Search Results (321)

To address the limitations of traditional Voltage-Current (VI) trajectory features in appliance load identification—such as the difficulty in distinguishing similar appliances, weakened amplitude information, and the absence of dynamic characteristics—this paper proposes a dual-stage cyclic training method for load identification that integrates multi-feature reconstruction with Particle Swarm Optimization (PSO). First, to overcome the high similarity of original VI trajectories, a PSO-based threshold optimization algorithm is designed to reconstruct VI trajectories through reflection operations and normalization, thereby enhancing the geometric morphological differences among similar appliances. Second, to supplement dynamic impedance information and energy level features, conductance-time trajectories and mean-square current color-block maps are introduced to characterize dynamic impedance variations and energy level differences, respectively. Finally, a three-channel classification network based on ResNet18 is constructed, where the reconstructed VI trajectories, conductance-time trajectories, and mean-square current color-block maps are fused via RGB channels as inputs, forming a closed-loop “threshold optimization—feature reconstruction—cyclic training” framework. Experimental results on the PLAID dataset demonstrate that the proposed method achieves an identification accuracy of 98.29% and a macro-averaged F1-score of 97.93%. Comparative experiments verify the effectiveness of the reconstructed VI trajectories, the complementarity of multi-feature fusion, and the superiority of the combinatorial optimization network, significantly improving the identification of multi-state and similar-condition appliances. Full article

Let $X_n=\{1,2,\dots ,n\}$. Previous work has focused on ordinary semigroup identities and the structural properties of individual transformation monoids. Building on related identity-based work involving one of the present authors, we compare the full transformation semigroup ${\mathcal{T}}_n$, the order-preserving semigroup ${\mathcal{O}}_n$, the order-preserving-or-order-reversing semigroup ${\mathcal{OD}}_n$, the orientation-preserving semigroup ${\mathcal{OP}}_n$, and the anti-cyclic one-line family ${\mathrm{OR}}_n$, treated only as a subset of ${\mathcal{T}}_n$. For each family S, we determine the least positive exponent $E_S$ such that $a^{E_S}$ is idempotent for every $a\in S$. This gives $z^{E_S}=z^{2E_S}$. For $n\ge 2$, the exponents for ${\mathcal{T}}_n$, ${\mathcal{O}}_n$, ${\mathcal{OD}}_n$, and ${\mathcal{OP}}_n$ are $lcm(1,\dots ,n)$, $n-1$, $2⌈{\displaystyle \frac{n-1}{2}}⌉$, and $lcm(1,\dots ,n)$, while $\widehat{E}\left({\mathrm{OR}}_n\right)=2⌈{\displaystyle \frac{n-1}{2}}⌉$ is the subset exponent for ${\mathrm{OR}}_n$. We then study $x^{E_S}{y}^{E_S}{x}^{E_S}=y^{E_S}{x}^{E_S}$. With $p=x^{E_S}$ and $q=y^{E_S}$, it reduces to $pqp=qp$. This holds exactly when q maps each kernel block of p into a single kernel block of p, and fails exactly when q splits a block. Together with the automatic cases, this test gives a classification of all ordered pairs into automatic, positive, and negative classes. Full article

SystemVerilog preprocessing determines the effective source code consumed by subsequent front-end compilation stages, yet in many toolchains it is implemented as an internal component tightly coupled with lexical, syntactic, or downstream analysis. Such integration is effective for end-to-end compilation flows, but it can obscure preprocessing behavior and make independent validation difficult, especially for macro expansion, conditional compilation, file inclusion, and source-location tracking. This paper presents Stagira svpp, a standalone SystemVerilog preprocessor implemented in OCaml and designed to follow the preprocessing semantics of IEEE Std 1800–2023. Preprocessing is modeled as an independent, directive-driven frontend stage, in which macro definitions, conditional-compilation contexts, file-inclusion hierarchies, source-location mappings, diagnostics, and auxiliary directive states are maintained through explicit state updates. The implementation supports object-like and function-like macros, token-level macro operators, nested conditional compilation, macro-driven file inclusion with cycle detection, predefined macros, source-location remapping, command-line macro/include configuration, and selected stateful directives. The evaluation uses a focused semantic test suite to exercise implemented preprocessing mechanisms and representative boundary cases. Within the evaluated scope, Stagira svpp passes all 48 test cases and exhibits consistent behavior for tested scenarios including nested conditionals, token-level macro operations, macro-expanded include arguments, cyclic inclusion detection, and source location-related diagnostics. A representative comparison with Icarus Verilog and Quartus II further illustrates observable behavioral differences in selected include-related scenarios. The current evaluation focuses on semantic correctness and boundary behavior rather than large-scale performance benchmarking, which has been identified as a direction for future work. By decoupling preprocessing from later frontend phases and making preprocessing state explicit, Stagira svpp provides a reusable reference implementation for studying and validating SystemVerilog preprocessing behavior. Full article

