Search Results (929)

To address the sampling misalignment and detail loss caused by fixed-grid downsampling for small-scale defects, as well as the insufficient differentiated modeling and interaction of defect details and structural context in UAV-acquired power-line images, an enhanced lightweight YOLOv8n-based framework for power-line defect detection is developed. First, a content-adaptive downsampling (CAD) module is developed to predict input-dependent sampling offsets and normalized aggregation weights and to perform differentiable resampling. Combined with local-global interactive depthwise separable convolution, CAD improves the preservation of small-object details while maintaining relatively low computational complexity. Second, a dynamic subspace gated exchange (DSGE) module is proposed to adaptively partition features into a high-frequency detail subspace and a low-frequency structural subspace according to the input content. Heterogeneous branches and bidirectional gated exchange are then employed to jointly model fine-grained details and structural context. In addition, the lightweight mixed local channel attention (MLCA) mechanism is incorporated in the detection head as an auxiliary feature-enhancement component. Experimental results show that the proposed model achieves mAP@0.50 and mAP@0.50:0.95 values of 92.3% and 62.9%, respectively, outperforming the compared models under the current evaluation protocol. With 1.90 M parameters and 5.6 G FLOPs, the model reaches an inference speed of 134.7 FPS on the desktop GPU platform, demonstrating that content-adaptive sampling and dynamic detail–structure interaction can improve small-defect detection and complex-background suppression while maintaining relatively low model complexity. Full article

This paper examines whether prolonged sanctions and major geopolitical episodes associated with financial fragmentation alter exchange-rate dynamics and weaken the explanatory power of domestic macroeconomic channels and strengthen external financial dominance of traditional exchange-rate transmission mechanisms. Standard exchange rate theories are based on the concepts of Purchasing Power Parity (PPP) and Uncovered Interest Parity (UIP). However, the application of continuous sanctions could weaken this explanatory power and change exchange rate dynamics. The present study applies a combined framework of Autoregressive Distributed Lag (ARDL), Error Correction Modeling (ECM), Vector Autoregression (VAR) and structural break analysis to study the exchange-rate behavior in response to repeated geopolitical shocks using monthly data for Russia from 2005 to 2025. The results indicate that external variables such as the US dollar index and oil prices are important determinants of exchange rates, while inflation and interest rate differentials associated with PPP and UIP have little explanatory power. Structural break tests detect major regime shifts associated with the Global Financial Crisis, Crimea-related sanctions episode, COVID-19 pandemic and Russia–Ukraine conflict. The error correction process indicates that the speed of adjustment to equilibrium is slow, which means that traditional exchange-rate relationships will continue to diverge. In general, the results suggest a regime-dependent exchange rate environment in which external financial factors tend to dominate domestic adjustment mechanisms. Our study contributes to the literature on exchange rates, sanctions and financial fragmentation by providing evidence on how geopolitical shocks shift the relative importance of domestic and external determinants in a highly sanctioned economy. Full article

Tunnel lining defects pose significant threats to structural integrity and operational safety. Traditional image processing and machine learning methods often suffer from limited accuracy and poor generalization under complex backgrounds. To address these limitations, this study proposes a lightweight intelligent recognition method based on You Only Look Once version 11 nano (YOLOv11n) for Ground Penetrating Radar (GPR) images of tunnel linings. The backbone is replaced with Mobile Network Version 3 (MobileNetV3) to reduce parameters and Floating Point Operations (FLOPs), while depthwise separable convolution and a streamlined Compressed 2-Stage Fused-Lite (C2f-Lite) structure are integrated into the Neck to further decrease computational overhead. Channel mapping layers are employed to ensure smooth feature transfer, and selective use of Squeeze-and-Excitation (SE) attention and Hard-Swish (H-swish) activation balances detection accuracy with efficiency. Evaluated on a low-power mobile workstation acting as an edge-precursor proxy platform, experimental results demonstrate that the improved YOLOv11n_MobileNetV3 model achieves high accuracy with a mean Average Precision (mAP) at 0.5 of 94.4% and mAP@0.5:0.95 of 62.4%, low computational cost of 4.7 Giga Floating Point Operations (GFLOPs), and fast inference speed of 45 Frames Per Second (FPS). Comparative analysis further confirms its superior balance of detection performance and efficiency over YOLO version 5 (YOLOv5) and YOLO version 8 (YOLOv8) baselines. The proposed approach provides a highly optimized, edge-oriented engineering solution for real-time tunnel lining defect inspection, establishing strong structural and theoretical feasibility for future deployment in embedded systems. Full article

