Search Results (823)

Inspired by the Tesla valve, the island-type fishway is a novel design whose biological performance remains unelucidated. This study integrated hydraulic experiments, CFD modeling, and 3D computer vision to investigate the passage performance and swimming behavior of juvenile silver carp (Hypophthalmichthys molitrix). The results confirmed high biological feasibility, with upstream success rates exceeding 70%. The island and arc-baffle configuration create a heterogeneous flow field with an S-shaped main flow and low-velocity zones; each island unit contributes 8.9% to total energy dissipation. Critically, fish utilize a multi-dimensional navigation strategy to avoid high-velocity cores: temporally adopting an intermittent “rest-burst” pattern for energetic recovery; horizontally following an “Ω”-shaped bypass trajectory; and vertically preferring the bottom boundary layer. Passage failure was primarily linked to suboptimal path selection near the high-velocity main flow. These findings demonstrate that fishway effectiveness depends less on bulk hydraulic parameters and more on the spatial connectivity of hydraulic refugia aligning with fish behavioral traits. This study provides a scientific basis for optimizing eco-friendly hydraulic structures. Full article

Point-of-care testing (POCT) has emerged as a vital diagnostic approach in emergency medicine, primary care, and resource-limited environments because of its convenience, affordability, and capacity to provide immediate results. Here, we present a multifunctional portable nucleic acid detection platform integrating reverse transcription polymerase chain reaction (RT-qPCR) and reverse transcription loop-mediated isothermal amplification (RT-LAMP) within a unified microfluidic device. The system leverages Tesla-valve-based passive flow control to enhance reaction efficiency and operational simplicity. A four-channel optical detection unit allows for multiplex fluorescence quantification (CY5, FAM, VIC, ROX) and has high sensitivity and reproducibility for RT-LAMP. The compact design reduces the overall size by approximately 90% compared with conventional qPCR instruments. For RT-PCR, the system achieves a detection limit of 2.0 copies μL⁻¹ and improves analytical efficiency by 27%. For RT-LAMP, the detection limit reaches 2.95 copies μL⁻¹ with a 14% enhancement in analytical efficiency. Compared with commercial qPCR instruments, the device maintains equivalent quantitative accuracy despite significant miniaturization, ensuring reliable performance in decentralized testing. Furthermore, the total RT-LAMP assay time is reduced from more than two hours to 42 min, enabling truly rapid molecular diagnostics. This dual-mode platform offers a flexible, scalable strategy for bridging laboratory-grade molecular assays with real-time POCT applications, supporting early disease detection and epidemic surveillance. Full article

This article offers a comprehensive technical and mechanical review of the Tesla turbine, an innovative device conceived by Nikola Tesla. The core research question guiding this review is: How can the design and application of the Tesla turbine be optimized to overcome its current efficiency limitations and unlock its full potential across various energy recovery technologies? The analysis focuses on the mechanical design of the turbine, illustrating the configuration of co-axial discs without blades mounted on a central shaft, and on the fluid dynamic phenomena that generate torque through the viscous boundary layer between the discs. Mathematical models based on the equations of viscous motion and CFD simulations are used to evaluate mechanical and fluid-dynamic losses, such as viscous friction, edge losses, and inlet duct losses. The work describes mechanical engineering challenges related to efficiency and performance, highlighting optimization techniques for the number and spacing of the discs, nozzle geometry, and thermal management to mitigate the risk of overheating. Finally, potential application areas in microturbine technology for low-enthalpy thermal cycles and energy recovery are examined. The article makes a significant contribution to applied mechanical engineering, offering design guidelines and an updated overview of the challenges and opportunities of Tesla turbine technology. Full article

