Search Results (10,603)

In recent years, special function theory has played an increasingly important role in the development of advanced mathematical models and statistical distributions. In this paper, a new extension of the Euler Beta function is introduced by employing the Wright function as a kernel, leading to the formulation of the Beta–Wright function. Several fundamental properties of the proposed function are systematically investigated, including summation formulas, functional relations, Mellin transforms, integral representations, and derivative formulas. Furthermore, extended forms of Gauss and confluent hypergeometric functions are constructed within this framework. In addition to its theoretical significance, the proposed function is applied to statistical modeling, and the associated distributions are analyzed using graphical and analytical techniques. The obtained results demonstrate that the Beta–Wright function provides a flexible and effective tool for both analytical investigations and statistical applications. Full article

Recent scientific evidence confirms that there is no safe threshold for bisphenol A intake, prompting strict regulatory actions and new prohibitions in the European Union. As a result, bisphenol A has increasingly been replaced by other analogues that are also toxic but less regulated and insufficiently studied, posing a new risk to human health due to cumulative exposure. Since food is the primary source of exposure to these compounds, this review aimed to evaluate the most appropriate existing chromatographic methods for their determination under newly introduced near-zero tolerance limits, as well as to assess current cumulative dietary exposure and associated health risks. A systematic literature search was conducted in major scientific databases and relevant regulatory sources covering the period from 2015 to 2025, following PRISMA guidelines. Of the 489 identified publications, 22 met the eligibility criteria for full-text analysis. The findings indicate a clear methodological shift towards simultaneous quantification of multiple bisphenol analogues, with LC-MS/MS emerging as the dominant and most robust analytical technique. Dietary exposure to bisphenol A is expected to decline due to stricter regulations; however, this may trigger a rise in the use of its structural analogues as alternatives. Exposure assessments indicate that combined dietary intake of bisphenol A and its analogues can result in a Hazard Index exceeding 1, primarily due to the substantially reduced Tolerable Daily Intake for bisphenol A. This highlights the need for continuous monitoring under stricter regulatory frameworks. Full article

A major drawback of many proposed biobased alternatives of the most commonly used petroleum-based packaging materials is their relatively poor physical properties. In order to develop more viable alternative packaging materials, these properties need to be modified, while maintaining and improving the other desired characteristics. An investigation was done on corona-charged curcumin-containing PLA films to determine how the addition of the polyphenol impacts its physical properties. Measurements of the surface potential of the films were performed, as was the impact of low pressure on the electret properties. The effect of the corona discharge treatment on the physicochemistry of the surface of composite PLA films was investigated systematically using some complementary surface analytical techniques, such as surface wettability and morphology by scanning electron microscopy. The mechanical properties and conductance of the films were also investigated. A dependency of the decay of the surface potential on the film type and the polarity of the corona was found. It was also established that modifying the surface of the films with corona discharge can cause an increase in their wettability and surface free energy, while also improving their adhesion properties. This is caused by the creation of polar functional groups on the film surface during the charging process. It was also determined that the introduction of curcumin in the PLA films decreases their stiffness, which may be caused by a decrease in intramolecular cohesion. Full article

The growing urgency of resource-efficient construction in water-stressed and rapidly urbanizing regions necessitates integrated decision support frameworks that move beyond isolated sustainability metrics. This study operationalizes the water-energy nexus within building design evaluation by developing a structured hybrid multi-criteria decision-making (MCDM) framework tailored to the Indian construction context. Unlike conventional sustainability assessments that treat water and energy independently, the proposed approach integrates life cycle-based water consumption, operational and embodied energy demand, environmental impacts, economic feasibility, and project constraints within a unified analytical hierarchy. A Delphi-validated criterion structure comprising five main criteria and twenty sub-criteria is weighted using the Analytic Hierarchy Process (AHP), and ranked using the VIKOR compromise solution method. To strengthen methodological robustness, ranking outcomes are validated across three independent MCDM logics including TOPSIS, PROMETHEE, and COPRAS. The framework evaluates four representative building strategies aligned with Indian regulatory and certification systems (NBC, ECBC, IGBC/GRIHA, and net-zero water-energy design). Using expert-informed weights derived from a Delphi–AHP involving a panel of experienced practitioners, the VIKOR compromise ranking consistently identifies the net-zero alternative as the most favorable option within the evaluated framework. The results are therefore interpreted as an expert-informed assessment demonstrating the applicability of the proposed decision support methodology rather than as statistically generalizable priorities for the entire Indian construction sector. The study contributes by (i) embedding nexus-based resource interdependence into building-level MCDM modeling, (ii) enhancing transparency through explicit benefit-cost classification and decision matrix disclosure, and (iii) demonstrating ranking stability across multiple validation techniques. The proposed framework provides a transferable methodological approach that can be adapted to different regional contexts through locally derived expert inputs. Full article

