Search Results (537)

This paper establishes a local fixed point theorem for multivalued mappings in zero-complete strong partial b-metric spaces and develops a framework for modelling multistage processes with several admissible terminal states. The contractive condition is formulated using a Bianchini–Grandolfi gauge function and the associated excess functional. Unlike global principles, the theorem requires contractive assumptions only within a prescribed closed ball. A localization condition keeps the successive approximations inside this ball, while an adapted chain estimate proves that the iterative sequence is zero-Cauchy. The proof does not require the family of partial b-metric balls to form a topological basis; the ball serves only as a localization set, and the limiting argument relies on zero-completeness and the zero-closedness of the mapping values. The main theorem guarantees a fixed point for a multivalued mapping and, under an additional condition, uniqueness in the single-valued case. Its consequences include local and global principles for linear set-valued contractions, a partial metric version, and a local Banach-type theorem. A further contribution is a method for constructing strong partial b-metrics from bounded metric spaces and prescribed nonempty target families. A nonlinear transformation of the original metric is combined with the transformed distances from the target family. Thus, the self-distance of each point is determined by its position relative to that family and vanishes precisely on it. This provides a natural model for processes with several distinct but equally stable terminal states. The theory is illustrated through a finite model of linguistic enrichment. Twenty formulations of the same mathematical statement are arranged into successive levels of grammatical, terminological, logical, and stylistic refinement. A weighted revision graph generates the generalized distance, while the self-distance represents the remaining effort required to reach a stable formulation. A multivalued revision mapping allows several admissible improvements at every stage. The model admits two distinct stable formulations: a concise formal version and a more explanatory, pedagogically oriented version. Both require no further essential revision but remain distinct. This demonstrates that stabilization need not imply uniqueness and that different enrichment trajectories may lead to different acceptable terminal texts. More generally, the example shows how generalized fixed point methods can describe local, nonunique, and multistage stabilization processes in language dynamics and related nonlinear systems. Full article

The hybrid operation of grid-following (GFL) and grid-forming (GFM) renewable energy units can lead to phase-reference inconsistencies and distorted transient impedance trajectories, which in turn cause maloperation or failure-to-operate of conventional distance protection. To address this issue, this paper proposes a novel distance protection method based on transient impedance fitting. First, a dynamic phase transformation is applied to map the currents of GFL units into a unified reference frame, enabling consistent representation of heterogeneous currents from the hybrid renewable energy station. Second, a transient equivalent impedance model is established based on the transient voltage-current relationship of the transmission line, revealing the influence mechanisms of the rates of change in current amplitude and phase angle on the transient additional impedance. Finally, the magnitude of the transient impedance within a short post-fault data window is selected as the fitting object. The least-squares method is employed to extract the linear fitting slope and intercept, which characterize the evolution trend and initial position of the transient impedance trajectory, respectively, thereby forming the criteria for distinguishing internal and external faults. Simulation results demonstrate that the proposed method correctly identifies fault sections under various fault locations, transition resistances, and renewable power output conditions, with the protection decision completed within 15 ms. The proposed method effectively overcomes the susceptibility of conventional distance protection to maloperation and failure-to-operate in scenarios with high renewable energy penetration. Full article

Wearable lumbar rehabilitation exoskeletons require body-size adaptability and deterministic kinematic modeling. Adjustable wearing structures may alter actuator anchor-point positions, creating inconsistencies in fixed-geometry inverse kinematic models. This study proposes a linear-actuator-driven parallel lumbar exoskeleton using a split semi-ring variable-width platform. Transverse opening increments, $h_1$ and $h_2$, are introduced into the anchor-point coordinates to reconstruct the worn geometry. A geometry-adaptive kinematic model is developed to map task-space motions to actuator-space commands. Multibody simulations indicate that compared with a fixed-geometry model, the proposed approach decreases the position RMSE from $1.30\mathrm{mm}$ to $0.32\mathrm{mm}$ and the orientation RMSE from ${0.62}^{\circ }$ to ${0.05}^{\circ }$. Human-worn experiments across three healthy adult male participants yielded a global actuator displacement RMSE of $0.08$–$0.09\mathrm{mm}$, a task-space position RMSE of $4.73$–$7.64\mathrm{mm}$, and an orientation RMSE of ${0.71}^{\circ }$–${1.09}^{\circ }$. These task-space errors characterize the exoskeleton-platform motion measured by optical marker clusters rather than directly measured anatomical lumbar-spine kinematics. Rigid testbench validation isolated the kinematic mechanism from human-interface compliance, reducing the testbench position RMSE from $3.07\mathrm{mm}$ to $1.86\mathrm{mm}$ and the orientation RMSE from ${0.78}^{\circ }$ to ${0.34}^{\circ }$ compared to the fixed-geometry assumption. These results provide a proof-of-concept validation for the feasibility of the architecture and model for lumbar exoskeletons with transverse width adjustability. Full article

