Search Results (11,886)

The intelligent transformation of the wastewater treatment industry, as a core component of the modern environmental governance system, is of decisive significance for achieving sustainable development goals. This study focuses on the issue of multi-stakeholder collaborative governance in the intelligent transformation of the wastewater treatment industry, with differential game theory as the core framework. A tripartite game model involving the government, wastewater treatment enterprises, and digital twin platforms is developed to depict the dynamic interrelations and mutual influences of strategy choices, thereby capturing the coordination mechanisms among government regulation, enterprise technology adoption, and platform support in the transformation process. Based on the dynamic optimization properties of differential games, the Hamilton–Jacobi–Bellman (HJB) equation is employed to derive the long-term equilibrium strategies of the three parties, presenting the evolutionary paths under Nash non-cooperative games, Stackelberg games, and tripartite cooperative games. Furthermore, the Sobol global sensitivity analysis is applied to identify key parameters influencing system performance, while the response surface method (RSM) with central composite design (CCD) is used to quantify parameter interaction effects. The findings are as follows: (1) compared with Nash non-cooperative and Stackelberg games, the tripartite cooperative strategy based on the differential game model achieves global optimization of system performance, demonstrating the efficiency-enhancing effect of dynamic collaboration; (2) the most sensitive parameters are β, α, μ₃, and η₃, with β having the highest sensitivity index (STᵢ = 0.459), indicating its dominant role in system performance; (3) significant synergistic enhancement effects are observed among α–β, α–μ₃, and β–μ₃, corresponding, respectively, to the “technology stability–benefit conversion” gain effect, the “technology decay–platform compensation” dynamic balance mechanism, and the “benefit conversion–platform empowerment” performance threshold rule. Full article

Coordinating robotic teams across multiple concurrent search tasks is a critical challenge in search and rescue operations. This work presents a new multi-agent framework designed to manage and optimize search efforts when several missing-person reports occur in parallel. The method extends iso-probability curve-based trajectory planning to the multi-target case and introduces a dynamic task allocation scheme that distributes search agents (e.g., UAVs) across tasks according to evolving probabilities of success. Overlapping search regions are explicitly resolved to eliminate duplicate coverage and to ensure balanced effort among tasks. The framework also extends the behavior-based motion prediction model for missing persons and the non-parametric estimator for iso-probability curves to capture more realistic search conditions. Extensive simulated experiments, with multiple concurrent tasks, demonstrate that the proposed method tangibly improves mean detection times compared with equal-allocation and individual static assignment strategies. Full article

The continuous reduction in critical dimensions and the escalating demands for higher throughput are driving motion platforms to operate under increasingly complex conditions, including multi-axis coupling, structural nonlinearities, and time-varying operational scenarios. These complexities make the trade-offs among precision, speed, and robustness increasingly challenging. Traditional Proportional–Integral–Derivative (PID) controllers, which rely on empirical tuning methods, suffer from prolonged trial-and-error cycles and limited transferability, and consequently struggle to maintain optimal performance under these complex working conditions. This paper proposes an adaptive β–Proximal Policy Optimization with Random Network Distillation (β-PPO-RND) parameter optimization within the Prescribed Performance Control (PPC) framework. The adaptive coefficient β is updated based on the temporal change in reward difference, which is clipped and smoothly mapped to a preset range using a hyperbolic tangent function. This mechanism dynamically balances intrinsic and extrinsic rewards—encouraging broader exploration in the early stage and emphasizing performance optimization in the later stage. Experimental validation on a Permanent Magnet Linear Synchronous Motor (PMLSM) platform confirms the effectiveness of the proposed approach. It eliminates the need for manual tuning and enables real-time controller parameter adjustment within the PPC framework, achieving high-precision trajectory tracking and a significant reduction in steady-state error. Experimental results show that the proposed method achieves MAE = 0.135 and RMSE = 0.154, representing approximately 70% reductions compared to the conventional PID controller. Full article

