Search Results (15,615)

Rainfall, as the main driving force of natural disasters such as floods and droughts, has strong non-linear and abrupt characteristics, which makes it difficult to predict. As extreme weather events occur frequently in the Yellow River Basin, it is especially critical to reveal the physical mechanism of rainfall in the basin and integrate monthly scale meteorological data to achieve monthly rainfall prediction. In this paper, we propose a rainfall prediction model coupled with a physical mechanism and a temporal convolutional network (TCN) to achieve the prediction of monthly rainfall in the basin, aiming to reveal the physical mechanism between rainfall factors in the basin based on the transfer entropy and the multidimensional Copula function and based on the physical mechanism which is embedded into the TCN to construct a dual-driven prediction model with both physical knowledge and data, while the SHAP is used to analyze the interpretability of the prediction model. The results are as follows: (1) Temperature, relative humidity, and evaporation are key characteristic factors driving rainfall. (2) The physical mechanism features between temperature, relative humidity, and evaporation can be described by the three-dimensional Gumbel–Hougaard Copula function, with a more concentrated data distribution of their joint distribution probability. (3) The PHY-TCN model can accurately fit the extremes of the rainfall series, improving the model accuracy in the training set by 3.82%, 1.39%, and 9.82% compared to TCN, CNN, and LSTM, respectively, and in the test set by 6.04%, 2.55%, and 8.91%, respectively. (4) Embedding physical mechanisms enhances the contribution of individual feature variables in the PHY-TCN model and increases the persuasiveness of the model. This study provides a new research framework for rainfall prediction in the YRB and analyzes the physical relationship between the input data and output results of the deep learning model. It has important practical significance and strategic value for guiding the optimal scheduling of water resources, improving the risk management level of the basin, and promoting the ecological protection and high-quality development of the YRB. Full article

Cyanobacteria harbor sophisticated molecular defense systems that have evolved over billions of years to protect against viral invasion and foreign genetic elements. These ancient photosynthetic organisms possess a diverse array of restriction-modification (R-M) systems and CRISPR-Cas arrays that present challenges for genetic engineering, but also offer unique opportunities for cancer-targeted biotechnological applications. These systems exist in prokaryotes mainly as defense mechanisms but they are currently used in molecular applications as gene editing tools. Moreover, latest developments in nucleases such as zinc finger nucleases (ZFNs), TALENs (transcription-activator-like effector nucleases) are discussed. A comprehensive genomic analysis of 126 cyanobacterial species found 89% encode multiple R-M systems, averaging 3.2 systems per genome, creating formidable barriers to transformation but also providing molecular machinery that could be harnessed for precise recognition and targeting of cancer cells. This review critically examines the dual nature of these defense systems, their ecological functions, and the emerging strategies to translate their molecular precision into advanced anticancer therapeutics. Hence, the review main objectives are to explore the recent understanding of these mechanisms and to exploit the knowledge gained in opening new avenues for cancer-focused targeted interventions, while acknowledging the significant challenges to translate these systems from laboratory curiosities to practical applications. Full article

Licorice (Glycyrrhiza glabra) is a perennial herb traditionally valued for its aromatic and therapeutic properties. In recent years, however, growing attention has shifted toward the technical and environmental potential of the plant’s industrial by-products, particularly the fibrous material left after extraction. This review integrates botanical knowledge with engineering and industrial perspectives, highlighting the role of licorice fiber in advancing sustainable innovation. The natural fiber obtained from licorice roots exhibits notable physical and mechanical qualities, including lightness, biodegradability, and compatibility with bio-based polymer matrices. These attributes make it a promising candidate for biocomposites used in green building and other sectors of the circular economy. Developing efficient recovery processes requires collaboration across disciplines, combining expertise in plant science, materials engineering, and industrial technology. The article also examines the economic and regulatory context driving the transition toward more circular and traceable production models. Increasing interest from companies, research institutions, and public bodies in valorizing licorice fiber and its derivatives is opening new market opportunities. Potential applications extend to agroindustry, eco-friendly cosmetics, bioeconomy, and sustainable construction. By linking botanical insights with innovative waste management strategies, licorice emerges as a resource capable of supporting integrated, competitive, and environmentally responsible industrial practices. Full article

