Search Results (1,765)

Island regions face dual challenges of renewable energy accommodation and freshwater scarcity, severely constraining operational economy and reliability. However, existing research regards wastewater treatment and seawater desalination as isolated subsystems, overlooking the significant synergistic potential in their water-energy nexus. This paper proposes a novel optimal operation framework for standalone island multi-energy microgrids, constructing a water-energy coupled system that integrates wastewater treatment, seawater desalination, hydrogen electrolysis, methanation, and diversified energy storage. A hierarchical collaborative dynamic weighting mechanism is proposed to facilitate system coupling coordination. At the system macro-level, a Sigmoid-based adaptive strategy responds to real-time operating conditions by dynamically adjusting the weighting ratios of four-dimensional objectives; at the water system micro-level, the load allocation between wastewater treatment and seawater desalination is optimized through a continuous regulation mechanism. This method establishes a framework to maximize the coupling coordination between wastewater treatment and seawater desalination, fully exploiting the flexible load characteristics of water treatment facilities to mitigate renewable energy fluctuations. Simulation results from a case study validate the effectiveness of the proposed strategy; the method achieves collaborative and efficient system operation alongside water-energy security assurance and significantly reduces the total system operating cost by 76,259.14 CNY compared to traditional methods. Full article

This study examines the psychological mechanisms through which anthropomorphic artificial intelligence (AI) relates to consumer adoption intentions in fragile, low-trust economies. Integrating the Stimulus–Organism–Response framework with the Computers Are Social Actors paradigm, Institutional Trust Theory, and Privacy Calculus Theory, we investigate how human-like AI design shapes cognitive and affective responses within Sierra Leone’s banking sector. Using survey data from 277 banking customers and partial least squares structural equation modeling, we find that AI anthropomorphism exhibits no direct association with adoption intention (β = −0.013, p = 0.760). Instead, its influence is entirely indirect—transmitted in parallel through perceived social presence (β = 0.144, 95% CI [0.062, 0.226]) and trust in the AI system (β = 0.139, 95% CI [0.068, 0.210]). Critically, customer skepticism—shaped by institutional fragility—functions as a boundary condition that substantially attenuates both pathways: among highly skeptical users (+1 SD), anthropomorphism’s conditional effect on social presence becomes non-significant (β = 0.098, p = 0.124) compared to low-skepticism users (β = 0.412, p < 0.001), while its effect on trust is reduced by more than half (β = 0.118 vs. 0.284). These findings identify a critical boundary condition on human-like AI design: in low-trust environments, anthropomorphism operates not as a standalone adoption driver but as a relational amplifier whose efficacy depends on foundational trust and is substantially weakened when skepticism is high. The study challenges universalist assumptions in human–AI interaction research and underscores the need for institutionally sensitive design approaches in fragile economies. Full article

Production diseases in dairy cattle impose economic and welfare burdens, yet few studies quantify costs using on-farm cases. This study aimed to estimate costs and lost revenues at the individual-animal level in 10 German dairy farms (average of 592 cows; 9694 kg marketed milk/cow/year; 32.9% culling rate). Each farm was visited for three weeks; diseased cows and calves were examined by a trained veterinarian. Diagnoses, treatments, labour times, and outcomes were recorded, and costs calculated for labour, products, veterinary and orthopaedic services, discarded milk, decreased milk yield, culling, book loss, and reduced carcass value. In total, 1272 single-animal cases were included: 68% were stand-alone diseases, 11% involved multiple diagnoses within one organ system, and 21% affected several organ systems. When several diseases occurred in the same animal, total costs and lost revenues were greater than the sum of stand-alone cases, indicating compounding effects. High-impact conditions included mastitis, claw disorders, left displaced abomasum, and multimorbidity; per-case losses ranged from €43 (digital dermatitis) to >€1200 (left displaced abomasum with complications). Labour and culling-related costs were higher than reported, and productivity losses exceeded treatment costs in many cases. Findings support farm-level decision-making, prevention, and parameterization of future dynamic models. Full article

