Search Results (23,449)

Conventional health risk assessments do not adequately reflect short-term exposure characteristics following chemical accidents. We aimed to evaluate the efficacy of existing assessment methods and propose a more suitable risk assessment approach for short-term exposure to hazardous chemicals. We analyzed foundational studies used to derive reference concentration (RfC), reference dose (RfD), and minimal risk level (MRL) values and applied these health guidance values (HGVs) to a hypothetical chemical accident scenario. An analysis of the studies underlying each HGV revealed that, except for the RfC for formaldehyde and the RfD for toluene, all values were derived under research conditions comparable to their respective exposure durations. Given the differing toxicity mechanisms between acute and chronic exposures, MRLs that were aligned with the corresponding exposure durations supported more appropriate risk management decisions. The health risk assessment results showed that RfC/RfD-based hazard quotients (HQs) were consistently higher than MRL-based HQs across all age groups and both substances, indicating that RfC/RfD values tend to yield more conservative risk estimates. For formaldehyde, the use of RfC instead of MRL resulted in an additional 208 tiles (2.08 km²) being classified as areas of potential concern (HQ > 1) relative to the MRL-based evaluation. These findings highlighted that the selection of HGVs can significantly influence the spatial extent of areas of potential concern, potentially altering health risk determinations for large population groups. This study provides a scientific basis for improving exposure and risk assessment frameworks under short-term exposure conditions. It also serves as valuable foundational data for developing effective and rational risk management strategies during actual chemical accidents. To the best of our knowledge, this is the first study to apply MRLs to a short-term chemical accident scenario and directly compare them with traditional reference values. Full article

Sustainable energy entrepreneurs promote sustainable development by focusing more on energy efficiency. This study examines the interdependence and driving–dependent relationships among the socioeconomic factors (SEFs) influencing sustainable energy enterprises (SEEs). A mixed-methods approach is used, beginning with a literature review and expert consensus, followed by total interpretive structural modeling (TISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis. Seven key SEFs are finalized through interviews with 12 experts. Data are then collected from 11 SEEs. The study reveals that the regulatory and institutional framework emerges as the primary driving factor influencing other SEFs, including financial accessibility, market demand, technological innovation, and infrastructure readiness. Social and cultural acceptance is identified as the most dependent factor. The study proposes future research directions by identifying the United Nations sustainable development goals (SDGs) related to the antecedents, decisions, and outcomes with theoretical linkages through the Antecedents–Decisions–Outcomes (ADO) framework. The major SDGs identified are SDG 4 (education), SDG 7 (energy), SDG 9 (industry), SDG 11 (communities), and SDG 13 (climate). The study highlights that regulatory support, funding access, skill development, and technology transfer are required areas for strategic focus. Understanding the hierarchy of SEs supports business model innovation, investment planning, and risk management. Full article

This study examines the intersection of environmental, social, and governance (ESG) disclosure (operationalized through sustainability reporting), corporate financial performance, and the behavioral dynamics of herding in capital structure decisions among non-financial firms in five ASEAN countries. As ESG and sustainability finance gain prominence in addressing climate change and climate risk, understanding the behavioral factors that relate to ESG adoption is crucial. Employing a quantitative approach, this research utilizes a purposive sample of 125 non-financial firms from Indonesia, Malaysia, the Philippines, Singapore, and Thailand, gathered from the Bloomberg Terminal spanning 2018–2023. Managerial Herding Ratio (MHR) is used to assess herding behavior, while Sustainability Report Disclosure Index (SRDI) measures ESG disclosure. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multigroup Analysis (MGA) were applied for data analysis. This research finds that while sustainability reporting enhances return on assets (ROA) and Tobin’s Q, it does not significantly relate to net profit margin (NPM). The findings also confirm that herding behavior—where companies mimic the financial structures of peers—moderates the relationship between sustainability reporting and performance outcomes, with leader firms gaining more from transparency efforts. This highlights the double-edged nature of herding: while it can accelerate ESG adoption, it may dilute the strategic depth of climate action if firms merely follow rather than lead. The study provides actionable insights for regulators and corporate strategists seeking to strengthen ESG finance as a driver for climate resilience and long-term stakeholder value. Full article

