Search Results (2,930)

Efficient interaction between PHP (Hypertext Preprocessor) applications and MySQL databases is essential for the performance of modern web systems. This study systematically compares the two most widely used PHP APIs for working with MySQL databases—MySQLi (MySQL Improved extension) and PDO (PHP Data Objects)—under identical experimental conditions. The analysis covers execution time, memory consumption, and the stability and variability of results across different types of SQL (Structured Query Language) queries (simple queries, complex JOIN, GROUP BY/HAVING). A specialized benchmarking tool was developed to collect detailed metrics over several hundred repetitions and to enable graphical and statistical evaluation. Across the full benchmark suite, MySQLi exhibits the lowest mean wall-clock execution time on average (≈15% overall). However, under higher query complexity and in certain connection-handling regimes, PDO prepared statement modes provide competitive latency with improved predictability. These results should be interpreted as context-aware rankings for the tested single-host environment and workload design, and as a reusable benchmarking framework intended for replication under alternative deployment models. Statistical analysis (Kruskal–Wallis and Mann–Whitney tests) confirms significant differences between the methods, while Box-plots and histograms visualize deviations and the presence of outliers. Unlike earlier studies, this work provides a controlled and replicable benchmarking environment that tests both MySQLi and PDO across multiple API modes and isolates the impact of native versus emulated prepared statements. It also evaluates performance under complex-query workloads that reflect typical reporting and analytics patterns on the ClassicModels schema. To our knowledge, no previous study has analyzed these factors jointly or provided a reusable tool enabling transparent comparison across PHP–MySQL access layers. The findings provide empirical evidence and practical guidelines for choosing the optimal API depending on the application scenario, as well as a tool that can be applied for further testing in various web environments. Full article

Extractive regions are among the most visible frontlines of the Anthropocene as they are areas where the environmental and social consequences of intensive resource exploitation are concentrated. In Serbia, mining areas such as Bor and Majdanpek represent complex socio-spatial assemblages in which everyday life, work, and governance intersect under pressures of neoliberal development and ecological degradation. This study aims to identify the challenges and opportunities for citizen participation in mining regions, providing guidance on enhancing participatory processes in decision-making. To operationalise this aim, the study pursues three objectives: (1) to assess residents’ awareness, participation practices, access to information, and motivation to engage in planning; (2) to identify perceived barriers and opportunities for participation; and (3) to formulate recommendations for improving participatory and communication processes in extractive-region governance. Accordingly, the research is guided by the main question: How do residents of the Bor–Majdanpek mining region perceive opportunities and barriers to public participation in planning and decision-making processes? To address this question, a face-to-face field survey was conducted in the summer of 2024 with a random sample of residents (N = 300). In this mixed-methods exploratory study, primary survey data were analysed using descriptive and inferential statistical methods. In contrast, open-ended questions were analysed qualitatively to capture respondents’ detailed perceptions and suggestions. Findings indicate limited awareness of planning procedures, low participation experience, and structural barriers related to information access, trust, and institutional responsiveness. At the same time, respondents show a strong interest in more transparent, accessible, and dialogic forms of engagement. This study demonstrates that citizen participation in extractive landscapes is not only a procedural requirement but a mechanism to strengthen democratic governance and rebuild trust. Insights from Bor–Majdanpek provide an evidence base for improving participatory practices in mining regions undergoing socio-environmental transformation. Full article