Traditional cloud pricing models are allocation-centric, where users are charged based on reserved resources rather than workload energy consumption. However, modern AI workloads exhibit substantial and heterogeneous power behavior, limiting the effectiveness of such allocation-centric pricing. This paper presents a comprehensive experimental study of nine widely used workloads across 50 controlled configurations, including standalone and concurrent executions under varying resource partitions. Our results show that total system power is largely unaffected by how resources are divided among co-located workloads, except in cases of explicit resource under-provisioning or severe resource contention. Across 45 workload–core groups, 41 exhibit a coefficient of variation below 3% across different co-located workloads, demonstrating structural stability of workload-level power profiles under heterogeneous execution environments. In contrast, deployment choice (e.g., CPU versus GPU execution) can shift the same model into distinct power regimes. Based on measured power decomposition and scaling behavior, we derive an empirical categorization framework distinguishing GPU-dominant and CPU-dominant workloads, further characterized by utilization and memory dimensions. From an energy perspective, CPU utilization (for CPU-dominant workloads) and SM utilization (for GPU-dominant workloads) emerge as the primary determinants of power magnitude, while memory-related parameters contribute marginally to overall power. These findings provide empirical evidence that allocation-based pricing is a weak proxy for actual energy cost and motivate energy-aligned cloud management strategies grounded in workload power profiles. As our findings are derived from a controlled single-node experiment, evaluations under more realistic data center environments will be required for further generalization. Full article

Background/Objectives: Selective transfer of an ulnar nerve fascicle to the motor branch of the musculocutaneous nerve (Oberlin technique) is widely used to restore elbow flexion following upper brachial plexus injury. Intraoperative neurophysiological mapping allows quantitative recording of compound muscle action potentials (CMAPs) during donor fascicle selection; however, its prognostic relevance remains unclear. This study evaluated whether intraoperative flexor carpi ulnaris (FCU) CMAP amplitude is associated with time to electromyographic reinnervation of the biceps brachii and with final functional outcomes. Methods: A retrospective observational study was conducted including patients who underwent selective nerve transfer to the biceps brachii between 2006 and 2025 at two tertiary referral centers. Donor fascicles were selected using intraoperative neurophysiological mapping with quantitative CMAP recordings from three ulnar-innervated muscles. Primary outcomes were time to electromyographic evidence of reinnervation and final elbow flexion strength assessed using the British Medical Research Council grading system. Associations were analyzed using nonparametric statistical methods. Results: Twenty patients met the inclusion criteria. Higher intraoperative FCU CMAP amplitudes were associated with a shorter time to electromyographic reinnervation (Spearman ρ = −0.572, p = 0.0106). No association was observed between CMAP amplitude and final elbow flexion strength (Spearman ρ = −0.168, p = 0.479), or between time to reinnervation and final functional outcome (Spearman ρ = −0.276, p = 0.253). A positive association was found between the injury-to-surgery interval and intraoperative CMAP amplitude (Spearman ρ = 0.681, p = 0.000943). Conclusions: The intraoperative FCU CMAP amplitude facilitates objective donor fascicle selection and is associated with earlier electromyographic reinnervation. Nevertheless, it was not associated with final elbow flexion strength in this cohort and should be interpreted as a technical adjunct rather than a standalone prognostic indicator. Functional recovery following nerve transfer appears to reflect multifactorial biological and temporal determinants beyond a single intraoperative neurophysiological measurement. These findings should be interpreted cautiously given the limited sample size. Full article

This paper presents an efficient stand-alone photovoltaic water pumping system (PVWPS) intended for agricultural irrigation applications, operating without energy storage. The system employs a three-phase induction motor supplied by a three-level neutral point clamped (NPC) inverter. The proposed control strategy integrates the advantages of two distinct controllers to enhance both energy extraction and drive performance. On the photovoltaic side, a fuzzy logic-based maximum power point tracking (MPPT) algorithm is implemented to ensure continuous operation at the global maximum power point under rapidly varying irradiance conditions. On the motor drive side, a direct torque control (DTC) scheme is combined with the multilevel NPC inverter to regulate electromagnetic torque and stator flux. The use of a multilevel inverter significantly mitigates the inherent drawbacks of conventional DTC, notably torque and flux ripples, as well as stator current harmonic distortion. The overall control architecture maximizes power transfer from the photovoltaic generator to the pumping system, resulting in improved dynamic response and energy efficiency. The proposed system is validated through detailed MATLAB/Simulink simulations under abrupt irradiance variations and a realistic daily solar profile corresponding to August conditions in Kairouan, Tunisia. Simulation results demonstrate substantial performance improvements, including an 88% reduction in torque ripples, a 50% decrease in flux ripple, a 77.9% reduction in stator current THD, and a 33.3% enhancement in speed transient response compared to conventional DTC-based systems. Full article

