Search Results (29,548)

The increasing demand for sustainable and intelligent energy solutions in electric vehicles (EVs) has led to a significant interest in the development of advanced hybrid energy storage systems (HESS) and efficient wireless charging architectures. In this work, a dynamic sliding mode control (DSMC) technique is optimized through a swarming heuristics framework for a battery-ultracapacitor HESS integrated with a wireless power transfer (WPT) system. Leveraging an LCC-S topology, the WPT system enables high-efficiency, contactless energy transfer to the storage modules under varying load and alignment conditions. To address the nonlinearities and parameter uncertainties inherent in such systems, a robust DSMC approach is formulated to ensure smooth system tracking and disturbance rejection. The control design is further refined using a bio-inspired moth–flame optimization algorithm hybridized with gravitational search and fractional-order PSO (MFOGSAPSO)—enhanced with adaptive entropy regulation and fractal-based memory—to dynamically tune the sliding-surface coefficients and switching gains. The proposed methodology is validated through comprehensive simulations in MATLAB/Simulink and a controller hardware-in-the-loop (C-HIL) setup on TI F28379D LaunchPads. Among the three MFO variants, MFOGSAPSO-A achieves the fastest objective function convergence, stabilizing near 685 within 10 iterations and substantially outperforming the optimized PID (715) and Optimized SMC (708). The proposed DSMC attains an overall RMSE of $0.1081$, reducing the tracking error by $60.69\%$ relative to PID and $14.84\%$ relative to SMC, while shortening the settling time to $0.102$ ms against PID ($84.84\%$) and SMC ($23.88\%$) improvements. The C-HIL results closely match the offline simulation waveforms without retuning, confirming superior energy management, improved power sharing between the battery and ultracapacitor, and enhanced overall efficiency of the wireless charging process under realistic embedded execution. Full article

Spam detection on SMS messaging has not received as much attention from researchers recently as the spam detection studies on emails or social media platforms. However, spam SMS messaging can be more intrusive, annoying, and harmful. Thus, detecting and filtering spam SMS messages is becoming a priority that saves human productivity. This work aims to introduce a dynamic, accurate, and efficient machine learning-based spam detection technique for SMS messaging. It aims at protecting users and businesses from spam SMS attacks. It aims to detect and identify suspicious messages that contain promotional, misleading, irrelevant, or harmful content. It primarily aims to test and evaluate the impact of feature extraction methods on the performance of machine-learning-based spam detection. Several text feature extraction techniques have been used and tested, including classical, statistical, contextual, and advanced embedding techniques. An extensive set of experiments has been presented on benchmark datasets in this field. From the comparative study, we can infer that all investigated feature extraction techniques have achieved high accuracy (90%+) on the in-domain dataset. However, their performance decreased when they were tested on the out-of-domain dataset (70%+). The advanced embedding techniques achieved the best performance across both datasets compared to the other tested feature extraction models. Full article

In curling, quantitative assessment of ice surface conditions during actual play is challenging, and existing sensor-on-stone approaches cannot simultaneously capture ice-contact vibration and athlete-related measurements. We developed a self-contained wearable slider unit embedding two IMUs (LSM6DSV16X), a wideband accelerometer (IIS3DWB), and plantar pressure sensors in a commercially compatible curling slider. Measurements were conducted with two participants under four ice surface conditions (used pebble, no-pebble, extra-fine, and coarse-fine pebble) at an actual curling hall. The root mean square (RMS) amplitude of the DC-subtracted IIS3DWB Z-axis vibration signal distinguished no-pebble from pebbled ice but did not distinguish among the three pebbled conditions. The spectral centroid of the no-pebble condition was also biased toward higher frequencies, although participant-dependent effects remained among pebbled conditions. The slider acquired plantar-pressure variation during delivery, and an exploratory pooled analysis showed a pattern consistent with greater right/left balance variation in trials with greater MOCAP-derived lateral wobble. Because only two participants were tested and the lateral pressure channels reached the calibration limit, participant-specific validation requires a wider measurement range and additional participants. These results support the feasibility of simultaneous ice-contact vibration and plantar-pressure measurement during curling delivery. Full article