Camera-only BEV 3D object detectors are trained under highly imbalanced category distributions, and their matched object queries can exhibit directional semantic errors toward frequent classes. We investigate this behavior as a diagnostic problem: given fixed geometric predictions and fixed query–ground-truth assignments, how much class information remains accessible in frozen decoder features, which errors can be recovered, and where does recovery fail? We establish a scene-disjoint protocol in which recovery fitting and model selection use separate subsets of the official nuScenes training set, while all 150 validation scenes (6019 samples) remain final-only until all model and post-processing choices are fixed. Geometry-only Hungarian matching produces 158,253 fixed positive pairs on the full validation set. The frozen detector obtains a macro accuracy of 0.6136 on these pairs, while a lightweight factorized head trained on frozen features from decoder layer 4 reaches 0.6959 ± 0.0012 across three seeds. A linear probe achieves a macro accuracy of 0.8628 on the internal tuning split, whereas a shuffled-label control remains at chance (0.1000), indicating that substantial class information remains decodable from the frozen features. Tail-focused analysis further shows that recovered errors are more separable in frozen feature space than unrecovered errors across all 15 class-by-seed comparisons. However, recovery is not consistently observed across the controlled ResNet-18 and ResNet-50 configurations, and locked end-to-end evaluation decreases mAP from 0.2565 to 0.1620 and NDS from 0.3582 to 0.2796. These results support a geometry-controlled diagnosis of partial and class-dependent semantic recoverability, rather than improved localization, architecture-independent recovery, or deployable detection performance. Full article

Soybean pod-related traits are important agronomic characteristics associated with seed development, domestication, cultivar identification, and breeding improvement. However, conventional phenotyping methods mainly rely on manual measurements, which are time-consuming and labor-intensive and capture only limited dimensions of pod variation, while the genetic basis of skeleton- and curvature-based pod descriptors remains insufficiently characterized in biparental populations. In this study, eight quantitative traits representing pod size, shape, and color components were extracted from an existing mature pod image dataset of an interspecific soybean recombinant inbred line (RIL) population using the established deep learning-based image phenotyping framework. These traits exhibited substantial phenotypic variation, with across-year entry-mean broad-sense heritability (H²) estimates ranging from 0.30 to 0.80. Composite interval mapping (CIM) based on a high-density genetic linkage map identified 54 quantitative trait loci (QTLs), which were integrated into 39 non-redundant loci, including six cross-year stable QTLs and three QTLs supported by best linear unbiased prediction (BLUP) analysis. Candidate genes within selected focal QTL regions were prioritized through functional annotation and pod and seed developmental expression analyses. Among them, Glyma.17G109100 (GmSW17) was prioritized as a positional candidate gene for pod size-related variation, whereas Glyma.19G120400 (L1), a previously validated causal gene for pod color, was located within qV19. These findings demonstrate the effectiveness of combining deep learning-based phenotyping with genetic analysis for dissecting the genetic architecture of complex soybean pod-related traits and provide valuable stable QTLs and candidate genes for future functional studies and soybean molecular breeding. Full article

