Search Results (12,894)

The rapid proliferation of resident space objects has made space situational awareness critically dependent on accurate characterization of non-cooperative targets using photometric light curves. This review provides a comprehensive examination of data-driven approaches for space object photometric prediction, synthesizing research across optical scattering characterization, shape and attitude inversion methodologies, and intelligent analysis techniques based on machine learning and deep learning. The evolution from traditional physics-based models to contemporary data-driven paradigms is systematically analyzed, revealing fundamental trade-offs between physical interpretability, computational efficiency, and predictive accuracy. Key findings indicate that while physical bidirectional reflectance distribution function (BRDF) models provide rigorous foundations, their computational demands and prior knowledge requirements limit operational applicability; conversely, deep learning has demonstrated superior predictive accuracy in existing comparative studies, although this conclusion is qualified by the absence of standardized public benchmarks, and it also suffers from interpretability deficits and simulation-to-reality generalization gaps. Critical research gaps are identified, including the absence of public benchmark datasets, inadequate handling of temporal multi-scale phenomena, and the persistent challenge of bridging simulated and real-world observations. Future directions should pursue physics-guided machine learning frameworks that integrate domain knowledge with data-driven capabilities, develop explainable artificial intelligence techniques tailored for photometric analysis, and establish standardized evaluation protocols to advance next-generation space object characterization essential for collision avoidance and space traffic management. Full article

Metallurgical batching—governing raw material proportioning across sintering, blast furnace ironmaking, converter steelmaking, and non-ferrous smelting—critically determines product quality, energy consumption, and production cost throughout the full process chain. Its inherent complexity, characterized by strong nonlinear physicochemical coupling, measurement delays of up to 1.5 h, and multi-source raw material disturbances, renders conventional linear programming and empirical methods inadequate for dynamic, multi-objective industrial environments. This review systematically examines 98 representative studies (2020–2026) on intelligent algorithms applied to metallurgical batching optimization. A two-dimensional analysis framework of the fusion algorithm function and metallurgical scene is established. All kinds of methods are divided into three categories: prediction-oriented, optimization-oriented and decision-oriented, covering four typical scenes of sintering burdening, blast furnace ironmaking, converter steelmaking and non-ferrous metal smelting. Traditional machine learning models achieve sintering burn-through point prediction with R² ≈ 0.85 and offer superior interpretability via SHAP analysis. Deep learning architectures deliver blast furnace silicon content prediction with RMSE ≈ 0.04%, while multi-objective evolutionary algorithms provide mature Pareto optimization for batching cost and carbon objectives. Reinforcement learning holds long-term potential for closed-loop adaptive control but remains constrained by Sim-to-Real safety barriers. Converter steelmaking and non-ferrous smelting are identified as underexplored domains. Three priority directions are proposed: domain-adaptive predictive modeling for cross-plant generalization, real-time re-optimization embedding mechanism constraints, and safe reinforcement learning transfer via high-fidelity digital twins. Full article

Ball-end copy-milling is widely used for finishing complex components, yet its influence on surface integrity is generally overlooked and remains insufficiently addressed. Milling often generates tensile residual stresses at the machined surface, which are detrimental to fatigue performance and commonly require costly postprocessing, particularly in fatigue-critical parts such as turbine blades. In this context, the present study evaluates the capability of Prestress-Assisted Machining under uniform bending loads to improve the surface integrity of ball-end copy-milled Aluminium 5083 workpieces. Experimental tests were conducted on slender specimens with different thicknesses and curvature radii while maintaining constant cutting conditions. After machining and unclamping, surface residual stresses were measured by X-ray diffraction, and the effects of prestressing on geometry, cutting forces and surface roughness were also assessed. The results demonstrate that this method markedly increases compressive residual stresses in the prestressing direction, from approximately 30 MPa to about 180 MPa, and that this variation can be accurately described by subtracting the elastic prestressing stress from the residual stresses obtained without external loads applied. Moreover, no relevant adverse effects were observed in cutting forces or roughness, and corrected toolpaths allowed a uniform slot depth. These findings identify bending-based Prestress-Assisted Machining as an effective and predictable strategy for improving surface integrity in ball-end copy-milling and extend its applicability beyond previously reported pocket and slot milling operations. Full article

