I examine national environmental security policy cultures to assess the opportunities for increasing global environmental security. The question is whether the difficulties in modeling the state's ecological security are a consequence of limitations in the coherence of the four dogmas: ecological security, sustainable development, social responsibility and environmental management. The presented fragments of theoretical research explain the question by presenting the logic of the constructive value of the national culture of ecological safety policy. I conducted empirical research based on 18 development strategies of regional cities in Poland. The multi-criteria method made it possible to identify and describe a micro-community reflecting the macroscopic observation of regional cities as an emerging whole of the state’s development. Assumption: ownership of the environmental aspects of cities is a natural quantity influencing the ecological security of the state in the contemporary global civilization system. I have formulated new definitions: ecological security of the state, culture of ecological security policy, sustainable development and socially responsible and sustainable development of the city. I have developed 13 catalogues of elements of the structure of the development strategy of regional cities in Poland, containing 1 390 public services. I have shown when there is a lack of culture of the state's ecological security policy in the global ecological security system according to the cause-effect principle. I have been conducting research since 2019.

In the knowledge era, intellectual capital has been considered a key factor in creating value within organisations. This study examines the relationships and interactions between the components of intellectual capital and the profitability of Panamanian banking, and financial institutions listed on the Latin American Stock Exchange (LATINEX) from 2014 to 2020. Utilising the Valued-Added Intellectual Coefficient (VAIC)™ model, which evaluates the intellectual capital of organisations based on information from financial statements, the study constructs a regression model to examine the relationship between the Return on Equity (ROE) and the components of the VAIC™. The findings confirm the study’s hypothesis, demonstrating that the Structural Capital Efficiency (SCE) and company size (SIZE) variables explain 57% of the variance in ROE for the analysed institutions. The results suggest that the Intellectual Capital (IC) of financial sector institutions listed on LATINEX is significantly influenced by the SCE coefficient, which shows a negative relationship, suggesting that investment in structural capital does not enhance profitability. On the other hand, larger institutions exhibited higher profitability during the study period.

Flood frequency analysis at large scales, essential for the development of flood risk maps, is hindered by the uncertainty in outputs of process-based hydrological models and the scarcity of gauge flow data. We develop a Bayesian hierarchical model (BHM) based on the Generalized Extreme Value (GEV) distribution for regional flood frequency analysis at high resolution across North America. Our model leverages annual maximum flow data from ≈20000 gauged stations and a dataset of 130 static catchment-specific covariates to predict extreme flows at all catchments over the continent as well as their associated statistical uncertainty. Additionally, a modification is made to the data layer of the BHM to include daily gauge discharge data when available, which improves the precision of the discharge level estimates. We validated the model using a hold-out approach and found that its predictive power is very good for the GEV distribution location and scale parameters and improveable for the shape parameter, which is notoriously hard to estimate. The resulting discharge return levels yield satisfying agreement when compared with available design peak discharge from various government sources. Assessment of the covariates’ contributions to the model also informs on the most relevant underlying factors influencing flood-inducing peak flows. The key covariates in our model are temperature-related bioindicators, the catchment drainage area and geographical location.

This qualitative study explores the role of supply chain partnerships in enhancing brand value through collaborative marketing efforts. By analyzing diverse cases from industries including consumer electronics, apparel, automotive, and food and beverage, the research examines how different forms of partnerships—strategic alliances, co-marketing, joint ventures, and supplier integrations—contribute to brand value. The study employed a multiple case study design, incorporating semi-structured interviews, document analysis, and participant observations to gather data. The findings reveal that these partnerships enable companies to leverage complementary strengths, resulting in enhanced innovation, product quality, sustainability, market reach, and ethical standards. Collaborative marketing strategies such as joint product launches, sustainable branding campaigns, and co-developed customer programs are identified as key drivers of brand value, effectively increasing brand awareness, customer loyalty, market share, and perceived quality. Despite challenges like coordination complexity, goal misalignment, cultural differences, and cost management issues, the benefits realized—such as improved brand image, faster time-to-market, and reduced costs—demonstrate the substantial value of effective supply chain partnerships. The study underscores the importance of strategic alignment between supply chain activities and brand objectives, highlighting that successful partnerships foster deeper consumer engagement and stronger market positions. This research provides actionable insights for companies seeking to optimize their supply chain collaborations to enhance brand value, offering a foundation for future studies and practical applications in supply chain management and marketing. The findings contribute to a deeper understanding of how supply chain partnerships can be strategically managed to drive brand excellence in a competitive market environment.

Recently, there appeared in this journal (Beh and Lombardo \textbf{2022}, {\it Symmetry}, 14, 1103) a paper that showed how to perform a correspondence analysis on a two-way contingency table where Bowker's statistic lies at the numerical heart of this analysis. Thus, we showed how this statistic can be used to visually identify departures from perfect symmetry. Interestingly, Bowker's statistic is a special case of the symmetry-version of the Cressie-Read family of divergence statistics. Therefore, this paper presents a new framework for visually assessing departures from perfect symmetry using a second-order Taylor series approximation of the Cressie-Read family of divergence statistics.

The production of Moringa oleifera capsules has emerged as a promising agricultural activity in Mexico, driven by the plant’s well-documented health and nutritional benefits. In response, farmers have begun producing Moringa oleifera as a dietary supplement, using artisanal methods with minimal production controls. This study aimed to estimate the processing times of the Moringa oleifera production system using simulation modeling with Arena® software, and to evaluate the economic feasibility of capsule production. Methodologically, it was approached as a case study in the state of Veracruz, processing times were estimated through systematic sampling and modeled with Arena simulation software. Three simulation scenarios were considered to select a technically and economically viable alternative for agricultural producers: one with a 200-capsule encapsulator (1E200C), another with two 200-capsule encapsulators (2E200C) and a third with an 800-capsule encapsulator (1E800C). For the initial diagnosis a research stay was conducted for 180 days, and the production capacity was set at 24,000 capsules due to raw material limitations. Results indicated that the 1E800C scenario reduced processing time by 60% compared to the1E200C scenario, and by 35% compared to the 2E200C scenario. Additionally, the 1E800C model required only 2 days (16 hours) of work, while the 2E200C required 4 days (32 hours), and the 1E200C required 7 days (56 hours). In terms of production costs per unit of 90 capsules, the 1E200C was 3.93 USD the 2E200C 3.64 USD and the 1E800C 3.45 USD The study concluded that due to raw material limitations, the company can produce 12,800 units per year of 90 capsules. It is recommended to adopt the 1E800C encapsulator, which would lower overall production costs by 12.23%, representing 0.48 USD per unit produced, resulting in an additional profit of 6,150.50 USD Over a five-year evaluation period, the benefit-cost ratio was 5.03, the NPV was 922,370.11 and the IRR was 42.09%, indicating that this type of agribusiness in Mexico is both technically and economically feasible.

This research explores the behaviors of Generation Z consumers in China, particularly focusing on their interaction with companies through social media for the joint creation of value. Adopting a qualitative methodology, the research initially constructs a scale for assessing co-creation of value on social media platforms. Subsequently, it delves into the motivational aspects of users' engagement in these co-creative activities. The study unveils a range of utilitarian and hedonic motivations that drive Generation Z's value co-creation actions on social media. Insights derived from the findings offer both theoretical and practical implications for the field.

Agarwood, a highly valuable fragrant wood of Aquilaria spp. has been widely used in perfumes, traditional medicine, religious rites and cultural activities. Due to the presence of aromatic and medicinal properties, the Agarwood has gained its economic importance in recent years. It has been providing livelihood and employment facilities to many people who are cultivating those. Owing to high demand and profitability, the people of Assam has started to cultivate Agarwood in a commercial manner in recent days. This study was conducted to examine the economic viability of Agarwood cultivation in Assam and to understand the problems of the cultivators to consider Agarwood cultivation as the primary source of income. The primary data were collected during the months of May and June, 2022 from 210 households using structured questionnaire. The cost-benefit analysis revealed that INR 100 of initial investment yields a net benefit of INR 623 which indicates that the return from the Agarwood cultivation among the cultivators are very high. Thus, the present study argues that the government should encourage the cultivators by providing the necessary support in the form of a legalized market, credit requirement, creating the awareness about the demand of Agarwood and making provision of training.

Due to combined climate and biodiversity crisis, the sustainable utilization of phytogenetic resources stands as one-way alternative while nutrient management strategies gain an increasing role in agriculture. Building on previous studies regarding the Endangered local endemic of Crete (Greece) Carlina diae (Asteraceae) with medicinal and ornamental value, this investigation focused on its pilot cultivation and fertilization (foliar or root application). Foliar application included chemical fertilizer (conventional) or integrated nutrient management (INM). Root application consisted of chemical fertilizer, biostimulant, or INM with biostimulant. Above-ground biomass content of nutrients, together with leaf chlorophyll fluorescence and colour parameters (SPAD meter, DA meter, Chroma Meter) were determined. The leaf content of chlorophyll, three key antioxidant compounds and minerals’ titers were also assessed. The fertilization scheme did not influence plant growth and visually perceived quality (leaf color and shape). Notably, foliar INM fertilization increased biomass partitioning to inflorescences (harvestable organ for either medicinal or ornamental purposes), and decreased tissue water content (facilitating processing). Considering all three antioxidants together, INM with biostimulant appeared the optimum scheme, being associated with the highest (carotenoids, phenolics) or the second highest (flavonoid) content. In C. diae, therefore, INM fertilization was optimal for upgrading yield (foliar) and herbal quality in terms of antioxidant profile (INM with biostimulant), which might be embraced as an eco-friendly approach for high quality yields.

The devastating effects of COVID-19 permeates virtually all aspects of life across geographic boundaries. As the lockdown is eased, and reinstated, across many parts of the world, attention is now shifting towards how to navigate the ‘now normal’ rather than the ‘next normal,’ of doing business with a view to achieving sustainable development. This study highlights some of the challenges and opportunities for micro, small, and medium enterprises (MSMEs) in times of crisis such as COVID-19. Conceptual by orientation, the study provides a broad overview of trends in crises times, drawing upon a range of qualitative accounts. The overarching message is the current direction of travel for MSMEs in sustaining their bottom-line, not after the crisis, but at this particular moment in time. The study highlights some implications for ‘pivoting,’ in order to cushion the impact of the pandemic on efforts to capacitate MSMEs in leveraging value co-creation with the consumers. The implications of the here and now are discussed.

Extreme value analysis (EVA) is well-established to derive hydrometeorological design values for infrastructure that has to withstand extreme events. Since there is evidence of increased extremes with higher hazard potential under climate change, alterations of EVA are introduced for which statistical properties are no longer assumed to be constant but vary over time. In this study, both stationary and non-stationary EVA models are used to derive design life levels (DLLs) of daily precipitation in the pre-alpine Oberland region of Southern Germany, an orographically complex region characterized by heavy precipitation events and climate change. As EVA is fraught with uncertainties, it is crucial to quantify its methodological impacts: two theoretical distributions (i.e., Generalized Extreme Value (GEV) and Generalized Pareto (GP) distribution), four different parameter estimation techniques, (i.e., Maximum Likelihood Estimation (MLE), L-moments, Generalized Maximum Likelihood Estimation (GMLE), and Bayesian estimation method) are compared. Discrepancies due to the parameter estimation methods may reach up to 45 % of the mean absolute value, while differences between stationary and non-stationary models are of the same magnitude (differences in design levels up to 40%). Despite the underlying large methodological uncertainties, there is a robust tendency for increased DLLs in the Oberland region towards the end of this century based on the (non-)stationary models.

Environmental concern has attained a higher level than in the past, with numerous organisations, including the pioneering "Green Banking" institution, expressing their concern. This study examines the relationship between green brand innovativeness and perceived value, as well as its effect on green brand loyalty. It expands on previous research that investigated the differences between innovation and innovativeness and their effects on brand loyalty. In addition, the role of perceived green value as a mediator and consumer green knowledge as a moderator in the formation of green brand loyalty is investigated. To test the conceptual model, a convenience-sampling survey of 600 Ethiopian customers was administered to capture a sample of respondents for the conceptual model. For data analysis, structural equation modelling (SEM) was employed. The results indicate that consumers' perceptions of green value have indirect effects on brand loyalty and direct effects on the innovativeness of green brands. In addition, green knowledge mediates the relationship between green brand innovation and green value perception in a significant way. Therefore, organisations should concentrate on enhancing consumers' environmental literacy and nurturing positive attitudes towards green brands in order to increase green brand loyalty.

Energy imports and the transition to renewable energy sources are of critical importance in the current geopolitical context, which necessitates concrete actions to tackle the energy crisis at the European Union level. The study aims to explore the impact of imported non-renewable energy resources on the EU-27 economy. It examines the correlations and causal relationships between GDP, GVA, R&D investments, and energy imports from 2000 to 2021. Data normality was assessed using the Shapiro-Wilk test, while Pearson's test identified correlations between variables. Linear and multiple regression analyses were conducted to determine the effects of changes in independent variables on dependent variables. The study found a strong association between natural gas imports and GDP, with increases in GDP leading to a more than fourfold rise in imports. Furthermore, multiple regression analysis indicated that a 1% increase in R&D investments results in a 2.21% decrease in fossil fuel imports in 91.7% of cases. This suggests that R&D investments contribute to improved efficiency and the use of renewable energy sources.

