It is estimated that 3 billion tons of concrete are used globally each year. Cement is a critical component of concrete, and its production accounts for nearly 5-7% of global carbon dioxide (CO2) emissions. Producing one ton of concrete (OPC) releases approximately one ton of CO2. These CO2 emissions contribute to the greenhouse effect, leading to global warming, polar ice cap melting, sea level rise, coastal city flooding, land desertification, decreased agricultural productivity, ecosystem damage, and adverse effects on human health. This situation is concerning, and there is an urgent need to minimize CO2 emissions from the cement industry. This paper focuses on incorporating various materials into concrete to increase its strength and reduce emissions of CO2 and other harmful substances, thereby mitigating the impacts of the greenhouse effect and achieving the great vision of carbon neutrality. The study theoretically analyzes the issues of coconut shell disposal in Southeast Asia and waste tire disposal in Europe and the United States. This paper uses Abaqus to model and analyze various green concretes and compares the data with that of ordinary concrete.

Daylighting design is not only a stage of dimensioning glazed surfaces to provide sufficient natural light to an occupied space, but also a method of analysing how this matter can be achieved without unwanted effects, such as: gains and losses of heat, glare, and variations of the daylighting intensity, at various indoor distances and levels. According to the international regulations in force, glazed surfaces must be designed with a careful balance between the admission of natural light and thermal performance, such as heat loss in winter and heat gain in summer. In a novel project, it is essential to determine the optimal ratio of window-to-wall surface area. This necessity arises because the materials constituting the glazed surfaces of windows typically exhibit lower insulation properties compared to those of the building's walls. This methodology could be applied to building renovations when envelope modifications are possible and suitable. The case study presented in the paper highlights the energy consumed due to a group of windows with a large glazed area in an existing building, located in a temperate continental climate area. The energy consumption results from supplementary artificial lighting required to maintain adequate illumination for indoor activities and that to counterbalance heat loss during colder periods are evaluated. The analysis performed by modifying the glazed surface led to the identification of an optimum value of windows dimensions for an energy consumed minimum.

This study evaluated ground-level concentrations (GLCs) of aerosols and criteria pollutants using the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis dataset over the Himawari-8 observational land area, including China, Indonesia, and Australia, for the past eight years. A square root transformation of the data was used to normalize and mitigate skewness. Monthly-hourly long-term averages were subtracted to determine abnormalities and identify significant pollution events. Geographic Weighted Regression (GWR) and the Jacobian matrix (dY/dX) methods were used to determine the spatial variability in pollutant concentrations and correlations with meteorological factors. The findings revealed complex interactions between atmospheric dynamics, such as wind components, specific humidity, and pollutant distribution. The analysis highlighted the limitations of traditional point monitoring methods and the need for incident-based spatiotemporal approaches to understand pollution dynamics. This comprehensive evaluation, covering roughly 10% of the globe, demonstrated CAMS's effectiveness in providing detailed insights into air quality trends, which can support targeted public health interventions and compliance with sustainability goals. Integrating CAMS data with high-resolution satellite data like Himawari-8 could revolutionize air quality monitoring by delivering dynamic, near real-time outputs with high spatiotemporal resolution.

This article develops duality principles and numerical results for a large class of non-convex variational models. The main results are based on fundamental tools of convex analysis, duality theory and calculus of variations. More specifically the approach is established for a class of non-convex functionals similar as those found in some models in phase transition. Finally, in the last section we present a concerning numerical example and the respective software.

MiR-204 is a small noncoding RNA with diverse regulatory roles in breast cancer (BC) development and progression. miR-204 is implicated in the instauration of fundamental traits acquired during the multistep development of BC known as hallmarks of cancer. It may act as a potent tumor suppressor by inhibiting key cellular processes like angiogenesis, vasculogenic mimicry, invasion, migration, and metastasis. It achieves this by targeting multiple master genes involved in these processes, including HIF-1α, β-catenin, VEGFA, FAK, FOXA1, among others. Additionally, miR-204 modulates signaling pathways like PI3K/AKT and interacts with HOTAIR and DSCAM-AS1 lncRNAs, further influencing tumor progression. Beyond its direct effects on tumor cells, miR-204 shapes the tumor microenvironment by regulating immune cell infiltration, suppressing pro-tumorigenic cytokine production, and potentially influencing immunotherapy response. Moreover, miR-204 plays a crucial role in metabolic reprogramming by directly suppressing metabolic genes within tumor cells, indirectly affecting metabolism through exosome signaling, and remodeling metabolic flux within the tumor microenvironment. This review aims to present an update on the current knowledge regarding the role of miR-204 in the hallmarks of BC. In conclusion, miR-204 is a potential therapeutic target and prognostic marker in BC, emphasizing the need for further research to fully elucidate its complex roles in orchestrating aggressive BC behavior.

Gastric cancer (GC) remains a significant global health challenge, with high mortality rates, especially in developing countries. Current treatments are invasive and have considerable risks, necessitating the exploration of safer alternatives. Quercetin (QRC), a flavonoid present in various plants and foods, has demonstrated multiple health benefits, including anticancer properties. This study investigates the therapeutic potential of QRC in the treatment of GC. We utilized advanced molecular techniques to assess the impact of QRC on GC cells, examining its effects on cellular pathways and gene expression. Our findings indicate that QRC significantly inhibits GC cell proliferation and induces apoptosis, suggesting its potential as a safer therapeutic option for GC treatment. Further research is required to validate these results and explore the clinical applications of QRC in cancer therapy

Vascular rings are a rare congenital anomaly of the aortic arch in which a ring-shaped structure forms surrounding the trachea and/or esophagus, potentially causing compression. We describe the case of a 14-month-old female patient with failure to thrive secondary to dysphagia, and a vascular ring formed by a right aortic arch, an aberrant left subclavian artery, and a left liga-mentum arteriosum. Surgical repair involved ligamentum arteriosum division, Kommerell’s di-verticulum obliteration, and left subclavian artery re-implantation into the left carotid artery. Endoscopy and EndoFLIPTM evaluated intraoperative improvement in esophageal narrowing and impedance, respectively. Postoperative period was uneventful and follow-up visits demonstrated dysphagia resolution and a patent re-implanted left subclavian artery.

This critical review revisited the new classification system for periodontitis, specifically for staging, suggesting modifications, and introducing a new flowchart for a better clinical evaluation. It evaluated articles published between 2018 and 2024 in the English language, which had an educational motivation focused on staging periodontitis. PubMed/MEDLINE, Web of Science, and Embase databases were used to retrieve the articles. The focus questions involved the analysis of all parameters for staging periodontitis. A total of 836 articles were initially found; 388 duplicated were excluded; 448 were evaluated by title and abstract; 26 articles followed for full-text reading, and 6 articles were included in this critical review (k=0.98). All articles included detailed parameters and steps about how to diagnose periodontitis. Therefore, it was possible to observe instability and “gray zones” in the staging step, which was due to a lack of priority and an organized order sequence. This review suggests the severity parameters cannot be overcome by complexity parameters, following a cumulative sequence: CAL (1^st), RBL (2^nd), TLP (3^rd), and, after, complexity parameters. An exception must be permitted only for complexity factors between Stages III and IV that can change the initial Stage (III or IV) obtained through the severity analysis, but only between themselves. Moreover, for patients without tooth loss or with TLP of ≤4 teeth (without need for complex rehabilitation) and presenting any type of drifting or flaring or a secondary traumatic occlusion, it cannot be a justification for moving the diagnosis from Stage III to Stage IV.

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.

Supply chain flexibility plays a pivotal role in enabling organizations to adapt their marketing strategies to evolving consumer preferences in dynamic market environments. This qualitative study explores how supply chain flexibility dimensions—responsiveness, agility, resilience, and sustainability—impact marketing strategy adaptation. Through semi-structured interviews and document analysis, insights were gathered from industry practitioners across diverse sectors. Key findings highlight that responsive supply chains facilitate quick adjustments in production, distribution, and sourcing to meet changing consumer demands. Agility enables rapid reconfiguration of operations to capitalize on market opportunities and respond to disruptions effectively. Resilient supply chains mitigate risks and maintain continuity during crises, safeguarding customer satisfaction and brand reputation. Integrating sustainability practices not only meets regulatory standards but also aligns with consumer preferences for eco-friendly products, enhancing corporate social responsibility. Technological advancements such as AI, IoT, blockchain, and cloud computing enhance supply chain visibility, optimize decision-making, and support real-time responsiveness. Despite benefits, challenges like legacy systems, organizational silos, resistance to change, and resource constraints hinder effective implementation. Overcoming these barriers requires strategic leadership, cross-functional collaboration, and continuous investment in technology and talent. Embracing supply chain flexibility empowers organizations to navigate complexities, drive innovation, and sustain competitive advantage. By aligning supply chain capabilities with marketing strategies, companies can enhance market responsiveness, customer satisfaction, and long-term growth in today's dynamic business landscape

Animals use dietary lipids to sustain their growth and survival. Insects can synthesize fatty acids (FAs) and are autotroph for a number of lipids, but auxotroph for specific lipids classes (e.g. sterols, polyunsaturated FAs). Once ingested, lipids are hydrolyzed in the intestinal lumen and taken up into intestinal cells within specific regions of the insect digestive tract. These lipids can be either stored in the intestinal cells or exported through the haemolymph circulation to specific organs. In this chapter, we describe the various lipids provided by insect diets, their extracellular hydrolysis in the gut lumen and their intake and metabolic fate in the intestinal cells. This review emphasizes the critical role of the digestive tract and its regionalization in processing dietary lipids prior to their transfer to the requiring tissues.

Background: In 2023, ChatGPT-4 has been impactful across several sectors including healthcare, where it aids in medical information analysis and education. Electronic Patient-Reported Outcomes (ePROs) play a crucial role in monitoring cancer patients’ post-treatment symptoms, enabling early in-terventions. However, managing the voluminous ePRO data presents significant challenges. This study assesses the feasibility of utilizing ChatGPT-4 for analyzing side effect data from ePROs. Methods: Thirty cancer patients were consecutively collected via a web-based ePRO platform, reporting side effects over 4 weeks. ChatGPT-4, simulating oncologists, dietitians, and nurses, analyzed this data and offered improvement suggestions, which were then reviewed by professionals in those fields. Results: Two oncologists, two dieticians and two nurses evaluated the AI’s performance across roles with 540 reviews. ChatGPT-4 excelled in data accuracy and completeness, and was noted for its empathy and support, enhancing communication and reducing caregiver stress. It was particularly effective as a dietician. Discussion: ChatGPT-4 shows promise in processing ePRO data with high accuracy, suggesting that AI can enhance clinical decision-making. Nevertheless, its limitations in emotional engagement highlight the need for AI to supplement, not replace, human care. Emphasizing the role of dietitians underscores the importance of nutritional advice in cancer care. Future studies will further explore the feasible application of AI and ePRO in cancer care clinical processes.

This article explores the application of machine learning techniques, specifically focusing on ensemble methods like Random Forests, for detecting fraudulent activities in digital financial transactions. Highlighting the evolution from traditional statistical approaches to modern machine learning models, it underscores the effectiveness of Random Forests in handling the inherent challenges of imbalanced datasets typical in fraud detection scenarios. Using a Kaggle dataset of credit card transactions, the study optimizes Random Forest parameters through rigorous parameter tuning, achieving significant improvements in model performance metrics such as Area Under the Curve (AUC). The findings underscore the critical role of machine learning in enhancing fraud detection capabilities, emphasizing the ongoing evolution and future potential of these methodologies in financial risk management.

This research introduces an Intelligent Robotic Arm with Adaptive Collision Avoidance, employing real-time current fluctuation analysis for human-proximity detection. Precision and versatility in motors and actuators are given top priority in the robotic arm setup. Motor power lines are continually monitored by integrated high-precision current sensors, and a real-time data processing system separates natural fluctuations from those caused by outside influences. When a human comes into contact with the surface of the robotic arm, the system excels at detecting their closeness by identifying unique patternsin the variations of current. This sets off an adaptive collision avoidance system, which quickly flips the main power supply’s kill switch to bring the vehicle to an emergency stop. A crucial component is adaptability, as the system dynamically modifies sensitivity in response to environmental condi- tions. Comparative studies show that this approach performs better than conventional static sensing techniques. Experiments under control confirm the great precision, accuracy, and low false positive rate. The use of a failsafe technique improves system integrity by preventing false alarms and enabling recovery after an emergency

The Fourier Continuous Derivative ($D_C$) offers a unique perspective on fractional differentiation grounded in the theory of Fourier series. This approach has the potential to address problems across various disciplines, including physics, engineering, and mathematics. The primary insight underpinning this approach is that a convex function defined on $\mathbb{Z}$ retains its convexity on $\mathbb{R}$. This paper delves into the Fourier Continuous Derivative, compares it with traditional fractional derivatives, and outlines its possible real-world applications, such as modeling viscoelastic materials, solving wave equations, and financial data analysis.