Biochars are currently proposed as soil amendments or sorbent materials. There is an extensive scientific literature that deals with biochars originating from different raw materials. However, a holistic physicochemical characterization with simple analytical techniques is needed to provide insights on the characteristics of the biochars produced from malt spent rootlets (MSRs) and how they vary using different pyrolysis temperatures. This way, their properties can be fully understood, and they can be used for commercial purposes more effectively. Initially, the texture of the biochars were visualized by SEM and was quantified by the adsorption/desorption of nitrogen and the Brunauer, Emmett, and Teller (BET) equation. Additionally, the moisture content, the ash content and the pH of each sample were measured. Furthermore, the electrical conductivity of each sample was measured. Different techniques were used to determine the properties of carbon and of the surface functional groups (Total Carbon, XRD, ATR-FTIR) and leachable organic matter. Also, sorption of the methylene blue dye solution has been studied, which is an indication of mesopores for each biochar. Molasses number was also determined, as this is an indicator of macropores. Finally, the chlorine removal rate was determined for each type of biochar. The experiments marked that the change in mass of biochars has stopped after three hours at 50 °C in the drying oven. The measured moisture content ranged from 6 to 11%. The specific surface area of our materials, calculated through the BET equation, for low temperature biochars (e.g., 28 m²/g, at 350 °C), is much lower than that of high temperature pyrolyzed biochar (e.g., 286 m²/g, at 850 °C). The pH value ranged from 7 to 10. The electrical conductivity values of samples ranged from 800 μS/cm to 2.55 mS/cm, and these decreased during the measurement after the second wash with deionized water. Crystallinity increased with increasing pyrolysis temperature whereas the number of functional groups decreased. MSR biochars produced at temperatures equal or higher than 750 °C demonstrate different characteristics to the ones produced at lower temperatures. Full article

The global edible oil market is consistently at risk of economically motivated adulteration, underscoring the necessity of robust analytical methods essential for authentication. Among various phytochemicals, phytosterols have emerged as powerful diagnostic markers and compositional indicators for verifying the botanical origin, purity, and quality of edible oils. This review summarizes recent advancements in phytosterol analysis, highlighting its application in detecting adulteration in high-value oils such as olive oil, tea seed oil, and sesame oil. We discuss the approaches of multiple chromatographic and mass spectrometry techniques (GC-MS, LC-MS) with chemometric analysis of novel markers like fatty acyl sterol esters and sterol degradation products. Furthermore, we discuss significant challenges, including the need for comprehensive databases, the identification of complex sterol compositional profiles, and the limitations of current standardized methods. The advancement of phytosterol-based authentication increasingly depends on the development of rapid, high-throughput, and non-targeted sterol profiling approaches, supported by artificial intelligence and bioinformatics, to ensure vegetable oil authenticity and safeguard market integrity. Full article

