Search Results (11,434)

The Hilfer fractional derivative effectively captures non-locality, historical dependence, and memory effects, making it valuable for modeling real-world systems, and exponential growth can describe explosive growth phenomena in real-world problems. This paper focuses on the existence of mild solutions for infinite-delay differential equations involving Hilfer fractional derivatives, fractional Laplacian operator ${(-\Delta )}^{\delta }$, and exponentially growing functions in Orlicz spaces. First, by utilizing standard $L^p$-$L^q$ estimates for strongly continuous semigroups generated by fractional Laplacian operator, the existence of global solutions in the Orlicz space $exp{L}^p\left({\mathbb{R}}^d\right)$ and the time-weighted $L^z\left({\mathbb{R}}^d\right)$ space is established. Then, by leveraging Hölder’s interpolation inequality, the existence of local solutions in $L^1\left({\mathbb{R}}^d\right)\cap L^{\infty }\left({\mathbb{R}}^d\right)$ is established. Full article

Grid-connected photovoltaic–battery energy storage–electric vehicle (PV-BESS-EV) charging stations require supervisory energy management that can coordinate tariff response, carbon-intensity signals, peak constraints, storage utilization, and converter-level operability within a transparent evidential framework. This study develops a bounded-reference rule-based supervisory energy management system (RB-SEMS) that preserves lower-level local converter controllers while generating operating modes and saturated reference commands for BESS power, grid exchange, and EV charging limits. A dual-time-scale evaluation framework is established by combining short-time switching/control simulations for dynamic traceability and SOC-sensitive protection with 24 h, 15 min EMS-level energy-balance simulations for cost, carbon, peak, PV utilization, EV service, and storage throughput assessment. Selected daily reference-injection cases are retained as copied-model diagnostic checks rather than as full-day switching-level validation. Under the D4-LSOC condition, RB-SEMS reduces the reported post-startup DC-bus deviation from 46.13 V to 40.60 V and the filtered BESS peak from 269.18 kW to 84.42 kW. In the E1-TOU scenario, E1-TOU-cost reduces daily total cost from 623.57 CNY to 564.05 CNY, lowers peak-period grid import from 183.75 kWh to 126.75 kWh, and increases local PV utilization from 71.13% to 78.71%; E1-PC66 further reduces the maximum 15 min grid import from 77.88 kW to 66.00 kW. Under the prescribed E2-PCC scenario, E2-CP reduces the calculated grid-related CO₂ emissions from 550.29 kg to 500.42 kg, whereas the price-only diagnostic increases them to 572.29 kg. Same-metric PV-SC and MILP comparisons, tested-range sensitivity analysis, and a throughput-based degradation proxy clarify that RB-SEMS is an interpretable supervisory baseline for cost–carbon–peak–cycling trade-off analysis rather than a cost-optimal controller or regionally validated proof of carbon reduction. Full article

The Fifth Industrial Revolution (Industry 5.0) requires human-centric mechanisms that preserve the integrity, reproducibility, and interpretability of AI-driven decisions in smart manufacturing. Injection molding generates heterogeneous, imbalanced, and weakly labeled process data, posing reliability and integrity risks to data-driven quality control. This study proposes an integrity-verified and reproducibility-instrumented secure machine learning framework for operating-regime analysis in injection molding that integrates (i) SHA-256-based data-integrity verification at ingestion, (ii) Pearson correlation-based feature selection, and (iii) a Gaussian Mixture Model (GMM) under a passive-adversary threat model with Transport Layer Security (TLS)-secured transmission. Evaluated on real industrial data (n = 6719 cycles, seven process variables), correlation-based feature selection retained four non-redundant variables and improved the GMM Silhouette Score from 0.274 ± 0.075 (all features) to 0.323 ± 0.014 (95% CI [0.318, 0.329]), a +18.2% relative improvement (paired t(29) = 3.39, p = 0.002; Cohen’s d = 0.62; Wilcoxon p = 0.022), while lowering the Davies–Bouldin Index from 1.63 to 1.17. The Silhouette standard deviation of 0.014 over 30 seeds meets the σ ≤ 0.02 reproducibility target. The GMM resolves four interpretable operating regimes—one low-load regime consistent with nominal operation and three elevated-load regimes (left-side, right-side, and bilateral)—with operator-readable per-variable signatures. Relative to hard-partition and projection baselines, the GMM is not Silhouette-optimal but provides an interpretable, generative regime model that meets the σ ≤ 0.02 reproducibility target. The framework operationalizes human-centric manufacturing security as measurable integrity, reproducibility, and interpretability. Full article

