Background: Absolute uterine factor infertility (AUFI) is a kind of infertility that is completely attributable to uterine absence (surgical or congenital for women with Mayer-Rokitansky-Küster-Hauser syndrome: MRKH) or anatomic or functional abnormality that prevents embryo implantation or completion of pregnancy to term. Until recently, the only viable option to parenthood for couples with AUFI were adoption or surrogacy. Since a first attempt of uterus transplant (UTx) in 2000, nine babies were born from women with a transplanted uterus from 2014, eight of which in Sweden, and one in the United States. These promising results are raising immense hopes for the women with AUFI and there is optimism about the possibility for UTx to become part of clinical care even though, besides encouraging results, the procedure has also resulted in increased risks and harms for both the donors and recipients and increased risks of premature birth for the fetus. At present UTx is still considered as experimental and requiring more research and safety assessment before becoming a therapeutic option for AUFI. The transition from experimental procedure to therapeutic care would result in less strict ethical scrutiny for UTx and in the possibility for patients to get reimbursement for the procedure by the relevant healthcare insurance or public healthcare providers. In turn, an increase in the number of UTx performed yearly by specialized surgical teams would result in a general improvement of the “field strength”. However, at present it is difficult to establish the amount of evidence that we need in order to consider UTx as no longer experimental but routine clinical practice. The literature on UTx provides recommendations on the different outcomes that should be monitored in this experimental phase but no study is anticipating the number of subjects that should be followed and for how long. Conclusion: As for other transplants that have become routine practice, like renal transplant and heart transplant, it is likely that the decision on “routine practice readiness” will result from available cumulated evidences, from expert capacity to find a consensus on best practices and on political considerations as well, including pressures form patients and patient groups.

There have been several cases of large-scale wind generators (WGs) tripping off caused by untimely fault removing in recent years. Currently, the discoordination between the box-type transformer fuse protection (BTFP) and two-section collecting line current protection (CLCP) brings a security risk to wind farm. In order to ensure the selectivity, the first section (Sec-I) CLCP should be set a enough interval that is longer than the fuse melting time, and another interval is set for the Sec-II CLCP, which weakens the speed of the CLCP. When a fault occurs on the collecting line, there is no doubt that WGs cannot work too long in abnormal operation, which may cause WGs to be placed off the grid. For a power system with high penetration of wind power, large-scale WGs tripping off will cause a great power shortage, and affect the stability of the power system. The selectivity and sensitivity of the CLCP is analyzed in detail to make the CLCP speed better. Considering the fault ride-through ability of WGs, the fault clear time is an important factor to lead to large-scale WGs tripping off. Two main works are done in this paper. The first is to accelerate the speed of the Sec-I CLCP though reducing the protection zone. Another one is introduce the risk assessment module into the CLCP, which not only improve the speed of the CLCP but also ensure the safety of the wind farm during faults. According to the deference in trip-off causes of WGs, the matching functions are created to assess the trip-off risk of WGs on the spot. In the case of fault, the trip-off risk indicators of WGs are timely updated to data sharing center and open to the CLCPs. The set of risk indicators is divided into several subsets according to the risk range. The dynamic changes of the subsets during fault help to improved CLCP scheme. This scheme can accelerate protection speed based on the increasing risk of large-scale WGs tripping off in wind farms. Compared with traditional CLCP, this approach can make the CLCP combines selectivity and speed better based on the analysis of the ride- through ability of WGs.

There is a need for developmental screening that is easily administered in resource-poor settings. 1) We hypothesized that known risk factors would predict failed developmental screening on an adapted screening tool in East African children living in poverty. 2) The sample included 100 healthy Ugandan children aged 6-59 months. We adapted a parent-reported developmental screener based on the Child Development Review chart. The primary outcome was failure to meet age-appropriate milestones for any developmental domain. Venous blood was analyzed for lead, and caregivers completed a demographics questionnaire. We used multivariate logistic regression models to determine if elevated blood lead and stunting predicted failure on the screener, controlling for maternal education level, age in months past the lower bound of the child’s developmental age group, and absence of home electricity. 3) In the sample, 14% (n=14) of children failed one or more milestones on the screener. Lead levels or stunting did not predict failing the screener after controlling for covariates. 4) Though this tool was feasibly administered, it did not demonstrate preliminary construct validity and is not yet recommended for screening in high-risk populations. Future research should include a larger sample size and cognitive interviews to ensure it is contextually relevant.

We introduce and analyse a simple formal thought experiment designed to reflect a qualitative decision dilemma humanity might currently face in view of climate change. In it, each generation can choose between just two options, either setting humanity on a pathway to certain high wellbeing after one generation of suffering, or leaving the next generation in the same state as this one with the same options, but facing a continuous risk of permanent collapse. We analyse this abstract setup regarding the question of what the right choice would be both in a rationality-based framework including optimal control, welfare economics and game theory, and by means of other approaches based on the notions of responsibility, safe operating spaces, and sustainability paradigms. Despite the simplicity of the setup, we find a large diversity and disagreement of assessments both between and within these different approaches.

Flash flood is one of the most significant natural disasters in China, particularly in mountainous area, causing heavy economic damage and casualties of life. Accurate risk assessment is critical to an efficient flash flood management. There are more than 530,000 small watersheds in 2058 counties in China where flash flood should be prevented. In practice, with limited fund and different risk levels, the priorities of each small watershed for flash flood prevention and control are also needed for an efficient flash flood management. This paper, take Licheng county in China as an example, aims to give out these priorities for management. First, sensitive indexes are identified among index system, which includes 9 indexes based on underlying surface characteristics of small watershed in hilly region. Second, the range of each index and the rank division of each index for evaluation are determined. Based on the rank divisions for evaluation, the flash flood risk grade eigenvalue (H) is calculated by Variable Fuzzy Method (VFM ) using 1000 samplings generated by Latin hypercube sampling method. Third, the key sensitivity factors that affect flash flood risk grade eigenvalue (H) are assessed by two different global sensitivity analysis methods -- stepwise regression analysis and mutual entropy. Both results indicate that watershed slope (S) is the most sensitive factor; the second is antecedent precipitation index (CN); while other factors are slightly different sensitive in sequence. This study shows that stepwise regression analysis and mutual information analysis are appropriate for the sensitivity analysis of mountain flash flood risk. Finally, based on watershed slope (S), the priorities of flash flood prevention and control of 119 small watersheds in Licheng county are given out.

This paper presents a limited scope dynamic probabilistic risk assessment (D-PRA) on the survivability of commercial of the shelf (COTS) drones tasked with surveilling areas with varying radiation levels after a nuclear accident. The D-PRA is based on a discrete-dynamic event tree (D-DET) approach, which couples with the OpenEPL error propagation framework to model sequences leading to Loss of Mission (LOM) scenarios due to component failures in the drone’s navigation system. Radiation effects are simulated by calculating the total ionizing dose (TID) against the permissible limit per component, and errors are propagated within the electronic hardware and software blocks to quantify navigation system availability per radiation zone. The proposed methods are integrated into the traditional event tree/fault tree approach and the most vulnerable components are radiation-hardened (RAD-HARD) to the extent specified by a predefined mission success criterion. The results demonstrate the usefulness of the proposed approach in performing trade studies for incorporating COTS components into RAD-HARD drone designs.

Confusion, fear and mixed sentiments prevail in the minds of people towards what is arguably one of the most important of dynamics of modern human society: Artificial Intelligence (AI). This study aims to explore the contributions of news media towards this phenomenon - we analyze nearly seventy thousand recent news headlines on AI, using natural language processing (NLP) informatics methods, machine learning (ML) and large language models (LLMs) to draw insights and discover dominant themes. Our theoretical framework was derived from extant literature which posits the power of fear producing articles and news headlines which produce significant impacts on public behavior even when available in small quantities. We applied extensive textual informatics methods using word and phrase frequency analytics, sentiment analysis and human experts based thematic analysis to discover insights on AI phobia inducing news headlines. Our rigorous analysis of nearly seventy thousand headlines using multiple validation methods in NLP (exploratory informatics including BERT, Llama 2 and Mistral based topic identification), ML (supervised informatics) and LLMs (neural nets for sentiment classification, with BERT, Llama 2 and Mistral) demonstrates the presence of an unreasonable level of emotional negativity and fear inducing verbiage in AI news headlines. The framing of AI as being dangerous or as being an existential threat to humanity can have a profound impact on public perception, and the resulting AI phobia and confusion in public perceptions are inherently detrimental to the science of AI. Furthermore, this can also impact AI policy and regulations, and harm society. We conclude with a discussion deducing implications for society and make recommendations for education and policies that could support human identity and dignity.

This study examines the DCMA concerning the future risk of the water security status. We considered three risk factors: population growth, economic growth, and natural water supply demand differences. In the risk analysis part, we consulted with experts from several sectors including academia, Non-Governmental Organization (NGOs), and industry, to predict that the probability of future water stresses in high, medium, and low scenarios are 0.73, 0.24, and 0.03, respectively. In the decision analysis part, we adopted two Multiple Criteria Decision Analysis (MCDA) approaches that include Multiple Attribute Value Theory (MAVT) and Analytic Hierarchy Process (AHP) methods to evaluate the best alternative decision to alleviate future water stresses in the DCMA. The sensitivity analysis demonstrates the best option closely connects to the weighting scheme of the criteria considered in the framework. This study provides a valuable risk and decision analysis framework to analyze the water security status associated with the future water supply and demand gap decrease caused by three risk factors: population growth, climate change, and natural water supply.

We investigated and quantified bacterial accumulation in facemasks used by the general population, using 16S rRNA (Sanger Sequencing), culture and biochemical analysis along with Rose Bengal staining. Additionally, a systematic overview of the literature on face mask contamination was undertaken. We found an average bacterial load of 4.24 × 10⁴ CFU recovered/mask, with a maximum load of 2.85 × 10⁵ CFU. This maximum is 310 times higher than the limit value for contamination of ventilation system outlet surfaces specified by the German standard VDI 6022. Biochemical and molecular identification predominantly found Staphylococcus species (80%), including Staphylococcus aureus, along with endospore-forming Bacillus spp. Literature reports also indicate contamination of masks by bacterial and fungal opportunists of the genera Acinetobacter, Aspergillus, Alternaria, Bacillus, Cadosporium, Candida, Escherichia, Enterobacter, Enterococcus, Klebsiella (including K. pneumoniae), Micrococcus, Microsporum, Mucor, Pseudomonas, Staphylococcus and Streptococcus. Bacterial counts increase linearly with wearing duration, and prolonged use may affect the skin and respiratory microbiomes, promoting consequential eye, skin, oral and airway conditions. These aspects underscore the urgent need for further research and a risk-benefit analysis in respect of mask use, particularly given their unproven efficacy in disrupting the transmission of respiratory viruses and their adverse social consequences.

Parkinson’s disease (PD) is characterized by motor deficits and a wide variety of non-motor symptoms. The age of onset, rate of disease progression and the precise profile of motor and non-motor symptoms display considerable individual variation. Neuropathologically, the loss of substantia nigra dopaminergic neurons is a key feature of PD. The vast majority of PD patients exhibit alpha-synuclein aggregates in several brain regions, but there is also great variability in the neuropathology between individuals. While the dopamine replacement therapies can reduce motor symptoms, current therapies do not modify the disease progression. Numerous clinical trials using a wide variety of approaches have failed to achieve disease modification. It has been suggested that the heterogeneity of PD is a major contributing factor to the failure of disease modification trials, and that it is unlikely that a single treatment will be effective in all patients. Precision medicine, using drugs designed to target the pathophysiology in a manner that is specific to each individual with PD, has been suggested as a way forward. PD patients can be stratified according to whether they carry one of the risk variants associated with elevated PD risk. In this review we assess current clinical trials targeting two enzymes, leucine-rich repeat kinase 2 (LRRK2) and glucocerebrosidase (GBA), which are encoded by two most common PD risk genes. Because the details of the pathogenic processes coupled to the different LRRK2and GBA risk variants are not fully understood, we ask if these precision medicine-based intervention strategies will prove “precise“ or “personalized“ enough to modify the disease process in PD patients. We also consider at what phases of the disease that such strategies might be effective, in light of the genes being primarily associated with the risk of developing disease in the first place, and less clearly linked to the rate of disease progression. Finally, we critically evaluate the notion that therapies targeting LRRK2 and GBA might be relevant to a wider segment of PD patients, beyond those that actually carry risk variants of these genes.

Background: Only few studies have studied the link between risk perception and sleep in the context of the COVID-19 pandemic. This study investigates the effect of two distinct risk appraisals—risk perception and perception of collective coordinated defense (PCCD) on Chinese adults’ sleep quality during the COVID-19 pandemic, and tested COVID-19-related fear and rumination as potential mediators of the relationships. Methods: Data were collected using a self-report online questionnaire from a sample of 224 Chinese adults during the fourth wave of the COVID-19 pandemic in Hong Kong. Results: COVID-19 risk perception and PCCD were related to poor sleep quality. Mediation analysis showed that both fear and rumination mediated the relationship between risk perception and sleep quality, whereas only fear mediated the relationship between PCCD and sleep quality. The model showed an excellent fit to the data and accounted for 44% of the variance in sleep quality in Chinese adults. Conclusions: This study demonstrates the distinct perceptual processes—risk appraisals in particular—contributed to poor sleep quality in Chinese adults during the COVID-19 public emergencies. These findings would be helpful for policy makers to address the sleep problems induced by psychological consequences of the pandemic.

The current methods for lightning risk warnings that are based on atmospheric electric field (AEF) data have a tendency to rely on single features, which results in low robustness and efficiency. Additionally, there is a lack of research on cancelling warning signals, contributing to the high false alarm rate (FAR) of these methods. To overcome these limitations, this study proposes a lightning risk warning method that incorporates enhanced empirical Wavelet transform-Adaptive Savitzky Gorey filter (EEWT-ASG) and one-dimensional morphology, using time-frequency domain features obtained through the Wavelet transform (WT). The proposed method achieved a probability of detection (POD) of 77.11%, miss alarm rate (MAR) of 22.89%, FAR of 40.19%, and critical success index (CSI) of 0.51, as evaluated on 83 lightning processes. This method can issue a warning signal up to 22 minutes in advance for lightning processes.