Cervical spondylosis is a common cause of spinal cord dysfunction, and anterior cervical discectomy and fusion (ACDF) is widely employed when conservative treatment fails. Conventional implant systems such as the cervical cage with plate (CCP) and zero-profile stand-alone cage (ZPSC) are commonly used to enhance spinal stability and promote fusion, but they are associated with complications including dysphagia and adjacent segment degeneration. To address these limitations, a novel flexible plate cage system (FPCS) has been developed to optimize biomechanical performance while minimizing surgical risk. In this study, a finite element model of the C3–T1 cervical spine was constructed to simulate ACDF at the C5–C6 level using CCP, ZPSC, and FPCS implants. Under standardized loading conditions, von Mises stress was analyzed in the bone, intervertebral disc, endplates, cage, and screws, using the mean of the top 5% stress values to ensure accuracy. All surgical models showed increased stress compared to the intact reference spine. The ZPSC model exhibited the highest stress in the cage and screws, suggesting a more concentrated load path. The CCP model showed a more evenly distributed stress profile, particularly affecting the inferior adjacent segment. The FPCS model demonstrated moderate cage stress, reduced screw stress, and the highest plate stress, indicating a design that effectively redirects mechanical load from the screw-bone interface toward the anterior plate. This may be related to the unique structural configuration of the FPCS, which secures screws horizontally into the anterior vertebral body without penetrating the endplates. These findings suggest that the FPCS may offer a biomechanically favorable alternative to existing ACDF implants. Full article

The widespread commercialization of wireless chargers for electric vehicles generally suffers from one main problem, which is the perfect alignment between the two coils, leading to a decrease in mutual inductance, which causes a drop in magnetic coupling and even a failure to transfer power. To address this persistent problem, this work proposes a comprehensive and integrated method for optimizing the coils and control architecture for reliable and safe battery charging. To address the challenges of a complex, nonlinear design space and the need for misalignment-tolerant geometries, we employ a memetic algorithm (MA) that hybridizes Particle Swarm Optimization (PSO) for broad global exploration with Mesh Adaptive Direct Search (MADS) for precise local refinement. This combination effectively avoids poor local solutions—a limitation of standalone PSO or GA approaches reported in recent studies—while efficiently converging to coil geometries that maintain strong magnetic coupling under misalignment. After the coils have been designed, electromagnetic validation is tested using finite element analysis (FEA), which allows the magnetic field distribution to be evaluated, as well as the coupling coefficient under different scenarios of misalignment and variation in the air gap between the ground side and the vehicle side. At the same time, a comprehensive control strategy for the primary side of the system has been developed. This control method ensures power management on the primary side, enabling system interoperability for charging multiple types of vehicles, as well as reducing vehicle weight for greater range. All this is combined with an innovative pulsed current charging method, chosen for its advantages in terms of thermal stability, ensuring safe and efficient recharging that is mindful of battery health. Simulation and experimental validation demonstrate that the proposed framework maintains stable wireless power transfer and achieves over 87% DC–DC efficiency under lateral misalignments up to 100 mm, fully complying with SAE J2954 alignment tolerance requirements. Full article