This paper proposes a novel fault-identification method for micro-coils in relays with forcibly guided contacts, a type of electromechanical elementary component (EMEC), combining a composite analytical model, a 2D thermal model, and a 1D-CNN. A low-order thermal circuit with one central node and four boundary nodes is established, while a two-dimensional anisotropic Poisson equation is used as a high-order calibration model. The two models are coupled through iterative correction of reusable thermal resistances. For thermal aging, enamel-film delamination, and inter-turn short-circuit faults, thermal-conductivity attenuation, asymmetric branch-resistance perturbation, and localized abnormal heat-source injection are introduced to generate physically constrained temperature sequences. Orthogonal centerline temperature distributions are extracted as one-dimensional feature vectors for 1D-CNN classification. Simulation results show that the hybrid model has an error of approximately 1.7% compared with finite-element results, and the trained 1D-CNN achieves 98.13% accuracy on 160 test samples. Experimental reconstruction and deep-feature visualization further verify its ability to distinguish normal, aging, delamination, and local short-circuit states. Full article

The $XY$ model is one of the basic models of statistical mechanics. It is often used for the description of important physical systems like ⁴He films and liquid crystals. In the present work, we consider the Ginzburg–Landau mean-field approximation of the 3D $XY$ model. We study the Lie-point symmetries of the corresponding Ginzburg–Landau Hamiltonian and the associated Euler–Lagrange equations for finite systems with film geometry and establish the variational symmetries among them in the presence of an external ordering field. Then, using the Noether theorem, we find conservation laws that allow us to obtain and present in analytic form, by means of Weierstrass elliptic, zeta, and sigma functions, the general solution of the so-called twisted boundary value problem in the absence of an external ordering field. It should be remarked that exact solutions in the absence of an external ordering field are only known within the so-called $\mathsf{\Psi }$-theory and some other mean-field-like theories. Full article

The creation of Basic Probability Assignment (BPA) still represents a basic problem in the Dempster-Shafer (D-S) theory of evidence especially when it comes to representing continuous uncertainty and class ambiguity. In order to overcome this problem, this paper suggests a BPA construction model depending on Gaussian overlap. The main principle behind the approach is the creation of focal elements based on the overlaps between conditional probability distributions of classes, allowing characterisation of uncertainty in a data driven manner. Namely, attribute level evidence is represented by Gaussian distributions, and singleton and composite focal elements are composite focal elements are generated through Gaussian product responses and normalized to obtain BPAs. Composite focal elements are further projected into singleton-level decision scores through proportional belief and plausibility transformations for decision-making and attribute-weight calculation. Moreover, to dynamically modify the role played by different attributes, a Kullback-Leibler (KL) divergence-based weighting scheme is used. These parts combine to form a full pipeline of continuous evidence modeling to BPA generation as proposed by the given method. The experimental results show that the proposed method achieves 98.00 ± 2.67% accuracy on the Iris dataset, 97.21 ± 1.76% accuracy on the Wine dataset, and 90.86 ± 1.20% accuracy on the Breast Cancer Wisconsin dataset. Compared with existing BPA generation methods, the proposed method obtains the best performance on the Iris and Wine datasets. Compared with classical machine learning models, the method also achieves the highest accuracy on the Iris dataset and remains competitive on the Wine and Breast Cancer Wisconsin datasets. Full article

