Search Results (2,699)

As the global digital transformation of agriculture accelerates, the widespread deployment of farming equipment has triggered an exponential surge in agricultural production data. Consequently, traditional cloud computing frameworks face critical challenges: communication latency in the field, the demand for low-power devices, and stringent real-time decision constraints. These bottlenecks collectively exacerbate bandwidth constraints, diminish response efficiency, and introduce data security vulnerabilities. In this context, edge computing offers a promising solution for smart agriculture. By provisioning computing resources to the network periphery and enabling localized processing at data sources adjacent to agricultural machinery, sensors, and crops, edge computing leverages low-latency responses, bandwidth optimization, and distributed computation capabilities. This paper provides a comprehensive survey of the research landscape in agricultural edge computing. We begin by defining its core concepts and highlighting its advantages over cloud computing. Subsequently, anchored in the “terminal sensing-edge intelligence-cloud coordination” architecture, we analyze technological evolution in edge sensing devices, lightweight intelligent algorithms, and cooperative communication mechanisms. Additionally, through precision farming, intelligent agricultural machinery control, and full-chain crop traceability, we demonstrate its efficacy in enhancing real-time agricultural decision-making. Finally, we identify adaptation challenges in complex environments and outline future directions for research and development in this field. Full article

Unmanned aerial manipulators (UAMs), composed of unmanned aerial vehicles (UAVs) and manipulators, have great application potential in aerial manipulation like precision inspection, disaster rescue, etc. However, strong dynamic coupling exists between UAVs and manipulators. In addition, UAMs meet external disturbances such as gusts of wind during movements. Also, the control performance metrics, such as tracking accuracy and control stability, are seriously affected. Therefore, a cooperative control method is developed for a UAM system with a UAV and a 2-degree-of-freedom manipulator. First, the Euler–Lagrange formulation is employed to study the UAM dynamics like inertial forces and coupling effects. Then, an integral sliding mode control (ISMC) method with an integral term is developed to enhance robustness and eliminate steady-state errors. Finally, the proposed ISMC method is validated through numerical simulations in Matlab R2024a, introducing comparative analyses with the Proportional–Integral–Derivative (PID) and SMC controllers. The simulation results and the comparative analyses validate the effectiveness of ISMC, showing its superiority over the PID and SMC controllers in handling dynamic coupling and external disturbances, where the overshoot of ISMC is reduced by an average of more than 90%. The ISMC method provides a high-performance control strategy to promote the practical application of UAMs in various aerial manipulation tasks and lays the foundation for further optimizing control methods for more complex UAM systems. Full article

To reveal how the altitude gradient regulates the effects of rhizosphere microbial dynamics on ecosystem multifunctionality in Hylodesmum podocarpum, a field experiment was conducted across four elevation transects (a.s. 896–1805 m) in the Qinling Mountains. The results showed that rhizosphere soil exhibited peak microbial diversity richness at 1805 m (HB4), with bacterial communities showing a strong interspecific cooperative relationship, while the fungal communities showed a competitive relationship. In addition, this study found the assembly process to be different. Bacterial assemblages changed from random processes (HB1, HB2, HB3) to deterministic processes (HB4), whereas fungal assemblages remained stochastic processes across all elevations. Our results also revealed that synergistic interactions among soil carbon, phosphorus, and nitrogen nutrient functions collectively enhanced nutrient-centered soil multifunctionality. Notably, carbon and phosphorus nutrient functions emerged as the primary drivers of soil multifunctionality. Further mechanistic analysis revealed that while soil pH exerted significant control over both carbon and nitrogen nutrient functions, microbial mediation exhibited functional specialization: bacterial communities predominantly regulated carbon cycling, whereas fungal communities played a more comprehensive role in modulating carbon, nitrogen, and phosphorus dynamics along with overall ecosystem multifunctionality. This finding suggested that altitude gradients indirectly affect the characteristics of the microbial community by regulating soil nutrient status, thereby driving changes in ecosystem multifunctionality. This finding provides new insights into how nutrients regulate ecosystem functions through microbial pathways. Full article

