Search Results (22,671)

The undergraduate nursing students’ final year project, commonly called a ‘dissertation’ is an important component of the bachelor’s nursing programme. It can take the form of a literature review and proposal for a research or service improvement project. While crucial for developing research competence and evidence-based practice skills in preparation for their future careers, nursing students often find the dissertation process highly stressful. An online qualitative survey comprising open-ended questions was used to elicit nursing students’ rich, reflective accounts of the dissertation process at a university in the Northeast of England (hereafter referred to as the study site) from those who have recently completed their dissertations. The data obtained from 24 pre-registration nursing students who responded to the survey were thematically analysed. The findings revealed that critical relationships and essential support systems were key mediators of the challenges students faced, particularly a lack of readiness for the dissertation module, but they ultimately achieved transformative outcomes of an effective learning experience. Their navigational challenges can inform curriculum design and practices to better support students in their dissertation journey. Full article

Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by intelligently shaping the propagation environment. However, non-line-of-sight (NLoS) blockage between the access point (AP) and user equipment (UE) can still significantly degrade communication performance. This paper investigates the channel degradation caused by NLoS blockage in a single-antenna AP and multi-antenna UE system and proposes a joint power allocation and phase optimization scheme based on RIS and deep reinforcement learning (DRL). Under a composite channel model with direct and RIS-reflected links, the objective is to maximize the weighted sum rate subject to total power constraints, unit-modulus constraints on RIS elements, and quality of service (QoS) requirements. Due to the coupled variables and the non-convex unit-modulus constraint, conventional alternating optimization (AO) and convex approximation methods usually incur high complexity and yield suboptimal solutions. To address this issue, a DRL algorithm based on an Actor–Critic architecture is developed to learn adaptive power allocation and reflection coefficient adjustment policies through interaction with the environment, without requiring full global channel state information (CSI). Simulation results demonstrate that the proposed method achieves higher signal-to-interference-plus-noise ratio (SINR) and throughput while providing faster convergence and better generalization than existing methods. Full article

Background: As Taiwan transitions into a super-aging society, the government has launched “Long-term Care (LTC) 3.0,” a policy initiative that marks a strategic shift from service expansion to integrated quality verification, digital oversight, and social resilience. This transition demands a robust quality verification mechanism. Ensuring perceptual consistency between service providers and external evaluators is critical for systemic fairness and sustainable service quality. Objective: This study utilized a two-dimensional gap analysis to examine the discrepancy in service quality benchmarks between home-based LTC center managers and assessment committee members, identifying critical divergence zones for institutional improvement. Methods: A cross-sectional evaluative study was conducted, involving center managers (evaluatees, n = 50) and external experts (evaluators, n = 28). The data were collected via a structured instrument covering 20 consensus benchmarks. Results: Significant perceptual gaps were identified across all dimensions (p < 0.001), with “Professional Care Quality” exhibiting the largest effect size (Cohen’s d > 1.5). Benchmarks with low external scores but high internal ratings were categorized into the “Overestimation (Management Blind Spot)” quadrant, signaling a systemic overestimation bias in administrative and clinical risk management. Conclusions: This study provides empirical evidence for the refinement of LTC 3.0 assessment systems. The results offer a strategic roadmap for policymakers to enhance organizational resilience by transitioning from subjective self-perception to objective, data-driven quality management through the two-dimensional gap model. Full article

The industrial metaverse is a digital twin space that integrates the real world with virtual environments through bidirectional synchronization. It supports critical services, such as time-sensitive machine control and large-scale collaboration, which require Time-Sensitive Networking and scalable Data Distribution Services. DDS, developed by the Object Management Group, provides excellent scalability and diverse QoS policies but struggles to guarantee transmission delay and jitter for time-critical applications. TSN, based on IEEE 802.1 standards, addresses these challenges by ensuring time-criticality. However, current research lacks comprehensive integration mechanisms for DDS and TSN, particularly from the viewpoints of semantics and system framework. Additionally, there is no adaptive QoS mapping converting the abstract DDS QoS policies to the sophisticated TSN QoS parameters. This paper presents a novel DDS-over-TSN framework that incorporates three key functions to address these challenges. First, Cross-layer QoS Mapping automates correspondences between DDS and TSN parameters, deriving technical constraints from standard documentation through retrieval-augmented generation. Second, Semantic Priority Estimation extracts substantial priority levels by utilizing language model embedding vectors as high-dimensional feature extractors. Third, Adaptive Resource Allocation performs dynamic bandwidth distribution for each priority level through reinforcement learning. Simulation results reveal over 99% mapping accuracy and 97% consistency in priority extraction. The applied Deep Reinforcement Learning paradigm allocated 99% of required resources to high-priority classes and reduced resource wastage by 15% compared to conventional methods. This methodology meets industrial requirements by ensuring both deterministic real-time performance and efficient resource isolation. Full article