Background/Objectives: Out-of-hospital cardiac arrest (OHCA) has persistently low survival rates. While advanced airway management (AAM) is crucial during cardiopulmonary resuscitation, optimal timing remains unclear. This study examined the association between early AAM and clinical outcomes in adult OHCA patients. Methods: This retrospective study analyzed Korean nationwide OHCA registry data (August 2019–December 2022). Adult patients with emergency medical service-treated OHCA of presumed medical origin receiving AAM were included. Early AAM was defined as airway placement within 5 min of CPR initiation. Time-dependent propensity score matching controlled for selection bias and time-related confounding. Structural equation modeling examined associations between AAM timing and other prehospital interventions. Primary outcome was survival to hospital discharge with good neurological recovery (cerebral performance category 1–2). Results: Among 51,869 patients receiving AAM, 27,591 received early AAM and 24,278 received delayed AAM. After propensity score matching, 12,014 patients were included per group with balanced characteristics. Early AAM was associated with higher prehospital return of spontaneous circulation (11.8% vs. 10.5%; adjusted RR 1.21, 95% CI 1.12–1.29) and favorable neurological recovery (5.8% vs. 5.1%; adjusted RR 1.12, 95% CI 1.01–1.23). AAM timing correlated with timing of other critical interventions, including rhythm analysis and epinephrine administration. Conclusions: Early AAM within 5 min of CPR initiation was associated with improved neurological outcomes and increased prehospital ROSC in OHCA. Airway timing may indicate overall resuscitation quality, emphasizing the importance of coordinated, timely prehospital interventions. Full article

The loading rate of coal is significantly influenced by the number of vanes on shearer drums. However, in actual production, 1400 mm diameter drums feature two-vane and three-vane designs, while 2240 mm diameter ones have three-vane and four-vane designs, with the vane number corresponding to the optimal coal-loading rate remaining unclear. To reveal the correlation between vane number and coal-loading rate for such drums, parameters were calibrated through multiple physical tests in this study. Supported by field data, simulation analyses were conducted via the discrete element method to investigate the effect of the vane number on the drum coal-loading rate under different moisture contents and traction speeds. The results indicated that particle adhesion initially increases and then decreases with the moisture content, with the peak characteristics influenced by the particle size. Particle movement during drum coal mining is jointly governed by multiple factors. For 1400 mm drums, two or three vanes should be selected depending on moisture fluctuations and coal transportation requirements, whereas for 2240 mm drums, three or four vanes are recommended based on the balance between coal-cutting volume, conveying capacity, and traction speed. Full article

This study investigates an integrated solar-driven single-effect H₂O–LiBr absorption chiller powered by low-grade thermal energy. A detailed thermodynamic model, comprising a solar collector, a thermal storage tank, and an absorption cycle, was developed using the Engineering Equation Solver (EES) software V10.561. A comprehensive parametric analysis and multi-objective optimization were then conducted to enhance both the energy and exergy performance of the system. The Response Surface Methodology (RSM), based on the Box–Behnken Design, was employed to develop regression models validated through analysis of variance (ANOVA). The generator temperature (78–86 °C), evaporator temperature (2.5–6.5 °C), and absorber/condenser temperature (30–40 °C) were selected as key variables. According to the results, the single-objective analyses revealed maximum values of COP = 0.8065, cooling capacity = 20.72 kW, and exergy efficiency = 39.29%. Subsequently, the multi-objective RSM optimization produced a balanced global optimum with COP = 0.797, cooling capacity = 20.68 kW, and exergy efficiency = 36.93%, achieved under optimal operating conditions of 78 °C generator temperature, 6.5 °C evaporator temperature, and 30 °C absorber/condenser temperature. The obtained results confirm the significance of the proposed low-grade solar absorption chiller, demonstrating comparable or superior performance to recent studies (e.g., COP ≈ 0.75–0.80 and ≈ 35–37%). This agreement validates the RSM-based optimization approach and confirms the system’s suitability for sustainable cooling applications in low-temperature solar environments. Full article