Deep learning has been applied in many areas that have had a significant impact on applications that improves real-life challenges. The success of deep learning in a wide range of areas is due in part to the use of activation functions, which are particularly effective at solving non-linear problems. Activation functions are a key focus for researchers in artificial intelligence who aim to improve the performance of neural networks. This article provides a comprehensive explanation and comparison of different activation functions, with a focus on the arc tangent and its variations specifically. The paper presents experimental results that show that variations of the arc tangent using irrational numbers such as pi, the golden ratio, and Euler’s number, as well as a self-arctan function, produce promising results. Since we experimented with promising activation functions on two different problems, and datasets, we reached a result that different irrationals work well for different problems. In other words, arctan ϕ gives the best results mostly for multiclass classification and arctan e gives the best results for time series prediction problems. The paper focuses on a multiclass classification problem applied to the Reuters Newswire dataset and a time-series prediction problem on Türkiye energy trade value to show the impacts of activation functions.

Chicory, a horticultural crop cultivated worldwide, presents many botanical varieties and local biotypes. Among these, Italian radicchio group cultivars of pure Cichorium intybus L. and interspecific hybrids with Cichorium endivia L., as the “Red of Chioggia” biotype which in turn includes several phenotypes. This study deals with a pipeline for the marker-assisted breeding of F1 hybrids: it presents the genotyping-by-sequencing results of four elite inbred lines using a RADseq approach and an original molecular assay based on CAPS markers for screening mutants with nuclear male-sterility in radicchio of Chioggia. Two thousand nine hundred fifty-three SNP-carrying RADtags were identified and used to compute the actual estimates of homozygosity, and overall genetic similarity and uniformity of the populations, as well as to determine their genetic distinctiveness and differentiation. Molecular data were further used to investigate the genomic distribution of the RADtags among the two Cichorium species, allowing their mapping in 1,131 and 1,071 coding sequences in chicory and endive, respectively. In parallel, an assay to screen the genotype at the male-sterility locus Cims-1 was developed to discriminate wild-type and mutant alleles of the causative gene myb80-like. Moreover, a RADtag mapped close to this genomic region proved the potential application of this method for future marker-assisted selection tools. Finally, combining the genotype information of the core collection, the best ten individuals from each inbred line were selected to compute the observed genetic similarity as a measure of uniformity as well as the expected homozygosity and heterozygosity estimates scorable by the putative progenies derived from selfing (pollen parent) and full-sibling (seed parent) or pair-wise crossing (F1 hybrids). This predictive approach was conducted as a pilot study for understanding the potential application of RADseq in improving molecular breeding strategies aimed at the development of inbred lines and F1 hybrids in leaf chicory.

Rare diseases are characterized by a wide diversity of signs and symptoms and vary not only from disease to disease, but also from person to person, and living with a disease leads patients to peculiar experiences and treatments, without limits of time and space, as they extend to various environments and relationships of their lives. The objective of this study is the theoretical interaction between Value Co-creation (VC) and the Stakeholder Theory (ST) with the Shared Decision Making (SDM) health care theory. It is configured as a multiparadigmatic proposal by enabling the analysis of multiple perspectives of different stakeholders in health care. Thus, Co-created Decision Making (CDM) emerges in a logic dominated by service, with emphasis on intangible aspects and the interactivity of the relationships. It goes beyond the clinical office and the doctor-patient relationships, as studied in SDM, extending to all environments and interactions that add value to the patient's treatment. It was concluded that the essence of this new theory proposed here is neither in patient-centered care nor in patient self-care, but in co-created relationships with and between stakeholders in both directions, including non-health care environments that are important to the patient, such as relationships with friends, family, other patients with the same disease, social media, public policies, and the practice of pleasurable activities.

We examine a nonlinear initial value problem both singularly perturbed in a complex parameter and singular in complex time at the origin. The study undertaken in this paper is the continuation of a joined work with A. Lastra published in 2015. A change of balance between the leading and a critical subdominant term of the problem considered in our previous work is performed. It leads to a phenomenon of coalescing singularities to the origin in the Borel plane w.r.t time for a finite set of holomorphic solutions constructed as Fourier series in space on horizontal complex strips. In comparison to our former study, an enlargement of the Gevrey order of the asymptotic expansion for these solutions relatively to the complex parameter is induced.

We examine a family of linear partial differential equations both singularly perturbed in a complex parameter and singular in complex time at the origin. These equations entail forcing terms which combine polynomial and logarithmic type functions in time and that are bounded holomorphic on horizontal strips in one complex space variable. A set of sectorial holomorphic solutions are built up by means of complete and truncated Laplace transforms w.r.t time and parameter and Fourier inverse integral in space. Asymptotic expansions of these solutions relatively to time and parameter are investigated and two distinguished Gevrey type expansions in monomial and logarithmic scales are exhibited.

Background: Shared value creation in Base of the Pyramid (BoP) communities is a crucial process towards building sustainable societies. BoP communities in developing countries represent more than four billion people who live on low incomes with limited access to basic products and services. Current or emerging technologies offer promising solutions for organisations pursuing manufacturing opportunities in BoP communities. This study seeks to explore the literature on how BoP communities may become active participants in sustainably manufacturing products using micro-manufacturing factories. The research question posed is: What are the core concepts that need to be taken into consideration for creating shared value through micro-manufacturing factories in BoP communities? Method: A Systematised Literature review (SLR) was completed following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) method for data selection criteria and analysis. The SLR is used to explore the state of literature with regards to creating manufacturing shared value in BoP communities with the objective to identify study gaps and to explore shared value creation concepts. Results: Literature indicates BoP initiatives that have pursued to engage BoP communities through various innovation strategies. The findings of the review is organised under three strategic pillars: Capability building strategy, Implementation process, and Growth strategy. The capability building strategy defines the users’ intention to create shared value in BoP communities with Micro-manufacturing factories (MMF). It is followed by the implementation process which guides the users to create manufacturing shared value in BoP communities. This is followed by a growth strategy to scale for impact.

The connection of two phenomena - non-conservative friction forces and dissipation-induced instability can lead to many interesting engineering problems. The paper studies general material-dependent damping influence on dynamical instability of disc brake systems leading to brake squeal. The effect of general damping is demonstrated on a minimal and complex model of a disc brake. A complex system including material-dependent damping is defined in the commercial finite element software. The finite element model validated by experimental data on the brake-disc test bench is used to compute the influence of a pad and a disc damping variations on system stability by complex eigenvalue analysis. Analyzes show a significant sensitivity of the experimentally verified unstable mode of the system to the ratio of the damping between the disc and the friction material components.

In this paper, the existence of two nontrivial stationary solutions for the nonlinear Gilpin Ayala two species competition model is given by using the mountain pass lemma, and the local stability criterion of the trivial solution is given by using Lyapunov function method. Based on the local stability criterion, we give some suggestions on how to avoid the population extinction. This is, when the population is on the verge of extinction, we should try our best to avoid the diffusion behavior and reduce the diffusion coefficient, otherwise the species are easy to go extinct. Numerical example shows the effectiveness of the proposed method.

Densimetric Froude (Fr) is the minimum velocity required to prevent sediment deposition in pipes. Prediction of Fr is of utmost importance in numerous applications in civil engineering. In this paper through using a new hybrid method. Genetic Algorithm (GA) is used for optimum selection of membership functions of Adaptive Neuro-Fuzzy Inference System (ANFIS), and Singular Value Decomposition (SVD) method is used to compute the linear parameters of ANFIS’s results section (ANFIS-GA/SVD). Also, two different target functions are known as training error (TE) and prediction error (PE) by Pareto curve, the trade-off between these functions is selected as the optimal modeling point. First, different models will be presented using the parameters affecting Fr prediction, classifying them in different groups; then the Fr parameter will be predicted for all the different models through utilizing three different sets of data and the ANFIS-GA/SVD technique. The results of the models indicate that the best Fr prediction is obtained when independent parameters such as volumetric sediment concentration (CV), ratio of median diameter of particle size to pipe diameter (d/D), ratio of median diameter of particle size to hydraulic radius (d/R) and overall friction factor of sediment (λs) use as input variables in prediction of Fr. A sensitivity analysis is also conducted for the purpose of examining the effect of each of the dimensionless parameters on Fr prediction accuracy. Comparing the results of the suggested models with the existing regression-based equations shows that ANFIS-GA/SVD (R²=0.986, MAPE=4.397, RMSE=0.206, SI=0.053, ρ=0.026, BIAS=-0.025) is more accurate than the rest of the models.

Growing acreage and changing consumer preferences cause increasing interest in the cereal products originating from organic farming. Lack of results of objective test, however, does not allow drawing conclusions about the effects of cultivation in the organic system and comparison to currently preferred conventional system. Field experiment was conducted in organic and conventional fields. Thirty modern cultivars of winter wheat were sown. They were characterized for disease infection including Fusarium head blight, seed sowing value, the amount of DNA of the six species of Fusarium fungi as well as concentration of ergosterol and trichothecenes in grain. The intensity Fusarium head blight was at a similar level in both systems. However, Fusarium colonization of kernels expressed as ergosterol level or DNA concentration was higher for the organic system. It did not reflect in an increased accumulation of trichothecenes in grain, which was similar in both systems, but sowing value of organically produced seeds was lower. Significant differences between analyzed cropping systems and experimental variants were found. The selection of the individual cultivars for organic growing in terms of resistance to diseases and contamination of grain with Fusarium toxins was possible. Effects of organic growing differ significantly from the conventional and grain obtained such way can be recommended to consumers. There are indications for use of particular cultivars bred for conventional agriculture in the case of organic farming, and the growing organic decreases plant stress resulting from intense fertilization and chemical plant protection.

Artificial general intelligence (AGI) progression metrics indicate AGI will occur within decades. No proof exists that AGI will benefit humans and not harm or eliminate humans. I propose a set of logically distinct conceptual components that are necessary and sufficient to 1) ensure various AGI scenarios will not harm humanity and 2) robustly align AGI and human values and goals. By systematically addressing pathways to malevolent AI we can induce the methods/axioms required to redress them. Distributed ledger technology (DLT, ‘blockchain’) is integral to this proposal, e.g. ‘smart contracts’ are necessary to address evolution of AI that will be too fast for human monitoring and intervention. The proposed axioms: 1) Access to technology by market license. 2) Transparent ethics embodied in DLT. 3) Morality encrypted via DLT. 4) Behavior control structure with values at roots. 5) Individual bar-code identification of critical components. 6) Configuration Item (from business continuity/disaster recovery planning). 7) Identity verification secured via DLT. 8) ‘Smart’ automated contracts based on DLT. 9) Decentralized applications - AI software modules encrypted via DLT. 10) Audit trail of component usage stored via DLT. 11) Social ostracism (denial of resources) augmented by DLT petitions. 12) Game theory and mechanism design.

In this study, a mixed rule of degree of precision nine has been developed and implemented in the field of electrical sciences to obtain the instantaneous current in the RLC- circuit for particular value .The linearity has been performed with the Volterra’s integral equation of second kind with particular kernel . Then the definite integral has been evaluated through the mixed quadrature to obtain the numerical result which is very effective. A polynomial has been used to evaluate Volterra’s integral equation in the place of unknown functions. The accuracy of the proposed method has been tested taking different electromotive force in the circuit and absolute error has been estimated.

This study identifies the optimal management policy of a given energy storage system (ESS) installed in a grid-connected wind farm for maximizing the monetary benefits and provides guidelines for defining the economic value of the ESS under the optimal management policy and selecting the optimal size of the ESS based on the economic value. Considering stochastic models for wind power and electricity price, we develop a finite-horizon periodic-review Markov decision process (MDP) model to seek the optimal management policy. We also use a simple optimization model to find the optimal storage capacity and charging/discharging capacity of the ESS. By applying our analytic approach to a real-world grid-connected wind farm located in South Korea, we verify the usefulness of this study. Our numerical study shows that the economic value of the ESS is highly dependent on the management policy, wind electricity variability, and the electricity price variability. Thus, the optimal size of ESS should be carefully determined based on the locational characteristics and management policy even with limited investments. Furthermore, this study provides a meaningful policy implication on how much a subsidy the government should provide for installing ESS in a wind farm.

In this paper, by means of the Banach fixed point theorem and the Krasnoselskii's fixed point theorem, we investigate the existence of solutions for some fractional neutral functional integro-differential equations involving infinite delay. This paper deals with the fractional equations in the sense of Caputo fractional derivative and in the Banach spaces. Our results generalize the previous works on this issue. Also, an analytical example is presented to illustrate our results.

Indoor air quality (IAQ) is a critical determinant of human health, with fungal contamination posing significant risks. In 2023, a total of 160 samples were collected from 40 selected rooms. These samples were cultured on Sabouraud Dextrose Agar (SDA) medium using a portable air sampler and incubated at 28°C for 7 days. Initial identification of fungal isolates was performed based on their morphology. Suspected isolates underwent further analysis using internal transcribed spacer (ITS) rDNA analysis. The results revealed 11 species across 8 genera in various rooms. The most prevalent fungi were Aspergillus niger (183 CFU) , Penicillium chrysogenum (139 CFU) and Cladosporium halotolerans (135 CFU). On the other hand, Curvularia hawaiiensis and A. ustus were rarely isolated (10 and 19 respectively). Effective treatment involves a combination of preventive measures, such as controlling humidity and ensuring proper ventilation, alongside targeted interventions like antifungal treatments and thorough cleaning. Continuous monitoring and management of indoor environments are essential to reduce fungal contamination, enhance air quality, and protect human health from the harmful effects of airborne fungi. This abstract underscore the importance of addressing indoor fungal contamination to promote safer, healthier living and working spaces.

