Search Results (4,617)

In this paper, a new hybrid prediction method is proposed for estimating remaining useful life, emissions, and performance parameters using experimental data obtained from a micro-turbojet engine. Experiments were conducted under various rotational speed conditions, yielding a total of 342 measurement points. Turbine speed, exhaust gas temperature, fuel flow rate, and thrust were considered as input variables in the study. Thermal efficiency, total power, CO₂, and NO₂ were considered as output variables. The experimental findings showed that thermal efficiency varied between 0.49% and 7.1%, total power between 0.266 and 13.94 kW, and CO₂ emissions by volume between 0.317% and 2.183%. The proposed RL-MH-LR-CBR approach combines the advantages of multiple methods. In this method, the interpretable formulation of linear regression serves as the foundation. Additionally, in the adaptive meta-heuristic optimization process, a hyper-heuristic selection mechanism based on the UCB1-based multi-arm bandit approach is used to select the optimal algorithm from among the meta-heuristic methods. Finally, the CatBoost-based residual error learning component aims to capture non-linear patterns that cannot be explained by the linear model. The method was compared with 14 different methods on both the NASA C-MAPSS FD001 dataset and real engine data. The results demonstrate that the proposed framework exhibits more balanced, stable, and higher generalization capabilities compared to classical regression models and powerful AI methods, particularly in non-linear, noisy, and heterogeneous outputs. In the real engine dataset, the proposed method produced R² values of 0.968 for CO₂ and 0.936 for NO₂, while the predictive performance was even stronger for thermal efficiency and total power, with corresponding R² values of 0.998 and 0.995, respectively. Additionally, the method demonstrated a clear advantage in hard-to-model outputs by reducing the error level to 0.061 in NO₂ predictions. These findings demonstrate that the proposed approach is not limited to micro-turbojet-engines. The developed method provides a robust decision support framework that is applicable, scalable, and generalizable to predictive maintenance, emissions monitoring, energy systems, aviation analytics, and other highly dynamic engineering problems. Full article

Metal Digital light processing (MDLP) offers high resolution and excellent surface quality, but the final properties of printed parts are highly dependent on post-processing. In this study, the effects of debinding, decarburization, and sintering on the shape fidelity, microstructure, and mechanical properties of MDLP-fabricated 316L stainless steel were systematically investigated. The optimal post-processing route consisted of debinding in an inert atmosphere, decarburization in air within 400–600 °C, and sintering at 1370 °C for 4 h under flowing nitrogen. Under these conditions, the sintered parts achieved a relative density of 98.03 ± 0.23%, hardness of 380.63 ± 9.15 HV, elastic modulus of 213.47 ± 5.5 GPa, tensile strength of 519.7 ± 22 MPa, and elongation at fracture of 76.8 ± 9.3%. Microstructural analysis showed that increasing the sintering temperature reduced porosity and smoothed the morphology of Cr-rich oxygen-containing second phase regions, thereby alleviating stress concentration and improving mechanical properties. This study provides an effective post-processing strategy for MDLP-fabricated 316L stainless steel and examines the microstructural origins of the observed property evolution. Full article

Wear remains a dominant cause of performance loss and premature failure in mechanical components, motivating the development of environmentally benign surface-engineering solutions. Among thermal spray systems, high-velocity oxy-fuel (HVOF)-sprayed WC-Co coatings are widely applied under severe wear conditions. The development of nanophase coatings offers the potential for enhanced mechanical performance. However, retaining the nanostructure and limiting decarburization during deposition remain key challenges. In this study, nanophase WC-12Co feedstocks with two particle size ranges, together with Al-modified nanophase powders, were used to deposit coatings under optimized HVOF spraying conditions (spray distance 200 mm, reduced O₂/fuel ratio, and high particle velocity) and were benchmarked against a conventional WC-12Co (12 wt.% Co) coating. The coatings were characterized in terms of microstructure and phase constitution (OM, SEM/EDS, XRD) as well as thickness, porosity (0.5–3.6%), adhesion strength (up to 65 MPa), and microhardness (~1040–1210 HV). Tribological behavior was assessed by ASTM G99 pin-on-disk testing and counterbody wear was quantified via geometric volume loss estimations. The use of larger nanophase particles enabled effective nanostructure retention with limited decarburization, whereas reducing particle size intensified decarburization, promoting increased W₂C formation, and markedly reduced coating cohesion, despite lower porosity and higher hardness. Aluminum additions enhanced coating microhardness and suppressed Co₃W₃C formation, indicating improved phase stability with minimal additional decarburization. Although coating wear remained negligible for all systems, Al-containing coatings exhibited increased friction (up to 35%) and significantly higher counterbody wear (up to sevenfold) compared to the Al-free nanophase coating, which was found to correlate with coating microhardness. Overall, the results demonstrate that optimizing nanophase WC-Co coatings requires balancing competing mechanisms between microstructural stability, cohesive integrity, and tribological response, highlighting the critical role of feedstock design in tailoring coating performance. Full article