Wireless sensor networks (WSNs) are vulnerable to physical attacks in which adversaries gain partial or full control of sensor nodes, compromising the integrity of the network. Conventional security mechanisms impose excessive computational overhead and are not well suited to resource-constrained WSN devices. This paper proposes a lightweight, two-layer intrusion detection system (IDS) that integrates blockchain (BC) technology with machine learning for physical attack detection in WSNs. The first layer employs a lightweight BC protocol among cluster heads (CHs) and the base station (BS) to detect data integrity violations through hash-based consensus. The second layer applies an artificial neural network (ANN) at the base station to detect attacks that bypass blockchain verification, without imposing any processing load on sensor nodes. Simulation experiments on a 100-node WSN demonstrate that the combined system achieves 97.42% accuracy and 98.35% recall, outperforming five established classifiers and both standalone components. The system sustains detection rates above 99.98% under 30 simultaneous attackers and maintains reliable operation under packet loss conditions up to 10%. Full article

We present PIRATE (Precision Imaging Real-time Autonomous Tracker and Explorer), a fully autonomous unmanned surface vehicle designed to enable self-operating data collection and persistent tracking of mobile underwater targets through the tight integration of acoustic localization, onboard visual perception, and closed-loop navigation. PIRATE employs a single mobile acoustic receiver to estimate target position using time-difference-of-arrival (TDoA) measurements acquired at different times and locations through planned autonomous motion and uses these estimates to drive adaptive vehicle behavior and activate fine-grained visual sensing in real time. This architecture enables sustained target-driven operation, in which navigation, acoustic monitoring, and visual processing are dynamically coordinated based on mission context and localization uncertainty. The system integrates real-time AI-based visual detection and tracking with automatic mission control, allowing visual perception to operate opportunistically within an acoustically guided tracking loop rather than as a standalone sensing modality. Field experiments in a shallow-water environment demonstrate reliable autonomous navigation, single-receiver acoustic localization with meter-scale accuracy, and stable onboard visual inference under sustained operation. By enabling coupled acoustic tracking and onboard visual perception in a fully autonomous surface platform free of external infrastructure, PIRATE provides a practical foundation for fine-scale behavioral observation, adaptive marine monitoring, and long-duration studies of mobile underwater organisms. We demonstrate this advantage with two possible applications. Full article

Open-air asphalt milling generates hazardous respirable crystalline silica (RCS), posing severe risks to operators of legacy machines lacking enclosed cabs. This study evaluates a novel, standalone retrofit water spray system designed to intercept fugitive dust. Field validation across 11 road maintenance sites involved particle characterization and paired system-off/on exposure monitoring. Results indicated a Mass Median Aerodynamic Diameter (MMAD) of 6.12 µm, confirming the efficacy of fine-atomizing nozzles (0.3 mm) for capturing respirable fractions. The system achieved RCS suppression efficiencies ranging from 60% to over 85% under low-to-moderate wind conditions (<2.5 m/s). A comparative analysis revealed no significant performance gain from larger 0.5 mm nozzles, supporting the use of smaller orifices for optimal water conservation. However, suppression efficacy degraded significantly when crosswinds exceeded 2.5 m/s, indicating a potential operational boundary. This retrofit solution provides a scientifically validated, cost-effective engineering control for reducing occupational silica exposure in aging road maintenance fleets. Full article

Artificial intelligence (AI) is increasingly embedded in smart city strategies, yet its role in advancing participatory urban planning remains underexamined, particularly in rapidly urbanizing metropolitan contexts of the Global South. This exploratory, governance-centered study investigates how AI can support participatory urban planning for sustainable smart cities, emphasizing institutional mediation and trust dynamics. Using a convergent mixed-methods design, the research combines a purposive stakeholder survey (n = 260) with qualitative thematic analysis to assess AI awareness and use, participation quality, institutional and technical readiness, and public trust in the Dammam Metropolitan Area, Saudi Arabia. The findings reveal a participation paradox: relatively high AI awareness and digital readiness coexist with low perceived influence and limited confidence in participatory outcomes. Institutional coordination gaps, skill constraints, and regulatory ambiguity mediate the translation of AI adoption into meaningful engagement. Stakeholders favor AI applications, such as interactive mapping, predictive analytics, and digital twin visualization, that enhance transparency and deliberation over automated decision systems. Qualitative evidence further indicates that AI is perceived not as a standalone solution, but as a catalyst for institutional reform, capacity development, and sustainability-oriented governance. The study contributes to urban science by empirically validating a socio-technical framework that positions AI as a facilitative governance instrument embedded within institutional and trust-building processes. The findings offer policy-relevant insights for cities seeking to align AI-driven innovation with inclusive, accountable, and sustainable urban development. Full article