Objective: African Americans (AA) carry the largest burden for almost every type of cancer in the US and are also more likely to die from cancer. Approximately 10% of cancers can be explained by a hereditary factor and detected earlier. Many AA however have inequitable access to hereditary cancer risk assessment (HCRA) tools and information, further exacerbating cancer disparities. Innovative communication strategies to promote community-based HCRA information have promise to bridge AA to optimal primary cancer screening. The current pilot study followed a participatory process where researchers engaged with a Community Advisory Board (CAB) to explore how Communication Asset Mapping could assist Lay Health Advisors with dissemination of evidence-based HCRA information within AA faith communities. Methods: The research team and CAB conducted exploratory community group discussions with residents (N=21) guided by Communication Infrastructure Theory, and a community-engaged mapping process to inform the development of a Communication Asset Mapping (CAM) dissemination strategy. Results: Themes included: (1) Optimal locations (e.g. community centers) within identified networks (specified neighborhood networks) should have representatives who are trusted ambassadors to assist with HCRA information dissemination; (2) Trusted community member voices should fully represent the trusted network; and (3) Well-known and frequented geographic locations should be a true representation of participants’ neighborhoods for creating a robust health information network concerning HCRA. Conclusion: CAB and community residents appreciated the engagement process however, felt the impact was limited due to missing community voices within their neighborhoods to identify communication assets within the network for optimal HCRA information dissemination. CAM therefore is an important public health strategy in identification of trusted networks. The strategy was also helpful in pinpointing people who are critical communicators of emerging health information akin to HCRA.

Sick leave is influenced by various modifiable lifestyle factors and sex differences. This study examines the impact of body mass index, fruit and vegetable consumption, physical activity, smoking, and alcohol consumption on sick leave among Serbia's working population, focusing on sex differences. Data from the 2019 National Health Survey of Serbia were analyzed, focusing on a representative sample of 4,652 individuals aged 18-65. Chi-square tests and logistic regression models assessed the relationships between lifestyle factors and sick leave, adjusting for sociodemographic, work-related, and health-related variables. Among respondents, 15.8% reported sick leave in the past 12 months, with higher rates among women in both, short-term (13.9% vs. 10.6%) and long-term sick leave (3.4% vs. 2.2%). For men, obesity (OR = 2.6), poor dietary habits (fruit OR = 2.1; vegetables OR = 2.8), smoking (OR = 1.9), and risky alcohol consumption (OR = 4.1) significantly increase the likelihood of sick leave. In women, smoking (OR = 1.8) and risky alcohol consumption (OR = 3.1) are major predictors, while BMI and physical activity show less impact. Fruit and vegetable consumption and physical activity have inconsistent effects on sick leave, indicating the need for further research. This study identifies obesity, poor dietary habits, smoking, and alcohol consumption as key predictors of sick leave in men. In women, smoking and risky alcohol consumption are major predictors. Smoking is a risk factor for both sexes, while risky alcohol consumption emerges as a prominent predictor and is a significant predictor overall. These findings highlight the need for targeted public health interventions to address these lifestyle factors and reduce sick leave rates. Further research is needed to clarify the roles of dietary habits and physical activity in influencing sick leave.

The rapid proliferation of Internet of Things (IoT) devices has transformed various sectors, from smart homes to industrial systems. However, this growth has introduced significant security risks due to the increased attack surface and diverse nature of IoT devices. This paper presents a comprehensive assessment of IoT security risks and explores effective mitigation strategies. We first categorize the primary risks associated with IoT, including data breaches, device manipulation, and network vulnerabilities. Using a multi-layered approach, we analyze these risks in different contexts, such as consumer IoT, industrial IoT, and healthcare. The study then reviews existing security frameworks and protocols, highlighting their strengths and limitations. We propose a hybrid mitigation strategy combining encryption, access control, and anomaly detection to enhance IoT security. Furthermore, we discuss the role of regulatory standards and best practices in reinforcing IoT security. The findings underscore the need for a proactive and adaptive security posture to address evolving threats in the IoT landscape.

Cardiovascular diseases (CVDs) and their risk factors are a major cause of illness and death worldwide, especially in low- and middle-income countries like Cameroon. Physiotherapist-led health promotion (PLHP) has proven effective in improving health and reducing CVD risks. Understanding patient perspectives is crucial for designing effective, context-specific PLHP interventions. This study explored patients' views, experiences, perceived usefulness, acceptability, and preferred methods of PLHP through a sequential explanatory mixed-methods approach. The quantitative data highlights a significant burden of CVD conditions and risk factors among patients seen in physiotherapy services. Qualitatively, three themes were identified and included: 1) perspectives and experiences of people at risk or with CVDs (pwCVDs) on PLHP; 2) Perceived usefulness and acceptability of PLHP; 3) Preferred delivery methods of PLHP. Participants reported positive feedback on PLHP and physiotherapy services. Barriers to effective PLHP included high workloads for physiotherapists, limited service access in rural areas, and prohibitive costs. Despite these challenges, participants expressed strong confidence in physiotherapists' competence, though they also called for improved regulation and ongoing professional development. PLHP components, especially physical treatment and dietary advice, were deemed highly useful and acceptable. Patients suggested various delivery methods, including peer support groups, home visits, and mass media interventions. This study highlights the need to improve the scope of practice, competence of physiotherapists and accessibility of physiotherapy services in Cameroon for pwCVDs. It is necessary to adopt multidisciplinary approaches to achieve better outcomes for risk factors like diabetes and hypertension in context.

The integration of Artificial Intelligence (AI) in the FinTech industry has substantially reshaped operational workflows, product innovation, and risk management, all of which influence company valuation. This study investigates the impact of AI-enhanced multilayer networks on FinTech valuation. Utilizing a novel scalable multilayer network model with AI-driven Copula Nodes, the research reveals that operational efficiency is a significant driver of market value. However, the findings emphasize the necessity of a balanced strategy that integrates AI across all operational layers, particularly in risk management and product innovation, to maximize valuation. This study offers a comprehensive framework that enhances understanding of AI’s complex role in FinTech valuation, providing critical insights for industry stakeholders.

Background: Rabies, a zoonotic disease caused by the lyssavirus genus, is widely distributed and a significant cause of human death in Africa and Asia. This study aimed to investigate human rabies exposure and associated risk factors in Northwest Ethiopia. Methods: The study encompassed four zones, seven districts, and four kebeles from each district. The research team developed a questionnaire for data collection. Results: A total of 642 households were interviewed. The current study rabies exposure was revealed 10.47%. Over 98% of respondents displayed a strong understanding of rabies, with 99.69% correctly identifying rabies as a zoonotic disease. However, 80% of the interviewees were trusted traditional medicine could treat rabies. Only 21.93% of victims were took post exposure prophylaxis. Dogs were the primary affected animals (87.73%), of this 48.70% was bites by own dogs. Metema district had higher risk (p < 0.001) compared to others six districts. Dog ownership (p<0.05) and a higher number of households (p<0.01) were also identified as risk factors for rabies exposure. Conclusion: Residence, low education levels, high household density, free-roaming dogs, and dog ownership as key risk factors for rabies exposure. Public health awareness campaigns are crucial to emphasise the risks associated with animal bites.

Asbestos, also known by its commercial name of amianthus, has been a widely used material in various industries due to its unique properties. However, its extensive use has led to serious consequences for human health, most notably asbestosis, an irreversible chronic lung disease. Asbestosis increases the risk of lung cancer and malignant meso-thelioma both fatal diseases. To understand the role of asbestos in human health, applied sciences such as microscopy (optical and scanning electron microscopy) and geochemistry are fundamental to characterize the mineral fibers. In previous work we used these technics to characterize the fibers and, in this paper, we explore their issues associated with asbestos and asbestosis, as well as the challenges of science communication strategies to effectively inform society and workers about these risks. The lack of scientific culture, in general, leads to the lack of public awareness of risks, and to address these challenges, it is essential to implement effective communication and outreach plans and strategies, including the visualization of the fibers to understand why there may be a problem if inhaled. Educational campaigns, guidelines and plans that are informative and action-able, targeting workers, communities and the public about asbestos risks are very im-portant. General knowledge of mineralogy and geochemistry is needed and providing proper scientific communication and dissemination may help to cover the knowledge gap. We use examples and experience in Spain and Italy to illustrate this subject.

Lessons learned and risk-based outcomes from modernising the Australian Standard for post-mortem inspection and disposition judgement of beef, sheep, goat and pig carcases are described. Initial Risk Profiles identified priorities for quantitative assessments. The main difficulty encountered was the paucity of quantified performance of current inspection. Resolving this involved acquiring gross abnormality data representing regional production/proportional abattoir volumes, the range of gross abnormalities appearing nationally, proportional occurrence at carcase sites, and seasonality to enable comparisons. Evidence and discussion are provided for associated challenges experienced including preventing contamination, use of food chain information to support amendment, inspection as a part of industry Quality Assurance and opportunities to improve inspector training. Criteria considered by the Competent Authority for determination of equivalence of alternative post-mortem inspection techniques included comparisons of public health risk, non-detection rates for gross abnormalities, and microbial contamination resulting from inspection activities, as appropriate. Most gross abnormalities detected arose from animal health and welfare conditions affecting wholesomeness and did not present as food safety hazards. Non-detection rates between current and alternative inspection (observation) were negligible. A Quantitative Risk Assessment for Cysticercus bovis was conducted. Carcases with multiple gross abnormalities predominantly reflected historic infections (prior septicaemia) where trimming achieved wholesomeness unless cachexic.

Background: Shoulder pain is the third most common musculoskeletal issue in primary care, affecting up to 50% of patients six months post-consultation, leading to significant functional impairments and societal costs, especially due to sick leave. Shoulder injuries are particularly prevalent among 'overhead athletes' in sports like swimming, volleyball, and handball, with high injury rates reported annually. Screening tools like the Arm Care Screen (ACS) have been used to identify athletes at risk of shoulder injuries effectively. However, their applicability to the general population is less understood. This study aimed to assess the discriminant validity of a modified ACS (MACS) in detecting musculoskeletal risk factors among non-athletes. Methods: A prospective cross-sectional study was conducted with 30 asymptomatic subjects over 18, excluding those with a history of shoulder injuries or surgeries. The MACS, comprising four tests, was administered, and its diagnostic performance was evaluated through sensitivity, specificity, predictive values, and likelihood ratios. Results: Results indicated low sensitivity (0%-47.62%) and variable specificity (55.56%-100%), suggesting that MACS may not effectively identify risk factors in the general population. Positive and negative predictive values were inconsistent, highlighting the need for non-athlete-specific screening tools. Conclusion: While the MACS showed promise in athletes, its application in the general population requires further refinement. The study underscores the necessity for tailored screening methods to enhance early detection and prevention of musculoskeletal issues in diverse populations.

Digitalization has fundamentally changed the global economy and will continue to do so. This study investigates how the US Federal Reserve and US Treasury may work together to use decentralized finance (DeFi) systems to promote economic growth in local communities. The study looks into the potential for transformation and the difficulties in incorporating DeFi into conventional financial institutions. Research shows that the use of cash as a means of payment is widely expected to decline in the future. As a result, the public’s ability to make transactions using central bank money may decline rapidly. This study's goals are to solve economic issues, promote innovation, and increase financial efficiency. The use of in-depth interviews, theme analysis, case studies, stakeholder perspectives, comparative analysis, and document analysis is suggested as part of a qualitative research methodology. The goal of these approaches is to offer a sophisticated comprehension of the dynamics of collaboration and the consequences of DeFi integration in the context of the Federal Reserve-Treasury relationship. Important data sources that are necessary to answer the research question are indicated, such as US Treasury statistics, Federal Reserve publications, DeFi platforms, and International Monetary Fund economic indicators. Informed decision-making and policy formation may pave the way for a more inclusive and efficient financial ecosystem, which will eventually drive regional economic growth in the United States and promote monetary sovereignty for the aforementioned monetary entities. This can be achieved by investigating creative collaboration tactics between the Federal Reserve and the US Treasury.

Background and objectives: Due to resistance and lack of treatment options, hospital-acquired Acinetobacter baumannii (A. baumannii) infections are associated with high mortality. The aim of this study was to identify the characteristics and outcomes of patients with infections caused by multidrug resistant A. baumannii in a tertiary-care teaching hospital and to determine the risk factors for in-hospital mortality. Materials and methods: A retrospective cohort study including 196 patients with A. baumannii strains isolated from different clinical specimens in the Hospital of the Lithuanian University of Health Sciences in 2016, 2017 and 2020, 2021 was conducted. Data on patients’ characteristics, comorbid diseases, treatment, length of hospital and ICU stay, outcome were collected. To determine risk factors for in-hospital mortality, logistic regression analysis was performed. Results: There were 60 (30.6%) women 136 (69.4%) men with a mean age of 61.7 ± 16.6 years (range, 52–74). More than three-fourths (76.5%, n = 150) of the patients had at least one comorbid disease. The highest number of A. baumannii strains were isolated from patients hospitalized in ICUs (43.4%, n = 85). A. baumannii strains producing three types of β-lactamases were more frequently isolated from women than men (77.8 % vs. 22.2%, p = 0.006). Infections caused by A. baumannii strains producing two types of β-lactamases were significantly more often treated with combination therapy than infections caused by strains producing one type of β-lactamase (78.9% vs. 60.0%, p = 0.019). Patients with A. baumannii strains producing two different types of β-lactamases (AmpC plus KPC, AmpC plus ESBL, or ESBL plus KPC) stayed significantly shorter at the ICU compared to patients with A. baumannii strains not producing β-lactamases (median of 9, IQR 2–18, vs. median of 26, IQR 7–38, p = 0.022). Death occurred in 58.7% (n = 115) of patients. Logistic regression analysis showed that combination therapy, length of hospital stay of < 10 days after A. baumannii infection, age of > 58 years, and absence of co-infection were independent predictors of in-hospital mortality. Conclusions: Patients with infection caused by A. baumannii strains producing two different types of β-lactamases were more frequently treated with combination therapy. Mortality of patients with multidrug resistant A. baumannii infection was high, and it was associated with patient’s age, length of hospital stay after the diagnosis of A. baumannii infection, absence of co-infection, and combination treatment.