A novel amorphous Hf-MOFs precursor was successfully synthesized and converted into HfC nanoparticles via one-step pyrolysis. The effects of metal/ligand molar ratios, solvent types, and pyrolysis temperature were systematically studied. High-purity spherical HfC nanoparticles (44.30 ± 9.63 nm) were obtained at 1500 °C using a 1.5:1 metal/ligand molar ratio with mixed anhydrous ethanol/deionized water solvents. At a pyrolysis temperature of 1700 °C, the as-synthesized HfC nanoparticles possessed an exceptionally low oxygen content of 0.76%, alongside a carbon content of 6.42% that almost perfectly matches the theoretical value of stoichiometric HfC. The formation mechanism involving Hf-O-C coordination and carbothermal reduction was clarified. Additive-free HfC ceramics were fabricated using the as-synthesized HfC nanoparticles via spark plasma sintering (1950 °C, 30 MPa, 20 min). The resulting ceramics exhibited a relative density of 96.7% and a Vickers hardness of 20.2 GPa, both of which are significantly superior to those of ceramics sintered from commercial HfC powders under identical conditions (95.8% and 17.8 GPa, respectively). This work provides a promising and feasible pathway for the preparation of other high-quality ultra-high temperature hafnium-based carbide powders and ceramics. Full article

High-entropy alloys (HEAs) are typically produced using high-temperature metallurgical routes; however, alternative synthesis approaches based on wet-chemical processing remain relatively unexplored. In this study, a compositionally complex two-phase AgCuCoNiFe high-entropy alloy was synthesized using a precipitation–reduction strategy involving co-precipitation of mixed metal carbonates followed by thermal reduction in a reducing atmosphere. The objective of the work was to evaluate the feasibility of this hydrometallurgical route for preparing compositionally complex alloys and to investigate the structural evolution of the material as a function of reduction time. Quantitative MP-AES analysis confirmed efficient co-precipitation of all five elements, enabling the preparation of a precursor with near-equimolar metal composition. Structural characterization using SEM, EDS, and XRD revealed the presence of surface compositional heterogeneity in the as-reduced state, characterized by Ag-enriched domains. After controlled surface abrasion, the internal material exhibited significantly more uniform elemental distribution, although the obtained composition was not equimolar. X-ray diffraction patterns showed a transition from multiple sharp reflections at the surface to broadened peaks in the bulk, consistent with enhanced alloying within the bulk compared to the surface, while still revealing a two-phase character. Microhardness measurements indicated moderate hardness with mean values in the range of 187–221 HV with no significant dependence on reduction time, while wettability analysis revealed moderately hydrophilic behavior with contact angles in the range of approximately 75–83°. The results suggest that precipitation–reduction can be a viable alternative route for the synthesis of multicomponent HEAs, enabling the formation of chemically mixed alloy structures without the use of conventional melting-based processing. However, the obtained alloy exhibits incomplete chemical homogeneity, indicating that further optimization of the synthesis conditions is required to achieve a fully uniform composition. Full article

With the increasing demand for intelligent fault monitoring, acoustic-based diagnosis has emerged as a promising solution for industrial applications such as pipeline leakage and electrical equipment fault detection. However, complex working conditions and domain shifts significantly degrade model performance, especially when unseen target domain data is unavailable. To address this, we propose an amplitude-phase collaborative augmentation network named AP-CANet tailored for acoustic fault diagnosis. Specifically, the network adaptively aligns amplitude and phase features across multiple source domains and performs label-consistent sample augmentation to enrich data diversity while preserving semantic consistency. A frequency–spatial interaction module further integrates global spectral information with local temporal details to improve feature discriminability. Moreover, we introduce a manifold triplet loss that scales shortest path distances in the feature manifold, encouraging the model to better capture subtle distinctions among hard samples and improving intra-class compactness and inter-class separability. We evaluate the proposed method on two publicly available datasets: the Pipeline Leak Acoustic Dataset (GPLA-12) and the Electrical Sound Dataset (MIMII-DG). Experimental results demonstrate superior performance under domain-shift scenarios, highlighting the method’s potential for scalable and low-cost acoustic fault diagnosis in real-world industrial environments. Full article

