Search Results (4,150)

Accurate power load forecasting is critical for the efficient operation of industrial microgrids. However, raw meteorological and consumption data typically exhibit non-stationary characteristics, complicating the hyperparameter tuning of deep learning models, and subsequently degrading the prediction accuracy of these frameworks. To address the aforementioned challenges, a new hierarchical forecasting structure denoted as INRBO-SSA-LSTM is proposed in this paper. First, Pearson correlation analysis is employed for feature reduction, identifying the four main factors to mitigate the dimensionality curse. Building upon this foundation, a refined Newton-Raphson-Based Optimizer (INRBO) is introduced, integrating a cosine adaptive t-distribution perturbation, a boundary-aware non-uniform steering scheme, and a fitness-aware hybrid perturbation mechanism. Evaluated against the CEC2022 benchmark suite, comprehensive evaluations reveal that the INRBO demonstrates superior global exploration and local refinement capabilities compared to baseline algorithms when assessed on the CEC2022 benchmark suite for foundational optimization performance. Furthermore, rigorous testing on the CEC2017 suite across 10, 30, and 50 dimensions successfully validates its exceptional robustness and search capabilities in high-dimensional spaces. INRBO functions as a dual-stage optimizer within the proposed framework; in the initial phase, it dynamically calibrates the parameters of Singular Spectrum Analysis (SSA) to extract deterministic load patterns, achieving a maximum signal-to-noise ratio of 15.87 dB; in the second phase, it optimizes the global hyperparameters of the Long Short-Term Memory (LSTM) network. Validated using actual industrial microgrid data in Jiangsu Province, China, the proposed method significantly outperforms traditional baseline models across all indicators; specifically, the prediction error (RMSE = 10.9764, MAPE = 3.7866%) is substantially minimized, and the coefficient of determination ($R^2$ = 0.9741) is highly optimal. This adaptable framework effectively accommodates temporal demand variations, offering a robust foundation for the advancement of intelligent power management technology. Full article

Navigation lock manifolds are key components of filling-and-emptying systems, and port-flow distribution affects chamber flow stability and filling efficiency. Under unsteady filling conditions, port-flow distribution is governed by discharge variation and manifold geometry, making rapid prediction and engineering-constrained screening challenging. This study develops a surrogate-assisted prediction and Pareto-screening framework for a large-scale navigation lock manifold. Three-dimensional computational fluid dynamics (CFD) simulations were used to examine unsteady port-flow evolution. The peak-flow condition was selected as a representative control condition, and the flow non-uniformity coefficient α and system resistance coefficient ξ were used as performance indicators. Based on 243 parametric CFD samples and 144 independent external test samples, artificial neural network (ANN), Gaussian process regression (GPR), and support vector regression (SVR) models were evaluated. ANN performed best, with independent-test R² values of 0.9999 and 0.9928 for α and ξ. Feature-attribution analysis identified port width, culvert height, and port number as dominant variables. Pareto screening within a predefined engineering design space identified representative candidates with CFD verification errors below 1.1%. The TOPSIS-based candidate reduced ξ by 32.2% while maintaining α nearly unchanged. Full article

The insulated gate bipolar transistor (IGBT) is widely applied in industrial fields such as rail transit, wind power generation, smart grids, and renewable energy. The temperature distribution, stress variation patterns, thermal performance, and modeling damage in the solder layer of IGBT modules under thermal and stress loadings have rarely been studied. This study first established a three-dimensional geometric model based on the actual dimensions of the IGBT module. A finite element model was successfully constructed for thermal/mechanical multi-physics coupled simulation based on the ANSYS Workbench platform to simulate the temperature, deformation trends, and stress distribution patterns of the solder layer in the IGBT module. Secondly, the solder layer defects of the IGBT module were categorized into five major types, and 37 sets of 3D models of IGBT with damaged solder layers were designed, followed by thermal/mechanical coupled simulation analysis for each. Finally, the influence of the void positions, sizes, and distribution types in the solder layer on the module temperature, heat dissipation path, and thermal stress was simulated during thermal cycling. The results showed that the highest stress at the edge of the solder layer is 6.2504 × 10⁷ Pa, the lowest junction temperature is 70.79 °C, and the average thermal stress is 1.2388 (m/m). The highest junction temperature reached 72.562 °C under central solder layer damage states as determined by a thermal/mechanical coupled simulation analysis of four different types of solder layer defects. This research provides a theoretical basis and reliable technical support for the anti-damage and failure of IGBT modules and high-power devices. Full article