Accurate and up-to-date data on built-up areas are crucial for urban planning, disaster management, and sustainable development, yet Romania still lacks a unified, official database. In this study we integrated the three widely used global data sources—OpenStreetMap (OSM), Microsoft Building Footprints (MSBFs), and Global Human Settlement Layer Built-up surface (GHS)—onto a 10 m resolution raster grid and applied this consistently at the national scale across 3181 settlement polygons to produce a more accurate, unified ensemble model for Romania. The methodological basis was Triple Collocation Analysis (TCA), extended with ETC/CTC to estimate per-settlement scale factors, enabling the quantification and optimal weighting of the relative errors and accuracy in the absence of independent reference data. Weight patterns vary by settlement type: OSM receives relatively higher weights in smaller rural settlements with less redundant error; in municipalities the stronger OSM–MSBF correlation reduces both of their weights and increases the GHS share; cities exhibit a more balanced weighting. At cell level, the ensemble provides uncertainty quantification via confidence intervals that typically range from 2% to 14% at settlement scale. The resulting model—like any model—does not perfectly reflect reality; however, the ensemble improves the accuracy and timeliness of the available data. The resulting model is replicable and updatable with newer data, making it suitable for numerous practical applications, especially in spatial development and risk analysis. Full article

With the rapid growth of demands in marine resource exploitation, environmental monitoring, and maritime safety, cooperative operations based on Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) have emerged as a promising paradigm for intelligent ocean missions. UAVs offer flexibility and high coverage efficiency but suffer from limited endurance due to restricted battery capacity, making them unsuitable for large-scale tasks alone. In contrast, USVs provide long endurance and can serve as mobile motherships and energy-supply platforms, enabling UAVs to take off, land, recharge, or replace batteries. Therefore, how to achieve cooperative path planning and energy replenishment scheduling for USV–UAV systems in complex marine environments remains a crucial challenge. This study proposes a USV–UAV cooperative path planning and energy replenishment optimization method based on BeiDou high-precision positioning. First, a unified system model is established, incorporating task coverage, energy constraints, and replenishment scheduling, and formulating the problem as a multi-objective optimization model with the goals of minimizing total mission time, energy consumption, and waiting time, while maximizing task completion rate. Second, a bi-level optimization framework is designed: the upper layer optimizes the USV’s dynamic trajectory and docking positions, while the lower layer optimizes UAV path planning and battery replacement scheduling. A closed-loop interaction mechanism is introduced, enabling the system to adaptively adjust according to task execution status and UAV energy consumption, thus preventing task failures caused by battery depletion. Furthermore, an improved hybrid algorithm combining genetic optimization and multi-agent reinforcement learning is proposed, featuring adaptive task allocation and dynamic priority-based replenishment scheduling. A comprehensive reward function integrating task coverage, energy consumption, waiting time, and collision penalties is designed to enhance global optimization and intelligent coordination. Extensive simulations in representative marine scenarios demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves around higher task completion rate, shorter mission time, lower total energy consumption, and shorter waiting time. Moreover, the variance of energy consumption across UAVs is notably reduced, indicating a more balanced workload distribution. These results confirm the effectiveness and robustness of the proposed framework in large-scale, long-duration maritime missions, providing valuable insights for future intelligent ocean operations and cooperative unmanned systems. Full article

Climate change and livestock expansion have affected forage supply in the dry tropics. Therefore, optimizing planting methods adapted to adverse tropical environments is essential for establishment and yield. The objective of this study was to evaluate the effect of different planting methods on the establishment rate, morphology, yield, and nutritional composition of Cuba OM-22 under the soil and climate conditions of the dry tropics of Peru, using a block design with four replicates and five methods for propagation by cuttings. The S4 (two-node cuttings, 25 cm in length; horizontal position 180°, parallel to the soil surface; fully buried at 8 cm depth; no spacing between cuttings along the furrow) method offered the best balance between yield and quality, with higher establishment rate (55.93%), height (182.15 cm; higher than S1 and S5), and more tillers (surpassing S1 and S2 by 16.97% and 18.86%). In addition, it obtained good green forage yields (137.43 t ha⁻¹) and was better than all planting methods in dry matter yield (37.45 t ha⁻¹). In nutritional composition, S4 ranked among the highest averages for nitrogen-free extract (NFE) (43.22%) and ash (11.06%). However, protein, crude fiber, and fat content did not differ between methods. On the other hand, planting methods showed negative correlations between the number of tillers and ash content (p = 0.006; r = −0.79), ash and NFE (p = 0.000; r = −0.92), and protein with crude fiber (p = 0.029; r = −0.68). These findings highlight S4 as a key strategy for optimizing establishment, yield, and quality in Cuba OM-22 in the dry tropics. Full article