Direct antibody labeling is essential for immunoassays, multiplexed imaging, and biosensing; however, current methods are often time-consuming, restricted by antibody source, risk compromising protein performance, or vary with multiple steps. We introduce multiplex in situ tagging (MIST) Linker, a rapid and Fc-site-specific labeling tool that conjugates fluorophores or DNA oligonucleotides to antibodies from diverse commercial sources in as fast as 10 min using minimal starting material. MIST Linker achieves >90% cleavage upon UV exposure, facilitating rapid cyclic imaging on a single specimen. Validated across multiple species and sources, the platform outperforms conventional two-step immunofluorescence and immunohistochemistry in various tissues and cell lines. By enabling the rapid, cost-effective customization of antibody panels, MIST Linker significantly lowers the barrier to accessing antibody–DNA conjugates for spatial biology. When integrated with the spatial MIST platform and MIST-Explorer, it enables high-plex, single-cell spatial proteomics at high signal-to-noise ratios in human clinical biopsies, mouse specimens and cell lines. This toolkit provides an efficient, accessible solution for high-resolution spatial mapping, allowing for the in-depth analysis of cell subpopulations, biomarker distributions, and signaling events in complex biological specimens. Thus, MIST Linker offers a versatile, accessible, and scalable solution for antibody-labeling-based research and clinical diagnosis. Full article

The ride stability and driving comfort of vehicles are highly dependent on the performance of the damping system. The fatigue life prediction of damping components using rubber as the core damping material has become a research hotspot in the field of vehicle vibration isolation. Taking an automotive engine rubber isolator as the research carrier, this paper jointly carries out finite element simulation analysis and structural component fatigue life tests. A dual-parameter mapping framework is proposed, which integrates maximum strain energy density and cyclic energy dissipation instead of using a single damage indicator. This approach comprehensively accounts for the coupling effect of energy storage and energy dissipation coexisting under actual service conditions. Through uniaxial tensile tests on rubber specimens, combined with finite element simulations and physical model parameters, a quantitative mapping relationship between laboratory specimens and full-scale engine rubber isolators is established. Based on this mapping, the fatigue life curve of the isolator is derived from the specimen-based failure characteristics. Validation tests under two randomly selected operating conditions yield prediction errors of 7.5% and 6.9%, demonstrating that the proposed model can accurately achieve equivalent fatigue life transformation from small specimens to actual components. Unlike conventional direct extrapolation methods, this approach does not require complex multiaxial fatigue tests on the component itself; it only needs simple specimen fatigue data, significantly reducing development costs, while providing a reliable theoretical basis for material selection, fatigue performance optimization, and forward structural design of rubber isolators. Full article

