Search Results (4,347)

Evaluating students’ knowledge and competencies to achieve the desired learning goals is one of the most important stages of the teaching process. The purpose of this study is to create a dataset consisting of programming questions and determine the level of these questions according to the Bloom taxonomy and the weight of each concept they contain, by taking expert opinion. The student’s score, question difficulty, and complexity levels are considered to determine the extent to which the student has learned a concept. A total of 96 students participated in this study, 51 in the experimental group and 45 in the control group. Random design for a pre-test–post-test control group was used to measure the students’ learning performance and self-efficacy regarding programming. While the experimental group students were given detailed feedback on how much they learned a concept, the control group students were only informed about the total score they received from the exam. The learning performance and self-efficacy perception regarding programming were analyzed using the paired samples t-test. Results show that the learning performance and self-efficacy perception regarding programming of the experimental group students improved significantly compared to the control group. Full article

Introduction: In recent decades, the burden of mental disorders has become a major determinant of population health in the European Union, generating profound clinical, socioeconomic, and institutional consequences. Despite political recognition of this silent crisis, substantial methodological challenges persist in the transnational monitoring of mental health and in linking disease burden with the resources allocated to address it. The present analysis develops a multivariate taxonomy of EU Member States from a psychosocial perspective, using an integrative quantitative approach. Methods: This cross-sectional, comparative study follows international standards for transparent and reproducible quantitative reporting and is based on 18 harmonized clinical, epidemiological, and institutional indicators collected for 27 EU Member States over the period 2014–2023. The indicators used in this study were grouped according to their position along the care continuum. Hospital-based indicators refer to inpatient activity and institutional capacity, including total hospital discharges, psychiatric admissions (affective disorders, schizophrenia, dementia, alcohol- and drug-related disorders), and hospital bed availability. Outpatient and community-level indicators reflect the capacity of systems to provide non-hospital psychiatric care and consist primarily of psychiatrist density and total specialist medical workforce. Finally, subjective perception indicators capture population-level self-assessed health status, complementing clinical and institutional measures by integrating a psychosocial perspective. After harmonization and standardization, Principal Component Analysis (PCA) with Varimax rotation was applied to identify latent dimensions of mental health. Model adequacy was confirmed using the Kaiser–Meyer–Olkin coefficient (0.747) and Bartlett’s test of sphericity (p < 0.001). Results: Three latent dimensions explaining 77.7% of the total variance were identified: (1) institutionalized psychiatric burden, (2) functional capacity of the health care system, and (3) suicidal vulnerability associated with problematic substance use. Standardized factor scores allowed for the classification of Member States, revealing distinct patterns of psychosocial risk. For example, Germany and France display profiles marked by high levels of institutionalized psychiatric activity, while the Baltic and Southeast European countries exhibit elevated suicidal vulnerability in the context of limited medical resources. These results highlight the deep heterogeneity of psychiatric configurations in Europe and reveal persistent gaps between population needs and institutional response capacity. Conclusions: The analysis provides an empirical foundation for differentiated public policies aimed at prevention, early intervention, and stigma reduction. It also supports the case for institutionalizing a European mental health monitoring system based on harmonized indicators and common assessment standards. Overall, the findings clarify the underlying structure of mental health across the European Union and underscore the need for coherent, evidence-based strategies to reduce inequalities and strengthen system performance at the continental level. Full article

Trichoderma species are globally distributed fungi with remarkable biotechnological relevance. In this study, we describe two new species, T. jilotepecense and T. sanisidroense, and report T. endophyticum as the first record for Mexico. All isolates were obtained from soils of coffee agroecosystems in Veracruz. Species identification was based on the integration of cultural and micromorphological characteristics (PDA, SNA, CMD; 25–35 °C) with multilocus phylogenetic analyses using the ITS, tef1, and rpb2 markers. The concatenated dataset provided strong support for species delimitation and clarified phylogenetic relationships within the Harzianum and Virens clades. Full article

The wavelet transform (WT) is an integral transform primarily used for processing and analyzing nonstationary signals due to its multiresolution property. Multiresolution analysis is one method that finds applications in many fields because of the characteristics of the transform. Over the years, WT has become standard and is integrated into many coding protocols and applications without special mention. Decades of research in the field of wavelets have revealed several stages of development. In the initial stage, the focus was on wavelet families, with scientists deriving new families for emerging applications. The second stage addressed implementation issues, emphasizing more efficient implementation techniques. The next stage involved artificial neural networks (ANNs) that perform WT. This paper reviews the development of WT with examples from maritime applications. We also provide an overview of cutting-edge trends in wavelets and propose the aforementioned stages as a new taxonomy of WT development. Full article