Efficient documentation and traceability of construction waste are essential for meeting the objectives of the European Green Deal and the Circular Economy. In Poland, the national Database on Products, Packaging, and Waste Management (BDO) serves as the central platform for recording and reporting waste flows, including those generated by the construction sector. However, its current structure imposes substantial administrative burdens, particularly on large-scale projects involving thousands of waste transports. This study examines the documentation workflow within the BDO system as applied to construction activities. Using process mapping, field studies, and interviews, the research identifies key bottlenecks and opportunities for improvement, especially through automation enabled by the integration of external applications connected to BDO via its public Application Programming Interface (API). Among nine identified systems, one was selected due to its comprehensive functionalities tailored to construction-sector needs. A study involving thirty users demonstrated that implementation of this system reduced the time required to issue a Waste Transfer Card (KPO) by 77% and fully automated entries in the Waste Records Register (KEO). As a result, the average administrative workload decreased by 87%. For a representative demolition company generating approximately 46,000 KPOs annually, the total time savings correspond to 8.2 months of full-time administrative work. This reduction translates into annual savings exceeding PLN 47,000 and yields a return on investment of over 100% within the first year. Sensitivity analysis indicates that the system’s effectiveness decreases with lower documentation volumes. The findings confirm that targeted automation and improved interface design can significantly enhance the efficiency, accuracy, and transparency of construction waste documentation. Full article

Artificial intelligence (AI) is transforming accounting by automating cognitive tasks and redefining mechanisms of governance and risk control. This study examines how AI operates as an intelligent control system—one that substitutes manual accounting procedures while enhancing transparency, internal control, and fraud detection. Integrating the Technology Acceptance Model (TAM) with Organizational Information Processing Theory (OIPT), the research develops a behavioral–organizational framework linking perceived usefulness, ease of use, AI literacy, technology readiness, social influence, and facilitating conditions to AI adoption and perceived substitution benefits. A structured survey was administered to accounting students and practitioners in Northern Italy (n = 185) and analyzed through reliability tests and partial least squares structural equation modeling (PLS-SEM). The results show that AI literacy, facilitating conditions, and social influence significantly drive adoption intention, while perceived substitution benefits fully mediate the relationship between adoption and governance outcomes. The findings demonstrate that AI adoption enhances governance and risk management effectiveness by functioning as an intelligent control mechanism. The study introduces the AI-to-Control (A2C) Blueprint to guide responsible integration of AI into accounting systems, reframing AI adoption as a structural evolution in corporate governance rather than a mere technological upgrade. Full article

One Health envisions integrated governance across human, animal, and environmental systems to prevent and respond to complex health threats. Despite its global endorsement, One Health implementation often falters due to institutional fragmentation, power asymmetries, and ethical tensions that erode trust and cooperation. This paper proposes the integration of medical-bioethics mediation within One Health governance as a structured, relational mechanism to manage conflict, foster ethical deliberation, and strengthen trust between sectors and communities. We develop a conceptual framework to apply the mediation principles of neutrality, confidentiality, respect, and shared problem-solving beyond clinical ethics toward multisectoral One Health contexts. The framework is illustrated through domain-specific examples from zoonotic disease control, antimicrobial resistance, and environmental health. Medical bioethics mediation can advance conflict transformation, ethical reflection, participatory decision-making, and policy alignment, thereby supporting transparent negotiation of values and institutionalized dialogue of different One Health actors. Future research should pilot mediation-based governance models and assess their effects on intersectoral trust, collaborative capacity, and integrated health outcomes. Full article

Carboxymethylcellulose (CMC) is a biocompatible and biodegradable polysaccharide suitable for biomedical applications. Herein, an epichlorohydrin (ECH)-crosslinked CMC hydrogel (CMCG) was developed as a carrier for sustained drug release. Ether-type crosslinking between the hydroxyl groups of CMC and ECH yielded a transparent, highly water-absorbent gel. Structural analyses employing Fourier-transform infrared and solid-state ¹³C nuclear magnetic resonance spectroscopies confirmed successful crosslinking, and the hydrogel exhibited pH-dependent swelling. Carboplatin (CBP), a platinum-based anticancer drug, was incorporated into CMCG to prepare CBP-CMCG. In phosphate-buffered saline (pH 7.4), approximately 70% of CBP was released within 12 h, followed by a plateau phase, indicating diffusion-controlled release. Cytocompatibility assays using WI-38 normal human fibroblasts demonstrated that CMCG was non-cytotoxic, whereas free CBP induced significant cell death. In colorectal cancer HT-29 cells, CBP-CMCG exhibited gradual cytotoxicity, resulting in >80% nonviable cells after 24 h, indicating a sustained antitumor effect compared with free CBP. These results demonstrate that the newly developed ECH-crosslinked CMC hydrogel is a safe and effective platform for controlled drug delivery, enabling sustained release and prolonged therapeutic activity of CBP. Full article