Everyday public parenting encounters may influence immediate stress-relevant appraisal processes. Guided by interactionist and narcissistic self-regulation frameworks, the present study examined how recognition-based versus status-challenging norm framing in a standardized playground interaction influences young parents’ immediate responses, and whether narcissistic admiration and rivalry shape these processes. A community sample of 776 Israeli parents aged 25 to 41 was randomly assigned to view one of two ultra-realistic video vignettes depicting an identical turn-taking situation framed either in recognition-based terms that emphasized fairness, shared legitimacy, and respectful coordination, or in status-challenging terms that emphasized priority claims, non-negotiability, and implied hierarchy. Participants responded from the perspective of the focal parent (i.e., a parent from the family being spoken to in the interaction). Narcissistic admiration and rivalry were assessed using the Narcissistic Admiration and Rivalry Questionnaire. Parallel moderated mediation analyses revealed that condition was strongly associated with both perceived recognition and perceived freedom threat. These appraisals, in turn, predicted state reactance, negative affect, evaluations of the initiating parent, and behavioral preferences. Recognition-based framing indirectly reduced reactance and negative affect and increased favorable evaluations through higher perceived recognition and lower perceived freedom threat. Contrary to moderated mediation predictions, narcissistic admiration and rivalry did not moderate the indirect effects. However, narcissistic rivalry, and to a lesser extent narcissistic admiration, showed consistent direct associations with reactance-related and entitlement-oriented responding. These findings identify proximal appraisal mechanisms linking subtle norm framing in public parenting contexts to immediate affective, evaluative, and behavioral reactions. More broadly, the results highlight an immediate appraisal-based process that may inform future longitudinal and intervention-focused research on parenting stress in shared community settings. Full article

Induced seismicity is a recognized challenge in hot dry rock (HDR) geothermal development. Based on a systematic review of previous studies on induced seismicity mechanisms, characteristics, risk assessment, and mitigation measures, we compiled publicly available data from 13 HDR projects worldwide. Our statistical analysis shows that felt earthquakes occurred in 62% of the projects, and a post-injection “tailing effect” was observed in 54% of the projects. The spatial influence range of induced seismicity is typically within 2 km of the injection well, and the duration of the “Kaiser effect” varies from months to years depending on local conditions. A cross-site comparison of injection parameters suggests that the maximum wellhead pressure may be a more useful indicator than injected volume for estimating the largest possible earthquake magnitude, especially when comparing different tectonic settings. Furthermore, we examine the applicability and limitations of b-value trends, seismogenic indices, and existing maximum magnitude prediction models in seismic risk assessment. Dynamic adjustment of injection parameters based on real-time risk indicators, combined with safer injection schemes, may represent an important research direction for improving the conventional traffic light system. These findings provide a data-driven basis for site-specific safety management of HDR development. Full article

Next-generation heterogeneous wireless networks (HetNets) comprising LTE macro-cells, 5G New Radio (NR) small cells, and WiFi 6 access points aim to provide seamless connectivity under diverse mobility scenarios. However, vertical handover (VHO) remains a performance bottleneck because of the highly variable radio environments, dynamic user mobility, stringent quality of service (QoS) requirements, and the coexistence of multi-tier access technologies. Existing handover approaches based on deep learning and deep reinforcement learning (DRL) suffer from limitations: deep learning models lack decision-making capabilities, whereas DRL models, particularly deep Q-network (DQN)-based policies, face Q-value overestimation and unstable convergence. To overcome these limitations, this paper introduces a Hybrid deep double-Q networks (DDQN)–bidirectional long short-term memory (Bi-LSTM) Framework that integrates bi-directional mobility prediction and DRL-based adaptive decision-making. The Bi-LSTM module captures forward and backward temporal dependencies and predicts future Received Signal Strength (RSS) trajectories, mobility dynamics, and cell-edge transitions. The DDQN module stabilizes the action value estimation, mitigates overestimation bias, and enables context-aware handover decisions. A multi-tier simulation environment consisting of LTE, 5G NR, and WiFi 6 networks was developed using realistic path loss, shadowing, interference, and mobility models. Extensive evaluations demonstrated substantial improvements in mobility prediction accuracy, handover stability, radio link reliability, throughput efficiency, and latency reduction compared to conventional RSS-based and DQN-based schemes. The findings highlight the effectiveness of integrating predictive intelligence with reinforcement learning for reliable mobility management in 5G-Advanced and emerging 6G networks. Full article