The cashmere fibers produced by the secondary hair follicles of cashmere goats are precious textile raw materials. The secondary hair follicles exhibit a distinct annual cyclical pattern, comprising anagen, catagen, and telogen phases. Although a large amount of skin transcriptome data is currently available for cashmere goats, this study aims to systematically investigate the regulatory factors controlling the cyclical dynamics of secondary hair follicles through re-analysis of these data. Skin transcriptome data of Inner Mongolian cashmere goats were downloaded from the Sequence Read Archive (SRA) database. Bioinformatics analyses, including quality control, read mapping, quantification, differential expression analysis, Gene Ontology (GO) enrichment analysis, and KEGG pathway analysis, were performed to identify key regulatory genes governing the secondary hair follicle cycle. The results revealed a total of 1232 differentially expressed genes (DEGs) from comparisons across different phases. KEGG pathway analysis identified three signaling pathways associated with cashmere development: the cAMP signaling pathway (chx04024), the relaxin signaling pathway (chx04926), and the estrogen signaling pathway (chx04915). GO analysis yielded 335 terms, of which 236 were statistically significant (P-adj < 0.05). The DEGs were predominantly involved in biological processes such as regulation of transcription by RNA polymerase II, proteolysis, and positive regulation of transcription by RNA polymerase II. Significantly enriched cellular components included the plasma membrane, membrane, and extracellular space, and molecular functions were mainly related to protein binding, identical protein binding, and DNA-binding transcription factor activity, RNA polymerase II-specific. Based on their expression patterns, five key genes were selected: KRT25, KRT39, MAPK12, SPP1, and TCHHL1. These genes displayed significantly distinct expression profiles across the hair follicle cycle. TCHHL1 exhibited the highest expression in anagen, intermediate expression in catagen, and the lowest expression in telogen, with significant differences among all three phases. MAPK12 was specifically upregulated in telogen and was significantly more highly expressed than in both anagen and catagen. SPP1 showed high expression in anagen and catagen but extremely low expression in telogen. KRT39 displayed significantly higher expression in both anagen and catagen compared to telogen. KRT25 reached its highest expression in catagen, which was significantly different from that in telogen, while its expression in anagen showed no significant difference from either telogen or catagen. This study characterized the regulatory genes of the secondary hair follicle cycle in cashmere goats at the transcriptional level and analyzed the association between their expression patterns and cycle regulation. Full article

Reference evapotranspiration (ET₀) represents the atmospheric demand for water from a well-watered vegetated surface and is a key component of the hydrological cycle and agricultural water management. This study evaluated the performance of seven machine learning (ML) models: linear regression (LR), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Decision Trees (DT), and Cubist, for predicting monthly FAO-56 Penman–Monteith ET₀ in Benin. The target variable was calculated from data collected at six synoptic stations over the 2017–2021 period. Ten remote-sensing and topographic predictors were used: MODIS Land Surface Temperature (LST), six Sentinel-2 optical vegetation indices (NDVI, EVI, NDMI, NDWI, MSI, NDRE), elevation, and cyclic month encoding. Models were trained on the 2017–2019 period and evaluated on an independent temporal test set (2020–2021). All models showed positive predictive performance, with the BMA ensemble achieving the highest accuracy (RMSE = 7.0% of mean ET₀, R² = 0.802), followed by Cubist (RMSE = 7.3%, R² = 0.787) and DT (RMSE = 7.5%, R² = 0.776). The seven models were combined via Bayesian Model Averaging (BMA) with posterior weights estimated by the EM algorithm to produce 1 km monthly ET₀ maps for Benin for 2025. BMA-derived inter-model standard deviation provided spatially explicit uncertainty estimates, revealing that prediction uncertainty is greatest in the northern Sudanian zone during the dry season. The ET₀ target variable was constructed as a hybrid product combining station temperature observations with solar radiation, wind speed, and vapor pressure deficit extracted from the TerraClimate gridded reanalysis dataset; this methodological choice is discussed as a study limitation. Full article

Crime prediction in urban environments is a complex and pressing challenge driven by the intricate interplay of spatiotemporal dependencies, hierarchical geographic patterns, and socio-environmental determinants. We propose a multi-scale geo-temporal crime embedding (MSG-TCE) framework, which hierarchically models these dynamics via three novel components: a hierarchical residual temporal encoder (HRTE), a periodic transformer Encoder (PTE), and a hyperbolic spatial pooler (HSP). The HRTE captures multi-scale temporal trends by combining dilated convolutions with residual connections, while the PTE explicitly encodes periodic crime patterns using self-attention conditioned on cyclical positional encodings. The HSP maps spatial crime hotspots into hyperbolic space to better represent their inherent hierarchical structure, spanning city–district–neighbourhood–street-segment scales, and aggregates neighbourhood information via graph convolutions. These components are fused through a gated cross-attention mechanism, yielding a unified embedding for crime prediction. Experiments on real-world datasets from Chicago, Los Angeles, and New York City demonstrate that MSG-TCE achieves consistent improvements over five competitive baselines across RMSE, Precision@20, and DTW metrics, with statistically significant gains at longer prediction horizons. Ablation studies confirm the contribution of each component. Spatial visualisation maps, robustness analyses, and an exploratory covariate-augmented variant further substantiate the empirical validity of the framework. This paper also discusses limitations, including data reporting biases, the need for full covariate integration, and ethical considerations, pertaining to algorithmic fairness in crime prediction. Full article