This paper presents the design and analysis of solar systems for agricultural applications and the sustainable energy supply of villages, based on a case study of a rural settlement comprising 30 households. The village energy demand is quantified through a detailed assessment of hourly load profiles for daytime and nighttime operation, identifying peak loads and total daily energy consumption. Energy usage patterns are established for residential buildings, agricultural water pumping, public lighting, healthcare facilities, and commercial services. To meet these energy requirements sustainably, a 60 kW photovoltaic (PV) system is proposed in combination with a solar thermal water heating system designed to supply domestic and agricultural hot water. This study details the design methodology and simulation of the solar thermal system, including heat transfer modeling and system dimensioning. MATLAB (V.22b) simulations are conducted to evaluate system performance, covering PV energy generation, battery charge–discharge cycles, and thermal behavior over a 24 h period. Comparative analyses of standalone PV, hybrid PV/T, and combined PV and solar thermal configurations demonstrate that separate PV and thermal systems provide superior cost-effectiveness, operational reliability, and reduced maintenance requirements. The results confirm the technical feasibility, economic viability, and environmental benefits of solar-based solutions for rural electrification and agricultural applications. The results indicate that the analyzed rural settlement has an estimated daily electricity demand of approximately 590 kWh. Based on this demand, a 60 kW photovoltaic system was selected to ensure sufficient daytime electricity production while also allowing battery charging for nighttime consumption. In addition, the solar thermal system can increase the water temperature from approximately 10 °C to 55–80 °C, depending on solar irradiance conditions. The combined PV and solar thermal configuration demonstrates the potential to provide a reliable and sustainable energy solution for rural off-grid communities. Full article

Open-source and commercial fifth-generation (5G) deployments are difficult to compare because they are built for different goals and reported under different conditions, which slows down validation and technology transfer from research to practice. This study explores the deployment and evaluation of two 5G Standalone (SA) disaggregated Radio Access Network (RAN) systems, using open-source research RAN, commercial RAN, and Software-Defined Radio (SDR) hardware. The first testbed is a SDR-based prototype, containing a Universal Software Radio Peripheral (USRP) B210 device, using Software Radio System RAN (srsRAN) as the RAN. The commercial-based testbed contains a Benetel RAN550 Radio Unit (RU), connected via an optical fiber to a Commercial Off-the-Shelf (COTS) server acting as the Distributed Unit (DU) and Centralized Unit (CU) using the Accelleran virtualized Baseband Unit (vBBU) platform. The Core Network (CN) is implemented using the open-source Open5GS in both testbeds. To evaluate the network’s functionality, throughput and latency are tracked using a Motorola Edge 50 Pro mobile terminal. The experimental results are analyzed and compared with representative performance metrics reported in the literature to place the measurements in a broader research context. This study further assesses trade-offs related to cost, portability, and scalability by comparing SDR-based research prototypes with commercial deployments. Full article

Accurate and continuous outdoor pedestrian positioning using smartphones remains challenging in complex environments like urban canyons, where Global Navigation Satellite System (GNSS) signals are frequently degraded or blocked, and Pedestrian Dead Reckoning (PDR) suffers from cumulative errors. To address this, this paper proposes a novel fusion method based on a Robust Adaptive Cubature Kalman Filter (RACKF). The core of our approach is a two-stage filtering architecture: the first stage employs a quaternion-based RACKF to optimally fuse gyroscope and magnetometer data for robust heading estimation; the second stage performs the core fusion of GNSS observations with an enhanced 3D PDR solution. Key innovations include an adaptive noise estimation strategy combining fading and limited memory weighting, a robust M-estimator-based mechanism to suppress outliers, and the integration of differential barometric height measurements. Experimental results demonstrate that the proposed method achieves a horizontal positioning accuracy of 3.28 m (RMSE), outperforming standalone GNSS and improving 3D PDR by 25.97% and 10.39%, respectively. This work provides a practical, infrastructure-free solution for robust smartphone-based outdoor navigation. Full article