Radio Frequency Fingerprinting (RFF) would be a promising Physical Layer Security (PLS) solution for the Internet of Things (IoT) that requires robust, low-overhead security techniques. However, practical implementation of RFF may pose challenges, in particular, performance instability and ethical-regulatory conflicts. Based on authors’ previous research, this paper elaborates these challenges in potential deployment of a resilient and compliant RFF system. First, we analytically show how hardware-induced feature drift, primarily driven by device aging and temperature variations, degrades RFF performance. We then critically survey existing temperature variation and aging models, one of which is being studied by one of the authors’ research team. We look into this from a purely hardware-design perspective, and then compensation methods for an RFF perspective. This reveals a significant gap: current techniques are insufficient to maintain the long-term, high-accuracy RFF for real-world IoT security requirements. Finally, we introduce inherent privacy risks by enabling device tracking. This property conflicts with General Data Protection Regulation (GDPR) mandates, raising significant regulatory challenges and privacy risks. Overall, this work highlights the key technical and legal challenges that must be addressed for RFF to evolve into a robust, privacy-compliant and deployable security primitive for IoT and future wireless systems. Full article

Ion-selective electrodes (ISEs) are widely used analytical tools for the determination of specific ions in a variety of analytical applications due to their simplicity, selectivity, and low cost. Recent developments in materials science and digital fabrication have opened new opportunities for redesigning ISEs using modern manufacturing techniques. Here, we present a new application of 3D printing for fabricating potassium-selective electrodes using a simplified membrane composition. The 3D printing cocktail was prepared by mixing potassium tetraphenylborate, silver sulfide or graphite, and industrial ABS (acrylonitrile Butadiene Styrene) polymer. Membranes were tested both without and with the addition of ZnO nanoparticles. Incorporation of ZnO NPs significantly enhanced the electrode slope, while graphite-based membranes exhibited faster response, with potential stabilizing within 3–7 s across a concentration range of 4.88 × 10⁻⁵ mol L⁻¹ to 1.00 × 10⁻² mol L⁻¹. The optimized 3D printed membrane containing 0.6% ZnO NPs showed near-Nernstian behaviour (slope: 59.178 mV per decade and R² = 0.9989), a limit of detection of 2.06 × 10⁻⁵ mol L⁻¹ and high selectivity against common interfering ions. These results demonstrate that 3D printing combined with a suitable membrane composition and nanoparticle incorporation provides a versatile platform for rapid, reproducible, and high-performance potassium ISEs. Full article

Traffic accidents are among the leading causes of injury worldwide, highlighting the urgent need to better understand the factors that contribute to accident occurrence and severity in order to improve road safety and reduce injuries and fatalities. This study analyzes the U.S. Accidents dataset, comprising data collected from 2016 to 2023, to identify the key determinants of accident severity and to evaluate feature-selection techniques for predictive modeling. To this end, several feature-selection methods are examined, including L1-regularized logistic regression, minimum redundancy maximum relevance (mRMR), conditional mutual information maximization (CMIM), ReliefF, and tree-based importance measures; these are compared with the Weighted Conditional Mutual Information (WCFR). The selected feature subsets are then evaluated using three machine learning models: logistic regression, random forest, and XGBoost. Experimental results show that WCFR consistently outperforms the competing methods, achieving higher validation accuracy (up to approximately 0.84) and Macro-F1 scores (up to approximately 0.55), while using fewer features and maintaining model interpretability. These results indicate that WCFR is particularly effective for accident severity modeling and highlight its potential as a robust feature selection strategy for large-scale transportation safety analytics and future severity prediction studies. Full article

Adaptive reuse has emerged to become a tool for implementing the understanding of sustainability in the domain of architectural conservation, as it encourages the continued usage of old buildings as means of reducing environmental impact, as well as preserving socio-cultural capital while generating economic income. However, in its practice, the decisions regarding granting meanings, interpretation, and preserving memories within adaptation processes are dominated by expert-driven approaches that inadequately incorporate stakeholder values or intangible heritage dimensions. To this end, this study aims to contribute to the current debate by adopting a participatory co-evaluation framework that integrates both authenticity perspectives and sustainability dimensions using Multi-Criteria Decision Analysis (MCDA) for evaluating adaptive reuse alternatives for an abandoned prefabricated wooden heritage building. Stakeholder priorities were drawn through a workshop and transformed into normalized weights using the Simos technique. Four design alternative typologies—namely, Continuity, Cultivation, Differential, and Optimization—were assessed and compared against 20 performance indicators across heritage, social, ecological, and economic criteria using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Indicator-level analyses and sensitivity tests (±10% and ±20% weight variations) were applied to confirm the robustness of rankings. The results from the best-performing alternative demonstrated the trade-offs between heritage authenticity and sustainability objectives, as well as demonstrating how combining participatory methods with quantitative evaluation can support evidence-based decision-making for adaptive reuse. The applied integrated framework helps bridge the gap between heritage theory and practice by combining authenticity, participation, and sustainability in one analytical approach, supporting evidence-based decisions for adaptive reuse. Full article

