Search Results (225)

With the rapid evolution toward 6G networks, ensuring robust physical layer security (PLS) in highly dynamic and heterogeneous wireless environments has become a key challenge. Traditional security methods often struggle to adapt to time-varying channels, especially in the absence of perfect channel state information. Furthermore, the dynamic nature of node selection and power allocation in heterogeneous networks creates a complex hybrid action space operating across multiple timescales, significantly complicating the design of efficient and adaptive security strategies. To address this, this paper proposes a novel constrained hierarchical reinforcement learning (CHRL) framework for secure cooperative communications in next-generation wireless systems. The framework is designed to optimize secrecy performance within a hybrid action space comprising both discrete node selection and continuous power allocation, operating at different timescales. By hierarchically decoupling the joint optimization problem, the upper layer performs risk-aware node selection to maximize long-term secrecy capacity (SC) while guaranteeing a stable and secure link. At the lower layer, we develop a constrained MiniMax Multi-objective Deep Deterministic Policy Gradient (M3DDPG) algorithm that optimizes power allocation considering worst-case conditions. Lagrange multipliers are integrated to enforce a strictly positive SC constraint throughout transmission, effectively preventing security outages. Simulation results under time-varying Rayleigh fading channels demonstrate that the proposed CHRL framework outperforms existing HRL methods, achieving up to 17% improvement in SC while strictly maintaining security constraints. These results validate the effectiveness of the proposed approach for enhancing PLS in next-generation cooperative wireless networks. Full article

Extensive research and empirical evidence demonstrate that nanofluids enhance heat transfer efficiency in microchannels, but this improvement is often accompanied by increased pressure drop and particle clogging. This study aims to determine the optimal parameters for preparing stable nanofluids and to discuss the effects of different parameters on thermal and hydraulic performance. By analyzing the impact of varying ultrasonication time, particle concentration, particle size, surfactant type, and mixing ratios on stability, the most stable nanofluid was selected for evaluation of flow heat transfer and cost-effectiveness. Results indicate that a 1:1 mixed nanofluid of TiO₂ (20 nm)-SiO₂ (50 nm) exhibits optimal stability under conditions of 90 min ultrasonication, 0.20 vol% total particle concentration, and 0.15 wt% xanthan gum. At a Reynolds number of 550, this mixed nanofluid exhibits superior thermal performance. Compared with deionized water, its convective heat transfer coefficient and Nusselt number increase by 40.25% and 37.94%, respectively, while the pressure drop rises by only 17.18%. The performance evaluation criterion reaches 1.43, accompanied by a high cost–performance factor. These findings demonstrate that mixing large and small particles of TiO₂ and SiO₂ not only significantly enhances thermal performance but also positively impacts stability and hydraulic properties. A 90 min ultrasonic treatment time markedly improves stability and optimizes dynamic light scattering results. Full article

Occupant presence is a primary driver of Heating, Ventilation, and Air Conditioning (HVAC) and lighting energy consumption in office environments. Existing occupancy-sensing solutions often rely on privacy-sensitive modalities or require costly infrastructure, limiting their applicability in Small and Medium Offices (SMOs). To address these limitations, this study proposes a lightweight CSI-based occupancy-sensing framework based on a dual-core ESP32-S3 architecture, enabling concurrent CSI processing, environmental sensing, and cloud communication. A multi-stage signal preprocessing pipeline compresses raw CSI streams into a compact $56\times 8$ statistical feature matrix, achieving 98.86% classification accuracy for multi-level occupancy estimation. Compared with image-based baselines such as DenseNet121, the proposed approach reduces input data size to 24 kB and model parameters to 138 K, yielding over 129× reduction in transmission volume without sacrificing performance. These results demonstrate that the proposed framework provides a practical, privacy-preserving, and edge-deployable solution for occupancy-aware energy management in SMOs. Full article

