Search Results (3,395)

This study investigates how Environmental, Social, and Governance (ESG) performance is shaped across 31 countries in the European Union (EU) and the Association of Southeast Asian Nations (ASEAN) from 1990 to 2020. To explore these relationships, we employed the Continuously Updated Generalized Method of Moments (CUE-GMM) and the Limited Information Maximum Likelihood (LIML), with additional robustness checks using Instrumental Variables Two-Stage Least Squares (IV-2SLS), Panel-Corrected Standard Errors (PCSE), and Driscoll-Kraay regressions. The results highlight democratic governance as a consistent driver of ESG advancement. Military expenditure can also support sustainability by reinforcing institutional stability, particularly in developing and upper-middle-income countries. Economic factors such as foreign direct investment, industrialization, and human capital show context-dependent effects, whereas globalization and natural resource rents generally enhance ESG performance, and inflation tends to constrain it. Overall, the findings underscore the importance of tailored, context-specific sustainability policies, showing that effective ESG progress depends on the interaction between institutions, economic structures, and global integration. Full article

The application of artificial intelligence in education, particularly in learning programming, is gaining increasing significance. However, research on secondary school students specializing in IT at an early stage has received relatively little attention in this field. The aim of this study is to assess how vocational secondary school IT students utilize Generative artificial intelligence in learning programming. The study employed a survey-based methodology, where students with varying levels of knowledge were surveyed to understand their AI usage patterns. The sample consisted of students from vocational IT schools, and data were analyzed using descriptive statistics and independent samples t-tests. The results indicate that students with different levels of knowledge use AI tools differently, with ChatGPT being the most popular tool. The study further highlights that AI usage brings significant benefits, such as providing a personalized learning experience and enabling quick error correction. However, excessive reliance on AI tools may hinder students from acquiring fundamental programming skills. The findings support the idea that while AI can effectively complement teachers’ explanations, overdependence on it can be risky, potentially reducing students’ creativity and problem-solving abilities. The study emphasizes the crucial role of educators in teaching the responsible and ethical use of artificial intelligence. The results of this research offer new perspectives on the effective integration of Generative artificial intelligence into vocational secondary school programming education and suggest further studies to compare its applications at the university level. However, the study acknowledges certain limitations, such as the potential bias of self-reported data, which may affect the generalizability of the results. Unlike other studies, the age groups we surveyed, and the cohorts formed from them are nearly evenly distributed, making our sample representative of the region in question. Full article

Serious games for children with Autism Spectrum Disorder (ASD) require rigorous evaluation frameworks that capture neurodivergent interaction patterns. This pilot study designed, developed, and evaluated a serious game for children with ASD, focusing on operability assessment aligned with ISO/IEC 25010:2023 standards. A repeated-measures design involved ten children with ASD from the Carlos Garbay Special Education Institute in Riobamba, Ecuador, across 25 gameplay sessions. A bespoke operability algorithm incorporating four weighted components (ease of learning, user control, interface familiarity, and message comprehension) was developed through expert consultation with certified ASD therapists. Statistical study used linear mixed-effects models with Kenward–Roger correction, supplemented by thorough validation including split-half reliability and partial correlations. The operability metric demonstrated excellent internal consistency (split-half reliability = 0.94, 95% CI [0.88, 0.97]) and construct validity through partial correlations controlling for performance (difficulty: r_partial = 0.42, p = 0.037). Eighty percent of sessions achieved moderate-to-high operability levels (M = 45.07, SD = 10.52). In contrast to requirements, operability consistently improved with increasing difficulty level (Easy: M = 37.04; Medium: M = 48.71; Hard: M = 53.87), indicating that individuals with enhanced capabilities advanced to harder levels. Mixed-effects modeling indicated substantial difficulty effects (H = 9.36, p = 0.009, ε² = 0.39). This pilot study establishes preliminary evidence for operability assessment in ASD serious games, requiring larger confirmatory validation studies (n ≥ 30) to establish broader generalizability and standardized instrument integration. The positive difficulty–operability association highlights the importance of adaptive game design in supporting skill progression. Full article

The variability of B-type stars offers valuable insights into the interiors of stars and the processes that drive pulsation and rotation in massive stars. In this study, we present the classification of the variability of 197 B-type stars observed in various Kepler/K2 campaigns, including 73 newly classified stars from Campaigns 13–18. For these stars, we derived atmospheric and evolutionary parameters using space-based photometry and ground-based spectroscopy. We obtained spectroscopic data for 34 targets with high-resolution instruments at OPD/LNA, which were supplemented by archival LAMOST spectra. After correcting for instrumental systematics, we analyzed the light curves using Fourier transforms and wavelet decomposition to identify both periodic and stochastic signals. The identified variability types included SPB stars, $\beta$ Cephei/SPB hybrids, fast-rotating pulsators, stochastic low-frequency variables, eclipsing binaries, and rotational variables. We also revised classifications of misidentified stars using Gaia astrometry, confirming the main-sequence nature of objects once considered subdwarfs. Our results indicate that hot-star variability exists along a continuum shaped by mass, rotation, and internal mixing rather than distinct instability domains. This study enhances our understanding of B-type star variability and supports future asteroseismic modeling with missions like PLATO. Full article