Field-Programmable Gate Array System-on-Chip (FPGA-SoC) platforms are increasingly adopted in modern industrial Cyber-Physical Systems (CPSs), enabling real-time control, monitoring, and automation of critical industrial processes. The increasing integration density of modern FPGA-SoC architectures introduces new thermal security challenges, where heat evolves from a reliability concern into a potential source of information leakage. Thermal Side-Channel Attacks (TSCAs) exploit runtime thermal variations to infer sensitive operational, architectural, or cryptographic information from the underlying hardware. While this study is centered on FPGA-SoC platforms, comparable thermal security challenges are increasingly reported across other densely integrated computing architectures, including Multiprocessor System-on-Chip (MPSoC), System-in-Package (SiP), and emerging Three-Dimensional Integrated Circuit (3D-IC) technologies. Consequently, the detection of thermal side-channel intrusions has become a critical hardware security challenge for next generation industrial CPS infrastructures. To address this challenge, an AI-enabled Digital Twin (DT) framework is introduced for TSCA detection in densely integrated FPGA-SoC microarchitectures. By combining thermal behavioral modeling, feature engineering, and machine learning-based anomaly detection, the proposed framework extends conventional Thermal Digital Twin (TDT) approaches beyond monitoring and mitigation toward autonomous thermal threat detection. The proposed framework is experimentally validated using an NI myRIO-1900 platform integrating a Xilinx Zynq-7010 FPGA-SoC representative of modern industrial embedded control architectures. Experimental results demonstrate the feasibility of the proposed framework, achieving an accuracy of approximately 75% with an Area Under the ROC Curve (AUC) of 0.76 using a lightweight Isolation Forest model. These results validate the capability of the proposed AI-enabled Digital Twin framework to learn normal thermal behavioral patterns and autonomously detect anomalous thermal activities potentially related to TSCAs. Full article

Wood-reinforced rubble walls (so called timber-laced masonry), which were widespread in South Arabia and East Africa in the architecture of the first millennium BC, can be easily reconstructed in terms of their wall structure. They were mostly used for the construction of multi-story buildings, as evidenced by ancient sources. However, what has been lacking until now are static analyses of the load-bearing systems, which could provide information about how the wall systems and their individual components functioned. In Yeha, in the Ethiopian highlands, the Grat Be’al Gibri palace, which was equipped with such a wall system, is being investigated as part of an Ethiopian–German archaeological research project. This is the largest known palace-like structure from the early first millennium BC in South Arabia and East Africa. The special feature of the construction of its walls is that the beams integrated into the masonry were installed exclusively horizontally. There are many indications that this must have been a multi-story building, even though only parts of the ground floor have survived. Based on its virtual three-dimensional reconstruction, investigations of two representative wall elements in the supporting structure will be carried out using the finite element method under consideration of material uncertainties and by applying various failure mechanisms of the present structural components. The load resulting from multiple factors is applied purely vertically, and the masonry is defined as a homogeneous material, taking into account uncertain material parameters. The numerical simulations show that the variation in wood parameters has very little influence on the result and that a multi-story building was feasible with the present wall constructions. It can be concluded from the carrying reserves that an exceptional load must have caused the system failure. This is consistent with the fact, that the building was completely destroyed by a devastating fire in ancient times. By this interdisciplinary collaboration and by using modern simulation techniques, key questions in archaeology and building history can be answered and assumptions can be confirmed or refuted. Even if it is not certain that the ancient builders fully exploited the structural potential of the wall construction presented here to its limits, it is certain that they were able to develop highly efficient building structures and create impressive architecture through experience and the transmission of knowledge. Further research can now follow based on these findings. Full article

