Abstract: This study presents the Structural–Typological–Value Sensitivity Model (STVSM), a multidimensional framework for evaluating vulnerability in historic buildings where physical fragility cannot be adequately captured through structural indicators alone. While existing approaches primarily prioritize load-bearing behaviour, they often overlook typological discontinuity, spatial fragmentation, and the erosion of architectural and cultural value. STVSM addresses this limitation through three weighted sub-indices: structural vulnerability (SV), typological degradation (TV), and heritage value (HV), each calibrated using expert-derived micro- and macro-level weighting coefficients. Field-based deterioration scores (0–1) are combined with these weights to generate SV, TV, and HV values, which are then integrated into a Conservation Priority Index (CPI). Although conceptually informed by building-scale seismic vulnerability literature, the model does not aim to simulate earthquake performance or replace numerical structural analysis. Instead, it operates as a comparative decision-support framework that incorporates seismic-informed deterioration patterns within a broader, conservation-oriented logic. The model is applied to twenty-five historic buildings across three heritage contexts: traditional houses in Cumalikizik, vernacular dwellings in Balıkesir–Karesi, and nineteenth-century Greek Orthodox churches in Bursa. The results demonstrate that integrating structural condition, typological integrity, and heritage value provides a transparent, repeatable, and scalable basis for conservation prioritization across diverse historic building stocks.

Abstract: Epidemiological Surveillance of Nursing Diagnoses (ESND) represents an emerging field within Community and Public Health Nursing, aiming to strengthen the visibility of nursing‑sensitive phenomena in health information systems. This study applied the Community Assessment, Intervention and Empowerment Model (MAIEC) to evaluate the empowerment level and diagnose the community process of a Primary Health Care Island Unit in the Autonomous Region of the Azores, Portugal, regarding the promotion of ESND. A descriptive, cross‑sectional design was used, combining documental analysis, a community empowerment assessment, and a structured questionnaire administered to 172 nurses. Results revealed substantial gaps in community leadership, including low levels of knowledge about ESND, the Local Health Diagnosis, and documentation in priority ICNP® foci. Community participation indicators showed limited clarity of the ESND process, low awareness of organizational structures and partnerships, and a lack of visible formal leadership. Community coping was characterized by minimal prior ESND experience and low training levels, although more than half of participants identified contextual strengths. Overall, the findings indicate a community with developmental potential but requiring targeted interventions to strengthen leadership, participation, and coping capacities. Enhancing training, communication, and organizational structures will be essential to support the sustainable implementation of ESND and reinforce the contribution of nursing to public health surveillance.

Abstract: Edible insects are increasingly recognized as a sustainable protein source, yet systematic evidence on the safety and efficiency of indigenous processing practices remains limited. This study combines ethnographic surveys with microbiological analysis to document traditional harvesting, processing, and preservation methods for edible insects across nine ecological zones in Tanzania. Findings reveal edible insect harvesting practices through wild harvesting and varied by insect type, habitat, and seasonal availability. Unexpectedly, 30.9% of respondents reported raw consumption of edible insects. A wide diversity of insect specific processing methods was observed, including dry toasting, frying, boiling, sun drying, and smoke drying, reflecting adaptations to insect morphology, perishability, and intended use. While thermal processing practices were generally effective in elimination of major pathogens (Salmonella spp., Escherichia coli and Listeria monocytogenes), preservation challenges related to drying efficiency, post-processing handling, and storage were evident. Although most samples complied with East African Standards (EAS 1186:2023), sun-dried and toasted products exhibited high total viable counts and yeast & mold levels. No formal training to handlers was recorded, and processing practices were primarily transmitted orally and experientially through storytelling and observation. These findings demonstrate the potential of indigenous knowledge as a foundation for safe insect food systems, while identifying priority interventions such as improved drying infra-structure, hygienic handling, and Hazard Analysis Critical Control Point (HACCP) aligned protocols to support commercialization and regional trade without eroding cultural integrity.

