Search Results (4,575)

Chinese cloze question generation for educational assessments requires identifying gap phrases that accurately reflect key knowledge points, posing significant challenges to automated systems. We observe that the syntactic boundaries revealed by part-of-speech (POS) tags closely align with the semantic boundaries of target gap phrases. Motivated by this observation, we propose a multi-task learning framework in which gap phrase identification serves as the primary task and POS tagging as a complementary auxiliary task. The two tasks share a common BERT-BiLSTM encoder, enabling mutual reinforcement of both syntactic and semantic representations through joint training. To further capture the interaction between label semantics and contextual word representations, we introduce a label-attention mechanism that models dependencies between the global word sequence and candidate label embeddings. Additionally, we construct a refined POS tag subset by excluding categories whose boundaries show no alignment with gap phrase boundaries, thereby strengthening the correspondence between the two tasks. Evaluated on a real-world dataset of 20.5K questions spanning five academic disciplines, our method achieves an F1 score of 65.85%, with a Recall of 67.79%, representing improvements of 2.12% and 4.35% over the prior state-of-the-art, respectively. These results demonstrate that exploiting the alignment between syntactic and semantic structures through joint learning is effective for generating educationally meaningful fill-in-the-blank questions. Full article

Construction management, from the contractor’s perspective, is led by the Construction Manager (CM). The work motivation and leadership style of the CM are critical variables for the successful execution of construction projects. The scientific literature identifies participative leadership as the most effective style for mitigating conflicts among various stakeholders. However, analyzing the specific variables that influence a CM’s conflict resolution capacity remains an underexplored area. Furthermore, while the CM must act as a leader for their team (subcontractors, suppliers, etc.), they remain accountable to the contractor’s senior management. Therefore, this study aims to analyze the mediating role of CM motivation in the relationship between leadership and conflict resolution capacity using Partial Least Squares Structural Equation Modeling (PLS-SEM). In the construction industry, conflict resolution is not merely a situational fix but a critical process of capturing and externalizing tacit knowledge. Knowledge management and the ability to resolve conflicts in the construction sector are directly linked, critical, and strategic in nature. Construction is an industry characterized by fragmentation, the temporary nature of its projects, diversity of stakeholders (developers, builders, subcontractors, engineering firms) and a high level of uncertainty. In this environment, conflict is virtually inevitable. However, the way in which a CM handles a conflict determines whether it becomes a destructive dispute or an opportunity for improvement. Full article

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings. Full article

Precise manipulation of mitochondrial DNA (mtDNA) by CRISPR-Cas systems remains challenging, largely due to inefficient import of guide RNAs, motivating the exploration of alternative programmable nucleases. Here, we show that prokaryotic Argonaute nucleases (pAgos) of various classes can be efficiently targeted to human mitochondria. Using the Su9 mitochondrial targeting sequence from Neurospora crassa, we achieved robust mitochondrial import of four pAgos—DecAgo, CbuAgo, KmaAgo and RslAgo. As a functional readout of their activity in cells, we targeted the single-stranded D-loop region, which plays a central role in mtDNA replication and maintenance, reasoning that cleavage at this site was expected to potentially result in a reduction in mtDNA copy number. Of the four enzymes, only RNA-guided DecAgo induced a pronounced reduction in mtDNA levels, decreasing copy number approximately fivefold within 48 h. Unexpectedly, this effect occurred independently of exogenous guides, suggesting that DecAgo may utilize endogenous mitochondrial guide RNAs. These findings identify DecAgo as an active nuclease in human mitochondria and reveal a previously unrecognized mode of targeting, highlighting the need to further investigate the underlying mechanism and the potential role of endogenous guide molecules, as well as improving targeting specificity. Full article

The adoption of large language models (LLMs) is reshaping how organizations approach automation, decision-making, and user engagement across sectors. This study investigates the trends, theoretical frameworks, and adoption factors influencing the integration of LLMs in five key domains: education, commerce, banking, healthcare, and service. By employing a systematic literature review and meta-analysis, this paper synthesizes research published between 2022 and early 2026, corresponding to the period when LLMs became widely accessible for public and enterprise use, to evaluate both conceptual and empirical dimensions of LLM adoption. The review identifies the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, including its extensions, as the most frequently applied frameworks. It also highlights the growing incorporation of complementary models such as the diffusion of innovation, the information system success model, and self-determination theory. The meta-analysis examines 59 pairwise relationships drawn from 154 studies with a cumulative sample size of 88,886 participants. Using correlation coefficients, $I^2$ statistics, and Egger’s test, the analysis reveals strong, consistent associations between behavioral intention and both use behavior and actual use, while also identifying high heterogeneity across contexts. Constructs such as trust, hedonic motivation, and personal innovativeness emerged as influential but were underrepresented in the theoretical modeling. The study underscores the importance of facilitating conditions, infrastructure, and organizational readiness for enabling sustained use while also drawing attention to gaps in addressing perceived risks, privacy concerns, and ethical implications. Full article