Urban logistics systems face growing delivery demand and complex traffic and operational constraints, which make unmanned delivery carriers, including unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), a promising solution. Existing studies typically focus on a single delivery carrier type and rely on idealized assumptions, overlooking heterogeneous cooperation under multiple stations, multiple time windows, and real-world transport conditions. To address these gaps, we propose the Multi-Station UAV–UGV Cooperative Delivery Scheduling Problem with Temporally Discontinuous Service Availability (MSUUCDSP) to minimize the total travel and waiting time of UAVs and UGVs. To solve the problem, we propose a mixed-integer linear programming (MILP) model with a novel mathematical approach and a Hybrid Large Neighborhood Search (HLNS) algorithm. Additionally, we adopt a Hidden Markov Model (HMM)-based map-matching method and big data techniques to capture realistic operational characteristics. Computational experiments are conducted on various realistic instances under four diverse scenarios. Results show that UAV–UGV cooperation significantly improves efficiency, reducing total time cost by 17.12% compared with single-mode delivery, and they reveal substantial discrepancies between idealized assumptions and realistic scenarios. We further develop an ArcGIS-based simulation to support practical implementation. The findings provide valuable insights for decision-making and engineering applications for logistics operators. Full article

Achieving high-level self-reliance in science and technology requires a science and technology finance ecosystem that is aligned with the needs of technological innovation. To overcome bottlenecks in core technologies, firms must accelerate R&D, strengthen their core competitiveness, and pursue innovation-led, quality-oriented development. Using provincial-level data for 2013–2023, this paper constructs an index system for China’s science and technology finance ecosystem from four dimensions: science and technology financial services, science and technology capital markets, science and technology financial organizations, and government guidance for science and technology. We then measure the development level of this ecosystem and employ a panel data model to examine its impact on innovation in Specialized and Sophisticated SMEs. The results show that a more developed science and technology finance ecosystem significantly promotes innovation in these firms, with a stronger effect on substantive innovation than on strategic innovation. These findings remain robust across a series of robustness checks. Further analysis reveals significant heterogeneity across regions and levels of government intervention: the positive effect is stronger in eastern China and in regions with weaker government intervention. Mechanism tests indicate that the science and technology finance ecosystem promotes innovation by facilitating the accumulation of R&D capital and the agglomeration of scientific and technological talent. This study enriches the literature on science and technology finance ecosystems and SME innovation, and provides policy-relevant evidence for ecosystem development and the cultivation of Specialized and Sophisticated SMEs. Full article

The study of river basins is key to understanding the dynamics of microplastic (MPs) generation, transport, and accumulation in regions where various productive activities converge and waste management is limited. The objective of this study was to characterize economic activities in the Tecolutla River basin, Mexico, to identify risk factors associated with MPs generation and release throughout the production chain. A descriptive applied research study was conducted using a structured questionnaire administered to 19 economic units distributed across seven municipalities in the Tecolutla River basin, Veracruz, Mexico. The instrument allowed for the evaluation of the use of plastic materials in inputs, production processes, final products, and waste management practices. Among the economic units analyzed (n = 19), 94.7% reported the use of polymeric materials, with a predominance of thermoplastics such as polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polypropylene (PP), which have a high potential for secondary fragmentation. Within the tertiary sector, accommodation and food preparation services account for the highest proportion of units with limited separation and recycling practices. Activities in the secondary sector, especially the textile and construction industries, showed a high potential for releasing this pollutant due to the use of synthetic fibers, composite materials, and the absence of retention systems. The results provide a basis for the design of mitigation strategies targeting priority productive sectors at the watershed scale. Full article