Melon (Cucumis melo L.) is a globally significant horticultural crop, characterized by high nutritional value and substantial commercial status. However, frequent outbreaks of powdery mildew severely threaten its yield and fruit quality. Current early detection methods primarily focus on detached leaf assays, which often lack sufficient model generalization. This study proposes a temporal 3D multispectral point cloud reconstruction method for melon plants by integrating multispectral imaging with 3D reconstruction technology. An Artificial Neural Network (ANN) model for 3D spatial light field distribution was developed based on a hemispherical white reference to achieve precise reflectance calibration of the multispectral point clouds. Post-calibration, the coefficient of variation (CV) for the spectral reflectance of the hemispherical reference in 3D space was reduced to less than 2.4%. On this basis, an early classification model for melon powdery mildew was constructed using Partial Least Squares Discriminant Analysis (PLS-DA) based on the mean reflectance spectra of individual plant point clouds. The results demonstrate that the average recognition accuracy reaches 85.94% from 4 days post-inoculation onwards, enabling disease early warning three days in advance. This research provides critical theoretical support and technical reference for the non-destructive early monitoring and precision smart plant protection of crops in facility agriculture. Full article

As calibration errors have a direct impact on epipolar consistency, rectification accuracy, and metric 3D reconstruction performance, stereo camera calibration is a fundamental requirement for high-accuracy 3D modeling and reliable digital twin data acquisition. Because current calibration workflows (based on pairwise calibration methods) lack systematic data-quality checks mechanisms, there is a clear need for more robust data selection strategies. The novelty of the approach consists in the development of a new outlier-aware stereo calibration algorithm (OutAw) that introduces a unified multi-stage approach that integrates hard geometric selection, candidate subset generation, multi-criterion ranking, bootstrap stability analysis, and triangulation assessment into a comprehensive and systematic calibration framework. Unlike conventional approaches, OutAw (through its mechanism of detecting and rejecting inconsistent pairs) redefines the calibration strategy from arbitrary to criterion-based data selection. Also, the proposed algorithm is compared with BSC (a baseline OpenCV all-pairs calibration algorithm) and InterFil (an intermediate filtered variant) using 49 stereo pairs (at 1280 × 720 resolution) captured using a planar checkerboard. OutAw algorithm achieved (using only nine image pairs) superior results (epipolar error 0.5119 px, stereo RMS 0.7666 px) to the BSC ones (epipolar error 1.3687 px, stereo RMS 1.9385 px), representing statistically significant improvements (60.5%, respectively 62.3%). OutAw geometric consistency was validated by triangulation-based metrics (square-length standard deviation 0.1140 mm and square absolute error 0.1097 mm). Contamination analysis revealed that as the outlier rate increases, the calibration process degrades progressively. Also, the results obtained highlight that geometric quality-driven image selection is critical for achieving a reliable stereo calibration for DT applications. Full article

This paper proposes the Continuous Operational Feedback Loop (COFL) architecture, a fully localized, event-driven operational monitoring and response system for Direct-to-Consumer (D2C) commerce. The architecture integrates the n8n workflow engine with on-premise large language model (LLM) inference via the Ollama framework, forming a containerized stack deployable on commodity CPU-only edge hardware (~USD 1640). Using a multi-source dataset of 1800 records constructed from publicly available e-commerce corpora and evaluated with a silver-standard automated labeling protocol, empirical validation demonstrates an end-to-end latency of 3.22 s and a macro-F1 sentiment classification score of 0.836—representing 98.2% of the full-precision baseline and 94.0% of cloud GPT-4o API generation quality measured by ROUGE-L—at approximately 1/200th of the per-request inference cost. A systematic quantization ablation study across six model-quantization configurations establishes LLaMA 3 8B Q4_K_M as the Pareto-optimal selection for the target hardware. An Analytic Hierarchy Process (AHP) multi-criteria framework with criterion weights derived from published literature confirms the COFL implementation achieves a higher composite score than cloud API deployment under the stated evaluation assumptions. Failure mode and effects analysis (FMEA) is summarized to characterize system reliability under identified failure scenarios. Full article

Two-dimensional (2D) human pose estimation is one of the key research directions in Computer Vision (CV), which has wide application prospects in behavior recognition, such as gesture tracking, intelligent monitoring, and identity recognition. Therefore, it has recently attracted extensive attention from academia and industry. However, although a large amount of literature has been published, existing reviews often lack a unified theoretical perspective and fail to capture the latest paradigm shifts brought by foundation models. To this end, this paper reviews the applications of deep learning in the domain of 2D body pose estimation from 2010 to 2025 through a cascading approach. First, the mainstream body pose datasets and related evaluation metrics are introduced in a comprehensive and convincing way through mathematical formulas. Subsequently, an in-depth analysis of the performance of the algorithms in single-person and multi-person scenarios, and a comprehensive comparative analysis of the strengths and weaknesses of each algorithmic model, are conducted. A comprehensive comparative analysis encompassing both traditional architectures and the latest deep learning breakthroughs are provided, specifically highlighting Vision Foundation Models (VFMs), generative Diffusion processes, and State Space Models (SSMs). Finally, the current state of research in the field of 2D human pose estimation is summarized, and three main challenges, emerging solutions, and expected development trends are pointed out. This survey is an exhaustive compilation of existing research in 2D human pose estimation, providing a blueprint for researchers in the field and laying the foundation for future research work. Full article

