Search Results (1,861)

In this research, an innovative, integrative method is applied, which not only links media discourse and statutory planning documents but also involves both quantitative and qualitative analysis. By going beyond the traditional extreme of either policy review or text-based SETS frameworks, this study becomes the pioneer of a dual-coded, matrix-driven approach, which is capable of measuring policy–implementation gaps and empirically revealing the impact of media framing on disaster management outcomes. The 29 October 2024 Valencia flood, which claimed over 229 lives, highlights critical shortcomings in the region’s flood management policies. This study evaluates media and institutional sources to examine how public discourse aligns with post-flood management strategies. It focuses on Valencia’s statutory flood management plan, the “Pla d’acció territorial de caràcter sectorial sobre prevenció del risc d’inundació a la Comunitat Valenciana” (“Regional Action Plan for Flood Risk Prevention,” PATRICOVA) and its limited integration with the Socio–Ecological–Technological Systems (SETS) framework, which we identify as a central weakness. By analyzing Spanish media coverage, particularly from sources such as El País, ABC, and La Vanguardia, alongside government policy documents, the study reveals a gap between theoretical flood risk planning and practical disaster response. Our keyword-based text mining of leading newspapers highlights the neglect of social, ecological, and technological interactions. While PATRICOVA emphasizes nature protection and technological infrastructure, it overlooks critical societal dimensions and climate adaptation scenarios. Media analysis reveals significant failures at the SETS interfaces, especially in early warning systems, intergovernmental coordination, and community preparedness. Full article

Existing observation instruments to measure differentiated instruction often lack insight into the degree to which teachers’ decisions match the actual needs of their students, and neglect the importance of preparation and evaluation. This article describes the psychometric evaluation of a comprehensive instrument (Assessing Differentiation in All Phases of Teaching; ADAPT) that does not suffer from these shortcomings. To assess its quality, 41 raters used it to score videos of lessons and interviews of 86 primary school teachers. A 5-dimensional item-response model showed good fit and high internal consistency, and a decision study was conducted to determine the reliability and agreement coefficients for different numbers of raters. For the intended low-stakes use, a single rater would be enough to provide a reliable estimate of a teacher’s overall score. Finally, rater experiences showed that ADAPT has high practical value due to the comprehensive manual and detailed score descriptions and examples. The instrument can therefore not only be used for research purposes, but can also serve as a valuable resource for teachers and teacher educators in practice. Full article

Take-over time is a critical factor affecting safety. Accurately predicting the take-over time provides a more reliable basis on issuing take-over requests, assessment of take-over risks, and optimization of human–machine interaction modes. Although there has been substantial research on predicting take-over time, there are still shortcomings in personalized prediction (particularly in accounting for individual differences in driving experience, cognitive abilities, and physiological responses). To gain a comprehensive understanding of the characteristics and applicability of take-over time prediction methods, this review covers four aspects: literature search information, factors influencing take-over time, data acquisition and processing methods, and take-over time prediction methods. Through literature search, research hotspots in recent years have been summarized, revealing the main research directions and trends. Key factors influencing take-over time, including driver factors, autonomous driving systems, and driving environments, are discussed. Data preprocessing stages, including data acquisition and processing, are systematically analyzed. The advantages and disadvantages of classical statistical, machine learning, and cognitive architecture models are summarized, and the shortcomings in current research are highlighted (for instance, the limited generalizability of models trained predominantly on simulator data to real-world driving scenarios). By thoroughly summarizing the strengths and weaknesses of existing research, this review explores under-researched areas and future trends, aiming to provide a solid theoretical foundation and innovative research perspectives for optimizing take-over time prediction, thereby promoting the widespread application and efficient development of autonomous driving technology. Full article

