Search Results (7,757)

Software-Defined Wide Area Networks (SD-WAN) have emerged as a rapidly evolving technology designed to meet the growing demand for flexible, secure, and scalable network infrastructures. This paper provides a review of SD-WAN techniques, focusing on their principles of operation, mechanisms, and evolution, with particular attention to applications in resource-constrained environments such as mobile, satellite, and radio networks. The analysis highlights key architectural elements, including security mechanisms, monitoring methods and metrics, and management protocols. A classification of both commercial (e.g., Cisco SD-WAN, Fortinet Secure SD-WAN, VMware SD-WAN, Palo Alto Prisma SD-WAN, HPE Aruba EdgeConnect) and research-based solutions is presented. The overview covers overlay protocols such as Overlay Management Protocol (OMP), Dynamic Multipath Optimization (DMPO), App-ID, OpenFlow, and NETCONF, as well as tunneling mechanisms such as IPsec and WireGuard. The discussion further covers control plane architectures (centralized, distributed, and hybrid) and network monitoring methods, including latency, jitter, and packet loss measurement. The growing importance of Artificial Intelligence (AI) in optimizing path selection and improving threat detection in SD-WAN environments, especially in resource-constrained networks, is emphasized. Analysis of solutions indicates that SD-WAN improves performance, reduces latency, and lowers operating costs compared to traditional WAN architectures. The paper concludes with guidelines and recommendations for using SD-WAN in resource-constrained environments. Full article

Varroa mites are a major global threat to honeybee colonies. Combining digital twins with scenario-generating models can be an enabler of precision apiculture, allowing for monitoring Varroa spread, generating treatment scenarios under varying conditions, and running remote interventions. This paper presents the conceptual design of this system for large-scale Varroa management in honeybee apiaries, with initial validation conducted through simulations and feasibility analysis. The design followed a design research framework. The proposed system integrates a wireless sensor network for continuous hive sensing, image capture, and remote actuation of treatment. It employs generative time-series models to forecast colony dynamics and a statistical network model to represent inter-colony spread; together, they support spread scenario prediction and what-if evaluations of treatments. The system evolves through continuous updates from field data, improving the accuracy of spread and treatment models over time. As part of our design research, an early feasibility assessment was carried out through the generation of synthetic data for spread model pretraining. In addition, a node-level energy budget for sensing, communication, and in-hive treatment was developed and matched with battery capacity and life calculations. Overall, this work outlines a path toward real-time, data-driven Varroa management across apiary networks, from regional to cross-border scales. Full article

Power-system safety is fundamental to protecting lives and ensuring reliable grid operation. Yet, hierarchical text classification (HTC) methods struggle with domain-dense accident narratives that require cross-sentence reasoning, often yielding limited fine-grained recognition, inconsistent label paths, and weak evidence traceability. We propose EG-HPT (Evidence-Grounded Hierarchy-Aware Prompt Tuning), which augments hierarchical prompt tuning with Global Pointer-based nested-entity recognition and a sentence–entity heterogeneous graph to aggregate cross-sentence cues; label-aware attention selects Top-k evidence nodes and a weighted InfoNCE objective aligns label and evidence representations, while a hierarchical separation loss and an ancestor-completeness constraint regularize the taxonomy. On a HFACS-based power-accident corpus, EG-HPT consistently outperforms strong baselines in Micro-F1, Macro-F1, and path-constrained Micro-F1 (C-Micro-F1), with ablations confirming the contributions of entity evidence and graph aggregation. These results indicate a deployable, interpretable solution for automated risk factor analysis, enabling auditable evidence chains and supporting multi-granularity accident intelligence in safety-critical operations. Full article

Under the global climate change, variations in climatic elements such as temperature, precipitation, and sunshine duration significantly impact the growth, development, and yield formation of winter wheat. A precise understanding of the impact of climate change on winter wheat growth and the scientific use of meteorological resources are crucial for ensuring food security, optimizing agricultural planting structures and agricultural sustainability. This study uses statistical methods and focuses on the Beijing–Tianjin–Hebei region, utilizing data from 25 meteorological stations from 1961 to 2010 and winter wheat yield data from 1978 to 2010. Twelve refined indicators encompassing temperature, precipitation, and sunshine duration were constructed. Path analysis was employed to determine their weights, establishing a comprehensive climate indicator model. Results indicate: Temperature indicators in the region show an upward trend, with accumulated temperature of the whole growth period increasing at a rate of 61.1 °C·d/10a. Precipitation indicators reveal precipitation of the whole growth period rising at 3.9 mm/10a and pre-winter precipitation increasing at 4.2 mm/10a. Sunshine duration exhibits a declining trend, decreasing at 72.7 h/10a during the whole growth period. Comprehensive climate indicators decrease from south to north, with the southwest region exhibiting the highest tendency rate (18.41), while the central and southern regions show greater variability. This study provides scientific basis for optimizing winter wheat planting patterns and rational utilization of climate resources in the Beijing–Tianjin–Hebei region. It recommends prioritizing cultivation in western southern Hebei and improving water conditions in the central and northern areas through irrigation technology to support sustainable crop production. Full article