Organophosphate flame retardants (OPFRs) are widely used as flame-retardant additives in plastics, electronics, and building materials. However, growing evidence suggests these compounds may pose significant neurotoxic risks. This study evaluated phenotypic alterations, such as cell viability, reactive oxygen species generation, and acetylcholinesterase activity, induced by seven widely detected OPFRs in SH-SY5Y human neuroblastoma cells. Aromatic OPFRs such as triphenyl phosphate (TPhP), 2-ethylhexyldiphenyl phosphate (EHDPhP) and tricresyl phosphate (TCP) exhibited the strongest effects, including decreased cell viability, increased oxidative stress and AChE inhibition. Therefore, 3D brain organoid models were used to further explore the potential lipidomic alterations induced by aromatic OPFRs. Lipidomic analysis of brain organoids exposed to aromatic OPFRs (TPhP, EHDPhP and TCP) showed significant alterations across major lipid classes, especially glycerophospholipids, sphingolipids, and glycerolipids. The depletion of bis(monoacylglycerol)phosphate (BMP) species suggests perturbations in endolysosomal lipid homeostasis and membrane trafficking pathways. Increased levels of ether-linked lysophosphatidylcholine (LPC-O) species, together with altered phosphatidylethanolamine (PE) and phosphatidylserine (PS) species, indicate extensive membrane lipid remodeling and changes in cellular signaling. Furthermore, the accumulation of diacylglycerol (DG) and triacylglycerol (TG) species points to disturbances in lipid storage and metabolism. Overall, these findings indicate that aromatic OPFRs induce cytotoxicity, oxidative stress, and alteration of cholinergic function, and are associated with lipid dysregulation linked to neurotoxicity in brain organoids. Future research should explore chronic low-dose exposure and long-term neurological effects. Full article

Cancer risk may be influenced by genetic variation and altered expression of xenobiotic-metabolizing enzymes, yet their role in endometrial cancer remains incompletely understood. This study evaluated the association between four polymorphisms in xenobiotic metabolism-related genes CYP1A1 rs1799814, CYP2D6 rs3892097, NAT1 rs72554606, and NAT2 rs1799930 and the risk of endometrial cancer, and assessed CYP1A1 and CYP2D6 expression in tumor and control tissues. Genetic association analyses, including multivariate and histology-stratified models, were performed, and gene expression levels were compared between cancer and control tissues. Variants in NAT2, CYP1A1, and CYP2D6 were significantly associated with an increased risk of endometrial cancer, whereas NAT1 rs72554606 showed a protective effect, particularly in the dominant model. The strongest association was observed for NAT2 rs1799930 in additive and recessive models. Expression analysis revealed significantly higher CYP1A1 and CYP2D6 levels in tumor tissues than in control tissues. Stratified analyses showed generally consistent effects, especially for endometrioid carcinoma, although estimates for the serous subtype were limited by sample size. These findings suggest that polymorphisms and altered expression of xenobiotic-metabolizing genes may contribute to endometrial carcinogenesis. Further studies, including independent validation and analyses of gene–environment interactions, are needed. Full article