The escalating demand for efficient energy harvesting in CubeSat missions necessitates advanced maximum power point tracking (MPPT) techniques. This work presents a comprehensive time-domain analysis and simulation of three MPPT algorithms: perturb and observe (PO), particle swarm optimization (PSO), and a novel hybrid PO-PSO method, tailored explicitly for CubeSat photovoltaic (PV) solar modules. Utilizing MATLAB R2025a/Simulink, a detailed model of a PV module based on the Azur Space 3G30C datasheet and a DC-DC boost converter was developed. The conventional PO MPPT, while simple, demonstrated limitations in tracking the global maximum power point (GMPP) under rapidly changing temperature conditions and exhibited significant oscillations around the GMPP. The PSO algorithm, known for its global search capabilities, was investigated to mitigate these shortcomings. This research introduces a hybrid PO-PSO MPPT technique that synergistically combines the low computational complexity of PO with the robust global optimization of PSO. Time-domain simulation results demonstrate that the proposed hybrid PO-PSO MPPT significantly reduces oscillations around the GMPP, enhances tracking accuracy under varying temperature conditions, and stabilizes output parameters more effectively than standalone PO or PSO methods. These findings validate the hybrid approach as a superior and reliable solution for optimizing power generation in constrained CubeSat applications. Full article

To address the shortcomings of traditional polyurethane (PU) sealants, including inadequate weather resistance, low curing efficiency, and limited environmental performance, this study synthesized a functional silicone oligomer (DQPSi) featuring both dynamic crosslinking and hydrophobic properties via the sol–gel method, which was subsequently incorporated into the polyurethane matrix. The effects of DQPSi content (0–20 wt%) on the properties of silane-modified polyurethane (SPU) sealants were systematically investigated. Results demonstrate that DQPSi significantly enhances the comprehensive performance of the material. At 15% loading, the sealant achieves optimal performance balance: surface-drying time shortens to 110 min (45% reduction), curing rate increases to 1.7 mm/d (112.5% improvement), tensile modulus rises by 14% to 0.88 MPa, elongation at break substantially increases to 420%, and contact angle improves to 78° with markedly enhanced hydrophobicity. Microscopic analyses (SEM, nanoindentation) confirm that these improvements stem from DQPSi forming a uniform interpenetrating network (IPN) structure with the PU matrix, where dynamic Si-O-Si bonds provide rigidity and stress dissipation while hydrophobic groups (methylpropyl) migrate to the surface to form a barrier. However, excessive addition (20%) induces silicone phase separation and over-crosslinking, causing mechanical degradation (tensile strength decreases to 0.70 MPa, elongation at break drops to 331%) and microcrack formation. This research elucidates DQPSi’s reinforcement mechanism and critical loading threshold, establishing theoretical and technical foundations for developing high-performance eco-friendly silane-modified polyurethane sealants. Full article

Corrosion-induced cracking poses a significant threat to the longevity of reinforced concrete (RC) structures, yet precisely forecasting its advancement continues to be a considerable scientific obstacle. The principal shortcoming of current numerical models is their excessive simplification, frequently presuming totally saturated conditions and disregarding the dynamic interplay between environmental (hygro-thermal) variations and developing mesoscale damage. This study presents a thorough hygro-thermo-electro-chemo-mechanical (HTECM) phase-field model to fill this research need. The model uniquely combines dynamic unsaturated hygro-thermal transport with multi-ion reactive electrochemistry and meso-scale fracture mechanics. A rigorous comparison with published experimental data validates the model’s exceptional accuracy. The anticipated progression of fracture width exhibited remarkable concordance with experimental data, indicating a substantial enhancement in precision compared to uncoupled, saturated-state models. A key finding is the quantification of the damage-induced “transport-corrosion” positive feedback loop: initial corrosion-induced microcracks significantly expedite the transport of local moisture and corrosive agents, leading to nonlinear structural degradation. This work presents a high-fidelity numerical platform that enhances the understanding of linked deterioration in materials science and improves the durability design of reinforced concrete structures. Full article