Background and Objectives: Because of its consistently high stone-free rates (SFRs), percutaneous nephrolithotomy (PCNL) continues to be the first-line treatment for renal stones larger than 20 mm. Standard 24 to 30 Fr access tracts, however, are linked to access-related morbidity, such as bleeding, pain, and extended hospital stays. These restrictions have led to progressive tract miniaturization and the development of mini-PCNL, ultra-mini PCNL, and micro-PCN techniques. Materials and Methods: We performed a narrative review of studies published through January 2026 using PubMed and Google Scholar. Search terms included percutaneous nephrolithotomy, mini-PCNL, ultra-mini PCNL, micro-PCNL, and vacuum-assisted PCNL. Original studies, systematic reviews, and meta-analyses reporting clinical outcomes, complications, and advancements were selected, whereas conference abstracts, non-English papers, and articles without accessible full text were excluded. Results: Across randomized trials, miniaturized PCNL generally preserves efficacy when patients are selected appropriately. Across randomized trials and meta-analyses, miniaturized PCNL achieved stone-free rates comparable to standard PCNL (typically ~80–90% for stones ≤20 mm and similar rates in selected stones >2 cm), while demonstrating lower hemoglobin decrease (mean difference approximately −0.6 to −1.0 g/dL), reduced transfusion rates, and shorter hospital stays, at the cost of longer operative time (mean difference ~8–12 min). On the other hand, operative time may increase, and smaller working channels can make visualization and fragment evacuation more demanding as stone burden rises. Raised intrarenal pressure is a recurring safety issue because it may increase infectious risk unless drainage is actively managed. Recent innovations aim to address these limitations, including vacuum-assisted access sheaths, pressure-controlled irrigation, improved laser and lithotripsy platforms, image-fusion guidance, navigation systems, and robotic assistance. Conclusions: PCNL now spans a spectrum of tract sizes rather than a single standard approach. When chosen appropriately and performed with attention to pressure control and fragment evacuation, miniaturized PCNL can reduce morbidity without sacrificing stone clearance. Future advancements in percutaneous stone surgery are more likely to rely on integrated technological solutions that improve accuracy, safety, and repeatability than on additional tract size reduction. Full article

For few-shot image classification tasks, the recognition accuracy of existing models remains limited due to the inherent complexity of the few-shot learning setting. To address this challenge, this paper proposes a few-shot image classification approach, termed GDFSIC, which integrates a Global–Local Channel Attention Module (GLCAM) with a graph-propagation-based Distance–Direction Similarity Earth Mover’s Distance (DDS-EMD). The GLCAM module is incorporated into the feature extractor to enhance focus on discriminative regions and increase model attention to critical feature areas. Furthermore, a Distance–Direction Similarity (DDS) metric is introduced as a more effective distance criterion for capturing subtle differences in latent spatial representations. The proposed method is evaluated on four widely used few-shot image classification benchmarks: CIFAR-FS, CUB-200-2011, mini-ImageNet, and Tiered-ImageNet. Experimental results demonstrate that our approach achieves a clear competitive advantage in classification accuracy across these datasets. Ablation studies and further analyses confirm the effectiveness of each component of the proposed framework. Full article

The escalating heat flux density and temperature in highly integrated microelectronic devices adversely affect their reliability and service life, making efficient thermal management crucial for stable operation. This study utilizes Galinstan liquid metal as the coolant to investigate the flow and heat transfer performance in microchannel heat sinks incorporating various turbulator configurations. It is revealed that for microchannels featuring expanded regions, turbulators that create highly symmetric flow fields are preferable due to improved flow distribution. The long teardrop-shaped turbulator provides the best heat transfer performance among all the investigated heat transfer enhancement structures. And this turbulator yields a 13.8–25.9% higher enhancement effectiveness compared to other configurations, at the expense of a 28–41% increase in pressure loss. However, the sudden cross-sectional expansion in the expanded region causes a significant reduction in fluid velocity. Consequently, microchannels with expanded regions and turbulators exhibit a higher bottom surface temperature than the original, straight microchannels, leading to an overall deterioration in heat transfer performance. Full article

