Search Results (362)

An investment portfolio consists of a set of financial assets, such as stocks, fixed-income securities, mutual funds, and real estate, held to achieve diversification and to optimize returns. Accurate asset forecasting provides investors with valuable information to support decision-making. Although existing studies have proposed models for forecasting and portfolio optimization, most rely mainly on traditional techniques and metaheuristic approaches. This work introduces ESIPO (Ensemble Strategies for Investment Portfolio Optimization), a methodology that integrates deep learning and metaheuristic algorithms to perform asset forecasting and investment portfolio optimization. The dataset is obtained from the S&P 500 index, one of the main stock markets. To enhance forecasting accuracy, ESIPO combines five methods from the top-performing models of the international M4 competition: (a) ARIMA (AutoRegressive Integrated Moving Average) and ETS (the statistical exponential-smoothing state-space), which represent classical statistical approaches; (b) FFORMA (Feature-based FORecast Model Averaging) and JAGANATHAN, two ensemble-based methods; (c) CNN (Convolutional Neural Network), which is one of the most common deep learning models. ESIPO improves the forecast performance of the portfolio by applying the TAIPO (Threshold Accepting Investment Portfolio Optimization) metaheuristic to select the best assets and optimize portfolio composition. The results obtained 45% of improvement according to the Sharpe Ratio metric. Full article

Plasma etching is a critical step in semiconductor manufacturing, yet existing approaches are either computationally expensive or limited to predicting scalar etching metrics rather than full profile evolution. We propose EPreNet, a condition-guided spatio-temporal network for pixel-level prediction of plasma etching profile evolution from historical profile frames and process parameters. To support this task, we construct a benchmark dataset of 18,360 images spanning 918 process conditions simulated via TCAD, sampled with Latin Hypercube Sampling to ensure uniform parameter-space coverage, and further establish an evaluation framework combining image-level and geometry-based metrics for etching-profile prediction. Experiments demonstrate that EPreNet reduces MSE by 16% and achieves SSIM of 0.992 and PSNR of 30.323 dB, while achieving manufacturing-relevant geometric accuracy with $1.4°$ sidewall angle error and 1.6% depth error rate. Inference requires only 38.92 ms per frame faster than TCAD simulation 1300 s, supporting rapid surrogate-based evaluation and accelerated TCAD-assisted process exploration. The model also shows strong generalization to unseen initial critical dimensions and encouraging initial transferability to preprocessed experimental SEM images, suggesting its potential as an efficient surrogate for TCAD-assisted process development while maintaining high geometric fidelity. Full article

Accurate retrieval of visibility grades is critical for transportation safety. Due to the highly complex meteorological backgrounds, traditional global deep learning models frequently struggle with limited physical traceability and feature heterogeneity. To address these challenges by enhance physical traceability and reduces heterogeneity, this study proposes a scenario-adaptive visibility retrieval framework based on multi-source synergy, namely TabPFN-ExtraTrees (TabPFN-ET), targeting major transportation routes in Anhui Province, China. Fusing Fengyun-4 (FY-4A/4B) satellite multispectral observations with ground meteorological data, this framework utilizes the divide-and-conquer routing mechanism of ExtraTrees to decouple the complex, heterogeneous feature space into highly homogeneous sub-scenarios. Subsequently, the TabPFN model conducts high-precision inference within each specific subspace. Evaluations on a class-balanced benchmark demonstrate that TabPFN-ET achieves an Overall Accuracy of 0.681, outperforming baseline models such as SAINT across various metrics. Furthermore, this paper conducts a physically consistent analysis of the framework. Feature importance and node profiling corroborate its physical consistency: the FY-4 upper-level water vapor channel (Channel 09) and near-surface humidity act as the macroscopic atmospheric stability and microscopic thermodynamic constraints, respectively, driving the model’s scene decoupling and inference. Cross-regional tests in Jiangsu provide preliminary indications of context-specific transferability. Full article