This paper investigates robust output-feedback consensus of linear fractional-order multi-agent systems under external disturbances and communication constraints. A fully distributed framework is developed by integrating local dynamic observers, fractional adaptive edge couplings, and dynamic event-triggered communication. The resulting protocol requires neither the network size nor algebraic connectivity. An explicit locally verifiable triggering condition is derived directly from the weighted broadcast-error term, while positivity of the fractional internal variable and Hölder continuity of Caputo trajectories are used to exclude Zeno behavior. A three-channel fractional bounded-real analysis characterizes the consensus, observer-to-coupling, and observer-to-output mappings and yields a generalized $H_{\infty }$ attenuation bound for the actual plant disagreement. In a six-agent benchmark, the proposed trigger requires 366 transmissions, compared with 3164 for a static trigger and 6000 for periodic communication, corresponding to reductions of $88.43\%$ and $93.90\%$, respectively, with comparable consensus accuracy. Finite-energy disturbance tests, attenuation-certificate analysis, numerical sensitivity studies, and a fractional servo synchronization example further demonstrate the effectiveness and applicability of the proposed method. Full article

This paper develops a physics-informed neural network (PINN) framework for the lossy telegrapher equations and presents a coupled IEEE 738 thermal balance formulation intended as a structural blueprint for environmentally aware transmission-line digital twins. The baseline electromagnetic PINN maps $(x,t)\mapsto (\widehat{V},\widehat{I})$ and is empirically validated against a finite-difference time-domain (FDTD) reference solver. An augmented parametric framework ${\mathcal{N}}_{\mathit{\theta }}:(x,t,\mathbf{e})\mapsto (\widehat{V},\widehat{I},{\widehat{T}}_{\mathrm{line}})$ is mathematically derived, wherein the ambient vector $\mathbf{e}$ modulates a temperature-dependent resistance $R^{\prime }\left(T_{\mathrm{line}}\right)$ and couples to the telegrapher residuals via a non-linear thermal balance residual $r_T$. Two further constraints, a sag-tension consistency residual $r_S$ and a dynamic line rating (DLR) one-sided penalty $r_{\mathrm{DLR}}$, are formulated for completeness but are explicitly designated as architectural extension hooks running at zero weight (${\omega }_{\mathrm{th}}={\omega }_{\mathrm{sag}}={\omega }_{\mathrm{dlr}}=0$) within the reported microscale numerical benchmarks. Consequently, the empirical validation presented herein strictly concerns the baseline electromagnetic telegrapher PINN. The numerical results demonstrate robust $L^2$ field convergence against FDTD reference data, highly structured error accumulation along physical characteristic curves, and reliable recovery of strongly observable parameters $(L^{\prime },C^{\prime })$ from sparse, noisy terminal measurements. Conversely, the recovery of loss parameters $(R^{\prime },G^{\prime })$ exhibits a severe structural weak identifiability that precisely matches the analytical predictions of a comprehensive Fisher Information Matrix analysis. The core contributions of this work are primarily methodological: (i) a dimensionally consistent, corrected residual formulation for the lossy telegrapher equations; (ii) an explicit positioning of the proposed multi-physics framework within the parametric PINN literature; (iii) a Fisher information identifiability diagnostic illustrating the near-degeneracy of baseline parameter estimation; and (iv) a clean algorithmic separation of forward training, inverse parameter identification, and prospective online updates. Full article

Tea plantations are high-input agricultural systems and have been recognized as hotspots of soil nitrous oxide (N₂O) emissions; however, the key controlling factors of these emissions and their quantitative prediction remain insufficiently understood. We compiled 115 field-observation records from 26 published studies into a multi-factor database covering climate, soil properties, and fertilization management, and compared five machine learning models—multiple linear regression (MLR), ridge regression, support vector regression (SVR), random forest (RF), and gradient-boosting regression trees (GBRTs)—using 5-fold cross-validation, combined with Spearman correlation and feature-importance analyses. Annual N₂O emissions varied widely (0.40–73.20 kg·hm⁻²·a⁻¹; mean 9.85 kg·hm⁻²·a⁻¹), and the mean direct emission factor (EFd, 2.04%) far exceeded the IPCC default value. Emissions were significantly positively correlated with total nitrogen (TN) input but negatively correlated with mean annual temperature (MAT) and mean annual precipitation (MAP). GBRT performed best, effectively capturing nonlinear multifactor interactions; TN input and soil pH were the dominant predictors, followed by rainfall. However, feature importance rankings were method-dependent: the RF/SHAP analysis ranked MAT first rather than fifth, reflecting the different algorithmic mechanisms of the two approaches. The GBRT-based model provides a useful tool for estimating tea-plantation N₂O emissions (LOOCV R² = 0.668) and quantitative support for sustainable nitrogen management and targeted greenhouse gas mitigation strategies in tea production systems. Full article