As the whisky industry applies circular economy principles to maximize resource utilization and minimize environmental impact, companies become exposed to several risks, which require complex assessments to ensure reliable outcomes. This study provides an organized framework to identify, prioritize and rank various risk factors commonly observed in whisky industry through the development of an Analytical Hierarchy Process (AHP) multi-criteria analysis model. Experts from 18 small European distilleries identified five main risk criteria and nineteen sub-criteria from brainstorming workplace observations, and categorized them as: environmental (5), operational (4), technological innovation (3), food safety (3) and economical (4) risks. The Analytical Hierarchy Process (AHP) approach was used to determine the weights and ranks of the main criteria and sub-criteria based on the survey responses received from experts from each distillery. The final judgements are consistent, as indicated by consistency values (CR) of less than 0.1 for all risk criteria. Unlike traditional risk assessment methods, the AHP model effectively integrates qualitative and quantitative data, aiding strategic decision-making in the whisky industry by breaking down complex problems into manageable sub-problems. Future research directions may expand the criteria and explore additional sustainable practices.

This paper explores the effectiveness of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and traditional Mean-Variance optimization in financial portfolio management. Using a dataset comprising global financial assets, we applied both methodologies to optimize portfolios based on multiple objectives, including risk, return, skewness, and kurtosis. The findings reveal that NSGA-III outperforms the Mean-Variance method in achieving a more diverse set of Pareto-optimal portfolios. NSGA-III portfolios exhibited superior performance in balancing risk and return, demonstrated by higher Sharpe ratios, more favorable skewness, and lower kurtosis. Additionally, NSGA-III's ability to simultaneously optimize across multiple conflicting objectives highlights its robustness in navigating complex financial landscapes, offering enhanced portfolio resilience. In contrast, the Mean-Variance approach, while effective in achieving balanced risk and return, was limited in addressing higher-order moments of the return distribution. These results underscore NSGA-III's potential as a powerful tool for portfolio optimization, providing a comprehensive alternative to traditional methods in modern financial markets where multiple objectives must be considered.

Background/Objectives: Bacterial infections (BI) are a major cause of mortality in patients with alcohol-associated hepatitis (AH); however, only a few studies have investigated BI in AH in the last decade. Therefore, we aimed to assess the features and outcomes of BI in patients with AH. Methods: This observational descriptive study included patients with AH admitted to a tertiary academic hospital between 2016–2021. Clinical and complete microbiological data were recorded and complications, including acute-on-chronic liver failure (ACLF), and mortality over 90-days were compared between infected and non-infected patients. Results: Overall, 115 patients with AH were recruited and 75 had severe AH; among them, 66 started corticosteroid treatment. We identified 69 cases of BI in 44 patients; the incidence of BI at hospital discharge was 32.2%, which reached 38.2% at 90 days. The predominant infection site was the chest (35%). Among the identified bacteria (52.1%), half were gram-positive and half gram-negative. A low rate of multidrug-resistant bacteria (14%) was also noted. Infected patients during hospitalization (n=37) exhibited higher rates of hepatic decompensation and ACLF p=0.001) and lower survival (81.8% vs. 95.8%, p=0.015) than did non-infected patients (n=78). In-hospital infected patients (n=22) exhibited worse survival (72.7%) than did those infected upon admission (93.3%) or non-infected patients (94.9%) (p=0.009). Corticosteroid-treated patients displayed a nonsignificant increase in the total number of BI; however, without greater mortality. Conclusions: BI were common in our cohort of patients with AH. Patients with in-hospital infections commonly experienced serious complications, including high ACLF and death rates. Infections diagnosed upon admission were treated without affecting survival.

This study examines the integration of Environmental, Social, and Governance (ESG) factors into global banking practices and their impact on financial stability, measured by the Bank to Capital Asset Ratio. Through a comprehensive literature review and data analysis, the paper highlights the dual role of banks as catalysts for ESG investment and risk managers. It discusses the challenges and opportunities in transitioning to ESG-aligned business models, emphasizing the importance of regulatory frameworks and risk management approaches. The findings suggest that ESG integration enhances bank stability and competitiveness, contributing to sustainable economic development. The paper concludes with policy implications and recommendations for further integration of ESG factors into banking governance.

This study explores the transformative impact of machine learning on predictive analytics within supply chain management. As supply chains become increasingly complex and dynamic, traditional methods of forecasting and management are proving inadequate. Machine learning offers advanced algorithms capable of analyzing vast datasets to uncover patterns and trends that enhance decision-making processes. This research investigates how machine learning improves demand forecasting accuracy, optimizes inventory management, strengthens supplier relationships, and enhances risk management. The study employs a qualitative approach, analyzing data from industry case studies and expert interviews to identify key themes and challenges associated with the adoption of machine learning. Findings reveal that machine learning significantly improves forecasting precision, reduces inventory costs, and enables proactive supplier and risk management. However, challenges such as ensuring data quality, acquiring specialized skills, integrating new technologies with existing systems, and addressing ethical considerations are critical. The study also highlights the potential of emerging technologies like blockchain, IoT, and 5G to further enhance machine learning applications in supply chain management. This research contributes to a deeper understanding of how machine learning can drive innovation and efficiency in supply chains, while also addressing the hurdles that organizations must overcome. The results underscore the need for a balanced approach that incorporates technological advancements and ethical practices to fully leverage the benefits of machine learning.

This study investigates the critical role of Supplier Relationship Management (SRM) in enhancing the e-commerce user experience. In an increasingly competitive digital marketplace, effective SRM practices are essential for optimizing supply chain operations and meeting customer expectations. The research employs a qualitative approach, combining semi-structured interviews with industry stakeholders, extensive literature review, and case studies to explore various dimensions of SRM. Key findings reveal that strategic supplier selection criteria, including quality, cost efficiency, reliability, innovation, and sustainability, are pivotal in ensuring a robust and adaptable supply chain. Effective communication practices, such as transparency, frequent updates, and digital tools, foster strong supplier relationships and enable timely issue resolution. Performance management strategies, centered on KPI monitoring and continuous improvement, support operational excellence and customer satisfaction. Risk management practices, including diversification, contingency planning, and advanced analytics, are crucial for mitigating disruptions and ensuring supply chain resilience, as highlighted by the challenges posed during the COVID-19 pandemic. Technological integration with blockchain, AI, IoT, data analytics, and automation enhances efficiency, transparency, and decision-making capabilities in SRM. Furthermore, the study underscores the evolving integration of sustainability and ethical sourcing into SRM strategies, reflecting broader corporate responsibility goals. Ultimately, this research contributes to understanding how effective SRM strategies drive competitive advantage in e-commerce by enhancing supply chain reliability, responsiveness, and customer-centricity. As e-commerce continues to evolve, adapting and innovating SRM practices will be vital for companies aiming to sustain growth and meet the dynamic demands of global markets.

Supplier Relationship Management (SRM) plays a pivotal role in enhancing delivery performance within the e-commerce sector, yet understanding its nuanced impacts remains crucial for optimizing operational efficiencies and customer satisfaction. This qualitative research explores the intricate dynamics of SRM in e-commerce, focusing on strategic alignment, technological integration, trust, collaboration, and risk management as key determinants of delivery reliability. Through in-depth interviews and thematic analysis, the study reveals that strategic alignment of supplier capabilities with firm objectives is fundamental for maintaining a responsive and synchronized supply chain. Technological integration, including AI-driven analytics and blockchain applications, emerges as critical for real-time monitoring and adaptive decision-making, thereby improving delivery accuracy and efficiency. Trust and collaboration are identified as essential pillars for building resilient supplier relationships, fostering transparent communication and joint problem-solving. Effective risk management practices, such as supplier diversification and contingency planning, mitigate disruptions and ensure consistent delivery performance. Despite these benefits, challenges such as technological adoption barriers, cultural differences, communication complexities, resource constraints, and supplier reliability issues persist, necessitating strategic interventions. Recommendations include investing in advanced SRM technologies, promoting cultural understanding, enhancing communication protocols, allocating adequate resources, and fostering collaborative initiatives with suppliers. These strategies aim to overcome challenges and optimize SRM practices, ultimately enhancing delivery performance and competitiveness in the e-commerce landscape.

This qualitative research explores the intricate relationship between cultural influences on supply chain practices and their implications for marketing strategies. Cultural dimensions such as individualism versus collectivism, uncertainty avoidance, and contextuality significantly shape decision-making processes, risk management strategies, and relationship dynamics within global supply chains. Through semi-structured interviews with supply chain managers, marketing executives, and cultural experts across diverse industries and regions, this study investigates how cultural values impact supply chain operations and consumer engagement strategies. Key findings highlight that collectivist cultures prioritize consensus-building and long-term relationships, fostering trust and collaboration among supply chain partners. In contrast, individualist cultures emphasize efficiency and accountability but may face challenges in relationship building. Risk management practices vary with cultural orientations towards uncertainty, influencing the adoption of structured versus flexible approaches to mitigate disruptions. In terms of marketing implications, the study underscores the importance of cultural sensitivity in crafting effective strategies. High-context cultures require marketing messages that resonate emotionally through implicit communication and cultural symbolism, while low-context cultures prefer clear, direct messaging focusing on product benefits. The study concludes by advocating for cultural competence as a strategic imperative for global businesses, enabling them to leverage cultural diversity for innovation and competitive advantage. It emphasizes the need for continuous adaptation to cultural nuances and market dynamics to foster inclusive collaboration and achieve sustainable growth in a globalized economy.

This qualitative research study delves into the intricate dynamics of how organizations adapt to supply chain disruptions during crises, offering profound insights into the strategies, practices, and challenges encountered in enhancing supply chain agility. Through in-depth interviews with supply chain managers across diverse industries, key themes such as proactive risk management, technological integration, collaboration with supply chain partners, and adaptive leadership have emerged as crucial determinants of agility and resilience. Additionally, the study highlights the significance of organizational culture, effective communication, and external factors such as regulatory requirements and market dynamics in shaping supply chain agility. By embracing a holistic approach that integrates these multifaceted factors, organizations can bolster their capacity to anticipate, detect, and respond to disruptions, thereby maintaining or enhancing overall performance even in the face of uncertainty and complexity. The insights gleaned from this research are poised to inform theory development, guide managerial practice, and influence policy-making in the realm of supply chain management, equipping organizations with the knowledge and tools needed to navigate future disruptions successfully.

The prevalence and incidence of obesity and the comorbidities linked to it are increasing in the world population. Current therapies for obesity and associated pathologies have proven to cause a broad number of adverse effects and often, they are overpriced or not affordable for all patients. Among the alternatives currently available, natural bioactive compounds stand out. These are frequently contained in pharmaceutical presentations, nutraceutical products, supplements, or functional foods. The clinical evidence for these molecules is increasingly solid, among which epigallocatechin-3-gallate, ellagic acid, resveratrol, berberine, anthocyanins, probiotics, carotenoids, curcumin, silymarin, hydroxy citric acid, and α-lipoic acid stand out. The molecular mechanisms and signaling pathways of these molecules have been shown to interact with the endocrine, nervous, and gastroenteric systems, as well as regulate the expression of multiple genes and, therefore, proteins involved in starvation-satiety processes, activation of brown adipose tissue, increased lipolysis, decreased lipogenesis, and inflammation, beneficial changes in metabolism and improved insulin sensitivity. This review provides a comprehensive view of nature-based therapeutic options to address the increasing prevalence of obesity. It offers a valuable perspective for future research and subsequent clinical practice, addressing everything from the molecular, genetic, and physiological bases to the clinical study of the bioactive compound.

The movement toward prevention trials in people at-risk for Parkinson’s is rapidly becoming a reality. The authors of this article include a genetically at-risk advocate with the LRRK2 G2019S variant and two patients with rapid eye movement sleep behavior disorder (RBD), one of whom has now been diagnosed with Parkinson’s disease (PD). These authors participated as speakers, panelists, and moderators in the “Planning for Prevention of Parkinson’s: A Trial Design Forum” hosted by Massachusetts General Hospital in 2021 and 2022. Other authors include a young onset person with Parkinson’s (PwP) and retired family physician, an expert in patient engagement in Parkinson’s, and early career and veteran movement disorders clinician researchers. Several themes emerged from the at-risk participant voice concerning the importance of early intervention, the legitimacy of their input in decision-making, and the desire for transparent communication and feedback throughout the entire research study process. Challenges and opportunities in the current environment include lack of awareness among primary care physicians and general neurologists about PD risk, legal and psychological implications of risk disclosure, limited return of individual research study results, and undefined engagement and integration of individuals at-risk into the broader Parkinson’s community. Incorporating the perspectives of individuals at-risk as well as those living with PD at this early stage of prevention trial development is crucial to success.

Radiological imaging is essential for acute patient management in Intensive Care Units (ICUs); however, it introduces the risk of exposure to ionizing radiation. This review synthesizes research on radiation exposure in intensive care unit (ICU) settings, highlighting its rise during the COVID-19 pandemic and repetitive imaging. Our analysis extends to radiation safety thresholds, revealing that some ICU patients exceed annual occupational limits, emphasizing the need to balance diagnostic utility against potential long-term risks, such as cancer. Prospective studies have demonstrated an increase in the median Cumulative Effective Dose (CED) in patients with trauma over time, prompting calls for improved awareness and monitoring. Moreover, innovative dose-reduction strategies and optimized protocols, especially in neuro-ICU units, promise to mitigate these risks. This review highlights the essential but risky role of radiological imaging in critical care. It advocates rigorous radiation management protocols to safeguard patient health, while ensuring the continuity of high-quality medical care.

The occurrence rate of occupational accidents caused by slipping and falling is 50% worldwide. Determining the slip safety risk is necessary to minimize the accidents caused by slipping in pedestrian walking areas. By on-site testing, this study aims to determine the slip risks of floor coverings used in walking areas with many pedestrians in public service buildings (PSB). For this purpose, on-site measurements were performed in two environments and six locations in PSB using portable GMG 200 and pendulum testers operating according to DIN 51131 and TS EN 14231 standards. These tests were performed on four-floor coverings with polished surface treatment in PSB. Since the floor coverings measured have a polished surface, it can be seen that the dynamic friction coefficients (DCOF) values are very low, and the slip risks are high. By statistically analyzing the slip test data using the K-means method, a new safety classification was made according to the usage areas of floor coverings. To minimize the risk of slip safety, especially for students, patients, and employees in PSB, it is recommended to use floor coverings with high DCOF values and low slip risk.