Analyzing the long-term evolution of meso-scale urban morphological types is constrained by the “small-sample, high-dimensional” nature of historical data, which weakens the robustness and interpretability of conventional data-driven methods and limits the use of morphological evidence in digital urban analysis and built environment governance. To address this, we propose a theory-guided modular principal component analysis (TG-MPCA) framework for sample-constrained morphological research. By embedding domain knowledge into dimensionality reduction, the framework extracts compact, low-dimensional morphological indices that are both morphologically interpretable and internally stable within the selected sample set. Applied to Harbin’s representative districts through unsupervised hierarchical clustering, evolutionary lineage analysis, and transition node identification, it reveals a “layered response pattern” among morphological modules, an asymmetrical mapping between morphological types and historical periods, a structural breakpoint around 1946 that parallels post-war Western urban restructuring, and anomalous deceleration and premature convergence in typological evolution during transitional periods. These observations offer a meso-scale morphological perspective for understanding both the spatial transformation of Chinese cities and the developmental challenges of Northeast China’s old industrial bases, while demonstrating the value of theory-guided quantitative analysis for transforming fragmented historical spatial information into interpretable morphological evidence in data-constrained contexts. Full article

Background and Objectives: Long-standing contact between oral mucosa and restorative materials may be associated with adaptive epithelial and stromal remodeling. This study evaluated non-dysplastic epithelial plasticity in the oral mucosa adjacent to long-term restorative materials. Materials and Methods: This retrospective study included 150 formalin-fixed, paraffin-embedded oral mucosal specimens divided into five groups: control, dental amalgam, methacrylate-based resin composite, clinically documented all-ceramic restorations, and metallic/metal–ceramic restorations (n = 30 each). Exposed cases had documented mucosal adjacency or repetitive contact with a dominant restorative material for at least 5 years. Histopathological parameters, CK19, Ki67, p53, and COX-2 expression were assessed, and MAERS was calculated. Statistical significance was set at p < 0.05. Results: MAERS differed significantly among groups (p < 0.001), with the highest mean values in metallic/metal–ceramic specimens (10.3 ± 2.4) and dental amalgam specimens (9.7 ± 2.1), intermediate values in resin composite specimens (7.1 ± 1.8), and the lowest values in all-ceramic (5.0 ± 1.4) and control specimens (4.8 ± 1.3). Suprabasal CK19 redistribution was observed in 22/30 metallic/metal–ceramic specimens (73.3%) and 20/30 amalgam specimens (66.7%), elevated Ki67 expression in 22/30 (73.3%) and 19/30 (63.3%), and moderate-to-strong COX-2 expression in 21/30 (70.0%) and 19/30 (63.3%), respectively. p53 expression was heterogeneous and did not show dominant, strong, or diffuse overexpression. Conclusions: Metallic/metal–ceramic restorations showed the highest remodeling burden, dental amalgam showed a similarly high profile, resin composite showed an intermediate profile, and all-ceramic-associated mucosa remained close to control specimens. These findings suggest material-dependent, inflammation-associated epithelial plasticity without evidence of epithelial dysplasia. Full article

Low-altitude unmanned aerial vehicle (UAV)-based traffic object detection is challenged by substantial scale variations from aerial perspectives, the extremely small pixel proportions of distant traffic participants, complex road background interference, unstable illumination, and severe occlusion in dense traffic scenes. To address these problems, this paper proposes ACP2-YOLO, an improved YOLO11-based detection framework for low-altitude UAV traffic scenarios, with the goal of enhancing the detection of vehicles, pedestrians, and non-motorized traffic participants. The proposed framework introduces two key improvements. First, a lightweight hybrid ACmix module that integrates convolution and self-attention is embedded into the network, enabling the model to jointly capture local detailed features and global contextual dependencies and thereby strengthen feature representation under complex backgrounds. Second, a P2 small-object detection layer is added to the original three-scale detection structure of YOLO11 to construct a four-scale P2–P5 feature pyramid. By allowing shallow high-resolution features to directly participate in object prediction, this design effectively reduces spatial information loss caused by deep downsampling and improves small-object perception. Experiments on the VisDrone2019 dataset show that the improved model achieves 53.1% Precision, 41.1% Recall, 42.9% mAP@50, and 26.3% mAP@50–95, outperforming the baseline YOLO11 by 4.2, 4.2, 5.0, and 3.6 percentage points, respectively. Comparisons with mainstream YOLO-series detectors further demonstrate its superior overall accuracy, small-object detection capability, and adaptability to complex scenes, indicating its potential for UAV-based traffic monitoring, road safety inspection, and intelligent transportation perception. Full article