This study comprehensively examines the banking, macroeconomic, and institutional quality dynamics determining the return on equity (ROE) of the banking sector in 27 European Union countries over the 2014–2024 period. Adopting a purely explanatory framework rather than a predictive exercise, the primary aim is to identify and rank the factors driving cross-country profitability variability. The traditional multiple linear regression (MLR) method and three machine learning models (CatBoost, Extra Trees, Gradient Boosting) are comparatively analyzed, with model transparency ensured via the Shapley Additive Explanations (SHAP) algorithm. Empirical findings provide evidence consistent with strong, non-linear interactions among profitability dynamics that traditional econometric models tend to overlook. Comparative analyses indicate that the best-performing CatBoost algorithm possesses notably higher explanatory power compared to the MLR model, an advantage that persists when the linear benchmark is augmented with country fixed effects. According to SHAP results, the non-performing loan (NPL) ratio is the most dominant factor eroding profitability. Conversely, inflation is associated with a positive impact on ROE through the repricing channel up to a certain threshold, after which its marginal contribution flattens, exhibiting a concave structure. These thresholds should be read as model-implied patterns within the present sample rather than as general economic constants. The direct explanatory power of the institutional quality indicators employed here—and of a principal-component composite of the broader governance set—remains relatively limited, suggesting an indirect role operating through macroeconomic channels. These findings, supported by leave-one-country-out (LOCO), fixed-effects, and lagged-regressor robustness checks, suggest that explainable machine learning offers a valuable analytical infrastructure for characterizing the asymmetric effects of macro-financial shocks on bank performance. Full article

Smart wearable electrocardiogram (ECG) monitoring enables continuous cardiac assessment beyond clinical settings, but ECG signals are often degraded by baseline wander, muscle artifacts, and electrode motion artifacts. At present, pure end-to-end spiking neural networks (SNNs), with the advantage of low computational complexity, have rarely been explored for ECG noise suppression, particularly under dynamic conditions. To address this gap, this study proposes Direct-Coding-Physics-Aware Spiking Neural Network (DCPA-SNN) for wearable ECG denoising. The proposed method integrates a direct-coding SNN, channel attention, residual noise learning, and a physics-aware multi-domain loss function to preserve diagnostically important waveform structures. Clean ECG signals from the MIT-BIH Arrhythmia Database and real-noise segments from the MIT-BIH Noise Stress Test Database were used to construct single-noise and mixed-noise evaluation scenarios with input SNRs ranging from −6 dB to 4 dB to reflect the noise characteristics of wearable devices. Experimental results demonstrate that DCPA-SNN achieves robust denoising performance under different noise conditions. In the mixed-noise scenario, which serves as the primary evaluation setting of this study, the average denoised SNR reached 5.80 dB, with an average SNR improvement of 6.80 dB, while the R-peak detection rate increased from 90.71% to 95.72%. These results demonstrate that the proposed model, DCPA-SNN, provides a promising approach for wearable ECG denoising with potential for low-power deployment. Full article