As the global overprovisioning of electronic devices continues to rise, the management of electronic waste (e-waste) has developed as a significant challenge. E-waste is comprised of hazardous materials; moreover, the toxic constituents in them can impact the environment and public health through improper disposal. On the beneficial side, this e-waste also contains some valuable materials. If the valuable materials are recovered, they can reduce the dependence on new, raw materials. Conventional linear models of production and consumption have led to a momentous buildup of e-waste, posing environmental and economic risks. This paper examines how the adoption of a circular economy approach can improve e-waste recycling in Nairobi by evaluating its economic recovery potential and e-waste residuals. It introduces metrics to assess the development of circular economy practices to help in the handling of e-waste residuals and examines the economic consequences associated with the adoption of circular strategies.

More than 400 million tons of livestock waste are generated annually by approximately 25,000 concentrated animal feeding operations (CAFOs) located in the United States (US). These operations give rise to externalities, including adverse local and global health impacts from CAFO waste emissions, which can potentially outweigh their economic viability. However, a shortage of evidence synthesis research on US-based CAFO emissions may hinder effective policy development. This scoping review (ScR) study, conducted following guidance from the Joanna Briggs Institute, searched databases including Scopus, Web of Science, PubMed and Embase in May 2020, yielding ten publications meeting inclusion criteria. Results suggest possible CAFO worker exposures to multidrug-resistant Staphylococcus aureus (MDRSA), campylobacteriosis and cryptosporidiosis. Communities near CAFOs experienced higher rates of adverse health impacts compared to those in non-CAFO areas, with patterns suggesting proximity may correlate with increased odds of detrimental health effects. Implicit global health threats include Methicillin-resistant Staphylococcus aureus  (MRSA), MDRSA, campylobacteriosis, tuberculosis, and cryptosporidiosis. These studies provide foundational insights into CAFO proximity, density patterns, and adverse public health effects, indicating a need for evidence-informed environmental health policies to minimize local and global risks.

Backgorund and Objectives: In this study, we aimed to evaluate and compare the changes in visual and structural outcomes in patients with intravitreal aflibercept (IVA)-resistant neovascular age-related macular degeneration (nAMD) switched to either intravitreal brolucizumab (IVBr) or intravitreal faricimab (IVF) injections in a clinical setting. Materials and Methods: This obser-vational clinical study included 20 eyes of 20 patients switched to brolucizumab and 15 eyes of 14 patients switched to faricimab for aflibercept-resistant nAMD. We measured the structural outcome (central macular thickness (CMT)) and the visual outcome (best-corrected visual acuity (BCVA); logMAR) as follows: just before the most recent IVA injection (B0), one month after the most recent IVA injection (B1), just before the first IVBr or IVF injection (A0), one month after (A1) and three months after (A3) the first IVBr or IVF injection. Results: BCVA showed significant im-provement at A1 (0.25 ± 0.34) and at A3 (0.19 ± 0.24) compared to A0 (0.38 ± 0.35) in IVBr group (p = 0.0156, p = 0.0166, respectively). CMT (μm) was significantly thinner at A1 (IVBr; 240.55 ± 51.82, IVF; 234.91 ± 47.29) and at A3 (IVBr; 243.21 ± 76.15, IVF; 250.50 ± 72.61) compared to at A0 (IVBr; 303.55 ± 79.18, IVF; 270.33 ± 77.62) in IVBr group (A1; p = 0.0093, A3; p = 0.0026) and in IVF group (A1; p = 0.0161, A3; p = 0.0093). There was no significant difference in BCVA and CMT improvement observed between two groups at any time point (p > 0.05 for all). Conclusion: Switching from aflibercept to either brolucizumab or faricimab has a significant anatomical ef-fect on eyes with aflibercept-resistant nAMD and both treatments appear to be effective short-term treatment options. There is a trend towards greater visual improvements and reductions in CMT with brolucizumab.

Assessing carbon stocks in tropical forests is crucial for understanding their role in mitigating climate change. (1) Background: However, existing assessments have underestimated certain factors that can substantially influence carbon dynamics. (2) Methods: This study focuses on one such factor - buttress roots from a tropical forest. (3) Results: Our findings reveal that a significant proportion of trees (69.57%) had 3 to 5 buttress roots per tree. The total biomass of the buttress roots and the aboveground portion of the trees with buttress roots were calculated to be 8.5 tonnes/ha and 10.7 tonnes/ha respectively. The buttress root biomass accounted for 16.18% of the total tree biomass. It was observed that the presence of buttress roots during both the rainy and dry seasons increased the soil organic carbon content by an average of 20.8% in the upslope areas with buttress roots. During the rainy season, the presence of buttress roots did not affect the heavy fraction organic carbon in the soil layers in a statistically significant way. Regardless of the season, the soil respiration rate in the areas without buttress roots was higher than that in the areas with buttress roots. The presence of buttress roots had a positive effect on soil nutrient concentration in all seasons, creating a healthier environment for trees. Tree species with buttress roots had on average 20% higher organic carbon content during both the wet and dry seasons. (4) Conclusions: These findings highlight the importance of considering buttress roots in carbon stock assessments and forest management strategies. By integrating buttress roots into carbon accounting models, we can obtain more accurate estimates of carbon stock potential and develop more effective conservation and restoration strategies for tropical forests.

In the context of healthcare technology management (HTM) in Saudi Arabia and the Gulf region, this study addresses a significant gap by exploring both external and internal risk factors affecting HTM performance. Previous research has primarily focused on modeling or predicting failures in medical devices, mostly examining internal (endogenous) factors that impact device performance and the development of optimal service strategies. However, a comprehensive investigation of external (exogenous) factors has been notably absent. This research introduces a novel hierarchical risk management framework, designed to accommodate the broad array of healthcare technologies, not limited to medical devices. It significantly advances the field by thoroughly investigating and validating a comprehensive set of 53 risk factors and assessing their influence on HTM. Additionally, this study embraces the perspective of enterprise risk management (ERM) and expands it to identify and incorporate a wider range of risk factors, offering a more holistic and strategic approach to risk assessment in healthcare technology management. The findings reveal that several external and internal factors previously underexplored significantly impact HTM performance. Notably, the Fuzzy AHP survey identified “design risk” under facility and environmental risks as the highest risk for HTM in the region. Furthermore, the study reveals that three out of the top ten risks are related to “facility and internal environmental” factors, six are related to technological endogenous factors, and only one is related to managerial factors. This distribution underscores the critical areas for intervention and the need for robust facility and technology management strategies. In conclusion, this research not only fills a critical void by providing a robust framework for healthcare technology risk assessment but also broadens the scope of risk analysis to include a wider array of technologies, thereby enhancing the efficacy and safety of healthcare interventions in the region. Additionally, the proposed hierarchy provides insights into the underlying risk factors for healthcare technology management, with potential applications extending beyond the regional context to a global scale. Moreover, the equation we propose offers a novel perspective on the key risk factors involved in healthcare technology management, presenting insights with far-reaching implications applicable not only regionally but also on a global level. This framework also supports sustainability goals by encouraging the efficient and responsible utilization and management of healthcare technologies, essential for ensuring the long-term economic and environmental sustainability of medical technology use.

Abstract: Background: The gut microbiota, a diverse community of microorganisms in the gastrointestinal tract, is essential for human health, primarily through nutrient metabolism and production of anti-inflammatory short-chain fatty acids. Dysbiosis, a disruption in microbial composition, is linked to diseases like inflammatory bowel disease, colorectal cancer, obesity, diabetes, asthma, and allergies. Dietary habits significantly influence gut microbiota diversity, with balanced diets crucial for maintaining microbial equilibrium. Culinary herbs and spices, known for their antioxidant and antimicrobial properties, may positively modulate the gut microbiota by inhibiting inflammation, stimulating beneficial bacteria, and exhibiting prebiotic effects. Methods: A comprehensive literature search was conducted using the PubMed database to identify relevant research articles on the impact of culinary spices on the gut microbiome. The search terms included "culinary spices," "gut microbiome," "gut modulation," "gut dysbiosis," "common spices," and "gut health." Thirty-five articles published between 2016 and 2024 were reviewed, with eight directly addressing the effects of culinary spices on gut microbiome modulation. Priority was given to peer-reviewed journals and reputable scientific sources. Results: Studies demonstrate that mixed spices and individual spices like ginger, turmeric, and chili can induce favorable changes in gut microflora, promoting beneficial probiotics and inhibiting pathogenic bacteria. Spices such as turmeric and curcumin exhibit potent anti-inflammatory properties, contributing to their protective effects against chronic diseases like colon cancer and metabolic disorders. Conclusions: Incorporating culinary spices into dietary interventions is important for overall well-being. Further research is needed to elucidate the mechanisms of action and optimize the therapeutic applications of spices in clinical settings.

The solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the quantity of solar panels and the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction method is proposed to forecast the solar irradiance of typical irrigation areas. The impact of the meteorological parameters on the solar irradiance is studied, and four different parameter combinations are formed and considered as input to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict the solar radiation. The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. The differences between the prediction performances of models trained based on single-station data and mixed data from multiple stations are also compared. The obtained results show that the proposed model achieves the highest prediction accuracy when the maximum temperature, minimum temperature, sunshine hours ratio, relative humidity, wind speed, and extraterrestrial radiation are used as the input parameters. The prediction effectiveness is also verified based on measured data. The WOA-XGBoost model has higher prediction accuracy than the XGBoost model, with predicted values falling within acceptable ranges. The model developed using mixed data of multiple stations can be applied for forecasting solar irradiance in different regions. This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines.

(1) Background: Emotional resilience is defined as the capacity of individuals to cultivate positive emotions and effectively navigate through negative emotions in terms of stress or adversity. Recent research has focused on the significant impact of adolescents’ negative emotions, as opposed to positive emotions, on mental health. The examination of positive emotion provides a distinct viewpoint on the relationship between emotional resilience and mental health in adolescents. Consequently, the current research conducted two separate investigations to examine the impact of adolescent emotional resilience on mental health, as well as the mediating influence of positive emotion. (2) Methods: Study 1 comprised a cross-sectional analysis of 3086 Chinese adolescents, while Study 2 involved a longitudinal examination of 266 Chinese adolescents. (3) Results: Results indicated that emotional resilience was associated with enhanced levels of life satisfaction, self-esteem, and overall mental health, while also demonstrating a negative correlation with symptoms of depression and anxiety. Positive emotions were found to have a partial mediating effect on the relationship between emotional resilience and mental health. These results indicate that emotional resilience not only has a direct impact on mental health but also influences mental health indirectly through positive emotions. (4) Conclusions: The findings highlighted the role of emotional resilience in mitigating psychological problems and enhancing mental health in Chinese adolescents. The implications and limitations were discussed.

Creatine has been linked to the development of kidney damage since 1998. However, uncertainties persist regarding the actual impact of creatine supplementation on individuals with kidney disease before supplementation or with pathological conditions that have an increased risk of developing kidney dysfunction. This study performed a comprehensive review of the literature and utilized bioinformatics tools to investigate the effects of creatine on renal tissue and function. Moreover, we identified 44 genes modulated explicitly in response to creatine exposure using the Comparative Toxicogenomics Database, including IGF1, SLC2A4, and various creatine kinase genes. The analysis revealed associations with metabolic processes such as amino acid metabolism, indicating a connection between creatine and tissue physiology. Gene expression analysis using the Genotype-Tissue Expression Portal highlighted tissue-specific expression patterns in kidney and pancreas tissues. Furthermore, the study explored creatine metabolism pathways and kinase regulation through biological databases and Gene Expression Omnibus data, shedding light on the potential regulatory roles of creatine in cellular processes during kidney diseases. Understanding the impact of the balance between the endogenous creatine synthesis and the creatine uptake, particularly the genes GATM, GAMT, SLC6A8 and IGF1, insights into molecular mechanisms underlying creatine's physiological effects on renal tissues. Overall, this work provides new insights into the molecular interactions and pathways influenced by creatine in renal function.

Hydroperoxides (ROOH) are known as damaging agents capable of mediating mutation, while a role as signaling agents through oxidation of protein sulfhydryls that can alter cancer-related pathways has gained traction. Glutathione peroxidase 2 (GPX2) is an antioxidant enzyme that reduces ROOH at the expense of glutathione (GSH). GPX2 is noted for a tendency of large in-creases or decreases in expression levels during tumorigenesis that leads to investigators focusing on its role in cancer. However, GPX2 is only one component of multiple enzyme families that metabolize ROOH and GPX2 levels are often very low in the context of these other ROOH reducing activities. Colorectal cancer (CRC) was selected as a case study for examining GPX2 function as colorectal tissues and cancers are sites where GPX2 is highly expressed. A case can be made for a significant impact of changes in expression levels. There is also a link between GPX2 and NADPH oxidase 1 (NOX1) from earlier studies that is seldom addressed and is discussed, presenting data on a unique association in colon and CRC. Tumor-derived cell lines are quite commonly used for pre-clinical studies involving the role of GPX2 in CRC. Generally, selection for this type of work is limited to identifying cell lines based on high and low GPX2 expression with the standard re-search scheme of overexpression in low expressing lines and suppression in high expressing lines to identify impacted pathways. This overlooks CRC subtypes among cell lines involving a wide range of gene expression profiles and a variety of driver mutation differences along with a large difference in GPX2 expression levels. A trend for low and high GPX2 expressing cell lines to segregate into different CRC subclasses, indicated in this report, suggests that choices based solely on GPX2 levels may provide misleading and conflicting results by disregarding other properties of cell lines and failing to factor in differences in potential protein targets of ROOH. CRC and cell line classification schemes are presented here that were intended to assist workers in performing pre-clinical studies but are largely unnoted in work on GPX2 and CRC. Studies are often initiated on the premise that the transition from normal to CRC is associated with up-regulation of GPX2. This is probably correct. However, the source normal cells for CRC could be almost any colon cell type, some with very high GPX2 levels. These factors are addressed in this study.