Osteoarthritis and chronic pancreatitis comorbidity are insufficiently studied, and the reciprocal impact of these conditions remains poorly understood. This study aimed to investigate potential predictors for the development of osteoarthritis and chronic pancreatitis comorbidity, as well as complications associated with this comorbidity. A cohort of 150 patients was categorized into three groups: a control group (n=30), patients with osteoarthritis (n=68), and patients with comorbid osteoarthritis and chronic pancreatitis (n=52). Potential predictors for comorbidity development and associated complications were explored. The study revealed that concurrent osteoarthritis and chronic pancreatitis exacerbate each other's progression and contribute to malnutrition. Body Mass Index (BMI) emerged as a potential predictor for chronic pancreatitis development in osteoarthritis patients. In osteoarthritis patients, fecal elastase-1 levels were identified as potential predictors for exocrine pancreatic insufficiency. Factors such as WOMAC total score, fecal elastase-1, C-reactive protein (CRP), ferritin, retinol, tocopherol, 25-hydroxy vitamin D3, and BMI were found to influence comorbidity development between osteoarthritis and chronic pancreatitis. Additionally, Gastrointestinal Symptom Rating Scale-Diarrhea Syndrome (GSRS-DS), Gastrointestinal Symptom Rating Scale-Constipation syndrome (GSRS-CS), Qualitative Assessment of the Symptoms and Impact of Pancreatic Exocrine Insufficiency domain A (PEI-Q-A), retinol, tocopherol, and iron were identified as potential predictors for exocrine pancreatic insufficiency in osteoarthritis patients. In conclusion, the comorbidity of osteoarthritis and chronic pancreatitis exacerbates disease progression and complications, necessitating further investigation.

Gastrointestinal bleeding (GIB) after heart transplantation (HT) remains a significant clinical issue. The study aimed to explore its incidence, trends, outcomes and clinical predictors of GIB in HT patients. Adult patients who underwent HT between 2015 and 2021 at Union Hospital were recruited and divided into two groups based on the presence or absence of postoperative GIB. Primary outcomes were evaluated by follow-up. Independent predictors of GIB after HT were identified by logistic regression analysis. A nomogram prediction model was constructed according to these independent variables, and the accuracy of the model was assessed using the receiver operating characteristic (ROC) curve and the calibration curve. Among 461 patients, 40 (8.7%) developed GIB post-HT. HT patients with postoperative GIB exhibited higher in-hospital, 30-day, 90-day and 1-year mortality (all P < 0.05). Multivariate analysis identified age, preoperative warfarin, postoperative continuous renal replacement therapy and postoperative nasogastric tube as independent risk factors for GIB following HT. The nomogram prediction model was performed using the 4 variables. The area under the curve (AUC) of this model was 0.852(95% CI: 0.787-0.917, P < 0.001) and the calibration curve was close to the ideal diagonal line. GIB following HT is associated with a poor clinical prognosis. The constructed nomogram, incorporating its independent risk factors, demonstrated a favorable predictive value for GIB.

The energy transition is a complicated process that involves changing production and consumption habits, boosting the use of renewable energy sources, and reducing greenhouse gas emissions. This article examines the management of risks associated with this shift, focusing on investment, commercial, technological, regulatory, and environmental challenges. The paper proposes an effective way for dealing with new difficulties by assessing risks and the management techniques that accompany them. Furthermore, as a critical component of the energy revolution, the concept of Nearly Zero Energy Buildings (NZEB) is given, with a focus on the ambitions of INGV – Irpina Headquarters and the EU regulatory framework. Finally, the actions and methods employed to achieve the NZEB objectives are discussed, including the utilization of cutting-edge technologies and renewable energy sources. Furthermore, the concept of Nearly Zero Energy Buildings (NZEB) is introduced as an important component of the energy transition, with an emphasis on the European regulatory framework and the ambitions of the INGV – Irpina Headquarters. Finally, the interventions and techniques employed to achieve the NZEB targets are highlighted, such as the employment of advanced technologies and renewable energy resources. This study contributes significantly to understanding the processes and issues associated with the energy transition and the implementation of NZEB, hence boosting sustainability in the construction sector.

Previous reviews have investigated machine learning (ML) models used to predict the risk of developing preeclampsia but have not described how the ML models are intended to be deployed throughout pregnancy or feature performance. The aim of this study is to provide an overview of the existing ML models and their intended deployment patterns and performance along with identified features of high importance. This review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. PubMed, Engineering Village, and the Association for Computing Machinery were searched between January and February 2024. A total of 86 studies were found of which 14 were included. Out of 12 studies, eight showed the intent to use the ML model as a single-use, two intended a dual-use, and two intended multiple-use. A total of seven studies listed the features of the highest importance. Systolic and diastolic blood pressure were listed along with mean arterial pressure to be of high importance. Out of four studies intending to use the ML model more than a single-use, three of them were conducted in the years 2023 and 2024, whereas the remaining study is from 2011. No ML model emerged as superior across the subgroups of PE. Utilizing body mass index and either mean arterial pressure or diastolic blood pressure and systolic blood pressure may benefit the performance. The deployment patterns are mainly single use being within the gestation weeks 11+0 to 14+1.

Objective: This study aimed to explore potential interventions or preventive measures to reduce the incidence of falls among the elderly in the Madinah region, and to provide recommendations for healthcare professionals, policymakers, and community stakeholders to enhance fall prevention strategies and improve the overall well-being of the elderly population in this area. Methods: The study adopted a cross-sectional study design, and was carried out in the Madinah region using convenient physical assignment among of Saudi and non-Saudi local residents aged 65 years and over. Questionnaire surveys were circulated and demographic characteristics were collected, including participants’ fall experience and health status data over the past year, in order to assess the incidence of falls among the elderly population. This design allows for the collection of data at a single point in time, aiming to evaluate the prevalence, risk factors, and consequences of falls among the target population. Results: A total of 189 participants were recruited to the study, and the data indicated that the prevalence of falls among those aged 65 years and older in the past year was 41.5%. We found that 73% of the participants had experienced falls and post-fall injuries within the past 12 months. There were minimal differences in terms of physical handicapped cases found between those with and without a fall history. Logistic regression analysis revealed a significant link between fall experience and maximum bite force. Conclusion: There is a high prevalence of falls in the elderly population in the Madinah region. Our study reveals that 50.79% of our elderly participants had experienced more than one fall during the preceding year. Some preventable risk factors were identified in an analysis of their daily routines. The impacts of these risk factors have potential consequences for their healthy lives.

In this study, a critical review was carried out using the Web of ScienceTM Core Collection database to analyse the scientific literature published to date to identify lines of research and future perspectives on the presence of chemical pollutants in beer brewing. Beer is one of the world's most popular drinks and the most consumed alcoholic beverage. However, a widespread challenge with potential implications for human and animal health is the presence of physical, chemical and/or microbiological contaminants in beer. Biogenic amines, heavy metals, mycotoxins, nitrosamines, pesticides, acrylamide, phthalates, bisphenols, microplastics and, to a lesser extent, hydrocarbons (aliphatic chlorinated and polycyclic aromatic), carbonyls, furan-derivatives, polychlorinated biphenyls and trihalomethanes are the main chemical pollutants found during the beer brewing process. Pollution sources include raw materials, technological process steps, the brewery environment and packaging materials. Different chemical pollutants have been found during the beer brewing process, from barley to beer. Brewing steps such as steeping, kilning, mashing, boiling, fermentation and clarification are critical in reducing the levels of many of these pollutants. As a result, their residual levels are usually below the maximum levels allowed by international regulations.

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by the loss of dopaminergic neurons and the buildup of misfolded alpha-synuclein proteins. While its exact cause remains unknown, several factors may influence its development. This review examines how factors like chronic constipation, head injuries, cholesterol and uric acid levels, pesticide exposure, smoking, NSAIDs, and caffeine intake may affect PD risk. Chronic constipation appears to be linked to a higher risk of PD, potentially serving as an early warning sign. Head injuries, especially mild-to-moderate ones, may also play a role in increasing PD risk. Surprisingly, higher levels of cholesterol seem to protect against PD, while lower levels of uric acid might increase the risk in the presence of some differences according to gender type. Exposure to certain pesticides is associated with a higher PD risk. NSAIDs, on the other hand, may or may not have a protective effect, with conflicting evidence. Interestingly, caffeine and tobacco smoking appear to lower the PD risk. Further studies are needed to fully understand the complex interplay between these factors and PD. It's also important to remember that these are just general trends, and individual risk factors will vary depending on a person's unique genetic makeup, medical history, and overall health.

OBJECTIVE: To investigate the value of serum inflammatory factor levels in acute ischemic stroke patients after revascularization therapy in assessing acute prognosis. METHODS: 94 patients with acute ischemic stroke who underwent revascularization therapy admitted to our hospital from January 01, 2022 to January 01, 2024 were studied. The main evaluation index was modified Rankin Scale score (mRS) after 3 months, and patients with mRS score >2 were categorized into the poor prognosis group, and the rest were the good prognosis group. As well as the treatment of revascularization in all patients was used as a division into a thrombus extraction group and an intravenous thrombolysis group. Logistic regression analysis was applied to explore the independent risk factors for the prognosis of patients treated with recanalization for acute ischemic stroke. RESULTS: Two groups with good (59 cases) and poor (35 cases) prognosis were divided according to the mRS score. There were some differences in age, gender, emergency GLU, INR, whether they had diabetes or hypertension and other contents between the two groups, but the differences were not statistically significant. The NIHSS score, hospitalization days, previous cardiac history, hospitalization days, LDL, fasting glucose, IL-6, and TNF-a at the time of admission were lower than those in the poor prognosis group, and the difference was statistically significant. In the patients in the embolization group, there were some differences in age, gender, emergency GLU, INR, whether they had diabetes or hypertension and other contents between the two groups, but the differences were not statistically significant. The pre-hospital NHISS score, IL-6, and TNF-a of patients in the good prognosis group were lower than those in the poor prognosis group, and the difference was statistically significant. CONCLUSION: IL-6 and TNF-α may be used as predictors of prognosis after revascularization therapy for acute ischemic stroke, and this conclusion also applies to patients in the embolization group.

In recent years, the digital economy has played an increasingly important role in enhancing productivity, fostering growth, and facilitating the emergence of digital banking. With the widespread adoption of digital financial services for financial inclusion, the banking sector is experiencing a demand for digital transformation. It is important to note that despite the many advantages of this change, it also disrupts the familiar banking experience that customers have grown to expect, as well as introducing potential risks to maintaining customer protection at previous levels. Many critics have raised concerns regarding customer protection and potential exposure to various risks that may arise from digital financial services, which may result in both financial and non-financial losses. These risks can impact customer retention as dissatisfied customers may switch to alternative service providers. Additionally, reputation can be affected since merely offering digital financial services may not sufficiently safeguard customers. To remain competitive, financial institutions must deliver a secure experience that aligns with customer expectations while considering economic factors and societal needs. This is why it is essential to comprehend the severity of the risk factors that influence the protection of customers in the realm of digitalized banking services and products. In this study, five factors have been identified that influence customer protection when using digital financial services in Pakistan. Based on the analysis conducted using SmartPLS, it appears that all the factors proposed in this study have a significant and positive impact on the protection of customers. In particular, the information provided remains of paramount importance when it comes to determining the level of protection that a customer is entitled to within digital financial services, followed by responsiveness, privacy, authentication, and encryption mechanisms. Due to these findings, the implementation of enhanced information security management principles becomes imperative for the development and progress of the Pakistani digital banking industry.

Objective: The goal of this review is to provide a succinct, yet comprehensive, guide to the an-tepartum management of multiple gestations. Methods: This narrative review on twins and multiple gestation provides a concise guide for providers with this ever-growing patient population. PubMED was utilized for literature search. Each reference was evaluated and graded for quality of evidence by US Preventative Services Task Force and Cochrane Review guidelines. Screening, surveillance, and management strategies are delineated succinctly and organized by type of multiple gestation being managed from the lowest risk dichorionic twin pair to the increasingly escalating risks of twins and higher order multiples sharing a placenta and/or amniotic sac. Results: The incidence of twin pregnancies has grown over the past several years, due in large part to delayed reproductive timing and surge in assisted reproductive technologies. Twin pregnancies are associated with higher morbidity for both gravidas and offspring, warranting evidence-based approach in counseling and management. Conclusions: We note the important differences among multiples in chorionicity and amnionicity, in the importance of close surveillance and early delivery. Graded clinical pearls and evi-dence-based recommendations are presented to guide obstetric providers.

The assessment of soil contamination in the vicinity of integrated siderurgical plants is of outmost importance for agroecosystems and human health, and sensitive techniques should be employed for accurate assessment of chemical elements (metals, potential toxic elements, rare earths, radioelements) in soil and further evaluation of potential ecological and safety risk. In this paper a total of 45 major, minor and trace elements (Al, As, Au, Ba, Br, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Eu, Fe, Hf, Hg, I, K, La, Mg, Mn, Mo, Na, Nd, Ni, Pb, Rb, Sb, Sc, Sm, Sn, Sr, Ta, Tb, Th, Ti, Tm, U, V, W, Y, Yb, Zn and Zr) were quantified in soils located around a large siderurgical works (Galati, SE Romania) using instrumental neutron activation analysis (INAA) in combination with X-ray fluorescence (XRF) and inductively coupled plasma mass spectrometry (ICP-MS). The statistical analysis results and vertical distribution patterns for three depths (0-5 cm, 5-20 cm, 20-30 cm) indicate inputs of toxic elements in the sites close to the ironmaking and steelmaking facilities and industrial wastes dumping site. For selected elements, a comparison with historical, legislated and world reported concentration values in soil was performed and depth migration, contamination and toxic risk indices were assessed. The distribution of major, rock forming elements was closer to the Upper Continental Crust (UCC), and to the Dobrogea loess, finding confirmed by the ternary diagram of the incompatible trace elements Sc, La and Th, as well as by the La to Th rate. At the same time, the La/Th vs. Sc and Th/Sc vs. Zr/Sc bi-plots suggested a felsic origin and a weak recycling of soils mineral components.