Effective disaster management is critical for safeguarding lives, infrastructure and economies in an era of escalating natural hazards like floods and landslides. Despite advanced early-warning systems and coordination frameworks, a persistent “last-mile” challenge undermines response effectiveness: transforming fragmented and unstructured multimodal data into timely and accountable field actions. This paper introduces ResQConnect, a human-centered, AI-powered multimodal multi-agent platform that bridges this gap by directly linking incident intake to coordinated disaster response operations in hazard-prone regions. ResQConnect integrates three key components. It uses an agentic Retrieval-Augmented Generation (RAG) workflow in which specialized language-model agents extract metadata, refine queries, check contextual adequacy and generate actionable task plans using a curated, hazard-specific knowledge base. The contribution lies in structuring the RAG for correctness, safety and procedural grounding in high-risk settings. The platform introduces an Adaptive Event-Triggered (AET) multi-commodity routing algorithm that decides when to re-optimize routes, balancing responsiveness, computational cost and route stability under dynamic disaster conditions. Finally, ResQConnect deploys a compressed, domain-specific language model on mobile devices to provide policy-aligned guidance when cloud connectivity is limited or unavailable. Across realistic flood and landslide scenarios, ResQConnect improved overall task quality scores from 61.4 to 82.9 (+21.5 points) over a standard RAG baseline, reduced solver calls by up to 85% compared to continuous re-optimization while remaining within 7–12% of optimal response time, and delivered fully offline mobile guidance with sub-500ms response latency and 54 tokens/s throughput on commodity smartphones. Overall, ResQConnect demonstrates a practical and resilient approach to AI-augmented disaster response. From a sustainability perspective, the proposed system contributes to Sustainable Development Goal (SDG) 11 by improving the speed and coordination of disaster response. It also supports SDG 13 by strengthening adaptation and readiness for climate-driven hazards. ResQConnect is validated using real-world flood and landslide disaster datasets, ensuring realistic incidents, constraints and operational conditions. Full article

This entry introduces an integrated model of reading that situates the Lexical Quality Hypothesis (LQH) within the Reading Systems Framework (RSF). The LQH posits that skilled reading depends on high-quality lexical representations—precise and flexible mappings of orthographic, phonological, morpho-syntactic, and semantic features—stored in the mental lexicon. These representations facilitate automatic word identification, accurate meaning retrieval, and efficient word-to-text integration (WTI), forming the foundation of text comprehension. Extending this micro-level perspective, the RSF positions lexical quality (LQ) within a macro-level cognitive architecture where the lexicon bridges word identification and reading comprehension systems. The RSF integrates multiple knowledge systems (linguistic, orthographic, and general world knowledge) with higher-order processes (sentence parsing, inference generation, comprehension monitoring, and situation model construction), emphasizing the bidirectional interactions between lower-level lexical knowledge and higher-order text comprehension. Central to this model is WTI, a dynamic mechanism through which lexical representations are incrementally incorporated into a coherent mental model of the text. This integrated model carries important implications for theory refinement, empirical investigation, and evidence-based instructional practices. Full article

Problem Statement: Upper crossed syndrome (UCS), as first described by Janda, refers to a group of muscle imbalances in which tightness in the upper trapezius and levator scapulae dorsally cross with tightness in the pectoralis major and minor muscles, and weakness of deep cervical flexors cross ventrally with weakness of the middle and lower trapezius. Postural alterations from this dysfunction, including forward head, rounded shoulders, and scapular dyskinesis, contribute to upper-back and shoulder pain, particularly among office workers who spend long periods of the workday on a computer. Upper crossed syndrome is a significant contributor to both neck pain and shoulder pain among computer users, which have been rated at 55–69%, and 15–52%, respectively. Despite its prevalence, knowledge about UCS and its treatment remains spotty among primary care physicians. In addition, improvements in workstation ergonomics along with hourly work breaks may be considered as primary prevention strategies for UCS. Objectives: This narrative review examines and synthesizes evidence about the epidemiology and diagnosis of UCS, along with clinical recommendations for physiotherapeutic approaches to treatment. Ergonomic measures in the workplace, including changes in the design of computer workstations so that both the keyboard and monitor are at the proper heights to minimize the risk of long-term musculoskeletal disorders, are also critical. Methods: The first author, a Doctor of Behavioral Health, performed the initial literature search, which was reviewed by the second author, a PhD in sports injury epidemiology. The third author, a chiropractor and practice owner, provided clinical recommendations for stretching and strengthening exercises, which were also described in the literature. Discussion: While easily treatable when caught early, UCS may become resistant to noninvasive approaches over time, and more severe pathologies of the neck and shoulder, including impingement, thoracic outlet syndrome, and cervicogenic headaches may result. Because there is no specific ICD code for UCS, it is important for physicians to recognize the early signs, consider them in the context of workplace-related injuries, and understand physiotherapeutic strategies for symptom resolution. Full article