Objective: Survival prediction in oncology and cardiology requires models that can capture nonlinear prognostic structure while remaining interpretable, calibrated, and clinically safe. This study develops and evaluates a diagnostic-first hybrid quantum–classical framework for right-censored survival analysis. Methods: We introduce KTA-Survival (Kernel-Target Alignment for survival), a pre-training feasibility diagnostic that adapts kernel-target alignment to censored outcomes by comparing a quantum fidelity kernel with a concordance-based survival target kernel. We then propose QResid-Boost (Quantum Residual Boosting), a Cox-LASSO–anchored residual framework in which a variational quantum circuit is trained on martingale residuals through a Quantum-Skip-Residual architecture. A sigmoid-bounded scalar gate, α, constrains the quantum contribution and allows the model to reduce to the classical baseline when the residual signal is uninformative. The framework was evaluated on GBSG2 (German Breast Cancer Study Group 2; n = 686), FLChain (serum free light chain; n = 1500), WHAS500 (Worcester Heart Attack Study; n = 500), and a synthetic Weibull positive-control dataset containing high-frequency periodic interactions. Results: On the GBSG2 hold-out partition, Random Survival Forest achieved the highest concordance (C = 0.7188), followed by the Stacking ensemble (C = 0.7128), Cox-LASSO (C = 0.7019), and QResid-Boost (C = 0.7016). The leading classical and hybrid models did not differ significantly by paired bootstrap testing, whereas all outperformed the pure quantum variants. In the synthetic positive-control cohort, QResid-Boost improved over Cox-LASSO by ΔC = +0.0397, demonstrating that the quantum residual can add value when nonlinear periodic structure remains after the linear baseline. KTA-Survival yielded positive ΔKTA values across the evaluated datasets and correctly identified the regime in which the quantum residual produced its largest measurable gain. Conclusions: The proposed diagnostic-first framework reframes quantum survival modelling as a gated enrichment strategy rather than an unconstrained replacement for classical risk models. In low-dimensional clinical cohorts where linear structure already explains most prognostic signal, the framework behaves conservatively; when residual nonlinear structure is present, it can provide measurable improvement without uncontrolled model drift. Full article

This study examines how different prompt anchoring strategies influence the conceptual representation of LLM-generated keywords and compares those effects with the effects of model selection. A controlled exploratory experiment evaluated four prompt conditions—No Examples, Brief Keywords, Detailed Explanations, and Author-Based Examples—across T. D. Wilson’s four information behavior dimensions using 1068 abstracts. Four LLMs (GPT-4o-mini, Claude-3-haiku, Gemini-2.0-flash-lite, and DeepSeek V3) were evaluated under all prompt conditions, yielding 17,036 valid observations. Results indicate that model identity accounts for substantially more variance in keyword generation (η² = 0.309) than prompt condition (η² = 0.069), although these estimates should be interpreted with caution given the repeated-measures design and assumption violations. Prompt anchoring, however, consistently reconfigured the conceptual distribution of outputs across all models, indicating that it influences conceptual representation even when model effects are larger. Author-Based Examples substantially increased representation of the typically underrepresented Information Sharing dimension, whereas Detailed Explanations produced the highest overall generation rates and the broadest dimensional coverage. These findings further indicate that different anchoring strategies involve consistent trade-offs in dimensional coverage. The study thereby identifies prompt anchoring as a source of methodological variation in LLM-assisted content analysis, indicating that anchoring strategies should be explicitly specified, justified, and reported as part of the study methodology. Full article