Abstract: Rising atmospheric CO₂ levels have increased the demand for robust, scalable adsorbents for practical CO₂ capture and separation. Porous organic polymers (POPs) are attractive candidates because their pore architecture and binding site properties can be precisely tuned via building blocks and linkage formation. This review summarizes experimental and computational studies of azo-linked POPs and, more broadly, nitrogen-nitrogen (N−N) linked systems, emphasizing how synthetic routes, building blocks, and framework topology govern CO₂ uptake. We highlight key synthetic strategies and representative systems, including porphyrin-azo networks, and discuss the relatively sparse experimental literature on alternative N−N linked POPs incorporating azoxy and azodioxy motifs. Emphasis is placed on reversible nitroso/azodioxide chemistry as a potential pathway to ordered porous organic materials. Computational studies provide a practical route to connect structure with adsorption behavior in largely amorphous or partially ordered networks. We review hierarchical workflows combining periodic DFT and electrostatic potential properties, grand canonical Monte Carlo (GCMC) simulations and binding-energy calculations to rationalize trends and identify favorable binding environments. Computational findings demonstrate that pore accessibility and stacking models can strongly influence predicted CO₂ adsorption. This review provides guidelines for designing POPs with enhanced CO₂ adsorption, offering an outlook and discussing challenges for future studies.

Abstract: Performance variability in MgO-based cements stems partly from poorly characterized dissolution kinetics of commercial lightly burned magnesia (LBM). Existing studies focus on high-purity materials under acidic conditions, but LBM dissolves also in alkaline condition where Mg(OH)₂ precipitation prevents reliable sampling at high pH. We validated pH monitoring against ICP-AES for tracking initial LBM dissolution kinetics across pH 2.0-11.0 and temperatures 25-85°C. Commercial LBM (32 ^m2/g, 7.5 wt% CaO) exhibited rates one to two orders of magnitude higher than synthetic magnesia (10⁻⁸ to 10⁻¹² mol/cm²·s). X-ray diffraction, electron microscopy with energy-dispersive spectroscopy, and BET analysis revealed enhanced reactivity from poor crystallinity, multiphase composition, and high surface area with textural porosity. Temperature effects peaked at 75°C before declining due to Mg(OH)₂ passivation. The validated method provides practical guidance for MBC quality control and performance optimization.

Abstract: A single channel Rayleigh Density Temperature Lidar (RDTL) with a receiver telescope of 85 cm diameter was installed at Poker Flat Research Range (PFRR), Chatanika, Alaska (65°N, 213°E) in November 1997. To increase the incoming signal count the receiver diameter was increased to 1 m in 2016. In order to prevent damage of the photomultiplier tube due to the high incoming signal counts, the RDTL receiver system was modified to a three-channel system. However, temperature calculations from the individual channel retrieval showed a mismatch between them and this created a problem in combining the signal counts from the three channels into one to achieve higher confidence in the data. In this study, a correction procedure has been developed and deployed to the signal counting statistics of the RDTL to eliminate instrumental biases and get 100% agreement in temperatures between the three channels.

Abstract: This study evaluates the extent and quality of tax transparency reporting among the Top 40 firms listed on the Johannesburg Stock Exchange (JSE), distinguishing between mandatory tax disclosures and voluntary transparency practices. A qualitative, disclosure-based research design was employed, involving content analysis of publicly available annual reports, integrated reports, and sustainability reports. A structured tax transparency framework grounded in stakeholder theory and legitimacy theory, and adapted from prior empirical studies was applied to systematically assess tax-related disclosures. Findings indicate high compliance with mandatory tax disclosure requirements, reflecting strong adherence to accounting standards and regulatory obligations. In contrast, voluntary tax transparency shows considerable variation: firms predominantly provide narrative, policy-oriented, and governance-related information, while detailed, forward-looking, and jurisdiction-specific disclosures remain limited. The discussion highlights that voluntary transparency is shaped by stakeholder expectations, legitimacy concerns, and perceived reputational and commercial risks, leading to selective disclosure. Regulatory compliance emerges as the primary driver of tax reporting, whereas voluntary practices are influenced by firm-specific and contextual factors. The results hold relevance for investors, regulators, and policymakers seeking greater corporate accountability, and for standard-setters aiming to enhance the consistency and depth of tax transparency reporting. Overall, the study enriches the limited literature on corporate tax transparency in emerging markets by offering contemporary empirical evidence from South Africa and identifying key areas requiring improvement in voluntary tax disclosures.