This study explores the sol–gel synthesis, structural characterization, and photocatalytic performance of Ho³⁺-doped TiO₂ nanopowders at two dopant levels (0.5 and 2 mol%). Transparent, homogeneous gels were prepared using titanium (IV) butoxide and holmium (III) nitrate pentahydrate in ethanol, followed by drying and optional annealing at 500 °C. X-ray diffraction confirmed anatase TiO₂ as the dominant crystalline phase, with Ho incorporation suppressing crystal growth and yielding smaller crystallite sizes than undoped TiO₂. FT-IR and UV-Vis spectroscopy verified complete hydrolysis–condensation during gel formation, while diffuse reflectance spectra revealed a red-shifted absorption edge, indicating reduced band gaps. SEM analysis showed nanoscale particles with agglomeration, which intensified after annealing. Photocatalytic activity was tested under UV irradiation using Crystal Violet (anionic dye) and Carmoisine (cationic dye). Annealed Ho-doped powders exhibited markedly higher degradation rates, with the 2 mol% sample achieving the greatest efficiency, particularly against Crystal Violet. These findings demonstrate that Ho³⁺ doping enhances TiO₂’s UV-driven photocatalytic activity by tailoring its structural and optical properties. Full article

Geographic Information Systems (GIS) are increasingly applied to public health and urban security challenges, yet current evidence remains fragmented across methods, disciplines, and regions. This study integrates Systematic Mapping (SM) and Systematic Review (SR) within a unified PICOS–SPICE framework to consolidate existing GIS-based research. From an initial corpus of 7106 records, 65 studies met all methodological and reproducibility criteria. Scientific production shows consistent growth, peaking in 2023, with research concentrated in Asia and North America and limited representation from Africa and South America. Methodologically, the literature is dominated by accessibility assessments and spatial autocorrelation, while advanced analytical models—such as Bayesian inference and machine learning—remain scarce. GIS workflows rely mainly on ArcGIS and QGIS, complemented by open-source tools, including R, Python, and SaTScan. The fused SM + SR pipeline provides a transparent and replicable structure that highlights current strengths in spatial resolution and analytical versatility while revealing persistent gaps in data openness, reproducibility, and global equity. These findings offer a consolidated evidence base to guide future GIS research and support informed decision-making in public health and urban security. Full article

Despite seven decades of international commitment—from the 1948 Universal Declaration of Human Rights through SDG 3.8—universal health coverage remains stubbornly out of reach. Two billion people, predominantly informal sector workers, lack access to sustainable health insurance. This entry explains the underlying cause: sustainable health insurance requires specific behavioral and institutional conditions for collective action—conditions that existing health insurance models systematically fail to satisfy, thereby structurally excluding informal populations. The Trinity Law framework formalizes these conditions as three multiplicatively interacting requirements—Trust (T), Consensus (C), and Dual Benefit (DB)—expressed as S = T × C × DB. Empirical analysis of community-based health insurance schemes across 24 countries identifies a robust trust threshold (τ* ≈ 0.68) operating as a behavioral phase transition: below this level, cooperation collapses; above it, participation becomes self-sustaining. Cross-country evidence from 274 organizations across 155 countries confirms consensus thresholds (C* ≈ 0.59), while analysis of 158,763 observations validates dual benefit mechanisms. The multiplicative structure explains why partial reforms fail: weakness in any single component drives overall sustainability toward zero. Applied to health insurance, this framework distinguishes conventional systems—Bismarckian employment-based, Beveridgean tax-financed, and commercial health insurance from sustainable systems like participatory community-based microinsurance that satisfy all three Trinity Law conditions through participatory design, transparent governance, and aligned incentives. The persistent UHC gap reflects not implementation failures but fundamental design incompatibilities that the Trinity Law makes explicit. This entry has three objectives: first, it states the Trinity Law conditions; second, it summarizes the empirical evidence for each component; third, it applies the framework to classify major health insurance models. Supporting datasets and code are available in the referenced Zenodo repositories. The term ‘law’ follows the tradition of social science regularities like the ‘law of demand’: a robust empirical pattern with strong predictive validity, not a claim to physical certainty. Full article