The environmental conditions to which dams are exposed play a major role in dictating the progression and manifestation of the alkali-aggregate reaction (AAR). However, in the numerical thermal-mechanical simulation of AAR affected dams, the solar radiation and its associated shadow effects have received little attention. The spatiotemporal distribution of the solar radiation incidence on the dam surfaces has often been addressed in a simplified way or has just been neglected. Yet, far less attention has been given to shadows cast by the dam’s own geometry or the slopes. To illustrate the influence of the nonuniform temperature distribution on dam surfaces due to solar radiation and shading, the analysis of an AAR affected arch dam is presented in this paper.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4,202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 27 for fiber length (FL), 16 for fiber strength (FS), 24 for micronaire (MC), 17 for fiber uniformity (FU), and 24 for fiber elongation rate (FE). Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosome D1 and D11. Each QTL contributed 1.98%–21.45% to the phenotypic variation (PV), including 13 major effect QTLs that contributed more than 10% toward PV. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18%–21.45% of PV, qFS-A11-1 in F2:3, F2:4 and F2:5 with 6.05%–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

Dinoflagellates are one of the largest groups of marine microalgae and exhibit diverse trophic strategies. Within these niches, dinoflagellates can produce secondary metabolites that are known to be toxic, which can lead to ecologically harmful blooms. Amphidinium carterae is one species of dinoflagellate that produces toxic compounds and is used as a model for dinoflagellate studies. The extent to which the microbiome surrounding A. carterae affects its growth and secondary metabolite synthesis is still being assessed, as well as the impact of bacterial data on sequencing and assembly. An antibiotic cocktail was previously shown to eliminate 16S amplification from the dinoflagellate culture. Even with drastically reduced bacterial numbers, bacterial populations are likely to be present. In this experiment we used novel Nanopore long-read sequencing techniques on A. carterae cultures to assemble 15 full bacterial genomes ranging from 2.9-6.0 Mb and found that the use of antibiotics decreased the percentage of reads mapping back to bacteria. We also identified shifts in the microbiome composition and identified a potentially deleterious bacterial species arising in the absence of the antibiotic-treatment. Multiple antibiotic-resistance genes were identified, as well as evidence that the bacterial population do not contribute to toxic secondary metabolite synthesis.

In developing his cosmological model of 1917, de Sitter theoretically predicted the phenomenon of cosmological redshift (the de Sitter effect), which he did long before the discovery of this phenomenon in observations. The de Sitter effect is gravitational by its nature, as it is due to differences between the coordinate systems of the observer and the distant source. However, the relationship between the redshift and distance derived from the de Sitter metric is at odds with observations, since this relationship is nonlinear (quadratic) for small redshifts, while the observed relationship between the same quantities is strictly linear. This paper discusses the possibility that cosmological redshift is gravitational by its nature, as in de Sitter’s 1917 model. At the same time, here, as in de Sitter’s model, an elliptical space is used, the main characteristic of which is the identification of its antipodal points. But, unlike de Sitter’s model, here, in order to ensure a strict linear dependence of the redshift on distance, the origin of the reference system is transferred to the observer’s antipodal point. The Schwarzschild-de Sitter metric used in this model allows you to estimate the curvature of space from observational data. To do this, a theoretical Hubble diagram is built within the framework of the model with the Schwarzschild-de Sitter metric, which is compared with observations from the Pantheon+ catalogue of type Ia supernovae and the Amati catalogue of gamma-ray bursts in the redshift range of 0<z<8. As a results of the comparison, the lower estimate of the radius of curvature of space turned out to be quite large: 2.3·1015 Mpc. This means that the observational data indicate a negligible curvature of space.

This paper investigates the strategic dimensions influencing the adoption of environmental inno-vations (EIs) in small and medium-sized manufacturing enterprises (SMEs) in Mexico City. The study aims to understand the fundamental strategic dimensions defining eco-innovation and ex-amine how SMEs manage the complexities of incorporating these practices. Employing a mixed methods approach with documentary analysis and expert interviews; the study identifies key factors influencing EI adoption through causal maps and the Viable System Model. Findings high-light the significance of external and internal factors such as competitive pressure, economic con-straints, technological capabilities, political support, and social influence. This integrative approach emphasizes the importance of adaptability and responsiveness, presenting eco-innovation as a source of sustainable competitive advantage for SMEs.

Based on non-invasive neuroimaging techniques and graph theory-based network analyses, many researchers have tried to use small-world network model to elucidate sex differences in the brain. This manuscript aims to compile the related research findings from the past few years and summarize the sex differences in human brain networks from the perspective of small-world properties. We reviewed published reports examining altered small-world properties in both the functional and structural brain networks between males and females. Based on four patterns of altered small-world properties proposed--randomization, regularization, stronger small-worldization and weaker small-worldization, we found that current results point to a significant trend toward more regularization in females and more randomization in males in the functional brain networks. As for particular topological properties, the most consistent findings include a significant shift toward higher global efficiency in males and higher local efficiency in females in the functional network studies. On the other hand, there seems to be no consensus to date on the sex differences in small-world properties of the structural brain networks. Nevertheless, we noticed that the sample sizes in many published studies are limited, and future studies with larger samples are warranted to obtain more reliable results. We anticipate that the conclusions in this manuscript will contribute to a deeper understanding of neurobiological mechanisms underlying the sex differences in the brain.

A suitable waste management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and requires precise data on the volume of demolition waste (DW) gen-erated. Therefore, we developed an optimal machine learning (ML) model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. A dataset comprising infor-mation on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, and landfill mix waste, and recyclable minerals) was constructed. ML models were developed to predict the quantities of such waste. Artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest (RF), and support vector regression were applied, and optimal models were derived via hyperparameter tuning. The RF model demonstrated superior performance. In both validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies of of 0.987 and 0.972, re-spectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. SHapley Additive exPlanations analysis highlighted floor area as the primary input variable influencing model performance. The type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill wastes generated. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.

ABSTRACT: Background: Virtual Reality (VR) emerges as a promising non-pharmacological intervention for managing symptoms and providing distraction during chemotherapy. This study aims to assess VR's effectiveness on cancer-related symptoms, vital signs, and patients' perception of the chemotherapy in lung cancer patients. Methods: A quasi-experimental study was conducted on 100 patients. Participants were allocated into an intervention group (n = 55), which experienced immersive VR, and a comparison group (n = 45), which received usual care. Data were collected through questionnaires and checklists, including feedback on the VR experience, pain, vital signs, and common cancer symptoms, assessed through the Edmonton Symptom Assessment Scale. Results: VR had a significant impact on reducing the perception of the chemotherapy length. Patients reported high levels of satisfaction and tolerability. No adverse events were observed. VR did not have significant influence on pain intensity and vital signs. The only exceptions were oxygen saturation, where a significant difference (p = 0.02) was reported, and perception of chemotherapy duration. Conclusions: As a non-pharmacological intervention, VR proves beneficial in minimizing the perceived length of chemotherapy session for lung cancer patients, enhancing their overall treatment experience. The intervention showed to be a safe, feasible, and well-accepted distraction technique. Future research should explore VR's potential effects on a wider range of symptoms and evaluate its impact on long-term outcomes.

This study explores the dynamic interplay between supply chain integration and marketing strategies in achieving customer satisfaction. Through a qualitative approach involving semi-structured interviews with supply chain and marketing professionals, the research delves into how these traditionally distinct functions can be aligned to enhance operational efficiency and deliver superior customer experiences. Findings reveal that strategic alignment between supply chain management (SCM) and marketing fosters better coordination of product availability and service delivery, thereby meeting customer expectations consistently and enhancing brand loyalty. Information sharing and technological integration emerge as critical enablers, facilitating real-time data exchange and collaborative decision-making. The study also identifies challenges such as organizational silos, cultural resistance, and technological constraints that impede seamless integration, highlighting the need for proactive strategies to foster cross-functional collaboration. Additionally, the evolving roles of sustainability and customer-centricity are emphasized as key drivers for differentiating brands and achieving competitive advantage. By integrating sustainable practices and adopting a customer-centric approach, organizations can build stronger customer relationships and enhance their market position. The research underscores the importance of leadership support, investment in technology, and a collaborative organizational culture in overcoming integration barriers. The study’s findings provide actionable insights for organizations aiming to optimize their SCM and marketing strategies, drive innovation, and sustain growth in an increasingly complex and competitive business environment. This research contributes to the growing body of knowledge on supply chain and marketing integration, offering practical recommendations for achieving customer satisfaction and long-term success.

Marine litter, comprising persistent and human-made solid materials discarded in marine environments, poses a severe threat to Nigeria's coastal and marine ecosystems. Despite existing international, regional, and national regulations, ineffective enforcement and compliance have led to widespread environmental degradation (UNEP, 2021). This study examines the factors contributing to the proliferation of marine litter in Nigeria, focusing on regulatory gaps, insufficient waste management infrastructure, and the lack of public awareness. The findings reveal that the marine ecosystem is deteriorating due to inadequate waste disposal practices and limited understanding of the impacts of marine litter on both the environment and human health (UNEP, 2021). The paper concludes with recommendations for improving regulatory enforcement, enhancing public education, and investing in waste management infrastructure to address the growing crisis of marine litter. Throughout this article, the limited information on the impacts of marine pollution across Africa is highlighted, emphasizing the value of these insights despite their scarcity.

As split learning (SL) becomes popular for addressing data privacy and resource limitation issues in edge model training process, it is crucial to pre-validate whether the extensive use of wireless and computing resources in SL can achieve the expected training latency and convergence. While digital twin network (DTN) can potentially estimate network strategy performance in pre-validation environments, they are still in their infancy for SL tasks, facing challenges like unknown non-i.i.d. data distributions, inaccurate channel states, and misreport resource availability across devices. To address these challenges, this paper proposes a TransNeural algorithm for DTN pre-validation environment to estimate SL latency and convergence. First, the TransNeural algorithm integrates transformers to efficiently model data similarities between different devices, considering different data distributions and device participate sequence greatly influence SL training convergence. Second, it leverages neural network to automatically establish the complex relationships between SL latency and convergence with data distributions, wireless and computing resources, dataset sizes, and training iterations. Deviations in user reports are also accounted for in the estimation process. Simulations show that the TransNeural algorithm improves latency estimation accuracy by 13.44% and convergence estimation accuracy by 116.74% compared to traditional equation-based methods.

Heat pumps are increasingly becoming a key solution in the quest for decarbonization, offering a significant advantage over traditional boilers due to their superior energy efficiency. Not only can heat pumps provide both heating and cooling, but they also achieve thermal efficiencies exceeding 500%, which can seem to defy conventional expectations. The outdoor unit of a heat pump consists of about twenty main components, one of which is a complex network of copper pipes and sheets welded to valves, connectors, and other fittings. This set involves over thirty welded joints, highlighting a significant opportunity for reducing manufacturing time through process optimization. Copper is a critical material extensively used in various applications due to its excellent thermal and electrical conductivity. However, these very properties make welding copper particularly challenging, especially when aiming to automate and streamline industrial processes. Traditionally, connecting copper pipes and fittings is done through brazing, yet, alternative methods such as LASER welding, electron beam welding, resistance welding, friction welding, and ultrasonic welding also exist. This study focuses on the ultrasonic welding of copper, aiming to identify the optimal parameters that maximize the mechanical strength of overlapping joints and ensure effective tube sealing. Experimental results showed that for copper sheets, the best outcomes were achieved with a welding time of 3 s and a pressure of 6 bar. For tube sealing, the optimal conditions were a welding time of 3 s and a pressure of 4 bar. For both scenarios, the same variables were used, retention time of 0.62 s, initial pressure of 2 bar, retention pressure of 2 bar and sonotrode rising pressure of 3 bar. The analysis reveals that ultrasonic welding of copper can achieve both robust mechanical strength and reliable sealing, making it a promising technique for industrial applications.

Parkinson’s disease (PD) is a common multisystem neurodegenerative disorder affecting 1% of the population above 60 years. The main neuropathological features of PD are the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the presence of alpha synuclein (Syn)-rich Lewy bodies both manifesting with classical motor signs. Syn has emerged as a key protein in PD pathology as it can spread through synaptic networks to reach several anatomical regions of the body contributing to the appearance of non-motor symptoms (NMS) considerate prevalent among individuals before PD diagnosis and persisting throughout the patient’s life. NMS mainly include loss of taste and smell, constipation, psychiatric disorders, dementia, rapid eye movement (REM) sleep behavior impairment, urogenital dysfunction, and cardiovascular impairment. This review summarizes the more recent findings showing the impact of Syn deposits on several prodromal NMS and emphasizes the importance of early detection of Syn toxic species in biofluids and peripheral biopsies as prospective biomarkers in PD.