Blended learning is widely adopted in higher education, yet little is known about how students experience its motivational and instructional features. In this study, we examined undergraduate students’ experiences regarding blended learning by integrating Herzberg’s two-factor theory with the TPACK framework. Semi-structured interviews were conducted with 24 undergraduates at a large Vietnamese university. A theory-informed qualitative content analysis approach was used to identify codes, categories, and themes. These were then mapped onto the pedagogical content knowledge (PCK), technological content knowledge (TCK), and technological pedagogical knowledge (TPK) intersections of the TPACK framework. The findings showed that hygiene factors included unengaging teaching practices, inadequate digital infrastructure, and limited online interaction. These factors often produced frustration and reduced engagement. Motivator factors included active and relevant pedagogical strategies, engaging and accessible digital resources, and technology-facilitated autonomous, expressive, and creative learning work. These factors encouraged deeper learning and stronger motivation. It is concluded that blended learning design must address both hygiene and motivator factors to improve student engagement. Integrating these factors with the TPACK intersections offers a practical model for improved course structures, enhanced digital resources, and the design of more interactive technology-supported pedagogy. The findings provide actionable implications for higher education institutions seeking to improve the quality of blended learning. Full article

This paper investigates how agricultural practices impact mountain biodiversity. Within the PROMONT project this has been realized across six ADRION pilot areas. By combining species surveys, land-use mapping, and stakeholder input, PROMONT identifies how intensification, agrochemical use, and abandonment threaten ecological integrity. Findings show that traditional agro-pastoral systems support biodiversity, while modern intensification leads to habitat loss and species decline. Agroecological practices, such as organic farming and landscape heterogeneity, offer viable pathways for sustainable coexistence. The study proposes a replicable assessment methodology and recommends integrating biodiversity objectives into agricultural policy, promoting knowledge transfer, and supporting conservation-friendly farming to enhance ecological resilience in mountain environments. Full article

Real-time visual inference on resource-constrained hardware remains a core challenge for edge computing and embedded artificial intelligence systems. Recent deep learning architectures, particularly Vision Transformers (ViTs) and Detection Transformers (DETRs), achieve high detection accuracy but impose substantial computational and memory demands that limit their deployment on low-power edge platforms such as NVIDIA Jetson and Raspberry Pi devices. This paper presents a systematic review of model compression and optimization strategies—specifically quantization, pruning, and knowledge distillation—applied to lightweight object detection architectures for edge deployment. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, peer-reviewed studies were analyzed from Scopus, IEEE Xplore, and ScienceDirect to examine the evolution of efficient detectors from convolutional neural networks to transformer-based models. The synthesis highlights a growing focus on real-time transformer variants, including Real-Time DETR (RT-DETR) and low-bit quantized approaches such as Q-DETR, alongside optimized YOLO-based architectures. While quantization enables substantial theoretical acceleration (e.g., up to 16× operation reduction), aggressive low-bit precision introduces accuracy degradation, particularly in transformer attention mechanisms, highlighting a critical efficiency-accuracy tradeoff. The review further shows that Quantization-Aware Training (QAT) consistently outperforms Post-Training Quantization (PTQ) in preserving performance under low-precision constraints. Finally, this review identifies critical open research challenges, emphasizing the efficiency–accuracy tradeoff and the high computational demands imposed by Transformer architectures. Future directions are proposed, including hardware-aware optimization, robustness to imbalanced datasets, and multimodal sensing integration, to ensure reliable real-time inference in practical agricultural edge computing environments. Full article