Upper limb deficiencies can limit the range of tasks children can perform. Current prosthetics provide overall good performance to increase the activities that users can complete, but challenges remain. Body- or electrically powered prostheses struggle to restore the full range of motion needed for specific tasks. Currently, these systems do not allow for controlled hand closure or opening across all possible postures. A breathing-powered prototype named Airbender, which extracts energy from a breathing input by means of a Tesla turbine, provides the possibility of operation in any position. This paper introduces a novel design for a multi-finger actuated breathing-powered upper limb prosthetic concept and analyses its performance through a series of lab-based experiments. Results show that such a design could provide a fully controllable system. The final assembled design is capable of achieving full actuation under a flow rate of 340 Ls/min. The results obtained demonstrate that a functional multi-finger actuated breathing-powered upper limb prosthesis could be feasible and opens a path for future research in the field, with the ultimate goal of reducing the minimum flow rate required and actuation time to further improve its functionality. Full article

We examined the acute effects of flywheel eccentric overload (FEO) on countermovement jumps (CMJs), changes of direction (COD), and isometric mid-thigh pulls (IMTPs) in top-level team sports athletes (three females and seven males). FEO was carried out by performing 3 × 6 reps with 0.025 kg·m² inertia and a 2 min passive rest period. Its post-activation potentiation was compared to a control warm-up. Performance was tested at 0, 3, and 6 min post-intervention. Significant improvements were reported in the COD5m times for the left (F = 8.38, p < 0.001, ES = 1.92) and right legs (F = 11.3, p < 0.001, ES = 2.24), as well as for CMJ height (F = 12.4, p < 0.001, ES = 2.35). Significant differences were observed in COD5m between baseline and 3 min (p < 0.001, ES = 0.99 and p = 0.003, ES = 1.25) and 6 min (p = 0.04, ES = 1.19 and p < 0.001, ES = 1.09) for the left and right legs, respectively. Jump height increased significantly at 3 min (p < 0.001, ES = 1.62) and remained elevated at 6 min (p < 0.001, ES = 1.02). CMJ peak power (CMJPP) decreased significantly (F = 6.4, p = 0.002, ES = 1.68), with a drop at 0 min (p = 0.024, ES = 0.85) and a return to baseline at 3 min (p = 0.002, ES = 1.35). No significant effects were found for the CMJ eccentric rate of force development (CMJRFDecc) or IMTP. It was found that FEO can acutely enhance jumping and changes of direction but not strength in elite team sports athletes. A three-minute rest appears to maximize these effects. Full article

This article presents the design, simulation, and real-time implementation of an intelligent water level control system using Deep Reinforcement Learning (DRL) with the Deep Deterministic Policy Gradient (DDPG) algorithm. The control policy was initially trained in a MATLAB-based simulation environment, where actor–critic neural networks were trained and optimized to ensure accurate and robust performance under dynamic and nonlinear conditions. The trained policy was subsequently deployed on a low-cost embedded platform (Arduino Uno), demonstrating its feasibility for real-time embedded applications. Experimental results confirm the controller’s ability to adapt to external disturbances. Quantitatively, the proposed controller achieved a steady-state error of less than 0.05 cm and an overshoot of 16% in the physical implementation, outperforming conventional proportional–integral–derivative (PID) control by 22% in tracking accuracy. The combination of the DDPG algorithm and low-cost hardware implementation demonstrates the feasibility of real-time deep learning-based control for intelligent water management. Furthermore, the proposed architecture is directly applicable to low-cost Internet of Things (IoT)-based water management systems, enabling autonomous and adaptive control in real-world hydraulic infrastructures. This proposal demonstrates its potential for smart agriculture, distributed sensor networks, and scalable and resource-efficient water systems. Finally, the main novelty of this work is the deployment of a DRL-based controller on a resource-constrained microcontroller, validated under real-world perturbations and sensor noise. Full article