This study investigates the relationship between authentic leadership and socially responsible behavior among employees in Saudi Arabia's service sector. Utilizing social cognitive theory as the theoretical framework, the research proposes that authentic leadership indirectly influences socially responsible behavior through psychological empowerment and psychological capital as parallel and serial mediators. Additionally, the study examines the moderating effect of CSR perceptions on these relationships. A cross-sectional survey was conducted with 349 professionals from the service sector. The results, analyzed using partial least squares structural equation modeling (PLS-SEM), revealed that authentic leadership does not directly impact SRB. Instead, the relationship is fully mediated by psychological empowerment and psychological capital. Moreover, CSR perceptions significantly moderate the relationship between authentic leadership and psychological empowerment, as well as the indirect effects of authentic leadership on SRB through psychological empowerment and psychological capital. The findings highlight the importance of fostering an authentic leadership style and how psychological resources of empowerment, and psychological capital carry forward this effect to promote SRB in the service sector. Additionally, the study underscores the role of CSR perceptions in strengthening the impact of authentic leadership on employee outcomes. The research contributes to the literature on leadership, CSR, and employee behavior, and offers practical implications for organizations aiming to enhance their social responsibility initiatives.

COVID-19 infection in high-risk populations is fatal and has a poor prognosis, necessitating a test to determine the protectiveness of immune response. Antibody testing is necessary to determine the body's immune response to COVID-19 infection and also vaccination strategies. Among the various methods available, chemiluminescent immunoassay (CLIA) test is more widely used and accessible to determine antibody levels. This study aimed to determine the protection level of S-RBD SARS-CoV-2 IgG using CLIA compared to Surrogate Virus Neutralization Test (SVNT). The population of this study comprised all healthcare professionals who experienced S-RBD SARS-CoV-2 IgG antibody level examinations. S-RBD SARS-CoV-2 IgG antibody levels were examined using CLIA and SVNT. The cut-off was determined using Receiver Operating Characteristic (ROC) curve and AUC (Area Under the Curve) measurements were evaluated. The result showed a strong positive correlation between S-RBD SARS-CoV-2 IgG CLIA and SVNT with a value of r=0.933 and p <0.001. The value ≥37.29 BAU/mL was determined as the cut-off based on SVNT 30% inhibition level with sensitivity, specificity, positive, and negative predictive values of 96.5%, 90.9%, 96.5%, and 90.9%, respectively. A titer of antibodies greater than or equal to 37.29 BAU/mL with CLIA showed the presence of protective antibodies compared to SVNT.

Let's define $\delta(x) = (\sum_{{q\leq x}}{\frac{1}{q}}-\log \log x-B)$, where $B \approx 0.2614972128$ is the Meissel-Mertens constant. The Robin theorem states that $\delta(x)$ changes sign infinitely often. Let's also define $S(x) = \theta(x) - x$, where $\theta(x)$ is the Chebyshev function. It is known that $S(x)$ changes sign infinitely often. We define the another function $\varpi(x) = \left(\sum_{{q\leq x}}{\frac{1}{q}}-\log \log \theta(x)-B \right)$. We prove that when the inequality $\varpi(x) \leq 0$ is satisfied for some number $x \geq 3$, then the Riemann hypothesis should be false. The Riemann hypothesis is also false when the inequalities $\delta(x) \leq 0$ and $S(x)\geq 0$ are satisfied for some number $x \geq 3$ or when $\frac{3 \times \log x + 5}{8 \times \pi \times \sqrt{x} + 1.2 \times \log x + 2} + \frac{\log x}{\log \theta(x)} \leq 1$ is satisfied for some number $x \geq 13.1$.

The algorithm described in this paper inherits the concepts of layered fusion, insertion, and set from four convex hull-based heuristic algorithms. It summarizes relevant definitions and theorems, proposes valuable research hypotheses, combines grouping and insertion, and innovatively introduces the practice of "grouped insertion."

This study aimed to enhance the compatibility of glass ionomer cement (GIC) with tooth tissue by chemically modifying its primary component, calciumfluoroaluminosilicate (CFAS), through the incorporation of nanorod hydroxyapatite (nHA). The process involved binding L-glutamic acid to nHA, followed by activation and binding of albumin to create nHA immobilized with albumin. This modified nHA was then used to surface-modify CFAS particles, producing nHA-CFAS powder. The modified powder was mixed with poly(acrylic acid) and UV-cured to form GIC containing nHA-CFAS (GIC-nHA). Fourier transform infrared spectroscopy and scanning electron microscopy (SEM) confirmed the successful modification. Cytocompatibility tests with osteoblasts showed that GIC-nHA had superior cell viability and bone formation capabilities compared to the control GIC. The improved histocompatibility is attributed to nHA enhancing the biological activity of osteoblasts, indicating that the surface modification method significantly improves the functional integration of GIC with tooth tissue.

The unique structure of G4.0 PAMAM dendrimers allows the drug to be enclosed in internal spaces or immobilized on the surface. In the conducted research, the conditions for the formation of the active G4.0 PAMAM complex with doxorubicin (DOX) were optimized. The physicochemical properties of the system were monitored using DLS, CD, and fluorescence spectroscopy. The QCM-D method was chosen to determine the preferential conditions for the complex formation. The highest binding efficiency of the drug to the cationic dendrimer was observed under basic conditions when the DOX molecule was deprotonated. The decrease in the zeta potential of the complex confirms that DOX immobilizes through electrostatic interaction with the carrier's surface amine groups. The binding constants were determined from the fluorescence quenching of the DOX molecule in the presence of G4.0 PAMAM. The two-fold way of binding doxorubicin in the structure of dendrimers was visible in the ITC isotherm. Fluorescence spectra and release curves identified reversible binding of DOX to the nanocarrier. Among the selected cancer cells, the most promising anticancer activity of G4.0-DOX was observed in A375 malignant melanoma cells. Moreover, the preferred intracellular location of the complexes concerning the free drug was found, which is important from a therapeutic point of view.

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

Making cities health-promoting places is an evolving theme. Numerous research confirm the health-promoting qualities of contact with nature and problems resulting from the deprivation of access to public green spaces. Easy access to safe and inclusive open public green spaces is still one of the long-lasting problems of urbanized areas around the globe. It is one of the sustainable development goals - SDGs proposed by the UN, 11. Make cities and human settlements inclusive, safe, resilient and sustainable. Point 11.7 By 2030, provide universal access to safe, inclusive and accessible, green and public spaces, in particular for women and children, older persons and persons with disabilities. The major question is how to implement this goal in practice and design cities to provide easy access to safe and inclusive open public green spaces. One of the important concepts for sustainable urban development is the urban block - Macrolot coined by Christian de Portzamparc, which led to the new urban morphology of eco-neighborhoods in France. It is a combination of the traditional, walkable urban grid with the Le Corbusier vision for a healthy modernist city offering daylight, fresh air, and greenery for everyone. Among the advantages of this particular urban morphology are the increased presence of green spaces which form public parks, and possibilities for placemaking. Studying the effects of the urban form of the Macrolot is of great significance for sustainable urban development. In this study, five neighborhoods: three eco-neighborhoods from France designed according to the open urban block – Macrolot urban morphology – ZAC Massena, ZAC Trapeze, and ZAC Clichy-Batignolles and two award-winning developments from Po-land – Riverview and Ostoja Wilanów were chosen as case studies. The application of the Macrolot concept to sustainable urban planning & design and possibilities for operationalization of the SDGs: 11. Make cities and human settlements inclusive, safe, resilient and sustainable are discussed. This study offers valuable evidence to inform urban planning and design.

Multiple Sclerosis (MS) is a chronic autoimmune disorder characterized by the demyelination of neural fibers in the central nervous system. Understanding the impact of demyelination on neural network efficiency is crucial for elucidating the pathophysiological mechanisms underlying MS. This study aims to model the effects of demyelination on neural network efficiency, specifically focusing on how increased resistance and path lengths due to demyelination correlate with the clinical symptoms of MS. We employed a graph-theoretical approach to simulate neural networks, introducing varying degrees of demyelination. The demyelination was represented by increasing the resistance (weights) of selected edges within the network, affecting both random and clustered distributions of network connections. The primary metric analyzed was the average shortest path length, indicative of network efficiency. The models demonstrated a significant increase in the average shortest path length in demyelinated networks compared to intact ones. Specifically, networks with clustered demyelination exhibited localized inefficiencies, correlating with the focal nature of MS lesions. The increase in path length was associated with reduced signal transmission efficiency, providing insights into a range of MS symptoms including motor and sensory impairments, cognitive dysfunction, visual disturbances, and fatigue. The graph-theoretical model of neural network demyelination offers a novel perspective on how structural changes in the brain's connectivity due to MS can lead to diverse clinical manifestations. While the model simplifies the complexity of neural networks, it underscores the potential of computational approaches in understanding and predicting the course of neurological disorders like MS.

Hybrid methods are a suitable option to extract dietary patterns associated with health outcomes. This study aimed to identify dietary patterns of Brazilian adults (20-59 years old; n=28,153) related to dietary components associated with the risk of obesity. Data from the 2017-2018 Brazilian National Dietary Survey were analyzed. Food consumption was obtained through 24-hour recall. Dietary patterns were extracted using partial least squares regression. The selected response variables were energy density (ED), percentage of total fat (%TF), and fiber density (FD). In addition, 32 food groups were established as predictor variables. The first dietary pattern, named as energy-dense and low-fiber (ED-LF), included with positive factor loadings: solid fats, breads, added-sugar beverages, fast foods, sauces, pasta, and cheeses, and with negative factor loadings: rice, beans, vegetables, water, and fruits. Higher adherence to the ED-LF dietary pattern was observed for individuals >40 years old, from urban areas, in the highest income level, who were not on a diet, reporting away-from-home food consumption, and having ≥1 snack/day. Eating patterns with similar characteristics are often associated with an increased risk of obesity. The results are consistent with recommendations to increase the consumption of fresh foods and to reduce ultra-processed products.

How hematopoietic stem and progenitor cell (HSPC) fate decisions are affected by genetic alterations acquired during AML leukemogenesis is poorly understood and mainly explored in animal models. Here, we studied isocitrate dehydrogenases (IDH) genes mutations in the human model of HSPC and provide a review of the literature on this topic. IDH1/2 mutations occur in ~20% of AML, are recognized among the mutations earliest acquired during leukemogenesis, and are target of specific inhibitors (ivosidenib and enasidenib, respectively). In order to investigate the direct effects of these mutations on the HSPC we expressed by lentiviral transduction either IDH1-R132H or IDH2-R140Q mutants into human CD34+ healthy donor cells and analyzed the colony-forming unit (CFU) ability. CFU ability was dramatically compromised with a complete trilineage block of differentiation. Strikingly, the block was reversed by the specific inhibitors, confirming it was a specific effect induced by the mutants. In line with this observation, the CD34+ leukemic precursors isolated from a patient with IDH2-mutated AML at baseline and during enasidenib treatment showed over time a progressive and marked improvement of their fitness in terms of CFU ability and propensity to differentiate, attaining clonal trilinear reconstitution of hematopoiesis and complete hematological remission.

In the bar of our Milky Way, there should be another supermassive black holes B which is with the mass almost 10^-2--10^-5 the Sgr A* and with a distance10,000--14,500 lightyears from Sgr A*. The bar is formed by the stellar orbiting around both of the black holes. In the Hill radius of B, a lots of stars orbit around B while another lots of stars orbit Sgr A*. It determines that there is a “x-shaped structure” in the bar and the shape of bar appears a peanut. As the orbits of the stars is out of the Hill radius of B, they are repulsed to a distant place by B to form the arms.

These findings have several policy implications, emphasizing the need for enhanced data collection, climate-resilient agricultural policies, and continued investment in research. The study contributes to the understanding of climate-smart agriculture, providing insights for informed decision-making towards resilient and productive agricultural systems in the face of climate change challenges.

The goal of farmers operating in a market economy is to maximise profit. In the face of the changing political situation, the main social interest, besides food security, should be energy security. This article aims to indicate the direction of agricultural policy development so that it guarantees both the economic security of producers—farmers—and Europe’s energy security. The work uses data obtained from the agri benchmark Cash Crop database. DEA method and stepwise multiple regression were used. The research showed that plants are already grown in Europe which will ensure high energy efficiency of production without compromising farm financial results (including oats, peas and winter rye) and that improving the involvement of certain inputs results in improved production efficiency (among others, by expenditure on agricultural advisory services). The significance of the presented research results from the fact that the recent COVID pandemic and escalation of the military conflict in Ukraine have led to a renewed interest in the concept of EU energy security, i.e. generation of generating the greatest possible amount of energy within the EU, to be used both for food and non-food purposes.

Climate change continues to be a problem that is being discussed in various parts of the world. The hottest issue of concern is carbon emissions. The increase in carbon emissions was reported by the SAINS Global Carbon Project team in the Earth Systematic Science Data Journal. Disclosure of carbon emissions is part of sustainability reporting. Although several studies have revealed the determinants of carbon emission disclosure in companies, it is still not fully disclosed, one of the determinants of carbon emission disclosure is media exposure. Therefore, this study aims to fill this gap with the intention of revisiting the factors that influence carbon emission disclosure with the main focus on exploring the role of media exposure and disclosure practices. This study uses the SLR method with the help of Watase-Uake to search for relevant articles. The 43 articles deemed relevant were systematically reviewed to fill the gaps of previous research. The results show that the role of the media in encouraging disclosure of corporate carbon emissions information is mixed and inconsistent. The results also indicate that stakeholder pressure, good corporate governance, and the unique characteristics of each company are key factors that can influence voluntary disclosure of carbon emission information by companies.