Aero-engine blades operate under extreme conditions involving high temperature, pressure, rotational speed, and cyclic loads, making them susceptible to surface defects such as micro-cracks. Due to their small scale, weak edges, low contrast, and elongated morphology, micro-cracks are easily affected by metallic reflections, uneven illumination, and complex background textures in borescope images, resulting in high missed-detection rates for conventional detection methods. To address these challenges, this study proposes an improved YOLO11-based framework for aero-engine blade micro-crack detection. The proposed method introduces P1/P2 shallow high-resolution detection branches to enhance the perception of fine crack edges and textures, incorporates Focal Loss to alleviate foreground–background imbalance, applies object-level Tversky Loss to strengthen false-negative constraints, and adopts a hard mining strategy to improve learning for difficult crack samples. Experiments conducted on a real aero-engine borescope image dataset demonstrate that the proposed model achieves a Precision of 0.9981, Recall of 0.9606, F1-score of 0.9790, mAP50 of 0.9781, and mAP50-95 of 0.6938 on an independent test set. Compared with the YOLO11 baseline, the proposed method significantly improves crack detection accuracy, localization quality, and robustness in complex borescope inspection scenarios. Full article

Direction-of-arrival (DOA) estimation of cyclostationary signals is an important problem in array signal processing, especially in sensor-limited and underdetermined scenarios. Sparse arrays and cyclostationary statistics can improve virtual degrees of freedom and target selectivity, but incomplete difference coarray information caused by missing lags may degrade virtual covariance reconstruction and reduce the reliability of DOA estimation in closely spaced, coherent, and interference-contaminated environments. To address this issue, this paper proposes a cyclostationary DOA estimation method based on an augmented extended coprime array (AECA), SVT-based hole recovery, and weighted atomic norm minimization (ANM). The proposed method first constructs the cyclic correlation matrix at the target cyclic frequency and maps it into the AECA-based virtual coarray domain. Redundant lag observations are then aggregated, and an iterative hole recovery procedure is applied to obtain an initial structured virtual covariance matrix. On this basis, a weighted ANM-based covariance refinement model is introduced, where directly observed lags and SVT-recovered hole entries are assigned different confidence levels. The final DOA estimates are obtained using MUSIC on the refined virtual covariance matrix. Simulation results under the considered underdetermined, closely spaced, coherent-source, and interference-contaminated scenarios show that the proposed method achieves lower RMSE and clearer spectral responses than the selected baseline methods. Additional ablation, parameter sensitivity, cyclic frequency mismatch, non-Gaussian noise, and runtime analyses further clarify the contribution, robustness range, and computational cost of the proposed framework. Full article

Background: Anterior cruciate ligament (ACL) injury is one of the most common injuries among athletes and demonstrates significant sex-based differences in incidence, with a higher documented risk in females. Various anatomical, biomechanical, neuromuscular, and hormonal factors have been proposed to explain this disparity; however, the available evidence remains inconclusive due to methodological heterogeneity across studies, variability in outcome measures, and inconsistencies in the assessment of hormonal and biomechanical variables. Objective: To map and synthesize the scientific evidence regarding risk factors associated with ACL injury during sports activity, incorporating a sex-specific analytical perspective. Methods: A scoping review was conducted following the methodological framework proposed by Arksey and O’Malley, the Joanna Briggs Institute guidelines, and the PRISMA Extension for Scoping Reviews (PRISMA-ScR). A systematic search was performed in PubMed and Scopus through September 2025. Observational and experimental studies assessing ACL injury risk factors and analyzing sex-based differences were included. Findings were synthesized using a descriptive and narrative approach. Results: Nineteen studies were included. Biomechanical and neuromuscular factors were the most frequently investigated domains among the included studies (68.4%), followed by hormonal (42%) and anatomical factors (36.8%). These percentages reflect the distribution of research focus across the literature rather than the relative strength or importance of each risk factor. In females, injury risk was primarily associated with high-risk biomechanical patterns, cyclical hormonal variations, and specific anatomical characteristics. In males, risk factors were mainly related to muscular weakness, joint laxity, and structural ligament characteristics. Conclusions: ACL injury risk in athletes appears to be influenced by multiple interacting intrinsic and extrinsic factors. The available evidence suggests that sex-related differences may exist in the contribution of biomechanical, anatomical, hormonal, and neuromuscular factors; however, these relationships are multifactorial and should be interpreted cautiously given the heterogeneity of the included studies. Full article