The limited availability of high-quality timber and the increasing demand for wood-based panels have encouraged the exploration of alternative and sustainable lignocellulosic resources. Rattan waste is abundant in Indonesia; however, its low mechanical strength and limited durability restrict its direct application in composite materials. This study investigated the effect of furfuryl alcohol (FA) modification and different adhesive systems on the performance of rattan-based particleboard. Rattan particles were immersed in FA for 24 h and used to produce particleboards (300 × 300 × 10 mm) bonded with phenol formaldehyde (PF), melamine formaldehyde (MF), and urea formaldehyde (UF) adhesives at a resin content of 12%. The boards were manufactured under controlled hot pressing conditions and conditioned for 14 days prior to testing. Furfurylation significantly improved dimensional stability by reducing moisture content, water absorption, thickness swelling, and leaching, with anti-swelling efficiency values ranging from 43.25% to 71.06%. Some selected mechanical properties, including internal bonding strength, hardness, and screw holding power, were also enhanced. However, the modification showed limited influence on the modulus of elasticity and, in some cases, reduced the modulus of rupture. Among the adhesive systems, MF-bonded boards exhibited the most balanced mechanical performance. Furfurylation also produced darker and more uniform board surfaces. These findings indicate that furfurylated rattan particleboards are suitable for non-structural and decorative applications. Full article

Travel companion matching presents unique challenges compared with conventional recommendation domains, as it involves real-world interpersonal interaction, perceived safety risks, and limited historical user data under cold-start conditions. Existing platforms often lack structured multi-factor matching and transparent integration of trust and safety constraints. This study makes three contributions. First, it introduces a methodology for deriving interpretable compatibility weights from user preference data under cold-start conditions. Second, it presents a four-algorithm comparative evaluation framework that identifies user-preferred matching strategies through controlled real-user testing. Third, it proposes a safety-enhanced empirical hybrid algorithm that integrates a hard trust gate (T ≥ 0.7), safety-oriented components (51.3% normalised weight), and empirically derived preference personalisation (48.7%) within a single scoring framework. A three-phase empirical methodology is adopted: Phase 1 (n = 26 survey) derives compatibility weights, revealing safety (69.2%), travel pace (76.9%), and budget (73.1%) as dominant factors; Phase 2 (n = 15) compares four algorithms, with safety-first matching receiving the highest acceptance rate (60.0%, 95% Wilson CI: 35.7–80.2%); Phase 3 (n = 13 journeys) evaluates the hybrid algorithm, achieving an 84.6% selection rate with Precision@6 = 0.333, MRR@6 = 0.554, and NDCG@6 = 0.597. These results provide preliminary evidence that trust-aware constraints can be integrated with empirically derived preference modelling to produce actionable recommendations under cold-start conditions, offering a reproducible approach for peer-to-peer travel platforms prioritising user safety. Full article

To address statistical heterogeneity and update-level privacy risks in federated learning for geospatial data, this paper proposes a hierarchically decoupled collaborative framework that integrates client-side privacy perturbation with server-side consistency-aware aggregation, while incorporating governance as a system-level support module. Under strong non-IID conditions, the proposed soft-weight aggregation strategy mitigates update mismatch and improves convergence stability without hard filtering legitimate but distributionally shifted client contributions. Meanwhile, the risk-aware perturbation mechanism adaptively adjusts clipping and noise strength across clients to better balance privacy protection and model utility. An on-chain governance and off-chain training coordination mechanism is further introduced to support auditable and traceable collaboration without interfering with the main optimization process. Experimental results on EuroSAT_RGB with ResNet-18 show that the proposed design achieves more stable training and better overall performance than the compared baselines, especially under severe heterogeneity. These findings highlight the value of jointly considering privacy-aware perturbation and consistency-aware aggregation for improving training stability and preserving utility in geospatial federated learning under statistically heterogeneous settings. Full article