Multilingual online reviews combine ordinal ratings, asymmetric positive and negative evidence, sparse attribute occurrence, and substantial cross-country imbalance. This study presents an integrated inferential framework. Signed topic scores are converted to within-country rank intensities, and country-specific cumulative-logit models distinguish positive and negative occurrence baselines from their corresponding intensity contrasts. The two intensity contrasts are then synthesized jointly through a bivariate Bayesian normal-normal random-effects model that retains their within-country covariance. The primary analysis uses all 30,042 reviews observed in the common 2019–2024 window; the complete 85,373-review corpus and repeated country-capped samples are sensitivity analyses. Separating the two occurrence baselines improves summed AIC from 32,347.7 to 32,167.8, while a transformation-by-function comparison shows that natural splines improve AIC and quadratic-weighted agreement. The linear-rank model is retained to provide comparable scalar intensity contrasts. Targeted partial proportional-odds fits substantially improve in-sample AIC in China and Japan but leave repeated-validation performance and all nine average effect directions essentially unchanged. A secondary semantic mapping audit agrees with 30 of 31 topic assignments. Simulation results show generally adequate interval coverage but reduced Kano-state accuracy under small K, sparse occurrence, and proportional-odds violations. The findings therefore support tiered, uncertainty-aware prioritization rather than a deterministic global ranking. Full article

Urban traffic environments can elevate physiological stress, yet most existing studies assess this indirectly through infrastructural or traffic-related proxies rather than direct physiological measurement. This pilot study proposes a geographic information system (GIS)–Analytical Hierarchy Process (AHP) framework that integrates wearable heart-rate sensing with spatial analysis to identify localized physiological activation patterns at urban intersections. The proposed framework is presented as a methodological proof-of-concept and is not yet validated as a decision-support tool; application to urban health assessment or smart-city planning would require testing on a substantially larger and independently sampled spatial dataset. Data were collected from ten participants across 118 repeated commuting passes by private automobile at six intersections in Kragujevac, Serbia. An AHP-weighted urban stress index combining heart rate, the traffic-intensity proxy, time of day, and acceleration events (CR = 0.0115) was computed and mapped using inverse-distance-weighted interpolation. A linear mixed-effects model showed a significant positive association between an ordinal, time-of-day-based traffic-intensity proxy and heart rate across the 118 passes (8.90 bpm per ordinal unit, p < 0.001); because this proxy is derived from time-of-day categories, the association is best interpreted as an exploratory time-of-day–heart-rate relationship rather than a validated causal effect of traffic, and a sensitivity analysis confirmed that the same three intersections ranked highest across alternative weighting scenarios. The results indicate a consistent spatial relationship between intersections associated with higher traffic-intensity proxy values and elevated physiological activation. Although based on a limited pilot-scale dataset, the proposed framework demonstrates the feasibility of combining wearable physiological sensing with GIS–AHP spatial analysis and offers a methodological proof-of-concept for smart-city and urban-health research in medium-sized cities, pending validation on larger, independently sampled spatial datasets. Full article

In long-aperture forward-looking radar, nonlinear Doppler mismatch caused by target relative motion can lead to positioning deviation and image defocusing. To address this issue, an adaptive segmented Doppler compensation method based on phase error constraints is proposed. As synthetic aperture time increases, high-order terms in the slant range history broaden the Doppler spectrum and enhance spatially variant phase errors. Conventional global compensation cannot achieve stable focusing, and fixed-length segmentation fails to adapt to varying motion nonlinearity. Accordingly, the high-order nonlinear characteristics of the slant range are first analyzed, and an adaptive sub-aperture partitioning criterion constrained by second-order phase error is derived, ensuring each sub-aperture satisfies the local quasi-linear hypothesis. A cross-segment mapping relationship between different sub-apertures is then established, and the compensation process is formulated as a two-dimensional separable operator. To manage the high computational complexity of solving spatially variant mapping under long apertures, the Alternating Direction Method of Multipliers (ADMM) is introduced to iteratively optimize the operator, achieving phase alignment and coherent reconstruction among sub-apertures. Simulation and experimental results show that the proposed method effectively suppresses nonlinear defocusing under long-aperture conditions. Compared with conventional global methods, it achieves superior energy concentration and focusing resolution in extended target scenarios. Full article