In this research article, we introduce and develop the notion of complex-valued b-suprametric spaces as a natural generalization of existing metric-type structures. Fundamental concepts, including convergence, Cauchy sequences, and completeness, are examined in this new setting. We establish new common fixed point theorems for generalized and cyclic rational contractive mappings. The obtained results extend and unify various known fixed point theorems available in the current literature. To demonstrate the applicability and effectiveness of our theoretical findings, illustrative nontrivial examples are provided. As an application, we investigate the existence and uniqueness of solutions for Caputo fractional differential equations, which naturally arise in systems with hereditary and memory effects, particularly in biomedical modeling of viscoelastic biological tissues such as arteries, cartilage, and brain tissue. This demonstrates both the mathematical strength and the practical relevance of the proposed framework. Full article

Antibiotic resistance genes (ARGs) are prevalent in livestock environments due to antimicrobial use, yet their airborne dispersal into human-occupied indoor spaces remains poorly characterized. We investigated whether airborne ARGs disperse from livestock stables into farmers’ homes and surrounding outdoor environments. Electrostatic dust collectors were deployed in paired pig and cow stables and their associated homes in Jutland, Denmark, to collect settled airborne dust. Pooled samples were analyzed using shotgun metagenomic sequencing. ARG dispersal patterns were assessed using FEAST source tracking and ecological similarity metrics, including shared ARG ratios and Jaccard indices. Pig production systems exhibited higher antibiotic use and stronger resistome continuity with farmers’ homes than cow systems, reflected by greater FEAST contributions (P = 0.029) and Jaccard similarity (P = 0.029). Beta-diversity analysis supported higher compositional similarity between pig stables and homes (PERMANOVA R² = 0.23, p = 0.052), whereas cow environments showed greater divergence (R² = 0.41, P = 0.035). Across environments, tetracycline, macrolide–lincosamide–streptogramin B, and aminoglycoside resistance genes dominated, consistent with livestock-specific antibiotic use patterns. Supplementary indoor–outdoor comparisons across cow, pig, and chicken stables (from an independent 2024 sampling campaign not directly comparable to the 2008 EDC-based survey) revealed contrasting dispersal dynamics, with higher bacterial species spillover from cow stables but stronger ARG overlap from pig stables. Collectively, these findings are consistent with airborne ARG connectivity across occupational and environmental interfaces and support consideration of air as a potential pathway in One Health AMR surveillance. Full article

Under rapid urbanization, street crime poses growing challenges to urban safety. Existing studies often treat gray and green spaces as independent variables, limiting the understanding of nonlinear crime patterns and spatiotemporal heterogeneity. Using day–night street crime data from Shanghai between 2010 and 2020, this study applies an interpretable machine learning framework combining CatBoost and SHAP to examine how the coupling of gray–green spatial structures influences street crime. Gray–green spatial morphology is quantified using both MSPA- and Fragstats-based indicators, which are integrated into composite coupling indices. The results indicate that gray–green structural coupling exhibits significant nonlinear and threshold-dependent effects on street crime. Compared with conventional Fragstats metrics, MSPA-based structural indicators demonstrate stronger explanatory power. Theft-specific analysis further indicates that gray-space core–edge structures exhibit higher crime risk at night, with this effect becoming more pronounced in the later period. Across both study periods and day–night contexts, green branch areas (G_BRANCH) consistently show stable inhibitory effects, with the strongest suppression occurring when G_BRANCH values range between 0 and 1.6 and interact with gray core–edge structures (B_CORE and B_EDGE). These findings provide quantitative evidence that gray–green spatial structures function through coupled, nonlinear interactions and offer targeted spatial planning implications for crime prevention in high-density cities. Full article