Background: The present study aims to evaluate the effectiveness of ESYSTA^® (Emperra GmbH E-Health Technologies, Germany), a CE-certified digital health application made to support insulin-treated diabetes patients to improve their disease management through better self-empowerment. Methods: To evaluate the effectiveness of ESYSTA^®, data from patients who used ESYSTA^® for at least 12 months and participated in an originally prospective one-arm study were evaluated. This study was conducted in cooperation with the German health insurance company AOK Nordost (2012–2015). From a real-world data pool of insured AOK Nordost patients, a control group was matched to mimic a controlled trial that allows the use of ESYSTA^® to be compared with standard care in the context of a disease management program (DMP). Results: The study results show significant and clinically relevant reductions in HbA1c values of at least 0.4% in ESYSTA^® users after 6 months. After 12 months, users achieved, on average, an HbA1c reduction of approximately 0.7%. These reductions are more pronounced compared to the matched control group. Conclusions: The present study shows the effectiveness of the digital health application ESYSTA^®. Using a matched control group further increased the internal and external validity of the study results. Full article

Background/Objectives: Pediatric acquired brain injury (ABI) often results in persistent challenges that extend beyond motor impairments, affecting quality of life (QoL), social participation, and engagement in physical activity. Given the complexity and chronicity of these outcomes, there is a pressing need for multidimensional interventions grounded in the International Classification of Functioning, Disability and Health (ICF). Sport-based exercise interventions, when developmentally adapted and tailored to individual interests, may promote intrinsic motivation, peer connection, and sustainable engagement—factors especially relevant in pediatric ABI populations, who often experience reduced physical activity and social isolation. However, standardized, replicable protocols specifically tailored to this population remain scarce. This study presents the protocol for a randomized controlled trial evaluating the effects of a 16-week sport-based intervention on QoL, social participation, physical activity engagement, and motor functioning tailored for adolescents with pediatric ABI. Methods: Participants will be randomly assigned to an intervention group or a control group receiving usual care. The intervention consists of one weekly 60-minute session, led by trained professionals in adapted physical activity and pediatric neurorehabilitation. It combines sport-based motor skill training, cooperative games, and group activities specifically tailored to each child’s developmental level, motor abilities, and preferences. Outcomes will be assessed at baseline and following the 16-week intervention period, focusing on QoL, participation, physical activity engagement, and motor functioning. Discussion: This study introduces a structured, child-centered model that bridges clinical rehabilitation and community-based sport. By integrating motor and psychosocial targets through a group sport-based intervention, it aims to enhance recovery across ICF domains. Findings may inform interdisciplinary practice and support the development of sustainable strategies to promote long-term engagement and well-being in adolescents with ABI. Full article

To address concrete cracking in submerged box-shaped hollow thin-walled piers under static ice and hydrostatic pressure, this study proposes a composite strengthening method employing externally bonded steel plates coupled with concrete infill blocks. Based on mechanical theoretical derivation, the strengthened structure is simplified as a cooperative system comprising compression–truss and suspended-cable mechanisms. Key design parameters—including steel plate span, thickness, infill block height, and plate corner configuration—are optimized using a genetic algorithm. The optimization objective minimizes strengthening cost, subject to constraints of concrete crack resistance, steel plate strength, and deformation control, ultimately determining the numerically optimal composite strengthening solution. Validation through planar finite element models demonstrates that: (1) the proposed system effectively suppresses cracking in the original structure; (2) peak stresses in the steel plates remain below the yield strength of Q345 steel; and (3) the theoretical design is reasonable and effective, which can solve the cracking problem of the wading-tank hollow thin-walled pier under the action of surrounding load. Full article