Lirianthe delavayi has valuable ornamental and medicinal value, is an important economic tree, and has a long history of cultivation. As important regulatory molecules, miRNAs are involved in important physiological processes, such as the plant stress response, organ morphogenesis, hormone secretion, and signaling. In this study, based on miRNA sequencing analysis of wild and cultivated Lirianthe delavayi, we explored the differences in posttranscriptional regulation during the cultivation of Lirianthe delavayi from the aspect of miRNA molecular regulation. The results revealed the following: 1. A total of 365 miRNAs were sequenced and identified, and their first bases had obvious uracil (U) base preferences. Among them, 242 miRNAs were classified into 32 miRNA families; 2. Analysis of miRNA expression differences revealed that 39 miRNAs had significant expression differences between wild and cultivated populations; 3. Bioinformatics target gene prediction and target gene enrichment analysis of significantly differentially expressed miRNAs revealed that the target genes were significantly enriched in plant‒pathogen interactions, sphingolipid metabolism, defense responses, and ADP binding. 4. The plant‒pathogen interaction pathway involves seven differentially expressed miRNAs and 30 miRNA‒target gene regulatory pairs, which are involved mainly in pathogen signature molecule recognition and binding, signal transduction, and the regulation of disease resistance molecules or gene expression; Ath-MIR169g-p3_2ss7AG19GA, ath-MIR169g-p5_2ss7AG19GA, and ath-miR408-3p_L-3 are expressed only in wild population, and it is hypothesized that the domestication process from wild to cultivation species may be accompanied by the loss of disease resistance-related miRNAs. This study provides a reference for exploring the posttranscriptional-level regulatory mechanisms of adaptive changes in Lirianthe delavayi from wild to cultivated.

Abstract: The COVID-19 pandemic has brought about significant psychological challenges, particularly depressive and anxiety-related behaviors in those who have recovered from the virus. This study focuses on individuals aged 30-75+ to understand the prevalence, severity, and impact of these symptoms. By exploring their lasting consequences and potential risk factors, we aim to provide valuable insights for healthcare and mental health professionals, guiding the development of effective support systems to address the psychological aftermath of COVID-19. In this study, a quantitative research design was used to investigate the effects of depressive and anxiety behaviors in patients aged 30-75+ who had experienced COVID-19. The research employed a cross-sectional approach, collecting data at a specific time to assess the prevalence and severity of depressive and anxiety symptoms in the target population. A total of 422 patients were recruited through convenience sampling from various sources, including healthcare facilities, community organizations, and online platforms. Inclusion criteria encompassed individuals aged 30-75+ who had tested positive for COVID-19 and had recovered from the infection. Those with pre-existing mental health conditions significantly affecting depressive and anxiety symptoms were excluded. The sample was diverse in terms of age, gender, employment status, education level, marital status, and past chronic problems. Data collection involved administering standardized questionnaires, such as the Patient Health Questionnaire (PHQ-9) for depressive behaviors and the Generalized Anxiety Disorder 7-item scale (GAD-7) for anxiety behaviors. These questionnaires assessed symptom severity, while demographic variables were collected separately. The collected data underwent analysis using the Statistical Package for the Social Sciences (SPSS) software, encompassing descriptive statistics, reliability assessments, normality tests, non-parametric tests for demographic comparisons, correlation analyses, and linear regression. Ethical approval and informed consent were obtained, ensuring participant confidentiality and data privacy. Several limitations were acknowledged, including the cross-sectional design's inability to establish causal relationships, potential selection bias due to convenience sampling, susceptibility to response biases with self-report measures, and assumptions and limitations inherent in statistical tests. Overall, the study aimed to provide insights into the effects of depressive and anxiety behaviors in COVID-19 survivors aged 30-75+ and contribute to the development of targeted interventions and support services for this population. This study explored the impact of depressive and anxiety behaviors in COVID-19 survivors aged 30-75+. It involved data collection through validated questionnaires and rigorous statistical analysis. The findings underscore the importance of tailored interventions and support systems to address the long-term psychological effects of the pandemic. Healthcare professionals, researchers, and policymakers must collaborate to prioritize mental well-being and allocate resources accordingly, ultimately improving the quality of life for survivors.

Brucellosis is a zoonoses caused by bacteria of the genus Brucella, which causes economic losses in livestock and threatens public health. A cross-sectional study was conducted to determine the prevalence of Brucella species and risk factors in smallholder dairy cattle in six regions of Tanzania from July 2019 to October 2020. Dairy cattle (n=2048) were sampled from 1371 farms. DNA ex-tracted from blood and vaginal swabs was tested for Brucella using qPCR targeting the IS711 gene and positives were tested for the alkB marker for B. abortus and BMEI1172 marker for B. melitensis. The molecular prevalence was 3.5% (95% CI: 2.8-4.4) with the highest prevalence 8.1% (95%CI: 4.6-13.0) in Njombe region. B. melitensis was the predominant species detected (66.2%). Logistic regression revealed PCR positivity in dairy cattle was higher in the animals with a history of abortion (OR: 3.17, 95%CI: 1.05-9.62) and farms without dogs around (OR: 2.68, 95%CI: 1.12-5.90). Further studies are recommended to understand the source of B. melitensis and the implications to veterinary public health. Livestock keepers should be informed of the risks and biosecurity practices to reduce introduction and control Brucella. Cattle and small ruminant vaccination pro-grams could be implemented to control brucellosis in high-risk populations in the country.

In computer vision, human action recognition is a hot topic, popularized by the development of deep learning. Current models have achieved high accuracy results on public datasets. Despite this success, they require significant computational resources for training. Given that transfer learning based techniques allow reusing what other models have already learned and training models with less computational resources, in this work we propose using a transfer learning based approach for action recognition in videos. We describe a methodology for human action recognition using transfer learning techniques in a custom dataset. The proposed method consists of four stages: 1) human detection and tracking, 2) video preprocessing, 3) feature extraction (using pretrained models with ImageNet), and 4) action recognition using a two-stream model consisting of TCNs, LSTMs, and CNNs layers. The custom dataset is imbalanced with 189, 390, 490, 854, and 890 videos per class, respectively. For feature extraction, we analyzed the performance of seven pretrained models: Inception-v3, MobileNet-v2, MobileNet-v3-L, VGG-16, VGG-19, Xception, and ConvNeXt-L. We show that the best results were obtained with the last one. Finally, using pretrained models for feature extraction allowed training in a PC with a single GPU with an accuracy of 94.9%.

In this study, the team employed full-scale crash tests to investigate the impact of post-densification technology on the safety performance of separated central median W-beam barriers under low compaction conditions. By precisely simulating the design and construction environment of the central median, the study comprehensively evaluated the barrier's containment, redirection, buffering, and anti-intrusion performances, as well as the deformation of both the barrier and vehicles under various collision scenarios. The results indicate that under low compaction soil conditions, the A-level separated central median barriers reinforced with post-densification technology effectively meet the specified protection requirements. By comparing the influence of soil strength variations on barrier performance under simulated rainfall conditions, it was confirmed that enhancing soil compaction significantly improves soil stability, effectively increases the overall strength and stability of the barriers, reduces deformation, and significantly enhances safety performance. This study not only validates the effectiveness of the reinforcement scheme but also provides new perspectives for the scientific research of safety barriers and offers crucial technical support for the optimal design of road safety protection facilities.

Background. The minimum wage has become a standard measure in economic and social policies of countries all over the world and it directly or indirectly affects between 20% and 30% of the labour force. The primary objective of this measure is to guarantee that workers receive a minimum wage that allows them to lead a decent life, thereby reducing inequality and poverty. However, studies on the minimum wage have not focused on assessing the effects on these dimensions, but on employment, anticipating a negative effect (in line with the traditional theory of perfect competition), which they have been unable to substantiate. Objectives and Methods. The objective of this study is to address this research gap by analysing the effects of minimum wage increases on income inequality and poverty. To this end, firstly, a systematic review of the empirical analyses was conducted using the PRISMA methodology, with a view to ensuring that all empirical evidence was available. Secondly, the Spanish case was examined. As the significant increase in minimum wage in Spain in 2019 (21,3% in real terms) presents an invaluable opportunity to utilise this event as a natural experiment to generate new evidence, a difference-in-differences approach was employed to assess the impact of this phenomenon. Results. In relation to the first objective, a number of non-exhaustive and outdated literature reviews have been identified, which have been subjected to a critical review. In addition, a total of 31 articles that empirically analysed this relationship in recent years (2020-2024) have been included. The available evidence indicates a clear inverse relationship between the minimum wage and inequalities and poverty. Regarding the Spanish case, there has been a dearth of scientific studies on this subject, particularly in the context of the significant increase observed in 2019, which makes this article of particular relevance. The results are statistically significant, demonstrating an increase of approximately 18% in annual gross income among low-income households, relative to the control group. Furthermore, there is evidence of a spillover effect and a lighthouse effect towards income groups closer to the treatment group. This suggests that the minimum wage increase has a positive impact on the income of workers earning just above the new minimum and even below it. This result is consistent with the existing literature, which shows that minimum wage increases have a positive effect on the income of workers earning slightly above or below the new minimum. Scientific contribution. In doing so, two new basic scientific contributions were made: a systematic, exhaustive, and up-to-date literature review (up to June 2024), as there is, to our knowledge, no recent systematic review of this relationship (minimum wage vs. inequality). Then, the introduction of new scientific evidence demonstrating that a significant increase in the minimum wage can significantly improve the income of low-wage earners without the adverse effects that would otherwise disrupt this positive effect, thereby reducing income inequality and poverty. As a limitation of the study, it would be desirable to delve deeper into other aspects not analysed here, such as the effects on productivity and consumption. Furthermore, it has not been possible to analyse the medium and long-term effects, due to data limitations. Nevertheless, there is evidence that inequality and poverty are on a downward path.

With the increasing use of data-driven modeling methods new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. Literature shows the advantages of data-driven methods reduce computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitude for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation data sets, with Gaussian Process Regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

This qualitative research investigates the role of collaborative planning in synchronizing supply chain and marketing activities among multinational companies (MNCs). Collaborative planning integrates traditionally separate functions to enhance operational efficiency, customer responsiveness, and strategic alignment. The study employs semi-structured interviews with key informants from diverse MNCs across industries and geographies to explore practices, challenges, and outcomes associated with collaborative planning initiatives. Findings highlight the pivotal role of cross-functional collaboration and advanced technologies, such as artificial intelligence and big data analytics, in improving demand forecasting accuracy, optimizing inventory management, and enabling personalized marketing strategies. Challenges include organizational resistance, cultural barriers, and the complexity of aligning global strategies. Strategic outcomes encompass improved operational efficiency, cost savings, enhanced customer satisfaction, and strengthened competitive advantage. Moreover, the integration of sustainability goals underscores the importance of environmental responsibility and regulatory compliance in collaborative planning frameworks. The study also identifies emerging trends in digital transformation and agile supply chains shaping future practices. Overall, collaborative planning emerges as a strategic imperative for MNCs to navigate complexities, capitalize on opportunities, and sustain growth in a globalized marketplace.

Introduction: Left ventricular dysfunction in an independent predictor of mortality in Chagas disease. Global longitudinal strain (GLS) is an emerging echocardiographic modality with possible incremental value in early detection of cardiac involvement. Objective: Compare left ventricular function with multiple echocardiographic parameters between patients with the indeterminate and chronic forms of Chagas and perform a literature review. Methods: In this observational study, 11 patients with a confirmed diagnosis of Chagas disease were evaluated. Individuals were distributed as follows: 5 with the indeterminate and 6 with the chronic forms. Conventional echocardiographic evaluation was followed by GLS measurement. Results: The mean left ventricle ejection fraction LVEF was 33 ± 2.88 and the mean GLS of the left ventricle was - 10.76 ± 2.20 in the chronic form as compared to 57.80 ± 6.49 and - 17.34 ± 3.82, respectively in the indeterminate group. Contractility was reduced in segments of the inferior, inferoseptal and anteroseptal walls in both groups. Conclusion: Analysis of longitudinal strain in the indeterminate form has potential incremental value to the conventional analysis of ejection fraction when compared to patients with chronic Chagas cardiomyopathy, especially with individual segmental analysis but the impact and clinical significance of these techniques is still unknown.

In this study, the determination of static E50 and υ parameters of rock materials was investigated using P-S wave velocities and Shore hardness (SH), non-destructive measurement methods. We used multiple linear regression (MLR), multiple non-linear regression (MNLR) and artificial neural network (ANN) models to estimate and determine these parameters. When the models defined by MLR, MNLR, and ANN were compared to the R2 values, ANN models that estimate E50 and υ parameters of rock materials using non-destructive methods (Vp, Vs, Vp/Vs, d, and SH) could be determined with higher accuracy than MLR and MNLR models. The study’s originality is based on the fact that ores such as galena, chromite, and barite have been studied for the first time on rock mechanics, and in this respect, it offers an innovative perspective. In addition, the use of all non-destructive measurement methods, Vp, Vs, and Shore hardness tests, also increases the originality of the study.

