Search Results (1,982)

In situ process monitoring systems (IPMSs) are rapidly gaining importance in quality assurance of laser powder bed fusion (L-PBF) parts, yet standardized methods for their objective evaluation are lacking. This study introduces a novel, system-independent assessment method for IPMSs based on a specially designed Energy Step Cube (ESC) test specimen. The ESC enables systematic variation in volumetric energy density (VED) by adjusting laser scan speed, without disclosing complete process parameters. Two industrially relevant IPMSs—PrintRite3D by Divergent and Trumpf’s integrated system—were evaluated using the ESC approach with AlSi10Mg as the test material. System performance was assessed based on sensitivity to VED changes and correlation with actual porosity, determined by metallographic analysis. Results revealed significant differences in sensitivity and effective observation windows between the systems. PrintRite3D demonstrated higher sensitivity to small VED changes, while the Trumpf system showed a broader stable observation range. The study highlights the challenges in establishing relationships between IPMS signals and resulting part properties, currently restricting their standalone use for quality assurance. This work establishes a foundation for standardized IPMS evaluation in additive manufacturing, offering valuable insights for technology advancement and enabling objective comparisons between various IPMSs, thereby promoting innovation in this field. Full article

Lightweight Single-Image Super-Resolution (SISR) faces the core challenge of balancing computational efficiency with reconstruction quality, particularly in preserving both high-frequency details and global structures under constrained resources. To address this, we propose the Periodically Enhanced Cascade Network (PECNet). Our main contributions are as follows: 1. Its core component, a novel Multi-scale Adaptive Feature Aggregation (MAFA) module, which employs three functionally complementary branches that work synergistically: one dedicated to extracting local high-frequency details, another to efficiently modeling long-range dependencies and a third to capturing structured contextual information within windows. 2. To seamlessly integrate these branches and enable cross-window information interaction, we introduce the Periodic Boundary Padding Shift (PBPS) mechanism. This mechanism serves as a symmetric preprocessing step that achieves implicit window shifting without introducing any additional computational overhead. Extensive benchmarking shows PECNet achieves better reconstruction quality without a complexity increase. Taking the representative shift-window-based lightweight model, NGswin, as an example, for ×4 SR on the Manga109 dataset, PECNet achieves an average PSNR 0.25 dB higher, while its computational cost (in FLOPs) constitutes merely 40% of NGswin’s. Full article

Background: Youth and young adults with early psychosis frequently use cannabis, yet the reliability of self-reported use is uncertain in clinical practice. We examined the concordance between self-reported cannabis use and urine toxicology among patients enrolled in an Early Psychosis Intervention (EPI) program in Southeast Ontario, Canada. Methods: We conducted a cross-sectional chart review of 116 EPI patients (2016–2019). Demographics, self-reported cannabis use (yes/no), concurrent substance use, and urine toxicology results from the initial clinical assessment were extracted. Diagnostic indices (sensitivity, specificity, positive/negative predictive values, and accuracy) were calculated using urine toxicology as the reference. The clinical panel used a 50 ng/mL threshold for THC-COOH; the specific assay platform (immunoassay vs. confirmatory GC-/LC-MS) was not specified in records and is noted as a limitation. Results: Overall, 82.8% (96/116) self-reported cannabis use. Self-report showed high sensitivity (88.4%) but very low specificity (20.3%), with PPV 39.2%, NPV 75.0%, and accuracy 45.30%, indicating limited concordance with urine toxicology. Self-reported cannabis use was significantly associated with self-reported cocaine and MDMA use, while associations with methamphetamine, opioids, and benzodiazepines were not significant. Conclusions: In this EPI cohort, self-reports overestimated cannabis use relative to urine toxicology (high sensitivity, low specificity, and accuracy <50%). These findings support cautious clinical interpretation of self-report and the complementary value of biological verification, especially when use is infrequent or the testing window/threshold may miss exposure. Future work should incorporate use frequency, potency, and timing relative to testing, and clearly specify toxicology assay methods. Full article