Exoskeleton robots function as augmentation systems that establish mechanical couplings with the human body, substantially enhancing the wearer’s biomechanical capabilities through assistive torques. We introduce a lumbar spine-assisted exoskeleton design based on Variable-Stiffness Pneumatic Artificial Muscles (VSPAM) and develop a dynamic adaptation mechanism bridging the pneumatic drive module with human kinematic intent to facilitate human–robot cooperative control. For kinematic intent resolution, we propose a multimodal fusion architecture integrating the VGG16 convolutional network with Long Short-Term Memory (LSTM) networks. By incorporating self-attention mechanisms, we construct a fine-grained relational inference module that leverages multi-head attention weight matrices to capture global spatio-temporal feature dependencies, overcoming local feature constraints inherent in traditional algorithms. We further employ cross-attention mechanisms to achieve deep fusion of visual and kinematic features, establishing aligned intermodal correspondence to mitigate unimodal perception limitations. Experimental validation demonstrates 96.1% ± 1.2% motion classification accuracy, offering a novel technical solution for rehabilitation robotics and industrial assistance. Full article

The Marine Pollution Control Act (MPCA) in Taiwan aims to align with international conventions such as the United Nations Convention on the Law of the Sea (UNCLOS), the International Convention for the Prevention of Pollution from Ships (MARPOL), the International Convention on Civil Liability for Oil Pollution Damage (CLC), the International Oil Pollution Compensation Funds (FUNDs), and the International Convention for the Control and Management of Ships’ Ballast Water and Sediments (BWM). However, Taiwan’s particular international status prevents formal participation in these treaties. This study evaluates Taiwan’s legal and institutional frameworks on ship emission control, pollution liability and compensation, and interagency coordination, identifying key gaps compared with global standards. By analyzing Japan’s and South Korea’s best practices in port management, cross-border pollution prevention, and vessel monitoring, this study proposes legal and policy reforms that are tailored to Taiwan. Recommendations include strengthening liability mechanisms, enhancing interagency collaboration, monitoring vessels, and fostering regional cooperation. Our findings suggest that these reforms will improve Taiwan’s marine environmental governance and contribute to regional and global ocean sustainability. Full article

This paper addresses the core issues of slow coverage rate growth and high repeated detection rates in multi-UAV cooperative search operations within unknown areas. A distributed cooperative search algorithm based on the maximum entropy mechanism is proposed to resolve these challenges. It innovatively integrates the entropy gradient decision framework with DMPC-OODA (Distributed Model Predictive Control-Observe, Orient, Decide, Act) rolling optimization: environmental uncertainty is quantified through an exponential decay entropy model to drive UAVs to migrate toward high-entropy regions; element-wise product operations are employed to efficiently update environmental maps; and a dynamic weight function is designed to adaptively adjust the weights of coverage gain and entropy gain, thereby balancing “rapid coverage” and “accurate exploration”. Through multiple independent repeated experiments, the algorithm demonstrates significant improvements in coverage efficiency—by 6.95%, 12.22%, and 59.49%, respectively—compared with the Search Intent Interaction (SII) mode, non-entropy mode, and random mode, which effectively enhances resource utilization. Full article

Efficient surface water allocation in reservoir-equipped basins is essential for balancing competing demands within the Water–Energy–Food (WEF) nexus. This study investigated the applicability of Particle Swarm Optimization (PSO) for optimizing water distribution in the Machángara River Basin, Ecuador; a complex, constraint-rich hydrological system. Implemented via the Pymoo package in Python, the PSO model was evaluated across calibration, validation, and execution phases, and benchmarked against exact methods, including Linear Programming (LP) and Mixed Integer Linear Programming (MILP). The results revealed that standard PSO struggled to satisfy equality constraints and yielded suboptimal solutions, with elevated penalty costs. Despite incorporating MILP-inspired encoding and repair functions, the algorithm failed to identify feasible solutions that met operational requirements. The execution phase, which includes reservoir construction decisions, resulted in a total penalty exceeding EUR 164.95 billion, with no improvement observed from adding reservoirs. Comparative analysis confirmed that LP and MILP outperformed PSO in constraint compliance and penalty minimization. Nonetheless, the study contributes a reproducible implementation framework and a comprehensive benchmarking strategy, including synthetic test functions, performance metrics, and diagnostic visualizations. These tools can facilitate systematic evaluation of PSO’s behavior in high-dimensional, nonlinear environments and provide a foundation for future hybrid or adaptive heuristic models. The findings underscore the limitations of standard PSO in hydrological optimization but also highlight its potential when enhanced through hybridization. Future research should explore PSO variants that integrate exact solvers, adaptive control mechanisms, or cooperative search strategies to improve feasibility and convergence. This work advances the methodological understanding of metaheuristics in environmental resource management and supports the development of robust optimization tools under the WEF-nexus paradigm. Full article