We study cyclic codes over split two-branch finite local rings of the form $R_{\mathcal{l},m}={\mathbb{F}}_p[u,v]/⟨u^{\mathcal{l}},v^m,uv⟩,\mathcal{l},m\ge 2,$ whose radical filtration is governed by two independent nilpotent chains: $u\to u^2\to \cdots \to u^{\mathcal{l}-1}\to 0\mathrm{and}v\to v^2\to \cdots \to v^{m-1}\to 0.$ For the structural part, we develop a residue–torsion framework in which a cyclic code is described by one residue layer together with $\mathcal{l}-1$u-torsion layers and $m-1$v-torsion layers over ${\mathbb{F}}_p$. This yields divisibility constraints, a layered generator description, and an explicit cardinality formula in terms of the associated field cyclic codes. We then specialize to the split cube-zero ring $R=R_{3,3}={\mathbb{F}}_p[u,v]/⟨u^3,v^3,uv⟩,$ a non-chain local ring of type $(2,2)$ with basis $\{1,u,v,u^2,v^2\}$. For this ring, the general theory becomes a five-layer structure consisting of one residue layer, two first torsion layers, and two second torsion layers. Using an ${\mathbb{F}}_p$-linear Gray map adapted to this split radical filtration, we show that, when $gcd(n,p)=1$, the Gray image is linearly equivalent to a direct sum of five cyclic codes over ${\mathbb{F}}_p$, so the dimension is additive across the layers. The minimum distance, however, is not determined by this decomposition alone and requires separate analysis. When $n=p^s$, we derive exact distance formulas by reducing the problem to the five associated repeated-root cyclic codes over ${\mathbb{F}}_p$. For $p=3$ and $n=9$, we compute explicit examples whose Gray images are ternary codes of length 45, illustrating the theory and producing several optimal codes. These results give a structural and metric description of cyclic codes over the split local ring $R_{3,3}$ while placing its algebraic framework in the broader family $R_{\mathcal{l},m}$. Full article

Recent technological advances and the reinvigoration of NASA’s Artemis program have increased the feasibility of lunar habitats and supporting infrastructure, necessitating the development of specialized foundation systems capable of maintaining stability under transferred structured loads. Site investigation techniques, including in situ testing, sampling, and geophysical mapping, must therefore be adapted for lunar conditions, while construction using regolith requires an improved understanding of lunar soil mechanics. Foundations must also endure extreme thermal fluctuations, reduced gravity, radiation exposure, micrometeoroid impacts, and lunar seismicity to ensure long-term performance. Consequently, enhanced knowledge of the monotonic and cyclic geotechnical behavior of lunar soils is essential. Owing to the limited availability of in situ testing opportunities and returned lunar materials, high-fidelity simulants that replicate regolith behavior are required for experimental studies. This research investigates the static behavior of several contemporary lunar simulants and compares their responses with terrestrial benchmark soils. The results indicate that the overall stress–strain trends of lunar simulants broadly resemble those of terrestrial soils; however, the particle morphology and distinctive mineralogical compositions, including basaltic and anorthositic constituents, yield higher values of certain geomechanical parameters. Comparison with terrestrial datasets further suggests that carefully selected benchmark soils may facilitate the development of a next generation of lunar simulants with improved fidelity to lunar regolith. Full article