Accurate maize yield forecasting under climate uncertainty remains a critical challenge for global food security, yet existing studies predominantly rely on single-model frameworks, limiting generalizability and actionable insights. This study selected three regions, specifically Dali, Lijiang, and Zhaotong, and collected data on 12 agronomic traits of 114 varieties, along with eight sets of meteorological data, covering the period from 2019 to 2023. We employed three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. The results revealed a strong correlation between yield and multiple agronomic traits, particularly grain weight per spike (GWPS) and hundred-kernel weight (HKW). Notably, the XGBoost model emerged as the top performer across all three regions. The model achieved the lowest RMSE (0.22–191.13) and a good R² (0.98–0.99), demonstrating exceptional predictive accuracy for yield-related traits. The comparative analysis revealed that XGBoost exhibited superior accuracy and stability compared to RF and SVM. Through feature importance analysis, four critical determinants of yield were identified: GWPS, shelling percentage (SP), growth period (GP), and plant height (PH). Furthermore, partial dependence plots (PDPs) provided deeper insights into the nonlinear interactive effects between GWPS, SP, GP, PH, and yield, offering a more comprehensive understanding of their complex relationships. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. The results highlight the importance of integrating agronomic and meteorological data in yield forecasting, paving the way for development of agricultural decision-support systems in the context of future climate change scenarios. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. Full article

Urban sustainability poses a deeply interdisciplinary challenge, spanning technical fields like data science and environmental science, design-oriented disciplines like architecture and spatial planning, and domains such as economics, policy, and social studies. While numerous advanced tools are used in these domains, ranging from geospatial systems to AI and network analysis-, they often remain fragmented, domain-specific, and difficult to integrate. This paper introduces a semantic framework that aims not to replace existing analytical methods, but to interlink their outputs and datasets within a unified, queryable knowledge graph. Leveraging semantic web technologies, the framework enables the integration of heterogeneous urban data, including spatial, network, and regulatory information, permitting advanced querying and pattern discovery across formats. Applying the methodology to two urban contexts—Thessaloniki (Greece) as a full implementation and Marine Parade GRC (Singapore) as a secondary test—we demonstrate its flexibility and potential to support more informed decision-making in diverse planning environments. The methodology reveals both opportunities and constraints shaped by accessibility, connectivity, and legal zoning, offering a reusable approach for urban interventions in other contexts. More broadly, the work illustrates how semantic technologies can foster interoperability among tools and disciplines, creating the conditions for truly data-driven, collaborative urban planning. Full article

Objectives: Hepatocellular carcinoma (HCC) remains a major indication for liver transplantation (LT), but accurate pretransplant risk stratification is critical to improve long-term outcomes. Traditional morphometric criteria such as tumor size and number are limited in predicting recurrence and survival. The HALP (hemoglobin, albumin, lymphocyte, platelet), gamma-glutamyl transpeptidase to platelet ratio (GPR), and FIB-4 indices are emerging systemic inflammatory and nutritional biomarkers that may provide additional prognostic value in HCC patients undergoing LT. Materials and Methods: This retrospective, two-center cohort study included 200 patients who underwent LT for HCC between 2012 and 2023. Preoperative HALP, GPR, and FIB-4 scores were calculated, and their associations with overall survival (OS) and recurrence-free survival (RFS) were assessed using ROC analyses and Cox proportional hazard models. Cut-off values were determined for each biomarker, and survival outcomes were analyzed using Kaplan–Meier methods. Results: A low HALP score (≤0.39) was independently associated with reduced OS but not with RFS. Conversely, low GPR (≤0.45) and FIB-4 (≤3.1) values were significantly associated with both poor OS and higher recurrence risk. Tumor size, number of lesions, and microvascular invasion also independently predicted poor outcomes. Multivariate analysis confirmed HALP, GPR, and FIB-4 as significant preoperative predictors of prognosis in this population. Conclusions: HALP, GPR, and FIB-4 are readily available, cost-effective indices that provide significant prognostic information in HCC patients undergoing LT. Their integration with morphometric criteria may improve pretransplant risk stratification and support individualized clinical decision-making. Full article

Background/Objectives: The extension of euthanasia and physician-assisted suicide to individuals with mental disorders presents a profound ethical, clinical, and legal challenge. While increasingly accepted in some jurisdictions, their application in psychiatric contexts—particularly in cases of depression—raises concerns about diagnostic precision, therapeutic adequacy, and the validity of informed consent. This study examines two controversial Belgian cases to explore the complexities of euthanasia for psychological suffering. Methods: A qualitative case analysis was conducted through a qualitative analysis of publicly available media sources. The cases were examined through clinical, psychoanalytic, and medico-legal lenses to assess diagnostic clarity, treatment history, and ethical considerations. No access to official medical records was available. Case Presentation: The first case involved a young woman whose depressive symptoms were reportedly linked to trauma from a terrorist attack. The second concerned a middle-aged woman convicted of infanticide and later diagnosed with Major Depression. Discussion: In both cases, euthanasia was granted on the grounds of “irreversible psychological suffering.” However, the absence of detailed clinical documentation, potential unresolved trauma, and lack of psychodynamic assessment raised doubts about the robustness of the evaluations and the validity of informed consent. Conclusions: These findings highlight the need for a more rigorous, multidisciplinary, and ethically grounded approach to psychiatric euthanasia. This study underscores the importance of precise diagnostic criteria, comprehensive treatment histories, and deeper exploration of unconscious and existential motivations. Safeguarding clinical integrity and ethical standards is essential in end-of-life decisions involving mental illness. Full article