The parameterization of vegetation indices (VIs) is crucial for sustainable irrigation and horticulture management, specifically for urban green infrastructure (GI) management. However, the constraints of roadside traffic, motor and industrially related pollution, and potential public vandalism compromise the efficacy of conventional in situ monitoring systems. The shortcomings of prevalent satellites, UAVs, and manual/automated sensor measurements and monitoring systems have already been reviewed. This research proposes a novel urban GI monitoring system based on an integration of gas exchange and various VIs obtained from computer vision algorithms applied to data acquired from three novel sources: (1) Integrated gas sensor data using nine different volatile organic compounds using an electronic nose (E-nose), designed on a PCB for stable performance under variable environmental conditions; (2) Plant growth parameters including effective leaf area index (LAIe), infrared index (Ig), canopy temperature depression (CTD) and tree water stress index (TWSI); (3) Meteorological data for all measurement campaigns based on wind velocity, air temperature, rainfall, air pressure, and air humidity conditions. To account for spatial and temporal data acquisition variability, the integrated cameras and the E-nose were mounted on a vehicle roof to acquire information from 172 Elm trees planted across the Royal Parade, Melbourne. Results showed strong correlations among air contaminants, ambient conditions, and plant growth status, which can be modelled and optimized for better smart irrigation and environmental monitoring based on real-time data. Full article

Amid the combined pressures of global carbon-reduction in architecture and the imperative of cultural heritage conservation, new courtyard-style buildings in hot-summer and cold-winter regions face a dual challenge of reconciling historical morphological constraints with contemporary comfort requirements. At the same time, the prevailing energy-efficiency codes in these regions, emphasizing high airtightness and strong insulation, have revealed shortcomings such as poor indoor air quality and insufficient summer ventilation. This study takes the Lu Residence in Dongyang, Jinhua, Zhejiang Province, as the primary case. It systematically examines the coupling mechanisms between the geometric configurations of transitional space in traditional courtyard dwellings and their environmental physical parameters using field surveys, multi-parameter environmental monitoring, and computer simulations. The results identify the optimal orientations and geometric parameters that balance summer ventilation with winter thermal buffering in hot-summer and cold-winter regions. The primary conclusions of this research are as follows: (1) The optimal orientation for axial buildings lies between 15° west of south and 15° east of south, as well as 30–60° east or west of south, with an angle of 45–60° in relation to the prevailing annual wind direction for all buildings. (2) The optimal height-to-width ratio of the courtyard is less than 1:2.5, while the range of the length-to-width ratio extends from 1:0.5 to 1:0.7. (3) The optimal eave depth varies from 900 to 1500 mm, effectively balancing winter heat retention and summer shading; however, a depth of 2400 mm is primarily advantageous for shading purposes. Furthermore, these findings are applied to the design of a new guesthouse within the conservation area of the Xu Zhen Er Gong Ancestral Hall in Yongkang, establishing a climate–geometry matching mechanism for transitional spaces. The study demonstrates that transitional space can serve as effective passive regulators, offering a scientific and sustainable pathway for the adaptive continuation of traditional courtyard architecture. Full article

Developing countries face the dilemma of balancing economic development with the governance of the water environment. In the 21st century, water environment governance has become a core theme in Chinese society, prompting governments at all levels to introduce numerous policies in this area. However, the effectiveness of governance varies widely across regions. To address the shortcomings of existing research, which often adopts overly simplistic perspectives and lacks explanatory power, this study integrates previous findings on water environment governance, drawing on theories such as structuration and policy implementation to construct an institutional-actor analytical framework. Through a qualitative comparative analysis of the “Five-Water Co-Governance” initiative in 28 counties in Zhejiang, this study identifies four distinct configurations leading to different governance outcomes: the strong upper-pressure and command-dominated type, the strong target-pressure and market-dominated type, the weak-pressure and command-market hybrid type, and the weak-pressure and command-market hybrid type. The revelation of these diverse governance types deepens the understanding of causal pathways in environmental governance and provides valuable insights into water environment governance practices in developing countries. Full article

This study was driven by two primary objectives. The first objective focused on exploring the relationship between transformational and transactional leadership practices and teachers’ perceptions of their principals’ effectiveness. The second aimed to evaluate the adequacy of leadership training programs in Cyprus, with particular emphasis on how well these programs foster transformational leadership skills. To effectively address these goals, the research employed an explanatory sequential mixed methods design. For the quantitative phase, the Multifactor Leadership Questionnaire was administered to 630 teachers from 28 schools across all districts. Confirmatory Factor Analyses indicated that the Transformational and Transactional leadership dimensions could be represented by a higher-order factor. Subsequently, Structural Equation Modeling revealed a positive association between Transformational/Transactional leadership and principal effectiveness, whereas Passive-avoidant leadership showed a small negative association. For the qualitative phase, semi-structured interviews were conducted with five principals exhibiting different levels of adoption of transformational practices. Thematic analysis revealed shortcomings in current training programs, including a lack of substantive focus on preparing principals to become transformational school leaders. Overall, the study highlights the importance of transformational school leadership practices in shaping teachers’ perceptions of their principal effectiveness and emphasizes the need to revise principal training programs by incorporating transformational content. Full article