Abstract:

The rapid integration of large language models (LLMs) into organizational workflows raises fundamental questions about the long-term effects of AI assistance on human creative capabilities. This article provides a comprehensive theoretical extension of Sun et al.'s (2025) field experiment, which demonstrated that LLM assistance enhances creative output during use but diminishes subsequent independent creativity—particularly for individuals with lower metacognitive ability. We develop the metacognitive paradox framework to explain this phenomenon: AI tools designed to augment human creativity may inadvertently suppress the very cognitive processes that sustain independent creative capacity. Drawing on metacognition theory, cognitive offloading research, automation and human factors literatures, and the componential model of creativity, we articulate the mechanisms through which LLM assistance affects creative cognition, specify temporal dynamics and boundary conditions, and generate testable propositions for future research. We explicitly compare our framework against alternative theoretical explanations—including cognitive load theory, motivational accounts, and expertise development perspectives—and provide methodological guidance for testing our propositions. Our analysis reveals that the relationship between AI assistance and human creativity is neither uniformly beneficial nor detrimental but contingent upon individual metacognitive capabilities, patterns of tool use, task characteristics, and organizational context. We conclude with implications for theory, research methodology, organizational practice, and the ethical dimensions of AI-augmented work.

Abstract: Background/Objectives: One of the greatest threats to global public health is antimicrobial resistance (AMR), due to the increasing number of infections caused by extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae. Escherichia coli and Klebsiella pneumoniae ESBL-producing bacteria possess resistance mechanisms that inactivate β-lactam antibiotics by hydrolyzing their β-lactam ring, thereby limiting conventional therapeutic options. In response to this problem, the objective in this exploratory in vitro study was to evaluate the antimicrobial activity of Origanum vulgare (oregano) essential oil and its interaction with the antibiotic cefepime using in vitro methods. Methods: Antimicrobial susceptibility tests were performed, including determination of the minimum inhibitory concentration by the microdilution method with statistical analysis, and evaluation of the fractional inhibitory concentration index using the checkerboard method. In addition, advanced methods such as bacterial identification by MALDI-TOF mass spectrometry and PCR were employed for the identification of resistance genes. Results: The studied strains exhibited both phenotypic and genotypic resistance. The MIC of the essential oil was 1024 µg/mL for ESBL-producing E. coli and 2048 µg/mL for ESBL-producing K. pneumoniae, whereas the ATCC strains showed higher susceptibility. The FICI values indicated synergism in E. coli (FICI = 0.188) and an additive effect in K. pneumoniae (FICI = 0.563). Conclusions: Oregano essential oil exhibits antimicrobial activity and the ability to potentiate the effect of cefepime, suggesting its potential as a therapeutic adjuvant. Additional studies are required, including a larger number of strains, cytotoxicity analyses, and clinical validation.

Abstract:

Poly(lactic acid) (PLA) devices can be functionalized with plant derived bioactives to introduce antioxidant activity while maintaining manufacturability and cytocompatibility. Here, a polyphenol rich mango leaf extract (MLE) was obtained by enhanced solvent extraction and incorporated into PLA using supercritical carbon dioxide assisted impregnation. Two manufacturing sequences were compared: impregnation after three dimensional (3D) printing of discs and impregnation of filaments prior to printing. Extract yield and radical scavenging capacity were quantified, and impregnation efficiency was assessed as a function of pressure and temperature. Biological performance was evaluated using adipose tissue derived endothelial colony forming cells (ECFCs) and adipose tissue derived mesenchymal stromal cells (MSCs), cultured separately and in co culture on functionalized substrates. Impregnation after printing provided higher and more reproducible loading while preserving disc geometry, whereas impregnation before printing promoted swelling and printing associated deformation that compromised structural fidelity. Cell based analyses supported improved adhesion, spatial distribution and proliferative status on discs produced by impregnation after printing under low temperature and high pressure conditions, without evidence of selective loss of either population in co culture by flow cytometry. These results support post print supercritical impregnation as a robust route to generate antioxidant, cell supportive PLA scaffolds from agricultural by products with potential relevance for vascular oriented biomedical applications.

