Search Results (2,713)

Population aging in middle-income economies produces macroeconomic and distributional consequences that aggregate frameworks cannot detect. This paper develops a multi-household CGE model calibrated to a 26-sector Social Accounting Matrix for Thailand (2024) and traces the labor, saving, and fiscal channels of aging across eleven counterfactual scenarios. Three findings emerge. First, aging’s primary macroeconomic cost operates through capital accumulation, not output contraction: investment falls seven times faster than the GDP under a savings-driven closure, because middle-aged households—the economy’s dominant net savers—compress lifecycle saving in response to aging. The saving channel alone amplifies the labor supply shock four-fold (range: 3.5–4.5). Second, aging can raise elderly welfare. When elderly households retain labor market attachment, wage gains from tighter factor markets outweigh declining capital returns—a welfare reversal invisible to representative agent and OLG frameworks by construction. The critical labor income threshold is ${\alpha }_L^{\ast }=35.5\%$ (range: 34.8–36.2%), confirmed across all participation increments tested (elderly welfare gain: THB 341–521 million). Third, no single instrument satisfies efficiency and equity simultaneously. Pension transfers crowd out investment nonlinearly above 12 percent of tax revenue (range: 10–14%); health demand expansion is the decisive complement that converts redistribution into a near-Pareto improvement. Policy complementarity is an empirical necessity, not a theoretical refinement. Collectively, these results reframe demographic aging as a factor price redistribution mechanism whose welfare incidence is determined by the cohort-level income composition—with direct implications for aging policy in middle-income economies facing rapid demographic transitions under tighter fiscal constraints than for advanced economies encountered at equivalent demographic stages. Full article

Forest Rights Mortgage Loans (FRMLs) have grown quickly in China’s collective forest areas, even though the basic conditions for this type of lending remain far from ideal. In many places, forest holdings are small and scattered, property rights are complex and not fully consolidated, and channels for disposing of collateral are limited. Under these circumstances, the Fulin Loan Model (FLM) in Fujian provides a useful case for understanding how forest-rights lending can still function in practice. Drawing on fieldwork, semi-structured interviews, and process tracing, this study explores both how the model was established and how it has been sustained over time. The analysis suggests that the FLM is neither a straightforward market-based lending tool nor merely a top-down policy arrangement. Rather, it relies on a more mixed form of governance in which local government support, banking procedures, and village-level social relations are brought together through specific organizational arrangements. These arrangements help lower the costs of early institutional experimentation, distribute and manage lending risks, and translate locally rooted trust into a form of credit support that formal financial institutions can recognize. As a single-case study, the FLM points to one possible way in which rural finance can be made workable under conditions of incomplete markets and strong social embeddedness. Full article

This paper examines whether climate, geopolitical and economic policy uncertainty indices improve Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts for green and brown stocks. We extend the Realized-ES-CAViaR framework by incorporating physical and transition climate risk, geopolitical risk and economic policy uncertainty indices alongside a high-low range volatility estimator. Using daily data for the iShares Global Clean Energy ETF (ICLN) and the iShares Global Energy ETF (IXC) over the period January 2012–December 2024, we evaluate alternative model specifications at the 1% and 2.5% risk levels through backtesting procedures, strictly consistent scoring rules and the Model Confidence Set methodology. Results reveal a pronounced asymmetry in the predictive content of risk indices across asset classes and quantile levels. Transition climate risk dominates tail-risk forecasting at the 1% level for both asset classes, while geopolitical risk and economic policy uncertainty emerge as the leading factors at the 2.5% level for green and brown stocks, respectively. These findings highlight the heterogeneous channels through which uncertainty shocks propagate into financial tail-risk, with direct implications for risk management and regulatory oversight during the low-carbon transition. Full article