Binary mixtures of HFE-73DE and ethyl acetate are investigated as dielectric working fluids for laminar minichannel cooling. Thermophysical properties of the pure components and four mixtures (10/90, 25/75, 50/50 and 75/25 mass % HFE-73DE/ethyl acetate) were measured over the relevant temperature range. Single-phase convective heat transfer tests were then carried out in a heated 1 × 4 × 180 mm minichannel test section under constant heat-flux conditions for pure HFE-73DE. A three-dimensional conjugate CFD model with temperature-dependent liquid properties was developed in Simcenter STAR-CCM+ and validated against these measurements; the average relative temperature difference between CFD and experiment remained below 0.5%, while a grid-convergence study based on the Grid Convergence Index (GCI) confirmed that the numerical uncertainty is comparable to the experimental one. The validated model was subsequently used to predict the axial evolution of wall temperature, fluid-core temperature, velocity and heat transfer coefficient for the four mixtures under identical conditions. The mean Nusselt numbers obtained from CFD were further compared with the classical Shah and London fully developed laminar solution for rectangular ducts, revealing that the present configuration yields values about 35–42% higher than the theoretical prediction owing to asymmetric heating and conjugate heat transfer. The results show that increasing the HFE-73DE mass fraction strengthens convective heat transfer and reduces fluid-temperature rise, while intermediate compositions (50/50 and 75/25) provide a favourable compromise between enhanced heat transfer performance and moderate pressure drop. The study provides guidance for composition selection and the design of dielectric minichannel heat exchangers operating with HFE-73DE/ethyl acetate mixtures. Full article

The increasing adoption of lithium-ion (Li-ion) batteries in electric vehicles (EVs) and renewable energy systems has heightened the demand for efficient Battery Thermal Management Systems (BTMS). Effective thermal regulation is critical to prevent performance degradation, extend battery lifespan, and mitigate safety risks such as thermal runaway. Liquid cooling has become the dominant strategy in commercial EVs due to its superior thermal performance over air cooling. However, optimizing liquid cooling systems remains challenging due to the trade-off between heat transfer efficiency and pressure drop. Recent studies have explored various coolant selection, nanofluid enhancements, and complex channel geometries, an ideal balance remains difficult to achieve. While advanced methods such as topology optimization offer promising performance gains, they often introduce significant modeling and manufacturing complexity. In this study, we propose a practical alternative: an interconnected straight-channel cooling plate that introduces lateral passages to disrupt the thermal boundary layer and enhance mixing. Comparative analysis shows that the design improves temperature uniformity and reduces peak battery temperature, all while maintaining a moderate pressure drop. The proposed configuration offers a scalable and effective solution for next-generation BTMS, particularly in EV applications where thermal performance and manufacturability are both critical. Full article

This study presents comprehensive experimental, analytical, and numerical analyses of heat transfer during countercurrent flow of Fluorinert FC-72 and distilled water within a multi-mini-channel (MMCH) module under steady-state conditions. The experimental investigation was conducted in a test section inclined at an angle of 165 degrees relative to the horizontal plane, utilizing an infrared camera to measure the external temperature of the heated mini-channel (MCH) wall. The test module comprised twelve MCHs: six hot (HMCH) and six cold mini-channels (CMCH), each with a rectangular cross-section. The dimensions of each MCH are 140 mm in length, 18.3 mm in width, and 1.5 mm in depth, with a hydraulic diameter of d_h = 2.77 mm. The heating system on the top wall of the external heated copper comprises a halogen heating lamp. Results include infrared thermographs, temperature distributions, and heat transfer coefficients (HTCs) along the channels. Local HTCs were calculated using a one-dimensional (1D) approach, a simple analytical method, at interfaces such as the heated plate—HMCHs, HMCHs—separating plate, separating plate—CMCHs, and CMCHs—closing plate. CFD simulations conducted with Simcenter STAR-CCM+ incorporated empirical data from experiments, using parameters like temperature, pressure, velocity profiles, and heat flux density to determine HTCs. The maximum difference between the 1D method and CFD results was 29% at the HMCHs/separating plate interface. In comparison, the minimum was 13.5% at the separating plate/CMCHs interface, with an average across all channels and heat flux densities. Full article