Background: Malnutrition was associated with worse survival outcomes, impaired quality of life, and deteriorated performance status across various cancer types. We aimed to identify risk factors for malnutrition in patients with epithelial ovarian(EOC) and impact on survival. Methods: In our prospective study, we included the patients with primary and recurrent EOC, tubal or peritoneal cancer conducted. We assessed serum laboratory parameters, body mass index, nutritional risk index, nutritional risk screening score(NRS-2002), and bio-electrical impedance analysis. Results: We recruited a total of 152. Patients >65 years-old, with ascites of >500 ml, or with platinum-resistant EOC showed statistically significant increased risk of malnutrition when evaluated by NRS-2002(p-values = 0.014, 0.001, and 0.007, respectively). An NRS-2002<3 was an independent predictive factor for complete tumor resectability (p=0.009). The patients with NRS-2002≥3 had a median overall survival(OS) of seven months (95% CI=0-24 months), as compared to the patients with NRS-2002<3 where median OS was 46 months(p=0,001). Phase angle(PhAα)≤ 4.5 was the strongest predictor of OS. Conclusion: Malnutrition is an independent predictor of incomplete cytoreduction in study population. It is an independent prognostic factor for poor OS. Preoperative nutritional assessment is an effective tool in identification of high-risk groups within EOC characterized by poor clinical outcome.

In these times, consumers mistakenly believe that the terms “expiration date” and “best before” found on packages only convey information about food safety. Consequently, a misunderstanding of these terms contributes to food waste. In this sense, the understanding and behavior of the con-sumer with respect to the two terms were evaluated; in addition, the consumer's perception of the health risks of consuming expired food was analyzed. The research was carried out with the population of the sector of Lurigancho-Chosica, Lima – Peru; sample size was 555 participants. An online survey was sent to consumers over 18 years of age, from different sociodemographic and socioeconomic conditions. In general terms, it was found that 45% (p <0.01) consider that both terms mean the same thing, this result is a problem for food safety. The 67% (p <0.05) and 45% (p <0.05) correctly defined the first and second terms, respectively; however, the behavior is not coherent with the understanding of the terms. In addition, irrational and dangerous behavior toward expired food was evidenced, i.e., they consume expired food - "expiration date", and discard expired food - "best before". Finally, the health risks they perceived from consuming expired foods of both terms were: upset stomach, vomiting, gas, intoxi-cation, and malnutrition; these perceptions are erroneous and unfounded. Sociodemographic and socioeconomic conditions influence the under-standing, behavior, and perception of health risks. Therefore, in general lines, the level of understanding of the terms was low (p<0.01), the be-havior was inappropriate toward expired food (p<0.01), and the percep-tion of the health risk of consuming expired food was incorrect (p<0.01).

Sudden water pollution accident is a tough environmental challenge to solve due to its uncertainty, unpredictability and destructiveness. Therein, the dangerous goods transportation accidents on the cross-tributary bridges are increasing worldwide with the acceleration of industrialization and urbanization, posing threats to the water ecological environment and human health. To prevent and control the risk, this study evaluated the risk of sudden water pollution accidents associated with dangerous goods transportation on the cross-tributary bridges by using the expert judgment fuzzy language method and Bayesian network model. Baiyangdian lake as the largest freshwater shallow lake in North China was chosen as a study case. Different accident scenarios were simulated to put forward risk prevention and control measures. The results showed that the primary risk factors were divided into five categories: personnel factors, vehicle factors, tank factors, weather factors, and road environment factors. The probability of dangerous goods transportation accidents was a possible event (P=0.115), with a high risk level. Furthermore, the Baiyangdian lake would suffer from serious environmental pollution and water quality degradation without prevention and control measures by simulating the oil leakage accident. Therefore, emergency prevention and control measures are expected to be formulated and implemented before and after the occurrence of the sudden water pollution accidents. This study provides useful insights into risk prevention and control strategies for sudden water pollution accidents, and the framework can be made applicable to other rivers facing similar risk challenges worldwide.

Middle East region experiences a high prevalence of hypovitaminosis D, yet most genetic studies on Vitamin D have focused on European populations. Furthermore, there is a lack of research on the genomic risk factors affecting the elderly population, who are more susceptible to health burden. We investigated the genetic determinants of 25-hydroxyvitamin D levels in elderly Lebanese individuals (n=199) through a whole exome-based genome-wide association study. We identified new loci with suggestive evidence of an association with Vitamin D levels, including rs141064014 in the MGAM gene (P-value of 4.40 × 10−06) and rs7036592 in PHF2 (P-value of 8.43 × 10−06). A meta-analysis of the Lebanese data and the largest European genome-wide association study confirmed consistence replication of numerous variants, including rs2725405 in SLC38A10 (P-value of 3.73 x 10-08). Despite the lower performance of European-derived polygenic risk scores compared to the European estimations, it still effectively predicted Vitamin D deficiency among elderly Lebanese individuals. Our findings provide novel insights into the genetic mechanisms of Vitamin D deficiency in Middle Eastern elderly populations, facilitating the development of personalized approaches for more effective management of hypovitaminosis D. Additionally, we demonstrated that whole exome-based genome-wide association study is an effective method for identifying genetic components associated with phenotypes.

Reading proficiency is requisite in our read-to-learn educational system, yet two-thirds of American students are not proficient readers. Assuring educational equity means supporting all learners with multiple component reading interventions that individually scaffold students while remediating weak literacy skills and providing intensive and sustainable intervention early. This study (N = 855) measured the efficacy of two different multiple component reading programs for students in grades three, four, and five. Grade levels of students were assigned to either the treatment intervention or the typical practice condition; and all students were pre-and post-tested using EasyCBM Reading Benchmarks. Students scoring at/below the 30^th percentile on either benchmark were also assessed with the WRMT-3 Passage Reading Comprehension and Oral Reading Fluency measures. Students in the treatment condition received Readable English and students in typical practice condition continued to receive Amplify CKLA during their regular ELA times for 45—60 hours. Students receiving Readable English significantly outperformed students in the typical practice condition on measures of oral reading fluency, reading rate, accuracy, and passage comprehension. Raw scores, growth scale values, and grade equivalents are reported, and implications for practice are discussed. In a school year fraught with pandemic instructional interruptions and learning loss, elementary students in the intervention condition averaged a year’s worth of growth in reading fluency and nine months of growth in reading comprehension compared to three- and five-months fluency and comprehension growth in the typical practice condition. Students in the Readable English condition experienced meaningful gains in reading rate and accuracy that will give exponential word reading volume dividends to students able to read text faster and more accurately going forward. This study adds to accumulating evidence that multiple component reading programs designed to reinforce fluency skills also support reading comprehension gains for all students.

Methods to handle uncertainty in economic evaluation have gained much attention in the literature and the cost-effectiveness acceptability curve (CEAC) is the most widely used method to summarize and present uncertainty associated with program costs and effects in cost-effectiveness analysis. Some researchers have emphasized the limitations of the CEAC for informing decision and policy makers as the CEAC is insensitive to radial shifts of the joint distribution of incremental costs and effects in the North-East and South-West quadrants of the cost-effective plane (CEP). Furthermore, it has been pointed out that the CEAC does not incorporate risk-aversion in valuing uncertain costs and effects. In the present article we show that the cost-effectiveness affordability curve (CEAFC) captures both dimensions of the joint distribution of incremental costs and effects on the CEP and is therefore sensitive to radial shifts of the joint distribution on the CEP. Furthermore, the CEAFC also informs about the budget impact of a new intervention as it estimates the joint probability an intervention is both affordable and cost-effective. Moreover, we show that the cost-effectiveness risk-aversion curve (CERAC) allows to incorporate risk-aversion into the analysis and can therefore be used to inform decision-makers who are risk-averse. We use data from a published cost-effectiveness model of palbociclib in addition to letrozole versus letrozole alone for the treatment of oestrogen-receptor positive, HER-2 negative, advanced breast cancer to demonstrate the differences between CEAC, CEAFC and CERAC and show how these can jointly be used to inform decision and policy makers.

Estimating the economic risks of climate shocks and climate stressors on spatially heterogeneous cities over time remain highly challenging. The purpose of this paper is to present a practical methodology to assess the economic risks of climate change in developing cities to inform spatially sensitive municipal climate response strategies. Building on a capital-based framework (CBF), spatially disaggregated baseline and future scenario scores for economic wealth and its exposure to climate change are developed for six different classes of capital and across 77 major suburbs in Cape Town, South Africa. Capital-at-risk was calculated by combining relative exposure and capital scores across different scenarios and with population impacted plotted against the major suburbs and the city’s 8 main planning districts. The economic risk assessment presented here provides a generic approach to assist investment planning and the implementation of adaptation options through an enhanced understanding of relative levels of capital endowment vis-à-vis relative levels of exposure to climate-related hazards over time. An informed climate response strategy in spatially heterogeneous cities need to include spatially sensitive estimates on capital-at-risk and populations disproportionally impacted by climate exposure over time. The economic risk assessment approach presented here helps in advancing to such a goal.

Several infectious agents are ascertained causes of cancer, but the burden of cancer mortality attributable to carcinogenic infections in Italy was still unknown. To tackle this issue, we calculated the rate and regional distribution of cancer deaths due to infections sustained by seven pathogens ranked as group-1 carcinogenic agents in humans by IARC. Population attributable fractions related to these agents were applied to annual statistics of cancer deaths coded according to 10th International Classification of Diseases. The estimated burden of cancer mortality attributable to carcinogenic infections in Italy during the period 2011-2015 was on average 8.7% of all cancer deaths registered yearly. Approximately 60% of deaths occurred in men and almost the whole burden was due to four infectious agents (Helicobacter pylori, HCV, HPV, and HBV). The analysis of regional distribution showed a higher number of infection-related cancer deaths in the northern regions, where the estimates reached 30 (Liguria) and 28 (Friuli Venezia Giulia) deaths per 100,000 inhabitants in 2015. Since one-twelfth of cancer deaths were attributable to these modifiable risk factors, the implementation of appropriate prevention and treatment interventions may help to reduce the impact of these infections on cancer mortality.

The basic principles of geological risk calculation through Probability of Success (PoS) are mostly applied for numerical estimation of additional hydrocarbon existence in proven reservoirs or potential hydrocarbon discoveries in selected geological regional subsurface volume. It can be tailored and validated for a comprehensive input dataset collected in the selected petroleum province, adapted by dividing up geological events into several probability categories and classes. The most applied categories are (existence of) reservoir rocks, traps and isolators, source rocks, migration pathways and preservation conditions for hydrocarbons The methodology results in unique probability values as multiplication of independent statistical events, which can also be applied in the assessment of a potential hydrocarbon discovery of desired minimal volume and its value in any virtual currency like risk-neutral dollars. Such methodology has been extensively developed in the last decades in the Croatian subsurface, mostly in the Croatian part of the Pannonian Basin System (CPBS). Through the adaptation of geological categories, it was also applied in hybrid, i.e., stochastical, models developed in the CPBS (Drava Depression). Stochastically estimation of porosity was already applied. As the robustness of this methodology is very high, it was also modified to estimate the influence of water-flooding in increasing oil recovery in some proven Neogene sandstone reservoirs in the CPBS (Sava Depression). This new modification is being presented to be applied to geological risk calculation, intending to assess the safety of geological environment in deep wells, where depleted radioactive fuel would be disposed, a subject of great importance. The case study encompassed the magmatic and metamorphic rocks in the pre-Neogene basement of the CPBS. For disposal purpose, these are regionally lithologies considered as to be the safest ones considering petrophysical values, water saturation, recent weathering and tectonic activity.

Today project management in every field of life is prerequisite for the success of project by increasing quality reduces cost and schedule. But selecting tools and techniques to achieve our objectives and implement our plan fully is very difficult task because choice creates complexity. So, we discuss AHP to make decision simple. Ranking critical success factor, cloud computing services, risk prioritization, selection of right ETL software and many other systems AHP plays its important role. For the improvement of today’s complex systems it is very important to prioritize and select projects to remove the root cause of the problem. To attain the right selection of construction equipment for carrying out schedule tasks with high efficiency, production and financial capability is the main purpose of procurement of construction equipment process. Certain conflicts can occur due to the construction of UML models in a collaborative way. AHP is used to remove these conflicts and for establishing and evaluating modelers judgments. Nowadays it is very important and critical decision to choose the best option from multiple alternatives for a successful career after passing 12th standard and also it affects our future. “AHP and TOPSIS” methods are used for this purpose. In this paper, an “AHP and Fuzzy AHP” based hierarchical trust model has been used to rate the service providers and their various plans for infrastructure as a service.

Genome-wide association studies have revealed a locus at 8p12 that is associated with breast cancer risk. Fine-mapping of this locus identified 16 candidate causal variants (CCVs). However, as these variants are intergenic, their function is unclear. To map chromatin looping from this risk locus to a previously identified candidate target gene, DUSP4, we performed chromatin conformation capture analyses in normal and tumoral breast cell lines. We identified putative regulatory elements, containing CCVs, that loop to the DUSP4 promoter region. Using reporter gene assays, we found that the risk allele of CCV rs7461885 reduced the activity of a DUSP4 enhancer element, consistent with the function of DUSP4 as a tumor suppressor gene. Furthermore, the risk allele of CCV rs12155535, located in another DUSP4 enhancer element, was negatively correlated with looping of this element to the DUSP4 promoter region, suggesting that this allele would be associated with reduced expression. These findings provide the first evidence that CCV risk alleles downregulate DUSP4 expression, suggesting that this gene is a regulatory target of the 8p12 breast cancer risk locus.