In this study, we propose a novel hybrid framework for assessing the invasiveness of an in-house dataset of 114 pathologically proven lung adenocarcinomas presenting as subsolid nodules on Computed Tomography (CT). Nodules were classified into group 1 (G1), which included atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinomas, and group 2 (G2), which included invasive adenocarcinomas. Our approach includes a three-way Integration of Visual, Spatial, and Temporal features with Attention, referred to as I-VISTA, obtained from three processing algorithms designed based on Deep Learning (DL) and radiomic models, leading to a more comprehensive analysis of nodule variations. The aforementioned processing algorithms are arranged in the following three parallel paths: (i) The Shifted Window (SWin) Transformer path, which is a hierarchical vision Transformer that extracts nodules’ related spatial features; (ii) The Convolutional Auto-Encoder (CAE) Transformer path, which captures informative features related to inter-slice relations via a modified Transformer encoder architecture; and (iii) a 3D Radiomic-based path that collects quantitative features based on texture analysis of each nodule. Extracted feature sets are then passed through the Criss-Cross attention fusion module to discover the most informative feature patterns and classify nodules type. The experiments were evaluated based on a ten-fold cross-validation scheme. I-VISTA framework achieved the best performance of overall accuracy, sensitivity, and specificity (mean ± std) of 93.93 ± 6.80%, 92.66 ± 9.04%, and 94.99 ± 7.63% with an Area under the ROC Curve (AUC) of 0.93 ± 0.08 for lung nodule classification among ten folds. The hybrid framework integrating DL and hand-crafted 3D Radiomic model outperformed the standalone DL and hand-crafted 3D Radiomic model in differentiating G1 from G2 subsolid nodules identified on CT. Full article

With the rapid advancement of artificial intelligence technology, the role of Artificial Intelligence Generated Content (AIGC) applications within digital learning communities has become increasingly significant. Enhancing the level of knowledge innovation through the integration of human and artificial intelligence has emerged as a critical issue. Grounded in social cognitive theory, this study utilizes a sample of 407 Super Star Learn community learners as a case study. It applies the Fuzzy Set Qualitative Comparative Analysis (fsQCA) method to investigate the synergistic effects of technological environment, cultural context, and individual cognitive factors in promoting learners’ knowledge innovation capabilities. The results show the following: (1) No single condition constitutes a prerequisite for learners to achieve high-level knowledge innovation when acting in isolation. However, enhancing technical capabilities has a relatively universal impact on promoting learners to achieve these results. (2) The multiple concurrency of the technological environment, cultural environment, and individual cognitive conditions has generated multiple configuration patterns that promote knowledge innovation, indicating that the paths leading to learners’ high-level innovation exhibit the characteristic of numerous concurrency. Therefore, it is suggested that digital learning communities actively explore new paths for sustainable knowledge innovation and development driven by generative artificial intelligence technology, thereby injecting sustainable impetus into the development and innovation process of learners, contributing to the goals of sustainable education. Full article

The simulation of multidimensional wave propagation with variable material parameters is a computationally intensive task, with applications from seismology to electromagnetics. While quantum computers offer a promising path forward, their algorithms are often analyzed in the abstract oracle model, which can mask the high gate-level complexity of implementing those oracles. We present a framework for constructing a quantum algorithm for the multidimensional wave equation with a variable speed profile. The core of our method is a decomposition of the system Hamiltonian into sets of mutually commuting Pauli strings, paired with a dedicated diagonalization procedure that uses Clifford gates to minimize simulation cost. Within this framework, we derive explicit bounds on the number of quantum gates required for Trotter–Suzuki-based simulation. Our analysis reveals significant computational savings for structured block-model speed profiles compared to general cases. Numerical experiments in three dimensions confirm the practical viability and performance of our approach. Beyond providing a concrete, gate-level algorithm for an important class of wave problems, the techniques introduced here for Hamiltonian decomposition and diagonalization enrich the general toolbox of quantum simulation. Full article