Dynamic multi-criteria group decision-making (MCGDM) is widely applied in complex real-world settings where multiple experts evaluate alternatives across diverse criteria under uncertain and evolving conditions. This study proposes a transparent and interpretable linguistic (L-) framework for dynamic MCGDM grounded in hedge algebras (HA), a mathematical formalism that provides explicit algebraic and semantic structures for L-domains. A novel binary L-aggregation operator is developed using the 4-tuple semantic representation of HA, ensuring closure, commutativity, monotonicity, partial associativity, the existence of an identity element, and semantic consistency throughout the aggregation process. Using this operator, a two-stage dynamic decision-making model is developed—(i) L-FAHP for determining the criterion weights in dynamic environments, and (ii) L-FTOPSIS for ranking alternatives—where both methods are formulated on HA L-scales. To address temporal dynamics, a dynamic L-aggregation mechanism is further proposed to integrate current expert judgments with historical evaluations through a semantic decay factor, enabling the controlled attenuation of outdated information. A case study on enterprise digital transformation readiness illustrates that the proposed framework enhances semantic interpretability, maintains stability under uncertainty, and more accurately captures the temporal evolution of expert assessments. These results underscore the practical value and applicability of the HA-based dynamic L-approach in complex decision environments where expert knowledge and temporal variability are critical. Full article

Creating trust in society for new technologies, such as a new types of powertrains, and making them marketable requires transparent, neutral, and independent technical verification. This is crucial for the acceptance and success of electrified vehicles in the used car markets. A key component of electric vehicles is the traction battery, whose current and future condition, particularly regarding aging, determines its residual value and safe operation. This review aims to identify and evaluate methods for predicting the lifetime of onboard traction batteries, focusing on their applicability in technical inspections. A systematic literature and patent review was conducted using targeted keywords, yielding 22 patents and 633 publications. From these, 150 distinct lifetime prediction methods were extracted and categorized into a four-level mind map. These methods are summarized, cited, and structured in detailed tables. The relationships between approaches are explained to clarify the current research landscape. Long Short-Term Memory, Convolutional Neural Networks, and Particle Filters were identified as the most frequently used techniques. However, no methods were found suitable for predicting the lifetime of traction batteries during technical vehicle inspections, which operate under short test durations, limited data access, and diverse real-world operating conditions. Most studies focused on cell-level testing and did not address complete battery systems in operational vehicles. This gap highlights the need for applied research and the development of practical methods to support battery assessment in real-world conditions. Advancing this field is essential to foster confidence in battery systems and enable a sustainable transition to electromobility. Full article

Mongolian patterns represent an important component of Mongolian cultural heritage, characterized by their dual structure of geometric symmetry and dynamic ornamental motifs. However, existing artificial intelligence-based generative methods struggle to preserve both low-frequency structural regularity and high-frequency decorative detail under limited data conditions. This study proposes a parameter-efficient digitization framework based on latent diffusion models (LDMs) fine-tuned with low-rank adaptation (LoRA) to achieve high-fidelity reconstruction of classic Mongolian patterns. A curated few-shot dataset and a low-rank constraint enable effective learning from only eight representative samples, while a dual-prompt mechanism and MSE-driven optimization improve geometric stability and semantic consistency. Integrated within a transparent ComfyUI workflow, the method supports controllable generation and reproducible experimentation. Experimental evaluations demonstrate that the proposed LoRA-LDM model achieves superior structural accuracy, reduced visual distortion, and enhanced motif preservation compared with baseline models. The results confirm the method’s applicability for digital preservation, reconstruction, and derivative design of structured cultural heritage motifs. Full article