Highway slope instability under coupled traffic and environmental loading poses critical threats to transportation safety in mountainous regions, where dynamic vehicular forces interact with complex geological conditions in ways that single-modality monitoring cannot fully resolve. This study proposes MMDF-DEWS, a multi-modal data fusion and deep learning-based early-warning system that, for the first time, treats quantified traffic-loading parameters as a first-class input modality alongside Interferometric Synthetic Aperture Radar (InSAR) displacement, Global Navigation Satellite System (GNSS) measurements, and embedded geotechnical sensor outputs. A hybrid Transformer–bidirectional LSTM backbone with hierarchical attention-guided fusion enables the model to capture both long-range temporal deformation trends and short-term dynamic responses triggered by heavy-vehicle passage. To guard against over-fitting on a limited number of instability events, we adopt chronological training/validation/test partitioning, five-fold cross-validation for hyper-parameter selection, stratified focal-loss training, and cross-dataset evaluation on two independent public benchmarks: the Three Gorges Reservoir Area Landslide Monitoring Dataset (TGRA-LMD) and the European Ground Motion Service Sentinel-1 (EGMS-S1) dataset. The framework outperforms six state-of-the-art baselines by 4.7–11.2% in F1-score, and ablation studies confirm that the explicit inclusion of traffic-loading features alone improves Warning-class recall by 6.3 percentage points, demonstrating a direct and physically grounded link between cyclic vehicular loading and slope-state prediction. The system satisfies operationally relevant engineering targets for warning lead time and false-alarm rate, and provides interpretable attention maps suitable for transportation-authority decision support. Full article

Vegetation phenology refers to the cyclical growth patterns of vegetation in nature, which are influenced by climatic conditions, human activities, and genetic factors. It plays an irreplaceable role in regulating carbon cycling and energy flow within natural ecosystems. However, the combination of a cloudy and rainy climate with a landscape characterized by the interplay of land and water and fragmented patches has long posed challenges for remote sensing phenological monitoring data, including a scarcity of valid observations, frequent temporal gaps, and spectral distortion in mixed pixels. These issues make it difficult to reliably support the needs of wetland phenological inversion and mapping. To address this issue, this study uses vegetation inversion in the Poyang Lake wetlands as a case study and reconstructs high-spatiotemporal-resolution time-series kNDVI data based on multi-source remote sensing data. Methodologically, we propose an improved and enhanced spatiotemporal adaptive reflectance fusion model, IESTARFM. This model enhances the homogeneity of similar pixel selection through adaptive matching windows and land cover constraints. Additionally, it explicitly incorporates cloud probability and time-lag factors into the weighting structure to systematically downweight unreliable observations, and further employs quadratic term corrections to account for the nonlinear growth response of kNDVI. Using the reconstructed dataset, key phenological information is extracted by combining third-order harmonic analysis with a dynamic thresholding method, thereby enhancing the robust characterization of seasonal trajectories under conditions of missing data and noise. Accuracy evaluation results show that the 10m/8d high-frequency kNDVI dataset reconstructed by IESTARFM achieves at least a 12.61% improvement in fusion accuracy compared to classical methods such as ESTARFM, STARFM, and FSDAF, with a maximum reduction in RMSE of 0.026, and effectively restores details in areas with thin cloud cover. The reconstructed kNDVI series achieved a coefficient of determination $R^2$ = 0.875 and RMSE = 0.066 relative to Sentinel-2 observations, indicating that the reconstructed series closely reproduces the reference imagery in both amplitude and spatial structure. The phenological parameters derived from kNDVI exhibit an RMSE of 4.81 days compared to field observations, demonstrating that the reconstructed time series reliably captures the timing of key phenological events. It should be noted that the proposed approach is designed for post-event time-series reconstruction and is not intended for real-time forecasting. In summary, this study collaboratively enhanced the reliability of high-resolution index time-series reconstruction and phenological identification in cloudy and rainy wetlands through three key aspects: cloud noise suppression, heterogeneous boundary preservation, and nonlinear growth characterization. It provides a generalizable technical foundation for dynamic monitoring of wetland vegetation, ecological restoration assessment, and refined management in regions with frequent cloud and rainfall. Full article