In response to growing pressures for sustainability in tourism, this paper examines the techno-economic evaluation of green innovations in small and medium-sized tourism enterprises (SMEs). Focusing on a single case study of a hotel in Greece, the research investigates how and why specific sustainability interventions were implemented and assesses their operational and economic impacts. The study adopts an interpretivist approach, combining process tracing with thematic analysis. The analysis is guided by innovation diffusion theory, supported by organizational learning perspectives, to explain the stepwise adoption of sustainability practices and the internal adaptation processes that enabled them. The techno-economic evaluation draws on quantitative indicators and qualitative assessments of perceived benefits and implementation challenges, offering a broader view of value beyond purely financial metrics. Data were collected through semi-structured interviews, on-site observations, and internal documentation. The findings reveal a gradual, non-linear path to innovation, shaped by adoption dynamics and organizational learning, reinforced by leadership commitment, contextual adaptation, supply chain decisions, and external incentives. Key interventions, including solar energy adoption, composting, and the formation of zero-waste partnerships, resulted in measurable reductions in energy use and landfill waste, along with improvements in guest satisfaction, operational efficiency, and local collaboration. Although it is subject to limitations typical of single-case designs, the study demonstrates how even modest sustainability efforts, when integrated into daily operations, can generate multiple types of outcomes (economic, environmental, and operational). The paper offers practical implications for tourism SMEs and policymakers and formulates propositions for future testing on sustainable innovation in the tourism sector. Full article

Background: High-performance work systems (HPWSs), while designed to boost corporate performance, can inadvertently create a core organizational paradox, triggering a negative feedback loop. Specifically, their intense focus on performance outcomes can create a climate conducive to unethical pro-organizational behavior (UPB), as employees navigate the pressures and perceived obligations, ultimately undermining the organization’s long-term sustainability and viability. While prior research has identified important singular pathways, the mechanisms through which HPWSs simultaneously generate both perceived obligations and performance pressures remain ambiguous. Methods: Drawing on the Job Demands–Resources (JD-R) model, we propose and test a moderated dual-mediation framework. Using survey data from 473 employees, we examine psychological contract fulfillment and bottom-line mentality as parallel mediators, with moral identity as a moderator, in the HPWS-UPB relationship. Results: The analysis demonstrated that HPWSs influence UPB through two distinct and paradoxical pathways: a pressure-driven path via an increased bottom-line mentality, and a reciprocity-driven path via enhanced psychological contract fulfillment. Moral identity emerged as a crucial, albeit asymmetrical, buffer, with its buffering role being particularly consequential for the pressure-driven pathway, as moral identity also significantly weakened the indirect effect of HPWSs on UPB channeled through bottom-line mentality. Conclusions: These findings offer a holistic, systems-based understanding of the performance-ethics paradox. The validation of a dual-pathway model provides a new blueprint for how a single management system produces contradictory outcomes through competing mechanisms. The identification of key intervention points (e.g., fostering moral identity) offers practical strategies for managers to foster systems that support both high productivity and a sustainable ethical climate. Full article

Accurate measurement of the Goos–Hänchen (GH) shift serves as a crucial foundation for the deepening of its theories and the expansion of its applications. To meet the requirements for GH shift measurement, this study constructed a complete experimental system. Composed of a stable laser light source, a high-precision optical path control unit with adjustable incident angles, a high sensitivity detection scheme, and an integrated control and data processing module, this system possesses the capability of full-process measurement covering optical signal generation, adjustment, detection, and data analysis. To effectively obtain the GH shift, this research adopted the TE/TM polarization differential method for measurement experiments and discussed the performance indicators of the system. Experimental verification shows that the system can accomplish the GH shift measurement task accurately and reliably. The experimental platform established in this study provides a practical tool for in-depth theoretical research and application exploration of the GH shift. Furthermore, its high-precision measurement capability not only lays a foundation for the research and development of optical sensing technologies based on the GH shift phenomenon but also offers important support for further revealing the physical essence of the beam shift effect and exploring its potential technical application value. Full article

The growing demand for sustainable solutions in the transportation sector and global decarbonization goals have fueled debate on using hydrogen as an energy source. Although hydrogen’s potential is recognized in Brazil, its application in heavy-duty vehicles still faces structural and technological barriers. This study aimed to analyze the viability of hydrogen as an energy alternative for trucks in Brazil. The research adopted an exploratory qualitative approach, based on the expert analysis method, through semi-structured interviews with development engineers, representatives of heavy-duty vehicle manufacturers, and researchers specializing in hydrogen technologies. The data were organized into a thematic framework and interpreted using content analysis. The results show that, although there is growing interest and ongoing initiatives, challenges such as the cost of fuel cells, the lack of refueling infrastructure, and low technological maturity hinder large-scale adoption. From a theoretical perspective, the study contributes by integrating specialized literature with practical insights from key industry players, broadening the understanding of the energy transition. In practical terms, it outlines some strategic paths, such as expanding technological development and forming partnerships. From a social perspective, it emphasizes the importance of hydrogen as a pillar for sustainable, low-carbon mobility, capable of positively impacting public health and mitigating climate change. Full article