In response to the persistent imbalance between the rapid expansion of scholarly publishing and the constrained availability of qualified reviewers, an empirical investigation was conducted to examine the feasibility and boundary conditions of employing Large Language Models (LLMs) in journal peer review. A parallel corpus of 493 pairs of human expert reviews and GPT-4o-generated reviews was constructed from the open peer-review platform PeerJ Computer Science. Analytical techniques, including keyword co-occurrence analysis, sentiment and subjectivity assessment, syntactic complexity measurement, and n-gram distributional entropy analysis, were applied to compare cognitive patterns, evaluative tendencies, and thematic coverage between human and AI reviewers. The results indicate that human and AI reviews exhibit complementary functional orientations. Human reviewers were observed to provide integrative and socially contextualized evaluations, while AI reviews emphasized structural verification and internal consistency, especially regarding the correspondence between abstracts and main texts. Contrary to the assumption of excessive leniency, GPT-4o-generated reviews demonstrated higher critical density and functional rigor, maintaining substantial topical alignment with human feedback. Based on these findings, a collaborative human–AI review framework is proposed, in which AI systems are positioned as analytical assistants that conduct structured verification prior to expert evaluation. Such integration is expected to enhance the efficiency, consistency, and transparency of the peer-review process and to promote the sustainable development of scholarly communication. Full article

The study aimed to interpret how citizen participation mechanisms contribute to rebuilding public trust in the Peruvian state, considering how citizens evaluate transparency, institutional legitimacy, and state responsiveness. A qualitative approach with an explanatory-interpretive scope was developed, based on a phenomenological-hermeneutic method, and included 4124 participants selected through purposive sampling, whose semi-structured interviews were analyzed through open and axial coding in Atlas.ti v23. The results showed that public trust is mainly shaped by the perceived consistency between institutional discourse and action, clarity of information, accessibility of services, ethical conduct of officials, and responsiveness to social demands. Likewise, it was identified that citizen participation is valued positively when it produces verifiable results, feedback, and continuity, while it is perceived as symbolic when it does not influence decision-making, there is one-way communication, or bureaucratic and technological barriers persist. In conclusion, the study shows that public trust is rebuilt when institutions guarantee transparency, clear communication, and participatory mechanisms with real impact, shaping governance oriented toward openness, shared responsibility, and democratic legitimacy. Full article

Sustainable tourism is increasingly important for local sustainable livelihoods; however, its development is often hindered by financial limitations. To address this critical constraint, this study investigates the relationship between tourist satisfaction and their Willingness to Pay (WTP) for Community-Based Tourism (CBT) service improvements. Employing a novel Culture Tower/KHUC Tower-based approach, Partial Least Squares Structural Equation Modeling with the Bayesian Mindsponge Framework (CPBM), we analyze data from 275 young respondents collected in mid-December 2024 and mid-August 2025. Our empirical results demonstrate that tourist satisfaction is a strong predictor of WTP, alongside financial status, environmental concerns, and environmental quality assessment. Additionally, we identify food quality, service delivery, and infrastructure as the primary determinants of satisfaction in CBT contexts. The findings reveal that satisfaction-driven financing mechanisms can unlock sustainable revenue streams for CBT destinations, provided that strategic investments address key service quality dimensions. Our results have important implications for tourism managers and policymakers seeking to enhance financial sustainability through coordinated stakeholder engagement, targeted infrastructure improvements, and transparent fund management systems in Lac Village and beyond. Full article