Single joint isometric assessments of force production using force plates have become popular in research and practice; however, there are currently no standardization recommendations. The purpose of the present review was to explore and discuss the use of force plates to assess single joint isometric force production characteristics and provide suggestions on protocol standardization for both laboratory and applied settings. Frequently used single joint isometric assessments currently performed using force plates involve the knee flexors/hip extensors and plantar flexors. Currently there are a range of protocols applied to assessing isometric force production; therefore, we provide recommendations on key methodological features to be considered. We also discuss the potential shortcomings and future research directions for single joint isometric testing in both laboratory and applied settings. Full article

With the growing emphasis on sustainable development, organizations and government agencies are increasingly incorporating environmental, social, and governance (ESG) factors into their strategic agendas. However, previous research has primarily examined ESG performance, stakeholder engagement, and financial outcomes in isolation, overlooking the systemic role of employee perceptions and psychological responses. To address this shortcoming, this study integrated social identity theory and social exchange theory to explain how ESG practices influence green innovation behavior through organizational pride. Furthermore, drawing on organizational climate theory, we explored the moderating role of innovation climate in this relationship. We used structural equation modeling (SEM) to analyze data from 346 employees across diverse Chinese companies, enabling us to capture the overall structure of the relationship rather than isolated causal relationships. Our results show that all three dimensions of ESG practices significantly enhance organizational pride, which in turn stimulates green innovation, highlighting the indirect, systemic relationship between ESG and innovation outcomes. Organizational climate is an important contextual variable influencing both individual behavior and organizational performance. When organizations have a favorable innovation climate, employees are more likely to translate their pride into concrete innovative behaviors. While the direct impact of ESG (S) and ESG (G) on green innovation has not been confirmed, the mediating role of organizational pride and the moderating role of innovation climate highlight the dynamic interplay between psychological and organizational subsystems. This study conceptualizes ESG practices, organizational pride, and innovation climate as interconnected subsystems within a broader organizational system, providing a systems-based perspective for sustainability research. It advances theoretical understanding of how sustainability initiatives spread through psychological and organizational mechanisms and offers practical insights for policymakers and decision makers seeking to promote long-term green innovation. Full article

Background: Electroencephalography (EEG) is a non-invasive technique that records millisecond-scale cortical electrical activity using scalp electrodes. In EEG-based neurofeedback (NFB), these signals are processed to provide real-time feedback that supports self-regulation of targeted brain rhythms; evidence suggests improvements in cognitive and neurophysiological performance in athletes and non-athletes. However, methodological inconsistencies—such as limited blinding, poor sham control, and outdated approaches to EEG spectral analysis—restrict reproducibility and hinder cumulative progress in the field. Methods: This scoping review aimed to identify and analyze the methodological characteristics, outcome measures, and reproducibility gaps in EEG-based NFB studies involving athletes and non-athletes. Following PRISMA-ScR guidelines, we systematically searched academic databases (PubMed, Embase, Scopus, Web of Science, PsycINFO, and Cochrane Library), as well as gray literature sources (ProQuest Dissertations, LILACS, Tripdatabase, and Google Scholar). Of 48 included studies, 44 were published in international peer-reviewed journals and 4 in regional journals. Data were extracted on study design, participant population, NFB protocols, targeted EEG rhythms, cognitive and neurophysiological outcomes, and methodological rigor. Results: The review revealed substantial heterogeneity in targeted rhythms, protocols, and reporting standards. None of the studies employed modern spectral parameterization methods (e.g., FOOOF), while only 29% used active sham protocols and 6% employed inert sham conditions. Reporting blinding procedures and follow-up assessments was limited or absent in most studies. Discussion: This review highlights critical methodological shortcomings that may bias interpretations of NFB effects in sport and cognitive domains. To strengthen future research, studies should rigorously implement sham and blinding procedures, ensure transparent reporting of EEG metrics, and adopt open-science practices, including modern approaches to spectral parameterization. Full article