A new model for risk assessment of urban rainstorm disasters, based on information diffusion method and variable fuzzy sets (IDM-VFS) was proposed. In addition, an integrated index system of urban rainstorm risk was established. In the proposed model, IDM was employed to calculate the classification standards of urban rainstorm risk levels, then the VFS was adopted to assess the dangerousness, sensitivity, vulnerability and comprehensive risk of urban rainstorm disasters. In the present study, the urban rainstorm risk of Jiangsu province was evaluated with the proposed model. The results show that Wuxi, Changzhou, Nanjing and Suzhou have higher dangerousness, due to sustained rainfall and strong rainfall intensity in short duration; Wuxi, Changzhou and Nanjing have higher sensitivity because of lower disaster resistance ability; and Wuxi and Suzhou have higher vulnerability because these cities have higher potential losses in face of urban rainstorm disasters. The comprehensive risk zoning map of urban rainstorm shows apparent regional characteristics: the northwestern cities have lower risk than the southern cities. Moreover, most cities of the Jiangsu province are of the moderate urban rainstorm risk level. The results are consistent with the actual situation of Jiangsu province, and the study can provide some decision-making references for the urban rainstorm management.

In order to summarized the polygenic background of psychiatric diseases, polygenic risk scores (PRS) have been developed. Recently, PRS have been use to predict patients with higher comorbidities in psychiatric diseases, like dual diagnosis. PRS are principally derived in analysis of Caucasian and Asian populations, we are not aware of how this PRS could be applied in populations with high admixture. In order to explored this, the present work has the aim to analyzed if previous calculated PRS for psychiatric diseases could predict dual diagnosis in Mexican population, and also, if PRS calculation could be influenced by Mexican Amerindian (MA) global ancestry. We performed PRS calculation, using PRSice, with summary genome-wide association statistics previously published for psychiatric diseases, and also, performed Nagelkerke correlation test in order to established if PRS are correlated with dual diagnosis. We found that dual diagnosis could be predicted by major depressive disorder polygenic risk score. Nevertheless, schizophrenia polygenic risk score is highly correlated with global MA ancestry, independently of the schizophrenia diagnosis. Our results reinforced the notion that PRS calculation could be deviated by the MA global ancestry, nevertheless analysis on larger sample sizes are required in order to clarified this issue.

In this article, we combine datasets from multiple research projects and remote sensing tech-nologies to evaluate the conservation conditions of La Fortaleza de Kuelap, a pre-Hispanic monument in Peru’s northeastern Andes that suffered significant damage during historically high seasonal rains in April 2022. Our analyses seek to identify the main causes of the collapse and locate places where the monument is likely at risk of further deterioration. To do so, we model surface hydrology using a digital elevation model (DEM) derived from drone LiDAR data, re-construct a history of previous collapses, and calculate the volume of the most recent by fusing terrestrial LiDAR and photogrammetric datasets. In addition, we examine subsurface water ac-cumulation through electrical resistivity data, reconstruct the stratification of the monument from seismic refraction data, and analyze vegetation loss and ground moisture accumulation using high resolution satellite imagery. Our results point to water accumulation as the most significant source of risk for La Fortaleza’s perimeter walls. Combined with adverse contemporary conser-vation interventions and natural conditions, this led to the collapse of April 2022. This integration of analytical results demonstrates how multi-scalar and multi-instrumental approaches provide comprehensive and timely assessments of conservation needs.

Objectives To develop and validate a prediction model for 28-day in-hospital mortality among adult patients critically ill with COVID-19 in the UK. Design Observational cohort study. Setting 287 adult critical care units in England, Wales and Northern Ireland, of which 260 admitted at least one eligible patient. Participants 10,933 patients with confirmed COVID-19 of whom 10,401 were eligible (excluding 532 patients with a duration of critical care less than 24 hours and 1 patient with unknown 28-day outcome): 8,666 development (March-April 2020) and 1,735 temporal validation (May-August 2020). Main outcome measures 28-day in-hospital mortality from start of critical care. Results Two models were developed using 14 patient level predictors selected from 30 candidate predictors, with and without adjustment for calendar time. In the temporal validation data, the model discrimination was maintained (c index 0.78) but calibration was poor, particularly for the model not adjusted for calendar time (ratio of observed to predicted mortality 0.74 versus 0.88 for the model adjusted for calendar time). Conclusions We developed and validated a prediction model for 28-day in-hospital mortality for patients critically ill with COVID-19. Although absolute predictions were inaccurate due to changing outcomes, the models will support risk-adjustment in analyses and monitoring changes in risk-adjusted outcome over time.

The Estimation of systemic risk in India is still in its infancy stage. There are several methods which are available but none of the methods are fully compatible to forecast the systemic risk since under different circumstances the factors responsible for the risk differs. In this paper the systemic risk estimation in India being carried out based on spread in daily stock market price(Difference between the bid and ask price of a share) of the top 100 firms in India according to market capitalization for the period of July2007 to March 2016. The results were compared with the Financial Stability Report published by Reserve Bank Of India for the period of March 2010 to June 2016.The results clearly indicates that there exits relationship between market illiquidity represented by spread and risks associated with the Financial System. In most of the cases the Z score (deviation from the mean/Standard Deviation) of the spread has become negative which provides the spread which is farther from the mean, also a good indicator of volatility in market and risk to financial system. It is also seen that the Systemic Risk Survey conducted by Reserve Bank of India which started during October 2011 has supported the results.

Introduction: Thyroid nodules are extremely common and require complex management to prevent unnecessary surgical intervention and ensure that no malignant disease is overlooked. Several diagnostic tools and scoring systems are available to evaluate the Risk of Malignancy (ROM). The goal is to assess variables that can aid and support the clinical recommendations suggested by the updated Bethesda System for Reporting Thyroid Cytopathology (TBSRTC), such as the ultrasonographic features of thyroid nodules, particularly for indeterminate categories III (atypia of undetermined significance) and IV (follicular neoplasm). Methods: We retrospectively analysed the correlation of the demographic and ultrasonographic characteristics of thyroid nodules with the cytopathological and histopathological diagnoses of TBSRTC categories III (atypia of undetermined significance), IV (follicular neoplasm), V (suspicious for malignancy), and VI (malignant) in patients who underwent surgery in a single Portuguese centre over a 10-year period. Results: In total, 360 nodules were evaluated in 341 patients and 57% were histopathologically malignant or borderline. The majority were included in the TBSRTC indeterminate categories III and IV with an ROM of 44% and 43%, respectively. The ultrasonographic characteristics associated with a higher TBSRTC category and a greater ROM value were hypoechogenicity, presence of microcalcifications, irregular margins, and the presence of cervical adenopathy. When correlating with a malignant histology, only adenopathy and the presence of microcalcifications were observed to be statistically significant. Discussion: The indeterminate categories of the TBSRTC have been the most challenging ones to manage. The new TBSRTC (2023) guidelines as well as the ultrasonographic characteristics of a patient can be helpful in assessing the ROM and deciding on an appropriate course of treatment. Other resources, such as molecular tests, are also gaining a more important role in the clinical decision process and may become crucial in the future. Conclusion: The clinical management of thyroid nodules requires a careful analysis of clinical history and an evaluation of demographic details, the symptoms of the patient, comorbidities, personal and family history, ultrasonographic features, and the results of cytopathology, thyroid function, and molecular/genetic tests to provide the best care possible. Patients with thyroid nodules of an indeterminate character can obtain a definitive diagnosis through surgery. However, by employing a complete analysis of all relevant data, some patients may be suitable for active surveillance instead of a surgical intervention.

Supplier Relationship Management (SRM) tools play a crucial role in enhancing efficiency and competitiveness within e-commerce supply chains. This qualitative research explores the effectiveness of SRM tools in optimizing procurement processes, mitigating risks, and fostering collaborative partnerships with suppliers. The study employs semi-structured interviews with key stakeholders in diverse e-commerce sectors to gather insights into the implementation and outcomes of SRM tools. Findings reveal that technological advancements, including AI, blockchain, and data analytics, facilitate real-time communication, decision-making, and supply chain visibility, thereby enabling organizations to achieve cost savings and operational efficiencies. Enhanced supplier relationships through SRM tools contribute to collaborative innovation, faster product development cycles, and improved customer satisfaction. However, challenges such as technological complexity, regulatory compliance, and supplier resistance necessitate strategic management and investment in cybersecurity and compliance frameworks. The research underscores the importance of executive sponsorship, stakeholder engagement, and continuous improvement in driving successful SRM implementations.

Supply chain disruptions pose significant challenges to organizations, highlighting the critical importance of resilience in contemporary supply chains. This qualitative research explores the implications of supply chain disruptions on organizational resilience, drawing insights from interviews with supply chain managers and executives across various industries. The study identifies key factors that contribute to resilience, including agility, collaboration, risk management, strategic planning, technology integration, leadership, and organizational culture. Through thematic analysis, the research elucidates how these factors interact to enable organizations to manage and recover from disruptions effectively. Findings underscore the importance of agility in adapting to changing circumstances, collaboration with stakeholders to coordinate responses, and proactive risk management to anticipate and mitigate disruptions. Strategic planning and technology integration emerge as vital enablers of resilience, along with effective leadership and a resilient organizational culture that fosters adaptability and continuous improvement. The implications of supply chain disruptions extend beyond operational and financial impacts to include reputational and relational dimensions, emphasizing the importance of transparent communication and stakeholder engagement. The practical insights offered by this study provide guidance for organizations seeking to enhance their resilience and ensure continuity of operations in an increasingly complex and dynamic supply chain environment.

A simple bounded stochastic motion is employed for modelling equity price dynamics under shocks, where the prices are constrained to lie between two positive bounds. While the stochastic process has an inaccessible boundary, the other boundary is quasi-bounded, implying that the boundary can be breached if the probability leakage condition is met. The quasi-boundedness of the process at the boundary can thus provide an indicator of possible risk of equities under price shocks. Empirical calibration of model parameters of the proposed process for equities can be performed easily due to the availability of an analytically tractable probability density function which generates fat-tailed distributions consistent with empirical observations. The volatility and mean-reversion of the S&P500 dynamics calibrated by the process is positively and negatively co-integrated respectively with the VIX index representing the level of market distress. The process captures the high likelihood of Hertz’s default about two months earlier, using only information until that point, and before the firm filed for Chapter 11 bankruptcy in May 2020 as a result of the Covid-19 pandemic. Empirical calibration of the process for GameStop’s stock price shows that the short squeeze of the stock occurred when the condition for breaching an upper boundary was met on 14 January 2021, i.e., about two weeks before major short sellers closed out their positions with significant losses. The trading volume of the stock was positively co-integrated with the probability leakage ratio.

When attempting to quantify future harms caused by carbon emissions and to set appropriate energy policies, it has been argued that the most important metric is the number of human deaths caused by climate change. Several studies have attempted to overcome the uncertainties associated with such forecasting. In this article, approaches to estimating future human deaths tolls from climate change are compared and synthesized, and implications for energy policy are considered. Several studies are consistent with the “1000-ton rule,” according to which a future person is killed every time 1000 tons of fossil carbon are burned (order-of-magnitude estimate). If warming reaches or exceeds 2°C this century, mainly richer humans will be responsible for killing roughly 1 billion mainly poorer humans through anthropogenic global warming. Such mass manslaughter is clearly unacceptable. On this basis, relatively aggressive energy policies are summarized that would enable immediate and substantive decreases to carbon emissions. The limitations to such calculations are outlined and future work is recommended to accelerate the decarbonization of the global economy while minimizing the number of sacrificed human lives.

As an emerging network paradigm, Space-Air-Ground integrated networks (SAGIN) has attracted the attentions from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortages of computing and storage resources in mobile devices greatly affect the quality of experiences for intelligent applications. Hence, we devise to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MEC). To facilitate efficient processing, we need to solve the optimal task offloading decisions. Different from the existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capability for an edge computing node, the uncertainty of the transmission latency caused by the heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we firstly describe a task offloading decision problem in new challenge environments. But, we cannot use the standard robust optimization and stochastic optimization methods to obtain the optimal result under the uncertain network environments. In this paper, we propose the condition value at risk-aware distributionally robust optimization algorithm, named as CVAR-DRO, to solve the task offloading decision problem. The proposed CVAR-DRO method combines the distributionally robust optimization and the condition value at risk model for solving the optimal result. And then, We have evaluated our approach in simulation SAGIN environments with the confidence interval, the number of mobile task-offloading and the various parameters. We compare our proposed CVAR-DRO algorithm with the state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the brute algorithm. The experimental results show that CVAR-DRO can get a sub-optimal mobile task-offloading decision. Overall, CVAR-DRO is more robust than others to the new challenges mentioned above in SAGIN.

In the present work, we investigate the possibility that long-range airborne transport of infectious aerosols could initiate an epidemic outbreak at distances downwind beyond one hundred kilometers. For this, we have developed a simple atmospheric transport box-model which, for a hypothetical case of a COVID-19 outbreak, was compared to a more sophisticated 3-dimensional transport-dispersion model (HYSPLIT) calculation. Coupled with an extended Wells-Riley description of infection airborne spread, it shows that, the very low probability of outdoor transmission can be compensated by high numbers and densities, such as occurs in large cities, of infected and susceptible people in the source upwind and in the target downwind respectively. This may result in the creation of a few primary cases. It is worth pointing out that the probability of being infected remains very small at the individual level. Therefore, this process alone, which depends on population sizes, geography, seasonality and meteorology, can only “trigger” an epidemic which could then spread by the standard infection routes

(1) Background: A set of relevant Police Ergonomics Parameters (PEP) has been used for the assessment and prevention of occupational risks involved in Police Physical Interventions (PITs). Through this set of PEPs, a comparison has been made between 2 traditional PITs against 2 novel OTPs (Operational Tactical Procedures). (2) Method: The data have been collected by means of a motion capture suit fitted with 19 Inertial Measurement Units, IMUs. A specific and powerful software package specific for ergonomic analysis has been used to manage the large amount of data registered and to generate a series of 3-dimensional plots. Traditional PITs and newer OTPs have been performed for their analysis. Specifically, the PEPs corresponding to the implementation of 4 PITs have been analyzed by collecting the measurements provided by a set of IMUs installed on a motion capture suit and their occupational risk assessments have been compared against those corresponding to newer OTPs. (3) Results: For the 4 PITs, the PEPs have been analyzed with the measured values from the IMUs, throughout the duration of each technique. The two traditional intervention techniques have scored higher than the new OTPs in the REBA system, the Assymetry angle, L5-Pelvis Shear and Joint Contac Forces, and the Total Muscle Power measurements. (4) Conclusions: It has been confirmed that the new OPTs minimize the risk of injuries, since their PEPs reached lower values than those corresponding to the traditional PITs, which are still being taught at Police academies.