Abstract: The globalization of high-technology supply chains has concentrated design and tech-nological control in advanced economies, limiting the industrial upgrading potential of emerging regions. At the same time, increasing sustainability pressures demand the integration of circular economy principles into production systems. However, existing research rarely integrates supply chain localization strategies, circular value creation mechanisms, and regional capability development within a unified explanatory framework. This study develops a conceptual circular supply chain framework for the sustainable localization of high-technology unmanned aerial vehicle (UAV) systems in emerging economies. Drawing on localization theory, circular supply chain design, and capability accumulation literature, the framework conceptualizes localization as a systemic config-uration composed of three interdependent structural dimensions: (1) core technological supply chain processes, (2) transversal circular value creation mechanisms, and (3) re-gional capability accumulation pathways. Unlike linear acquisition models, the proposed framework embeds modularity, re-pairability, remanufacturing, and lifecycle management within the supply chain's op-erational architecture. This integration enables simultaneous outcomes in environmental sustainability, economic resilience, and social upgrading. The framework further iden-tifies boundary conditions and aligns structurally with Sustainable Development Goals related to responsible production, industrial innovation, and climate action.

Abstract: This paper presents a comprehensive analysis of artificial intelligence (AI) adoption and governance frameworks in New Jersey, examining the state's strategic initiatives to become a national leader in AI innovation while ensuring ethical implementation and public trust. Through systematic review of recent developments including the $500 million Next New Jersey Program, establishment of the NJ AI Hub with founding partners Princeton University, Microsoft, and CoreWeave, and implementation of workforce training initiatives reaching over 65,000 -75,000 state employees, we analyze how governance structures can accelerate responsible AI adoption. Our research synthesizes findings from the New Jersey AI Task Force report, academic literature from Rutgers and Princeton, and industry implementations from leading technology providers to develop a multi-layered governance framework tailored to New Jersey's unique public-private-academic ecosystem. Key findings indicate that integrated approaches combining infrastructure investment, workforce development, and ethical guidelines yield optimal outcomes, with 60-70% of New Jersey adults now engaging with AI tools and over 1,200 - 1,500 jobs created in AI-related fields. The paper proposes actionable recommendations for policymakers, including standardized AI procurement protocols, cross-agency coordination mechanisms, and continuous stakeholder engagement strategies. This work contributes to both theoretical understanding of AI governance at the state level and practical guidance for jurisdictions seeking to balance innovation acceleration with responsible oversight, while addressing emerging challenges in agentic AI systems and algorithmic discrimination prevention.

Abstract: Ancient pathogen genomics has redefined how infectious disease histories are reconstructed,revealing unexpected origins, transmission routes and lineage turnovers that are invisible frommodern genomes alone. Yet this perspective remains heavily biased toward Eurasia and theAmericas, leaving Africa, central to human evolution, biodiversity and zoonotic emergence,largely unexplored. In this review, we assess the current state of ancient pathogen research inAfrica and synthesize insights from bacterial, parasitic and viral perspectives. We identify Africaas a pivotal frontier for the field and outline strategic priorities to move from isolated detectionstoward continent-scale reconstructions of past disease landscapes, with direct relevance forunderstanding present-day and future epidemic risk.

Abstract: Opioid use disorder is a national crisis in the United States, with 3 US FDA-approved pharmacotherapies available and rapidly rising overdose deaths driven by synthetic opioids, i.e. fentanyl. Recent population-level evidence suggests that GLP-1 receptor agonists (GLP-1RA) may reduce the risk of opioid overdose, yet underlying mechanisms remain unclear. This study investigated molecular mechanisms of fentanyl and GLP-1RA. We performed RNA-seq in human iPSC-derived forebrain organoids treated with fentanyl, liraglutide, or exenatide. We then extended this analysis to iPSC-derived forebrain neurons exposed to additional therapeutic candidates: anticonvulsants (topiramate, gabapentin) and a metabolic modulator (β-hydroxybutyrate). We performed RNA-seq and functional genomic assays using iPSC-derived cell models. All drugs were tested at clinically relevant concentrations. Our results showed modulation of endoplastic reticulum (ER) stress signaling as a shared molecular mechanism across fentanyl, GLP-1RA, and other drug classes with therapeutic potential for substance use disorders (SUDs). Fentanyl, liraglutide, and exenatide consistently down-regulated ER stress–related genes, with TRIB3 emerging as the most strongly suppressed target in brain organoids. Additional stress-response genes, including DDIT3, ATF4, and PPP1R15A, were similarly reduced, indicating broad attenuation of ER stress pathways. We further identified CEBPB as a key upstream driver of these transcriptional changes. Finally, we confirmed that diverse compounds, including anticonvulsants and metabolic modulators, suppressed ER stress genes and reduced CEBPB DNA-binding activity in neurons. In summary, these findings reveal ER stress modulation, mediated in part through CEBPB, as a convergent mechanism across multiple drug classes and highlight potential relevance to therapeutic action in SUD.