In recent years, interest in the application of ontologies in various domains of knowledge has grown significantly. Ontologies are widely used in a myriad of areas, such as artificial intelligence, data integration, knowledge management, and the semantic web, to name but a few. However, despite the widespread adoption, there exist a range of problems associated with ontologies, such as the complexity and cognitive challenges associated with ontology engineering, design, and development. One of the solutions to these challenges is to reuse existing ontologies rather than developing new ontologies afresh for new applications. The reuse of ontologies that describe a knowledge domain is a complex task consisting of many aspects. One of the key aspects involves ranking ontologies to aid in their selection. Various techniques have been proposed for this task, but many of them fall short in their expressiveness and ability to capture the cognitive aspects of human-like decision-making processes. Furthermore, much of the existing research focuses on an objective approach to ontology ranking, but it is unquestionable that a wide range of aspects pertaining to the quality of an ontology simply cannot be captured in a quantitative manner. Existing ranking models fail to provide a robust and flexible canvas for facilitating qualitative ontology ranking and selection for reuse. To address the aforementioned shortcomings of existing ontology ranking approaches, this study proposes a novel algorithm for ranking ontologies that extends the Elimination and Choice Translating Reality (ELECTRE) multi-criteria decision-making method with the Linguistic q-Rung Orthopair Fuzzy Set (Lq-ROFS-ELECTRE II), allowing the expression of uncertainty in a more robust and precise manner. The new Lq-ROFS-ELECTRE II algorithm was applied to rank a set of 19 ontologies of the machine learning (ML) domain. The ML ontologies were evaluated using a set of seven qualitative criteria extracted from the Ontometric framework. The proposed Lq-ROFS-ELECTRE II algorithm was then applied to rank the 19 ontologies in light of the seven criteria. The ranking results obtained were compared against the quantitative rankings of the same 19 ontologies using the traditional ELECTRE II algorithm, and confirmed the validity of the ranking performed by the proposed Lq-ROFS-ELECTRE II algorithm and its effectiveness in the task of ontology ranking. Furthermore, a comparative analysis of the proposed Lq-ROFS-ELECTRE II against existing MCDM methods and other existing fuzzy ELECTRE II methods displayed its superior modeling capabilities that allow for more natural decision evaluation from subject experts in real-world applications and allow the decision-maker to have much flexibility in expressing their preferences. These capabilities of the Lq-ROFS-ELECTRE II algorithm make it applicable not only in ontology ranking, but in any domain where there exist decision-making scenarios that comprise multiple conflicting criteria under uncertainty. Full article

This thorough study looks at the use of machine learning (ML) techniques to forecast energy usage in buildings, with an emphasis on mosques. As energy use has a greater impact on both the environment and the economy, it is becoming increasingly important to optimize energy usage in buildings, especially for religious organizations such as mosques. The study goes into a variety of ML methods and models, including neural networks, regression models, decision trees, and clustering algorithms, each customized to a distinct difficulty in energy management. The paper evaluates the efficacy of several ML techniques, noting their merits, shortcomings, and potential applications. Additionally, it investigates the impact of climate, mosque design, occupancy patterns, and geographical variables on energy use. To achieve accurate energy consumption projections, rigorous data collecting, pre-processing, and model validation procedures are required. The paper also discusses important data sources and methodologies for mosque-specific energy analysis. Furthermore, the study emphasizes the practical benefits of applying ML in energy prediction, such as cost savings, increased environmental sustainability, and better resource allocation. This study’s ramifications extend beyond mosques, providing useful insights into energy management in buildings in general. By summarizing the current state of ML applications in mosque energy prediction, this study is an important resource for researchers, decision-makers, and energy management practitioners, paving the way for future advancements and the adoption of more sustainable energy practices in religious institutions. Full article