To remain relevant in the green transition, companies are beginning to voluntarily account for the exchange of emissions in their supply chain transactions and using the resulting greenhouse gas inventories for climate resilient decision support. Market advantages of sustainability and transparency see a shift from internal decision support tools to external communication tools which potentially expose companies to the risk of uncovering greenwashing if claims are not supported by transparent data, sound modelling, and a climate just emissions inventory, which considers external impacts connected to the production system. The different methods and standards in place for such greenhouse gas inventories, despite all referring to the ISO life cycle analysis standards and guidelines, present mixed signals and leave room for different interpretations, that may ultimately lead to cascading greenwashing, misleading results, and false successes. The new GHG Protocol Land Sector and Removals Guidance draft addresses this in part. With the GHG Protocol moving into revision periods, we identify gaps that present barriers to companies, or allow for interpretations that goes against the intentions of reporting GHG emissions related to an activity or organisation. The literature agrees that not rectifying these subtle-ties present counterproductive decision support for the green transition’s overall goal: to reduce global emissions.

In the European Union (EU), cannabidiol products require pre-marketing authorisation under the novel food regulation. Currently, 19 CBD applications are under assessment at the European Food Safety Authority (EFSA). During the initial assessment of the application files, the EFSA Panel on Nutrition, Novel Foods and Food Allergens (NDA) located several knowledge gaps in their 07 June 2022 statement on safety of cannabidiol as a novel food that need to be addressed before the evaluation of CBD can be concluded. Namely, the effect of CBD on the liver, gastrointestinal tract, endocrine system, nervous system, psychological function, and reproductive system needs to be clarified. Nevertheless, the available literature allows a benchmark dose (BMD)-response modelling of several bioassays, resulting in a BMD lower confidence limit (BMDL) of 20 mg/kg bw/day for liver toxicity in rats. Human data in healthy volunteers found increases in the liver enzymes alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in a study at 4.3 mg/kg bw/day, which was defined by EFSA NDA panel as a lowest observed adverse effect level (LOAEL). The EFSA NDA panel currently concluded that the safety of CBD as a novel food cannot be evaluated, leading to a so-called clock stop of the applications until the applicants provide the required data. Meanwhile, the authors suggest that CBD products still available as food supplements on the EU market despite the lack of authorisation must be considered as “unsafe”. Products exceeding a health-based guidance value of 10 mg/day must be considered as being “unfit for consumption” (Article 14(1) and (2) (b) of Regulation No 178/2002), while the ones in exceedance of the human LOAEL must be considered “injurious to health” (Article 14(1) and (2) (a) of Regulation No 178/2002).

This paper investigates whether currency risk is priced differently in the different sectors (industrial, financial, and basic materials) of equity markets in a sample of developed United States of America (USA) and developing economies (Brazil, India, Poland, and South Africa). The paper makes use of the following techniques: (i) Univariate Autoregressive Fractionally Integrated Moving Average and Exponential General Autoregressive Conditional Heteroskedastic (ARFIMA-EGARCH), (ii) the Markov-Switching method (MS), and (iii) the Canonical Vine Copulas (C-Vine) techniques. Using a sample of daily data made of the foreign exchange rate against the domestic currency and equity market sectors; our findings show that there is an asymmetry effect between equities markets and the foreign exchange rate: there is a heterogeneous, strong, and positive dependence between the two. Higher equities prices are associated with depreciation of local currencies, according to US dollar (USD) exchange rates. In addition, we find that the selected emerging economies are pricing a positive and considerable currency risk. The pricing of currency risk has a varied effect in both regimes representing the states of the economy. In fact, when currency risk pricing has a beneficial impact on certain sectors of the economy, investors predict better returns.

The interaction between oceanographic, meteorological, and hydrological factors can result in an extreme flooding scenario in the low-lying coastal area, called compound flooding (CF) events. For instance, rainfall and storm surge (or high river discharge) can be driven by the same meteorological, tropical or extra-tropical cyclones, resulting in a CF phenomenon. The trivariate distributional framework can significantly explain compound events' statistical behaviour reducing the associated high-impact flood risk. Resolving heterogenous dependency of the multidimensional CF events by incorporating traditional 3-D symmetric or fully nested Archimedean copula is quite complex. The main challenge is to preserve all lower-level dependencies. An approach based on decomposing the full multivariate density into simple local building blocks via conditional independence called vine or pair-copulas is a much more comprehensive way of approximating the trivariate flood dependence structure. In this study, a parametric vine copula of a drawable (D-vine) structure is introduced in the trivariate modelling of flooding events with 46 years of observations of the west Coast of Canada. This trivariate framework searches dependency by combining the joint impact of annual maximum 24-hr rainfall and the highest storm surge and river discharge observed within the time ±1 day of the highest rainfall event. The D-vine structures are constructed in three alternative ways by permutation of the conditioning variables. The most appropriate D-vine structure is selected using the fitness test statistics and estimating trivariate joint and conditional joint return periods. The investigation confirms that the D-vine copula can effectively define the compound phenomenon's dependency. The failure probability (FP) method is also adopted in assessing the trivariate hydrologic risk. It is observed that hydrologic events defined in the trivariate case produce higher FP than in the bivariate (or univariate) case. It is also concluded that hydrologic risk increases (i) with an increase in the service design life of the hydraulic facilities and (ii) with a decrease in return periods.

Socioeconomic conditions and social attitudes are known to represent epidemiological determinants. Credible knowledge on socioeconomic driving factors of the COVID-19 epidemic is still incomplete. Based on a linear random effects regression, a predictive model is derived to estimate COVID-19 incidence in German rural districts from local socioeconomic factors and popularity of political parties in terms of their share of vote. Thereby, time series provided by Germany's public health institute (Robert Koch Institute) of weekly notified 7-day incidences per 100,000 inhabitants per district from the outset of the epidemic in 2020 up to December 1, 2021, have been used to construct the dependent variable. Local socioeconomic conditions including share of votes, retrieved from the Federal Statistical Office of Germany, have been used as potential risk factors. Popularity of the right-wing party Alternative for Germany (AfD) bears a considerable risk of increasing COVID-19 incidence both in terms of predicting the maximum incidences during three epidemic periods (alternatively, cumulative incidences over the periods are used to quantify the dependent variable) and in a time-continuous sense. Thus, districts with high AfD popularity rank on top in the time-average regarding COVID-19 incidence. The impact of the popularity of the Free Democrats (FDP) is markedly intermittent in the course of time showing two pronounced peaks in incidence but also occasional drops. A moderate risk emanates from popularities of the Green Party (GRÜNE) and the Christian Democratic Union (CDU/CSU) compared to the other parties with lowest risk level. Socioeconomic parameters like \emph{per capita} income, proportions of protection seekers and social benefit claimants, and educational level have negligible impact. To the contrary, incidence significantly increases with population density. In order to effectively combat the COVID-19 epidemic, public health policymakers are well advised to account for social attitudes and behavioural patterns reflected in local popularities of political parties, which are conceived as proper surrogates for these attitudes. Whilst causal relations between social attitudes and the presence of parties remain obscure, the political landscape in terms of share of votes constitutes at least viable predictive "markers" relevant for public health policy making.

Given that the outcome of hepatic arterial infusion chemotherapy (HAIC) with cisplatin for intrahepatic advanced hepatocellular carcinoma (HCC) is unclear, we aimed to compare prognostic factors for overall survival (OS) following HAIC with cisplatin versus sorafenib for intrahepatic advanced HCC using propensity score-matched analysis. We enrolled 348 patients with intrahepatic advanced HCC who received HAIC with cisplatin (n = 97) or sorafenib (n = 251) between June 2006 and March 2020. No significant difference was observed in OS between HAIC with cisplatin and sorafenib cohorts (median survival time [MST]: 13.9 vs. 12.7 months; p = 0.0989). To reduce confounding effects, 176 patients were selected using propensity score-matched analysis (n = 88 for each treatment). HAIC with cisplatin significantly prolonged OS compared with sorafenib (MST: 16.2 vs. 12.2 months; p = 0.0060). Following stratification according to the Child–Pugh classification, for both patients with class A (MST: 24.0 vs. 15.6 months; p = 0.0097) and class B (MST: 8.5 vs. 6.9 months; p = 0.0391), HAIC with cisplatin rather than sorafenib significantly prolonged OS. Our findings suggest that HAIC with cisplatin demonstrates longer prognostic effects than sorafenib in intrahepatic advanced HCC, regardless of the hepatic reserve.

China's implementation of public-private partnerships projects has been quite effective, involving infrastructure and other livelihood projects, a total of 19 industries, and an investment of nearly 1.5 trillion yuan. The characteristics of PPP projects such as long construction period and large investment amount determine the risks of PPP projects are also great, and the PPP projects of comprehensive environmental governance are also the same. The government and social capital use the PPP model to cooperate, and use the principal component analysis method to assess the risks of the entire life cycle of the comprehensive environmental governance PPP project. Therefore, it plays an important role in ensuring the smooth implementation of projects and reducing the losses caused by risks. According to the risk factors of the whole life cycle of the comprehensive environmental governance PPP project, an indicator system of 5 first-level indicators, 18 second-level indicators, and 43 third-level indicators has been established. Principal component analysis is used to analyze the influence weight of risk factors at each stage. The analysis shows that among the four stages, environmental pollution risk, project approval delay risk, completion risk, interest rate and financial fluctuation risk, and franchise life risk are the most influential risks in the implementation of PPP projects. Therefore, suggestions are made through the risk factors of each stage in the comprehensive environmental governance PPP project. For example, strengthen the response to the external environment risks of the comprehensive environmental governance PPP project, standardize the bidding and procurement of the comprehensive environmental governance PPP project, and strengthen the subsequent management of the transfer of the comprehensive environmental governance PPP project. In this way, the ability to resist risks of comprehensive environmental governance PPP projects is improved; the smooth implementation of the project is guaranteed, and the long-term development of comprehensive environmental governance PPP projects is promoted.

To assess the presence of organochlorine pollutants (OCP) in Alaskan sub-Arctic latitudes, we analyzed ice core and meltwater samples from Jarvis Glacier, a polythermal glacier in Interior Alaska. Jarvis Glacier is receding as atmospheric warming continues throughout the region, increasing opportunity for OCP transport both englacially and into the proglacial watershed. Across all meltwater and ice core samples we identify the pesticides DDT, DDE and DDD, α- HCH and ϒ-HCH. OCP concentrations in ice core samples were highest at the 7-14 m depth (0.51 ng/L of DDT) and decreased gradually approaching the bedrock at 79m. Meltwater concentrations from the proglacial creek slightly exceeded concentrations found in the ice core, potentially indicating aggregate OCP glacial loss, with peak OCP concentration (1.12 ng/L of DDD) taken in July and potentially associated to peak melt. Ongoing use of DDT to fight Malaria in Asia, and the extended atmospheric range of HCH may account for concentrations in near-surface ice, correlating with use and atmospheric transport. The opportunity for biota bioaccumulation of OCPs, or human uptake of OCPs from glacial meltwater, may increase as glacial melt continues.

This paper examines cognitive biases which affect the ability of decision makers to make rational decisions in an organizational context. The motivation for this analysis begins with the observation of catastrophic accidents caused by human error but in an organizational context. This paper expands on the concept of cognitive bias to define organizational biases which are the factors that affect decisions in an organizational context. The paper distinguishes between organizational biases, which are the focus of this paper, and individual biases, which are biases experienced by individuals but may have organizational consequences. The purpose of this paper is to identify methods to mitigate the risks of organizational accidents, accidents which involve many people operating at different levels of an organization. The methodology is to identify those decisions that would address the specific organizational biases. The focus of this paper is the decisions for mitigating the risks associated with decisions in an organizational context. Results are shown for seven organizational biases, six specific case studies, and four decision options. This paper concludes that organizational biases are intrinsically different from individual biases and that these differences lead to different decision options from those that mitigate individual biases; however, they may exist concurrently.

This paper introduces the AltumView Sentinare smart activity sensor for senior care and patient monitoring. The sensor uses an AI chip and deep learning algorithms to monitor the activity of people, collect activity statistics, and notify caregivers when emergencies such as falls are detected. To protect privacy, only skeleton (stick figure) animations are transmitted instead of videos. The sensor is highly affordable, accessible, and versatile. It was a CES 2021 Innovation Award Honoree, and has been selected by Amazon as one of only three fall detection devices integrated into its Alexa Together urgent response service, and has received very positive reviews from Amazon customers. It has also been used in different senior care settings in about ten different countries. The paper presents the main features of the system, the evidences and lessons learned from its practical applications, and future directions.

The paper discusses how joint damage and deterioration affect the seismic response of existing reinforced concrete frames with sub-standard beam-column joints. The available simplified modeling techniques are critically reviewed to propose a robust, yet computationally efficient technique for simulating the nonlinear behavior of substandard beam-column joints. Improvements over the existing models include simulation of the cyclic deterioration of joint stiffness and strength as well as pinching in the hysteretic response, implemented considering a deteriorating hysteretic rule. A fibre-section forced-based inelastic beam-column element is developed; considering improved material models and fixed-end rotation due to bond failure, rebars-slip and inelastic extension, to simulate the deteriorating cyclic behavior of existing pre-cracked beam-column members. For the assessment of frames with substandard exterior beam-column joints, a nonlinear model for the exterior joint is developed and validated through a full-scale quasi-static cyclic test performed on a substandard T-joint connection. The proposed model allows considering structural performance in risk assessment while accounting for true inelastic mechanisms at the joints.

Earth Observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: Atmosphere, Water, Agriculture, Land, Disaster Risk Reduction (DRR), Cultural Heritage, Energy, Marine Safety and Security (MSS) and Big Earth Data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and Land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high resolution and near real time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems and services.