Generative Artificial Intelligence (GAI) has become a topic of increasing societal and academic relevance, with its rapid diffusion reshaping public debate, policymaking, and scholarly inquiry across diverse disciplines. Building on this context, the present study explores the factors influencing GAI adoption among Spanish digital natives (Millennials and Zoomers), using data from a large national survey of 1533 participants (average age = 33.51 years). The theoretical foundation of this research is the Theory of Planned Behavior (TPB). Accordingly, the study examines how perceived usefulness (USEFUL), innovativeness (INNOV), privacy concerns (PRI), knowledge (KNOWL), perceived social performance (SPER), and perceived need for regulation (NREG), along with gender (FEM) and generational identity (GENZ), influence the frequency of GAI use. A mixed-methods design combines ordered logistic regression to assess average effects and fuzzy set qualitative comparative analysis (fsQCA) to uncover multiple causal paths. The results show that USEFUL, INNOV, KNOWL, and GENZ positively influence GAI use, whereas NREG discourages it. PRI and SPER show no statistically significant differences. The fsQCA reveals 17 configurations leading to GAI use and eight to non-use, confirming an asymmetric pattern in which all variables, including PRI, SPER, and FEM, are relevant in specific combinations. These insights highlight the multifaceted nature of GAI adoption and suggest tailored educational, communication, and policy strategies to promote responsible and inclusive use. Full article

Background/Objectives: This study analyzes how Health and Fitness Awareness influences quality of life (QOL), mediated by Health Behavior Action and Safety Behavior Practice, within the Social Determinants of Health (SDH) framework. Methods: Accordingly, a multi-group structural equation modeling (SEM) analysis was conducted on the data obtained from 6601 respondents selected from the 2024 National Sports for All Survey, jointly administered by the Ministry of Culture, Sports and Tourism and Korea Sports Promotion Foundation. Nationally representative survey data was collected across metropolitan, mid-sized, and rural areas in South Korea. The analysis further examined whether the structural pathways differed by regional size. Outcome measures included path coefficients and latent mean differences among Health and Fitness Awareness, Health Behavior Action, Safety Behavior Practice, and Improvement in QOL. Results: The analysis revealed that Health and Fitness Awareness significantly influenced both Health Behavior Action and Safety Behavior Practice; these, in turn, had positive effects on Improvement in QOL. Moreover, the structural pathways differed by region: whereas Health Behavior Action played a more significant mediating role in large cities, Safety Behavior Practice was more prominent in mid-sized ones. Conclusions: These findings are expected to provide a theoretical and policy-based foundation for region-specific health promotion strategy development and health equity advancement. Full article

Urban Air Mobility (UAM) has the potential to revolutionize urban transportation, largely with the deployment of Unmanned Aerial Vehicles (UAVs), commonly known as drones. After an initial stage focused on technology requirements, research is now shifting toward investigating operational requirements, which are unavoidably affected by urban characteristics. This study aims to explore the implementation of UAM services within urban environments by mapping the current scientific landscape from a city-focused perspective. Following a systematic search procedure, a bibliometric analysis was conducted on studies published between 2010 and 2024, examining over 350 articles that address UAM and urban-related topics. Trends in publication volume and scientific impact were analysed, along with influential manuscripts, collaborations, and leading countries in the field. Through a keyword co-occurrence analysis, five main research themes were identified: air traffic management, risk assessment, environmental factors (wind and noise), and vertiport location. These themes were further explored through a scoping review to assess current research and emerging directions. The findings highlight that urban characteristics are not just operational constraints but also fundamental elements that shape UAM strategies, influencing UAV path planning, safety, environmental constraints, and infrastructure design. Future research directions include the development of urban digital twins, comprehensive urban spatial databases, and multi-objective optimization frameworks to support the effective implementation of UAM into cities. Full article

Biofouling is the colonization and attachment of sessile organisms on submerged surfaces, whether natural or artificial. The presence of these communities compromises the structural integrity, operational efficiency, and durability of coastal structures, resulting in high economic and environmental costs, especially when conventional removal methods involve the use of toxic biocides. In this context, this article aimed to evaluate the scientific productivity of the literature related to sustainable antifouling strategies, with an emphasis on technologically and environmentally sustainable solutions, through a bibliometric analysis. We analyzed 160 research articles and 90 patents published between 2004 and 2024. It was observed that, since 2019, there has been an increase in publications about biofouling solutions, with a notable emphasis on China’s leadership in both scientific production and patent filings. This topic has also attracted extensive international collaboration. The most promising strategies for controlling marine biofouling involve a combination of physical, chemical, and biological methods, integrated with sustainable coatings. The growing demand for low-environmental-impact solutions has driven the development of safer, more effective, and economically viable antifouling technologies. Therefore, the integration of traditional techniques with advances in biotechnology represents a strategic path to mitigating the impacts of biofouling in marine environments. Full article