Supply chain transparency plays a crucial role in shaping consumer perceptions and building brand trust in today's competitive marketplace. This qualitative study explores the impact of supply chain transparency on consumer behavior, focusing on its influence on brand trust and purchase decisions. Through semi-structured interviews with 30 participants, the research examines consumer attitudes towards transparency, highlighting key factors that influence trustworthiness and credibility perceptions of brands. The findings reveal that consumers prioritize brands that demonstrate openness about their sourcing, production practices, and ethical standards, viewing transparency as a critical indicator of corporate responsibility and integrity. Factors such as product safety, environmental sustainability, and labor practices within supply chains emerge as significant concerns driving consumer preference for transparent brands. Demographic insights indicate that younger consumers and those with higher education levels exhibit heightened sensitivity to transparency issues, underscoring a generational and educational divide in consumer expectations. Moreover, income levels influence the perceived importance of transparency, with higher-income participants showing greater preference for brands that prioritize ethical and sustainable practices. Challenges associated with supply chain transparency, including information overload and concerns about greenwashing, highlight the complexities brands face in effectively communicating their ethical commitments to consumers. The study concludes by advocating for strategic transparency initiatives that integrate sustainability, technology-enabled verification, and stakeholder engagement to build consumer trust and competitive advantage. By addressing these insights, brands can navigate the evolving landscape of consumer expectations and regulatory requirements, fostering long-term relationships based on trust and ethical business practices.

Depressive disorders represent the leading contributors to mental health-related global burden, and they are often diagnosed in the context of many comorbid disorders, such as cardiovascular disorders, Stroke, Parkinson’s Disease, Major Neurocognitive Disorders and Headache, thus worsening their outcomes. Depression is a challenge for “real-world” clinicians, due to the low rates of remission despite the increasing number of antidepressant strategies currently available. Indeed, current antidepressant strategies often fail to achieve acceptable rates of remission. One of the main challenges of “real-world” management of depression is represented by geriatric depression, which is common and under diagnosed. The challenge of diagnosis and treatment of geriatric depression in real world calls for the need of a deeper exploration of its management in clinical practice. This is the purpose of the present cross-sectional survey, aimed at evaluating the clinical approach to late-life depression in a sample of expert physicians working in geriatric settings in Italy, thus trying to provide useful insights on geriatric depression care.

Ruderal plants designate native or exotic plant species that grow spontaneously in anthropized environments, being important from the ecological succession perspective. The present study aimed to identify ruderal plants that occur in an anthropized area of the Campus Floresta of the Federal University of Acre (UFAC), in the municipality of Cruzeiro do Sul, in the Southwest Amazon. Toward this end, botanical material was collected and herborized, and identified based on consultation of the specialized literature, virtual herbariums, and by comparison with the Campus Floresta Herbarium (CFCZS) collection. Finally, these data were incorporated to the referred collection, according to the usual plant systematics procedures. The results revealed 28 species and 28 genera, distributed in 14 families. The most representative families were Fabaceae, Malvaceae and Poaceae, with four species each; followed by Asteraceae with three; Cyperaceae, Rubiaceae and Amaranthaceae with two each; and the others, Plantaginaceae, Ochnaceae, Urticaceae, Verbenaceae, Iridaceae, Lamiaceae and Dennstaedtiaceae with one species each. The occurrence of predominant species in planted pastures, agricultural use, and mining area ecosystems is highlighted, such as Pteridium aquilinum (L.) Kuhn., Andropogon bicornis L., and Crotalaria micans Link.

The associations between dietary sodium intake (DSI), dietary potassium intake (DPI), and kidney stone disease (KSD) are not clear. We examined The National Health and Nutrition Examination Survey 2011-2018 to determine the independent associations between daily DSI, DPI, DSI/DPI and prevalent KSD. 19,405 participants were included for analysis, of which 1,895 had KSD. Higher DSI was not associated with increased odds of KSD in regression analysis when DSI was modeled as a continuous variable (OR=0.99, 95% CI: 0.99-1.00, p=0.2), or when comparing highest quartile of DSI to lowest quartile (OR=0.84, 95% CI: 0.68–1.04, p=0.1). Unlike DSI, higher DPI associated strongly with reduced odds of KSD in regression analysis when DPI was modeled as a continuous variable (OR=0.99, 95% CI: 0.99-0.99, p=0.02), or when comparing highest quartile of DPI to lowest quartile (OR =0.75, 95% CI: 0.60–0.94, p=0.01). Lastly, higher DSI/DPI also associated strongly with increased odds of KSD in regression analysis when DSI/DPI was modeled as a continuous variable (OR=1.1, 95% CI: 1.01-1.20, p=0.03), or when comparing highest quartile of DPI to lowest quartile (OR =1.30, 95% CI: 1.10–1.70, p=0.008). All the observed relationships were independent of total calorie intake. In conclusion, both lower DPI and higher DSI/DPI associate with an increased risk of KSD. Future prospective studies are needed to clarify these causal relationships.

A two-stage pilot plant study has been completed that evaluated the performance of a reverse osmosis (RO) membrane process for the treatment of a feedwater that consisted of a blend of nanofiltration (NF) concentrate and brackish groundwater. Membrane performance was assessed by monitoring the process operation, collecting water quality data, and documenting the blended feedwater’s impact on fouling due to a combination of, or solely caused by, plugging, scaling, microbiological, or organic means. Fluorescence and biological activity reaction tests were used to identify the types of organics and microorganisms present in the blended feedwater. Additionally, scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy (EDS) were used to analyze suspended matter that collected on the surfaces of cartridge filters used in the pilot’s pretreatment system. SEM and EDS were also used to evaluate solids collected on the surfaces of 0.45-µm silver filter pads after filtering known volumes of NF concentrate and RO feedwater blends. Water quality analyses confirmed that the blended feedwater contained little to no dissolved oxygen, and the blended feedwater was absent of a significant amount of particulate matter as defined by silt density index and turbidity measurements. However, water quality results suggested that the presence of sulfate, sulfide, iron, anaerobic bacteria, and humic acid organics likely contributed to the formation of pyrite observed on some of the membrane surfaces autopsied at the conclusion of pilot operations. It was determined that first-stage membrane productivity was impacted by the location of cartridge filter pretreatment; however, second-stage productivity was maintained with no observed flux decline during the entire pilot operation’s timeline. Study results indicated that the operation of an RO process treating a blend of NF concentrate and brackish groundwater could maintain a sustainable and productive operation that provided a practical zero-discharge process operation while the dilution of RO feedwater salinity would lower overall production costs.

It is important to consider both requirements and limitations for the generation and sharing of genomic data. For example, human genomic sequence data should typically not be deposited publicly without the appropriate "explicit consent" to do so. This includes genetic identifying data that may exist within raw reads for bulk RNA Sequencing (RNA-Seq) data and genomic data from cell lines that are available to purchase. At the same time, data sharing is important for re-analysis and reproducibility/replication in the scientific community. A review of known rules and guidelines for genomic data sharing is provided. This includes specific rules for NIH-funded data for controlled access deposit of HeLa cell line data, including bulk RNA-Seq data. The ability to create “partial” Gene Expression Omnibus (GEO) public deposits with only processed data is described, with search criteria that can identify many “partial” GEO deposits for a variety of data types where reads were not made public for patient privacy concerns. However, it is the opinion of the author that this should be considered a short-term solution, and additional considerations and action should be carried out for genomic data generated in future experiments. Information about how to learn more about what is known for consent for cell line samples is also briefly provided, along with search results for genomic data from widely available cell lines that is deposited in controlled access databases. Finally, it should be made clear that this article presents some amount of opinion. Additionally, open feedback for this preprint is encouraged to further enhance knowledge and communication.

Early detection of PDAC remains challenging due to the lack of early symptoms and absence of reliable biomarkers. The aim of the present project was to identify miRNA and proteomics sig-natures discriminating PDAC patients with DM from nondiabetic PDAC patients. Proteomics analysis and miRNA array were used for protein and miRNA screening. We used western blot-ting and Real-Time Quantitative Reverse Transcription polymerase chain reaction (qRT-PCR) for protein and miRNA validation. Comparisons between experimental groups with normal dis-tributions were performed using one-way ANOVA followed by Tukey’s post hoc test and pair-wise tests were performed using t tests. P≤0.05 was considered statistically significant. Protein cluster of differentiation 166 (CD166), glycoprotein CD63 (CD63), S100 calcium binding protein A13 (S100A13), and tumor necrosis factor-β (TNF-β) were detected in the proteomics screening. The miRNA assay revealed a differential miRNA 1285 regulation. Previously described in the literature target proteins of miR-1285 cadherin-1 (CDH-1), cellular Jun (c-Jun), p53, mothers against decapentaplegic homolog 4 (Smad4), human transglutaminase 2 (TGM2) and yes-associated protein (YAP), were validated via Western blotting. miR-1285-3p was successfully validated as differentially regulated in PDAC + DM via qRT-PCR. Overall, our data suggest miRNA1285-3p, TGM2, CDH-1, CD166 and S100A13 as potential meaningful biomarker candi-dates to characterize patients with PDAC + DM. Data are available via ProteomeXchange with identifier PXD053169.

The presence and localization of plant metabolites are indicative of physiological processes, e.g., under biotic and abiotic stress conditions. Further, the chemical composition of plant parts is related to their quality as food or for medicinal applications. Mass spectrometry imaging (MSI) has become a popular analytical technique for exploring and visualizing the spatial distribution of plant molecules within a tissue. This review provides a summary of mass spectrometry methods used for mapping and identifying metabolites in plant tissues. We present the benefits and the disadvantages of both vacuum and ambient ionization methods, considering direct and indirect approaches. Finally, we discuss the current limitations in annotating and identifying molecules and perspectives for future investigations.

Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes are being published increasingly. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of this methodology is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature.

Abstract Introduction PCa is one of the cancers that exhibits the widest disparity gaps. Geographical place of residence has been shown to be associated with healthcare access/utilization and PCa outcomes. Geographical Information Systems (GIS) are widely being utilized for PCa disparities research, however, inconsistencies in their application exist. This systematic review will summarize GIS application within PCa disparities research, highlight gaps in the literature and propose alternative approaches. Methods This paper followed the methods of the Cochrane Collaboration and the criteria set of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles published in peer-reviewed journals were searched through the PubMed, Embase, and Web of Science databases till December 2022. The main inclusion criteria were employing a GIS approach and examining a relationship between geographical components and PCa disparities. The main exclusion criteria were studies conducted outside the US and those that were not published in English. Results A total of 25 articles were included, 23 focused on PCa measures as outcomes: incidence, survival, and mortality while only two examined PCa management. GIS application in PCa disparities research was grouped into three main categories: mapping, processing, and analysis. GIS mapping allowed for the visualization of quantitative, qualitative, and temporal trends of PCa factors. GIS processing was mainly used for geocoding and smoothing of PCa rates. GIS analysis mainly served to evaluate global spatial autocorrelation and distribution of PCa cases, while local cluster identification techniques were mainly employed to identify locations with poorer PCa outcomes, soliciting public health interventions. Discussion Varied GIS applications and methodologies have been used in researching PCa disparities. Multiple geographical scales were adopted leading to variation in associations and outcomes. Geocoding quality varied considerably leading to less robust findings. Limitations in cluster-detection approaches were identified especially when variations were captured using the Spatial Scan Statistic. GIS approaches utilized in other diseases might be applied within PCa disparities research for more accurate inferences. A novel approach for GIS research in PCa disparities could be focusing more on geospatial disparities in procedure utilization especially when it comes to PCa screening techniques. Conclusion This systematic review summarized and described the current state and trend of GIS application in PCa disparities research. Although GIS is of crucial importance when it comes to PCa disparities research, future studies should rely on more robust GIS techniques, carefully select the geographical scale studied, and partner with GIS scientists for more accurate inferences. Such interdisciplinary approaches have the potential to bridge the gaps between GIS and cancer prevention and control to further advance cancer equity.

Background: Objective quantitative texture characteristics may be helpful in salivary glandular tumor differential diagnosis. This study uses machine learning (ML) to explore and validate the performance of ultrasound (US) texture features in diagnosing salivary glandular tumors. Material and methods: 122 patients with salivary glandular tumors, including 71 benign and 51 malignant tumors, are enrolled. A representative brightness mode US pictures are selected for further Gray Level Co-occurrence Matrix (GLCM) texture analysis. We use t-test to test the significance and use receiver operating characteristic curve method to fund optimal cut-point for these significant features. After splitting 80% data into training set and 20% data into testing set, we use five machine learning models k-nearest Neighbors (kNN), Naïve Bayes, Logistic regression, Artificial Neural Networks (ANN) and supportive vector machine (SVM) to explore and validate the performance of US GLCM texture features in diagnosing salivary glandular tumors. Results: This study includes 49 female and 73 male patients, with a mean age of 53 years old, ranging from 21 to 93. We find that six GLCM texture features (contrast, inverse difference movement, entropy, dissimilarity, inverse difference and difference entropy) are significantly different between benign from malignant tumors (p<0.05). On ML, the overall accuracy rates are 74.3% (95%CI: 59.8-88.8%), 94.3% (86.6-100%), 72% (54-89%), 84% (69.5-97.3%) and 73.5% (58.7-88.4%) for kNN, Naïve Bayes, Logistic regression, one node ANN and SVM, respectively. Conclusion: US texture analysis with ML has potential as an objective and valuable tool for assessment of salivary gland tumors.