The electronic cigarette or e-cigarette is the new alternative method to deliver nicotine. In 2007, the United States introduced vaping devices on the market. Due to their attractive taste and lack of adverse effects, these vaping devices have gained popularity among youth and adults. However, there has been increasing evidence that vaping can cause severe lung injury, and these devices release toxic chemicals into the body. E-cigarettes are recognized as a global threat to public health. Nicotine, the most prevalent chemical in vaping devices, is associated with neurological problems and cognitive decline. Long-term use of e-cigarettes can cause adverse health problems, including respiratory illnesses, asthma, bronchitis, and lung inflammation. In 2019, the Centers for Disease Control and Prevention (CDC) officially declared vaping-associated illness EVALI (e-cigarette or Vaping Product Associated Lung Injury). The US Surgeon General published an alarm in December 2016 that e-cigarette consumption among adolescents and teens in the US is a "major public health concern" and that more young people are now vaping than smoking traditional cigarettes and cigars. Since its commercial introduction in 2003, modern electronic cigarettes (or E-cigarette s) have become increasingly popular with smokers, non-smokers, and those who used to smoke who have switched to them. With such a rise in consumption, people are becoming more aware of the irritating, harmful, and perhaps cancer-causing effects on the lungs. (Chadi et al., 2019; Dinardo & Rome, 2019; Lyzwinski et al., 2022; Marrocco et al., 2022; O’Callaghan et al., 2022)

Lung cancer (LC) is the second most common cancer and the leading cause of cancer deaths in the U.S. Insulin therapy, a key treatment for managing Type 2 Diabetes Mellitus (T2DM), may increase LC risk. The impact of non-insulin antidiabetic drugs, particularly GLP-1 receptor agonists (GLP-1RAs), on LC risk is not well understood. This study evaluated LC risk in T2DM patients comparing seven non-insulin antidiabetic agents to insulin. Using the TriNetX Analytics platform, we analyzed de-identified electronic health records of 1,040,341 T2DM patients treated between 2005-2019, excluding those with prior antidiabetic use or LC diagnoses. We calculated hazard ratios and confidence intervals for LC risk and used propensity score matching to control for confounding factors. All non-insulin antidiabetic drugs, except alpha-glucosidase inhibitors, significantly reduced LC risk compared to insulin, with GLP-1RAs showing the greatest reduction (HR: 0.49, 95% CI: 0.41, 0.59). GLP-1RAs were consistently associated with lowered LC risk across all histological types, races, genders, and smoking statuses. These findings suggest that non-insulin antidiabetic drugs, particularly GLP-1RAs, may be preferable for managing T2DM while reducing LC risk.

Urbanization induced land use land cover (LULC) changes intensify the urban heat island effects. It magnifies the risk of urban dwellers and sometimes causes the loss of human life, defined as heat-related health risk (HRHR). Hence, urban LULC planning plays a crucial role. Present study analyses the impact of composition and configuration of urban LULC defined as urban landscape metric (ULM) on HRHR in Delhi at the ward level. Firstly, the HRHR is measured by using satellite thermal and other digital data. Then, measured HRHR is validated by conducting a rapid field survey. Thirdly, ULM measured at ward level using Fragstat 4 software. Finally, both, HRHR and ULM linked with bi-variate Moran's I and impacts of ULM are assessed using ordinary least square (OLS) and spatial error (SE) regression. The result indicates the high risk is found as clustered in north-east, central and middle of south-west Delhi. Built-up density intensifies HRHR and abundance of vegetation reduce it; however, it is not similar for all vegetation patches. Larger vegetation patches surrounded by dense built-up might not able to reduce the risk as much as a large vegetation patch could in other regions. Findings can be helpful for heat resilient city planning.

Several cities are experiencing an increase in flood hazard and exposure, and consequentially in flood risk, attributable to the coupled effect of climate change and urbanization. Non-structural protection strategies demonstrate significant potential in the short term for mitigating hydraulic risk in urban areas, particularly where the implementation of structural measures is impeded by a complex urban environment. This work proposes an Early Warning System (EWS) for fluvial floods developed for the city of Palermo (Italy). It is based on the preliminary definition of po-tential Flood Event Scenarios (FESs) as a function of typical potential precursors. Each FES pre-dicts first points of flooding, flood areas and water depths, also providing specific hazard maps for people, vehicles, and buildings. The EWS uses rainfall depth-duration thresholds to assess the expected hydrograph as a function of rainfall forecasts and the water stage observed in real time at an upstream section of the river crossing the city. This last also allows for estimating the ante-cedent wetness conditions. Based on this information, the EWS retrieves an expected FES from a pre-built library with a preannouncement time up to 36 h. The EWS is here successfully tested on a historical flood event.

A contemporary technique for calculating seismic risk is earthquake nowcasting (EN), which analyzes the progression of the earthquake (EQ) cycle in fault systems. 'Natural time' is a novel idea of time that serves as the foundation for EN assessment. The earthquake potential score (EPS), which has been discovered to have useful uses regionally and worldwide, is a unique tool EN uses to predict seismic risk. For the estimation of the EPS for the occurrences with the highest magnitude among these applications, we have since 2021 concentrated on Greece territory, applying sophisticated Artificial Intelligence (AI) algorithms, both wise (semi)supervised and unsupervised models, along with a customized dynamic sliding window technique that performs as a stochastic filter able to fine-tune the geo seismic occurrences. Long short-term memory (LSTM) neural networks, random forests, and clustering (geospatial) models are three machine learning techniques that are particularly good at finding patterns in vast databases and may be used to enhance earthquake prediction performance. In this study, we attempt to forecast whether practical Machine-learning and AI/Game-theoretic-based approaches can help predict big earthquakes and the normal future seismic cycle for 6-12 months. Specifically, we focus on answering two questions for a given region: (1) Is there a chance that a significant earthquake—say, one with a magnitude of M ≥ 6.0 will happen in the upcoming year? (2) What is the largest earthquake magnitude predicted in the upcoming year, and with which exact geographic coordinates (GCS)? Our results are quite promising and project a high precision accuracy score (≥ 98%) for seismic nowcasting in terms of four predictive parameters: the approximate (a) latitude, (b) longitude, (c) focal depth, and (d) magnitude of the phenomena.

Advanced air treatment systems have the potential to reduce airborne infection risk, improve indoor air quality (IAQ) and reduce energy consumption, but few studies reported practical implementation and performance. PlasmaShield®, an advanced multi-modal HVAC-integrated system was directly compared with a standard MERV-13 system in a post-surgical pediatric healthcare setting. The evaluation entailed monitoring of multi-size airborne particles, bioaerosols and key IAQ parameters. Measurements were made of outside air, recovery room supply air and air in the occupied space for 3 days prior to, and after, installation of the PlasmaShield system. Compared with the existing arrangement, very significant reductions in particle number concentrations were observed in the occupied space, especially with virus-like submicron particles. Significant reductions in airborne culturable bacteria and fungi were observed in the supply air, with more modest reductions in the occupied space. In the case of virus-like particles, there was an 8-fold improvement in equivalent clean air, suggesting a 5-fold infection risk reduction for long range exposure. The data suggest multiple benefits of airborne particle and biaerosol reduction, with applications beyond healthcare. Long term studies are recommended to confirm the combined IAQ, health and energy benefits.

Cervical carcinogenesis is the leading cause of cancer related death in women and the role of high-risk human papillomavirus (HR-HPV) as a possible risk factor in the development of this cancer is well recognised. Despite the availability of multi-therapeutic approaches, there is still a major concern regarding the prevention of metastatic dissemination and excessive tissue injuries. Therefore, it is imperative to develop a safer and more efficient treatment modality. Ficus Carica, a natural plant, has shown potential therapeutic properties through its fruit latex when applied to HPV-positive cervical cancer cell lines. However, the mechanisms of action of Ficus Carica (fig) latex are not well understood. This study aims to provide a deeper insight into the biological activities of fig latex on human cervical cancer cell lines expressing high-risk HPV types 16 and 18. The data obtained from this study reveals that fig latex influences the expression of genes in-volved in "Class I MHC-mediated antigen presentation" as well as "Antigen processing: Ubiquitination and Proteasome degradation." These genes play a crucial role in host immune surveillance and the resolution of infection. Findings from this study suggest that fig latex may enhance T cell responses against oncogenic HPV, which could be beneficial for the clearance of early-stage cancer.

The Victorian Government Inquiry into wildfires that killed 173 people in 2009 has driven an Australian policy shift from self-evacuation or staying and defending a well-prepared property (‘go or stay’) to self-evacuation under catastrophic fire weather (‘leave early’). The Inquiry also led to the establishment of national ‘performance standards’ for Private Fire Shelters (PFS, that also known as bunkers). Nonetheless, incorporation of PFS into national bushfire policy remains embryonic with only Victoria having streamlined accreditation and planning approval processes. Arguments against PFS include potentially engendering complacency about preparing dwellings to survive fire and encourage risky behavior in response to a fire threat. Counteracting these arguments is research that shows that residents without PFS have low engagement with bushfire preparation and typically delay evacuation. In any case, because wildfire is unpredictable it is accepted that self-evacuation plans must have fallback positions that include sheltering ‘in place’ from the bushfire, making properly used and well maintained PFS an important element of bushfire safety. A less discussed barrier to PFS uptake outside Victoria appears to hinge on a lack of clarity about obligations for their design, certification, and consistency with planning approvals. The escalating Australian fire crisis demands much greater research and development in legal frameworks, policy and planning processes for PFS, as well as design and construction standards.

Abstract: This is a retrospective study of patients admitted to Jackson Hospital, Montgomery, Alabama with a diagnosis of COVID-19 from January 1, 2021, to February 15, 2022. The independent variable used in the models were patient sex, age, race, BMI category, daily D-dimer categories, categories of anticoagulation doses (Enoxaparin 40 mg VS. Enoxaparin 80 mg VS Enoxaparin 1mg/kg or equivalent), bleeding episodes, and vaccination status. The three different categories of anticoagulation doses were considered for the purpose of the study. Results: The study reviewed a total of one hundred (100) hospitalized patients. Intermediate-dose anticoagulation was found to be the optimal dose as only fourteen percent (14%) patients died compared to a thirty-six percent (36%) and fifty percent (57%) death rate among those treated with low-dose and high-dose anticoagulation, respectively. The multivariate linear regression model predicting patient oxygen requirements revealed D-Dimer and bleeding status to be statistically significant predictors with a p value of <0.01. For the patients who had a D-dimer value ≥ 2 µg/mL, the oxygenation requirement was predicted to be 31 L higher than those with a D-dimer <2 µg/mL (99% CI; p<0.01). When mean D-dimer and corresponding oxygen requirements were calculated per hospitalization days category, the D-dimer levels and oxygen requirements were noted to follow the same trends indicating both values tended to increase and decrease simultaneously. Conclusion: The study concludes daily D-dimer trends can predict COVID-19 patient survival or daily oxygen requirements indicating D-dimer can be the miracle molecule for COVID-19 prognosis.

Flood events and their associated damages have escalated significantly in the last few decades. To add to the gruesome situation, many reports and studies warn that flood risk would aggravate significantly in future periods due to significant alterations in the climate patterns and socio-economic dynamics. Floodplain mapping is looked upon as a viable option to tackle this global issue as it provides both quantitative and qualitative information on flood dynamics. Moreover, with the increasing availability of global data and enhancement in computational simulations, it has become easier to simlate flooding patterns at large scales. This study deter-mines the usability of publicly available datasets in capturing flood hazards over Canada. Run-off data set from the North American Regional Reanalysis (NARR), along with a few other rele-vant inputs are fed to CaMa-Flood, a robust global hydrodynamic model to generate flooding patterns for 1 in 100 and 1 in 200-yr return period events over Canada . The simulated maps are compared and validated with the existing maps of a few flood-prone regions in Canada, thereby establishing their performance over both regional and country-scale. Later, the simulated flood-plain maps are used in conjunction with property related information at 34 cities (within the top 100 populous cities in Canada) to determine the degree of exposure due to flooding in 1991, 2001, and 2011. The results indicate that around 80 percent of inundated spots belong to high and very-high hazard classes in a 200-yr event, which is roughly 4 percent more than simulated for 100-yr event. NARR derived floodplain maps perform very well while compared over the six flood-prone regions. While analyzing the exposure of properties to flooding, we notice an in-crease in the number during the last three decades, with the maximum rise observed in Toronto, followed by Montreal, and Edmonton. To disseminate the extensive flood-related information, a web-based public tool, Flood Map Viewer (http://www.floodmapviewer.com/) is developed. The development of the tool was motivated by the commitment of the Canadian government to provide $63 M over the next three years for the completion of flood maps for higher-risk areas. The study reaches out to demonstrate how publicly available datasets can be utilized with a lesser degree of uncertainty in representing flooding patterns over large regions. The flood re-lated information derived from the study can be used along with vulnerability for quantifying flood risk, which will help in developing appropriate pathways for resilience building for long-term sustainable benefits.

Deep reinforcement learning (DRL) has achieved significant results in many Machine Learning (ML) benchmarks. In this short survey we provide an overview of DRL applied to trading on financial markets, including a short meta-analysis using Google Scholar, with an emphasis on using hierarchy for dividing the problem space as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market.

The rising incidence of complex illnesses and their costs have revolutionized basic research, patient management, and societal needs. Between 70 to 90% of the risk of developing a disease is due to the air we breathe, the water we drink, and the surroundings in which we work and live. Visibly polluted, infectious or not, the fact remains that we are more than ever exposed to environmental risks. Air pollution is the fourth most prevalent deadly risk factor worldwide and by far the leading risk factor for hundreds of diseases, including respiratory infections, inflammatory illness, and cancer. Thus, an unhealthy environment can be considered as a pandemic, affecting 280 million people and claiming 12 million deaths every year. Although critical for identifying of the people at risk, the causal environment components (pollutants and/or the microbiome), and the affected physiological mechanisms are not well understood. Herein, we consider the dysregulation of macroautophagy (hereafter referred to as ‘autophagy’), as the mechanism at the heart of an immediate response to environmental stress. We discuss the missing link between the autophagy gene variations, and the exposome in the susceptibility, prognosis, and management of complex diseases when embracing personalized medicine.