Shock-aware aerodynamic shape optimization of transonic airfoils requires surrogate models that capture both integral aerodynamic trends and shock-relevant pressure distribution features. This study addresses drag-oriented optimization of the RAE2822 transonic airfoil under a lift-targeted condition with baseline relative thickness feasibility, rather than strict target pressure inverse design. Each airfoil is parameterized by a 16-dimensional CST vector and mapped to a two-channel vertical signed distance field representation of the upper- and lower-surface $C_p$ curves, from which shock descriptors, including the shock location indicator $x_s$ and the pressure jump magnitude $\Delta C_p$, are extracted in a deterministic, implementation-consistent manner. To quantify the reliability of surrogate-derived shock metrics, a held-out uncertainty analysis is performed on 500 samples. The surrogate achieves MAE/RMSE values of 0.00474/0.00602 for $C_L$ and $4.66\times {10}^{-4}/6.33\times {10}^{-4}$ for $C_D$, while the recovered shock-related quantities yield 0.00201/0.01598 for $x_s$ and 0.00200/0.00336 for $\Delta C_p$. Scatter plots and error histograms show tight one-to-one trends for most samples, with limited outliers mainly associated with locally ambiguous pressure gradient patterns. Overall, the surrogate is more reliable for capturing shock intensity trends than for prescribing an exact shock location; accordingly, $x_s$ is interpreted as a trend-level descriptor, whereas $\Delta C_p$ is treated as the more stable engineering indicator inside the optimization loop. The trained surrogate is embedded in a differential evolution optimizer with soft penalties on lift deviation and thickness feasibility violation, and selected designs are re-evaluated through closed-loop SU2 RANS simulations. CFD verification shows that the optimized design reduces drag from $C_D=0.01463$ to $C_D=0.01229$ (a 16.0% reduction) and reduces the shock jump from $\Delta C_p=0.239$ to $\Delta C_p=0.046$ (an 80.7% reduction). For the optimized design, the prediction-to-CFD differences are $\Delta C_L=+0.0042$ and $\Delta C_D=+0.00012$. These results support an engineering-oriented and auditable shock-aware closed-loop optimization workflow, with final design conclusions established by CFD verification rather than surrogate-predicted shock location alone. Full article

With the increasing demand for privacy-preserving and real-time personalized services in large-scale video platforms, designing robust federated recommendation frameworks over practical communication networks has become increasingly important. To this end, this paper proposes a bias-corrected federated learning framework tailored for video recommendation over stochastic communication links. At the local training stage, a bias-corrected mechanism is introduced to explicitly account for video duration and user activity, mitigating feature-level bias and enabling the learned representations to more accurately reflect users’ intrinsic preferences. To meet the timeliness requirements of real-time federated learning, the successful upload probability of local model transmission is analytically characterized under time-varying channel conditions. Building upon this probabilistic model, a statistically corrected global aggregation strategy is designed to preserve the unbiasedness of the global update with respect to the ideal fully reliable FedAvg scheme, even when a subset of local nodes fails to upload their models within the specified delay constraint. Comprehensive experimental evaluations validate that the proposed framework significantly improves recommendation accuracy and maintains robustness against communication unreliability in practical distributed environments. Full article

Reconciling hydropower development with aquatic biodiversity conservation is a central challenge for sustainable river management worldwide. Cascade dam configurations, in which multiple impoundments are arranged in series along a single channel, impose longitudinal environmental gradients that restructure biological communities across trophic levels. Whether the resulting multi-trophic responses are independently driven by shared abiotic gradients (environmental filtering) or mechanistically coupled through direct food-web interactions (trophic cascading) remains unresolved. We surveyed phytoplankton, zooplankton, and benthic macroinvertebrates simultaneously at seven stations along a 430 km gradient downstream of Danjiangkou Dam in the Hanjiang River, the largest tributary of the Yangtze River and the source of China’s South-to-North Water Diversion Middle Route, over eight seasonal campaigns (2015–2017). Variance partitioning, piecewise structural equation modeling, Mantel tests, and co-occurrence network analysis were applied to partition environmental and trophic pathways. Environmental filtering dominated community restructuring at all three trophic levels, while the biotic proxy for direct trophic interactions explained less than 0.4% of community variation, consistent with weak detectable trophic coupling at seasonal resolution. Distance from Danjiangkou Dam shaped downstream transparency and turbidity gradients that mediated trophic-level-specific responses along distinct environmental axes (pH and water temperature for phytoplankton, conductivity for zooplankton, and transparency for benthic macroinvertebrates). Benthic macroinvertebrates were systematically decoupled from the pelagic analytical framework, absent from the cross-trophic co-occurrence network and structured more by spatial configuration than by water-column variables. Hub species in the network were associated with downstream mineralized conditions, confirming that network architecture reflects shared environmental preferences rather than biotic interactions. These findings support a management shift from single-dam mitigation toward cascade-scale coordination of environmental flow regimes, sediment connectivity, and substrate restoration as integrated strategies for sustaining multi-trophic biodiversity in regulated rivers. Full article