Conclusions: The quantitative method provides a large-scale overview of the biometry measurements of an entire practice. Image audits can then be focused on sonographers whose measurements are outliers, avoiding the time, complexity, and expense of auditing those whose measurements fall within the expected range. We encourage commercial software developers to include tools in their ultrasound reporting software to facilitate such quantitative review.

Recent developments in microfluidics and biomaterials have enabled the creation of organs-on-chips (OoCs), which provide a controlled and lifelike in vitro microenvironment resembling an actual organ. Organ-on-a-chip technology offers a tremendous opportunity for efficient, cost-effective, and ethical drug testing and research. However, the high throughput and large quantities of data these complex microenvironments produce make it difficult for researchers to effectively analyze the data and draw valuable conclusions. Convolutional Neural Networks (CNNs) are most aptly positioned to address many of OoC’s challenges because of their ability to interpret microscopic images and facilitate the analytical process. Despite the growing field of AI, there have been a limited number of studies summarizing the various applications of CNNs in OoCs. This review aims to provide 1) an overview of the technology involved with CNNs and OoCs 2) an insight into the state-of-the-art applications of CNNs in OoCs including device parameters, predicting and tracking cell trajectories, super-resolution image segmentation, and image classification, and 3) an overview of existing challenges and opportunities ahead for clinical translation of this technology. Various applications of CNNs have been classified by the type of task. Different CNN models such as Faster R-CNNs, fully convolutional networks (FCNs), and Mask R-CNNs are explained and highlighted. This review article can be used as a resource for a better understanding of the potential of CNNs in biomedical research and clinical applications, particularly OoCs.

The attempt of this study made us to Study the Competency Mapping (CM) in Software Companies”. CM is a process of categorizing the gap of a person or an employee’s performance routine or professional related skills to fill the gaps through effective programs. (1) Background: Integrating competencies such as “Developing people through Empowering and Training Programs, Providing Feedback, and Rewards, Recognizing the Employee for their work, Effective Communication, Aligning right values ensuring standards, etc. to evaluate and improve the performance an employee to get desired results; (2) Methods: This study involved 100 software employees (n = 100) to state that the competencies that are mapped have a significant impact on performance.. Chi- square and one – way ANOVA tests we used to identify the relationship between two variables; (3) Results: Goal oriented people can adopt the changes and work in any kind of circumstance for better outcomes; (4) Conclusions: competencies that are mapped have a significant impact on performance. And the person who is goal oriented will try to achieve his/her goals in any type of circumstances.

The comparison of three types of ionosonde data from Europe during an Interplanetary Coronal Mass Ejection (ICME) and a Stream Interaction Regions (SIRs) / Corotating Interaction Regions (CIRs) -driven geomagnetic storm event is detailed in this study. The selected events are 16-20 March 2015 for the ICME-driven and 30 May to 04 June 2013 for the SIR/CIR-driven one. Ionospheric data from three European ionosonde stations, namely Pruhonice (PQ), Sopron (SO) and Rome (RO), are investigated. The ionospheric F2-layer responses to these geomagnetic events are analyzed with the ionospheric foF2 and h’F2 parameter, the calculated deltafoF2 and deltahF2 values, ratio of Total Electron Content (rTEC) and Thermosphere, Ionosphere, Mesosphere, Energetics and Dynamics (TIMED) satellite Global Ultraviolet Imager (GUVI) thermospheric [O]/[N2] measurement data. The storm-time and the quiet day mean values are also compared, and it can be concluded that the quiet day curves are similar at all stations while the storm-time ones showed the latitudinal dependence during the development of the storm. As a result of the electron density comparison, during the two events it can be concluded that SSC that characterized the ICME induced a Travelling Atmospheric Disturbance (TAD) seen in the European stations, while this is not in the SIR/CIR-driven ionospheric storm, which showed a stronger and more prolonged negative effect in all stations probably due to the season.

Schizophrenia is a disease with a complex etiology that significantly impairs the functioning of patients. In recent years, there has been increasing focus on the importance of the gut microbiota in the context of the gut-brain axis. In our study, we analyzed data on the gut-brain axis in schizophrenia, as well as the impact of eating habits, the use of various supplements, and diets on schizophrenia. Additionally, the study investigated the impact of antipsychotics on the development of metabolic disorders such as diabetes, dyslipidemia, and obesity. There may be significant clinical benefits to be gained from therapies supported by supplements such as omega-3 fatty acids, B vitamins, and probiotics. The results suggest the need for a holistic approach to the treatment of schizophrenia, incorporating both drug therapy and dietary interventions.

An analysis of the physical and chemical phenomena accompanying the electrical discharges was carried out, principal factors inactivating microorganisms is studied. Characteristic factors of an electric discharge in cavitation mode providing a synergistic effect when disinfecting water with a discharge and chemical reagents are identified in order of importance. As a result of the integral action of cavitation, the inactivation of bacteria is intensified both due to the emergence of chem-ically active particles and due to local shock waves, acoustic currents and ultraviolet radiation in the area of cavitating bubbles. The similarity of cavitation processes in water systems implemented by underwater electric discharges and with the ultrasound has been experimentally proved. The main advantage of electric discharge cavitation in comparison with ultrasonic one is the wide band of ultrasonic frequencies of radiation inherent in electric discharge, high intensity and power of cav-itation processes, and, accordingly, the possibility of a significant increase in the volume of the disinfected liquid. The study enable better understanding and prediction of the effects that occur during a high-voltage underwater electrical discharge and have a bactericidal effect.

One of the most significant challenges that is facing urban-planning ,is the develop-ment of a transportation network, particularly in historical Syrian cities, which are characterized by their compact urban form. Historical Syrian cities, including historical city of Aleppo, present difficulties due to the essential balance required between saving cultural heritage and meeting the needs of a present-day population. This study basically addresses the city's key elements and planning issues, including traffic density, pedestrian safety, and the massive spreading of motor-ized vehicles. The objective of this study is to analyze the street structure, understanding its components and problems in order to develop a methodology that can be used to enhance urban mobility and to improve the structure of the historical city. for this purpose, two main methods were employed, method of analyzing geographical data based on Geographic Information Systems (GIS) and K-Means cluster analysis method. These two methods were employed to gain detailed and clear vision into mobility patterns of historical Aleppo. Which involved careful processing of data, such as street names, locations, components, quantities, and widths, based on the author’s observations and notes and a variety of local and international sources. This analysis process will not only reveal the current hierarchy of streets but also demonstrates the dynamical and complicated formation of transportation structures of the historical city. The findings of the study describe the challenges faced by historic city centers and highlight the interrelationship between the following: urban structure, objects of cultural heritage, tourism plans, and mobility patterns. The application of spatial analysis method serves as a technique that reveals the formulation of possible scenarios that helps to develop the transportation structure in the historical city of Aleppo. These generated scenarios will be used as typical solutions that can be generalized to all similar historic Syrian cities.

The production of recombinant proteins in Escherichia coli is often challenged by cytoplasmic expression due to proteolytic degradation and inclusion body formation. Extracellular expression can overcome these problems by simplifying downstream processing and improving protein yields. This study aims to compare the efficiency of two Bacillus subtilis chitosanase signal peptides in mediating extracellular secretion in E. coli. We identified a naturally occurring mutant signal peptide (mCsn2-SP) from B. subtilis CH2 chitosanase (CH2CSN), which is characterized by a deletion of six amino acids in the N-region relative to the signal peptide (Csn1-SP) from B. subtilis CH1 chitosanase (CH1CSN). The CH1CSN and CH2CSN genes were cloned into the pET-11a vector and protein secretion was evaluated in E. coli BL21(DE3) host cells. Expression was induced with 0.1 mM and 1 mM IPTG at 30°C for one and three days. CH2CSN showed higher secretion levels compared to CH1CSN under all experimental conditions, especially with 0.1 mM IPTG induction for 3 days, which resulted in a 2.37-fold increase in secretion. These results suggest that signal peptide deletion variants may have an effect on protein secretion efficiency and help to further understand the function of signal peptides.

Secondary prevention after SCA (acute coronary syndrome) is of paramount importance to reduce the incidence of MACE and improve prognosis in follow-up. The control of avoidable risk factors, lifestyle modifications, but especially careful control of cholesterolemia (with a target of LDL-C

Vehicular ad hoc networks (VANET) are a subset of mobile ad hoc networks used to communicate between vehicles and infrastructure. During vehicular congestion communication, nodes compete to acquire channels, causing the channels to become congested. The congestion on the vehicle network results in increased delay and packet loss, resulting in reduced VANET performance. To address this problem, we developed a load-aware and priority adaptive traffic congestion control method in vehicular ad hoc networks (VANETs). The proposed scheme identifies less-congested road segments based on the network's load and reduces traffic congestion by suggesting other routes between nearby roadside units (RSUs). This research aims to improve the efficiency of the vehicular environment by utilizing the movement of vehicles with (RSUs) and sharing the traffic load between them. Simulation results demonstrate the effectiveness of the proposed protocol in reducing congestion and enhancing the overall performance of VANETs. To validate the proposed algorithm, we have implemented and tested the proposed algorithm using Network Simulator 3 (NS3) for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication scenarios and computed the performance of the algorithm on different parameters of the network. The simulation result of the proposed load aware and priority adaptive traffic congestion control method in VANET improved the packet delivery ratio, packet lost ratio, and end-to-end delay by 96%, 4.1%, and 1102 milliseconds, when compared to TDCCA value of 92%, 5.7%, and 1154 milliseconds, respectively

Accurate measurement of water vapor is essential for researches and applications of meteorology, climatology and hydrology. Based on the BeiDou PPP-B2b service, real-time precipitable water vapor (PWV) is retrieved with the precise point positioning (PPP) software. The experiment was conducted in Beijing in January, 2023. Three solutions are designed with PPP using the BeiDou system only, the GPS system only and the BeiDou-GPS combined solution. Real-time PWVs for the 3 solutions are validated with the ERA5 reanalysis data. Between PWVs from single BeiDou and ERA, there are a bias of 0.7 mm and a RMSE of 1.8 mm. While for the GPS case, the bias is 0.73 mm and RMSE is 1.97 mm. The biases are less than 1 mm and RMSEs are less than 2mm. Both the BeiDou and the GPS processing performs very well. But little improvement is found for the BeiDou-GPS combined solution, comparing with the BeiDou system only and the GPS system only solution. This may be due to the poor handling of two different kinds of errors for the GPS and the BeiDou systems in our PPP software. A better PWV estimation with the two systems is to estimate PWV with single system at first step, and then to obtain the optimization by Bayesian model averaging.

Global wastes accumulate at an accelerating rate especially food and slaughterhouse waste constituting a major proportion owing to inefficient management. The high protein concentration of fish waste and keratinaceous byproducts is hazardous, harbouring pathogens, and emitting toxins. Transforming proteinaceous waste into amino acids (AAs) reduces waste bulk and offers a smarter nutrient approach for agriculture. However, this resource remains underutilized triggering eco-system pollution, diseases spreading, and climate change. Currently, small quantities of proteinaceous waste are valorized as animal feed, fertilizer, and in medical, and biotechnological sectors, which cannot solve bulk waste accumulation further, natural AA sources in agriculture are less recognized meanwhile researchers focus on synthetic AAs. Introducing various plant-nutrient formulas enriching fish AA with keratinaceous waste hydrolysate has not been broadly discussed. With this novel approach, using proteinaceous waste resources in agriculture would solve environmental pollution while minimizing land degradation by synthetic fertilizer. In this review, we illustrate the potential and benefits of valorizing proteinaceous waste in bulk through bioconversion. More importantly, we spotlight the biocompatibility of natural AAs and propose an enrichment, to use the ultimate product as catalysts for crops. Consequently, this approach reduces hazardous waste while paving a paradigm change in agriculture.

Azolla is the only plant with a co-evolving nitrogen-fixing (diazotrophic) cyanobacterial symbiont (cyanobiont), Nostoc azollae, resulting from Whole Genome Duplication (WGD) 80 million years ago in Azolla’s ancestor. Additional genes from the WGD resulted in genetic, biochemical and morphological changes in the plant that enabled transmission of the cyanobiont to successive generations via its megaspores. The resulting permanent symbiosis and co-evolution led to loss, downregulation or conversion of non-essential genes to pseudogenes in the cyanobiont, changing it from a free-living organism to an obligate symbiont. Upregulation of other genes in the cyanobiont increased its atmospheric dinitrogen fixation and provision of nitrogen-based products to the plant. As a result, Azolla can double its biomass in less than two days free-floating on fresh water and sequester large amounts of atmospheric CO2, giving it the potential to mitigate anthropogenic climate change through Carbon Capture and Storage. Azolla’s biomass can also provide local, low-cost food, biofertiliser, feed and biofuel that are urgently needed as our population increases by a billion every twelve years. This paper integrates data from biology, genetics, geology and paleontology to identify the location, timing and mechanism for the acquisition of a co-evolving diazotrophic cyanobiont by Azolla’s ancestor in the Late Cretaceous (Campanian) of North America.