Risk-based redispatch optimization is proposed as a methodology to support the Transmission System Operator (TSO) with preventive remedial actions obtained by extending the security constrained unit commitment/economic dispatch with constraints resulting from the risk assessed for the power system. Although being heuristic, the methodology is based on comprehensive dynamic security assessment as time-domain simulations are used, allowing to express the degree of all types of instabilities, e.g., caused by contingencies, in monetary terms. Therefore, the risk is assessed as the expected value of the cost incurred by the TSO. Such approach forms a new pathway to including risk in planning procedures already used by TSOs. Results obtained for the IEEE39 dynamic power system, with costs assigned to load shedding and generator tripping due to single transmission lines short-circuits, are shown as a reference case.

One of the most burning topics in cybersecurity in 2023 will undoubtedly be the compliance with the Software Bill of Materials. Since the US president issued the Executive Order 14028 on Improving the Nation’s Cybersecurity, software developers have prepared and bills are transmitted to vendors, customers, and users, but they don’t know what to do with the reports they are getting. In addition, since software developers have identified the values of the Software Bill of Materials, they have been using the reports extensively. This article presents an estimate of 270 million requests per month, just from form one popular tool to one vulnerability index. This number is expected to double every year and a half. This simple estimate explains the urgency for automating the process. We propose solutions based on artificial intelligence and machine learning, and we base our tools on the existing FAIR principles (Findable, Accessible, Interoperable, and Reusable). This methodology is supported with a case study research and Grounded theory, for categorising data into axis, and for verifying the values of the tools with experts in the field. We showcase how to create, and share Vulnerability Exploitability eXchange data, and automate the Software Bill of Materials compliance process with AI models and a unified computational framework combining solutions for the following problems: (1) the data utilisation problem, (2) the automation and scaling problem, (3) the naming problem, (4) the alignment problem, (5) the pedigree, and provenance problem, and many other problems that are on the top of mind for many security engineers at present. The uptake of these findings will depend on collaborations with government and industry, and on the availability and the ease of use of automated tools.

Neuroblastoma (NB) is a solid tumor occurring in infancy and childhood, whose high-risk form has currently a survival rate &lt; 50 %, despite aggressive treatments. Additionally, drugs-induced secondary tumorigenesis and the emergency of drug resistance worsen the already worrying scenario, calling for an urgent development of new extra-genomic treatments. Quaternary phosphonium salts (QPSs) possess both antimicrobial and anticancer effects. Triphenyl phosphonium salts (TPPs) are mitochondria-targeting compounds, exerting anticancer effects, impairing mitochondria functions and damaging DNA, at the same time. TPP-based bola-amphiphiles, previously proven to self-assemble nanoparticles (NPs) in water and successfully assayed as possible antibacterials, were never tested as suitable antitumor agents. Here, aiming at the development of new antitumor devices to counteract resistant forms of NB, the anticancer effects of a TPP-based amphiphile molecule have been investigated for the first time. To this end, we considered the water-soluble sterically hindered quaternary phosphonium salt (BPPB) encompassing two triphenyl phosphonium groups linked by a C12 alkyl chain, thus embodying both the characteristics of mitochondria-targeting compounds and those of bola-amphiphiles. The anticancer effects of BPPB were assessed against HTLA-230 human stage-IV NB cells and their counterpart resistant to etoposide (ETO), doxorubicin (DOX) and to many other therapeutics (HTLA-ER). Determined IC50 values were very low on both cell populations, already after 24 hours of treatment, when cells viability did not significantly differ from that observed for the longest exposure timing. Moreover, to investigate a putative future inclusion of BPPB in a chemotherapeutic cocktail for NB, its cytotoxic effects were assessed against mammalian cell lines. These included monkey kidney cells (Cos-7), human hepatic cells (HepG2), a lung-derived fibroblast cell line (MRC-5) and red blood cells. From appreciable to very high selectivity indices have been determined after 24 hours treatments, an exposure time after which both NB cell populations tested appeared already fully exterminated.

Psychiatric disorders are mental, behavioral or emotional disorders. These conditions are prevalent, one in four adults suffer from any type of psychiatric disorders world-wide. It has always been observed that psychiatric disorders have a genetic component, however new methods to sequence full genomes of large cohorts have identified with high precision genetic risk loci for these conditions. Psychiatric disorders include, but are not limited to, bipolar disorder, schizophrenia, autism spectrum disorder, anxiety disorders, major depressive disorder, and attention-deficit and hyperactivity disorder. Several risk loci for psychiatric disorders fall within genes that encode for voltage-gated calcium channels (Ca_Vs). Calcium entering through Ca_Vs is key for multiple neuronal processes. In this review, we will summarize recent findings that link Ca_Vs and their auxiliary subunits to psychiatric disorders. First, we will provide a general overview of Ca_Vs structure, classification, function, expression and pharmacology. Next, we will summarize tools and databases to study risk loci associated with psychiatric disorders. We will examine functional studies of risk variations in Ca_V genes when available. We will review pharmacological evidence of the use of Ca_V modulators to treat psychiatric disorders. Our review will be of interest for those studying pathophysiological aspects of Ca_Vs.

This review aims to show, by an analysis of the literature on the topic, that hyperinsulinemia associated with insulin resistance is a multiple risk factor for type 2 diabetes, cardiovascular diseases, cellular senescence and cancer, and neurodegenerative diseases. This condition is progressively increasing in developed and developing countries, and its prevalence has now exceeded 50% of the general population. Since it is asymptomatic or poorly symptomatic, it can last for many years before manifesting itself in the form of type 2 diabetes mellitus, cardiovascular disease, neoplasm, cognitive impairment or dementia, therefore determining enormous social and health care costs. For these reasons, we strongly believe that a screening plan for this pathology should be implemented, in order to identify affected subjects and promptly start them on preventive treatment.

This qualitative study investigates the management of supplier risks in e-commerce through relationship management strategies, focusing on trust-building, communication practices, digital technology integration, risk assessment methodologies, challenges, innovation initiatives, and ethical considerations. The research aims to provide nuanced insights into how e-commerce companies navigate the complexities of global supply chains to mitigate operational, financial, and reputational risks associated with supplier relationships. Through semi-structured interviews with 20 key stakeholders in the e-commerce sector, data were collected and analyzed using thematic analysis. The findings underscored the foundational role of trust in establishing resilient supplier relationships, facilitated by transparent communication, consistent performance evaluation, and mutual respect for contractual obligations. Effective communication practices, supported by digital platforms and technologies such as AI, blockchain, IoT, and cloud computing, enhanced operational transparency and decision-making capabilities across supply chains. The study identified diverse approaches to risk assessment, from qualitative evaluations to quantitative models utilizing predictive analytics and scenario planning. However, participants highlighted challenges including geopolitical uncertainties, trade disruptions, regulatory changes, and cultural barriers, necessitating adaptive strategies and diversified sourcing options. Innovation initiatives such as joint product development and technology adoption were crucial in enhancing supply chain agility and competitive advantage. Ethical considerations emerged as a critical aspect, influencing supplier selection criteria, CSR initiatives, environmental sustainability practices, and labor standards compliance. Integrating ethical guidelines into supplier contracts reinforced corporate values and enhanced brand reputation. Overall, this study contributes practical implications for e-commerce practitioners seeking to enhance supply chain resilience, mitigate supplier risks, and sustain competitive advantage in a dynamic global marketplace.

The qualitative research on the influence of supply chain risk management (SCRM) on marketing strategies during economic uncertainty explores the complex interplay between these two critical areas. Conducted through in-depth interviews with industry professionals across various sectors, the study examines how organizations manage supply chain risks and adapt their marketing strategies in response to challenges such as natural disasters, geopolitical tensions, economic downturns, and pandemics. The findings reveal that these risks significantly impact marketing efforts by causing supply shortages, delays, increased costs, and demand fluctuations, necessitating rapid adjustments in promotional activities, pricing strategies, and communication with customers. Key strategies for integrating SCRM with marketing include leveraging technology for real-time supply chain monitoring, building strong supplier relationships, diversifying supply sources, and enhancing communication and collaboration between supply chain and marketing teams. These practices enable organizations to enhance their resilience and responsiveness, ensuring that marketing strategies are aligned with the evolving realities of the supply chain. The study also identifies challenges such as organizational silos, lack of cross-functional collaboration, resistance to change, and limited technological capabilities, which can hinder effective integration. Addressing these barriers through fostering collaboration, investing in technology, and promoting change management is essential for achieving successful integration. The outcomes of this integration include improved supply chain resilience, enhanced customer satisfaction, increased marketing agility, and a stronger competitive position. The research underscores the importance of a holistic and adaptive approach to integrating SCRM with marketing strategies, providing valuable insights for organizations seeking to navigate economic uncertainty and sustain their growth in a rapidly changing environment.

To address the problems of wind abandonment in the regional integrated energy system (RIES), which occurs when cogeneration unit operates in a heat-determined electricity operation mode, and the low efficiency of the energy storage system in a low-temperature environment, we propose an optimal scheduling method for RIES based on fine energy storage and wind power dissipation. First, a fine energy storage model is established on the source side and the conditional value at risk(CvaR) theory is used to quantify the uncertainty of wind power; then a combined heat and power demand response mechanism is introduced on the load side to reduce the peak-to-valley difference between heat and power loads, and to promote the consumption of wind power. Finally, with the objective of minimizing the total cost of RIES optimal dispatch, the example is solved on the MATLAB platform. The simulation results show that compared with the traditional model, the proposed model is not only more adaptable to the low-temperature environment, but also can effectively improve the system economy and wind power consumption.

The global transport sector of the world economy contributes about 15% of the Greenhouse Gas (GHGs) emissions in the world today. The University of Saskatchewan has pursued the green energy transition over the years. They have spearheaded diverse sustainability projects and agendas, due to the importance of curbing climate change and advancing sustainability. The transport system in the university campus is one area of focus where the Sustainability Office plans to introduce some innovations, as a way of curbing GHG emissions while also advancing sustainability practice in the university campus. The study carried out an economic benefit analysis on the campus fleet (consisting of 91 ICE vehicles) to determine if it is economically or financially feasible to transition from Internal Combustion Engine (ICE) or PVs (Petrol Vehicles) to Electric Vehicles (EVs). The analysis used RETScreen Expert software for analyzing renewable energy technology projects. The variables of Payback Period (PBP), cash flow projections, savings made from transitioning (fuel cost savings and energy cost savings), Benefit-Cost-ratio, GHG emission reduction potential, etc. were analyzed. The findings revealed that the GHG emission from the campus fleet will be reduced by 100% (this will result in the removal of about 298.1 tCO₂ from the environment). Also, the fleet manager will save approximately $129,049 (88.9%) in fuel costs. Apart from these, the return on investment will be achieved in year 5 (all things being equal), but can be reduced to year 2 if the vehicles are put into constant and active use (eliminating most idle times. Also, the Sustainability Office will be making a GHG reduction revenue of $14,906.

The aim of this study was to evaluate the acceptability and feasibility of a theory-derived sedentary workplace intervention (single arm, pre-post design) for police office staff. Twenty-four staff participated in an 8-week intervention incorporating an education session, team competition with quick response (QR) codes, team trophy, and weekly leaderboard newsletters, a self-monitoring phone app, and electronic prompt tools. The intervention supported participants to reduce and break up their sitting time with three minutes of incidental movement every 30 minutes at work. Feasibility and acceptability were assessed using mixed methods via the RE-AIM QuEST and PRECIS-2 frameworks. The intervention was highly pragmatic in terms of eligibility, organisation, adherence, outcome, and analysis. It was slightly less pragmatic on recruitment and setting. Delivery and follow-up were more explanatory. Reach and adoption indicators demonstrated feasibility among police staff, across a range of departments, who were demographically similar to participants in previous office-based multi-component interventions. The intervention was delivered mostly as planned with minor deviations from protocol (Implementation fidelity). Participants perceived the intervention components as highly acceptable. Preliminary results showed improvements in workplace sitting and standing, as well as small improvements in weight and positive affect. Evaluation of the intervention in a fully powered randomised controlled trial to assess behaviour and health outcomes is recommended.

Economic risks for livestock production are caused by volatile commodities and market conditions, but also by environmental drivers like increasing uncertainties due to weather anomalies and global warming. These risks impact the gross margin of farmers and can stimulated investment decisions. For confined pig and poultry production, farmers can reduce the environmental impact by implementing specific adaptation measures to reduce heat stress. A simulation model driven by meteorological data was used to calculate heat stress impact as a projection for 2030. For a business-as-usual livestock building, the indoor climate for several adaptation measures was calculated. The weather-related value-at risk quantified the economic risks caused by global warming and the stochastic component of the weather. The results show that only energy-saving adaptation measures to reduce the inlet air temperature are appropriate to reduce the economic risk to the level of the year 1980. The efficiency of other adaptation measures to reduce heat stress is distinctly lower. The results in this study can support the decision making of farmers concerning adaptation management and investments. It can inform agricultural policy design as well as technological development.

In light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programs will identify hundreds of novel viruses that might someday pose a threat to humans. Our capacity to identify which viruses are capable of zoonotic emergence depends on the existence of a technology—a machine learning model or other informatic system—that leverages available data on known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions: What are the prerequisites, in terms of open data, equity, and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it, and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges?

NASA mission systems proposals are often compared using an equivalent system mass (ESM) framework wherein all elements of technology to deliver an effect- its components, operations and logistics of delivery- are converted to effective masses since this has a known cost scale in space operations. To date, ESM methods and the tools for system comparison have not considered complexities wherein systems that serve a mission span multiple transit and operations stages, such as would be required to support a crewed mission to Mars, and thus do not account for the different mass equivalency factors operational during each period and the inter-dependencies of the costs across the mission. Further, ESM does not account well for the differential reliabilities of the underlying technologies. Less reliability should incur an equivalent mass cost for technologies that might otherwise provide a mass advantage. We introduce an extensions to ESM to address these limitations and show that it provides a direct method for analyzing, optimizing and comparing different mission systems. We demonstrate our extended ESM (xESM) calculation with crop production technologies -- an aspect of the developing offworld biomanufacturing suite -- since it represents a case with strong coupling among stages of the mission and a relatively high-risk profile.