Surface solar radiation at the ground is affected by aerosols, clouds, and atmospheric moisture, as well as by circulation-related conditions that influence cloud formation and pollutant transport. In daily observations, these influences are mixed, which makes pollution-related variability difficult to interpret. We analyzed data from Beijing station 54511 (2015–2019), including daily integrated radiation components and collocated meteorological and pollution variables. We used wavelet coherence, pollution-stratified association analysis, and gray relational analysis, and compared two meteorological normalization methods: multiple linear regression (MLR) and random forest (RF). The results show that meteorological–radiation relationships vary systematically across pollution levels, indicating substantial meteorological confounding in daily radiation analyses. Among the radiation components, DR shows the clearest pollution-dependent shift in its relationship with RH, while several direct components become less sensitive to cloud cover under heavier pollution. RF reproduced daily radiation components with strong predictive performance (R² = 0.83–0.88), and the meteorologically adjusted anomalies from RF were consistent with those from MLR (r = 0.63–0.78 across components). These findings suggest that both MLR and RF can be effectively used to normalize meteorological effects in daily station records. The analysis supports routine interpretation of day-to-day surface radiation variability and can be extended to multi-site studies and finer temporal resolution. Full article

A probabilistic data-driven framework for voltage quality forecasting in distribution transformers based on Gaussian process regression and high-resolution field measurements is presented. Voltage time series acquired under real operating conditions were modeled using composite covariance functions designed to capture long-term trends and stochastic multi-scale fluctuations. The proposed approach enables simultaneous prediction and uncertainty quantification, allowing direct compliance assessment with voltage quality standards. The additive Gaussian process models achieved coefficients of determination above 0.75 and produced statistically uncorrelated residuals, indicating an adequate representation of the intrinsic temporal structure. However, the predictive intervals exhibit a certain level of undercoverage, indicating that, while uncertainty is effectively quantified, there is still room for improvement in calibration. The selected kernel structures revealed distinct physical regimes in the voltage dynamics, including smooth steady operation, moderately irregular behavior associated with localized disturbances, and multi-scale stochastic variability. For benchmarking purposes, results were compared with those obtained from a stochastic damped harmonic oscillator with restoring force, a naive model, a seasonal naive model and an Autoregressive Integrated Moving Average model. The oscillator model, the naive model, the seasonal naive model, and the Autoregressive Integrated Moving Average model generated strongly autocorrelated residuals, whereas the Gaussian process models yielded consistent white-noise residuals that outperformed all the other models. These findings demonstrate that probabilistic Gaussian process modeling provides an interpretable, scalable, and uncertainty-aware alternative for predictive voltage quality assessment in modern distribution systems. Full article

We suggest Josephson interferometry as a quantitative probe of spin–orbit-driven phenomena in superconducting heterostructures. Two distinct mechanisms are analyzed: (i) intrinsic helical superconductivity, producing asymmetric Fraunhofer patterns with lobe deformations and field-reversal asymmetry, and (ii) emergent interfacial magnetism in ferromagnet–superconductor hybrids, where Rashba spin–orbit coupling generates spontaneous fields that rigidly shift the interference fringes. The predicted signatures—flux-shifted interference minima, anisotropic critical current suppression, and angle-dependent pattern distortions—provide direct experimental access to finite-momentum pairing and interface-localized fields via standard Josephson current measurements. Full article