The effects of climate change health impacts on urban health care systems and vulnerable populations are a researched topic with foundations on urban risk management, urban health methodologies, and sustainable urbanization. As climate change is a dynamic and transformative global issue the gap between adaptation and mitigation efforts and urban planning relies, among other things, in the limitations of previous studies of risk management structures tailored to the existing strategy to calculate the uncertainties and predict their influence on urban systems and subsystems. Moreover, as climate and health is a well-researched issue, it is also complex as the methodologies and strategic approach to minimize health risks of climate change is intrinsically contingent to risk methodologies and policy-making processes that are efficient with support of knowledge-based information. This paper emphasis the need for focusing on urban health risk management methods that serves to identify potential opportunities, and then manage and take action to prevent adverse health effects. It also emphasizes the probability of events and their consequences, which are measurable both qualitatively and quantitatively.

Accurate measurement of systemic financial risk is critical to maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was con-structed by integrating six dimensions and employing Gray Relation Analysis (GRA) for dimensionality reduction of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA) optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trend of financial pressure indicators, the relationship between financial pressure and economic activity can be effectively identified. The research findings indicate that: (1) The CFSI is capable of accurately reflecting the current financial stress situation in China; (2) The IGSA-RBFNN demonstrates good robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology benefits policymakers and regulatory authorities to formulate preventive measures and risk management by identifying potential risks and vulnerabilities in advance.

Due to the growth of tourism, mining and manufacturing, economy of the Hainan Island, southern China has swiftly expanded, but also poses a risk of soil pollution by mercury (Hg) due to the traffic and mineral processing. In order to investigate the characteristics of soil Hg pollution in the Hainan Island and assess the health risk, a total of 239 samples were gathered from five representative regions across the island. A vehicle-mounted potable mercury analyzer was used to measure the atmospheric Hg and soil Hg concentrations in the collected samples. The results indicate that the soil Hg concentration of the five sub-study areas exhibits notable fluctuations, which was found to be influenced by various factors, including wind direction, mining activities, and economic development conditions. The degree of Hg pollution was assessed using both the Single Pollution Index Method and the Geoaccumulation Index Method. The results showed that although nearly half of the sampling points were contaminated, the current soil Hg level does not pose a non-carcinogenic risk to human health.

Purpose: Pancreatic cancer (PAAD) is one of the most lethal malignancies. Immunotherapy is largely ineffective in PAAD. T-cell exhaustion contributes to the resistance for immunotherapies. We investigated the prognostic potential of T cell exhaustion-related genes (TEXGs). Methods: Single-cell RNA (scRNA) sequencing dataset from TISCH, and bulk sequencing datasets from TCGA and GTEx were applied to screen differently expressed TEXGs. Kaplan-Meier survival, LASSO regression, and Univariate/Multivariate Cox regression analysis were performed to construct TEXGs risk model. This model was used to predict the prognosis, tumor immune microenvironment, and immunotherapy response. PAAD cohorts from ICGC and GSE71729 datasets were used to validate this risk model. Pan-cancer expression of SPOCK2 was performed in TISCH database. Results: A six-gene (SPOCK2, MT1X, LIPH, RARRES3, EMP1, and MEG3) risk model was constructed. Low-risk PAAD patients had longer survival time in both training dataset (TCGA-PAAD, n=178) and validation datasets (ICGC-PACA-CA, ICGC-PAAD-US, and GSE71729, n=412). Multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic variable for PAAD. High-risk patients correlated with cancer-related KEGG pathway enrichment, higher mutation frequency of KRAS and TP53 genes, and immunosuppressive status. Immunohistochemistry staining confirmed the changes of TEXGs in clinical samples. Moreover, pan-cancer scRNA sequencing datasets from TISCH analysis indicated that SPOCK2 might be a novel marker of exhausted CD8+ T cells. Conclusion: In conclusion, we established and validated a T cell exhaustion-related prognostic signature for PAAD patients. Moreover, our study suggests that SPOCK2 may be a novel marker of exhausted CD8+ T cells.

This paper introduces the exponential time differencing (ETD) technique as a numerical method to solve efficiently American vulnerable options pricing. We address several challenges, including removing cross-derivative terms through appropriate transformations, treating early-exercise opportunities using the penalty method, and substituting fixed boundary conditions with corresponding one-sided finite differences. The proposed method is shown to be both accurate and efficient through numerical experiments, which also compare the results with existing methods and analyse the numerical stability and convergence rate.

This study sought to spatially characterise and explain the differences of peoples’ perceptions on the impact of COVID-19 based on the socio-economic disparities in Gauteng, using choropleth mapping and Geographically Weighted Regression (GWR). Results indicate that respondents from relatively vulnerable municipalities like Merafong, Mogale City and Lesedi reported life being worse, information scant, and overall despondency being high since COVID-19. These are areas that fall under the High to Very High categories in terms of the Socio-economic Risk Index. GWR results, however, did not show a high explanatory power on the COVID-19 variables se-lected for the research, based on R2 values. For instance, the residual in overall satisfaction with life after COVID-19 was the lowest (-0.5 to 0.5) in the less affluent districts of the Rand West City and Sedibeng. Residual variables on life changes after COVID-19 were also the lowest in the southern parts of the same districts, with other low values almost evenly distributed throughout the province for the variable ‘Government information on COVID-19 was scant’. Although overestimation and underestimation existed, most were relatively low falling between -1.5 to -0.5 and distributed evenly across the province. In sum, however, the findings point to the complexity of socio-economic factors in spatially characterising social risk and vulnerability. Additionally, the negative sentiments expressed by people from the more vulnerable locations in the province emphasise the need for more targeted interventions by government to cushion the residents of the province to the continued negative impacts of COVID-19.

Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot discover hidden nonlinearities and interactions within the data. This study uses discrete time survival models with embedded neural networks as estimators of time to default. This provides flexibility to express nonlinearities and interactions between variables, and hence allows for models with better overall model fit. Additionally, the neural networks are used to estimate Age-Period-Cohort (APC) models so that default risk can be decomposed into time components for loan age (maturity), origination (vintage) and environment (e.g., economic, operational and social effects). These can be built as general models, or as local APC models for specific customer segments. The local APC models reveal special conditions for different customer groups. The corresponding APC identification problem is solved by a combination of regularization and fitting the decomposed environment time risk component to macroeconomic data, since the environmental risk is expected to have a strong relationship with macroeconomic conditions. Our approach is shown to be effective when tested on a large publicly available US mortgage data set. This novel framework can be adapted by practitioners in the financial industry to improve modelling, estimation and assessment of credit risk.

Purpose: To identify factors linked with nutritional risk via the Malnutrition Universal Screening Tool scale assessment among critically ill patients from a hospital in Cali (Colombia). Materials and methods: A retrospective, analytical observational study was conducted with patients admitted to an intensive care unit (ICU). Sociodemographic, clinical, hematological, and biochemical parameters were analyzed, as well as their association with nutritional risk and patient mortality. Binary logistic regression was applied to outline nutritional risk. Results: Overall, 28.4% prevalence of elevated nutritional risk was observed. An association was found between nutritional risk and hemoglobin and hematocrit values, primary diseases accounting for ICU admission, gastrointestinal symptoms, and nutritional support route (p < 0.05). Mortality was 11.6% and it was correlated with nutritional risk, hemoglobin, and blood urea nitrogen. Conclusions: Gastrointestinal symptoms and the type of nutritional support received during hospitalization may increase nutritional risk, whereas polycythemia was a protective factor. We found an associative model to determine nutritional risk with high specificity and an adequate diagnostic validity index.

To ensure sustainability of the future power grid, the rate of expansion of distributed renewable energy resources (DRER) has introduced operational challenges. These includes managing transmission constraints with DRER power injection, dispatching DRER efficiently, managing system frequency and ensuring sufficient reactive power for voltage support. Coupled with the intensification of wildfires, power infrastructure across the United States faces challenges to minimize the impact to maintain system reliability and resiliency. This research embarks on a comprehensive evaluation, beginning with an in-depth historical analysis to delineate regions most susceptible to wildfires. Utilizing a multidimensional approach, the study assesses wildfire-induced risks to power grids, by integrating historical wildfire occurrences, real-time wildfire proximities, Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation metrics, and system parameters, by employing Principal Component Analysis (PCA) based optimal weights, leading to the formulation of a novel risk factor model. This risk factor has the potential to be the key to ensuring the resilience of a renewable-rich smart grid when faced with a severe weather event. Our model’s applicability was further verified through an empirical assessment, selecting representative networks from diverse regions, providing insights into the geographical variability of risk factors. Ultimately, this study offers stakeholders and policymakers a comprehensive tool-set, empowering decisions regarding infrastructure investments, grid reinforcements, and strategic power rerouting to ensure consistent energy delivery using risk assessment against wildfire.

Objective: To evaluate which risk factors contribute directly or indirectly to Toxoplasma gondii infection in patients with ocular toxoplasmosis. Methods: Between 2018-2020, a total of 306 patients were selected, and treated at the Retinology Outpatient Clinic of the Specialty Hospital of Sāo José do Rio Preto, in the state of São Paulo. After the fundoscopy test, the Enzyme Linked Fluorescent Assay (ELFA) was used to determine IgG and IgM class anti-Toxoplasma gondii antibodies. Results: The results showed that individuals aged 60 years or older, and women who had a previous pregnancy, (p=0.000, OR= 8.137 and 95 % CI = 2.562 – 25.844), (p=0.016, OR= 7.395 and CI 95 % = 1,449 – 37,725) respectively, were 48.6 % more likely to develop ocular toxoplasmosis. Conclusion: In this way, previous pregnancy is a risk factor for the reactivation of toxoplasma, as well as for the generation of retinochoroiditis in previously infected women.

Visceral leishmaniasis (VL) is a severe parasitic disease transmitted by phlebotomine sandflies. VL is endemic in West Pokot County, Kenya, where effective strategies to interrupt transmission are impeded by the limited understanding of VL risk factors. Therefore, this case-control study aimed to explore environmental, behavioral and household determinants of VL in West Pokot. From November 2022 to January 2023, a structured questionnaire was administered to 36 symptomatic primary VL cases attending the Kacheliba Sub-County Hospital and to 50 controls from local villages. The VL status of all participants was confirmed using an rK39 rapid diagnostic test. In an age-corrected univariate logistic regression analysis, significant associations were found between VL and housing characteristics, such as window presence and floor type. Furthermore, outdoor activities, including traveling outside the residence for more than 2 weeks, activities near termite mounds, and forest activities during the rainy season increased the risk of VL. VL cases more frequently reported the presence of cattle, dogs and sheep in their house yards at night. VL was also associated with cutting down trees in the house yard and house proximity to several Acacia tree species. To conclude, this work reports a number of previously undescribed risk factors for VL in the understudied West Pokot focus. Our findings warrant further research into sandfly ecology and potential zoonotic parasite reservoirs in West Pokot.

HIV diagnosis and poor HIV management have various detrimental impacts on the lives of people living with HIV (PLHIV). As part of a large qualitative study investigating HIV risk factors and impacts, with the suicide topic not being a focus, this paper describes factors contributing to suicidal ideation and attempts that arose naturally in the stories of women living with HIV (WLHIV) in Yogyakarta and Belu districts, Indonesia. The participants were recruited using the snowball sampling technique. Guided by a qualitative data analysis framework, the data were thematically analysed. Findings indicated participants experienced immense psychological challenges due to the infection, spousal transmission, fear of mother-to-child transmission, and losing a child due to AIDS, which triggered suicidal ideation and attempts. The women’s lack of awareness of HIV management strategies resulted in them feeling trapped and overwhelmed. Associated negative thoughts and the anticipation and experience of stigma relating to their HIV status were influencing factors for thoughts of suicide among participants. Lack of social support from family and friends during the early stage of HIV diagnosis, compounded with pre-existing financial difficulties, lack of income, unemployment, and feeling overburdened, also triggered the women’s thoughts of suicide. Other influencing factors for suicidal ideation and attempts among the women were family breakdown following HIV diagnosis and this was reflected in family disputes, husband-wife separation or divorce, mother-child separation by other family members, reported concern about future relationships, and fear of being rejected or abandoned by their partner due to their HIV positive status. The findings indicate the need for a nuanced approach to counselling within HIV care interventions for couples to support the acceptance of each other’s HIV status whilst maintaining psychological wellbeing. Additionally, the findings indicate the importance of HIV education and awareness among community members for the de-stigmatisation of HIV and to increase the acceptance of PLHIV within families and communities.