The proliferation of Digital Twins (DTs) across industries like manufacturing, healthcare, and logistics is leading to the formation of complex ecosystems where heterogeneous DTs must cooperate. In such environments, establishing trust becomes paramount. However, trust in DTs remains an under-investigated problem, with current research predominantly focused on security and privacy, which are prerequisites but not sole constituents of trust. This paper presents a comprehensive framework for analyzing and enhancing the trustworthiness of Digital Twins. First, we propose a novel five-layer symmetrical reference architecture (Asset, Synchronization, Data, Application, Integration) that models physical and digital twins as peers, improving reusability and maintainability. Using this architecture as a foundation, we then develop a multi-dimensional taxonomy to categorize DT trust issues from three critical perspectives: (1) an architectural perspective, which identifies and maps trust issues (e.g., model accuracy, data latency, application usability) to specific layers and behavioral attributes like conformance and dependability; (2) a massive twinning perspective, which explores emergent challenges in ecosystems of cooperating DTs, such as relationship complexity and data management; and (3) a stakeholder perspective, which addresses the need for both qualitative and quantitative trust assurances. Our analysis reveals that trust is a composite property requiring a holistic approach beyond conventional security. The paper concludes by synthesizing these perspectives into a unified view of DT trust and outlining critical open challenges and future research directions, providing a foundational roadmap for developing truly trustworthy Digital Twin systems. Full article

The mobile network ecosystem is undergoing profound change driven by Artificial Intelligence (AI), Network Function Virtualization (NFV), and Software-Defined Networking (SDN). These technologies are well positioned to enable the essential transformation of next-generation networks, delivering significant improvements in efficiency, flexibility, and sustainability. AI is expected to impact the entire lifecycle of mobile networks, including design, deployment, service implementation, and long-term management. This article reviews the key characteristics of 5G and the anticipated technology enablers of 6G, focusing on the integration of AI within mobile networks. This study addresses several perspectives, including network optimization, predictive analytics, and security enhancement. A taxonomy is proposed to classify AI applications into 5G and 6G according to their role in network operations and their impact across vertical domains such as the Internet of Things (IoT), healthcare, and transportation. Furthermore, emerging trends are discussed, including federated learning, advanced AI models, and explainable AI, along with major challenges related to data privacy, adaptability, and interoperability. This paper concludes with future research directions, emphasizing the importance of ethical AI policies and cross-sector collaborations to ensure effective and sustainable AI-enabled mobile networks. Full article

The skull provides essential diagnostic features for species identification and sex determination. Cranial sexual dimorphism in guinea pigs (Cavia porcellus) is particularly important to understand, as these animals are frequently used in experimental research and veterinary practice, yet detailed morphometric evaluations remain limited. This study aimed to assess cranial size and shape variation between sexes using three-dimensional geometric morphometric (GM) methods. Computed tomography scans of 30 clinically healthy guinea pigs were used to reconstruct three-dimensional skull models. Twenty-one anatomical landmarks were digitized, and the dataset was subjected to generalized Procrustes analysis, followed by principal component analysis, Procrustes ANOVA, and regression-based allometric assessments. The results revealed pronounced sexual dimorphism. Males exhibited significantly larger centroid sizes and more robust cranial morphologies, whereas females presented smaller and more gracile skull forms. Procrustes ANOVA confirmed significant shape differences between sexes (p < 0.01), with size being the primary driver of morphological divergence. Body weight had a weaker but detectable influence on cranial variation, although its effect diminished after accounting for centroid size (R²: 0.085). These findings demonstrate that three-dimensional geometric morphometrics can effectively detect subtle patterns of cranial dimorphism in guinea pigs. Beyond their anatomical relevance, the results provide a valuable reference for veterinary sciences, taxonomy, and future biomedical research requiring precise morphological characterization. These findings show that three-dimensional geometric morphometrics can effectively detect subtle patterns of sexual dimorphism in cranial shape, which is particularly relevant for sex identification in skeletal collections and for the development of comparative anatomical databases in veterinary and experimental research. Full article