Seismic collapse can cause catastrophic losses, and acceptable annual collapse probability with its CMR target is a core metric in performance-based design. Existing ATC-63-based CMR research mainly addresses non-damped systems and often uses a single lumped dispersion, obscuring damper-reliability contributions and hindering alignment with CECS 392 limits. This study proposes a unified, code-consistent decision framework for acceptable annual collapse probability and CMR that jointly accounts for seismic hazard and damper-related uncertainty. The total collapse dispersion is decomposed as ${\sigma }_{\mathrm{total},\mathrm{damp}}^2={\sigma }_{\mathrm{base}}^2 +{ \sigma }_{\mathrm{damper}}^2$, where ${\sigma }_{\mathrm{b}\mathrm{a}\mathrm{s}\mathrm{e}}$ represents background dispersion independent of dampers and ${\sigma }_{\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{e}\mathrm{r}}$ captures incremental uncertainty induced by degradation and partial failure. A code-designed viscous-damped RC frame is evaluated under three scenarios (nominal damping, 20% damping-coefficient reduction, and 7% random damper failures). Using the same 14 records and $\mathrm{S}\mathrm{a}\left({\mathrm{T}}_1,5\%\right)$ as the intensity measure, multi-stripe IDA and Probit-based lognormal fragility fitting yield median collapse intensities $\mathrm{S}\mathrm{c}\approx 2.18-2.24 \mathrm{g}$, with only ~2–3% reduction under mild degradation/failure. A random-effects variance decomposition identifies σ_damper ≈ 0, indicating a limited marginal contribution of damper-related uncertainty within the degradation range considered in this study. Closed-form relationships between annual collapse rate, $\mathrm{S}\mathrm{c}$, and ${\sigma }_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l},\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}}$ are then derived under a power-law hazard model and inverted to generate acceptable-risk intervals and CMR target curves/matrices. Results show that higher design intensity and larger ${\sigma }_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l},\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}}$ demand substantially higher CMR, highlighting potential risk underestimation when relying solely on nominal CMR. The framework enables explicit identification of damper-related uncertainty from limited collapse data and provides a practical workflow for collapse-prevention design and post-assessment under explicitly defined scenario conditions, with a clear pathway for extension to broader scenario spaces. Full article

Background: The decoding of motor imagery electroencephalography (MI-EEG) is constrained by core issues including low signal-to-noise ratio (SNR) and cross-session as well as cross-subject domain shift, which seriously impedes the practical deployment of brain–computer interfaces (BCIs). Methods: To address these challenges, this paper proposes a novel end-to-end MI-EEG decoding method named BARN-DA. Two innovative modules, Band-Aware Channel Attention (BACA) and Multi-Scale Kernel Perception (MSKP), are designed: one enhances discriminative channel features by modeling channel information fused with frequency band feature representation, and the other captures complex data correlations via multi-scale parallel convolutions to improve the discriminability of the network’s feature extraction. Subsequently, the features are mapped onto the Riemannian manifold. For the source and target domain features residing on this manifold, a Riemannian Maximum Mean Discrepancy (R-MMD) loss is designed based on the log-Euclidean metric. This approach enables the effective embedding of Symmetric Positive Definite (SPD) matrices into the Reproducing Kernel Hilbert Space (RKHS), thereby reducing cross-domain discrepancies. Results: Experimental results on four public datasets demonstrate that the BARN-DA method achieves average cross-session classification accuracies of 84.65% ± 8.97% (BCIC IV 2a), 89.19% ± 7.69% (BCIC IV 2b), and 61.76% ± 12.68% (SHU), as well as average cross-subject classification accuracies of 65.49% ± 11.64% (BCIC IV 2a), 78.78% ± 8.44% (BCIC IV 2b), and 78.14% ± 14.41% (BCIC III 4a). Compared with state-of-the-art methods, BARN-DA obtains higher classification accuracy and stronger cross-session and cross-subject generalization ability. Conclusions: These results confirm that BARN-DA effectively alleviates low SNR and domain shift problems in MI-EEG decoding, providing an efficient technical solution for practical BCI systems. Full article