Background. In a changing European agricultural context, diversification of dairy farms is gaining attention. This study seeks to (1) assess musculoskeletal pain prevalence associated with tasks such as butter, yogurt, and cheese production; and (2) analyze associated risks. Methods. Observing 31 mostly female workers, it utilized the ERGOROM questionnaire, a methodology adapted from the Institut National de Recherche et de Sécurité, and Key Indicator Method forms. Results. Findings revealed that tasks like load carrying (42% of workers), manual work (17%), and awkward postures (14%) resulted in musculoskeletal pain, predominantly in the lower back (65%), neck (39%), and dominant upper limb areas (shoulder: 61%, elbow: 26%, and wrist: 65%). While psychosocial risks remained low, concerns arose from workload, hygiene standards, and resource unpredictability. Conclusions. As dairy farming evolves from artisanal to semi-industrial, the study emphasizes the importance of ergonomic adaptations to protect farmer health and prevent musculoskeletal disorders during diversification.

Machine Learning techniques can be used to identify whether deficits in cognitive functions contribute to antisocial and aggressive behavior. This paper initially presents the results of tests conducted on delinquent and non-delinquent youths to assess their cognitive functions. The dataset extracted from these assessments, consisting of 37 predictor variables and one target. was used to train three algorithms that aim to predict whether the data corresponds to that of a young offender or a non-offending youth. Prior to this, statistical tests were conducted on the data to identify characteristics that exhibited significant differences in order to select the most relevant features and optimize the prediction results. Additionally, other feature selection methods, such as Boruta, RFE, and Filter, were applied, and their effects on the accuracy of each of the three machine learning models used (SVM, RF, and KNN) were compared. 80% of the data were utilized for training, while the remaining 20% were used for validation. The best result was achieved by the K-NN model trained with 19 features selected by the Boruta method, followed by the SVM model trained with 24 features selected by the filter method.

The perception of the impacts of climate risks on health systems in the Grand Lomé region of Togo is diverse. This study was carried out based on information collected in the field. The investigation was based on a questionnaire, individual interviews and field observations. Data collection was carried out in a purposive manner in twenty-four (24) health facilities with a total of 112 health service people and 13 resource people affected. This article presents the results on the perception of the impacts of climate change on health personnel, the environment and buildings. The results show that 92.31% of respondents attest that climate change has impacts on health personnel; 84.62% on the environment and 84.62% on buildings and other health infrastructure. This study shows that climate change has real consequences on human health. It is therefore urgent that those responsible for health systems in the Grand Lomé region, which is home to the capital of Togo, can focus on finding adaptation measures in relation to each health structure and according to its intervention platform.

Background: Vitamin B12 is a water-soluble vitamin with important cellular functions and is an essential vitamin. The aim of this study is to determine the B12 levels of children in the period from the 6th month when they start taking additional foods to the age of 7 and the risk factors affecting them. Methods: One hundred pediatric patients aged 6-72 months who were diagnosed with vitamin B12 deficiency and their parents who agreed to participate in Istanbul Atlas University, Medical Faculty, “Medicine Hospital” Pediatric Clinic between September 2022 and June 2023 were prospectively included in this study. Results: B12 deficiency was significantly higher in the 6-11 (25%) month group than those in the 12-23 (5.8%) and 24-47 (2.8%) month groups. Homocysteine levels were highest in those with insufficient B12 levels compared to the other groups. There was no statistically significant difference in weekly dairy and meat consumption levels between age groups. B12 levels were lower in the 6-11-month group than in the other groups. Homocysteine levels were highest in those with insufficient B12 levels [<200 pg/mL (148 pmol/L)]. Folic acid levels were lower in the 24-47 month and 48-72 month groups than in the 6-11 month and 12-23 month groups. Conclusions: The results obtained in this study showed that low vitamin B12 and increased homocysteine levels seem to be important risk factors in children, especially from the 6th month when they start taking additional foods. The diagnosis of B12 deficiency can be confirmed by elevated serum total homocysteine levels, which are evidence of functional cobalamin deficiency.

Patients with advanced chronic liver disease (ACLD) or cirrhosis undergoing surgery have an increased risk of morbidity and mortality in contrast to the general population. Retrospective, observational study of ACLD patients to evaluate the predictive capacity of previous surgical risk scores in a European cohort of patients. ACLD was defined by the presence of thrombocytopenia <150.000/uL and splenomegaly; AST-to-Platelet Ratio Index >2; nodular liver edge by ultrasound; transient elastography >15 kPa and/or signs of portal hypertension. We assessed variables related to 90-day mortality and the discrimination and calibration of current surgical scores (Child-Pugh, MELD-Na, MRS, NSQIP, and VOCAL-Penn). Only patients with ACLD and major surgeries included in VOCAL-Penn were considered (n=512). The mortality rate at 90 days after surgery was 9.8%. Baseline disparities between H. Mar and VOCAL-Penn cohorts were identified. Etiology, obesity, or platelet count not were associated with mortality. The VOCAL-Penn showed the best discrimination (C-statistic90D= 0.876) and overall predictive capacity (Brier90D= 0.054) but calibration was not excellent in our cohort. VOCAL-Penn was suboptimal in patients with diabetes (C-statistic30D= 0.770), without signs of portal hypertension (C-statistic30D= 0.555), or with an abdominal wall (C-statistic30D= 0.608) or urgent (C-statistic180D= 0.692) surgeries. Our European cohort has shown a mortality rate after surgery similar to those described in American studies. However, some variables included in the VOCAL-Penn score were not associated with mortality, and VOCAL-Penn’s discriminative ability decreases in patients with diabetes, without signs of portal hypertension, and with abdominal wall or urgent surgeries. These results should be validated in larger multicenter and prospective studies.

Colorectal cancer is the second leading cause of cancer-related mortality worldwide. Numerous pathophysiological mechanisms, such as abnormal cell proliferation, cell differentiation, resistance to apoptosis, invasion of structures adjacent to colorectal tumor cells, and distant metastasis, are involved in colorectal carcinogenesis. These processes are initiated by the complex interaction of a number of genetic and environmental factors, including sedentary lifestyle, obesity, alcohol consumption, smoking or gut microbiota. Despite the significant progress achieved in the diagnostic and therapeutic management of patients with colorectal cancer, there has been recently a noteworthy increase in the incidence of colorectal cancer in individuals below the age of 50 years. Early-onset colorectal cancer has a different frequency of oncogenic mutations, a higher prevalence of mucinous histology, a distinct deoxyribonucleic acid (DNA) methylation profile, a more distal location, and lower survival rates. A significant improvement in the prognosis of these patients can be achieved through the detection and removal of modifiable risk factors, along with the implementation of personalized screening strategies for individuals at high risk for this malignancy. Furthermore, gaining comprehension of the pathophysiological mechanisms by which these risk factors contribute to the process of oncogenesis may facilitate the discovery of novel therapeutic targets.

The rapid development of the regional economy in China has led to the rise of local ecological risks. It is very important to provide enjoyable ecosystem services to residents while reducing ecological risks. In order to understand spatial relationships between ecosystem services and ecological risks, we took Chongqing as an example in this study to assess the spatial relationship between ecosystem services and ecological risks at the county scale based on the ES-DPSIR system. The main findings include: (1) significant variation in the spatial distribution of the comprehensive ecosystem service index, where the lowest ecosystem service index (0.013) was found in the main urban area of Chongqing and the scores gradually increased outward from this center, reaching 0.689 in the outermost areas, (2) the increase of the comprehensive ecological risk index from east to west, ranging from -0.134 to 0.333, (3) the spatial relationship between ecosystem services and ecological risks was prominent, with 52.63% of the districts and counties being imbalanced or mild imbalanced, and (4) the significant differences between development trends of ecosystem services-ecological risks, including 60.53% being imbalanced districts and 30.47% mildly balanced. Overall, it was necessary to improve the relationship between ecosystem services and ecological risks in Chongqing by reducing ecological risks., and these research results could provide effective approaches and technical support for improving regional ecological security and enhancing ecosystem service capabilities.

In this systematic review of the literature on using Machine Learning (ML) for credit risk prediction, we raise the need for financial institutions to use AI and ML to assess credit risk, analyzing large volumes of information. We posed research questions about algorithms, metrics, results, data sets, variables, and related limitations in predicting credit risk. We searched renowned databases to answer them and identified 52 relevant studies with the credit industry microfinance. Challenges and approaches in credit risk prediction using ML models we identified, difficulties with the implemented models such as the black box model, the need for explanatory artificial intelligence, the importance of selecting relevant features, addressing multicollinearity, and the problem of the imbalance in the input data. By answering the questions, we identified that the Boosted Category is the most researched family of ML models; the most commonly used metrics for evaluation are Area Under Curve (AUC), Accuracy (ACC), Recall, precision measure F1 (F1), and Precision; Research mainly uses public data sets to compare models, and private ones to generate new knowledge when applied to the real world. The most significant limitation identified is the representativeness of reality, and the variables primarily used in the microcredit industry are related data to the demographic, the operation, and payment behavior. This study aims to guide the developers of credit risk management tools and software towards the existing offer of ML methods, metrics, and techniques used to forecast it, thereby minimizing possible losses due to default and guiding risk appetite.

The rapid changes in technology on a global scale, combined with the widespread adoption of business operations in cyberspace, have intensified the need for robust protection against escalating risks posed by cyber threats. This research paper aims to identify fundamental cyber resilience management attributes that enable organizations to manage cybersecurity, sustain, and adapt amidst evolving cyber risks and threats. By integrating resilience theory and security theory, this study establishes the attributes for resilience within cyber domains, making a novel contribution to cyber resilience management in organizations. The study introduces a model featuring seven main variables: Rationale, Reliable, Readiness, Resistance, Robust, Rebound, Reflective, and sub-variables across the Physical, Logical, and Social cyber domains, providing a converged framework for achieving cyber resilience. The findings of the study highlight the significance of fundamental attributes for enhancing cyber resilience management in organizations, such as clarity in purpose, vision, and values for security management, an empowered culture, availability of resources, avoidance of single points of failure, development, and coordination of resources to respond to threats and risks, promotion of continual improvement, and the sharing of information and knowledge. In conclusion, this research paper presents a model for managing cybersecurity in organizations by identifying key attributes for achieving cyber resilience.

Stochastic mortality models seek to forecast future mortality rates; thus, apparently the objective variable should be the mortality rate expressed in the original scale. However, the performance of stochastic mortality models - in terms, that is, of their goodness-of-fit and prediction accuracy - is often based on the logarithmic scale of the mortality rate. In this article, we examine whether the same forecast outcomes are obtained when the performance of mortality models is assessed based on the original and log scales of the mortality rate. We compare four different stochastic mortality models: the original Lee-Carter model, the Lee-Carter model with (log-)normal distribution, the Lee-Carter model with Poisson distribution and the median Lee-Carter model. We show that the preferred model will depend on the scale of the objective variable, the selection criteria measure and the range of ages analysed.

In the overall context of national eco-conservation civilization development and considering the hills in the Beijing-Tianjin-Hebei-Shanxi region, the local forest coverage rate is gradually on the rise, and so is the risk of forest fire. Every year, satellites can monitor and detect hundreds of fire spots on average. In recent years, serious fire took place for several times, leading to loss of life and personal injury. Then the fire-fighting work consumed huge amount of labor and material resources, exerting negative social impacts in many ways. Thus, the analysis of fire climate background in this region and the forecast based on fire weather indexes constitute an important part in the forest fire risk management. Located in the tropical monsoon climate zone, four provinces and municipalities of Beijing, Tianjin, Hebei and Shanxi are similar in terms of climate characteristics. This study tries to analyze the characteristics about fire spots under satellite monitoring, fire climate and fire weather index. The probability distribution analysis of weather index about fire spots in history and key meteorological observation stations as well as the weather index analysis in the early stage of typical fire indicate that the Fine Fuel Moisture Content (FFMC) and the Initial Spread Index (ISI) in the Fire Weather Index (FWI) system is applicable to the fire risk analysis in the Beijing-Tianjin-Hebei-Shanxi region, which can also effectively indicate the fuel moisture and spread conditions. As regards to the average level in Beijing-Tianjin-Hebei-Shanxi, when the FFMC is smaller than 93.5 and the ISI is also smaller than 5.0, there is a low risk of fire; when the FFMC is above 94 and the ISI is larger than 10.0, there is a high risk of fire; and the fire risk will be extremely high if the FFMC is over 96 and the ISI is above 13.0. In the practical fire-fighting management, the fire danger class can be released in reference to the ISI classes and threshold values of key meteorological observation stations under analysis provided by this study.

Without doubt, the social and environmental business impact will become more important as the governments cannot take care about these problems. Thus, launching the EU Green deal rules, World Global Sustainable Development Goals, corporate social responsibility (CSR) activities and strategies are part of this global commitment. Additionally, CSR is strongly connected to the exploration of (social) market awareness and technology development and application as it focusses on social, environmental and economic business impact. The main goal of the paper is to verify that contemporary greener CSR strategies are beneficiary activity for high-tech companies because of better competitive advantages acquisition through management of corporate reputation risk and patenting innovative technologies. The structure of the paper is the next: 1. Introduction – explanation the previous researches that explore the relation: CSR strategy – high-tech company competitiveness; 2. State of art – exploring the corporate reputation and patent rights as factors in CSR strategy; 3. Patent monopoly and CSR – research the connection between reputation risks of patents rights and CSR strategy; 4. Business performance and CSR reporting – briefly research methodology is presented as some results from Bulgarian companies; 5. Discussion and conclusions – summarizing a methodology for patent rights and reputation risk management for better CSR effects.