Automated pixel-level detection of steel surface defects is a critical challenge in manufacturing quality control, complicated by the variation in defect size and shape, low contrast with background textures, and the diversity of defect patterns. This paper proposes ID-MSNet, an enhanced version of the UNet3+ architecture, designed specifically for the segmentation of three common steel surface defect types: inclusions, patches, and scratches. The proposed architecture introduces three targeted modifications: (1) a multi-scale feature learning module (MSFLM) in the encoder that uses dilated convolutions at multiple rates to capture contextual features across different scales, combined with DropBlock regularization and batch normalization to improve generalization; (2) an improved down-sampling (IDS) module that replaces standard max-pooling with learnable strided convolutions fused via 1 × 1 convolution, preserving richer feature representations; and (3) a convolutional block attention module (CBAM) integrated into the skip connections to selectively focus the model on spatially and channel-wise relevant defect regions. Experiments on the publicly available SD-saliency-900 dataset demonstrate that ID-MSNet achieved an 86.19% mIoU, outperforming all compared state-of-the-art segmentation models while using only 6.7 million parameters—approximately 75% fewer than the original UNet3+. These results establish ID-MSNet as a strong and efficient baseline for steel surface defect segmentation, with potential applicability to automated quality inspection in broader manufacturing contexts. Full article

Deepfake technology poses significant threats to information authenticity and social trust, necessitating effective detection methods. However, existing detection approaches predominantly rely on high-complexity network architectures that, while accurate in controlled environments, suffer from prohibitive computational costs that hinder deployment in resource-constrained scenarios such as social media platforms. To address this efficiency-accuracy dilemma, we propose a lightweight face forgery detection method that systematically learns multi-scale forgery traces. Our approach features a four-stage lightweight architecture that hierarchically extracts features from local textures to global semantics, mimicking the human visual system. Within each stage, a multi-scale dynamic perception mechanism divides feature channels into parallel groups equipped with lightweight attention modules to capture forgery cues spanning pixel-level anomalies, local structures, regional patterns, and semantic inconsistencies. Furthermore, rather than relying on conventional feature fusion that risks suppressing subtle artifacts, we introduce a novel Context-Guided Dynamic Convolution. This mechanism uses mid-level spatial anomalies as active anchors to dynamically modulate high-level semantic filters, with the goal of mitigating the disconnect between semantic content and forgery evidence. Our model achieves strong performance, yielding an AUC of 91.98% on FaceForensics++ and 93.50% on DeepFake Detection Challenge, outperforming current state-of-the-art lightweight methods. Furthermore, compared to heavy Vision Transformers, our model achieves a superior performance-efficiency trade-off, requiring only 3.06 M parameters and 1.36 G FLOPs, making it highly suitable for real-time, resource-constrained deployment. Full article

Detecting surface defects on steel products is crucial for maintaining quality standards in industrial manufacturing. However, existing detection algorithms face several challenges, including the difficulty of capturing multi-scale defect characteristics with fixed receptive fields, insufficient utilization of defect edge and frequency domain features, and simplistic feature fusion strategies. In response to the above challenges, this paper proposed the Multi-Scale Frequency-Enhanced YOLO (MSFE-YOLO) algorithm that integrates multi-scale frequency domain enhancement with defect-aware attention mechanisms. First, a Multi-Scale Frequency-Enhanced Convolution (MSFC) module was constructed, which extracted multi-scale spatial features in parallel through depth-adaptive dilated convolutions, explicitly modeled high-frequency edge information using the Laplacian operator, and achieved adaptive fusion of multi-branch features via learnable weights. Second, a Cross-Stage Partial with Multi-Scale Defect-Aware Attention (C2MSDA) module was designed, integrating Sobel operator-based edge perception, multi-scale spatial attention, and adaptive channel attention to collaboratively enhance features across spatial, channel, and edge domains through a gated fusion strategy. Finally, an Adaptive Feature Fusion Enhancement (AFFE) module was proposed to achieve adaptive aggregation of multi-level features through a data-driven weight generation network and cross-scale feature interaction mechanism. Experimental results on the NEU-DET and GC10-DET datasets demonstrated that MSFE-YOLO achieved the mAP@0.5 of 79.8% and 66.7%, respectively, which were 1.7% and 2.1% higher than the benchmark model YOLOv11s respectively, while maintaining an inference speed of 89.3 FPS, which satisfied the real-time detection requirements in industrial scenarios. Full article