Polar environmental research requires advanced detection methods to understand rapid changes in these regions. Unmanned aerial vehicles (UAVs) bridge the gap between satellite remote sensing and in situ ice-based buoy measurements, offering improved spatiotemporal resolution and operational efficiency. However, their widespread use in polar regions remains limited due to insufficient endurance capabilities. To address this problem, this paper presents a new monitoring system, the so-called UAV and Ice-based buoy cross-domain observation system (UBCOS). Particularly, the ice-based buoy integrates a Real-Time Kinematic (RTK) base station, a contact-based charging system, and an Iridium communication system, providing UAVs with centimeter-level positioning correction, low-temperature charging support, and remote data transmission capabilities. UAVs equipped with pod-mounted cameras capture imagery of sea ice surface characteristics within a 4 km radius of the buoy. Field tests conducted in the Arctic in 2024 demonstrate that the system achieved expected performance in both monitoring task execution and data collection, validating its practicality and reliability for polar sea ice monitoring. Full article

Catalysis plays a pivotal role in green chemistry practices, particularly in reducing waste generated during chemical synthesis. Decatungstate (DT) emerges as a potent photocatalyst for Type I oxidations, exhibiting remarkable resilience to oxygen quenching, a characteristic that sets it apart from other excited triplet state photocatalysts. While homogeneous DT catalysis demonstrates effectiveness, its solubility poses challenges for its separation and recycling. To address these limitations, we focus on the development and comparison of heterogeneous DT photocatalysts, aiming to optimize their yield, recovery, and reusability. We synthesized tetrabutylammonium decatungstate (TBADT)-supported catalysts using silica, alumina, titanium dioxide, and glass wool and characterized them using diffuse reflectance measurements. Subsequently, we evaluated their photocatalytic performance by monitoring the oxidation of 1-phenylethanol and cyclohexanol under UVA irradiation. Our findings reveal that TBADT@silica emerges as the most effective catalyst, achieving approximately 20% conversion of cyclohexanol and 50% conversion of 1-phenylethanol with good reusability. Interestingly, we observed that 3-aminopropyl-triethoxysilane (APTES) treatment, intended to enhance DT anchoring, unexpectedly quenches the ³DT* triplet state, reducing catalytic activity. This unexpected finding underscores the importance of careful consideration in designing robust and recyclable heterogeneous decatungstate catalysts. Our research contributes significantly to the advancement of heterogeneous photocatalysis, paving the way for future applications in flow photochemistry. Further, we share a Python code (Google 3.12.11) to correct spectra obtained in Cary spectrometers. Full article

To explore the spatiotemporal evolution of extreme temperature events in Guangxi (1940–2023), reveal regional response mechanisms, and assess future trends of persistence under climate warming, a multi-scale analysis was conducted using ERA5 reanalysis data. Methodologies included RH tests for homogeneity correction, collaborative kriging for data optimisation, Mann–Kendall tests for trend and abrupt change detection, Morlet wavelet analysis for cyclic pattern identification, Exploratory Spatio-Temporal Data Analysis (ESTDA) for spatial heterogeneity quantification, and Rescaled Range (R/S) analysis to calculate Hurst indices for future persistence assessment. Results showed the following: (1) The ERA5 dataset exhibited high applicability in Guangxi (R = 0.9989, RMSE = 1.9492 °C), supporting robust evidence of continuous warming—warm indices (e.g., SU25, TX90p) increased significantly (SU25 at 0.2044 d/10a), while cold indices (e.g., TN10p, FD0) declined (TN10p at −0.0519 d/10a); abrupt changes of cold indices were concentrated in 1942–1950, with warm indices accelerating post-2000 and TXx exhibited the highest warming rate (0.23 °C/decade). (2) Extreme temperature indices displayed a primary 19–21-year oscillation cycle (dominant in warm indices) and a secondary 13-year cycle (prominent in cold indices). (3) Spatial heterogeneity featured northwest–southeast cold–heat inversion, coastal–inland intensity gradients, and latitudinal zonation of extreme indices; ESTDA revealed intensified polarisation, with warm indices clustering in low-latitude regions (e.g., Baise) and cold indices declining homogeneously in mountainous areas (e.g., Guilin), indicating an irreversible transition to a warming steady state. (4) R/S analysis indicated all indices had Hurst indices of 0.65–0.92, reflecting persistent future trends consistent with historical evolution, with warm indices (e.g., TNn, SU25) showing stronger persistence (H > 0.85). This work clarifies the spatial polarisation mechanism and future persistence of extreme temperature dynamics in Guangxi, providing a multi-scale scientific basis for disaster early warning and adaptation planning in climate-sensitive karst-monsoon regions. Full article