This study identifies motivational profiles among high school students regarding access to the University of Guanajuato, Yuriria Campus, within the framework of Sustainable Development Goal 4 (SDG 4). Using a survey of 306 students from diverse public and private institutions in southern Guanajuato, we applied K-means clustering analysis with validation techniques (elbow method, silhouette, bootstrap) to examine five key dimensions: family support, university interest, academic perception, transport accessibility, and self-efficacy. The analysis revealed three distinct profiles: (1) “Privileged and committed” (21%), with high scores in all variables and predominantly from private schools; (2) “Supported but not captivated” (65%), with moderate resources but low specific interest in the institution; and (3) “Vulnerable and disconnected” (14%), facing multiple barriers including low family support, economic constraints, and rural origin. ANOVA confirmed significant differences between clusters (p < 0.001). The inclusion of socioeconomic variables allowed for a deeper characterization of equity gaps. These findings provide evidence-based insights for designing targeted recruitment and retention strategies aligned with SDG 4, demonstrating how educational data analytics can inform sustainable higher education policies in regional contexts. Full article

Applications for machine learning (ML) and deep learning (DL) are constantly growing and have already been adopted in the field of particle measurement technology. Even though analytical (ultra-)centrifugation (AC/AUC) is a widely used technique for characterizing dispersed particle systems, ML and DL have not yet been applied in this area. Data evaluation and interpretation in AC/AUC can be challenging and often requires expert knowledge. DL models can help, but their development is limited by a lack of annotated training data. One solution is to generate and use synthetic data instead. In the first part of this study, a model was trained to synthesize data from experiments using a combination of Variational Autoencoder (VAE) and Generative Adversarial Networks (GANs). The results appear highly realistic. Novice users could distinguish real from synthetic samples with only 63% accuracy. Then, a classifier was trained on experimental AC data to categorize real-world examples based on their underlying separation kinetics, testing different DL architectures. After initial training, the models were further fine-tuned with synthetic AC data. ResNet34 models achieved the best performance with 94% accuracy, comparable to an AC expert (91%), while inexperienced users reached only 53%. In the second part of our study, a regression model was trained for the analysis of sedimentation coefficients. Therefore, various generative models were developed and evaluated for synthesizing AUC data based on numerically simulated sedimentation boundaries. The best results were achieved by combining VAE and GAN architectures with embedded physical constraints. However, the generative networks have so far led to additional smearing of the profiles, resulting in a broadening of the sedimentation coefficient distribution and indicating that further refinement is necessary. Full article

The Logistics Performance Index (LPI) is a widely used benchmarking tool for assessing national logistics capabilities. However, its role in sustainability-oriented research remains unclear. This study reconceptualizes the LPI as a multidimensional analytical framework for examining the structural associations between logistics performance and sustainability outcomes. Using cross-country data from 2023, the analysis evaluates the alignment of the six disaggregated LPI dimensions with economic (GDP per capita), social (Human Development Index), and environmental (CO₂ emissions) indicators across approximately 120 countries. The analysis applies an integrated framework combining linear models, ensemble learning techniques, explainable artificial intelligence (SHAP), and clustering analysis to assess the consistency and interpretability of these relationships. The results indicate that logistics performance is more strongly aligned with economic and social outcomes than with environmental indicators. Infrastructure quality, tracking and tracing, and timeliness emerge as key logistics dimensions associated with higher income levels and human development. In contrast, the moderate alignment observed for CO₂-related outcomes highlights the influence of broader structural factors, such as energy systems and industrial composition, beyond logistics performance. Clustering analysis further reveals distinct logistics–environmental configurations, underscoring substantial heterogeneity in sustainability trajectories among countries with similar logistics capabilities. Overall, these findings establish the LPI as a system-level lens for diagnosing logistics–sustainability relationships and for designing context-sensitive policies aligned with the Sustainable Development Goals (SDGs), particularly SDGs 8, 9, 11, and 13. Full article