With the development of digital technology and artificial intelligence (AI), numerous studies have focused on the applications and impacts of digital technology in the public sector. However, few studies have explored how frontline public service employees, the core subject of public organizations, can improve their proactive service performance. Based on the model of proactive motivation, this paper investigates the influence of digital leadership on employees’ proactive service performance from a micro perspective, as well as the internal mechanisms and boundary conditions underlying this process. Through an analysis of three-wave questionnaire survey data from 234 employees, this study finds that digital leadership has a positive impact on public employees’ proactive service performance through the serial mediation effects of AI service awareness and AI crafting. Furthermore, as an important boundary condition, employees’ public service motivation strengthens the serial indirect effect of digital leadership on proactive service performance. This paper not only extends the literature on digital leadership by adopting a micro-level perspective within the context of public sector digital transformation but also identifies the individual and contextual antecedents of proactive service performance by examining the interactive effect of public service motivation and leadership. Furthermore, this paper offers valuable implications for the practice of digital transformation in public organizations. Full article

Background: Antimicrobial resistance (AMR) poses an escalating threat to global health, agriculture, and the environment, demanding urgent multisectoral action under the One Health framework. Despite global awareness efforts, understanding of AMR among the general population remains insufficient, particularly in low- and middle-income countries such as Brazil. This study aimed to evaluate the knowledge, attitudes, and perceptions (KAP) of the Brazilian population regarding AMR. Methods: An online questionnaire was distributed through social media platforms between April and August 2025, resulting in 945 valid responses after data cleaning. Quasi-Poisson models were applied to identify demographic predictors of KAP scores while logistic regression models were used to assess the association between KAP scores and antibiotic use-related practices. Results: Education level was the strongest predictor of higher KAP scores, whereas age and gender showed inconsistent influence. Only 40.3% of respondents correctly identified antibiotics among commonly used medicines, and 25.9% reported proper disposal of antibiotic packaging. More than half (54.2%) were willing to pay more for antibiotic-free products, although only 26.7% had ever noticed such labeling. Network analysis of open-ended responses indicated that concerns about potential health risks and AMR awareness were the primary motivators for purchasing antibiotic-free products. Conclusions: These findings reveal significant gaps in public understanding of antibiotic use and resistance in Brazil, emphasizing the urgent need for targeted educational initiatives, improved public communication, and behavioral interventions to support antimicrobial stewardship and sustainable antibiotic use. Full article

Over the past decade, developments in artificial intelligence (AI) have sparked a new wave of debate and research across nearly all areas of life, including education. In English as a Foreign Language (EFL) education, AI-based technologies are also widely adopted to support learners and instructors. This trend has led to numerous studies focused on understanding AI’s role in identifying potential opportunities and challenges. This study offers a systematic review of relevant research, highlighting the benefits and obstacles of AI use in the Saudi EFL context. About 60 peer-reviewed articles were selected following PRISMA guidelines. The findings reveal multiple opportunities for AI integration in Saudi Arabia, such as improved language skills, personalized learning experiences, increased self-regulated learning, boosted motivation and confidence among learners, expanded learning opportunities, and support for pedagogy and institutional performance. Major challenges include biased and inaccurate data, students’ overdependence on technology, ethical concerns, and a lack of technological skills among users. The study also suggests future directions, including localizing AI tools, conducting long-term impact studies, providing faculty and student training, and establishing ethical guidelines within institutions. Full article

Language embodies traditions, values, and collective identity, bridging gaps between generations and geographies. Maintaining consistent language policies at home and in communities remains challenging, with research showing that only a few families have explicit rules about language use and few enforce them regularly. The study explores strategies for heritage language maintenance (HLM) to mitigate language attrition among Anaañ bilinguals residing in the Akpabuyo and Calabar South Local Government Areas of Cross River State, Nigeria. The study draws on social identity theory, which links language use to identity, motivation, and group affiliation, thereby influencing language maintenance. Using a qualitative approach, data were collected through semi-structured interviews with 40 participants, selected purposively from Anaañ speakers in the study areas. Thematic analysis is employed to identify patterns and themes, revealing approaches for heritage language maintenance to curb language attrition. Findings reveal that despite some Anaañ speakers’ negative attitude towards their HL, others value it, keep it alive, and are ready to pass it to the future generations. This will preserve cultural identity and foster a sense of pride, belonging and shared values among Anaañ people, especially those residing in Southern Cross River State. Full article