A coupled modeling framework is developed to predict coating thickness after air knife wiping in hot-dip galvanizing. A 3D large eddy simulation (LES) using the WALE sub-grid scale (SGS) model is performed to resolve the jet impingement on the moving strip. Time-averaged wall static pressure $p_{\omega }\left(y\right)$ and wall shear stress ${\tau }_{\omega }\left(y\right)$ along the strip direction are extracted and used as driving inputs for a thin film model. Starting from the continuity and momentum equations, a lubrication-type formulation is derived, leading to a local cubic equation for film thickness $h\left(y\right)$ that accounts for both pressure gradient and gravity. A coupling workflow is established to preprocess the LES wall signals and compute the final coating thickness $h_{final}$. Parametric sweeps of inlet total pressure $P_0$ and the knife-to-strip distance $H$ are employed to construct operating window maps. The predicted trends show that increasing $P_0$ or decreasing $H$ intensifies wall loading and reduces $h_{final}$, while the operating window boundary is governed by the balance between the gas-induced shears. Representative results, including peak wall loading and thickness ranges, are reported for industrially relevant operating conditions. Full article

Transforming growth factor-β1 (TGF-β1) and tumor necrosis factor-α (TNF-α) are central regulators of vascular inflammation and remodeling in coronary artery disease. However, their cell-type-specific and context-dependent effects in primary human coronary artery endothelial cells (ECs) and vascular smooth muscle cells (VSMCs) remain incompletely defined. Primary human coronary artery endothelial cells (pHCAECs) and smooth muscle cells (pHCASMCs) were stimulated with TGF-β1 (10 ng/mL), TNF-α (100 ng/mL), or their combination. Canonical SMAD2/3 activation, Krüppel-like factor 11 (KLF11) expression, cytoskeletal and junctional remodeling, vascular cell adhesion molecule-1 (VCAM-1) expression, migration dynamics (wound healing and confluent assays), and endothelial tube formation were assessed using immunofluorescence microscopy, live-cell imaging, and quantitative trajectory analysis. Both cytokines were associated with increased nuclear pSMAD2/3 signal in ECs and VSMCs, consistent with functional interplay between inflammatory and TGF-β-related signaling pathways. In pHCAECs, TNF-α robustly induced VCAM-1 functional expression and disrupted VE-cadherin continuity, whereas TGF-β1 primarily promoted cytoskeletal remodeling without strong inflammatory activation. TGF-β1 increased endothelial migration velocity and accumulated distance. In contrast, TNF-α preferentially enhanced Euclidean displacement and directional persistence, shifting the migratory pattern toward more directed movement most evident under combined TGF-β1 + TNF-α stimulation. Notably, TGF-β1 significantly reduced endothelial tube formation, indicating impaired network organization rather than proangiogenic activity. In pHCASMCs, TGF-β1 enhanced migratory activity, particularly in confluent monolayers, whereas TNF-α enhanced directional displacement. KLF11 was induced by TGF-β1 in both pHCAECs and pHCASMCs. In pHCAECs, TNF-α also increased KLF11 and co-stimulation promoted nuclear enrichment, whereas in pHCASMCs TNF-α alone was not effective and combined treatment amplified the TGF-β1 response, supporting cell-type-specific integration of inflammatory and TGF-β-dependent signals. TGF-β1 and TNF-α differentially regulate the inflammatory activation and migration of primary human coronary vascular cells in a cell-type- and structural-context-dependent manner. TGF-β1 enhances migratory force generation, whereas TNF-α reinforces directional polarization, and their integration determines effective vascular repair dynamics. Canonical SMAD2/3 activation does not uniformly predict functional outcome, and KLF11 was identified as a context-sensitive transcription-associated factor showing differential nuclear localization in response to cytokine stimulation, representing a hypothesis-generating observation for future mechanistic studies. Full article