This study proposes a Transformer–NSGA-III multi-objective optimization framework for high-rise residential buildings in Haikou, a coastal city characterized by a hot summer and warm winter climate. The framework addresses four conflicting objectives: Annual Energy Demand (AED), Predicted Percentage of Dissatisfied (PPD), Global Cost (GC), and Life Cycle Carbon (LCC) emissions. A localized database of 11 design variables was constructed by incorporating envelope parameters and climate data from 79 surveyed buildings. A total of 5000 training samples were generated through EnergyPlus simulations, employing jEPlus and Latin Hypercube Sampling (LHS). A Transformer model was employed as a surrogate predictor, leveraging its self-attention mechanism to capture complex, long-range dependencies and achieving superior predictive accuracy (R² ≥ 0.998, MAPE ≤ 0.26%) over the benchmark CNN and MLP models. The NSGA-III algorithm subsequently conducted a global optimization of the four-objective space, with the Pareto-optimal solution identified using the TOPSIS multi-criteria decision-making method. The optimization resulted in significant reductions of 28.5% in the AED, 24.1% in the PPD, 20.6% in the GC, and 18.0% in the LCC compared to the base case. The synergistic control of the window solar heat gain coefficient and external sunshade length was identified as the central strategy for simultaneously reducing energy consumption, thermal discomfort, cost, and carbon emissions in this hot and humid climate. The TOPSIS-optimal solution (C = 0.647) effectively balanced low energy use, high thermal comfort, low cost, and low carbon emissions. By integrating the Energy Performance of Buildings Directive (EPBD) Global Cost methodology with Life Cycle Carbon accounting, this study provides a robust framework for dynamic economic–environmental trade-off analyses of ultra-low-energy buildings in humid regions. The work advances the synergy between the NSGA-III and Transformer models for high-dimensional building performance optimization. Full article

This study investigates the temperature-driven transmission switching behavior of our proposed smart glass, which utilizes a biphasic liquid crystal system under continuous application of a distinctive homeotropic (H) state voltage (V_H). By ascertaining V_H at temperatures near the phase transition point, the minimum voltage required to sustain the H state in the smectic A* (SmA*) phase is identified. Interestingly, this minimum V_H is unable to induce the H state in the chiral nematic (N*) phase, thereby maintaining a low-transmission scattering state; i.e., the focal conic (FC) state. This empowers passive, bidirectional optical switching between the transparent H state (in the SmA* phase) and the scattering FC state (in the N* phase) in an unaligned liquid crystal cell. This work employs two dissimilar chiral dopants, R811/S811 and CB7CB/R5011, both capable of inducing the SmA* phase. Neither resulting cell system underwent surface orientation treatment, and a black dye was incorporated to enhance the contrast ratio. The results indicate that the more efficacious CB7CB/R5011 system achieves a contrast ratio of 17 between the transparent and scattering states, with a corresponding haze level of 78%. To further reduce energy consumption, the experimental framework was transitioned from a continuous-voltage to a variable-voltage mode, giving rise to an increased haze level of 88%. The proposed switching scheme holds promise for diverse applications, notably in smart windows and light shutters. Full article

Background: Selective serotonin reuptake inhibitors (SSRIs) are widely prescribed, yet their direct impact on ovarian function remains poorly understood. While serotonin signaling is known to occur within the ovarian follicle, the specific molecular consequences of its disruption by SSRIs are unclear. This study aimed to elucidate the direct, intra-ovarian mechanisms by which fluoxetine, a common SSRI, affects follicular development and oocyte competence. Methods: We administered fluoxetine (20 mg/kg) or vehicle daily for seven days to both prepubertal and adult female mice to model short-term therapeutic exposure. Results: Fluoxetine treatment successfully blocked peripheral serotonin uptake, reducing serum levels by over 90%. Crucially, this occurred without altering circulating levels of estradiol, FSH, or LH and without disrupting the estrous cycle, indicating a mechanism independent of the central hypothalamic–pituitary–gonadal axis. Instead, we pinpoint a direct ovarian effect: fluoxetine inhibited serotonin transport activity in oocytes and significantly downregulated the expression of the pivotal oocyte-derived growth factor Gdf9. This was accompanied by reduced expression of genes crucial for granulosa cell function (Lhr, Fshr) and steroidogenesis (Cyp19a1). Functionally, these molecular changes manifested as a decline in oocyte quality and a significant reduction in ovulation rates in adult mice. Notably, these detrimental effects were more pronounced in prepubertal mice, indicating a heightened vulnerability during early follicular development. Conclusions: Our findings reveal a direct, intra-ovarian mechanism of fluoxetine-induced disruption. By inhibiting oocyte serotonin transport and downregulating GDF9, fluoxetine impairs critical oocyte–granulosa cell communication, thereby compromising oocyte competence and reducing fertility outcomes. This work identifies follicular development as a critical window of susceptibility to SSRI exposure, holding significant clinical implications for reproductive-aged and adolescent populations. Full article