This study presents a comprehensive experimental investigation into the frictional pressure drop of two-phase flows—boiling and condensation—in horizontal minichannels, emphasizing its impact on the energy efficiency of vapor compression systems. A total of 3553 data points were obtained using six low-GWP refrigerants (R32, R134a, R290, R410A, R513A, and R1234yf) across a wide range of operating conditions in multiport aluminum tubes with hydraulic diameters of 0.715 mm and 1.16 mm. The dataset covers mass fluxes from 200 to 1230 $\mathrm{kg}{\mathrm{m}}^{-2}{\mathrm{s}}^{-1}$, saturation temperatures between 5 °C and 55 °C, and vapor qualities from 0.05 to 0.95. Results showed a strong dependence of frictional pressure gradient on vapor quality, mass flux, and channel size. Boiling flows generated higher frictional losses than condensation, and high-density refrigerants such as R32 exhibited the largest pressure penalties, which can directly translate into increased compressor power demand. Conversely, higher saturation temperatures were associated with lower frictional losses, highlighting the role of thermophysical properties in improving energy performance. Additionally, an inverse correlation between saturation temperature and frictional pressure gradient was observed, attributed to variations in thermophysical properties such as viscosity and surface tension. Existing correlations from the literature were assessed against the experimental dataset, with notable deviations observed in several cases, particularly for R134a under high-quality conditions. Consequently, a new empirical correlation was developed for predicting the frictional pressure drop in two-phase flow through minichannels. The proposed model, formulated using a power-law regression approach and incorporating dimensionless parameters, achieved better agreement with the experimental data, reducing prediction error to within ±20%, improving the accuracy for the majority of cases. This work provides a robust and validated dataset for the development and benchmarking of predictive models in compact heat exchanger design. By enabling the more precise estimation of two-phase pressure drops in compact heat exchangers, the findings support the design of refrigeration, air-conditioning, and heat pump systems with minimized flow resistance and reduced auxiliary energy consumption. This contributes to lowering compressor workload, improving coefficient of performance (COP), and it ultimately advances the development of next-generation cooling technologies with enhanced energy efficiency. Full article

The transfer of momentum and kinetic energy is a key factor in turbomachinery performance, particularly influencing the efficiency of the bladeless Tesla turbine, which holds significant potential for applications such as Organic Rankine Cycle (ORC) systems and energy recovery processes. In this study, a comprehensive numerical analysis was carried out to simulate the effects of surface roughness on the flow between the co-rotating disks of a Tesla turbine, using R1234yf and n-hexane as working fluids. To capture roughness effects, a porous medium layer (PML) approach was employed, with porous material parameters adjusted to replicate real roughness behavior. The model was first validated against experimental data for water flow in a minichannel by tuning the PML parameters to match measured pressure drops. In contrast to previous studies, this work applies the PML model to a Tesla turbine operating with organic Rankine cycle (ORC) fluids, where the working medium is changed from air to low-boiling gases. Compared to the air-based cases, the gap between the co-rotating disks is rescaled to smaller dimensions, which introduces additional challenges. Under these conditions, the effective roughness thickness must also be rescaled, and this study investigates how these rescaled roughness effects influence turbine performance using the k-ω shear stress transport (SST) turbulence model combined with the proposed roughness model. Results showed that incorporating the PML roughness model enhances momentum transfer and significantly influences flow characteristics, thereby providing an effective means of simulating Tesla turbine performance under varying roughness conditions. Full article

The critical heat flux (CHF) of minichannel heat sinks is crucial, as it helps prevent thermal safety incidents and equipment failure. However, the underlying mechanisms of CHF in minichannels remain poorly understood, and existing CHF prediction models require further refinement. This study systematically investigates the characteristics and influencing factors of critical heat flux (CHF) in rectangular minichannels through combined experimental and theoretical approaches. Experiments were conducted using microchannels with hydraulic diameters ranging from 0.5 to 2.0 mm, with ethanol employed as the working fluid. Key parameters-including mass flux, channel geometry, system pressure, and inlet subcooling-were analyzed to assess their influence on CHF. Results indicate that CHF increases with mass flux; however, the increase rate diminishes under higher mass flux. Larger channel dimensions significantly enhance CHF by delaying liquid film dryout. System pressure further improves CHF by reducing bubble detachment frequency and promoting flow stability. Increased inlet subcooling enhances CHF by delaying the onset of nucleate boiling and improving convective heat transfer. Four classical CHF prediction models were evaluated, revealing significant overprediction-up to 148.69% mean absolute error (MAE)-particularly for channels with hydraulic diameters below 1.0 mm. An ANN deep learning model was developed, achieving a reduced MAE of 8.93%, with 93% of predictions falling within ±15% error. This study offers valuable insights and a robust predictive model for optimizing microchannel heat sink performance in high heat flux applications. Full article