This study examines the relationship between price discovery and trading performance across different investor types in Taiwan’s active order-driven market. Using five-second intraday data, we construct a stock-trader-direction information share (IS) measure and link it to trading performance. Our results reveal several key findings: institutional investors have a higher IS per order, reflecting greater contributions to price discovery, and they outperform individual investors in trading performance. While higher IS is associated with better contemporaneous outcomes, it does not predict long-term performance. Determinants of price discovery include investor type, price aggressiveness, trade size, herding behavior, firm characteristics, and macroeconomic conditions. Robustness tests, covering one-minute IS, high-volatility periods, earnings announcements, and macroeconomic influences, support these conclusions. Full article

The integration of artificial intelligence (AI) and big data is poised to significantly augment drug research and development, offering the potential to address persistent challenges such as lengthy timelines and high failure rates. This review provides a critical overview of AI applications across the preclinical drug discovery pipeline (the 2020–2026 literature), covering drug–target interaction prediction, structure prediction, de novo design, virtual screening, drug repurposing, and ADMET forecasting. Beyond surveying technical developments, we critically discuss key translational hurdles, including data quality, model interpretability, patient heterogeneity, and regulatory adaptation, and provide structured summaries of representative models. We conclude by outlining future directions, such as multimodal AI, digital twins, and closed-loop automation, that aim to bridge the gap between computational prediction and clinical application. This review aims to inform researchers and accelerate the delivery of safe and effective therapies. Full article

With the rapid growth of the global shipping industry and the increasing availability of Automatic Identification System (AIS) data, accurate vessel trajectory prediction has become crucial for ensuring navigational safety and optimizing maritime traffic management. This paper presents a systematic review of recent advances in deep learning-based methods for vessel trajectory prediction. We provide a comprehensive analysis of mainstream models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, Sequence-to-Sequence (Seq2Seq) models, and the Transformer architecture. Their performance is compared in terms of spatio-temporal data processing capability, prediction accuracy, and computational efficiency. Furthermore, this review examines practical applications of these methods in scenarios such as collision avoidance and route optimization. Despite notable progress, several challenges remain, including data quality issues, real-time prediction capability, and model interpretability. Future research directions may focus on multi-source data fusion and the development of lightweight model designs to further improve prediction performance. This survey aims to serve as a valuable reference for researchers and contribute to ongoing innovation in vessel trajectory prediction technology. Full article

Background and Aims: Plasma fibrinogen and fractional exhaled nitric oxide (FeNO) reflect neutrophilic and eosinophilic airway inflammation, respectively, and are associated with disease activity and severity in different directions in bronchiectasis. This study aimed to concurrently investigate fibrinogen and FeNO and further evaluate the clinical importance of fibrinogen-to-FeNO ratio (FFR) as a composite biomarker in bronchiectasis. Methods: This was a two-centre, observational, cross-sectional study involving stable bronchiectasis patients. Fibrinogen, FeNO, and the ratio of their normalised values (FFR) were investigated in relation to clinical indicators of disease activity and severity, including respiratory symptoms, inflammatory markers, pulmonary function, radiological extent, airway infection, severity scores, and patient-reported outcomes. Results: FFR was correlated with both circulating neutrophils (r = 0.36, p = 0.04) and eosinophils (r = −0.39, p = 0.03) and, more strongly compared to fibrinogen and FeNO, with the percentage of predicted forced expiratory volume in the 1st second (r = −0.61, p < 0.001). Interestingly, only FFR was found to be higher in patients with Pseudomonas aeruginosa isolation in respiratory secretions (p < 0.01). In receiver operating characteristic curves, FFR showed good discriminatory ability to differentiate patients with any level (AUC: 0.80, 95% CI: 0.64–0.96) or a severe level (AUC: 0.83, 95% CI: 0.64–1.00) of pulmonary functional impairment and patients with severe disease (AUC: 0.78, 95% CI: 0.62–0.94). Conclusions: FFR emerges as a candidate biomarker capturing the balance between neutrophilic and eosinophilic inflammation and the net disease activity and severity in bronchiectasis. Full article