The COVID-19 pandemic initially focused clinical efforts on hospitalized patients. However, as the pandemic progressed, attention shifted to outpatients who often experience milder symptoms yet still contribute to viral transmission. This scoping review aimed to document and evaluate the clinical outcomes assessed in randomized controlled trials (RCTs) involving outpatients with COVID-19, identifying gaps and areas for improvement in trial design. This review followed the PRISMA-ScR guidelines. A comprehensive search of four electronic databases (PubMed, Scopus, Cochrane CENTRAL, and Web of Science) was conducted for RCTs published between December 2019 and December 2023. Studies were included if they involved outpatients with confirmed COVID-19 and reported clinical outcomes. Data were extracted from eligible studies, and outcomes were categorized using the COMET taxonomy. A total of 91 studies were included, representing a wide geographical distribution, with the USA, Iran, and Brazil contributing the most studies. The most frequently investigated treatments included hydroxychloroquine, fluvoxamine, convalescent plasma, and ivermectin. Key outcomes focused on hospitalization rates, symptom resolution, and disease progression. Mortality, although less common in outpatients, was reported in 65 studies, underscoring the importance of outpatient interventions. This review highlights the need for standardized outcome measures in outpatient COVID-19 trials. Full article

As video games increasingly replicate real-world locations, they have become powerful tools influencing players’ perceptions and behaviors toward travel destinations. Based on the principles of Transfer Trust Theory (TTT), this research investigates how the trust established in a destination within a virtual game context, referred to as perceived destination trust, translates into real-world travel willingness. Using data from a survey of 262 Tunisian gamers who played games set in real-world environments, we employed a structural equation modeling approach incorporating SPSS and SmartPLS analyses. The results indicate that immersion and enjoyment of the game significantly strengthen emotional attachment and the image of the destination, thereby reinforcing perceived trust. This trust positively predicts the willingness to visit real-world destinations. Furthermore, moderation analysis reveals that this effect is more pronounced among individuals classified as Explorers and Achievers, highlighting the influence of motivational typologies on the translation of virtual behaviors into real-world actions. These results extend the scope of TTT to video game-induced tourism (VGIT), empirically validating the psychological mechanisms that link virtual trust to real-world travel behaviors. From a practical standpoint, tourism organizations and game developers are advised to collaborate on creating immersive and authentic environments that enhance destination credibility while aligning with brand objectives. Full article

Breast cancer (BC) is increasingly recognized as a heterogeneous disease, with complexity that extends beyond the classical luminal A/B, HER2-enriched, and triple-negative framework. Advances in molecular and functional profiling have uncovered emerging subpopulations, including HER2-low, claudin-low, BRCA-deficient (“BRCAness”), and refined TNBC subsets, such as luminal AR (LAR) and basal-like immune variants, that extend beyond traditional taxonomies. These novel classifications provide additional resolutions, offering both prognostic insight and therapeutic opportunities. In this comprehensive review, we integrate evidence from genomic, epigenetic, proteomic, immune-related, and liquid biopsy biomarkers, underscoring how they define the biology of these subgroups and predict responses to targeted therapies, such as antibody–drug conjugates, PARP inhibitors, and immune checkpoint blockade. We further highlight the role of the tumor microenvironment (TME) and intratumoral heterogeneity in shaping these entities. Collectively, recognition of emerging subtypes as clinically actionable groups represents a paradigm shift from static receptor-based models to dynamic, biomarker-driven frameworks that refine prognosis, enable patient stratification, and support precision oncology in aggressive BC. Full article

The transition from Industry 4.0 to Industry 5.0 emphasizes the need for ethical, transparent, and human-centric artificial intelligence systems. In this context, ensuring the authenticity of digital information has become crucial for maintaining societal trust. This study addresses the challenge of detecting manipulated multimedia content, including synthetic images, videos, and audio generated by artificial intelligence, commonly known as Deepfakes. We analyze and compare general-purpose and Deepfake-specific detection methods to assess their effectiveness in real-world scenarios. This work introduces a refined reference model that integrates both application-oriented and methodological criteria, grouping tools into Blind Forensic, Handcrafted Machine Learning, Deep Learning-based methods, and Toolkits. This structured taxonomy provides a clearer comparative framework than existing works, which typically classify detectors using only one of these dimensions. To ensure reproducible evaluation, all experiments were performed using the SAFL dataset, which consolidates real and synthetic multimedia content generated with publicly available tools under a unified protocol. Among the tested tools, Forensically achieved the highest accuracy in image forgery detection 86.9%, while Autopsy reached 69.5% among Deepfake-specific image detectors. In video analysis, Forensically obtained 98.6% accuracy, whereas Deepware Scanner achieved 91.2% as the most effective Deepfake-focused tool. These results highlight that general-purpose methods remain robust for images, while specialized detectors perform competitively in videos. Overall, the proposed model and dataset establish a consistent foundation for advancing hybrid detection strategies aligned with the ethical and transparent AI principles envisioned in Industry 5.0. Full article