Accurate prediction of tunnel water inflow in water-rich fault zones is important for groundwater control design and construction risk prevention. In this study, a per-linear-meter tunnel water inflow database containing 425 valid samples was established through orthogonal numerical simulations based on a three-dimensional steady-state seepage model with a grouting ring. The input variables included four hydraulic and grouting parameters and two excavation-position descriptors, namely the excavation-position distance and excavation-position category, thereby reflecting both the water-blocking effect of grouting reinforcement and the spatial variation in water inflow as the excavation face approached the fault zone. Considering that the samples were generated from 25 orthogonal simulation cases at different excavation positions, grouped validation was adopted to reduce information leakage at the simulation-case level. Four baseline machine learning models, including SVM, RF, XGBoost, and CatBoost, were evaluated using ten repeated grouped hold-out validations. CatBoost achieved the best overall baseline generalization performance, with an average test R² of 0.6209 ± 0.0405, MAE of 0.1084 ± 0.0079, and RMSE of 0.1555 ± 0.0085. CatBoost was therefore selected for further hyperparameter optimization. Subsequently, random search, Bayesian optimization, the Osprey Optimization Algorithm, and the Grey Wolf Optimizer were compared under the same search space and computational budget. Hyperparameter optimization was conducted only within the training set using grouped cross-validation, and the independent grouped test set was used only for final evaluation. The results showed that the unoptimized CatBoost model achieved the best overall balance between prediction accuracy, stability, and computational efficiency. Although RS-CatBoost slightly improved MAE and MAPE among the optimized models, none of the optimization strategies consistently outperformed the unoptimized CatBoost baseline, indicating that the choice of hyperparameter optimization algorithm played a secondary role under the current dataset and grouped-validation framework. The proposed framework is intended as a preliminary modeling reference under controlled numerical simulation conditions, and its practical engineering reliability requires further validation using field monitoring data or independent benchmark cases. Full article

Dinosaur footprints are among the most abundant trace fossils, but they are not direct records of anatomy, behaviour or faunal composition. They preserve locomotion, substrate interaction and occurrence data only after those signals have been filtered by foot anatomy, movement, sediment properties and preservation. Classical dinosaur ichnology has relied on two-dimensional outlines, linear and angular measurements, qualitative ichnotaxonomy and influential hypotheses about trackmaker identity, speed, social behaviour and evolutionary timing. Here we review how these hypotheses are being reassessed with three-dimensional digitisation, geometric morphometrics, supervised and unsupervised machine learning, and biomechanical simulation. We first consider how different footprint representations, including interpretive outlines, landmarks, silhouettes, depth maps and three-dimensional models, shape the questions that track data can answer. We then assess analytical approaches ranging from multivariate statistics and landmark-based classifiers to convolutional neural networks and β-variational autoencoders. Against this methodological background, we revisit four linked problem domains: ornithopod–theropod discrimination and the Grallator–Anchisauripus–Eubrontes plexus; speed and gait reconstruction; ecological and behavioural interpretations of track abundance, sauropod gauge and trackway arrangement; and macroevolutionary claims about body-size trends, functional morphotypes and avian-like pedal morphologies. Across these cases, newer methods rarely remove ambiguity. They more often show where classical interpretations are robust, where they depend on representation or prior labels, and where competing explanations remain hard to separate. We argue that footprint-based inference is strongest when tracks are treated as preservationally filtered products of anatomy, motion and substrate mechanics, and when they are integrated with skeletal data, experimental analogues and forward models in explicit, uncertainty-aware frameworks. Full article

With the development of three-dimensional high-density integration, low-temperature co-fired ceramic (LTCC) technology has become an important substrate platform for electronic packaging. However, screen-printed Au films on LTCC substrates often contain boundary roughness, local thickness variation, pores, and particle-packing non-uniformity caused by the printing process. These features may affect both the macroscopic resistivity of printed patterns and the local mechanical response of the film. In this study, edge-related structural non-uniformity in screen-printed Au/LTCC films was evaluated using SEM observation, macroscopic resistivity–temperature fitting, nano-indentation, XRD, and nano-scratch testing. The resistivity results show that the stripe pattern has approximately 10–13% higher resistivity than the grid patterns within the measured temperature range, indicating a geometry-dependent electrical response. Nano-indentation results reveal large spatial dispersion in reduced modulus and hardness, and statistical analysis shows that differences among annealing conditions are not significant at the 0.05 level when indentation data alone are considered. Therefore, nanomechanical data are treated as an indirect structural indicator rather than a direct proof of local electrical uniformity. Among the investigated temperatures, 200 °C provides a favorable balance of local hardness, scratch resistance, and microstructural stability, whereas 300 °C should be interpreted cautiously because the higher scratch load is not supported by direct post-scratch failure analysis. Overall, the results provide a cautious but clear structure–property correlation for screen-printed Au/LTCC conductors and identify 200 °C as the preferred annealing condition among the investigated temperatures. These results provide practical guidance for evaluating structural non-uniformity in screen-printed Au/LTCC conductors. Full article