High-cost innovative anticancer drugs pose challenges for health systems in balancing timely patient access with long-term financial sustainability. In Thailand, reliance on Health Technology Assessment for reimbursement decisions may delay access, highlighting the potential role of Managed Entry Agreements (MEAs) as complementary policy instruments to manage uncertainty related to price, effectiveness, and use; however, MEA application remains limited and lacks an analytical framework for technique selection. This study used real-world data from Thammasat University Hospital to examine and compare the cost-saving performance of five MEA techniques—discount, free initiation treatment, utilization cap, conditional treatment continuation, and pay-by-result—across six high-cost anticancer drugs representing dominant uncertainty characteristics. Drug procurement costs were modeled over a 24-month horizon from the payer’s perspective, and one-way sensitivity analyses were conducted using ±10% variation in median progression-free survival. Free initiation treatment generated the highest cost savings across uncertainty types, followed by conditional treatment continuation, while utilization cap and discount produced more moderate savings. Pay-by-result demonstrated the lowest cost-saving potential. Sensitivity analyses confirmed the robustness of comparative rankings. Overall, the findings indicate that MEA performance varies according to dominant sources of drug-related uncertainty and support a more structured, context-appropriate approach to MEA selection to strengthen market access and value-based pricing in Thailand. Full article

Background: Chronic kidney disease (CKD) and end-stage renal disease (ESRD) represent a growing global health burden, affecting nearly one in ten adults worldwide. CKD is associated with high morbidity, premature mortality, reduced quality of life and enormous healthcare costs, and is primarily driven by dialysis and kidney transplantation. The silent and progressive nature of CKD means that most patients are diagnosed late, when irreversible damage has already occurred and costly kidney replacement therapies (KRT) become necessary. Dialysis services are resource-intensive, requiring significant infrastructure, specialized staff, and consumables, which makes them especially challenging to sustain in low- and middle-income countries. Traditional models of nephrology, care center-based dialysis and fragmented follow-up are increasingly inadequate in meeting the demands of a rising CKD population. These challenges highlight the urgent need for innovative approaches that enhance efficiency, improve patient outcomes, and expand access. Objective: This review aims to analyze the current landscape of information and communication technology (ICT) applications in nephrology and to evaluate how digital innovations are reconfiguring kidney therapy. Specifically, it seeks to identify the major ICT tools that are currently in use, assess their clinical and operational impact, and discuss their role in creating more sustainable, patient-centered kidney care models. This study reviews and analyzes ICT tools that are reconfiguring nephrology, including remote monitoring, AI, wearables, patient engagement apps and data dashboards. Methods: Narrative and scoping review of recent innovations in nephrology, including remote patient monitoring (RPM), telehealth, artificial intelligence (AI) analytics, wearable sensors, and clinical decision support platforms. Results: ICT tools such as Sharesource, Versia, telenephrology platforms, medical assistant for Chronic Care Service (MACCS), AI-based predictive analytics, wearable devices and patient engagement apps have improved patient outcomes, adherence, and early detection of complications. Key metrics include technique survival, hospitalization rate, patient-reported outcomes, workflow efficiency, and prediction accuracy. The relevant literature describing the potential of digital health technologies, including ICT platforms, artificial intelligence tools, and remote monitoring systems, to transform nephrology care was retrieved and screened for inclusion in this narrative review. Conclusions: ICT has shifted nephrology from reactive to proactive care, enhancing accessibility, patient empowerment and clinical efficiency. Future directions include precision nephrology, fully wearable kidneys, AI integration and large language models for education and triage. Challenges include digital divide, regulatory heterogeneity, cost and the need for long-term evidence. Full article