Proportional, fair scheduling in OFDM-based vehicle-to-everything (V2X) uplink causes the resource-block allocation of each vehicle to vary from slot to slot, yet conventional semantic encoders produce a fixed number of output tokens regardless of the instantaneous channel capacity. When the encoder output exceeds the slot budget, transmitted features are truncated and the resulting federated learning gradient is corrupted—a problem that affected 23% of training rounds for non-line-of-sight vehicles in our experiments. The difficulty is worsened by a spatial pattern common in urban deployments: vehicles at congested intersections suffer the poorest propagation conditions while carrying the training data most relevant to safety, and throughput-driven client selection excludes them in favor of vehicles with strong channels but uninformative scenes. We address both issues within a single framework for OFDM-based V2X federated learning. On the transmission side, a Sensing-Guided Adaptive Modulation (SGAM) module derives a per-slot token budget from the current resource-block allocation and selects tokens through differentiable Gumbel-TopK pruning with a hard capacity clip, so the transmitted token count stays within the slot budget. On the scheduling side, a Channel-Decoupled Federated Learning (CDFL) module partitions clients independently by channel quality and data complexity, selects diverse representatives per partition via facility location optimization, and corrects for partition-size imbalance through inverse propensity weighting during model aggregation. Experiments on NuScenes with 20 non-IID vehicular clients under realistic OFDM channel simulation demonstrate a Macro-F1 of 0.710 (+8.7 points over the Oort-adapted baseline), zero budget violations throughout training, and a 75% reduction in training variance; the worst-class F1 more than doubles relative to FedAvg. Full article

The estuarine and coastal regions of India and Taiwan are under increasing threat from pollutants such as microplastics (MPs) and heavy metals including cadmium (Cd). These contaminants are known to have adversely affect biodiversity and water quality. In this study, the combined toxic effects of polyethylene microplastics (PE-MPs) and Cd were evaluated using Poecilia sphenops, a euryhaline fish species, selected for its adaptability to varying salinity conditions. P. sphenops were exposed to Cd (20, 40, and 60 μg/L), MPs (8, 16, 24 mg/L), and co-exposure combinations ranging from Cd 5 μg/L + MPs 4 mg/L to Cd 20 μg/L + MPs 16 mg/L Results showed significant (p< 0.05) negative effects on growth parameters including body weight gain, specific growth rate (SGR), and survival rate. Hematological analysis revealed significant (p< 0.05) decreases in hemoglobin (Hb), red blood cells (RBCs), and white blood cells (WBCs), indicating impaired oxygen transport and compromised immune function. Elevated blood glucose levels indicated physiological stress, while reduced total protein levels suggested a compromised nutritional status. Antioxidant enzyme activities, including catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GPx), were significantly (p < 0.05) decreased in the toxicant-treated groups compared with the control. Digestive enzyme activities (proteases, amylases, and lipases) were also reduced, suggesting impaired digestion and nutrient assimilation. The study also included a comparative assessment of water quality between the exposed and control tanks. Water quality parameters such as turbidity, salinity, hardness, alkalinity, chloride, fluoride, and total suspended solids (TSSs) were elevated in the toxicant-treated media, accompanied by a notable decline in dissolved oxygen (DO) levels. These findings highlight the urgent need for integrated pollution control and water quality monitoring, particularly in coastal regions vulnerable to desalination discharges and plastic contamination. Sustainable management strategies must address these complex interactions between multiple pollutants to protect aquatic ecosystems. Full article

At around 8:40 a.m. on 17 July 2024, the Wanshuitian landslide in the Three Gorges Reservoir Area (TGRA) experienced a deformation failure characterized by thrust load-caused deformations and high-speed sliding. Using geological surveys and unmanned aerial vehicle (UAV) photography, this study divided the Wanshuitian landslide area into five zones: sliding initiation (A1), secondary disintegration (A2), main accumulation (B1), right falling (B2), and left falling (B3) zones. Through monitoring data analysis and GeoStudio-based numerical simulations, this study revealed the mechanisms behind the landslide failure mode characterized by slope sliding approximately along the strike of the rock formation under the coupling effect of hydrological and geological conditions. The results indicate that factors inducing the landslide failure include the geomorphic feature of alternating grooves and ridges, the lithologic assemblage characterized by interbeds of soft and hard rocks, the slope structure with well-developed joints, and the sustained heavy rains in the preceding period. In the Wanshuitian landslide area, mudstone valleys are prone to accumulate rainwater, which can infiltrate directly into the weak interlayers of rock masses and soften the rock masses. Multi-peak rain events with a short time interval serve as a critical factor in groundwater recharge. Within 17 days preceding its failure, the Wanshuitian landslide experienced a superimposed process of heavy and secondary rain events with a short interval (four days). Rainwater from the first heavy rain event failed to completely discharge during the short interval, while the secondary rain event also caused rainwater accumulation. These led to a continuous rise in the groundwater table, a constant decrease in the shear strength of the slope, and ultimately the landslide instability. Since the landslide sliding in the dip direction of the rock formation was impeded, the main sliding direction of the landslide formed an angle of 88° with this direction. This led to a unique failure mode characterized by slope sliding approximately along the strike of the rock formation. Based on these findings, this study proposed characteristics for the early identification of the failure of similar landslides, aiming to provide a robust scientific basis for the monitoring, early warning, and prevention and control of the failure of similar landslides. Full article