This paper proposes a low-complexity residual-corrected loss-minimization current-reference generation method for permanent magnet synchronous motor (PMSM) drives. Existing loss-minimization control (LMC) methods often rely on lookup tables, numerical optimization, high-order algebraic equations, or explicit approximations based on maximum torque per ampere (MTPA) and maximum torque per voltage (MTPV) references. While effective, these simplified approximations often introduce residual errors relative to the true loss-optimal conditions. To address this, this paper adopts a more consistent iron-loss equivalent-circuit objective and analytically eliminates the torque equality constraint, reducing the LMC problem to a one-dimensional scalar minimization problem of the d-axis current. This paper demonstrates that this reduced objective is strictly convex over the admissible scalar domain, allowing for an exact benchmark solution via a scalar convex solver. The proposed method constructs a resistance-aware initial reference from explicit MTPA and MTPV approximations and then applies a one-step scalar Newton-type residual correction using the gradient and Hessian of the reduced loss objective. The initial reference reproduces the known exact surface-mounted PMSM (SPMSM) LMC solution in the SPMSM limit. The correction direction is proved to be a strict descent direction, and a safeguarded step-size ensures loss reduction compared to the initial approximation. The main novelty is the use of the scalar LMC optimality residual as a fixed-cost correction layer for explicit LMC references. The numerical validation is interpreted as steady-state current-reference mapping accuracy and model-based controllable loss-objective verification against the exact scalar optimum, not as hardware drive-efficiency validation. Tests including the no-MTPV case show reduced current-reference and loss-objective gaps while retaining a fixed-cost structure that may support future embedded implementation after validation; hardware transient and efficiency validation under inverter nonlinearity, temperature variation, saturation, and PWM effects remains for future work. Full article

Accurate characterization of flood dynamics in large river–lake systems remains challenging due to the difficulty of simultaneously capturing inundation extent and water surface elevation (WSE) variations under rapidly changing hydrological conditions. This study develops an integrated Synthetic Aperture Radar (SAR) and wide-swath interferometric altimetry framework to reconstruct the spatiotemporal evolution and storage dynamics of the 2024 flood event in the East Dongting Lake system, China. Sentinel-1 SAR imagery is utilized to derive high-resolution inundation extent, while the Surface Water and Ocean Topography (SWOT) mission, equipped with the Ka-band Radar Interferometer (KaRIn), provides two-dimensional WSE observations. To improve SAR-based flood extraction in heterogeneous floodplain environments, an Adaptive Spatially-Constrained Fuzzy C-Means (AS-FCM) algorithm is proposed by incorporating adaptive spatial regularization and structure-aware neighborhood weighting. Quantitative evaluation demonstrates that the proposed method achieves the highest performance among the evaluated conventional approaches, with an Overall Accuracy of 93.6%, an Intersection over Union of 0.89, and a Kappa coefficient of 0.87. The multi-temporal inundation sequence reveals a distinct flood evolution pattern characterized by rapid expansion during the rising stage and gradual recession during the post-peak period. SWOT-derived WSE observations exhibit strong agreement with synchronous in situ measurements after bias adjustment, with a correlation coefficient of 0.988. By integrating SAR-derived inundation extent with temporally matched water-level observations constrained by bias-adjusted SWOT and in situ gauge data, an empirical WSE–area relationship ($R^2=0.937$) is established to reconstruct daily flood dynamics and estimate cumulative water storage variation. The results indicate that the East Dongting Lake floodplain played an important buffering role during the 2024 flood event, with cumulative storage variation reaching approximately $10.7{\mathrm{km}}^3$ during the peak stage. Overall, the proposed framework demonstrates strong potential for flood monitoring and hydrological storage assessment in complex river–lake systems. Full article