Data centers are critical digital infrastructure supporting cloud computing, artificial intelligence, and global information services. Despite their high-reliability design, they remain vulnerable to fire incidents due to continuous operation, high electrical loads, dense power systems, and the increasing use of lithium-ion batteries. Although such events are rare, their consequences can be severe, including service disruption, equipment damage, financial loss, and risks to data integrity. This study presents a systematic literature review of fire safety risk management frameworks in data centers, following PRISMA guidelines. Peer-reviewed studies published between 2020 and 2025 were retrieved from Scopus and Web of Science, screened, and appraised using structured quality criteria. Twelve empirical studies were synthesized and benchmarked against NFPA 75 and NFPA 76 standards. The findings are organized into three domains: Strategic Management, Fire Risk, and Fire Preparedness. The results show a strong focus on technical prevention and electrical hazards, while organizational readiness, emergency response, and recovery remain underexplored. Benchmarking indicates that industry standards adopt a more comprehensive lifecycle approach than the academic literature. This study reframes data center fire safety as a socio-technical reliability system and highlights critical gaps, providing a foundation for future research and improved fire safety governance and resilience. Full article

Calcium–silicate–hydrate (C-S-H), the primary binding phase governing cement paste cohesion, undergoes progressive physicochemical transformation upon carbonation—a process that critically dictates concrete durability in atmospheric environments. When CO₂ penetrates the porous cement matrix, it triggers a cascade of degradation mechanisms: calcium leaching decalcifies the C-S-H structure, inducing polymerization of silicate chains from dimeric to longer-chain configurations, while concurrent precipitation of calcium carbonate and amorphous silica gel fundamentally reconstitutes the nanoscale architecture. These nanoscale alterations propagate to macroscopic property evolution, manifesting as initial strength and stiffness gains due to pore-filling carbonation products followed by eventual deterioration as the cohesive binding network deteriorates. This review synthesizes current understanding of carbonation-induced structural evolution, examining the coupled influences of environmental parameters—CO₂ concentration, relative humidity, and temperature—alongside C-S-H intrinsic chemistry (Ca/Si ratio, aluminum substitution, and alkali content) on reaction kinetics and material performance. However, significant knowledge gaps persist: predictive models for in-service carbonation rates remain elusive due to the disconnect between idealized laboratory conditions and the heterogeneous, cracked reality of field concrete; the causal linkage between nanoscale C-S-H alteration and macroscale cracking patterns along with physical performance is poorly resolved, and most mechanistic studies rely on synthetic C-S-H, neglecting the compositional complexity of real Portland cement systems. We further propose emerging protection strategies, including surface barrier coatings and low-carbon alternative binders (geopolymers, calcium sulfoaluminate cements, carbon-negative materials such as recycled cement), which demonstrate enhanced carbonation resistance. Future research priorities include developing effective coating barriers for carbonation protection, developing operando characterization techniques for real-time reaction monitoring, deploying machine learning algorithms to bridge atomistic simulations with structural-scale predictions, and establishing long-term field performance databases to validate laboratory-derived degradation models. Full article

Background/Objectives: Food insecurity affects college students at nearly twice the rate of US households, with documented impacts on student academic performance, physical and mental health, and socialization. While frameworks exist to conceptualize general food insecurity and food insecurity in specific contexts, researchers and practitioners lack resources to guide system-level responses to food insecurity on college and university campuses and assess those responses. In this study, we aimed to develop and validate a simple yet comprehensive framework for assessing food insecurity responses within the context of higher education. Methods: We adapted an eight-phase process for framework development: (1) map selected data sources within the multidisciplinary literature, (2) read and categorize selected sources, (3) identify and name concepts, (4) deconstruct and categorize concepts based on their features, (5) group similar concepts together, (6) synthesize concepts into a framework, (7) validate the framework using expert panel review, and (8) revise as necessary. Results: The developed Campus Food Aid Self-assessment (CFAS) framework consists of six dimensions: Student Services and Supports; Involvement; Advocacy; Awareness and Culture Efforts; Education and Training; and Research, Scholarship, and Creative Works. Expert panelists (n = 7) reviewed the proposed framework and confirmed the clarity, comprehensiveness, and representativeness of the proposed dimensions, conceptual definitions, and operational variables. Conclusions: With a comprehensive yet accessible structure, the CFAS framework supports the development, coordination, and improvement of campus-based strategies to address food insecurity and support positive student outcomes. Full article