Efficient irrigation planning in data-scarce regions remains challenging due to limited access to localized meteorological data, reliance on complex computer-based models, and the technical knowledge required to deploy them at the field scale. Hence, the need for accessible, smartphone-based tools that simplify soil water balance calculations using public data to support practical decision-making in resource-limited contexts. This smartphone-based application estimates Net and Gross Irrigation Requirements using a Soil Water Balance (SWB) framework. The app combines region-specific empirical formulations for Effective Rainfall (Pe) calculation. The application utilizes user-supplied crop and irrigation parameters and meteorological data available in the public domain and operates at multiple temporal scales (daily, 10-day, weekly, and monthly), thereby supporting flexible irrigation schedules. The performance of app was evaluated through simulation-based benchmarking against FAO-CROPWAT 8.0 using harmonized inputs across five representatives agro-climatic region: Central India, Southern Vietnam, Northern Thailand, Western Bangladesh, and Central Sri Lanka. Quantitative comparison showed deviations within ±5% for Effective Rainfall, crop evapotranspiration, Net Irrigation, and Gross Irrigation, and low mean bias values (−2.8% to +3.3%) show the absence of systematic over- or under-estimation compared to CROPWAT model. The application also demonstrated responsiveness to climatic variability. Although the validation is limited to few representative locations and assumed minimal runoff conditions, the results suggest that the proposed method is technically consistent and feasible in practice. This study demonstrates smartphone-based application as a decision support for field-level irrigation planning and water resource management, particularly in data-limited agricultural contexts. Full article

Construction projects are inherently exposed to high levels of uncertainty due to technical complexity, multiple stakeholders, and dynamic operating environments. However, empirical evidence on the systematic implementation of risk management practices in developing construction contexts remains limited. Unlike studies that assess the effectiveness or outcomes of risk management, this study addresses the gap by examining perception-based evidence of its implementation at the project and organizational levels in Jordanian construction projects. The study focuses on planning, control and monitoring, perceived advantages, and implementation barriers. A quantitative, survey-based research design was employed using purposive sampling. The statistical population consisted of engineers, project managers, and contractors working in the Jordanian construction sector. Out of 280 distributed questionnaires, 232 valid responses were received (response rate: 82.9%). Data were analyzed using descriptive statistics and one-sample t-tests, with the neutral midpoint of the five-point Likert scale (3.00) used as the reference value. The reliability of the instrument was confirmed by Cronbach’s alpha coefficients ranging from 0.814 to 0.868. The findings indicate generally positive perceptions of risk management implementation, with mean values ranging from 3.84 to 4.13. Risk management planning achieved the highest mean score (4.13), whereas control and monitoring practices were comparatively weaker (3.84). Although 82.3% of respondents reported applying risk management techniques, experience levels remain low to moderate. Key barriers include the lack of structured programs, limited knowledge, and insufficient experience. The results highlight the need for institutionalized risk management frameworks and targeted professional training to enhance systematic implementation. Full article

Current approaches to gaming disorder prevention remain comparatively narrow, and prevention efforts are frequently underdeveloped and fragmented. Using the socio-ecological model (SEM), this qualitative study mapped frontline practitioners’ perceived obstacles and opportunities to develop a multi-level, practice-grounded framework for policy and implementation. Semi-structured interviews were conducted with 18 prevention professionals in Flanders (Dutch-speaking Belgium), recruited via purposive and snowball sampling. A hybrid inductive–deductive analysis—iterative coding guided by Layder’s adaptive theory—organized findings across SEM levels. At the public policy level, participants highlighted insufficient sustainable funding but saw potential in coordinated frameworks moving prevention beyond substance-focused agendas. At the community level, a clear knowledge gap emerged, with opportunities in integrating gaming within broader digital well-being efforts. Institutionally, the absence of practical tools and clear referral pathways was noted, in addition to high participation barriers, whereas accessible programs with targeted outreach were viewed as promising. Interpersonally, parental disengagement was common, but early involvement and pedagogical guidance were seen as key levers. At the intrapersonal level, limited self-insight and emotion regulation impeded change, while resilience, self-confidence, and offline activities were protective. This first empirical application of the SEM to gaming disorder prevention highlights the need for a multi-level, context-sensitive framework that bridges public health and digital media perspectives. Full article