To evaluate microbump antenna performance for high-frequency communications, this study utilizes 3D electromagnetic simulations comparing solder-ball and copper-pillar structures across three feeding schemes. The simulation results show that the copper-pillar type exhibits a lower resonant frequency and more stable relative bandwidth (with a fluctuation of only 0.63%) under the same feeding condition, while the bandwidth fluctuation of the solder-ball type reaches 13.17%. Regarding gain characteristics, the absolute differences between the two structures across all feeding methods remain negligible (within 0.03–0.06 dBi). Both antenna types exhibit the highest realized gain among the investigated schemes under microstrip feeding, yielding 5.54 dBi for solder-ball and 5.48 dBi for copper-pillar configurations, while coaxial center feeding results in the minimum gain. Given the extreme difficulty of sub-THz measurements, a measurement-compatible GSG-fed copper-pillar-type model resonating at 477.9 GHz was designed and subsequently enlarged by a factor of 46 according to the electromagnetic similarity principle. It should be emphasized that the fabricated and measured prototype is the scaled 10.4–10.5 GHz model rather than the original sub-THz microbump antenna. Based on the electromagnetic similarity principle, the measured resonant frequency and gain of the antenna are 10.5 GHz and 7.1 dBi, respectively. The measured S parameters are generally consistent with the simulated ones in trend. Therefore, under the same conditions considered in this work, the copper-pillar-type microbump antenna can achieve a lower resonant frequency and more stable relative bandwidth; while microstrip feeding provides the highest realized gain among the three investigated feeding schemes. The conclusions provide data support for the antenna-in-package design and performance optimization of microbump antennas. Full article

To address the dilemma of ‘non-grain use of cultivated land’ and support China’s requisition–compensation balance policy, this study developed a multi-dimensional assessment framework integrating the production, ecological, and economic dimensions (3D evaluation model), using Shenyang City as a case study to demonstrate the framework’s operational application and policy relevance. Based on 34,704 Third National Land Survey (TNLS) parcels (27,408.39 ha), we applied the constraint factor assessment method and entropy-weighted composite index model. The results show that non-cultivated agricultural land (NCAL) is generally marginally suitable (citywide average score: 2.50/4), with highly suitable areas accounting for only 4.04% (1106.30 ha). These areas exhibit a triangular spatial pattern distributed across northeastern Faku County, central Sujiatun District, and southern Xinmin City. Sensitivity tests using equal weights and ±20% dimension-weight perturbations confirm that high-suitability area remains limited (3.37–5.63% under entropy-weight scenarios; 8.54% under equal weights). Primary limiting factors include severe organic matter deficiency (average 19 g/kg), shallow soil depth, unfavorable pH, land requiring engineering restoration (94%), and punctiform heavy metal contamination (7.53% of plots, 2065.05 ha as spatially excluded areas). Consequently, we propose a five-tier sequential restoration framework: (1) near-term priority recultivation of highly suitable areas; (2) mid-term topsoil reconstruction for moderately suitable areas; (3) medium-to-long-term topsoil stripping and thickening for low-suitability areas; (4) long-term soil amelioration and slope-to-terrace conversion for marginally suitable areas; and (5) strict prohibition of restoration in unsuitable areas. This study establishes a spatially explicit decision-making system integrating “evaluation–classification–sequencing”, and distinguishes technical suitability from economic, institutional, and policy feasibility, providing a decision-support framework for scientifically implementing the cultivated land requisition–compensation balance policy. Future empirical studies using post-restoration monitoring data are needed to test its predictive accuracy against observed restoration outcomes. Full article

Under the “dual carbon” targets, using green finance to support renewable energy use is an important way to reduce extreme climate risks. This study builds a balanced panel dataset of 271 Chinese cities from 2010 to 2021. We measured the level of Green Finance (GF) and renewable energy utilization (RE). Employing two-way fixed effects, the Spatial Durbin Model (SDM), and the Heterogeneous Spatial Autoregressive (HSAR) model, we systematically examine the promoting effects, transmission mechanisms, spatial heterogeneity, and economic–environmental consequences of GF on RE. The empirical results reveal that GF significantly enhances RE and generates pronounced positive spatial spillovers. Mechanism analysis indicates that R&D investment and environmental regulation serve as the primary transmission channels. The promotion effect is more pronounced in the eastern and central regions, as well as in areas with higher R&D investment and stricter environmental regulation, whereas the spatial spillover effect is particularly evident in coastal regions. Further consequence analysis demonstrates that GF contributes to reducing conventional energy intensity, improving green total factor productivity, and alleviating extreme climate events. Building on these findings, this study proposes spatially differentiated and sustainability-oriented policy strategies to advance China’s energy transition and foster coordinated economic and environmental sustainability. Full article