The purpose of work is to quantify the changes of the organism functional reserves during the successive phases of the human health Space-Time Continuum (STC) on the basis of postulates of reliability complex systems theory. The study is devoted to the problem of age-related human involution, which is evaluated not from the causal, but from the kinetic point of view. The analysis of 10 most important basic life support systems of human body — cardiovascular (CVS), respiratory (RS), nervous (NS), digestive (DS), endocrine (ES), immune (IS), excretory (EXS), brain (BS), musculo-skeletal (MSS), hematopoietic (HS) was carried out. Based on this analysis two levels of ensuring the reliability of organism’s work were revealed: sequential and parallel. The system of logical equations for reduced sequential system is: Ys1 = CVS ∧ RS ∧ BS, where ∧ is the notation for the conjunctions of set elements. The system of logical equations for the reduced parallel system is: Ys2 = NS ∨ DS ∨ ES ∨ IS ∨ HS ∨ EXS ∨ MSS ,where ∨ is the disjunction of the scheme elements. Visualization of human STC changes the concept of the kinetics of age-related changes in the organism and the role of determinants of health as a stable factor accompanying a uniform, smooth transition from the most pronounced functions of the body to their gradual extinction. For human STC is formulated the following regularity kinetics of involutionary processes: after 30 years of age in the human body morphological changes regress in arithmetic progression, and the functions of organs in a geometric one. Assumption of health as a state redundancy of functions is suggested.

The extensive use of engineered nanoparticles (ENPs) such as zinc oxide nanoparticles (ZnO NPs) in various commercial fields has spurred significant concern about its possible toxicological effects of ZnO NPs on human health and eco-system. Therefore, it is substantial to understand the aggregation phenomena which could attenuate this effect. This data article provides information on aggregation of ZnO NPs under the influence of various parameters of water chemistry. Moreover, Taguchi orthogonal array L27(313) design matrix was used to evaluate the effect of multiple parameters in aqueous solution. The data were obtained from single beam UV-Vis spectrophotometer (Optizen 2120 UV, Mecasys, Korea) using Optizenview 4.2.5 PC interference software, Fourier transform infrared spectrometer (FT/IR-4700, spectroscopy, JASCO Analytical Instruments, Easton, USA) and particle electrophoresis (NanoZS, Zetasizer, Malvern Instruments Ltd, Worcestershire, UK). The dataset draws attention on dominant parameters influencing aggregation of ZnO NPs in water. The analysis of variance (ANOVA) data revealed that electrolyte concentration, type and natural organic matter (NOM) concentration were the most significant parameters. Furthermore, FT-IR data presents a possible mechanism of ZnO NPs stabilization in the presence of different NOM. This data will be helpful for the development of environmental risk assessment strategies and prediction of fate and mobility of other ENPs in the aquatic environment.

The scarcity and pollution of freshwater are extremely crucial issues today and the expansion of water reuse have been considered as an option to reduce its impact. This study aims to assess the efficiency of an integrated greywater treatment system and hydroponic lettuce production as a part of a green wall structure and to evaluate the health risk associated with the production and consumption of lettuce through quantitative microbial risk assessment (QMRA) and chemical health risk assessment. The study was conducted based on the unique configuration of source separation system; on-site greywater treatment system; green wall structure as a polishing step; and hydroponic lettuce production in the green wall structure. The final effluent from the system was used to grow three lettuce varieties by adding urine as a nutrient solution. Both water samples and plant biomass were collected and tested for E. coli and heavy metals contamination. The system has gained a cumulative 5.1 log10 reduction of E. coli in the final effluent and no E. coli found in the plant biomass. QMRA results indicated that the system attained the health-based targets, 10–6 DALYs per person per year. Similarly, health risk index (HRI) and targeted hazard quotient (THQ) results did not exceed the permissible level, thus the chemical health risk concern was insignificant.

Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.

Cervical cancer, primarily caused by high-risk human papillomavirus (HR-HPV) types 16 and 18, is a major global health concern. Persistent HR-HPV infection can progress from reversible pre-cancerous lesions to invasive cervical cancer, which is driven by the oncogenic activity of human papillomavirus (HPV) genes, particularly E6 and E7. Traditional screening methods, including cytology and HPV testing, have limited sensitivity and specificity. This review explores the ap-plication of p16/Ki-67 dual-staining cytology for cervical cancer screening. This advanced im-munocytochemical method allows for simultaneously detecting p16 and Ki-67 proteins within cervical epithelial cells, offering a more specific approach for triaging HPV-positive women. Dual staining and traditional methods are compared, demonstrating their high sensitivity and negative predictive value but low specificity. The increased sensitivity of dual staining results in higher detection rates of CIN2+ lesions, which is crucial for preventing cervical cancer progression. However, its low specificity may lead to increased false-positive results and unnecessary biopsies. The implications of integrating dual staining into contemporary screening strategies, particularly considering the evolving landscape of HPV vaccination and changes in HPV genotype prevalence are also discussed. New guidelines and further research are necessary to elucidate the long-term effects of integrating dual staining into screening protocols.

Breast cancer is the most common cause of mortality due to cancer for woman both in Lithuania and worldwide. Chances of survival after diagnosis differ significantly depending on the stage of disease at the time of diagnosis and other factors. One way to estimate survival is to construct a Kaplan-Meier estimate for each factor value separately. However, in cases when it is impossible to observe a large number of patients (for example, in case of countries with lower numbers of inhabitants) dividing data into subsets, say, by stage at diagnosis may lead to results where some subsets contain too little data so that results of Kaplan Meier (or any other) method will become statistically incredible. The problem may become even more acute if the researcher would like to use more risk factors, such as stage at diagnosis, sex, place of living, treatment method, etc. Alternatively, Cox models are used to analyse survival data with covariates, and they don’t require dividing the data into subsets according to chosen risks factors (hazards).

Background: Over 900,000 cases of COVID-19 and 23,000 deaths have been reported till 13 July in India. Preserving the limited healthcare workforce is part of the strategy against the pandemic. Mortality and morbidity data have a role in customising this strategy. At this time, there is no published study on COVID-related mortality among doctors or other healthcare workers in India. Methods: A multi-pronged search was made for all reported deaths linked with COVID-19 among doctors in India. Details of COVID-linked deaths reported by mainstream media and by multiple professional social media sources were collected, screened, verified and analysed. Violent deaths occurring in the setting of pandemic-related work were separately listed. Deaths from other diseases were excluded. Results: Among 108 COVID-linked deaths among doctors, there were four pandemic-related violent deaths including three road accidents and a suicide. Of the 104 non-violent deaths, 55.5% were below 60 years of age, while 29.6% and 21% were below the age of 50 and 40 respectively. The average age at death was 56.3 (range 22 - 96). Over half of the deaths occurred among general practitioners, while surgical specialties accounted for 27% of the mortality. The geographic distribution of deaths of doctors correlated with the reported number of COVID-19 patients in each state. The total number of COVID-related healthcare worker deaths was 136, out of which eight (5.8%) were violent, and occurred in young individuals with an average age of 27.8 years. Conclusions: The majority of the 104 COVID-related non-violent deaths among doctors (55.5%) occurred below the age of 60. The average at death was 56.6 years. The states with highest number of COVID-19 cases had greater number of doctor deaths. Violent deaths among young healthcare workers in the setting of the pandemic requires special attention.

The planning and decision-making for a distributed energy supply concept in complex actor structures like in districts calls for the approach to be highly structured. Here, a strategy with strong use of energetic simulations is developed, the core elements are presented and research gaps are identified. The exemplary implementation is shown using the case study of a new district on the former Oldenburg airbase in northwestern Germany. The process is divided into four consecutive phases, which are carried out with different stakeholder participation and use of different simulation tools. Based on a common objective, a superstructure of the applicable technologies is developed. Detailed planning is then carried out with the help of a multi-objective optimal sizing algorithm and Monte Carlo based risk assessment. The process ends with the operating phase, which is to guarantee a further optimal and dynamic mode of operation. The main objective of this publication is present the core elements of the planning processes and decision-making framework based on the case study and to find and identify research gaps that will have to be addressed in the future.

Wave energy converter (WEC) arrays deployed in coastal regions may create physical disturbances potentially resulting in environmental stresses. Presently, limited information is available on the nature of these physical disturbance or the resultant effects. A quantitative Spatial Environmental Assessment Tool (SEAT) for evaluating potential effects of wave energy converter (WEC) arrays on nearshore hydrodynamics and sediment transport is presented for the central Oregon coast (USA) through coupled numerical model simulations of an array of WECs. Derived climatological wave conditions were used as inputs to the model to allow for the calculation of risk metrics associated with various hydrodynamic and sediment transport variables such as maximum shear stress, bottom velocity, and change in bed elevation. The risk maps provided simple, quantitative, and spatially-resolved means of evaluating physical changes in the vicinity of a hypothetical WEC array in response to varying wave conditions. Near-field risk of sediment mobility was determined to be moderate in the lee of the densely spaced array, where the potential for increased sediment deposition could result in benthic habitat alteration. Modifications to the nearshore sediment deposition and erosion patterns were observed near headlands and topographic features, which could have implications for littoral sediment transport. The results illustrate the benefits of a risk evaluation tool for facilitating coastal resource management at early market marine renewable energy sites.

Changes in extreme weather patterns are expected under climate change. In this study, a risk assessment was conducted using 4 building damage history datasets and 33 weather datasets (precipitation, wind speed, snow, and temperature) from 230 regions in South Korea to quantitatively analyze and predict building damage caused by potential future natural disasters. Decision tree analysis was used to evaluate building damage risk in 230 regions. The decision tree model to determine the risk of flood, gale, and typhoon was generated, which excluded gales, with less damage. The weight (variable importance) and limit value (damage limit) of the weather variables ware derived using the decision tree model. Using these two factors, we assessed the building damage risk in 230 regions in South Korea until 2100. The number of regions at risk of flood damage increased by more than 30% in average. Conversely, regions at risk of snowfall damage decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, and the number of regions at high risk of typhoon damage increased by up to 62.5% in RCP 8.5. These results can be used as objective data to minimize future building damage throughout South Korea, representing the first step towards sustainable development in the region with respect to disaster response.

High fructose and sugar-sweetened soft drink (SSSD) intakes are associated with elevated blood uric acid concentrations and increased risk of gout and cardiovascular disease. Fruits are naturally high in fructose but their effect on cardiometabolic risk is unknown. We examined the effect on serum uric acid and cardiometabolic risk factors of consuming fructose from either fruit or SSSD in overweight adults. 48 healthy, overweight (BMI≥ 28 kg/m²) men (n=21) and women (n=20) were randomised to either a fruit (n = 19) or SSSSD (n = 22) intervention for 4 weeks. The fruit group received 6 items of fresh and dried fruit per day and the SSSD group received 955ml of SSSD per day with treatments matched for energy and fructose content. Serum uric acid concentrations were significantly reduced in men in the fruit group compared to the SSSD group (difference 57.2 μmol/L [95% CI: 16.4, 98.0], p= 0.008) but there was no difference amongst women (1.3 μmol/L [95%CI: -9.5, 6.9], p= 0.295). There differences in weight change or other cardiometabolic risk factors. These findings suggest no need to restrict fruit intake in individuals with elevated serum uric acid concentrations, such as those with gout.

Mobile devices (MD) are used by mobile cloud computing (MCC) customers users because of their 11 portability and robust connectivity and the ability to house and operate third-party applications 12 (apps). However, the apps installed on a MD may pose data security risks to the MD owner and to 13 other MCC users, especially when the requested permissions include access to sensitive data (e.g., 14 user’s location and contacts). Calculating the risk score of an app or quantifying its potential 15 harmfulness based on user input or on data gathered while the app is actually running may not 16 provide reliable and sufficiently accurate results to avoid harmful consequences. This study 17 develops and evaluates a risk assessment framework for Android-based MDs that does not depend 18 on user input or on actual app behaviour. Rather, an app risk evaluator assigns a risk category to 19 reach resident app based on the app’s classification (benign or malicious) and the app’s risk score. 20 The app classifier (a trained machine learning model) considers the permissions and the intents 21 requested by the app. The apps risk score is calculated by a probabilistic function based on the 22 app’s use of a set of selected dangerous permissions. The results from the testing of an instance of 23 the framework on a MD with real-life resident apps indicated that the proposed security solution 24 was effective and feasible.

The infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates usually called ‘the clones of high risk’ often drive the spread of resistance within particular species. Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and developing of spread prevention measures. However, the availability and uniformity of the data derived from the genomic sequences often represents a bottleneck for such investigations. In this dataset, we present the results of a comprehensive genomic epidemiology analysis of 17,546 genomes of a dangerous bacterial pathogen Acinetobacter baumannii. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), intrinsic blaOXA-51-like gene variants, capsular (KL) and oligosaccharide (OCL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to nine known international clones of high risk. The presence of antimicrobial resistance genes within the genomes is also reported. These data will be useful for researchers in the field of A. baumannii genomic epidemiology, resistance analysis and prevention measure development.

Alzheimer's disease (AD) is a progressive and evolving neurodegenerative disease with an insidious onset that can lead to memory loss and cognitive impairment. There is no effective treatment for this disease. However, early diagnosis plays an important role in treatment planning to slow down its progression, as treatment has the greatest impact in the early stages of the disease. Neurological images obtained through different imaging techniques provide powerful information and help diagnose the disease. With the wide application of deep learning techniques in disease diagnosis, especially the prominence of Convolutional Neural Networks (CNNs) in computer vision and image processing, more and more studies are proposing the use of this algorithm for the diagnosis of AD. In this paper, we first systematically introduce the impact of AD on people, detailing the biomarkers, early clinical symptoms, and risk factors of this disease. Secondly, it goes on to detail the development of CNNs, their form, and methods to help diagnose AD. It is proposed that CNNs can help diagnose AD by analyzing medical imaging data, particularly structural brain scans such as magnetic resonance imaging (MRI) and functional scans such as positron emission tomography (PET). Finally, it is concluded that CNNs are of great importance for the diagnosis of AD and that they are likely to play an increasingly important role in the early detection of the disease, the understanding of disease mechanisms, and ultimately, in the development of effective AD therapies and interventions. CNNs are playing an increasingly important role in the Their potential impact on healthcare emphasizes the importance of continued research and innovation in neural networks and medical imaging.