Abstract: Tumor heterogeneity and microenvironmental complexity remain fundamental barri-ers to genomics-centered precision oncology, frequently causing discordance between molecular alterations and real-world therapeutic responses. Here, we reviewed pa-tient-derived organoid (PDO) technologies as functional platforms that complement molecular profiling by directly investigating patient-specific sensitivity, resistance, and microenvironment dependent vulnerability. We first summarize why convention-al preclinical systems, two-dimensional cell lines and patient-derived xenografts, are limited by reduced biological fidelity, impractical turnaround time, and scalability for clinical decision support. We then synthesized organoid-based evidence across three representative disease malignancies with distinct precision-medicine bottlenecks. Across these settings, we highlight advances that extend the PDO capability beyond the tumor epithelium alone, including air–liquid interface cultures, immune and stro-mal co-cultures, and microfluidic organoid-on-chip systems, as well as integration with multi-omics and artificial intelligence for scalable analytics. Finally, we discuss the key translational requirements, standardization of culture matrices and assay readouts, quality control, automation to reduce turnaround time, and regulato-ry/ethical frameworks, required to transition organoid-guided testing from proof-of-concept to routine implementation. Collectively, this review reframes organ-oids as functional stratification platforms supporting a conceptual shift from geno-type-guided to response-driven precision oncology.

Abstract: The education of gifted individuals has strategic importance today. It aligns directly with the United Nations Sustainable Development Goals and it supports inclusive quality education (SDG 4). This study examines theoretical approaches and application models in gifted education with a cross-cultural perspective and the document analysis method is used. International models and the historical development of gifted education in Türkiye are compared. Findings show that gifted education evolved from one-dimensional intelligence tests to holistic models. These models support the affective, cognitive, and psychomotor well-being of students. The applications in different countries are shaped by their cultural values and education policies. Systemic barriers in identification processes prevent equal opportunities in many countries. The Science and Art Centers (BILSEM) and Gifted Education Program (UYEP) offer important institutional foundations in Türkiye. But recent literature shows that these centers continue to face physical and material problems. Students from all socio-economic backgrounds must have fair access to advanced learning. Gifted education is a long-term public policy area. Sustainable gifted education systems require social equity, institutional continuity, and dynamic curriculum structures. Policy implications are presented to build inclusive and sustainable education models from a Türkiye perspective.

Abstract:

This study presents an AI-powered framework for predictive maintenance and prognostic health management (PHM) based on edge-enabled predictive algorithms to support intelligent fault diagnosis in industrial operations. The proposed framework is designed to monitor system conditions, detect early fault signatures, and anticipate degradation patterns using high-frequency operational data collected from two large industrial plants between 2024 and 2025. By leveraging edge computing, the approach enables localized anomaly detection with low latency, allowing deviations in system behavior to be identified close to the data source. The methodology integrates edge-based anomaly detection with predictive modeling techniques to estimate future system health states and fault-related risk dynamics. Anomalies identified at the edge level are aggregated and processed through forecasting models to infer degradation trends and support prognostic assessment. A health-oriented evaluation layer translates predictive outputs into actionable indicators that support maintenance planning and system recovery decisions. The framework is evaluated using standard predictive performance metrics, including MAPE, RMSE, and R², together with a health-related improvement measure reflecting system stability and recovery capability. The results demonstrate high predictive reliability, with the models explaining approximately 98.9% of the observed variability in system risk indicators and achieving measurable improvements in operational stability through early fault mitigation. This research contributes a scalable algorithmic framework that links data-driven condition monitoring, intelligent fault diagnosis, and PHM within an edge computing environment, strengthening maintenance decision accuracy in dynamic industrial settings.