FastDiag is a widely used algorithm for model-based diagnosis, computing minimal subsets of constraints whose removal restores consistency in knowledge-based systems. As applications grow in complexity, researchers have proposed parallel extensions such as FastDiagP and FastDiagP++ to accelerate diagnosis through speculative and multiprocessing strategies. This paper presents a reproducible and extensible framework for evaluating FastDiag and its parallel variants across a benchmark suite of feature models and ontology-like constraints. We analyze each variant in terms of recursion structure, runtime performance, and diagnostic correctness. Tracking mechanisms and structured logs enable the fine-grained comparison of recursive behavior and branching strategies. Technical validation confirms that parallel execution preserves minimality and structural soundness, while benchmark results show runtime improvements of up to 4× with FastDiagP++. The accompanying dataset, available as open source, supports educational use, algorithmic benchmarking, and integration into interactive configuration environments. The framework is primarily intended for reproducible benchmarking and teaching with open-source implementations that facilitate analysis and extension. Full article

Background/Objectives: The global burden of inflammatory bowel diseases (IBDs) continues to rise, with up to 50% of patients experiencing extraintestinal manifestations. Cardiovascular diseases (CVDs) are of particular concern, ranking as the second leading cause of mortality in this population. Despite a comparatively lower prevalence of traditional cardiovascular (CV) risk factors, the persistent inflammatory milieu and immune dysregulation inherent to IBD may contribute to heightened CVD risk. In this study, following a review of the current literature, an ongoing prospective trial designed to clarify CV risk profiles in IBD patients is detailed. Methods: A cohort of patients with IBD is being enrolled for comprehensive baseline evaluation of CV risk factors, lifestyle metrics, and disease characteristics. The incidence of major adverse cardiovascular events (MACEs) will be tracked and contrasted with a gender- and age-matched non-IBD cohort over a 2-year follow-up period. In cases of MACE occurrence, a multi-omics analysis—including genomic, proteomic, transcriptomic, and microbiome profiling—will be performed, along with a parallel evaluation in matched IBD controls without MACE. An artificial intelligence (AI) framework will support the analysis of this complex dataset. Results: To date, over 150 patients with IBD have been enrolled, and detailed phenotypic data and biological samples have been collected. Conclusions: We aim to introduce an IBD-specific correction factor for existing CV risk scores upon study completion. This is particularly relevant for individuals under 40 years of age, who are often inadequately assessed by current risk stratification models Full article

With the increasing frequency of global forest fires, research on the spread of forest fires has become one of the important directions in fire research. In order to improve the accuracy of surface fire spread simulation, based on relevant forest resources map preprocessing technologies, this paper takes the triangle mesh division idea of Tri-14 CA model for crowd evacuation and the Wang Zhengfei’s improved forest surface fire spread speed model as the basis, obtains the basic equation set of forest fire spread speed in 14 directions, and establishes the spatio-temporal spread mathematical model of forest surface fire. Based on the above, a software platform is established by applying computer technology to realize the calculation and visualization simulation of forest fire spread. Combined with examples, the correctness and practicability of the model software are illustrated, aiming to provide information support for forest disaster emergency departments. Full article

The purpose of this research is to investigate the misconceptions related to geological concepts among Secondary Education students in the region of Achaia, Greece. The study focuses on both Lower Secondary Education (Gymnasium, grades 7–9) and Upper Secondary Education, including General and Vocational Education (grades 10–12). Previous research has shown that students entering Lower Secondary Education or High School often possess several misconceptions about geological concepts. These misconceptions result in a fragmented or incorrect understanding, which may arise from intuitive perceptions of how the natural world evolves that are incorrect, or from stereotypes and assumptions acquired from the family environment or inadequacies in the school curriculum. Despite teachers’ efforts to clarify these concepts, a significant percentage of students continue to hold misconceptions, mainly related to minerals and rocks. A total of 1065 secondary students completed an online closed-ended questionnaire that was designed and validated based on previous research findings to highlight their misconceptions. This study results showed a clear differentiation between students from urban and rural areas, while demographic characteristics (such as gender, age, parents’ occupation, and parents’ marital status) did not appear to play a significant role. In addition, the responses to specific sets of questions varied depending on the student’s grade level. Identifying students’ misconceptions can support the development of appropriate educational tools and/or inform targeted interventions that aim to clarify these concepts and correct any incorrect assumptions. Full article