Total internal reflection (TIR) loss is a critical bottleneck limiting light extraction in AlGaN-based deep-ultraviolet (DUV) light-emitting diodes (LEDs), primarily due to the large refractive-index contrast at the light-emitting interface. Here, pyramid-shaped dielectric metasurfaces are designed and fabricated at the sapphire/air interface of flip-chip AlGaN-based DUV LEDs using a scalable nanoimprinting process. The metasurface functions as a light outcoupling layer that modifies the interfacial momentum-matching condition and redistributes photon propagation directions. Experimental results and theoretical simulations show that metasurfaces with different feature sizes enhance light extraction through distinct mechanisms. The subwavelength pyramid nanoarray perturbs the local optical field and provides additional in-plane momentum components, facilitating the coupling of high-angle photons into radiative channels, whereas the larger pyramid void structure mainly promotes photon extraction through geometrical redirection, tilted output interfaces, and dry-etching-induced rough surface scattering. As a result, an average light output power (LOP) enhancement of over 8% is achieved for AlGaN-based DUV LEDs emitting at approximately 275 nm. This work demonstrates a low-cost, scalable, and effective strategy for enhancing the LEE of DUV LEDs, with promising potential for high-efficiency ultraviolet optoelectronic application. Full article

It is important to detect the damage in the rollers and repair them since the damage to the rollers has a negative impact on the quality of the rolled products. Identifying the types of damage helps determine the repair process and normal production work. Ultrasonic testing technology has the advantages of large detection depth, accurate defect localization, low cost, convenient use, fast speed, and harmlessness to the human body. In order to improve the intelligence of ultrasonic detection for identifying damages in rollers, this article proposes a deep learning classification method of damages based on Rayleigh wave signals and power spectrum images with specific sampling rate, automatic identification of four common types of damages (void, hole, crack, and adhesion) is achieved by establishing end-to-end learning models for one-dimensional (1D) and two-dimensional (2D) data. Firstly, an organic glass inclined block and a clamping device were designed. In the experiment, time-domain signals were received on the right side of the damaged sample, and signal data sets were established for signals with different sampling rates. Then, the power spectrum image data sets were established after a short-time Fourier transform was performed. Next, a damage detection model is established based on a deep learning framework, which includes ResNet, GoogLeNet, DenseNet, and AlexNet with 1D and 2D convolutional channels to extract signal features for classifying damage. Finally, the performances of DenseNet models with different structures and depths are compared based on key indicators such as accuracy and training time. The experiment demonstrates that under high sampling rate conditions, using the power spectrum image of Rayleigh waves as data input yields better results than directly using Rayleigh wave signals. Moreover, for the power spectrum images of 0.5 MS/s Rayleigh waves, using ResNet-18 to establish a deep learning model can achieve high accuracy and shorter training time. Full article

Under the IMO net-zero framework, a GHG carbon-pricing mechanism must not only generate an effective price signal for emission reduction, but also address the fair distribution of carbon revenues among different types of member states. In view of the differences between developing and developed countries in transition needs, responsibility attribution, and policy affordability, this paper develops a multilateral bargaining model that incorporates a responsibility–need reference point, loss aversion, horizontal fairness preferences, and a voting threshold, and analyzes the equilibrium formation mechanism for the distribution of carbon-pricing revenues. Theoretical derivation shows that a higher responsibility–need weight raises the fairness reference point of developing countries and increases their minimum acceptable allocation requirement; loss aversion operates through a rejection-threat channel by shrinking the acceptable set of proposals below the reference point; and horizontal fairness preferences compress the relative deviation gap between the two types of countries and may trigger a transformation of the support structure once certain thresholds are reached. The simulation results show that, in the baseline L-proposer case, developing countries receive $105.48$ out of the normalized revenue $R=120$, while developed countries receive $14.52$. Under the stress scenario with $q=121>N_L=120$, the mechanism requires cross-type support and yields group-level allocations of $106.56$ and $13.44$, respectively. At the same time, horizontal fairness preferences and voting rules jointly weaken the proposer’s ability to obtain additional gains through agenda-setting power. The study indicates that the IMO carbon-revenue distribution mechanism should strengthen responsibility–need orientation and cross-type support constraints while maintaining a unified carbon price signal, so as to improve the fairness, acceptability, and institutional stability of the carbon-pricing mechanism. Full article