Abstract: Sweet taste is a fundamental sensory modality that plays a crucial role in food intake and preference. In recent years, many studies have shown that sweet taste perception is not an isolated physiological process but interacts significantly with other sensory systems, including other tastes modalities and olfaction. This review summarizes cross-modal sensory interactions between sweet taste and other sensory systems (saltiness, sourness, bitterness, and umami) as well as olfaction and trigeminal nerve. It clarifies that the interaction between sweet taste and other basic tastes presents concentration-dependent characteristics of enhancement, inhibition or masking, and reveals the synergistic or antagonistic effects of olfactory aroma compounds on sweet taste perception, as well as the modulation of sweet taste by trigeminal nerve-mediated temperature, texture and chemical stimulation of food. This review focuses on the mechanisms underlying these interactions and their potential applications in future food science and nutrition. These findings not only deepen the understanding of the complex sensory perception of sweet taste, but also provide important theoretical support and practical guidance for solving the health problems caused by excessive sugar intake and optimizing food sensory quality.

Abstract: Economic freedom is increasingly recognized as a key element of sustainable social and economic development, significantly affecting resilience and competitiveness, which translates into long-term stability of economies around the world. In this study, we analyze the extent to which state intervention, measured by the level of fiscal freedom, government spending, and monetary freedom, affects the sustainability of economic management in EU member states. Using the Index of Economic Freedom (IEF) and macroeconomic indicators such as GDP per capita and the Human Development Index (HDI), we analyze 27 EU countries [1,2, 32]. The study covers the period 2015–2025. The study uses correlation analysis and an econometric model estimated by the least squares method to assess how selected dimensions of economic freedom contribute to sustainable economic performance. The results confirm significant heterogeneity among EU countries and show that higher levels of economic freedom are associated with better socio-economic outcomes, including improved competitiveness, innovation capacity, and long-term growth potential. Countries that improved their IEF scores during the period under review tended to strengthen the foundations for sustainable development by improving regulatory efficiency and reducing excessive state intervention. The results of the study highlight the importance of predictable fiscal and monetary policies as factors conducive to sustainable economic management, thus providing evidence that economic freedom supports the conditions necessary for achieving resilient and inclusive economic growth in the European Union, which also translates into microeconomic aspects in the context of functioning of business entities.

Abstract: Meat condemnation at slaughterhouses reflects the burden of animal diseases, economic losses, and potential public health risks. In northern Nigeria, however, longitudinal and model-based assessments of condemnation patterns using routine abattoir data remain limited. To quantify species- and disease-specific meat condemnation rates, assess temporal trends, and identify factors associated with condemnation at the Sokoto State Main Abattoir. A retrospective longitudinal study was conducted using abattoir meat inspection records from January to June 2025. Condemnation rates per 1,000 animals slaughtered were calculated by species, disease category, and month. Temporal trends and associated factors were evaluated using negative binomial regression with an offset for slaughter volume. Model adequacy was assessed through dispersion diagnostics, multicollinearity checks, residual analyses, sensitivity analyses, and predictive calibration using observed versus model-predicted rates. A total of 317,685 animals were slaughtered during the study period, with 1,628 condemnation cases, corresponding to an overall condemnation rate of 5.12 per 1,000 animals (95% CI: 4.88–5.38). Condemnation rates varied markedly by species, with camels exhibiting the highest rates (27.4 per 1,000), followed by cattle, sheep, and goats. Disease-specific analyses identified contagious bovine pleuropneumonia, fascioliasis, hydatidosis, and tuberculosis as major contributors to condemnation. Temporal patterns demonstrated non-linear monthly variation, with elevated rates in mid-study months. The final negative binomial model showed good calibration, with close agreement between observed and predicted rates across species and diseases. Meat condemnation at the Sokoto State abattoir demonstrates substantial heterogeneity by species, disease, and time. Priority conditions such as CBPP, fascioliasis, hydatidosis, and tuberculosis-like lesions warrant targeted control efforts. These findings reinforce the value of routinely collected abattoir data as a practical and robust component of animal health surveillance in resource-limited settings.