This paper proposes a design and control methodology for a Quasi-Y-Source impedance source inverter (QS-YSI) as a power electronics interface for Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) applications in the context of virtual power plants (VPPs). The work presents an analysis of bidirectional power transfer using Electric Vehicles (EVs) to supply power to the utility grid, businesses, and homes, thereby acting as distributed energy resources. The proposed QS-YSI topology supports both V2G and G2V operation while providing reactive power compensation and enabling the decoupled tracking of active power (P) and reactive power (Q), demonstrating the capability of EVs to return energy to the grid and to provide ancillary services such as power factor correction. The key contributions are a detailed control design methodology that includes pulsating DC-link voltage regulation, inverter output current reference tracking in the synchronous $dq$ reference frame considering DC-link voltage dynamics, and a modified Pulse Width Modulation (PWM) technique for effective decoupling of DC link and inverter output current control. Finally, the feasibility and validity of the proposed approach are demonstrated through simulations of the complete system under nominal conditions and experiments conducted considering a small-scale prototype. Full article

European black pine (Pinus nigra Arn. subsp. nigra) persists in scattered montane stands across Greece, where isolated populations harbour genetic variation shaped by local environments and demographic history. In this study, we assessed the genetic diversity and population structure of P. nigra using nuclear microsatellite markers (nSSRs) across four populations: Mt. Athos, Sithonia, Thassos, and Perama. A total of 67 individuals were genotyped, and seven high-quality polymorphic loci were retained after rigorous filtering. The Mt. Athos population exhibited the highest allelic richness and heterozygosity, with all loci being polymorphic and a low inbreeding coefficient after null allele correction. In contrast, the Perama population displayed reduced diversity, fewer polymorphic loci, and persistent heterozygote deficits. Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC) revealed weak overall population structure, with Perama genetically distinct from the other sites. Spatial Principal Component Analysis (sPCA) further uncovered an east–west cline within Athos and localized structure potentially shaped by both natural isolation and human influence. These findings highlight regional variation in genetic diversity within P. nigra and identify Athos as a genetically rich population of particular interest. The results provide a foundation for long-term monitoring and support informed strategies for the management and conservation of P. nigra in Greece. Full article

Background: The objective of this study was to assess the efficacy and safety of faricimab in diabetic macular edema (DME) in patients who were treatment-naïve or previously treated in a real-world setting. Methods: This was a retrospective, observational, single-center study that included 105 eyes from 79 patients diagnosed with DME and treated with intravitreal faricimab between January 2024 and January 2025. Patients were categorized into two groups according to their treatment history, namely treatment-naïve eyes and eyes previously treated (switch group). Functional (best-corrected visual acuity, BCVA) and anatomical (central foveal thickness, CFT; macular volume, MV) outcomes were assessed. The safety of faricimab was evaluated from changes in intraocular pressure and the occurrence of adverse events. Results: BCVA improved significantly in both groups, with a mean gain of +0.16 in treatment-naïve eyes and +0.10 in switch eyes. The mean reduction in CFT was −53.7 µm in the naïve group and −37.8 µm in the switch group. MV decreased by −0.4 mm³ overall, with significant reductions in both groups. No adverse events were reported, confirming the safety of faricimab in routine clinical practice. Conclusions: Faricimab showed significant improvements in functional and anatomical outcomes in patients with DME, regardless of the use of previous anti-VEGF therapies. These findings support the effectiveness and safety of faricimab in a real-world clinical setting and reinforce its potential as a valuable treatment option for DME. Full article

In today’s competitive digital landscape, application usability plays a critical role in user satisfaction and retention. Negative user reviews offer valuable insights into real-world usability issues, yet traditional analysis methods often fall short in scalability and contextual understanding. This paper proposes an intelligent framework that utilizes large language models (LLMs), including GPT-4, Gemini, and BLOOM, to automate the extraction of actionable usability recommendations from negative app reviews. By applying prompting and fine-tuning techniques, the framework transforms unstructured feedback into meaningful suggestions aligned with three core usability dimensions: correctness, completeness, and satisfaction. A manually annotated dataset of Instagram negative reviews was used to evaluate model performance. Results show that GPT-4 consistently outperformed other models, achieving BLEU scores up to 0.64, ROUGE scores up to 0.80, and METEOR scores up to 0.90—demonstrating high semantic accuracy and contextual relevance in generated recommendations. Gemini and BLOOM, while improved through fine-tuning, showed significantly lower performance. This study also introduces a practical, web-based tool that enables real-time review analysis and recommendation generation, supporting data-driven, user-centered software development. These findings illustrate the potential of LLM-based frameworks to enhance software usability analysis and accelerate feedback-driven design processes. Full article