Hairpin windings are increasingly adopted in permanent magnet (PM) traction machines owing to their high slot fill factor, compact end-winding structure, and suitability for automated manufacturing. However, limited heat dissipation and high copper losses under peak loading and high-frequency operation result in severe thermal constraints, which restrict the power rating of the machine. This paper presents a multi-physics comparison of different winding cooling topologies for a PM machine with hairpin winding, including hollow conductor cooling, end-winding cooling, and cooling channel insertion at slot-bottom, slot-middle, and slot-opening regions. A coupled electromagnetic–thermal model based on the finite element method (FEM), which accounts the heat transfer between different components, is used to analyze temperature distribution, losses, efficiency, loading capacity, and hydraulic requirements. The results show that the position of the cooling channel has great influence on the thermal behavior and electromagnetic performance of the machine under different working conditions. The study emphasizes the strong coupling between cooling design, conductor geometry, AC loss behavior, and efficiency and provides practical design guidelines for selecting appropriate cooling techniques in high-power-density traction machines. Consequently, an improved cooling system results in a reduced amount of PM for the same output power range. Full article

Climate change risks deepening social inequalities and poses new challenges for social policy. Drawing on climate justice, this article examines how climate impacts and policies are experienced by people living in poverty situations and by social workers, how their knowledge by experience and professional practice circulates in local climate policy-making, and how social workers understand their role in promoting climate justice. Based on qualitative interviews with people experiencing poverty, social workers, and local policy officials in three Flemish municipalities, the study shows that vulnerable groups experience climate injustice as a combination of disadvantages, misrecognition, and limited influence over policy agendas. While experiential knowledge is widely acknowledged as valuable, it remains weakly integrated in policy processes and is often channelled indirectly or introduced only after key decisions, reflecting persistent epistemic marginalisation. These patterns can be explained by the interaction of dominant policy framings, unequal power relations, and institutional arrangements that privilege technical expertise. Together, these dynamics highlight both the strategic importance and structural constraints of social work as a mediating actor and point to the need for institutional arrangements that enable earlier, more substantive, and more direct integration of experiential or practice-based knowledge. Full article

This paper introduces a sociotropy-inspired game-theoretic framework for distributed multiple-input multiple-output (MIMO) beamforming systems, where each antenna element is modeled as a strategic agent that adapts its beamforming parameters by balancing individual transmission performance with coordinated interaction among neighboring antennas. The resulting distributed beamforming problem is formulated as an exact potential game, enabling a unified analysis of cooperative antenna behavior under per-antenna power constraints. A complete mathematical formulation is developed, including the derivation of the utility and potential functions, the associated KKT stationarity conditions, and distributed projected gradient dynamics for beamforming adaptation. In addition, a graph-based multi-agent coordination mechanism is introduced to incorporate structured information exchange among antennas through similarity-driven message passing. Numerical simulations compare the proposed sociotropic strategy against both a selfish non-cooperative baseline and a graph-regularized multi-agent learning approach. Results demonstrate that sociotropic coordination improves interference management, convergence stability, and robustness under dynamic channel conditions, while maintaining lower computational complexity than learning-based coordination methods. Finally, the proposed distributed sociotropic beamforming framework is evaluated against classical maximum ratio transmission (MRT), zero-forcing (ZF), and regularised zero-forcing (RZF) beamforming schemes under identical time-varying channel dynamics. Results demonstrate that while conventional baselines exhibit performance saturation under channel fluctuations, the proposed method achieves continuous adaptation and improved sum-rate evolution over time. Full article

Proton exchange membrane fuel cells (PEMFCs), characterized by high efficiency, zero carbon emissions, and low-temperature start-up capability, are among the most promising clean energy technologies. The design of flow channels and flow fields is critical for enhancing fuel cell power density, mitigating water flooding, and reducing costs. This paper systematically reviews the effects of key geometric factors in PEMFC flow fields and channels, including structural geometry, cross-sectional shape, and baffle design, on cell performance, with the aim of improving water management and enhancing PEMFC performance. Furthermore, the optimization of flow fields such as parallel, serpentine, and interdigitated configurations is reviewed as well. Particularly, the structural features and enhancement mechanisms of biomimetic and novel flow fields, as well as the advantages of three-dimensional flow fields in promoting mass transfer and improving water and thermal management, are discussed, thereby laying a foundation for the innovation and development of future high-performance proton exchange membrane fuel cells. Full article