This work investigates the application of HfZrO ferroelectric material for the tuning of high-frequency bandpass filters. By integrating HfZrO with a two-dimensional HfSe semiconductor to form a heterostructure, the device achieves wideband tunability with low power requirements. Under a bias of ±4 V, the bandpass filter demonstrates a 3.4 GHz tuning range—from 7.8 GHz to 11.2 GHz—corresponding to a fractional tunability of approximately 43% in the X-band. The insertion loss remains below −1.8 dB across the tuning window, indicating low-loss operation. These results highlight the potential of the HfZrO/HfSe heterostructure as a promising platform for energy-efficient, CMOS-compatible, high-frequency tunable devices. Full article

This review presents a technology roadmap for Thermal Energy Storage (TES) systems operating in the medium-temperature range of 100–300 °C, a critical window that accounts for approximately 37% of industrial process heat demand in Europe. Decarbonising this segment is essential to meeting climate targets, especially in sectors that are reliant on fossil-fuel-based steam. The study analyses 11 TES technologies, including sensible, latent, and thermochemical systems, covering both mature and emerging solutions. Each technology is evaluated based on technical, environmental, and socio-economic key performance indicators (KPIs), such as energy density (up to 200 kWh/m³), cost per storage capacity (€2–100/kWh), and technological readiness level (TRL). Sensible heat technologies are largely mature and commercially available, while latent heat systems—especially those using nitrate salts—offer promising energy density and cost trade-offs. Thermochemical storage, though less mature, shows potential in high-cycle applications and long-term flexibility. The review highlights practical configurations and integration strategies and identifies pathways for research and deployment. This work offers a comprehensive reference for stakeholders aiming to accelerate industrial decarbonisation through TES, particularly for applications such as drying, evaporation, and low-pressure steam generation. Full article

Background: In a fast-changing sector, apparel distribution centers (DCs) are under increasing pressure to meet labor intensive operational requirements, short delivery windows, and variable demand in the rapidly changing apparel industry. Traditional labor forecasting methods often fail in dynamic environments, leading to inefficiencies, inadequate staffing, and reduced responsiveness. Methods: This comprehensive review discusses AI-enhanced labor forecasting tools that support flexible workforce planning in apparel DCs using a PRISMA methodology. To provide proactive, data-driven scheduling recommendations, the model combines machine learning algorithms with workforce metrics and real-time operational data. Results: Key performance indicators such as throughput per work hour, skill alignment among employees, and schedule adherence were used to assess performance. Apparel distribution centers can significantly benefit from real-time, adaptive decision-making made possible by AI technologies in contrast to traditional models that depend on static forecasts and human scheduling. These include LSTM for time-series prediction, XGBoost for performance-based staffing, and reinforcement learning for flexible task assignments. Conclusions: The paper demonstrates the potential of AI in workforce planning and provides useful guidance for the digitization of labor management in the clothing distribution industry for dynamic and responsive supply chains. Full article

Compact fusion reactors are receiving increasing interest as a promising route for accelerating the path toward commercial fusion, thanks to their reduced size and cost. However, this compactness introduces new technological challenges, including higher radiation loads on critical functional components, such as the magnet system. Neutron shielding is therefore of utmost importance to guarantee the expected lifetime of the device, and its selection must account for the harsh environment imposed by the high radiation flux. Shielding materials should be structurally stable, not melt within the operational temperature windows, and be relatively low-cost. For nuclear reactor applications, binary compounds are typically the preferred choice as they often meet these requirements, particularly in terms of availability and cost. In this work, we present a systematic Monte Carlo analysis of more than 700 binary compounds, exposed to the neutron spectrum at the most loaded position of the vacuum vessel in a simplified model of a compact fusion reactor. Shielding performances were evaluated in a toroidal geometry in terms of neutron attenuation, power deposition, and activation, leading to the identification of several promising compositions for effective neutron shielding in future fusion applications. Full article

This study presents a technique for categorizing Aedes aegypti mosquitoes infected with the Zika virus under laboratory conditions. Our approach involves the utilization of the near-infrared spectroscopy technique and machine learning algorithms. The model developed utilizes the absorption of light from 350 to 1000 nm. It integrates Linear Discriminant Analysis (LDA) of the signal’s windowed version to exploit non-linearities, along with Support Vector Machine (SVM) for classification purposes. Our proposed methodology can identify the presence of the Zika virus in intact mosquitoes with a balanced accuracy of 96% (row C2HT, average of columns TPR (%) and SPC (%)) when heads/thoraces of mosquitoes are scanned at 4, 7, and 10 days post virus infection. The model was 97.1% (10 DPI, row C2AB, column ACC (%)) accurate for mosquitoes that were used to test it, i.e., mosquitoes scanned 10-days post-infection and mosquitoes whose abdomens were scanned. Notable benefits include its cost-effectiveness and the capability for real-time predictions. This work also demonstrates the role played by different spectral wavelengths in predicting an infection in mosquitoes. Full article