Accurate stress measurement is essential to evaluating structural integrity and plays a pivotal role in the health monitoring and predicting the service life of steel infrastructures. This study proposes a deep learning approach for stress prediction based on longitudinal critically refracted (LCR) ultrasonic waves. The model integrates gated recurrent units (GRU), attention mechanisms, and one-dimensional convolutional neural networks (1D-CNN), enabling direct stress prediction from raw ultrasonic signals without the need for manual feature extraction or explicit physical modeling. To validate the approach, LCR signals were acquired using a custom-built piezoelectric ultrasonic system from 20# steel specimens subjected to uniaxial stresses ranging from 0 to 200 MPa. A dataset comprising 4200 samples was augmented to enhance training efficiency. The proposed model achieved a mean absolute error of 1.94 MPa. Generalization tests demonstrated high accuracy across diverse stress levels, with average errors below 3 MPa, highlighting the model’s robustness. This research presents an accurate, intelligent, and calibration-free ultrasonic method for stress evaluation, providing practical support for stress evaluation in steel structures under actual operating conditions. Full article

Place-based approaches have gained significant attention as a means of addressing entrenched disadvantage through collaborative, locally responsive service delivery, yet implementation has yielded mixed results and the systemic factors that facilitate or impede inter-organisational collaboration remain inadequately understood. This study applied participatory systems mapping as part of a systemic inquiry to identify leverage points for place-based integrated service delivery in the City of Casey, an outer-metropolitan municipality in Melbourne, Australia. Twenty-one representatives from the Casey Futures Partnership engaged in group model building workshops, co-producing a causal loop diagram containing 33 factors and 104 directional connections. The resulting map was analysed using a blended analytical approach combining network metrics with the Action Scales Model. Funding availability and criteria emerged as the most central factor within the system, while belief-level factors, including territorial behaviour and resource and collaboration mindset, were found to be substantially shaped by upstream structural conditions. Factors combining network influence with deeper system positioning and amenability to local action included awareness of community needs and priorities, trust and willingness to collaborate from funders, inter-organisational communication, and advocacy effectiveness. The findings support multi-level place-based approaches that address underlying beliefs and structural conditions alongside operational improvements. Full article