Pollution of urban surface dust with hazardous elements (HEs) is a serious environmental issue due to its toxicity and potential hazardous effects. Surface dust samples were collected from core urban, urban, and suburban gradients in the Urumqi city of the arid NW China, and the concentrations of six HEs, such as arsenic (As), cadmium (Cd), nickel (Ni), lead (Pb), mercury (Hg), and chromium (Cr), were determined. The pollution load index (PLI) and the US EPA health risk assessment model were applied to analyze and compare the pollution levels and potential health risk of HEs in surface dusts in different urbanization gradients. The obtained results indicate that the average concentrations of Hg, Cd, and Ni in surface dust decrease in the order of core urban > urban > suburban, whereas the average concentrations of As, Cr, and Pb decrease in the order of urban > core urban > suburban. The PLI of HEs in surface dust decreased in the order of core urban > urban > suburban. The concentrations of HEs in the core urban and urban gradients are relatively higher than in the suburban gradient. Furthermore, the total non-carcinogenic and carcinogenic risk index of investigated HEs in surface dust decrease in order of urban > core urban > suburban, for both adults and children. In addition, the pollution of surface dust with HEs in all urbanization gradients is more harmful to the children's health than to the adults. Overall, the potential non-carcinogenic and carcinogenic health risks of the investigated HEs, instigated primarily by oral ingestion of surface dust, are found to be within the acceptable range. However, urbanization can effects the accumulation of HEs in surface dust, and Cr is the main non-carcinogenic risk factor, whereas Cd is the main carcinogenic risk factor among the analyzed HEs in surface dust in all urbanization gradients.

Background and Objectives: Since the beginning of COVID-19 pandemic, the infection primarily affected patients with following chronic conditions: cardiovascular disease, hypertension, chronic obstructive pulmonary disease, diabetes mellitus, obesity and cancer. The aim of this study was to explore clinical and epidemiological characteristics associated with COVID-19 outcomes in patients at the primary health care centre from March 2020 to September 2022. Materials and Methods: The study included 40,692 citizens of Banja Luka County, Bosnia and Herzegovina, who were confirmed and registered as RT-PCR positive on COVID-19. Differences regarding the distributions of patients between groups were analysed using Pearson chi square test and Mantel-Haenszel chi square test for trends, while differences in mean values were compared using independent samples t test. Relationship between mortality and independent variables were examined using logistic regression. Results: Out of 40,692 COVID-19 positive patients, 7.76% were hospitalized. The average age of hospitalized patients was significantly higher than the age of non-hospitalized patients (64.2±16.1 vs. 45.4±18.7; p&lt;0.001). The average age of patients with lethal outcome was nearly twice higher compared to patients with non-lethal outcome (74.6±11.5 vs. 45.7±18.6; p&lt;0.001). Male patients had higher hospitalization and mortality rate, compared to females (9.8% vs. 5.9%, p&lt;0.001; 4.8% vs. 3%, p&lt;0.001, respectively). The highest hospitalization rate was in patients with chronic renal failure, diabetes and cardiovascular diseases, while the death rate was the highest among patients with CRF and hearth comorbidities. Fever, cough, fatigue, nausea and vomiting, chest pain, shortness of breath and appetite loss favoured hospitalization. Patients with fatigue and appetite loss had higher percentage of lethal outcome. Vaccinated patients had significantly lower rate of lethal outcome. Conclusions: Clinical symptoms, signs and outcomes, are posing as predictive parameters for further management of COVID-19. Vaccination has an important role in clinical outcomes of COVID-19.

Owners and managers of small and medium-sized enterprises (SMEs) have changed their perspective on risk management due to the current global negative threats in the business environment. If they want to be successful and ensure the financial performance of their business, they must accept a proactive approach to reducing strategic risks in connection with operational risks. The aim of the article is to identify and quantify the differences in operational risk management and its effect on the financial management of SMEs between four Central European countries. The statistical sample of SMEs consisted of 1,090 owners and top managers. Data collection was carried out through a questionnaire in the period December 2022-January 2023. Statistical hypotheses were evaluated with the application of correlation analysis and linear regression modelling. Empirical findings have confirmed that operational risk management has a significant positive impact on improving the profitability of the enterprise; on reducing the enterprise's indebtedness as well as in the context of reducing the enterprise's inability to pay its obligations. However, there are disparities in the perception of this influence between owners/managers with regard to the country in which they carry out their business activities. The findings are important for both national and multinational organizations and entities dealing with risk management in the business environment of small and medium-sized enterprises.

Chronic diseases affect a disproportionate number of United States (U.S.) Veterans, causing significant long-term health issues and affecting entitlement spending. This longitudinal study examined the health status of U.S. Veterans as compared to non-Veterans pre- and post-COVID utilizing the annual Center for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) survey data. Age-adjusted descriptive point estimates were generated independently for 2003 through 2021, while complex weighted panel data were generated from 2011 and onward. General linear modeling revealed that the average U.S. Veteran reports a higher prevalence of disease conditions except for mental health disorders when compared to the non-Veteran. These findings were consistent with both pre- and post-COVID, however, both groups reported a higher prevalence of mental health issues during the pandemic years. The findings suggest that there have been no improvements in reducing Veteran comorbidities to non-Veteran levels and that COVID adversely affected the mental health of both populations.

This study probes the probabilistic features of major fire hazards in enclosed spaces to establish their importance to the occurrence of fires onboard ro-ro passenger ships and in turn to raise effective operational countermeasures. Distinct from the previous studies, the present research employs Bayesian Network (BN) analysis to determine the probabilities of fire hazards more effectively. The findings of the research include five critical basic events (BE) identified namely, vehicle engine fire (fuel system fault), vehicle electric fire (electrical equipment defect or short circuit), used car electrical fire, reefer units electrical fire (electrical appliances defects or short circuit), and cargo spontaneous combustion. Additionally, the risk of fire for lithium - ion battery powered vehicles is also highlighted in the process of BN analysis, which prompts the authors to propose preventive measures for mitigating the possibility of fire occurrence on this type of electric vehicles. It is hoped that these measures can be essential justifications for establishing relevant rules regarding carrying LIB vehicles in enclosed spaces on international level.

Epithelioid hemangioendothelioma (EHE) is an extremely rare vascular sarcoma with variable aggressive clinical behavior. In this retrospective study we aimed to investigate prognostic factors based on clinicopathologic findings in a molecular/immunohistochemical confirmed nationwide multicenter cohort of 57 EHE. Patients had unifocal disease (n=29), multifocal disease (n=5), lymph node metastasis (n=8) and/or distant metastasis (n=15) at the time of diagnosis. The overall survival rate was 69.6% at 1 year and 50.5% at 5 years. Survival did not correlate with sex, age or histo-pathological parameters. No survival differences were observed between multifocal and metastatic disease suggesting that multifocality represents early metastases and treatment options are limited in comparison to unifocal disease. In unifocal tumors, survival could be predicted using the risk stratification model of Shibayama et al. dividing the cases into low (n=4), intermediate (n=15) and high (n=3) risk-groups. Larger tumor size (>3cm) and higher mitotic count (>1/10HPF) was asso-ciated with progressive unifocal disease course. Lymph node metastases at time of diagnosis oc-curred in 14.0% of the cases and was mainly associated with tumor localization in the head and neck area proposing lymph node dissection. In conclusion, our results demonstrate the aggressive behavior of EHE, emphasize the prognostic value of a previous described risk stratification model and may provide new insights regarding tumor focality, therapeutic strategies and prognosis.

The sinking of cavities in urban areas is a very important geological risk to which many towns in Puglia are exposed. The study, mapping, geological and speleological description, classification and cataloging of forms and types of cavities is of fundamental importance for each urban center because it is linked to past local anthropic and natural processes, unique to each site. All this could lead to an enhancement of existing underground cavities in urban areas through conservation and continuous monitoring. Unfortunately in many cases these underground cavities have been used as landfills and subsequently abandoned. One of these cavities at the end of March 2007 collapsed in the inhabited center of Gallipoli causing damage to the structures and fortunately not to human lives. In the area relating to the collapsed cavity, a series of geophysical investigations were undertaken with the use of ground penetrating radar methodology in an attempt to delimit the area of collapse and understand the possible types of interventions for restoring the area. In the same area the measures were repeated 16 years later (December 2022) due to another collapse. The results show a much more widespread danger situation.

We present a hierarchical reinforcement learning (RL) architecture that employs various low-level agents to act in the trading environment, i.e. the market. The highest level agent selects among a group of specialised agents, and then the selected agent decides when to sell or buy a single asset for some period. This period can be variable according to a termination function. We hypothesized that due to different market regimes, more than one single agent is needed when trying to learn from such heterogeneous data, and instead, multiple agents will perform better, with each one specialising in a subset of the data. We use $k-means$ clustering to partition the data and train each agent with a different cluster. Partitioning the input data also helps model-based RL (MBRL), where models can be heterogeneous. We also add two simple decision-making models to the set of low-level agents, diversifying the pool of available agents and thus increasing overall behaviour flexibility. We perform multiple experiments showing the strengths of a hierarchical approach and test various prediction models at both levels. We also use a risk-based reward at the high level, which transforms the overall problem into a risk-return optimization. This type of reward shows a significant reduction in risk while minimally reducing profits. Overall, the hierarchical approach shows significant promise, especially when the pool of low-level agents is highly diverse.

Temporomandibular joint disorders (TMD) are characterized by their multifactorial etiology and pathogenesis. A 3-year prospective study was conducted in a Portuguese TMD department to study the prevalence of different TMD signs and symptoms and their association with risk factors and comorbidities. Five hundred ninety-five patients were included using an online database: EUROTMJ. Most patients were female (80.50 %), with a mean age of 38.20±15.73 years. The main complaints were temporomandibular joint (TMJ) clicking (13.26%), pain (12.49%), and masticatory muscle tension (12.15%). The main clinical findings were myalgia (74%), TMJ clicking (60-62%) and arthralgia (31-36%). Risk factors such as clenching (60%) and bruxism (30%) were positively associated with TMJ pain and myalgia. Orthodontic treatment (20%) and wisdom tooth removal (19%) were positively associated with TMJ clicking, while jaw trauma (6%) tracheal intubation (4%) and orthognathic surgery (1%) were positively associated with TMJ crepitus, limited man-dibular range of motion and TMJ pain, respectively. 42.88% of TMD patients had other associated chronic diseases, most of them were mental behavioral or neurodevelopmental disorders (33.76%), namely, anxiety (20%) and depression (13%). The authors also observed a positively association of mental disorders with the degree of TMJ pain and myalgia. Online database seems to be a relevant scientific instrument for healthcare providers who treat TMD. The authors expect that EUROTMJ database can serve as a milestone for other TMD departments.

The pathophysiology of bipolar disorder (BD) remains mostly unclear. Yet, a valid biomarker is necessary to improve early detection of this serious disorder. Patients with manifest BD display reduced volumes of the hippocampal subfields and amygdala nuclei. In this pre-registered analysis, we used structural MRI (N=271, 7 sites), to compare volumes of hippocampus, amygdala, and their subfields/nuclei between help-seeking subjects divided in risk groups for BD as estimated by BPSS-P, BARS and EPIbipolar. We performed between-group comparisons using linear mixed effects models for all three risk assessment tools. Additionally, we aimed to differentiate the risk groups using linear support vector machine. We found no significant volume differences between the risk groups for all limbic structures during the main analysis. However, the SVM could still classify subjects at risk according to BPSS-P criteria with a balanced accuracy of 66.90% (95% CI 59.2– 74.6) for 10-fold cross-validation and 61.9% (95% CI 52.0– 71.9) for leave-one-site-out. Structural altera-tions of hippocampus and amygdala may not be as pronounced in young people at risk, nonetheless, ma-chine learning can predict estimated risk for BD above chance. This suggests that neural changes may not merely be a consequence of BD and may have prognostic clinical value.

According to the Global Report on Internal Displacement (GRID, 2022), the number of displaced people worldwide at the end of 2021 was 59.1 million, with 53.2 million of them being the result of conflicts and violence, and 5.9 million caused by climate-related disasters. Furthermore, the In-ternational Organization for Migration (IOM) predicts that the climate crisis will be solely re-sponsible for one billion migrants by 2050. In this context, the research examines the issue of coastal multi-risk arising from a combination of heavy rainfall, river's overflow, and sea-level rise phenomena, analysing it through the disciplinary perspective of urban planning. Specifically, the study focuses on the need to upgrade the Local Urban Plan from a climate-proof perspective by defining a method aimed at updating the territory's cognitive framework through the preparation of a multi-risk map (which considers different time horizons), as well as the prescriptive apparatus of the Plan through the definition of a toolkit of site-specific design actions oriented towards urban adaptation. The proposed methodology is verified on the case study of Isola Sacra, Fiumicino (Italy), and it is intended to be applicable to other territorial contexts. The research aims to outline guidelines for updating urban planning tools from a climate-proof perspective to promote long-term resilient urban development in risk-prone areas.

Aortic dissection (AD) is a critical heart condition with potentially severe outcomes. Our study aims to investigate the existence of a "weekend effect" in AD by examining the correlation between patient outcomes and the timing of their treatment during weekdays vs weekends. Specifically, we assessed prospectively the effects of the timing of treatment on patient outcomes for acute aortic dissection, both before surgical intervention and during hospitalization, as well as during the follow-up period. The mean age of the study population used here was 62.5 years with a high degree of variability. We recorded a rate of mortality before surgery of 8.65% for the weekend group and 15% for the weekday group, with no significant differences noted in the results of statistical tests. During hospitalization, mortality was found to be 50% (n=52) in the weekend group and 25% (n=5) in the weekday group, again with no significant differences observed in the results of the same statistical tests. Patient mortality after discharge was 9.61% (n=10) in the weekend group and 5% (n=1) in the weekday group. Our findings suggest that there is no significant difference in mortality rates between patients admitted to the hospital on weekends versus weekdays. Therefore, the period of the week when a patient presents to the hospital with aortic dissection appears to not affect their mortality outcomes.