This study investigates trade-in channel selection in a closed-loop supply chain under a hybrid carbon policy framework that integrates cap-and-trade and carbon taxation. Game-theoretic models are developed for three manufacturer-led channels: manufacturer trade-in (M-CX), retailer trade-in (R-CX), and third-party trade-in (T-CX). The analysis examines pricing strategies, profitability, and carbon emission reductions across these channels. The key findings are as follows: (1) Carbon tax consistently compresses manufacturer profits, whereas cap-and-trade mechanisms exhibit a non-linear U-shaped effect. Manufacturer profits remain highest under the M-CX channel, irrespective of policy intensity. (2) Retail prices are most sensitive to carbon policies under the T-CX channel, where trade-in rebates increase with carbon intensity. The R-CX channel sustains higher retail prices and rebates than M-CX, while T-CX surpasses both under conditions of high carbon intensity. (3) Carbon emission reductions decline sharply under M-CX and R-CX as policy stringency increases. In contrast, the T-CX channel establishes a buffering mechanism through rising rebates, exhibiting the slowest rate of decline. At low carbon intensity, T-CX yields the lowest reduction levels; however, under high intensity, it overtakes the other channels to achieve the highest reduction. This study offers insights for manufacturers’ channel selection and government policy coordination under hybrid carbon regulation regimes. Full article

As a widely used computing substrate, the side-channel security of FPGAs has attracted considerable attention, yet a systematic understanding of how FPGA device types contribute to exploitable leakage remains limited. This work presents a device-centric evaluation that maps an S-box-like function onto common FPGA primitives, including look-up table (LUT), flip-flop (FF), block RAM (BRAM), and distributed RAM (LUTRAM), and assesses Correlation Power Analysis (CPA) outcomes under the Hamming Weight (HW) and Hamming Distance (HD) power models. The results show pronounced leakage differences across device types: FF- and BRAM-based implementations exhibit substantially stronger leakage than LUT- and LUTRAM-based ones, and they frequently achieve $\mathrm{GE}=0$ in our configurations, while the HD model is generally more effective than the HW model in the performed CPA evaluations. Notably, FF-, BRAM-, and LUTRAM-based implementations can already be breakable starting from one instance under the HD model in our device-level tests, indicating that exploitable leakage may manifest in real FPGA applications. These device-level observations are further validated on a practical cipher by analyzing two SM4 encryption modules that differ only in the S-box implementation style; the BRAM-based design shows significantly stronger leakage than the LUT-based design, achieving $\mathrm{GE}=2.58$ versus $\mathrm{GE}=78.3$ at 10,000 traces. This work highlights the critical role of device selection and implementation style in FPGA side-channel security, and it provides practical insights for designing secure FPGA applications against power side-channel analysis. Full article

During the interpretation of Borehole Radar (BHR) B-scan profiles, the accurate determination of the azimuth of geological targets in three-dimensional space is a critical issue for achieving precise anomaly localization and spatial structure inversion. However, existing directional BHR anomaly localization methods exhibit limited intelligence, insufficient adaptability to multi-site data, and weak generalization capability, rendering them inadequate for engineering applications under complex geological conditions. To address these challenges, a robust deep learning model, termed BSS-Pose-BHR, is developed based on YOLOv11n-pose for keypoint detection in directional BHR profiles. The model incorporates three key optimizations: Bi-Level Routing Attention (BRA) replaces Multi-Head Self-Attention (MHSA) in the backbone to improve computational efficiency; Conv_SAMWS enhances keypoint-related feature weighting in the backbone and neck; and Spatial and Channel Reconstruction Convolution (SCConv) is integrated into the detection head to reduce redundancy and strengthen local feature extraction, thereby improving suitability for keypoint detection tasks. In addition, a three-dimensional electromagnetic model of limestone containing a certain density of clay particles is established to construct a simulation dataset. On the simulated test set, compared with current mainstream deep learning approaches and conventional directional borehole radar anomaly localization algorithms, BSS-Pose-BHR achieves superior performance, with an mAP50(B) of 0.9686, an mAP50–95(B) of 0.7712, an mAP50(P) of 0.9951, and an mAP50–95(P) of 0.9952. Ablation experiments demonstrate that each proposed module contributes significantly to performance improvement. Compared with the baseline, BSS-Pose-BHR improves mAP50(B) by 5.39% and mAP50(P) by 0.86%, while increasing model weight by only 1.05 MB, thereby achieving a reasonable trade-off between detection accuracy and complexity. Furthermore, indoor physical model experiments validate the effectiveness of the method on measured data. Robustness experiments under different Peak Signal-to-Noise Ratio (PSNR) conditions and varying missing-trace rates indicate that BSS-Pose-BHR maintains high detection accuracy under moderate noise and data loss, demonstrating strong engineering applicability and practical value. Full article