The rapid deployment of Large Language Models (LLMs) in multilingual, production-scale systems has made inference-time energy consumption a critical yet systematically under-evaluated dimension of model quality. While accuracy-centric benchmarks dominate current evaluation practice, they fail to capture the energy cost of reasoning, particularly across languages and task complexities where consumption profiles diverge substantially. In this work, we present a comprehensive energy–performance evaluation of five instruction-tuned LLMs, spanning Transformer, Grouped-Query Attention, and State Space Model architectures, across thirteen typologically diverse languages and multiple task difficulty levels under controlled GPU-level energy measurement on NVIDIA H200 hardware. Our analysis encompasses 65 model–language configurations totaling over 5100 individual inference runs, supported by rigorous non-parametric statistical testing (Friedman tests, pairwise Wilcoxon signed-rank with Holm correction, and paired Cohen’s d effect sizes). We report four principal findings. First, energy consumption varies up to threefold across models under identical workloads (${\chi }^2=49.42$, $p=4.78\times {10}^{-10}$, Friedman test), stratifying into three distinct energy regimes driven by architecture and generation dynamics rather than parameter count. Second, energy expenditure and reasoning performance are only weakly coupled, as confirmed by Spearman rank correlation analysis ($r_s=0.109$, $p=0.386$). Third, task category and difficulty level introduce substantial and model-dependent variation in both energy demand and performance, with cross-lingual performance variance amplifying at higher difficulty levels. Fourth, language choice acts as a measurable deployment parameter as follows: Romance languages on average achieve lower energy consumption than English across multiple models, while model efficiency rankings shift across languages, yielding language-dependent Pareto-optimal frontiers. We formalize these trade-offs through multi-objective Pareto analysis and introduce a composite AI Energy Score metric that captures reasoning quality per unit of energy. Of the 65 evaluated configurations, only four are Pareto-optimal, three Mistral-7B configurations at the low-energy extreme and one Phi-4-mini-instruct configuration at the high-performance end, while three of the five models are entirely dominated across all language configurations. These findings provide actionable guidelines for energy-aware model selection in multilingual deployments and support the integration of AI Energy Scores as a standard complementary criterion in LLM evaluation frameworks. Full article

Coupling interference in densely integrated high-frequency communication architectures leads to significant degradation in transmission efficiency, particularly in modern 5G and GHz-range platforms. From a mathematical perspective, mitigating such interference can be formulated as a multi-criteria optimization problem involving competing design objectives and interacting control mechanisms. In this paper, we develop an equilibrium-based optimization framework by modeling coupling suppression as a finite non-cooperative game. Isolation mechanisms are represented as strategic players whose actions are defined over constrained design spaces, while utility functions incorporate coupling minimization, insertion-loss penalties, and fabrication complexity. Under this formulation, stable mitigation strategies are characterized through Nash equilibrium conditions. To address the inherent trade-offs among performance metrics, the equilibrium computation is integrated with a Pareto multi-objective optimization scheme, yielding Nash–Pareto optimal configurations that balance electromagnetic isolation performance with implementation feasibility. Numerical full-wave simulations in the 2–12 GHz frequency band demonstrate that the proposed equilibrium solutions achieve substantial interference suppression, with reductions exceeding 30 dB compared with conventional baseline designs. The proposed framework provides a mathematically structured approach for interference mitigation and offers a generalizable methodology for multi-objective optimization in high-frequency communication systems. Full article