The ramping requirement in new power systems primarily stems from net load variations and forecast errors of renewable energy and load. Designing an equitable cost allocation mechanism for ramping services based on these factors facilitates incentives for generation and load to actively reduce ramping demands, thereby alleviating system ramping pressure. Accordingly, this paper proposes a fair ramping cost allocation mechanism based on the ramping responsibility coefficients of market participants. Under this mechanism, a market-oriented operation model for wind–hydro-storage joint operation is established to verify its effectiveness in market applications. First, a ramping cost allocation mechanism is constructed based on ramping responsibility coefficients. According to the responsibility coefficients of market participants for deterministic and uncertain ramping requirements, ramping costs are allocated to the corresponding contributors in proportion to the ramping demands caused by net load variations, load forecast deviations, and renewable energy forecast deviations. Specifically, for costs arising from renewable energy forecast errors, an allocation mechanism is designed based on the difference between the declared error range and the actual error. Second, within this allocation framework, hydropower and storage (including cascade hydropower and hybrid pumped storage) are utilized as flexible resources to mitigate wind power uncertainty and reduce its ramping costs. A two-stage day-ahead and real-time bi-level game model for wind–hydro-storage cooperative decision-making is developed. The upper level optimizes bilateral trading and market bidding strategies for wind–hydro-storage, while the lower level simulates the market clearing process. Through Stackelberg game modeling, joint optimal operation of wind–hydro-storage is achieved, ensuring mutual benefits. Finally, simulation results validate that the proposed ramping cost allocation mechanism can guide renewable energy to improve output controllability through economic signals. Furthermore, the bilateral trading and coordinated market participation of wind–hydro-storage realize win–win outcomes, reduce the ramping cost allocation for wind power by 23.10%, effectively narrow peak-valley price differences, and enhance market operational efficiency. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. A total of 84 percent of large businesses use cloud computing services in 2025 to enable remote working and higher flexibility of operation with reduction in the cost of operation. Cloud environments are dynamic and multitenant, often demanding high computational resources for real-time processing. However, the cloud system’s behavior is subjected to various kinds of anomalies in which patterns of data deviate from the normal traffic. The varieties of anomalies that exist are performance anomalies, security anomalies, resource anomalies, and network anomalies. These anomalies disrupt the normal operation of cloud systems by increasing the latency, reducing throughput, frequently violating service level agreements (SLAs), and experiencing the failure of virtual machines. Among all anomalies, virtual machine failures are one of the potential anomalies in which the normal operation of the virtual machine is interrupted, resulting in the degradation of services. Virtual machine failure happens because of resource exhaustion, malware access, packet loss, Distributed Denial of Service attacks, etc. Hence, there is a need to detect the chances of virtual machine failures and prevent it through proactive measures. Traditional machine learning techniques often struggle with high-dimensional data and nonlinear correlations, ending up with poor real-time adaptation. Hence, quantum machine learning is found to be a promising solution which effectively deals with combinatorially complex and high-dimensional data. In this paper, a novel quantum-enhanced support vector machine (QSVM) is designed as an optimized binary classifier which combines the principles of both quantum computing and support vector machine. It encodes the classical data into quantum states. Feature mapping is performed to transform the data into the high-dimensional form of Hilbert space. Quantum kernel evaluation is performed to evaluate similarities. Through effective optimization, optimal hyperplanes are designed to detect the anomalous behavior of virtual machines. This results in the exponential speed-up of operation and prevents the local minima through entanglement and superposition operation. The performance of the proposed QSVM is analyzed using the QuCloudSim 1.0 simulator and further validated using expected value analysis methodology. Full article

With the rapid development of applications such as smart cities and the industrial internet, the computation-intensive tasks generated by massive sensing devices pose significant challenges to traditional cloud computing paradigms. Unmanned aerial vehicle (UAV)-assisted edge computing systems, leveraging their high mobility and wide-area coverage capabilities, offer an innovative architecture for low-latency and highly reliable edge services. However, the practical deployment of such systems faces a highly complex multi-objective optimization problem featured by the tight coupling of task offloading decisions, UAV trajectory planning, and edge server resource allocation. Conventional optimization methods are difficult to adapt to the dynamic and high-dimensional characteristics of this problem, leading to suboptimal system performance. To address this critical challenge, this paper constructs an intelligent collaborative optimization framework for UAV-assisted edge computing systems and formulates the system quality of service (QoS) optimization problem as a mixed-integer non-convex programming problem with the dual objectives of minimizing task processing latency and reducing overall system energy consumption. A multi-agent joint task association and trajectory optimization (MA-JTATO) algorithm based on hybrid reinforcement learning is proposed to solve this intractable problem, which innovatively decouples the original coupled optimization problem into three interrelated subproblems and realizes their collaborative and efficient solution. Specifically, the Advantage Actor-Critic (A2C) algorithm is adopted to realize dynamic and optimal task association between UAVs and edge servers for discrete decision-making requirements; the multi-agent deep deterministic policy gradient (MADDPG) method is employed to achieve cooperative and energy-efficient trajectory planning for multiple UAVs to meet the needs of continuous control in dynamic environments; and convex optimization theory is applied to obtain a closed-form optimal solution for the efficient allocation of computational resources on edge servers. Simulation results demonstrate that the proposed MA-JTATO algorithm significantly outperforms traditional baseline algorithms in enhancing overall QoS, effectively validating the framework’s superior performance and robustness in dynamic and complex scenarios. Full article