To address the insufficient utilization of network model features and low search efficiency in kill chain analysis for Weapon System of Systems (WSoS), a complex network model of WSoS based on OODA loop was constructed, which converts the indicator system into attribute features embedded in network nodes, and analyzes the kill chain mode through the metapath. Subsequently, a Depth First Search (DFS) algorithm combined with Graph Attention Network (GAT) is proposed for kill chain search evaluation. The algorithm utilizes GAT to extract topological information and node attribute features from graph data to obtain node-embedding vectors, and optimizes the DFS algorithm process by computing the cosine similarity of node-embedding vectors. Simulation results demonstrated that the proposed algorithm achieves high search efficiency and accuracy, providing robust support for combat decision-making. Full article

Background: Vaccinations are crucial for preventing infectious diseases. Parental knowledge and attitudes significantly impact vaccination decisions. Methods: This study analyzed parental knowledge and opinions on childhood vaccinations (focus: measles, pertussis) and assessed vaccination coverage rates in Radomsko, Poland. A cross-sectional study (Jan–Mar 2025) combined the following: (1) parent questionnaires (children aged 6–11 years), including opinions based on the validated VAX scale and (2) analysis of official vaccination coverage data (sanitary inspection). Statistical analysis included descriptive statistics and logistic regression; results are presented as odds ratios (OR). Results: A total of 459 parents participated (mean age 38.9 years, 95% female, 67% Master’s-level education). Conclusions: Most correctly identified measles (92%) and pertussis (85%) vaccines as mandatory. Considerable confusion existed about newer mandatory vaccines and varicella (78% incorrectly thought mandatory). Analysis revealed the influence of both knowledge and opinions from the VAX scale on vaccination decisions. Higher parental education significantly increased vaccination adherence for pertussis (OR = 2.03; p < 0.001) and both diseases (OR = 1.83; p < 0.001). While general vaccination awareness was high (97%), detailed knowledge of Poland’s mandatory schedule was alarmingly low, especially for newer vaccines. Parental education level is a key determinant of both accurate knowledge and vaccination compliance. Targeted educational interventions are urgently needed to improve parental understanding and support public health goals. Full article

Urban tree planting on single-family-home lots represents a critical yet underexplored component of municipal greening strategies. This study examines residents’ perceptions of tree planting in Westpointe, a diverse neighborhood in Salt Lake City, Utah, as part of the city’s Reimagine Nature Public Lands Master Plan development effort. Through a mixed-methods approach combining qualitative interviews (n = 24) and a tree signup initiative extended to 86 residents, with 51 participating, this research explores the complex interplay of demographic, economic, social, and infrastructure factors influencing residents’ willingness to plant trees on single-family-home lots. The findings reveal significant variations based on gender, with women expressing more positive environmental and aesthetic motivations, while men focused on practical concerns including maintenance and property damage. Age emerged as another critical factor, with older adults (65+) expressing concerns about long-term maintenance capabilities, while younger families (25–44) demonstrated future-oriented thinking about shade and property values. Property characteristics, particularly yard size, significantly influenced receptiveness, with owners of larger yards (>5000 sq ft) showing greater willingness compared to those with smaller properties, who cited space constraints. Additional barriers, i.e., maintenance, financial, and knowledge barriers, included irrigation costs, lack of horticultural knowledge, pest concerns, and proximity to underground utilities. Geographic analysis revealed that Spanish-speaking social networks were particularly effective in promoting tree planting. The study contributes to urban forestry literature by providing nuanced insights into single-family homeowners’ tree-planting decisions and offers targeted recommendations for municipal programs. These include gender-specific outreach strategies, age-appropriate support services, sliding-scale subsidy programs based on property size, and comprehensive education initiatives. The findings inform evidence-based approaches to increase urban canopy coverage through private property plantings, ultimately supporting climate resilience and environmental justice goals in diverse urban neighborhoods. Full article