This paper investigates the interval type-2 (IT2) fuzzy adaptive fixed-time bipartite consensus self-triggered control for multiple quadrotor unmanned aerial vehicles with deferred full-state constraints and input saturation under cooperative-antagonistic interactions. First, a uniform nonlinear transformation function, incorporating a shifting function, is constructed to achieve the deferred asymmetric constraints on the vehicle states and eliminate the restrictions imposed by feasibility criteria. Notably, the proposed framework provides a unified solution for unconstrained, constant/time-varying, and symmetric/asymmetric constraints without necessitating controller reconfiguration. By employing interval type-2 fuzzy logic systems and an improved self-triggered mechanism, an IT2 fuzzy adaptive fixed-time self-triggered controller is designed to allow the control signals to perform on-demand self-updating without the need for additional hardware monitors, effectively mitigating bandwidth over-consumption. Stability analysis indicates that all states in the closed-loop attitude system are fixed-time bounded while strictly adhering to deferred time-varying constraints. Finally, illustrative examples are presented to validate the effectiveness of the proposed control scheme. Full article

With the growing adoption of connected and autonomous vehicles (CAVs), their market penetration is expected to rise. This study investigates the mixed traffic flow dynamics of human-driven vehicles (HDVs) and CAVs at airport terminal curbsides. A two-lane parking simulation model is developed, integrating the intelligent driver model, PATH-calibrated cooperative adaptive cruise control, and a degraded adaptive cruise control model to capture different driving behaviors. The model accounts for varying time headways among HDV drivers based on their information acceptance levels and imposes departure constraints to enhance safety. Simulation results show that the addition of CAVs can significantly increase the average speed of vehicles and reduce the average delay time. Two metrics are inversely proportional. Specifically, as illustrated by a curbside length of 400 m and a parking demand of 1300 pcph, when the CAV penetration rate p is 10%, 30%, 50%, 70%, and 100%, respectively, compared to p = 0, the average traffic flow speed increases by 1.7%, 6.4%, 15.0%, 27.2%, and 48.7%, respectively. The average delay time decreases by 2.8%, 6.4%, 10.5%, 13.5%, and 20.0%, respectively. Meanwhile, CAVs and HDVs exhibit consistent patterns in terms of parking space utilization: the first stage (0–30% of parking spaces) showed a stable and concentrated trend; the second stage (30–70% of parking spaces) showed a slow downward trend but remained at a high level; the third stage (70–100% of parking spaces) showed a rapid decline at a steady rate. Full article

Background and Objectives: Low-density lipoprotein cholesterol (LDL-C) reduction is critical for cardiovascular disease (CVD) prevention. This study aimed to assess the proportion of patients achieving the LDL-C target in Lithuania and to identify factors associated with target achievement. Materials and Methods: This retrospective study used anonymized health data from the Electronic Health Services and Cooperation Infrastructure Information System (ESPBI IS) in Lithuania. Adults aged ≥40 years with at least one LDL-C measurement in 2023 and no documented cancer diagnosis were included. The primary outcome was the proportion of patients achieving LDL-C < 1.8 mmol/L, the target recommended by the European Society of Cardiology guidelines for high-risk individuals. Univariate logistic regression analysis was conducted to identify factors associated with achieving the LDL-C target. Results: The study included 396,835 patients (mean age, 66.9 years). The mean LDL-C concentration was 3.32 mmol/L, and only 8.1% of patients achieved LDL-C < 1.8 mmol/L. Target achievement was higher among patients in the secondary CVD prevention group compared to primary prevention (20.6% vs. 7.3%). Over half of patients (56.4%) received no lipid-lowering therapy (LLT). Statin monotherapy was the most prescribed LLT (31.3%), while only 2.7% of patients received statin and ezetimibe combination. In logistic regression analysis, secondary prevention status, more frequent cardiologist consultations, and higher LLT prescription frequency were associated with LDL-C target achievement. Compared to patients not receiving LLT, the odds of achieving LDL-C < 1.8 mmol/L were significantly higher in those receiving statin monotherapy (odds ratio [OR]: 3.153, 95% confidence interval [CI]: 3.069–3.240), statin and ezetimibe (OR: 7.631, 95% CI: 7.267–8.013), or statin and antihypertensive (OR: 3.945, 95% CI: 3.803–4.092). Conclusions: LDL-C target attainment remains low in Lithuania, with the underuse of LLT. Broader implementation of guideline-recommended lipid-lowering strategies is needed to improve LDL-C control. Full article