Background and objectives. White matter hyperintensities (WMHs) on brain magnetic resonance imaging (MRI) are linked to cognitive decline, but clinical assessment still relies mainly on visual grading (Fazekas), which is coarse and rater-dependent. We described the lesion volume of WMHs and the association of the anatomical distribution with the severity of cognitive impairment using automated lesion analysis. In addition, we evaluated whether automated volumetric quantification is more strongly associated with cognitive performance than visual grading. Materials and Methods. In a retrospective cross-sectional study, forty-one adults referred for cognitive concerns underwent standardised 3.0 tesla MRI. White matter hyperintensities were automatically segmented using Icometrix software to obtain total and regional volumes (periventricular, subcortical, brainstem, cerebellum). Visual grading used the Fazekas scale separately for periventricular and deep white matter, with a combined grade defined by the higher of the two. Cognitive performance was grouped based on the Montreal Cognitive Assessment (MoCA) into high (≥26), moderate (18–25), and low (≤17). Statistics included Spearman’s correlation and the Kruskal–Wallis test with Dunn’s post hoc test where applicable. Results. Higher total white matter hyperintensity volume was associated with lower Montreal Cognitive Assessment scores and showed significant differences across cognitive groups. The Fazekas combined grade correlated more weakly with the MoCA score. Regional volumetric differences showed trends, but were not statistically significant. Total volumetric burden increased stepwise across combined Fazekas categories, supporting convergent validity between methods. Conclusions. Our study found that automated volumetric quantification provides a more objective, sensitive, and scalable measure of white matter hyperintensity burden than visual grading, aligns more closely with cognitive status, and is better suited for longitudinal monitoring and research endpoints. Full article

This study used a rat model of coronary artery reperfusion imaged with preclinical 7-tesla magnetic resonance imaging (7T-MRI) to evaluate cardiac function, myocardial deformation, and the impact of infarction-to-reperfusion time. Wistar rats were assigned to control (n = 6), 20 min infarction (n = 10), 30 min infarction (n = 6), and 40 min infarction (n = 6) groups. Myocardial infarction occurred in all infarction groups but not in controls. Imaging included short- and long-axis slices. Cardiac function was assessed using end-diastolic volume, end-systolic volume, and left-ventricular ejection fraction. Myocardial deformation was analyzed by circumferential strain, radial strain (RS), and longitudinal strain (LS, four-chamber and two-chamber) using feature tracking. The 30 and 40 min infarction groups showed significant reductions in cardiac function and strain compared to the controls. RS decreased significantly between the control and 20 min infarction groups (40.6 ± 4.7% and 34.0 ± 4.1%, p < 0.05). No significant LS difference was observed between 30 and 40 min. Consequently, RS detects early myocardial changes (20 min), whereas LS may reflect compensatory contractility in severe infarction. Preclinical 7T-MRI provides valuable insights into the impact of infarction duration on cardiac function and myocardial deformation. Full article

The rise of connected and automated vehicles has transformed in-vehicle infotainment (IVI) systems into critical gateways linking user interfaces, vehicular networks, and cloud-based fleet services. A concerning architectural reality is that hardcoded credentials like access point names (APNs) in IVI firmware create a cross-layer attack surface where local exposure can escalate into entire vehicle fleets being remotely compromised. To address this risk, we propose a cross-layer security framework that integrates firmware extraction, symbolic execution, and targeted fuzzing to reconstruct authentic IVI-to-backend interactions and uncover high-impact web vulnerabilities such as server-side request forgery (SSRF) and broken access control. Applied across seven diverse automotive systems, including major original equipment manufacturers (OEMs) (Mercedes-Benz, Tesla, SAIC, FAW-VW, Denza), Tier-1 supplier Bosch, and advanced driver assistance systems (ADAS) vendor Minieye, our approach exposes systemic anti-patterns and demonstrates a fully realized exploit that enables remote control of approximately six million Mercedes-Benz vehicles. All 23 discovered vulnerabilities, including seven CVEs, were patched within one month. In closed automotive ecosystems, we argue that the true measure of efficacy lies not in maximizing code coverage but in discovering actionable, fleet-wide attack paths, which is precisely what our approach delivers. Full article