This study investigates eutectic morphology transitions in Ni-Sn alloys using Bridgman directional solidification with a transition variable speed coupled with cellular automaton (CA) simulations. Steady-state solidification (0.1–2000 μm/s) produced only regular lamellar/rod-like eutectics, while velocity jumps triggered anomalous eutectic formation. As the drawing speed increased, the lamellar spacing decreased from ~3 μm to 0.4 μm, while the microhardness increased from ~426 HV to 500 HV. The experiments on Ni-Sn alloys revealed that anomalous eutectic morphologies form specifically at velocity transition interfaces (0.1–1000 μm/s), consistent with CA simulations showing destabilization of the lamellae, epitaxial growth of the Ni₃Sn phase, and decoupled nucleation of the α-Ni phase for the formation. The work defines a processing window for anomalous eutectic formation and provides mechanistic insights bridging undercooling and directional solidification regimes. Full article

This paper proposes a deep reinforcement learning-based predictive control scheme to address cushion pressure prediction and stabilization in hovercraft systems subject to modeling complexity, dynamic instability, and system delay. Notably, this work introduces a long short-term memory (LSTM) network with a temporal sliding window specifically designed for hovercraft cushion pressure forecasting. The model accurately captures the dynamic coupling between fan speed and chamber pressure while explicitly incorporating inherent control lag during airflow transmission. Furthermore, a novel adaptive behavior cloning mechanism is embedded into the twin delayed deep deterministic policy gradient with behavior cloning (TD3-BC) framework, which dynamically balances reinforcement learning (RL) objectives and historical policy constraints through an auto-adjusted weighting coefficient. This design effectively mitigates distribution shift and policy degradation in offline reinforcement learning, ensuring both training stability and performance beyond the behavior policy. By integrating the LSTM prediction model with the adaptive TD3-BC algorithm, a fully data-driven control architecture is established. Finally, simulation results demonstrate that the proposed method achieves high accuracy in cushion pressure tracking, significantly improves motion stability, and extends the operational lifespan of lift fans by reducing rotational speed fluctuations. Full article

In response to the insufficient adaptability of power transformer remaining useful life (RUL) prediction under complex working conditions and the difficulty of multi-scale feature fusion, this study proposes an industrial time series prediction model based on the parallel Transformer–BiGRU–GlobalAttention model. The parallel Transformer encoder captures long-range temporal dependencies, the BiGRU network enhances local sequence associations through bidirectional modeling, the global attention mechanism dynamically weights key temporal features, and cross-attention achieves spatiotemporal feature interaction and fusion. Experiments were conducted based on the public ETT transformer temperature dataset, employing sliding window and piecewise linear label processing techniques, with MAE, MSE, and RMSE as evaluation metrics. The results show that the model achieved excellent predictive performance on the test set, with an MSE of 0.078, MAE of 0.233, and RMSE of 11.13. Compared with traditional LSTM, CNN-BiGRU-Attention, and other methods, the model achieved improvements of 17.2%, 6.0%, and 8.9%, respectively. Ablation experiments verified that the global attention mechanism rationalizes the feature contribution distribution, with the core temporal feature OT having a contribution rate of 0.41. Multiple experiments demonstrated that this method has higher precision compared with other methods. Full article

The interruption of real-time state space representation (SSR) corrections significantly degrades the performance of precise point positioning (PPP). To address this challenge, we propose a novel residual-enhanced iTransformer model specifically designed for BeiDou navigation satellite system (BDS) SSR prediction. Unlike conventional approaches including polynomial fitting, harmonic modeling, and autoregressive moving average (ARMA) methods, our framework innovatively integrates residual networks with the iTransformer architecture to effectively capture the complex nonlinear characteristics and non-stationary patterns in satellite clock offsets. The model demonstrates remarkable performance improvements, achieving 72–85% reduction in prediction error compared with traditional ARMA models. Experimental results show that, within 2 h prediction windows, orbit corrections achieve better than 0.1 m (radial), 0.2 m (along-track), and 0.2 m (cross-track) accuracy, while clock corrections maintain sub-0.5 ns precision. Most importantly, during 30 min SSR outages, BDS real-time PPP utilizing our predicted corrections sustains positioning accuracy within 10 cm in all east, north, and up directions, representing over 80% improvement compared with traditional time-differenced carrier phase (TDCP) methods. This work establishes an effective solution for maintaining high-precision positioning services during SSR interruptions. Full article