Recreational fisheries are one of the most widespread forms of inland water use in developed countries, and they are increasingly being interpreted as a social–ecological system that integrates ecosystems, users, and governance structures. This study aimed to evaluate the perceived status of fish resources, the effectiveness of the management system, and the obstacles to developing recreational fisheries in Poland. The evaluation was based on a nationwide survey (CAWI, N = 1574). The results indicate a critical perception of both resource conditions and management performance. Institutional barriers were identified as the most significant. While environmental factors remain significant, their role should be interpreted within the broader context of governance effectiveness. The structure of motivations confirms the dominance of recreational and environmental functions over utilitarian and economic ones, highlighting the growing importance of cultural ecosystem services. The findings reveal a discrepancy between the evolving recreational aspects of angling and the existing management system, indicating an institutional mismatch that poses a significant challenge to sustainable water resource management. These findings align with the Sustainable Development Goals, particularly those related to water quality and ecosystem protection. While the study focuses on Poland, the identified challenges are not unique and are relevant to many countries undergoing similar transformations. The results emphasize the need for more integrated, adaptive, and participatory governance models. Full article

Introduction: Inland fisheries in their diverse forms are an important activity in Portugal, currently involving about 100,000 fishers. Despite their relevance, there is still limited knowledge regarding the economic multiplier effect associated with this activity, including its contribution to local and regional economies, its broader socio-economic impacts, and its role in promoting nature-based tourism. Objective: The INFISHERIES.PT project aims to characterize the socio-economic value of inland fisheries in Portugal. Methodology: The three main fishing activities in Portugal (professional, sport, and recreational fisheries) were considered to assess inland fisheries’ economic value. Data on annual expenditures of competitive sport anglers were collected through an online questionnaire distributed by the Portuguese Federation of Sport Fishing, while data on recreational fishers were obtained through face-to-face surveys. The analysis of professional fisheries was based on official catch declarations submitted to the national licensing authority (ICNF) between 2012 and 2024. Interim Results: Results for sport fisheries indicate an estimated mean annual direct expenditure of €6.7 million, with fishing equipment accounting for the largest share, followed by travel, meals, and accommodation. Social interaction was identified as the main motivation for recreational fishing, followed by contact with nature, as well as motivations related to peace, relaxation, and entertainment. Respondents most frequently reported annual expenditures between €100 and €499 on fishing equipment, travel, and food during fishing trips. Regarding professional fisheries, results highlight the increasing importance of non-native species in total catches, particularly the red swamp crayfish, in recent years. Native migratory species, such as the European eel, sea lamprey, and allis shad, despite lower catch volumes, maintain high market value and make a significant contribution to total revenue. Conclusions: The results obtained to date in this project indicate that freshwater fishing in Portugal is a relevant activity, both in its commercial and non-commercial forms, and plays an important economic role at local and regional levels. Moreover, sport and recreational angling, in particular, also serve as drivers of nature-based tourism, potentially contributing to increased environmental awareness among the population and pressuring authorities to maintain freshwater ecosystems in good ecological condition. Full article

Field-programmable gate array (FPGA)-based reconfigurable system-on-chip (SoC) platforms are increasingly deployed in safety-critical domains such as autonomous driving and industrial automation, yet the existing literature lacks a systematic assessment of how these designs address functional safety requirements. This paper presents a systematic review of 36 peer-reviewed studies (core period 2010–2024, with historical context from 1998) on FPGA-based reconfigurable parallel processing SoCs, analyzed through three frameworks: a convergence–divergence analysis (CDA) that provides a structured exploratory lens for identifying research trajectory trends and informing hypothesis generation; a safety-critical gap analysis benchmarked against a three-layer standard framework comprising ISO 26262 (functional safety), ISO 21448/SOTIF (safety of the intended functionality), and ISO/PAS 8800 (AI safety properties); and a four-dimensional design space taxonomy spanning reconfigurability granularity, parallelism exploitation, design automation level, and safety criticality. The analysis reveals that 33 of the 36 surveyed studies (92%) ignore safety certification entirely. While recent work has begun establishing worst-case execution time (WCET) bounds for FPGA SoC platforms, none of the surveyed FPGA-based AI accelerator studies provide WCET bounds, although recent analytical models for multi-DPU architectures demonstrate the feasibility of such analysis. FPGA CNN accelerators achieve energy efficiencies of up to 60 GOPS/W, and dynamic partial reconfiguration (DPR) yields 2–5× throughput improvements, yet these gains remain unsupported by the formal verification or uncertainty quantification mandated for safety certification. The CDA framework reveals strong convergence between DPR, network-on-chip (NoC), and high-level synthesis research threads (scores 0.72–0.91), indicating maturation toward integrated design flows. We identify conformal prediction as a distribution-free hardware-compatible framework for uncertainty quantification on resource-constrained FPGAs, motivated by requirements from ISO 21448 (triggering event identification) and ISO/PAS 8800 (runtime confidence monitoring), and propose a prioritized research agenda to bridge the gap between FPGA performance optimization and safety-certified deployment in transportation systems. Full article