Since the late 1970s, during the period of reform and opening-up, China has emerged as one of the fastest-urbanizing countries in the world. The high speed of urbanization and three-dimensional (3D) morphological changes in urban buildings may lead to significant urban challenges, including air and light pollution, the urban heat island effect, and strained human-land relationships. It is therefore crucial to elucidate the pattern of urban 3D morphological change and to investigate the factors that influence these changes. This study proposed a novel method of filtering new buildings using the ALOS and GBH building height data, combined with statistical data and panel data, focusing on spatial-temporal variations and their driving factors of 3D building morphological changes between 2002 and 2020 for 11 prefecture-level cities and 2 municipalities in the Beijing–Tianjin–Hebei (BTH) region. Our findings indicate that the 3D morphological change mechanisms of the same types of cities in the BTH region are similar. The total population negatively impacts the height of newly constructed building areas, and the optimization of the industrial structure positively influences the height of newly developed land. Based on our findings, we propose actionable recommendations to foster sustainable urban development in the BTH region. Full article

Holographic synthetic aperture radar (HoloSAR) enables 360° three-dimensional reconstruction by incoherently stacking tomographic subaperture images. However, after conventional subaperture-wise TomoSAR reconstruction and non-coherent integration, the resulting 3D imagery suffers from severe dynamic range imbalance due to angle-dependent scattering responses: wide-angle strong scatterers are repeatedly amplified, whereas narrow-angle weak structures are buried below the noise floor. To address this post-processing challenge, we propose a joint statistical filtering framework operating on the reconstructed subaperture-domain 3D images that fuses the coefficient of variation, inter-subaperture correlation, and spectral entropy with adaptive discriminative-power weighting; target screening is then performed via a Gaussian mixture model-based Bayesian optimal threshold. For pixels classified as weak targets, a percentile-matching energy-balancing transformation is applied to adaptively rescale their energy to the main-target reference level while preserving relative amplitude relationships. Experiments on real-world Ku-band UAV circular SAR data demonstrate that the proposed method effectively compresses the dynamic range, suppresses background noise, and recovers weak narrow-angle structures that are lost in traditional non-coherent superposition, yielding more complete and interpretable HoloSAR 3D reconstructions. Quantitative evaluation on Ku-band UAV circular SAR data demonstrates that the proposed method improves the Target-to-Background Ratio by 0.7 dB (to 11.2 dB), achieves a Background Suppression Ratio of −5.2 dB, increases the Structural Completeness Index by 156% (to 1428.1), and compresses the original dynamic range imbalance, which exceeds 50 dB, while preserving scene physical realism (ENL ≈ 7.4 × 10⁻³). Full article

Urban congestion is difficult to alleviate through road expansion alone, making it necessary to improve existing road-network performance while maintaining public-transport priority. Under transit-priority policies, buses and private vehicles share limited road space, and bus lanes, stops, and lane-changing interactions may either improve or reduce network efficiency. This study treats capacity improvement as a constrained network-performance objective that preserves feasible bus operation and supports higher-occupancy, lower-emission public transport. A simulation-based capacity evaluation framework was developed using the three-dimensional macroscopic fundamental diagram (3D-MFD). A grid network was built in Simulation of Urban Mobility (SUMO), and 16 scenarios were designed by varying four factors: dedicated bus-lane proportion, average bus dwell time, driver lane-changing willingness, and bus-to-private-vehicle ratio. The 3D-MFD was fitted by nonlinear least squares, and range analysis and analysis of variance were used to identify significant factors. Results show that bus-lane proportion and bus-to-private-vehicle ratio dominate capacity variation. A supplementary simulation-based transferability assessment on a Beijing subnetwork further showed that the 4% bus-lane share and 7% bus-to-private-vehicle ratio produced the highest tested capacity response. The findings provide an assumption-bounded basis for screening bus-priority parameters rather than universal design values. Full article