Aircraft noise monitoring systems deployed at major airports typically rely on scalar energy-based indicators, which primarily describe integrated sound energy but provide limited representation of the spectral–temporal structure and perceptual attributes of aircraft noise. To address this limitation, this study proposes a sensor-based psychoacoustic feature extraction and spatiotemporal analysis framework for continuous aircraft noise monitoring under high-density operational conditions. An automatic noise monitoring system compliant with ISO 20906 was deployed to synchronously acquire acoustic waveforms and ADS-B trajectory data. A cascaded spatiotemporal fusion algorithm was developed to associate noise events with aircraft flight paths, followed by a model-stratified multidimensional IQR-based data cleaning strategy to suppress environmental interference and non-stationary outliers. Based on the cleaned dataset, a suite of psychoacoustic features—including loudness, sharpness, roughness, fluctuation strength, and tonality—was extracted to characterize the perceptual structure of aircraft noise beyond conventional energy metrics. Experimental results demonstrate that, under equivalent sound exposure levels, psychoacoustic features retain substantial discriminative information that is lost in scalar energy indicators. The coefficients of variation for fluctuation strength and tonality reach 43.2% and 22.1%, respectively, corresponding to 15–69 times higher sensitivity compared to traditional energy-based metrics. Furthermore, nonlinear manifold mapping using UMAP reveals clear topological separation between new-generation and legacy aircraft models in the psychoacoustic feature space, whereas severe overlap persists in energy-based representations. Correlation analysis further indicates decoupling between macro-level physical design parameters (e.g., bypass ratio, thrust) and perceptual feature dimensions, highlighting the limitations of energy-centric monitoring schemes. The proposed framework demonstrates the feasibility of integrating psychoacoustic feature extraction into continuous sensor-based aircraft noise monitoring systems. It provides a scalable signal processing pipeline for enhancing the resolution and interpretability of aircraft noise measurements in complex operational environments. Full article

Background/Objectives: This study aimed to characterize macro- and micro-morphological properties of the supraspinatus tendon (SST) in para swimmers during the chronic stage of rotator cuff tendinopathy, integrating ultrasound assessments of tendon thickness, peak spatial frequency radius (PSFR) for collagen organization, acromiohumeral distance (AHD), and occupation ratio to evaluate subacromial impingement risk. Methods: In a cross-sectional design, 43 elite para swimmers (aged 18–30 years, S7–S10 classes with lower extremity impairments) from Para Swimming Team Poland were divided into rotator cuff tendinopathy (RC; n = 22) and asymptomatic control (CON; n = 21) groups. Measurements on the dominant shoulder utilized B-mode ultrasound (Alpinion X-CUBE 90) to assess SST thickness at 5, 10, and 15 mm proximal to the greater tuberosity, PSFR via MATLAB-analyzed spatial frequency spectra, AHD, and occupation ratio. Two-way and one-way ANOVAs assessed group and measurement effects (p < 0.05); Pearson correlations examined the relationships between thickness and PSFR. Results: Para swimmers with tendinopathy exhibited greater SST thickness across sites (p < 0.001, η² = 0.63), higher PSFR at all intervals (p ≤ 0.009, η² = 0.53) peaking at 10 mm, wider AHD (p = 0.002, η² = 0.21), and lower occupation ratio (p < 0.001, η² = 0.44) versus controls. Strong positive correlations linked thickness and PSFR proximally (r = 0.75–0.79, p < 0.001). Conclusions: Chronic tendinopathy in para swimmers manifests as thickened SST with collagen disarray, altered subacromial space, and impingement risk, distinguishing pathological from healthy tendons. Integrated ultrasound metrics aid diagnosis and inform interventions for overhead athletes with locomotor disorders. Full article