Three-finger toxins (3FTxs) from snake venom are the most abundant toxin family of mini non-enzymatic proteins, comprising 40–70% of the venom proteome. Despite their common three-finger structural scaffold, 3FTxs exhibit diverse pharmacological functions. Other than neurotoxins, they also include analgesic acid-sensing ion channel blockers, sodium and potassium channel modulators, integrin- and G-protein-coupled-receptor-targeting ligands, and gamma-aminobutyric acid type A receptor modulators that collectively span pain, cardiovascular, oncologic, and neurologic indications. However, in this fast-growing 3FTx family, there are several hundred 3FTxs whose functions have not yet been determined. Here, we systematically analyzed over 550 amino acid sequences of 3FTxs. Based on their structural features, we have classified them into more than 150 distinct subgroups. This updated information on this novel 3FTx toolkit will provide an unexplored library of investigational ligands and pharmacophores with potential therapeutic and diagnostic leads, as well as research tools. Thus, this review will provide new impetus in toxin research and pave the way for the design of potent, selective ligands for new sets of target receptors, ion channels, and enzymes. Full article

The main objective of this study was to investigate boiling heat transfer during refrigerant flow in a mini-channel heat sink. The test section consisted of multiple parallel mini-channels, each with a depth of 1 mm. The working fluid was heated by a thin layer of Haynes-230 alloy with a thickness of 0.1 mm. The outer surface temperature of the heater was measured using infrared thermography, while other thermal and flow-based parameters were recorded via a dedicated data acquisition system. The mini-channel heat sink was tested in seven different spatial orientations, with inclination angles relative to the horizontal plane of 45°, 60°, 75°, 90°, 105°, 120°, and 135°. The analysis focused on the early stage of the experiment, corresponding to the forced convection, boiling incipience, and subcooled boiling region. A time-dependent, two-dimensional model of heat transfer during flow boiling of a refrigerant in asymmetrically heated mini-channels was developed. The temperatures of both the heating foil and the working fluid (Fluorinert FC-770) were described using appropriate forms of the Fourier–Kirchhoff equation, subject to relevant boundary conditions. Two sets of time-dependent Trefftz functions were employed to solve the governing equations: one set corresponding to the two-dimensional Fourier equation and another, newly derived, for the energy equation in the fluid. The results include thermographic images of the heated surface, temperature distributions, fluid temperatures, local heat-transfer coefficients, and boiling curves. A comparison of the heat-transfer coefficients obtained using the Trefftz-based approach and those calculated using Fourier’s law demonstrated satisfactory agreement. Full article

RGB-T semantic segmentation using thermal and RGB images simultaneously is actively being researched to robustly recognize the surrounding environment of vehicles regardless of challenging lighting and weather conditions. It is important for them to operate in real time on edge devices. As transformer-based approaches, which most recent RGB-T semantic segmentation studies belong to, are very difficult to perform on edge devices, this paper considers only CNN-based RGB-T semantic segmentation networks that can be performed on edge devices and operated in real time. Although EAEFNet shows the best performance among CNN-based networks on edge devices, its inference speed is too slow for real-time operation. Furthermore, even when channel pruning is applied, the speed improvement is minimal. The analysis of EAEFNet identifies the intermediate fusion of RGB and thermal features and the high complexity of the decoder as the main causes. To address these issues, this paper proposes a network using a ResNet encoder with an early-fused four-channel input and the U-Net decoder structure. To improve the decoder performance, bilinear upsampling is replaced with PixelShuffle. Additionally, mini Atrous Spatial Pyramid Pooling (ASPP) and Progressive Transposed Module (PTM) modules are applied. Since the Proposed Network is primarily composed of convolutional layers, channel pruning is confirmed to be effectively applicable. Consequently, channel pruning significantly improves inference speed, and enables real-time operation on the neural processing unit (NPU) of edge devices. The Proposed Network is evaluated using the MFNet dataset, one of the most widely used public datasets for RGB-T semantic segmentation. It is shown that the proposed method achieves a performance comparable to EAEFNet while operating at over 30 FPS on an embedded board equipped with the Qualcomm QCS6490 SoC. Full article