As dynamic random-access memory (DRAM) continues to scale down and achieve higher integration density, the cell layout has transitioned to 6F², resulting in narrower spacing between adjacent word lines (WLs). Consequently, cell-to-cell disturbance has become more severe. In particular, the row-hammer effect (RHE) has emerged as a critical reliability issue that must be mitigated to ensure stable operation in next-generation DRAM devices. In this study, a novel DRAM cell structure is proposed, in which a low-k dielectric material is embedded beneath the storage node (SN) to mitigate the electric field. This structural modification effectively suppresses the RHE compared to the conventional partial-isolation type buried channel array transistor (Pi-BCAT). The feasibility and performance of the proposed structure were verified through 2D Sentaurus technology computer-aided design (TCAD) simulations. The device embedding the low-k dielectric beneath the SN exhibits a mitigation of approximately 20.45% in D0 failure and about 12.12% in D1 failure. This improvement is attributed to the reduced electric field in the region underneath the SN, which suppresses stored charge leakage. These results confirm that the proposed structure not only enhances DRAM reliability in advanced process nodes but also provides an effective design guideline for highly integrated and low-power memory devices. Full article

This study examines how client ESG performance affects supplier total factor productivity (TFP) from a knowledge spillover perspective, using matched client–supplier–year data for Chinese A-share listed firms from 2010 to 2023. The results show that client ESG significantly improves supplier TFP; specifically, a one-unit increase in client ESG is associated with an average increase of approximately 8.3% in supplier TFP. These results remain robust across a series of robustness tests. Mechanism analysis indicates that client ESG enhances supplier productivity through three knowledge spillover channels: technical assistance, management sharing, and innovation induction. Heterogeneity analysis further shows that this positive effect is more pronounced in long-term cooperative relationships, among clients with stronger market power, for state-owned suppliers, and when clients and suppliers have aligned ownership structures. Further analysis shows that the positive effect of client ESG persists for at least three fiscal years and is more pronounced in industries characterized by lower volatility. These findings suggest that policymakers and firms should strengthen supply chain ESG governance to promote knowledge spillovers and improve productivity. Full article

Insulators are critical insulation components in power transmission lines; however long-term exposure to adverse environmental conditions may threaten the safety and stability of power delivery. Existing studies primarily emphasize detection accuracy, while deployment efficiency and inference speed have received insufficient attention, limiting their applicability to CPU-based edge computing devices. To address these limitations, this paper proposes PEBL-YOLO, a lightweight model for insulator defect detection. The proposed model retains the external C3k2 structure of YOLOv11 while simplifying its internal bottleneck module, in which PConv is embedded to improve spatial feature extraction and fusion efficiency. In the neck, the original Path Aggregation Feature Pyramid Network (PAFPN) is reconstructed by integrating a Bidirectional Feature Pyramid Network (BiFPN) with Efficient Channel Attention (ECA), enabling more effective aggregation of multi-scale features and stronger focus on defect-related regions with minimal parameter increase. Moreover, a lightweight shared decoupled detection head is designed to decouple classification and regression branches. By combining parameter sharing with Group Normalization (GN) the detection head further reduces model complexity while maintaining accurate localization capability. Experimental results show that PEBL-YOLO contains only 1.68 M parameters. It achieves Precision, Recall, mAP@0.5, and mAP@0.5:0.95 of 95.0%, 92.1%, 94.4%, and 53.6%, respectively. These results demonstrate that PEBL-YOLO achieves a favorable trade-off between detection accuracy and parameter efficiency, providing a practical solution for lightweight insulator defect detection in edge computing scenarios. Full article