The systemic shift in the energy consumption structure from high-carbon fossil fuels to low-carbon clean energy constitutes a critical pathway toward global climate governance and carbon neutrality. However, this sustainable transition is consistently impeded by deep-seated institutional frictions and structural barriers, such as governance fragmentation and carbon lock-in effects embedded in traditional industrial organization. Whether digital government development can overcome these barriers by nurturing resilient business ecosystems and thereby promote a systemic low-carbon energy transition remains an urgent question within sustainable development research. To address this issue, this study integrates digital government development, regional e-commerce ecosystem competitiveness, and the low-carbon transition of the energy consumption structure into a unified analytical and sustainable governance framework. Using panel data from 30 Chinese provinces from 2012 to 2022, we exploit the institutional reform of provincial big data administrations as a quasi-natural experiment to identify the impacts of digital government. Regional e-commerce ecosystem competitiveness is comprehensively evaluated across four sustainable dimensions: ecological innovation capacity, market connectivity, ecological global integration, and inclusive infrastructure. Methodologically, we employ a spatial difference-in-differences model to capture geographic interdependencies alongside a double machine learning framework to handle high-dimensional confounding and nonlinear disturbances. The empirical findings reveal that both digital government development and regional e-commerce ecosystem competitiveness significantly drive the low-carbon transition of the energy consumption structure. The institutional effect of digital government exhibits strong regional embeddedness with localized impacts, whereas e-commerce ecosystem competitiveness generates positive spatial spillovers that accelerate energy optimization in neighboring regions. Crucially, regional e-commerce ecosystem competitiveness serves as a significant partial mediator, constructing a reliable transmission channel from institutional design to market-based decarbonization. Further pathway analysis indicates that market connectivity and inclusive infrastructure function as the primary transmission channels, effectively mitigating transportation energy intensity and bridging the digital-green divide, while the mediating contribution of ecological innovation capacity is relatively constrained due to cross-organizational coordination thresholds. This study clarifies the interactive mechanism between public digital governance and market ecosystem competitiveness in advancing environmental sustainability, thereby offering fresh theoretical insights and actionable policy implications for emerging market economies striving for economic growth and decarbonization. Full article

Electricity price uncertainty directly affects the economy and reliability of scheduling, especially when flexible loads are scheduled only according to point forecasts. To improve the coupling between price forecasting uncertainty and load scheduling, this paper proposes a two-stage affine adjustable robust optimization method for flexible loads based on the confidence level of electricity price prediction via an improved hybrid convolutional neural network temporal convolutional network (CNN-TCN) model. An attention-enhanced CNN-TCN model is used to obtain day-ahead electricity price forecasts, and conformalized quantile regression (CQR) is introduced to construct calibrated asymmetric prediction intervals under different confidence levels. The interval bounds are then converted into a budgeted price uncertainty set and embedded in a two-stage affine adjustable robust optimization model for industrial, commercial, and residential loads. The model considers power limits, ramping constraints, total energy requirements, baseline deviation limits, and smoothing penalties, enabling load transfer from high-price periods to low-price periods while preserving operational feasibility. Case studies based on Spanish electricity market data show that the proposed method reduces operating costs under forecast, worst-case, and abnormal disturbance scenarios compared with the original load plan. The results also show that the 90% confidence level provides a suitable balance among cost reduction, risk coverage, and scheduling conservatism in the studied case. Full article

Ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) are physically complementary non-destructive evaluation methods for reinforced concrete, yet their integrated diagnostic use has been limited by the absence of controlled, ground-truth-validated calibration of the joint-signature space. This paper presents a laboratory calibration programme in which a single C30/37 reinforced concrete beam (3000 mm × 300 mm × 200 mm, three T12 bars at 35 mm cover, CEM I 42.5N, w/c = 0.50) was sequentially conditioned through four controlled deterioration states—intact reference (Model A), water-filled saw-cut crack (Model B), full saturation by seven-day top-surface ponding (Model C), and chloride-induced active corrosion (Model D). Seven RES2DINV inverted ERT sections at three electrode spacings (a = 7, 15, and 30 mm) and three 800 MHz GPR profiles were acquired across the four known ground-truth conditions. The intact-reference resistivity ρ₀ = 558 Ω·m (full-section median of the mlab dataset at a = 7 mm) and GPR-calibrated velocity v = 0.095 ± 0.008 m/ns (from hyperbola fitting at 35 mm rebar cover) establish the absolute baselines. The four conditions produce systematically distinct joint signatures: Model A exhibits uniform high resistivity with clean rebar hyperbolae and no anomalous reflections; Model B produces a localised ERT low-ρ anomaly (ρ_min = 1.46 Ω·m) co-located with a negative-polarity (R = −0.68) GPR crack-mouth reflection confirming water-fill; Model C produces pervasive low-ρ with a smooth depth gradient and 50–65% GPR amplitude attenuation (−6.0 to −9.1 dB); Model D produces the same bulk GPR signatures as Model C but with a critically different ERT spatial texture—a heterogeneous near-surface layer above a sharp boundary at z ≈ 40 mm with depth-preferential low-ρ concentrated at rebar level. This depth-preferential signature, quantified here by a reproducible Depth-Preferential Index (DPI), is the primary ERT-only diagnostic criterion distinguishing active corrosion from pervasive saturation. For the Model C versus Model D distinction, the GPR response is non-discriminating; this high-risk distinction is resolved exclusively by the ERT depth-preferential criterion. The calibration demonstrates that GPR and ERT are physically non-redundant in the strict sense: neither method alone can unambiguously discriminate all four states, but their combination yields correct classification within the controlled laboratory conditions and subject to the stated qualification conditions. The corrosion state was confirmed at the regime level (chloride above the depassivation threshold, under accelerated polarisation) but was not quantified electrochemically, so the depth-preferential signature is interpreted as an indirect spatial proxy for active corrosion rather than a measurement of corrosion rate. Seven failure modes are quantitatively characterised and embedded in the framework as a priori qualification conditions. The calibrated reference values (ρ₀, A₀, Stage 2 thresholds, depth-preferential criterion) are specific to the laboratory mix and curing history and require local Stage 1 recalibration for field application. Full article

Accurate prediction of enzyme turnover numbers ($k_{cat}$) is essential for applications in systems biology, metabolic engineering, and drug discovery, yet remains challenging due to the limited availability and uneven distribution of experimental data. Here, we present AUKAT, an integrated framework that combines conditional generative modeling with deep neural prediction to improve $k_{cat}$ estimation. A conditional variational autoencoder generates synthetic training instances in embedding space, followed by a selection pipeline that retains samples with strong agreement across independent evaluators, thereby ensuring data reliability. A hybrid convolutional neural network and transformer-based architecture is then used to predict $k_{cat}$ from substrate, enzyme functional, and species embeddings. Incorporating synthetic data improved predictive performance for both random forest and neural network models in five-fold cross-validation, with larger gains observed for the neural network architecture. Benchmarking against DLKcat demonstrated comparable predictive accuracy on the standard test set, while evaluation on stricter unseen subsets indicated improved generalization for low-similarity substrates and enzymes. Feature importance analysis further showed that AUKAT leverages substrate, enzyme functional, and species information in a more balanced manner rather than relying predominantly on a single feature source. In addition, AUKAT-human, a specialized model trained using a pre-training and fine-tuning strategy, achieved improved prediction accuracy for human enzyme kinetics. Overall, AUKAT provides a scalable approach for enzyme kinetics prediction and offers a practical solution to data scarcity in biochemical modeling. Full article