Wearable devices can be developed using hardware platforms such as Application Specific Integrated Circuits (ASICs), Graphics Processing Units (GPUs), Digital Signal Processors (DSPs), Micro controller Units (MCUs), or Field Programmable Gate Arrays (FPGAs), each with distinct advantages and limitations. ASICs offer high efficiency but lack flexibility. GPUs excel in parallel processing but consume significant power. DSPs are optimized for signal processing but are limited in versatility. CPUs provide low power consumption but lack computational power. FPGAs are highly flexible, enabling powerful parallel processing at lower energy costs than GPUs but with higher resource demands than ASICs. The combined use of FPGAs and CPUs balances power efficiency and computational capability, making it ideal for wearable systems requiring complex algorithms in far-edge computing, where data processing occurs onboard the device. This approach promotes green electronics, extending battery life and reducing user inconvenience. The primary goal of this work was to develop a versatile framework, similar to existing software development frameworks, but specifically tailored for mixed FPGA/MCU platforms. The framework was validated through a real-world use case, demonstrating significant improvements in execution speed and power consumption. These results confirm its effectiveness in developing green and smart wearable systems. Full article

Background/Objectives: Fluid management in shock remains a clinical challenge, with ongoing debate about optimal guidance. Despite advanced technologies, fluid balance assessment is often inadequate. The SkInShock study investigated whether transepidermal water loss (TEWL) measurements could improve fluid balance estimation and serve as a non-invasive marker of vascular tone in patients with septic or cardiogenic shock. Methods: In this prospective single-center feasibility study (DRKS00027981), TEWL was measured daily in eight mechanically ventilated patients using a Tewameter^® (Courage+Khazaka, Cologne, Germany), which quantifies transcutaneous water evaporation. Total daily skin water loss was calculated either via direct TEWL measurements or an estimation formula (6 mL/kg/day + 20%/°C deviation from 37 °C). Systemic vascular resistance index (SVRI) was measured simultaneously using PiCCO^® technology (Pulsion Medical Systems, Munich, Germany) to evaluate the relationship between TEWL and vascular tone. Results: TEWL values were consistent across most body sites, except the forehead. TEWL-based estimates of skin water loss were significantly lower than formula-based estimates (p < 0.01). Formula-based values overestimated water loss at low TEWL levels and underestimated it at higher levels, with deviations reaching ±100%. While absolute TEWL values did not correlate with SVRI, intra-individually normalized values showed a significant negative correlation, indicating that higher skin water loss corresponded to lower vascular tone. Conclusions: TEWL measurement is feasible in ICU patients and may enhance fluid balance assessment and vascular tone monitoring. Our preliminary findings indicate that this non-invasive method could complement current diagnostics but warrants further investigation in larger cohorts. Full article

Background/Objectives: Acute seizure models are widely used in epilepsy research to investigate seizure mechanisms and evaluate antiseizure drug efficacy. Among these models, pentylenetetrazole (PTZ)-induced seizures provide a controlled and reproducible approach to study acute seizure dynamics. However, existing PTZ protocols often suffer from inconsistent seizure induction, high mortality rates, and limited translational relevance. Methods: In this study, we systematically evaluated different PTZ dosing regimens in adult Sprague-Dawley rats to establish an optimized acute seizure model that balances seizure induction efficiency with minimal lethality. Results: We tested multiple PTZ administration protocols, identifying a two-step regimen of 50 mg/kg followed by 30 mg/kg (30 min later) as the most effective strategy. This dosing approach reliably induced generalized tonic-clonic seizures in 94% of the animals while eliminating mortality. Seizures induced by this regimen were validated through electrocorticography (ECoG) recordings. The behavioral and ECoG assessments of seizures showed a strong agreement in the latency and duration (r = 0.97, p < 0.0001; r = 0.81, p < 0.05, respectively) with minimal bias in Bland–Altman analysis, confirming that both methods provide statistically comparable and interchangeable measures of seizure characteristics. Conclusions: Our findings highlight the robustness and reproducibility of this modified PTZ-induced acute seizure model, offering an improved preclinical platform for studying seizure pathophysiology and screening for novel therapeutic interventions. Full article

Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), is rapidly reshaping higher education by transforming teaching, learning, assessment, research, and institutional management. This entry provides a state-of-the-art, comprehensive, evidence-based synthesis of established AI applications and their implications within the higher education landscape, emphasizing mature knowledge aimed at educators, researchers, and policymakers. AI technologies now support personalized learning pathways, enhance instructional efficiency, and improve academic productivity by facilitating tasks such as automated grading, adaptive feedback, and academic writing assistance. The widespread adoption of AI tools among students and faculty members has created a critical need for AI literacy—encompassing not only technical proficiency but also critical evaluation, ethical awareness, and metacognitive engagement with AI-generated content. Key opportunities include the deployment of adaptive tutoring and real-time feedback mechanisms that tailor instruction to individual learning trajectories; automated content generation, grading assistance, and administrative workflow optimization that reduce faculty workload; and AI-driven analytics that inform curriculum design and early intervention to improve student outcomes. At the same time, AI poses challenges related to academic integrity (e.g., plagiarism and misuse of generative content), algorithmic bias and data privacy, digital divides that exacerbate inequities, and risks of “cognitive debt” whereby over-reliance on AI tools may degrade working memory, creativity, and executive function. The lack of standardized AI policies and fragmented institutional governance highlight the urgent necessity for transparent frameworks that balance technological adoption with academic values. Anchored in several foundational pillars (such as a brief description of AI higher education, AI literacy, AI tools for educators and teaching staff, ethical use of AI, and institutional integration of AI in higher education), this entry emphasizes that AI is neither a panacea nor an intrinsic threat but a “technology of selection” whose impact depends on the deliberate choices of educators, institutions, and learners. When embraced with ethical discernment and educational accountability, AI holds the potential to foster a more inclusive, efficient, and democratic future for higher education; however, its success depends on purposeful integration, balancing innovation with academic values such as integrity, creativity, and inclusivity. Full article

Relief well designs mainly focus on conventional parameters such as well diameter and well spacing by engineering experience, lacking rigorous analysis. The impact of wellhead elevation remains unclear. This paper proposes a multi-objective optimization method for determining the design parameters (i.e., the wellhead elevation and number of wells) of relief wells. MODFLOW is used to develop a three-dimensional transient seepage numerical model of the levee. The design parameters of relief wells are optimized by balancing the safety factor and the economic cost by non-dominated sorting genetic algorithm-II (NSGA-II). To remove computational burden within NSGA-II, random forest (RF) is used to establish an intelligent surrogate model for evaluating the hydraulic characteristics of levees. The final optimal design parameters are determined by entropy weight and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Finally, the proposed approaches are illustrated using the Wuhan Yangtze River Levee, China. Results show that compared with the empirical approach, the optimal design parameters obtained by the proposed approaches can not only meet the safety threshold for the levee, but also reduce the costs by 15%. The importance of wellhead elevation on the hydraulic gradient is about six times that of the number of wells. Full article

The joint optimization of storage location assignment and order picking efficiency for fresh products has become a vital challenge in intelligent warehousing because of the perishable nature of goods, strict temperature requirements, and the need to balance cost and efficiency. This study proposes a comprehensive mathematical model that integrates five critical cost components: picking path, storage layout deviation, First-In-First-Out (FIFO) penalty, energy consumption, and picker workload balance. To solve this NP-hard combinatorial optimization problem, we develop a Particle Swarm-guided hybrid Genetic-Simulated Annealing (PS-GSA) algorithm that synergistically combines global exploration by Particle Swarm Optimization (PSO), population evolution of Genetic Algorithm (GA), and the local refinement and probabilistic acceptance of Simulated Annealing (SA) enhanced with Variable Neighborhood Search (VNS). Computational experiments based on real enterprise data demonstrate the superiority of PS-GSA over benchmark algorithms (GA, SA, HPSO, and GSA) in terms of solution quality, convergence behavior, and stability, achieving 4.08–9.43% performance improvements in large-scale instances. The proposed method not only offers a robust theoretical contribution to combinatorial optimization but also provides a practical decision-support tool for fresh e-commerce warehousing, enabling managers to flexibly weigh efficiency, cost, and sustainability under different strategic priorities. Full article