The North China Plain is among the regions most afflicted by ground fissure disasters in China. Recent urbanization has accelerated ground fissure activity in the three counties of the northern North China Plain, posing significant threats to both the natural environment and socioeconomic sustainability. Despite the increased attention, a lack of comprehensive understanding persists due to delayed recognition and limited research. This study conducted field visits and geological surveys across 43 villages and 80 sites to elucidate the spatial distribution patterns of ground fissures in the aforementioned counties. By integrating these findings with regional geological data, we formulated a causative model to explain ground fissure formation. Our analysis reveals a concentration of ground fissures near the Niuxi and Rongxi faults, with the former exhibiting the most extensive distribution. The primary manifestations of ground fissures include linear cracks and patch-shaped collapse pits, predominantly oriented in east-west and north-south directions, indicating tensile failure with minimal vertical displacement. Various factors contribute to ground fissure development, including fault activity, ancient river channel distribution, bedrock undulations, rainfall, and ground settlement. Fault activity establishes a concealed fracture system in shallow geotechnical layers, laying the groundwork for ground fissure formation. Additionally, the distribution of ancient river channels and bedrock undulations modifies regional stress fields, further facilitating ground fissure emergence. Rainfall and differential ground settlement serve as triggering mechanisms, exposing ground fissures at the surface. This research offers novel insights into the causative mechanisms of ground fissures in the northern North China Plain, providing valuable scientific evidence for mitigating ground fissure disasters during the urbanization process in the region.

We present a study of hybrid neutron stars with color superconducting quark matter cores at finite temperature that results in sequences of stars with constant entropy per baryon, s/nB=const. For the quark matter equation of state, we employ a recently developed nonlocal chiral quark model while nuclear matter is described with a relativistic density functional model of the DD2 class. The phase transition is obtained by a Maxwell construction under isothermal conditions. We find that traversing the mixed phase on a trajectory at low s/nB≲2 in the phase diagram shows a heating effect while at larger s/nB the temperature drops. This behavior may be attributed to the presence of a color superconducting quark matter phase at low temperatures and the melting of the diquark condensate which restores the normal quark matter phase at higher temperatures. While the isentropic hybrid star branch at low s/nB≲2 is connected to the neutron star branch, it gets disconnected at higher entropy per baryon so that the "thermal twin" phenomenon is observed. We find that the transition from connected to disconnected hybrid star sequences may be estimated with the Seidov criterion for the difference in energy densities. The radii and masses at the onset of deconfinement exhibit a linear relationship and thus define a critical compactness of the isentropic star configuration for which the transition occurs which for large enough s/nB≳2 is accompanied by an instability. The results of this study may be of relevance for uncovering the conditions for the supernova explodability of massive blue supergiant stars by the quark deconfinement mechanism. The accretion-induced deconfinement transition with thermal twin formation may contribute to explaining the origin of eccentric orbits in some binary systems and the origin of isolated millisecond pulsars.

Currently, there has been a tendency for companies to direct the development of their businesses following circular economy trends associated with sustainability. European Community guidelines have established deadlines for the implementation of management strategies that allow creating and maintaining an effective and resource-efficient economic system, reducing the amount of en-ergy and materials used in production. In this scenario, this study makes sense, which aims to an-alyze academic studies on these topics with the aim of understanding future trends. In this sense, the objective of this study is to understand the potential impacts on business development of the inte-gration of sustainability and the circular economy. This article uses a systematic literature review, which has the objective of gather and analyze materials from different authors, carrying out an analysis to answer a specific question. The findings indicate that CE and sustainability are con-nected and businesses should implement sustainability and CE appropriate strategies to increase their competitiveness and improve resource efficiency. These strategies may integrate innovative technologies, the use of sharing platforms, product life extension, resource recovery to minimize waste and the integration of sustainability concepts in business as well as their communication with their stakeholders to change trends and behaviors.

The present research focuses on the endogenous development theory and investigates the relationship between economic growth (dependent variable) and renewable energy consumption, technological innovation, and export diversification (independent variables) in seven emerging economies known as the E-7. Previous studies have examined these factors individually but have not explored their combined impact on the E-7 economies. Therefore, this study contributes to the existing literature on the effects of renewable energy consumption, technological advancement, and export diversification on economic development. The study analyses the dynamic connection among these variables in seven selected emerging countries: Brazil, China, Indonesia, India, Mexico, Russia, and Turkey. Panel data from 1990 to 2022 is utilized, and various methodologies, including panel cointegration, the PMG-ARDL estimator, and robustness tests such as FMOLS and DOLS, are employed. Empirical inferences are drawn using the Dumitrescu-Hurlin Panel Causality (DHC) test, and the long-run relationships among the variables are validated using the Westerlund residual cointegration tests. The results from the PMG-ARDL estimator show that renewable energy consumption, technological advancement, and export diversification have a significant and positive impact on economic expansion, confirming the validity of the endogenous growth model in the E-7 countries. The control variable of the financial sector has a positive but insignificant effect on economic growth, while trade openness has a negative and significant effect. The DHC test results indicate a neutral feedback effect of renewable energy consumption on economic growth. The findings also reveal a unidirectional causal relationship between technological innovation and economic growth. Overall, these findings provide valuable insights for economic policymakers in the E-7 countries. By removing barriers to renewable energy consumption, technological innovation, and export diversification, policymakers can promote sustainable economic development.

This study focuses on the use of gamification, specifically the UrbanGame activity, to enhance heritage education related to the Spanish Civil War bombings in Alicante. The research aims to evaluate the effectiveness of active methodologies in raising cultural heritage awareness among university students. By employing a combination of collaborative work, meaningful learning, problem-based learning, discovery learning, concentration, and motivational strategies, the study involved 122 students from primary and secondary education programs. The methods included the design and implementation of the UrbanGame, followed by surveys to collect quantitative and qualitative data on students' perceptions, satisfaction, and learning outcomes. The results indicated that while collaborative work did not have a significant impact on content comprehension, skill acquisition, or value acquisition, meaningful learning showed a positive trend towards content comprehension. Problem-based learning had a weak but notable relationship with content comprehension, while motivation and concentration significantly influenced learning outcomes. Additionally, the UrbanGame activity promoted memory and cultural heritage by emotionally connecting students with local history. The findings suggest that an integrated approach that combines various active methodologies is essential for maximizing educational effectiveness and cultural promotion.

This study addresses the formidable challenge of solving large-scale Max-Cut problems (MCP). We introduce a rapid computational procedure utilizing a hybrid 1-flip/r-flip local search heuristic. This innovative strategy significantly reduces the computational time required for MCP problems while consistently generating solutions of exceptional quality. The paper presents substantial computational insights, showcasing the effectiveness of our approach on large-scale Max-Cut instances with varying densities. Our proposed heuristic is rigorously evaluated by comparing its performance against a quantum annealing solver, leveraging a multi-start Tabu Search framework. The results underscore the potency of this unique combination as an efficient and effective solution for large-scale QUBO problems. Notably, our hybrid heuristic consistently delivers high-quality solutions within the stringent CPU time limits of 600 seconds, demonstrating its efficacy across Max-Cut instances ranging from 10,000 to 40,000 variables. This research contributes to advancing the state-of-the-art in large-scale QUBO problem-solving, offering a powerful and time-efficient approach with broad applicability.

In a meta-analysis, we examined the behavioral portfolio of invasive brown anoles (Anolis sagrei) and native green anoles (Anolis carolinensis) in urban and non-urban environments. We hypothesized that invasive anoles should display more agonistic and bold signals (head bobbing, dewlap extensions and pushups) than their native range counterparts, and their native competitors. We found that in urban settings, anoles of both species signaled more with dewlap extensions than with head-bobs. The brown anoles displayed significantly higher in non-urban habitats and their native range compared to urban habitats and invasive ranges. The outcome of our analysis suggests that brown anoles have plastic behavioral portfolios, whereas the green anole have relatively balanced preferences for head-bobbing irrespective of the habitat in which the populations were collected. We attribute the success of the brown anole invasion to the flexible strategy in face of higher mate competition, higher predation risks, and less resource competition in the urban environments and invasive ranges. Lastly, we observed publication biases. More studies are conducted with urban and invasive brown anoles and specifically in manipulative mesocosm experimental settings - transplanting populations from native field settings. We show this altered the display rates across all studied signals.

We propose a novel approach to quantum theory construction that involves solving a maximization problem on the Shannon entropy of all possible measurements of a system, relative to its initial preparation. This maximization problem is additionally constrained by a phase condition that vanishes under measurements. Specifically, enforcing a vanishing U(1)-valued phase constraint leads to standard quantum mechanics, while a vanishing Spin^c(3,1)-valued phase constraint extends the theory to relativistic quantum mechanics and to quantum gravity. The latter scenario is found to incorporate the SU(3)xSU(2)xU(1) symmetries of the Standard Model as invariants of the probability measure itself, and to construct the metric tensor as an operator via a double-copy mechanism applied to Dirac currents. Significantly, this solution is consistent exclusively with a 3+1-dimensional spacetime configuration---all other dimensional settings are shown to lead to fundamental obstructions. This framework seamlessly integrates fundamental concepts from quantum mechanics, relativistic quantum mechanics, quantum gravity, the dimensional specificity of spacetime, and particle physics symmetries as the solution to a simple entropy maximization problem.

The implementation of systems condition monitoring is of utmost importance in ensuring effective maintenance, optimal efficiency, and adherence to established design criteria. This research presents a novel and economically efficient Digital Twin Model (DTM) designed for the purpose of Live Condition Monitoring (LCM) and Predictive Lifecycles. This study discusses the enhancement of DTs modelling by decreasing the number of dimensions from five to three: Physical, Digital, and Connection entities. The resulting model exhibits a notable improvement in accuracy and efficiency. The suggested Dynamic Time Model (DTM) enhances the empirical pre-set average load for both simulation and experimentation by 35.7%, which is equivalent to a 1.6-fold increase. The DTM demonstrates a significant enhancement in the empirical predefined average lifecycles of the system, as shown in the real-life case study. Specifically, the DTM achieves a 12-fold improvement compared to the simulated results and a nine-fold improvement compared to the experimental findings. The suggested Dynamic Time Management (DTM) demonstrates a significant enhancement in the average lifecycles of the system, with a 19.7% improvement (equivalent to 1.2 times greater) compared to the wireless DTM results obtained through the application of the real system load. The suggested Dynamic Time Warping (DTW) method's accuracy and efficiency are shown via a practical case study using the suspension system of a Peugeot 3008 vehicle.

Within the tourism industry, the creation, popularization, and dissemination of visual assets and cultural symbols attract significant attention. However, sports tourism delves deeper into this realm, emphasizing content innovation, creativity, and the application of knowledge. Amid the rapid development of a national unified market, the sports tourism industry chain, with intellectual property at its core, has become a crucial factor. Currently, the industry chain faces issues such as "short chains," "broken chains," and "narrow chains." To address these challenges, decentralization is necessary to ensure the stability of industry chain connections and promote value-driven chain development. Furthermore, it is crucial to address the diminishing marginal returns and diversification of the industry chain.

The paper considers the possibility of rotating a small moon by 180 degrees from a dynamic point of view using two rocket engines mounted on the surface of the moon. A thrust control law for the engines which allows to turn the moon over and then stabilize it relative to a new equilibrium position by parrying a gravity gradient torque is proposed. The dynamic aspects of such an experiment are discussed using the example of the Mars satellite Deimos.

Background: Colorectal adenoma undergoes neoplastic progression via the normal epithelium-adenoma-adenocarcinoma sequence as reported in the Vogelgram. The risk of developing cancer is strongly associated with the number and size of adenoma and with the subtype. Currently, adenomatous polyps can be distinguished on the basis of histology: the prevalence are tubular, 5-15% are villous and tubular/villous. Considering the increased risk for malignant transformation described for tubular/villous adenomas, patients diagnosed with adenomatous polyposis are at increased risk of developing CRC. The Wnt/β-catenin pathway plays a key role in the onset of colorectal adenoma, in particular intestinal cells first acquire loss-of-function mutations in APC gene that induce the formation of adenomas. Methods: Wnt/β-catenin pathway APC, Wnt3a, Wnt5a, LEF1, BCL9 genes and protein expression analyses were conducted by qRT-PCR and western blot in 68 colonic samples (polyps and adjacent mucosa) from 41 patients, of which 17 affected by FAP. Ten normal colonic mucosal samples were collected from 10 healthy donors. Results: In this study both APC gene and protein resulted less expressed in colon tumor compared to the adjacent colonic mucosa. Conversely, activated β-catenin was more expressed in polyps than in the adjacent mucosa. All results confirmed literature data on carcinomas. A statistically significant correlation between Wnt3a and BCL9 both in polyps and in the adjacent mucosa underlies that the canonical Wnt pathway is activated in early colon carcinogenesis and that the adjacent mucosa is already altered. Conclusion: This is the first study analyzing the difference in expression of Wnt/β-catenin pathway in human colorectal adenomas. Understanding the progression from adenomas to colorectal carcinomas is essential for the development of new therapeutic strategies and improving clinical outcomes.

Drawing from literature and Geert Hofstede’s cultural dimensions, this study delves into the impact of cultural factors on family structures, roles, and relationships. Ghana and Latvia have different cultural and social orientations that influence family dynamics. Through a comparative lens, this paper will contribute to understanding how a country’s culture influences family dynamics, highlighting the influence of Power Dynamics of parenting style and Individualism vs. Collectivism on interaction levels within the family.