In this work, three novel optimization algorithms—collectively referred to as the BxR algorithms—and their multi-objective versions, referred to as the MO-BxR algorithms, are applied to diverse heat transfer systems. Five representative case studies are presented: two single-objective problems involving a heat exchanger network and a jet-plate solar air heater; a two-objective optimization of Y-type fins in phase-change thermal energy storage units; and two three-objective problems involving TPMS–fin three-fluid heat exchangers and Tesla-valve evaporative cold plates for LiFePO₄ battery modules. The proposed algorithms are compared with leading evolutionary optimizers, including IUDE, εMAgES, iL-SHADEε, COLSHADE, and EnMODE, as well as NSGA-II, NSGA-III, and NSWOA. The results demonstrated improved convergence characteristics, better Pareto front diversity, and reduced computational burden. A decision-making framework is also incorporated to identify balanced, practically feasible, and engineering-preferred solutions from the Pareto sets. Overall, the results demonstrated that the BxR and MO-BxR algorithms are capable of effectively handling diverse thermal system designs and enhancing heat transfer performance. Full article

This study examines the role of Digital Twin technologies in advancing Environmental, Social, and Governance performance within multinational corporations. Grounded in socio-technical systems theory and stakeholder theory, the research investigates how digital twins facilitate the integration of organizational capabilities with external accountability mechanisms. A multi-method research design is employed, comprising in-depth case studies, capital market event analysis, and machine learning-assisted regression to capture both qualitative and empirical insights. Case evidence from Siemens, Unilever, Tesla, and BP reveals that DT adoption is associated with measurable ESG gains, including reduced emissions, improved safety, enhanced supplier compliance, and accelerated reporting cycles. Event study findings show statistically significant abnormal returns following ESG-oriented DT announcements, while regression analysis confirms a positive association between DT adoption and ESG performance. Governance structures are explored as potential moderators of this relationship. The findings underscore DTs as strategic enablers of ESG value creation, beyond their technical utility. By enhancing transparency, auditability, and stakeholder trust, DTs contribute to both internal transformation and external legitimacy. This research advances the discourse on ESG digitalization and offers actionable implications for corporate leaders and policymakers seeking to foster sustainable, technology-driven governance in complex global value chains. However, because the quantitative component relies on cross-sectional data, the relationships identified should be interpreted as associations rather than definitive causal effects. Full article

The production and sales figures for electric vehicles are showing a steady upward trend, clearly indicating the growing importance of sustainability goals. A unique historical situation has developed in the US: the owner of the leading electric car manufacturer (Tesla), Elon Musk, has taken an active role in political life. Amid a rising trend in electric vehicle (EV) adoption aligned with global sustainability goals, the political activism of Musk has provoked public backlash, including acts of vandalism and aggression toward Tesla vehicles. Using a multidisciplinary approach, the study explores (1) the psychological underpinnings of object-directed violence, (2) the legal classification of politically motivated vandalism, and (3) the broader market implications of corporate politicization. Our findings confirm that object-directed aggression stems from displaced frustration, especially when individuals feel politically powerless or morally outraged. Our analysis revealed that most Tesla-related vandalism will likely be prosecuted as property crimes. Although U.S. officials have labeled some acts as domestic terrorism or hate crimes, legal thresholds are generally not met. Our interdisciplinary model suggests that the politicization of Tesla has broader implications. Tesla’s symbolic status in the electric vehicle market means that attacks on it risk triggering a decline in public trust toward electric mobility. Full article

In situ fixation of deep-sea nucleic acids remains challenging, as conventional sampling often causes degradation due to abrupt environmental changes. This study developed a novel deep-sea nucleic acid preservation structure based on the Tesla valve principle, comparing it with traditional straight-tube structure. Experimental and CFD simulation results showed that the Tesla-valve structure significantly reduced fixative consumption under the same inlet velocity. At an inlet velocity of 0.2 m/s with a chamber fixative mass fraction of 87.2%, the Tesla-valve structure reduced fixative use by 48.9%. The fixative consumption decreases to minimum at 0.4 m/s in Tesla-value structure. Moreover, we investigated the effects of annular baffle quantity, baffle inclination angle, and central aperture diameter on fixative consumption across varying flow regimes using a numerical simulation method. Results indicated that the number of baffles exerted a significant influence on fixative consumption, with reduced baffle numbers correlating with increased consumption across all flow velocities. Baffle inclination angle and central aperture diameter demonstrated negligible effects on consumption metrics. This work provides an efficient structural design for deep-sea nucleic acid preservation, providing technical support for maintaining nucleic acid integrity during abyssal biological investigations. Full article