Predicting short-term buy and sell signals in financial markets remains a significant challenge for algorithmic trading. This difficulty stems from the data’s inherent volatility and noise, which often leads to spurious signals and poor trading performance. This paper presents a novel algorithmic trading model for silver that combines fine-tuned Convolutional Neural Networks (CNNs) with a decision filter based on the Relative Strength Index (RSI). The technique allows for the prediction of buy and sell points by turning time series data into chart images. Daily silver price per ounce data were turned into chart images using technical analysis indicators. Four pre-trained CNNs, namely AlexNet, VGG16, GoogLeNet, and ResNet-50, were fine-tuned using the generated image dataset to find the best architecture based on classification and financial performance. The models were evaluated using walk-forward validation with an expanding window. This validation method made the tests more realistic and the performance evaluation more robust under different market conditions. Fine-tuned VGG16 with the RSI filter had the best cost-adjusted profitability, with a cumulative return of 115.03% over five years. This was nearly double the 61.62% return of a buy-and-hold strategy. This outperformance is especially impressive because the evaluation period was mostly upward, which makes it harder to beat passive benchmarks. Adding the RSI filter also helped models make more disciplined decisions. This reduced transactions with low confidence. In general, the results show that pre-trained CNNs fine-tuned on visual representations, when supplemented with domain-specific heuristics, can provide strong and cost-effective solutions for algorithmic trading, even when realistic cost assumptions are used. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

This research investigates the implementation of multi-criteria decision analysis (MCDA) within the framework of the Common Agricultural Policy (CAP) for the period of 2023–2027, focusing on optimizing agricultural sustainability and profitability in Northern Greece. Using data from three farmer groups across Central and Western Macedonia, the study explores the application of MCDA models within three distinct case studies: the first optimizes a farm system focused on input minimization (Loudias), while the second and third (Ryakio and Agia Paraskevi) adopt a more comprehensive approach to farm management. More specifically, the first case focused on maximizing gross margin, minimizing variable costs, and reducing fertilizer use without targeting a reduction in water usage. By contrast, the second case study adopted a holistic approach to farm management, integrating water conservation in the Ryakio farmer group. The third included the requirement to keep arable land fallow in the Agia Paraskevi farmer group, reflecting the CAP’s new mandates. The results indicate that MCDA facilitates strategic crop selection and land changes that significantly enhance farm management efficiency and sustainability. The optimization led to more significant percentage increases in gross margin for the second (Ryakio) and third (Agia Paraskevi) case studies compared to the first, with the Agia Paraskevi group showing the most substantial improvement. Full article

Non-nutritive sucking (NNS) is an instinctive behavior in newborns, consisting of two stages: sucking and expression. It plays a critical role in preparing the infant for oral feeding. In neonatal and pediatric units, NNS assessment is routinely performed to determine feeding readiness. However, these evaluations are often subjective and rely heavily on the clinician’s experience. While other medical devices that support the development of NNS skills exist, they are not specifically designed for the comprehensive assessment of NNS, and their high cost limits accessibility for many hospitals and tertiary care units globally. This paper presents the development and pilot testing of a low-cost, portable device and accompanying software for assessing NNS in newborns hospitalized in neonatal care units. Methods: The device uses force-sensitive resistors to capture expression pressure and a differential pressure sensor to measure NNS. Data were acquired through the analog–digital converter of a microcontroller and transmitted via Bluetooth for real-time graphical analysis. Pilot testing was conducted with six hospitalized preterm newborns, measuring intensity, number of bursts, and sucks per burst. Results demonstrated that the system reliably captures both stages of NNS. Significance: This device provides an affordable, portable solution to support clinical decision-making in clinical units, facilitating accurate, objective monitoring of feeding readiness and developmental progression. Full article

Pale lager dominates global beer markets. However, rising living standards and changing consumer expectations have reshaped sensory preferences, highlighting the importance of understanding consumers’ true sensory priorities. In this study, a twenty-eight-item questionnaire, refined through multiple rounds of optimization, was distributed across China and yielded 1837 valid responses. Spearman correlation analysis and partial least-squares regressions showed that educational background and spending willingness exerted the strongest independent effects on sensory priorities. A hybrid analytic hierarchy process–entropy weight method–Delphi procedure was then applied to quantify sensory attribute importance. Results indicated that drinking sensation (30.92%) emerged as the leading driver of pale lager choice, followed by taste (26.60%), aroma (24.77%), and appearance (17.71%), confirming a flavor-led and experience-oriented preference structure. Weighting patterns differed across drinking-frequency cohorts: consumers moved from reliance on overall mouthfeel, through heightened sensitivity to negative attributes, to an eventual focus on subtle hedonic details. Based on these findings, a new sensory evaluation scale was developed and validated against consumer preference rankings, showing significantly stronger alignment with consumer preferences (ρ = 0.800; τ = 0.667) than the traditional scale. The findings supply actionable metrics and decision tools for breweries, supporting applications in product development, quality monitoring, and targeted marketing. Full article