In the field of intelligent inspection, high-definition video data collected by quadruped robot dogs face severe transmission and storage constraints. Although existing advanced lossy video coding standards can significantly improve compression efficiency, they inevitably introduce severe compression artifacts in low-bit-rate scenarios. To address this issue, this paper proposes a video decoding quality enhancement network named Video Quality Restoration Network (VQRNet), based on a dual-stream architecture. Specifically, the Local Feature Extraction component incorporates a Progressive Feature Fusion Module (PFFM) with a four-stage progressive structure. By integrating reparameterized convolution and attention mechanisms, PFFM focuses on capturing high-frequency texture details to repair small-scale distortions. Simultaneously, the Multi-Scale Lightweight Spatial Attention Module (MLSA) performs spatial feature recalibration, leveraging multi-scale convolution to adaptively identify and enhance key spatial regions, specifically addressing multi-scale distortion. In the Global Feature Extraction component, the State-Space Attention Module (SSAM) combines State-Space Models (SSMs) with attention mechanisms to capture long-range dependencies and contextual information, for large-scale distortions caused by high-intensity compression. To verify the performance of the proposed algorithm, a dedicated dataset comprising 20 real-world video sequences captured by quadruped robot dogs (partitioned into 15 training and 5 testing sequences) was constructed, and the VTM 23.4 reference software was employed to simulate compression degradation using four quantization parameters (QP 30, 35, 40, and 45). Experimental results demonstrate that VQRNet outperforms state-of-the-art quality enhancement methods in terms of core metrics, including PSNR and SSIM, specifically including MIRNet, NAFNet, TRRHA, and CTNet. In the QP = 30 scenario, VQRNet achieves an average PSNR of 40.33 dB, a significant improvement of 3.32 dB over the VTM 23.4 baseline (37.01 dB), while demonstrating significant advantages in computational complexity and parameter efficiency—requiring only 5.27 G FLOPs and 1.40 M parameters, with an average inference latency of only 11.82 ms per 128 × 128 patch. This work provides robust technical support for the efficient video perception of quadruped robot dogs. Full article

Diseases of the gastrointestinal tract (GI) represent a major global health burden, leading to more than eight million deaths each year, largely driven by malignant conditions such as cancers and tumors. Early detection of such conditions can significantly improve survival rates. In this work, we present a compact two-port MIMO topology for high-speed telemetry and sensing. This system integrates two identical antennas, each operating at 915 MHz, positioned only 0.55 mm apart. It has just 11.9 mm³ (6.9 mm × 6.9 mm × 0.25 mm) volume, achieved through the use of meandered resonator and a high-dielectric laminate for miniaturization. Despite its small size, the design delivers a measured peak gain of −25.1 dBi at resonance. Low mutual coupling in the antenna-system is made possible by maintaining an optimized spacing and introducing a slot in the ground plane, resulting in isolation levels above 27.9 dB. The MIMO configuration was evaluated using standard performance metrics, and at an SNR of 20 dB, the system reached a better performance than single-element antenna. Beyond communication, this design also functions as a sensor, with its resonant frequency shifting in response to changes in the surrounding tissue’s permittivity: enabling real-time monitoring of internal physiological changes. Throughout the sensing process, the design maintains good gain and impedance matching, making it a strong candidate for biomedical implants. Full article

Hyperspectral–multispectral (HSI–MSI) image fusion aims to reconstruct high-spatial-resolution hyperspectral images (HR-HSIs) by combining the spectral fidelity of low-resolution HSIs (LR-HSIs) with the spatial details of high-resolution MSIs (HR-MSIs). A key challenge is preserving spectral–spatial consistency under cross-modal resolution mismatch, where inadequate long-range dependency modeling and unstable inter-modality interaction may induce spectral distortion and structural discontinuities. This paper proposes DSIR-Net (DSIR), a dual-stream state-space fusion architecture equipped with an implicit neural representation (INR) module. DSIR decouples spectral and spatial representation learning into two coordinated streams and leverages state-space modeling to aggregate global context efficiently during progressive fusion. Moreover, INR-based coordinate-conditioned refinement provides continuous sub-pixel compensation, enhancing high-frequency detail recovery while suppressing fusion-induced artifacts. Across four commonly used benchmark datasets, DSIR shows consistent advantages over the competing methods in both numerical metrics and visual reconstruction quality. In addition to sharper structural details, DSIR preserves spectral information more faithfully. Using the best result among the baselines on each dataset as reference, the PSNR improvements are 0.040 dB (Houston), 0.204 dB (PaviaU), 0.093 dB (Botswana), and 0.163 dB (Chikusei). Full article