Hispanics of Mexican American descent in South Texas show a very high prevalence of MASLD, with some studies reporting rates as high as 50% in adults. However, assessment of genetic risk factors underlying this prevalence is complicated by a high co-occurrence of other metabolic disorders and variable endogenous and exogenous environmental risk factors. To map the transcriptomic architecture of MASLD hepatic steatosis risk, we conducted an epidemiological-scale investigation using human induced pluripotent stem cell (iPSC)-derived hepatocyte cultures from 193 participants in our longitudinal South Texas Family Study (STFS). iPSC-based models offer greater power to map genetic risk factors by experimentally controlling for confounding organismal and environmental factors. We combined transcriptome-wide gene expression analysis with high-content cellular measurements of neutral lipids to define a core hepatic steatosis MASLD phenotype at baseline (vehicle-treated) and following a lipid challenge. The additive genetic heritability of hepatic steatosis measures was 0.44 (p-value = 0.03) at baseline and 0.42 (p-value = 0.03) at post-lipid challenge. Multivariable linear regression comparing each gene’s expression against hepatic steatosis measures identified 1070 genes at baseline and 1229 genes post-lipid challenge, whose expression showed a transcriptome-wide statistically significant association (standardized |β| ≥ 0.24; Bonferroni-corrected p-value ≤ 0.001) with baseline and post-lipid challenge hepatic steatosis measures, respectively. Functional annotation and pathway enrichment analyses of these genes implicated a broad range of hepatocellular functions, mapping an overall transcriptomic architecture of MASLD-associated steatosis risk in Mexican Americans. The genes whose expression was positively correlated with hepatic steatosis measures suggest a direct role of variation in fatty acid (FA) and cholesterol uptake, de novo lipogenesis (DNL), and carbohydrate shunts in hepatic steatosis risk, as well as a cellular stress-associated and high-turnover metabolic state marked by elevated FA-oxidation and ketogenesis. In contrast, the genes whose expression was inversely correlated with hepatic steatosis measures suggest a significant role of the cellular cytoskeleton, hepatocyte epithelial integrity, and endosomal and autophagic clearance machinery in steatosis risk. Full article

Larch casebearer (Coleophora laricella) poses a serious threat to forest ecological security, and remote sensing object detection is essential for early warning and targeted intervention. However, existing detectors are hindered by insufficient feature extraction, limited self-attention inductive bias, and weak spatial localization, which collectively constrain their practical deployment. To address these issues, we propose DROMAL-Net, which integrates global feature enhancement and positional dynamic clustering for forest pest detection. Built upon the YOLOv11n baseline, our model incorporates three key innovations. First, we design the MALAPSA module by embedding a magnitude-aware linear attention (MALA) mechanism into the feature extraction pipeline, achieving linear computational complexity while preserving global contextual modeling. Second, to address the inductive bias deficiency of C2PSA and better preserve spatial topology, we introduce C3kDR (DCCC3k), a positional dynamic clustering module that combines dynamic clustering convolution with complex-domain positional compensation to enhance multi-scale localization accuracy. Third, we integrate 2D Rotary Position Embedding (2D-RoPE) to further mitigate spatial ambiguity in dynamic clustering.Extensive experiments on the public SLFPD dataset validate the effectiveness of each component. Under the standard random partition, DROMAL-Net achieves 77.98% mAP@0.5, outperforming YOLOv11n by 2.46 percentage points. Furthermore, under a strict leave-one-block-out protocol to prevent spatial leakage, DROMAL-Net consistently outperforms YOLOv11n across all five held-out blocks, achieving a macro-average mAP@0.5 of 69.12% vs. 65.94%, corresponding to a 3.18 percentage point improvement, which confirms its strong generalization capability to unseen geographic regions. Results across five random seeds yield consistently low standard deviations, confirming the stability of these gains. We further evaluate generalization on the TreeFinder dataset, which encompasses diverse tree species and complex canopy structures, where DROMAL-Net maintains robust performance, underscoring the effectiveness of MALAPSA and C3kDR for global feature enhancement and accurate localization in cross-regional applications. Full article