Practical training and recruitment strategies are critical for the sustainable implementation of psychosocial interventions. However, few studies have examined how to prepare community partners and doctoral students to support culturally adapted psycho-oncology interventions. This pre-pilot study aims first to evaluate two distinct training programs and recruitment procedures, and second to explore preliminary pre-post outcomes of the Caregiver-Patients Support to Cope with Advanced Cancer (CASA) intervention, using the Consolidated Framework for Implementation Research (CFIR). Three clinical psychology graduate students received CASA training, and two community partners completed Recruitment training. We present descriptive pre- and post-assessments, along with qualitative feedback, for both training and institutional (Puerto Rico Biobank) and community-based recruitment outcomes. A related-samples nonparametric analysis examined pre- and post-CASA intervention signals. Results indicated knowledge gains among doctoral students (pre-test M = 3.3; post-test M = 9.3) and community partners (pre-test M = 4.5; post-test M = 9.5). Preliminary outcomes revealed significant improvements in spiritual well-being (Z = −2.618, p = 0.009) and quality of life (Z = −2.957, p = 0.003) and a reduction in depressive (Z = −2.764, p = 0.006), anxiety (Z = −2.667, p = 0.008), and distress (Z = −2.195, p = 0.028) symptoms following CASA. Of 26 recruited dyads, institutional referrals enrolled 16 dyads (61.5%), while community partners referred 10 dyads with a 90.9% success rate. Findings support the feasibility of both training and CASA exploratory outcomes suggest meaningful psychosocial benefits for Latino dyads coping with advanced cancer. Combining institutional infrastructure with community engagement may enhance sustainability and equitable access to psycho-oncology care. Full article

Socioscientific issues (SSIs) provide meaningful contexts for developing students’ competencies in scientific evaluation and decision-making. This study developed an SSI-based instructional unit to support primary school students in making decisions about genome-edited fish by considering risks and benefits and proposing risk mitigation. The study aimed to examine the unit’s effectiveness in improving students’ risk-related decision-making and their attitudes toward critical thinking and risk. Sixty-three fifth-grade students participated in an 18-lesson unit comprising two phases: information gathering and risk management practice. Students completed three decision-making tasks and a post-unit questionnaire on related attitudes. Written arguments were analysed using a rubric based on claims, risk knowledge, benefit knowledge, and risk mitigation. The results indicated that the unit improved the quality of students’ socioscientific arguments. By the final task, about 60% of arguments reached the highest level, demonstrating integration of risk knowledge and corresponding mitigation. However, students’ risk–benefit emphasis ratings showed that their decisions remained predominantly risk-focused, and questionnaire data revealed a persistent zero-risk mindset. These findings provide empirical evidence that an SSI-based unit incorporating risk management practice can foster primary students’ risk-related socioscientific decision-making. Further refinement is needed to shift students’ risk attitudes and support more balanced risk–benefit reasoning. Full article

In recent years, with the rapid advancement of science and technology, generative artificial intelligence has increasingly entered the public eye. Primarily through intelligent algorithms that simulate human logic and integrate vast amounts of network data, it provides designers with solutions that transcend traditional thinking, enhancing both design efficiency and quality. Compared to traditional design methods reliant on human experience, generative design possesses robust data processing capabilities and the ability to refine design proposals, significantly reducing preliminary design time. This study employs the CiteSpace visualization tool to systematically organize and conduct knowledge map analysis of research literature related to generative AI in architectural design within the Web of Science database from 2005 to 2025. Findings reveal the following: (1) International research exhibits a trend toward interdisciplinary convergence. In recent years, research in this field has grown rapidly across nations, with continuously increasing academic influence; (2) Research primarily focuses on technological applications within architectural design, aiming to drive innovation and development by providing superior, more efficient technical support; (3) Generative AI in architectural design has emerged as a prominent international research focus, reflecting a shift from isolated design to industry-wide integration; (4) Generative AI has become a core global architectural design topic, with future research advancing toward full-process intelligent collaboration. High-quality knowledge graphs tailored for the architecture industry should be constructed to overcome data silos. Concurrently, a multidimensional evaluation system for generative quality must be established to deepen the symbiotic design paradigm of human–machine collaboration. This significantly enhances efficiency while reducing the iterative nature of traditional methods. This study aims to provide empirical support for theoretical and practical advancements, offering crucial references for practitioners to identify business opportunities and policymakers to optimize relevant strategies. Full article