This study presents a comparative evaluation of Dijkstra’s algorithm, A*, Bellman-Ford, AI-Augmented A* and a neural AI-based model for shortest-path computation across diverse graph topologies, with a focus on time efficiency and memory consumption under standardized experimental conditions. We analyzed grids, random graphs, and scale-free graphs of sizes up to 103,103 nodes, specifically examining 100- and 1000-node grids, 100- and 1000-node random graphs, and 100-node scale-free graphs. The algorithms were benchmarked through repeated runs per condition on a server-class system equipped with an Intel Xeon Gold 6248R processor, NVIDIA Tesla V100 GPU (32 GB), 256 GB RAM, and Ubuntu 20.04. A* consistently outperformed Dijkstra’s algorithm when paired with an informative admissible heuristic, exhibiting faster runtimes by approximately 1.37× to 1.91× across various topologies. In comparison, Bellman-Ford was slower than Dijkstra’s by approximately 1.50× to 1.92×, depending on graph type and size; however, it remained a robust option in scenarios involving negative edge weights or when early-termination conditions reduced practical iterations. The AI model demonstrated the slowest performance across conditions, incurring runtimes that were 2.60× to 3.23× higher than A* and 1.62× to 2.15× higher than Bellman-Ford, offering limited gains as a direct solver. These findings underscore topology-sensitive trade-offs: A* is preferred when a suitable heuristic is available; Dijkstra’s serves as a strong baseline in the absence of heuristics; Bellman-Ford is appropriate for handling negative weights; and current AI approaches are not yet competitive for exact shortest paths but may hold promise as learned heuristics to augment A*. We provide environmental details and comparative results to support reproducibility and facilitate further investigation into hybrid learned-classical strategies. Full article

This review investigates how cell form factors (cylindrical, prismatic, and pouch) and electrode architecture (jelly-roll, stacked, and blade) influence the performance, safety, and manufacturability of lithium-ion batteries (LIBs) across the main commercial chemistries LiFePO₄ (LFP), Li (NiMnCo)O₂ (NMC), LiNiCoAlO₂ (NCA), and LiCoO₂ (LCO). Literature, OEM datasheets, and teardown analyses published between 2015 and 2025 were examined to map the interdependence among geometry, electrode design, and electrochemical behavior. The comparison shows trade-offs among gravimetric and volumetric energy density, thermal runaway tolerance, cycle lifespan, and cell-to-pack integration efficiency. LFP, despite its lower nominal voltage, offers superior thermal stability and a longer cycle life, making it suitable for both prismatic and blade configurations in EVs and stationary storage applications. NMC and NCA chemistries achieve higher specific energy and power by using jelly-roll architectures that are best suited for tabless or multi-tab current collection, enhancing uniform current distribution and manufacturability. Pouch cells provide high energy-to-weight ratios and flexible packaging for compact modules, though they require precise mechanical compression. LCO remains confined to small electronics owing to safety and cost limitations. Although LFP’s safety and affordability make it dominant in cost-sensitive applications, its low voltage and energy density limit broader adoption. LiMnFePO₄ (LMFP) cathodes offer a pathway to enhance voltage and energy while retaining cycle life and cost efficiency; however, their optimization across various form factors and electrode architecture remains underexplored. This study establishes an application-driven framework linking form factors and electrode design to guide the design and optimization of next-generation lithium-ion battery systems. Full article