Provincial innovation systems can be understood as complex adaptive networks in which connectivity and resilience-enabling conditions emerge from heterogeneous local capabilities, spatial constraints, and relational configurations. Taking 95 county-level units in Jiangsu Province, China, as the study area, this study constructs a county-level innovation capability index for 2020–2023 across four dimensions: innovation input, innovation output, innovation environment, and innovation performance. Using this index as the mass variable, a gravity-based potential innovation linkage network is developed, and network analysis indicators are applied to examine its stage-specific structural changes, functional differentiation, and key transmission pathways within the model-derived network. The results show that county-level innovation capability increased across the four observations, while the intra-provincial south–north gradient remained evident. The potential network became increasingly connected, but this increase in connectivity did not lead to structural equalization. Instead, linkages were selectively reinforced around high-capability nodes and spatially proximate areas, indicating local clustering, potentially path-dependent organization, and selective connectivity. County-level units performed differentiated systemic roles as core-organizing, system-supporting, and connector nodes, shaped jointly by innovation capability, spatial location, and network embeddedness. The identified key transmission pathways exhibited a multi-level structural configuration involving intra-cluster reinforcement, intercity corridor continuity along the Yangtze River, and short-chain embedding of peripheral nodes. These findings suggest that provincial innovation systems may exhibit selective structural organization rather than uniform relational development, shaped by capability asymmetry, spatial proximity, and relational configuration. Because the network is derived from innovation capability and geographical distance, the identified linkages and pathways represent model-estimated relational opportunities rather than directly observed knowledge, technology, or innovation flows. Within this interpretive boundary, county-level nodes and key transmission pathways provide insights into system connectivity and resilience-oriented governance. Full article

Megakaryocytes (MKs) and platelets are increasingly recognized as active regulators of the bone marrow (BM) microenvironment rather than passive effectors of thrombopoiesis and hemostasis. Recent single-cell and lineage-tracing studies have established that megakaryopoiesis generates functionally heterogeneous populations, including immune-biased and niche-supporting subsets that shape hematopoietic stem cell (HSC) behavior, inflammatory tone, and vascular homeostasis. In leukemia, these regulatory circuits are systematically rewired to establish a marrow niche that suppresses normal hematopoiesis while sustaining leukemic stem cell (LSC) fitness through cytokine gradients, stromal remodeling, and direct cell-to-cell communication. In this focused review, we propose that the immune MK (iMK)–platelet axis is a central driver of leukemic niche remodeling. We discuss how iMK states arise under leukemic pressure, how MK heterogeneity encodes distinct niche instructions, and how platelet-derived extracellular vesicles (EVs) distribute inflammatory signals across the marrow and systemic circulation. Within this framework, we position mitochondrial stress outputs—such as reactive oxygen species (mtROS), mitochondrial DNA (mtDNA) release, metabolic rewiring, and mitochondria-containing EV secretion—not as isolated phenomena, but as mechanistic amplifiers embedded within the broader inflammatory and niche-regulatory programs of MKs and platelets. We further highlight preleukemic inflammatory states as an underappreciated entry point for therapeutic intervention, and propose three clinically actionable axes: inflammatory niche interruption, mitochondrial stress modulation, and platelet–leukemia communication blockade. This framework aligns with emerging concepts in MK heterogeneity, innate immune sensing, endothelial remodeling, and preleukemic signaling, and positions the MK–platelet axis as a promising therapeutic framework in leukemia-associated niche remodeling. Full article