Bed separation is a common geological phenomenon in the overburden strata during coal mining, which easily induces water inrush hazards, surface subsidence hazards, and other engineering disasters, thus seriously threatening the safety and efficiency of coal mining operations. This paper presents the spatiotemporal evolution characteristics and dynamic prediction of bed separation. The different boundary conditions before and after coal mining disturbance are considered to calculate and predict the location, spatial dimension and spatiotemporal evolution process of bed separation development. Theoretical analysis and scale model tests are used to study the distribution and process of bed separation development with comparisons made between the pre- and post-mining conditions. Formulas for the dynamic prediction of bed separation and a criterion for identifying bed separation development locations are proposed. The vertical propagation coefficient (K_s) and the horizontal development coefficient (K_l) of bed separation are proposed to quantitatively predict the vertical propagation extent and horizontal expansion scale of bed separation space with the advancement of the panel, providing key indicators for the dynamic prediction of bed separation evolution. The results show that the size and duration of bed separation space increase abnormally in the presence of thick and hard strata. This study provides a theoretical basis and practical guidance for the design and optimization of bed separation water hazard prevention and overburden grouting for subsidence control. Full article

Background/Objective: In late adulthood, the increasing prevalence of diabetes overlaps with the highest prevalence of postural instability. A cross-sectional study was designed to explore the combined influence of age, gender, history of COVID-19 quadriceps strength, and Body Mass Index (B.M.I.) on the postural stability of adults with/without diabetes, under a variety of sensory conditions. Methods: A total of 263 adults aged 21 to 82 years old accepted to participate, 99 with and 164 without diabetes. They had no history of vestibular/otology/neurology/autoimmune/orthopedic disease or proliferative retinopathy/severe renal dysfunction/traumatic injury. After clinical and vestibular evaluations, postural sway was recorded on hard/soft surface, eyes open/closed, and without/with 30° neck extension. Bivariate analysis and repeated measures multivariate analysis of covariance were performed with 0.05 significance. Results: In the two groups, two thirds of the participants had excess weight and almost half had history of COVID-19. Overall conditions, gender and diabetes were the main factors contributing to sway area (multiple R = 0.28–0.31, p ≤ 0.001) and to sway length (multiple R = 0.34–0.47, p ≤ 0.00001). Compared to adults without diabetes, in those with diabetes, the age was not related to sway measurements; with contribution to sway from history of COVID-19 and quadriceps strength, and decreased contribution of the study variables to both the anterior–posterior position of the center of pressure and ankle movement (velocity as a function of the anterior–posterior position of the center of pressure) (p > 0.05). Conclusions: Diabetes may interfere with the influence of individual cofactors contributing to postural sway, including decreased influence of age and reduced ankle movement. A history of mild–moderate COVID-19 may have influence on postural control in varied sensory conditions. Full article

To address the challenges faced by virtual power plants (VPPs) in uncertain market environments and complex distribution networks, including strong market coupling, difficulty in multi-resource coordination, and strict safety constraints, this paper proposes a Hierarchical Hybrid Intelligent Framework (H2IF). The proposed framework integrates a market-aware meta-game mechanism, a topology-embedded graph attention coordination method, and a risk-aware soft/hard constraint safety mechanism to achieve economically optimal dispatch of VPPs in complex dynamic scenarios. By explicitly modeling competitive market interactions, the proposed method enhances strategy robustness; by exploiting grid topology priors, it improves multi-agent coordination capability; and by combining differentiable projection with risk-constrained optimization, it jointly ensures operational safety and revenue stability. Simulation results on a modified IEEE 33-bus system demonstrate that H2IF outperforms mainstream deep reinforcement learning methods and rule-based dispatch strategies in overall performance. In the 24 × 300-step testing scenario, H2IF achieves an average single-episode operating cost of 38.23 k$, which is 28.9%, 40.4%, and 26.5% lower than those of MADDPG, SAC, and the rule-based method, respectively, while also yielding the lowest constraint violation level. Ablation studies further verify the effectiveness of each key module in improving profit, reducing operating costs, enhancing tracking performance, and strengthening safety. The results indicate that the proposed method enables coordinated optimization of economy, safety, and robustness for VPP dispatch under uncertain market and operating conditions. Full article