Crabapples (Malus spp.) are widely planted ornamental and multipurpose trees in temperate regions and represent an important component of forest and landscape resources. However, the absence of a standardized classification framework has led to nomenclatural confusion, hindering germplasm conservation, breeding, and international exchange. In this study, 80 introduced crabapple cultivars preserved in the germplasm repository of Nanjing Forestry University were systematically evaluated using 55 morphological traits of flowers, leaves, fruits, and tree architecture. A hierarchical framework was established based on flower type and corolla color, dividing cultivars into Single, Semidouble, and Double Flower groups, with further subdivisions of Single cultivars by color. Numerical taxonomy (R- and Q-type clustering) validated the robustness of this framework, identifying petal number and corolla color as the most consistent traits across cultivars and seasons (inter-cultivar CV < 10%), serving as reliable diagnostic indicators, although within-cultivar variation was not quantified. The proposed system resolved frequent misidentifications (e.g., M. ‘Kelsey’ and M. ‘Molten Lava’) and provided standardized descriptors for cultivar identification. Beyond taxonomy, the framework enhances germplasm management, supports nursery production and landscape forestry, and facilitates international exchange of ornamental resources. These findings highlight the potential of integrating morphological and numerical approaches for germplasm diversity assessment and contribute to the development of a unified global classification system for ornamental crabapples. Full article

Traffic congestion continues to pose a significant challenge to contemporary urban transportation systems, exerting substantial effects on economic productivity, environmental sustainability, and the overall quality of life. This systematic literature review thoroughly explores the development of traffic congestion forecasting methodologies from 2014 to 2024 by analyzing 100 peer-reviewed publications according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examine the technological advancements from traditional machine learning (achieving 75–85% accuracy) through deep learning approaches (85–92% accuracy) to recent large language model (LLM) implementations (90–95% accuracy). Our analysis indicates that LLM-based systems exhibit superior performance in managing multimodal data integration, comprehending traffic events, and predicting non-recurrent congestion scenarios. The key findings suggest that hybrid approaches, which integrate LLMs with specialized deep learning architectures, achieve the highest prediction accuracy while addressing the traditional limitations of edge case management and transfer learning capabilities. Nonetheless, challenges remain, including higher computational demands (50–100× higher than traditional methods), domain adaptation complexity, and constraints on real-time implementation. This review offers a comprehensive taxonomy of methodologies, performance benchmarks, and practical implementation guidelines, providing researchers and practitioners with a roadmap for advancing intelligent transportation systems using next-generation AI technologies. Full article

This study reports one genus newly recorded from China and four new species of the weevil tribe Stromboscerini. The genus Dexipeus Pascoe, 1885, is recorded in China for the first time, with one new species described: Dexipeus tengchongensis sp. nov. Additionally, three new species of Tasactes Faust, 1894, are recognized and diagnosed: T. biunciatus sp. nov., T. liangi sp. nov., and T. sulcatus sp. nov. For all new species, detailed morphological descriptions, diagnostic illustrations, and habitus pictures are provided. Two updated identification keys are presented. High-resolution pictures of the type specimens of the previously described Chinese species Allaeotes niger He, Zhang & Pelsue, 2003, and Synommatoides scutellatus He, Zhang & Pelsue, 2003, are also provided for future taxonomic research. Full article

SRAM-based FPGAs, with their flexible programmability and parallel execution features, have been widely used, and the security of such devices has drawn significant attention. Especially in the era of artificial intelligence, FPGA architectural optimizations and evolving application models have introduced novel security characteristics and threats. In this work, we introduce a taxonomy of FPGA threats and explore new threat features and potential countermeasures in the era of AI. We focus on evaluating the research trends of FPGA security, including both security threats and protection measures. Then, we propose a new perspective on the involvement of FPGA manufacturers in FPGA security and introduce the main security measures used in COTS FPGA products. Finally, we summarize the security challenges and offer future research directions. Full article