Respirable crystalline silica is a well-established occupational hazard in construction work. Despite increased awareness, consistent exposure control remains a challenge, particularly in dynamic and resource-constrained environments. Respirable crystalline silica exposure in construction environments challenges the achievement of the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being) and SDG 8 (Decent Work and Economic Growth). Respirable crystalline silica particles cause severe health complications, including silicosis, lung cancer, cardiovascular diseases, and autoimmune disorders, representing a significant barrier to achieving SDG 3.9’s target of reducing deaths and illnesses from hazardous chemical exposures by 2030. This systematic review evaluates two decades of advancements (2004–2024) in respirable crystalline silica identification, characterisation, and mitigation within construction, synthesising evidence from 143 studies to assess progress toward sustainable occupational health management. This review documents a paradigmatic shift from traditional exposure assessment toward sophisticated monitoring approaches incorporating real-time detection systems, virtual reality–Computational Fluid Dynamics simulations, and wearable sensor technologies. Engineering controls, including local exhaust ventilation, wet suppression methods, and modified tool designs, have achieved exposure reductions exceeding 90%, directly supporting SDG 8.8’s commitment to safe working environments for all workers, including migrants and those in precarious employment. However, substantial barriers persist, including prohibitive costs, inadequate infrastructure, and regional regulatory disparities that particularly disadvantage lower-resourced countries, contradicting the Sustainable Development Goals’ principles of leaving no one behind. The findings advocate holistic approaches integrating technological innovation with context-specific regulations, enhanced international cooperation, and culturally adapted worker education to achieve equitable occupational health protection supporting multiple Sustainable Development Goals’ objectives by 2030 and also highlighting potential areas for future research. Full article

The escalating crisis of multidrug resistance, together with the persistence of antibiotic residues in clinical and environmental matrices, demands integrated strategies that couple sensitive detection, efficient decontamination, and controlled delivery. However, current techniques for quantifying avibactam (AVI)—a broad-spectrum β-lactamase inhibitor—such as HPLC-UV lack the sensitivity and specificity required for both therapeutic drug monitoring and environmental surveillance. Encapsulation of AVI within cyclodextrins (CDs) may simultaneously enhance its stability, bioavailability, and detectability, while the high binding affinities of CDs position them as molecular traps capable of scavenging residual AVI. In this study, the inclusion complexation of AVI with various CDs was examined through molecular dynamics (MD) simulations, experimental isothermal titration calorimetry (ITC), and non-covalent interaction (NCI) analysis. Stable 1:1 inclusion complexes were observed between AVI and β-cyclodextrin (β-CD), 2,6-dimethyl-β-cyclodextrin (DM-β-CD), and 2-hydroxypropyl-β-cyclodextrin (HP-β-CD), with standard Gibbs free energies of binding (ΔG°) of –3.64, –3.24, and –3.11 kcal/mol, respectively. In contrast, γ-cyclodextrin (γ-CD) exhibited significantly weaker binding (ΔG° = –2.25 kcal/mol). DFT-based NCI analysis revealed that cooperative interaction topology and cavity complementarity, rather than the sheer number of localized contacts, govern complex stability. Combined computational and experimental data establish β-CD derivatives as effective supramolecular hosts for AVI, despite an entropic penalty in the DM-β-CD/AVI complex. These CD–AVI affinities support the development of improved analytical methodologies and pharmaceutical formulations, and they also open avenues for decontamination strategies based on molecular trapping of AVI. Full article