Background: Perforation associated with intrauterine devices (IUDs) occurs in approximately 1 in 1000 insertions and may be either partial or complete. Perforation may be primary, occurring during device insertion, or secondary, developing after the device has remained in situ for more than eight weeks. Typical symptoms of IUD perforation include chronic pain and intestinal obstruction. Rarely, the device is found either completely within the rectal lumen or partially embedded in the rectal wall with partial intraluminal extension. Removal of the device may require colonoscopy, laparoscopy, or a combination of both. Case: A 45-year-old woman was referred for evaluation of intermittent rectal bleeding. Initial outpatient evaluation included contrast-enhanced computed tomography (CT), which demonstrated two intrauterine devices: one appropriately positioned within the uterine cavity and another located within the rectouterine pouch. A multidisciplinary discussion involving gastroenterologists, gynecologists, and colorectal surgeons was subsequently conducted. Under general anesthesia, laparoscopy was initiated. Upon entering the abdominal cavity, a free IUD string was visualized embedded within the pelvic peritoneum. Using simultaneous colonoscopic guidance with transillumination, a targeted peritoneal incision was made overlying the IUD. The arms were removed laparoscopically. Subsequent colonoscopy removed the remaining segment of the IUD traversing the rectal wall. Conclusions: Although uterine perforation and migration of intrauterine devices are uncommon, they may result in severe and potentially life-threatening complications. Early diagnosis, careful documentation, routine follow-up, and timely removal of misplaced devices remain essential to minimizing morbidity and preventing adverse outcomes. Full article

Background/Objectives: Sudden cardiac death (SCD) is a natural death of cardiac origin, accounting for an estimated 6–9 million deaths worldwide each year and representing a major public health challenge. Despite its clinical and forensic relevance, the molecular investigation of SCD remains limited. Peripheral blood or fresh tissue, the preferred specimens for post-mortem genetic testing, are not always available, and DNA extracted from formalin-fixed paraffin-embedded (FFPE) tissues is often suboptimal for conventional genetic analyses. This review evaluates the potential of archived FFPE cardiac tissue as a source for microRNA (miRNA) analysis in molecular autopsy. Methods: A narrative review was conducted by collecting studies investigating miRNA expression in FFPE cardiac tissue relevant to SCD. Ten studies met the inclusion criteria and were critically analyzed. Results: Although the available evidence remains limited, recent studies have identified several differentially expressed miRNAs associated with cardiac diseases relevant to SCD. Owing to their small size and remarkable stability, miRNAs remain detectable in FFPE tissues despite fixation and long-term storage, making them attractive molecular biomarkers. While most available studies were conducted in clinical rather than forensic settings, they demonstrate the feasibility and analytical robustness of miRNA profiling in archived FFPE cardiac specimens. Conclusions: This review underlines the importance of reconsidering archived FFPE tissues not merely as historical or morphological resources, but as promising matrices for molecular autopsy of SCD supporting the identification and validation of novel molecular biomarkers. Full article

Crack detection in composite structures remains a central challenge in structural health monitoring, particularly when sensing must rely on a small number of embedded multiplexed fiber Bragg gratings (FBGs). Here, we present a physics-guided convolutional neural network (CNN) framework for crack-associated event detection from multiplexed FBG interrogator signals acquired during the three-point bending of glass-fiber-reinforced polymer (GFRP) beams. The dataset was constructed from raw interrogator recordings and synchronized force–displacement metadata while preserving the cracked and non-cracked loading stages present in the experiments. Each candidate response was encoded by 13 synchronized optical, loading, and mechanics-guided descriptors, including Euler–Bernoulli expected strain and residual terms, where the residual denotes the difference between the measured response and the elastic response predicted by beam theory. A compact one-dimensional CNN operating on 30-response sequences was evaluated on 64 experimental runs under strict leave-one-run-out validation. At the selected operating point, the model reached window-level precision of 0.900, recall of 0.910, F1 score of 0.905, and balanced accuracy of 0.942, while the corresponding run-level decision reached a precision of 0.833, a recall of 1.000, an F1 score of 0.909, and a balanced accuracy of 0.969. Bootstrap resampling over runs yielded 95% confidence intervals of 0.787–0.978 for window-level F1 and 0.769–1.000 for run-level F1. To probe generalization beyond the initial fabrication batch, the final frozen pipeline was also tested once on seven later-batch runs from two newly manufactured specimens, where it reached a window-level precision of 0.908, a recall of 1.000, an F1 score of 0.952, a balanced accuracy of 0.969, an ROC-AUC of 0.979, a PR-AUC of 0.955, and perfect run-level classification. These results show that a compact sequence CNN, enriched with mechanics-guided strain interpretation, can extract robust crack-event signatures from multiplexed FBG measurements while preserving a simple and reproducible modeling pipeline. Full article