Machine-to-machine and Internet of Things endpoints operate in physically accessible environments, motivating decapsulation-path hardening against an adversary with full code access. We present PQ-WB-KEM, a feasibility study that is, to our knowledge, the first systematic exploration of the table-based white-box design space for a NIST-standardized lattice key-encapsulation mechanism (ML-KEM-768, FIPS 203); prior white-box post-quantum work targets hash-based SPHINCS+ and multivariate hidden field equations (HFE; 256 GB), while the only earlier lattice-based white-box is custom and non-standardized. Because the base multiply runs in the number-theoretic transform (NTT) domain, where the secret operand $\widehat{s}=\mathrm{NTT}\left(s\right)$ is full-range over ${\mathbb{Z}}_q$, coefficient smallness does not shrink the tables. We map the design space with two verified lookup-only constructions: a shared full multiply table (Construction A, a measured 22.16 MB base, 25.57 MB core) and per-component tables with the secret baked in (Construction B, 7.67 MB base, 11.08 MB core), with the base tables being about 11,600× (A) and 33,400× (B) smaller than the 2022 256 GB HFE white box. Three-share arithmetic masking drives the measured first-order differential computation analysis (DCA) correlation to near the noise floor (${\rho }_{\max }=0.011$, versus $0.85$ unmasked). The projected deployment overhead is ≈47×, anchored on the native-C protected primitive measured with its mask-generation random number generator (RNG) randomness included ($4.30\times$, times an ≈11× embedded cache factor); the RNG-excluded harness yields the $17\times$ lower bound. We delimit scope honestly: against the full white-box adversary this construction does not achieve key confidentiality because the base multiply forms the clear product coordinates $p_0,p_1$ before masking and these yield linear equations for the secret; every positive result holds only against strictly weaker adversaries, and the work maps the lattice white-box design space rather than delivering a fully white-box key-encapsulation mechanism. Full article

Addressing technical challenges such as inaccurate appearance detection, inaccurate weight estimation, and low automation levels in passion fruit sorting under postharvest conveyor-line conditions, this study proposes an intelligent detection and grading model, YOLOv11n-ACH, based on an improved YOLOv11n. By integrating the Hybrid Inverted Block (HIB) module, the ASF-YOLO scale fusion mechanism, and the Convolutional Attention Fusion Mechanism (CAFM), the model effectively mitigates severe fruit occlusion and background interference caused by conveyor surfaces, residual plant material, and illumination variation. Consequently, the high-precision metric mAP@50-95 reaches 99.4%, representing an increase of 3.9 percentage points over the baseline model. Building upon this foundation, a real-time grading and counting system incorporating a confidence-priority frame-selection mechanism was constructed by combining the ByteTrack multi-object tracking algorithm with horizontal dynamic scale calibration technology. The study establishes a multivariate linear regression mass-estimation model based on morphological features (R² = 0.9617). The regression model was developed using 500 fruits, and its performance was independently evaluated using a second, non-overlapping cohort of 500 fruits collected from the same orchard. Using 12 horizontal calibration points, a cubic spline interpolation function was constructed to compensate for horizontal position-dependent variation in the pixel-to-physical scale under the tested fixed imaging configuration. In the independent mass-validation cohort, the system achieved an MAE of 2.54 g, an RMSE of 3.21 g, and an MARE of 5.65%. A third, non-overlapping cohort of 1568 fruits was used for end-to-end passage-level counting and operational grading evaluation. This lightweight solution provides an engineering approach for passion-fruit sorting under the tested postharvest conveyor-line conditions. Full article