The discovery of the Higgs boson in 2012 at the Large Hadron Collider marked a major milestone in our understanding of electroweak symmetry breaking. Since then, increasingly precise measurements by the ATLAS and CMS Collaborations, based primarily on proton–proton collision data at \(\sqrt{s}\) = 13 TeV corresponding to about 140 fb−1 per experiment, have confirmed its compatibility with Standard Model predictions within current uncertainties. The Higgs boson mass is now measured with a precision of about 0.08%, while its couplings to fermions and bosons are determined at the 7–20% level. The completion of the LHC programme and the High-Luminosity LHC, will probe Higgs boson couplings at the fewpercent level. However, sub-percent precision is required for stringent tests of the Standard Model, as any deviation would signal new physics beyond it. This strongly motivates future collider facilities, designed both as high-precision Higgs factories and, in many cases, as energy-frontier machines. Within the framework of the update of the European Strategy for Particle Physics, we discuss the physics case and main characteristics of the proposed particle collider options, highlighting their complementarity, technological challenges, and expected performance. The 2026 Strategy Update identifies the FCC-ee collider as the preferred next flagship project at CERN. Operating at the Z pole and at centre-of-mass energies between 240 and 365 GeV, it would enable model-independent, per-mille-level precision on Higgs boson couplings, while providing a pathway to a future high-energy hadron collider. The Higgs sector thus constitutes a central laboratory for precision tests of the Standard Model and for exploring the fundamental structure of our universe. Full article

Background/aim: Physical inactivity remains widespread globally, with most adults not achieving recommended physical activity levels. Exercise motives and goals, central to Self-Determination Theory, strongly influence sustained participation. The Bernese Motive and Goal Inventory (BMZI) is a validated tool to assess exercise motivation; however, no Arabic version exists. This study aimed to translate, culturally adapt, and validate the BMZI for Arabic-speaking adults. Methods: A web-based cross-sectional study was conducted in Saudi Arabia between September and October 2025 among native Arabic-speaking adults via social media and community networks. The survey included sociodemographic data, the Arabic version of the Bernese Motive and Goal Inventory (Ar-BMZI), the Sport Motivation Scale (SMS), and the SF-12 Health Survey. Reliability was assessed through Cronbach’s α, McDonald’s ω, and ICC for test–retest consistency; construct validity via exploratory and confirmatory factor analysis; and convergent validity by correlating Ar-BMZI with the Arabic-SMS and Arabic-SF-12 physical component. Results: A total of 680 participants were included, with a mean age of 30.4 ± 12.9 years. Most were female (61.6%) and held a bachelor’s degree (73.5%). Nearly half (50.9%) reported a low monthly income. The Ar-BMZI demonstrated strong overall psychometric performance. Internal consistency was excellent (Cronbach’s α = 0.883; ω = 0.868), and test–retest reliability indicated high stability over time (ICC = 0.870, 95% CI = 0.786–0.933). Convergent validity was supported by a moderate correlation with the Arabic Sport Motivation Scale (r = 0.613, p < 0.001) and a weak correlation with the SF-12 physical health domain (r = 0.098, p = 0.011), which supported discriminant validity. Exploratory principal component analysis with Varimax rotation identified a five-factor structure explaining 69.2% of the total variance, and confirmatory factor analysis further supported this structure, demonstrating an excellent model fit. Conclusions: The Ar-BMZI demonstrates high reliability and good validity, supporting its use among Arabic-speaking adults. Full article

Quantum machine learning (QML) provides a framework for benchmarking wearable biosignal classification relevant to stress detection. Motivated by the burden of stress-related conditions, this study compares three quantum classifiers with seven classical baselines using heart rate and respiration rate features as inputs under noise-free and noisy conditions. Uncertainty was quantified using Nadeau–Bengio-corrected confidence intervals and percentile bootstrap ($B=1000$). The variational quantum classifier (VQC) achieved an accuracy of $99.47\%/97.30\%$ (noise-free/noisy), the quantum support vector classifier (QSVC) achieved $99.90\%/99.37\%$, and PegasosQSVC achieved $99.80\%/99.70\%$. Additionally, under the assessed proof-of-concept conditions, statistical equivalence between the QSVC and the best-performing classical model was established at $\Delta =1$ pp; PegasosQSVC under noise achieved equivalence at $\Delta =2$ pp with accuracy degradation of less than $0.10$ pp. The time feature was identified as the primary separability driver in a post hoc classical ablation. Tree-based models were robust on physiological features alone. The surveyed methods provide a reproducible, noise-aware benchmark for wearable physiological signal classification; however, the reported high accuracies are based on a deliberately separable proof-of-concept benchmark and do not demonstrate clinical utility or a quantum advantage. Full article