Employing data supplied by the Oxford-based UK Childhood Cancer Research Group in 2003 it is shown that after the Chernobyl accident, childhood leukemia by single age and year of birth in the 0-4y cohort increased significantly in Wales and Scotland combined in the period 1986-1990 compared with controls born 1970-1985 and 1991-1994. Relative risk RR = 1.41; 95% CI 1.20<RR<1.65; p = 0.00004. These exposure periods were chosen because whole body Caesium-137 monitoring had demonstrated that internal exposures persisted for more than 4 years. The dose-response exhibits saturation and reversal, interpreted as biological censoring effects in the developing individual.

The long-term laboratory aspects of the effects of COVID-19 on liver function are still not well understood. Therefore, this study aimed to evaluate the hepatic clinical-laboratory profile of patients with up to 20 months of long-term COVID-19. A total of 243 patients of both sexes aged 18 years or older hospitalised in the acute phase of COVID-19 were included in this study. Liver function analysis was performed. Changes were identified in the mean levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT), and ferritin. Inflammatory markers such as ferritin > 300 U/L were observed in the group that presented more changes in liver function markers (ALT, AST, and GGT). Age ≥ 60 years, male sex, AST > 25 U/L, and GGT ≥ 50 or 32 U/L were associated with ALT > 29 U/L. There was a correlation between ALT and AST, LDH, GGT, and ferritin. Our findings suggest that ALT and AST levels may be elevated in patients with long-term COVID, especially in those hospitalised in the acute phase. In addition, ALT > 29 U/L was associated with other markers of liver injury, such as LDH, GGT, and ferritin.

Previous literature highlights the impact of COVID-19 on family functioning. Less is known about the impact of the pandemic on families of pediatric cancer patients. In order to determine universal and unique risk and resilience factors of these families during the pandemic, a qualitative analysis was conducted on families currently receiving cancer treatment at a midwestern hospital. Results of the data analysis depict ways in which these families have been impacted by and have adapted to COVID-19. These findings suggest that families of pediatric cancer patients have unique experiences in the context of COVID-19, in addition to universal experiences outlined in previous literature.

Ovarian cancers are curable by surgical resection when discovered early enough. Unfortunately, most ovarian cancers are diagnosed in the later stages. One strategy to identify early ovarian tumors is to screen women who have the highest risk scores. This mini review summarizes the accuracy of different methods used to assess the risk of developing ovarian cancer, including family history, BRCA genetic tests, and polygenic risk scores. The accuracy of these is compared to the maximum theoretical accuracy, revealing a substantial gap. We suggest that this gap, or missing heritability, could be caused by epistatic interactions between genes. An alternative approach to computing genetic risk scores, using chromosomal-scale length variation should incorporate epistatic interactions. Future research in this area should focus on this and other alternative methods of characterizing genomes.

Oral health surveys are essential to assess the dental caries experience and to influence national policies. This retrospective cross-sectional study aims to analysis dental caries experience who sought dental treatment in a reference university dental hospital at the Lisbon Metropolitan Area between January 2016 and March 2020. Full-mouth examination, sociodemographic, behaviors and medical information were included. Descriptive analyses and logistic regression analysis were applied to ascertain risk indicators associated with dental caries experience. A final sample of 9,349 participants (5,592 females/3,757 males) were included, aged 18 to 99 years old. In this population, caries experience was 91.1%, higher in female participants. Age (OR= 1.01, 95% CI [1.00-1.02], occupation (OR= 2.94, 95% CI [2.37-3.65], OR= 3.35, 95% CI [2.40-4.67], OR= 2.55, 95% CI [1.66-3.91], for employed, unemployed and retired, respectively), overweight (OR= 1.52, 95% CI [1.18-1.96]), reporting to never visited a dentist (OR= 0.38, 95% CI [0.23-0.64] and self-reported week teeth status (OR= 2.14, 95% CI [1.40-3.28]) were identified as risk indicators for the presence of dental caries, according to adjusted multivariable logistic analyses. These results highlight a substantial rate of dental experience in a Portuguese cohort and will pave the way for future tailored oral public health programs in Portugal.

Diabetes mellitus is a popular life-threatening disease and patients may gradually have started suffering from other diabetes-causing diseases such as heart attacks, stroke, hypertension, blurry vision, blindness, foot ulcer, amputation, kidney damage and other organ failures before diagnosis. Early detection can help reduce the fatality of this disease. Deep learning models have proven very useful in disease detection and computer-aided diagnosis. In this work, we proposed a deep unsupervised machine learning model for early detection of diabetes using voting ensemble feature selection and deep belief neural networks (DBN). Dataset was obtained from an online repository containing responses of prediagnosed patients to direct questionnaires administered in Sylhet Diabetes Hospital in Sylhet, Bangladesh. The dataset was preprocessed and preprocessed. Features were reduced using the ensemble feature selector. The DBN model was pretrained and tuned to obtain optimal performance. The model was also compared with other models with no multiple hidden layers. The DBN performed at its relative best with F1-measure, precision and recall of 1.00, 0.92 and 1.00 respectively. We conclude that DBN is a useful tool for an unsupervised early prediction of Type II diabetes mellitus.

The perception in pair trading is to recognize two stocks that move together, and their prices will converge to a mean value in future. However, finding the mean-reverted point at which the value of the pair will converge, and optimal boundaries of the trade is not easy. As uncertainty and model misspecifications may lead to losses. To cater for the problems, this study employs the novel entropic approach that utilizes entropy as penalty function for the misspecification of the model. The use of entropy as a measure of risk in pair trading is a nascent idea and this study utilizes daily data for 64 companies listed on PSX for the years 2017, 2018, and 2019, respectively to compute the returns based on the entropic approach. These companies cover the major sectors including Cement, Chemical, Automobile Assembler, Food and Personal Care Products, Oil and Gas Marketing Companies, Oil and Gas Exploration Companies Ltd, Power Generation and Distribution, Refinery and Pharmaceuticals. The returns to these stocks are then evaluated and compared with the Buy and Hold strategy. The results show positive and significant returns from pair trading using an entropic approach.

Antibiotic resistance is a pressing global and planetary health challenge. Links between climate change, antibiotic use and the emergence of antibiotic resistance have been well documented, but less attention has been given to the impact(s) of earth systems on specific bacterial livestock diseases at a more granular level. Understanding the precise impacts of climate change on livestock health – and in turn the use of antibiotics to address that ill-health – is important in providing an evidence base to tackle such impacts and to develop practical, implementable and locally acceptable solutions within and beyond current antibiotic stewardship programmes. In this paper, we set out the case for better integration of earth scientists and their specific disciplinary skill set (specifically, problem-solving with incomplete/fragmentary data; the ability to work across four dimensions and at the interface between the present and deep/geological time) into planetary health research. We then discuss a methodology that makes use of risk mapping, a common methodology in earth science but less frequently used in health science, to map disease risk against changing climatic conditions at a granular level. This will enable predictions of future disease risk and risk impacts based on predicted future climate conditions, and thus provide an evidence base for planetary health activists to influence policy and develop mitigations. Our case study – of climate conditions’ impact on livestock health in Karnataka, India – clearly evidences the benefit of integrating earth scientists into planetary health research.

Introduction: This study explores exposure to misinformation, COVID-19 risk perception, and confidence towards the government as predictors to negative attitudes toward the COVID-19 vaccine. Methods: A cross-sectional survey was carried out from 30 June to 30 August 2021 involving 775 respondents. The survey instrument for the questionnaire is an adaptation from various different studies consisting of five main variables: 1) misinformation about vaccination; 2) risk perception toward COVID-19; 3) attitudes toward the vaccination programme; 4) intention to get vaccinated; and 5) public confidence in the government in executing the vaccination programme. Results: The results of this study indicate that higher exposure to misinformation led to higher levels of negative attitudes toward the COVID-19 vaccine. When the perceived risk of COVID-19 infection was high, mistrust of vaccine benefits was low but there were also higher worries about the future effects of the vaccine. Confidence in government was associated with lower negative attitudes toward the COVID-19 vaccine. Conclusion: The results of this study may help develop an understanding of negative attitudes toward vaccinations in Malaysia and its contributing factors.

Gestational diabetes mellitus (GDM) is a common complication of pregnancy that adversely affects maternal and offspring health. A variety of risk factors, such as BMI and age, have been associated with increased risks of gestational diabetes. However, in many cases gestational diabetes occurs in healthy nulliparous women with no obvious risk factors. Emerging data suggest that the tendency to develop gestational diabetes has genetic and environmental components. Here we develop a polygenic risk score for gestational diabetes. We further investigate relationships between the genetic architecture of GDM and genetically constructed risk factors and biomarkers. Our results show that genetics can be used as an early screening tool that identifies at-risk at higher risk of GDM before its onset to propose comprehensive monitoring and preventative programs to mitigate the risks.

Abstract Geographic Information System and Remote Sensing played an important role in analyzing environmental and socio-economic drivers that created favorable condition for malaria breeding as well as in identifying hazard and risk areas. This study gives great emphasis on mapping malaria hazard and risk areas in Gedio zone of SNNPs using geospatial technology. The study identifies two major drivers like Environmental (physical) factors: which provide for the endurance of mosquitoes and Socio-economic factors. The above data were presented and analyzed quantitatively. The content analysis shows that Malaria hazard prevalence areas were mapped based on the environmental factors which are potential of providing good environmental conditions for mosquito breeding. The hazard map was produced using elevation, slope, proximity to breeding sites, and soil type as the factors for breeding mosquitoes. The malaria hazard analysis of the Gedio zone revealed that from the total area, 9.83%, 35.29% is mapped as a very high and high-risk area, whereas, the remaining 38.73%, a 16.14%, and 0.01% were mapped as moderate, low, very low level of malaria hazard respectively. The total area of the study area more than 1/3rd of the area is identified as a very high and high malaria risk area while the rest 2/3rd of an area is considered as a moderate to very low hazard risk zone. Accordingly, very high malaria risk area is found around towns because of population density. Finally, I recommend that the concerned body should have to expand health center, creating awareness of society, especially around populated areas where the risk is high and environmental and individual sanitation can reduce the risk of malaria.

Risk management of electric power transmission lines requires knowledge from different areas such as environment, land, investors, regulations, and engineering. Despite the widespread availability of databases for most of those areas, integrating them into a single database or model is a challenging problem. Instead, in this paper, we use a single source, the Brazilian National Electric Energy Agency’s (ANEEL) weekly reports, which contains decisions about the electrical grid, comprising most of the areas. Since the data is unstructured (text), we employed NLP techniques such as stemming and tokenization to identify keywords related to common causes of risks provided by an expert group on energy transmission. Then, we used models to estimate the probability of each risk. Our results show that we were able to estimate the probability of 97 risks out of 233.

Urban flood risk mapping is an important tool for the mitigation of flooding in view of human activities and climate change. Many developing countries, however, lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can improve urban flood risk management in regions with scarce hydrological data. Given this, we estimated the flood risk map for Rasht City (Iran), applying a composition of decision-making and machine learning methods. Flood hazard maps were produced applying six state-of-the-art machine learning methods such as classification and regression trees (CART), random forest (RF), boosted regression trees (BRT), multivariate adaptive regression splines (MARS), multivariate discriminant analysis (MDA), and support vector machine (SVM). Flood conditioning parameters applied in modeling were elevation, slope angle, aspect, rainfall, distance to river (DTR), distance to streets (DTS), soil hydrological group (SHG), curve number (CN), distance to urban drainage (DTUD), urban drainage density (UDD), and land use. In total, 93 flood location points were collected from the regional water company of Gilan province combined with field surveys. We used the Analytic Hierarchy Process (AHP) decision-making tool for creating an urban flood vulnerability map, which is according to population density (PD), dwelling quality (DQ), household income (HI), distance to cultural heritage (DTCH), distance to medical centers and hospitals (DTMCH), and land use. Then, the urban flood risk map was derived according to flood vulnerability and flood hazard maps. Evaluation of models was performed using receiver-operator characteristic curve (ROC), accuracy, probability of detection (POD), false alarm ratio (FAR), and precision. The findings showed that the CART method is most accurate method (AUC = 0.947, accuracy = 0.892, POD = 0.867, FAR = 0.071, and precision = 0.929). The results also demonstrated that DTR, UDD, and DTUD played important roles in flood hazard modeling; whereas, the population density was the most significant parameter in vulnerability mapping. These findings indicated that machine learning methods can improve urban flood risk management significantly in regions with limited hydrological data.

Obesity has become a rising global health problem affecting adults’ quality of life. The objective of this study was to describe the prevalence of obesity in Indonesian adults based on the cluster of islands. The study was also aimed to identify the risk factors of obesity in each island cluster. This study analysed secondary data of Indonesian Basic Health Research 2018. Our data for analysis comprised 688,638 adults (>=15 years) randomly selected using proportionate to population size throughout Indonesia. We included 20 variables for sociodemographic and obesity-related risk factors for analysis. Obese status was defined using Body Mass Index (BMI) >= 27.5 kg/m2. Our current study defined seven major islands cluster as the unit analysis consisting of 34 provinces in Indonesia. Descriptive analysis was conducted to determine the characteristics of the population and to calculate the prevalence of obesity within provinces in each of the island’s clusters. Multivariate logistic regression analyses to calculate odds ratios (ORs) was performed using R version 3.6.3. The study results showed that all island clusters had at least one province with an obesity prevalence of more than 20%. Six out of twenty variables, comprising four diet factors (consumption of sweet food, high-salt food, meat food, and carbonated drinks) and two other factors (mental health disorders and smoking behaviour), varied across the island clusters. In conclusion, there was a variation of obesity prevalence of the provinces within and between island clusters. Variation of risk factors raised in each cluster island suggested the government rethink and reframe the intervention to address obesity.