Compared to antennas bearing a single port, MIMO antennas with several ports enable higher data throughput by exploiting spatial diversity. This capability is essential for next-generation implantable medical devices, where high channel capacity is a key requirement. A quad-element implantable MIMO antenna is designed and practically validated at 1420 MHz in this paper. It occupies a compact volume of $7\times 8\times 0.1 {\mathrm{mm}}^3$ ($5.6 {\mathrm{mm}}^3$). The compactness is realized by combining high-permittivity substrate (Rogers 3010 with relative permittivity of 10.2) with meandered radiator paths, which increase the effective current length while maintaining a small physical size. All antennas have very small mutual coupling with isolation of more than $31.78$ dB, which is mainly due to the spacing of 1 mm between the elements and the substrate, which is thin. The peak realized gain for each antenna element is $-27.3$ dBi. The simulation is performed within a capsule-like structure, which is embedded in the stomach tissue model. The experimental verification is carried out by embedding antenna within minced meat. The ECC, channel capacity, and link margin are also evaluated and found to be satisfactory. The proposed antenna ensures reliable communication performance, with the transmission range being as high as $2.5$ m, link margin being 15 dB, and the data rate being 120 Mb/s. The proposed antenna ensures a good level of ECC, which is less than $0.1$. The SAR is $52.3$ W/kg at 1420 MHz. This design is favorable for implants because of the small size, good impedance matching, high isolation, low correlation, good level of gain, and good link performance. Full article

EEG-based classification of mental disorders has increasingly relied on deep learning models, which are computationally intensive and difficult to interpret, limiting reproducibility and clinical deployment in resource-constrained or cross-site settings. We propose a configurable and modular machine learning framework for EEG-based classification that emphasizes interpretability, flexibility, and rigorous evaluation using schizophrenia detection as a representative use case. Our framework integrates standardized preprocessing, multiscale feature extraction, minimum redundancy–maximum relevance feature selection, and configurable ensemble learning. It also supports multiple validation strategies, including random splits, k-fold cross-validation, and leave-one-subject-out (LOSO), enabling systematic assessment of subject-level generalization. We evaluated the framework on two open EEG datasets: Warsaw IPN (Institute of Psychiatry and Neurology, 19 channels, 250 Hz; 28 subjects) and a Moscow adolescent cohort (16 channels, 128 Hz; 84 subjects). Results show that validation strategy strongly affects model performance. While K-fold validation yielded epoch-level accuracies of 98.06% and 91.47%, LOSO results were much lower: 76.12% (epoch-level) and 82.14% (subject-level) for Dataset 1, and 70.71% (epoch-level) and 77.38% (subject-level) for Dataset 2. These findings demonstrate the risk of overestimated performance due to data leakage and underscore the importance of subject-independent evaluation. Our proposed framework provides a low-complexity, interpretable, and extensible benchmark for reproducible EEG-based machine learning, with interpretable feature representations linked to EEG dynamics and potential applicability to broader neuroengineering and clinical decision-support systems. Full article

This study examines how firms’ financial heterogeneity shapes the transmission of monetary policy to investment in Latin American economies. It develops an extended theoretical model with heterogeneous firms, calibrated for Latin American economies, and validates it empirically through local projection models. These projections are applied to both a dataset of 72 of the most representative firms from the six analyzed Latin American economies and simulated data from the theoretical model, enabling direct comparison of the results. The research yields three main findings. First, it shows that financial heterogeneity is crucial and determines how firms respond to a monetary shock. Firms with fragile structures or high levels of indebtedness tend to restrict investment following monetary expansions, whereas firms with stronger financial positions or greater distance to default tend to increase it. The aggregate effect depends on the distribution of financial structures in the economy and which group dominates. Second, a transmission mechanism is identified via a financial channel based on a price–quantity sequence. The drop in the real rate compresses spreads and raises the price of capital; if financial constraints are active, the monetary relief is used to repair balance sheets rather than to invest; otherwise, the stimulus quickly translates into investment. Finally, the study shows that ignoring heterogeneity—as in representative–agent models—leads to a significant overestimation of both the magnitude and persistence of investment responses to monetary policy shocks. Full article