Search Results (10,285)

The growth of greenhouse gas emissions, driven by the use of internal combustion engines (ICE), highlights the urgent need for sustainable solutions, particularly in the shipping sector. Non-invasive predictive maintenance using acoustic signal analysis has emerged as a promising strategy for fault diagnosis in ICEs. In this context, the present study proposes a hybrid Deep Learning (DL) model and provides a novel publicly available dataset containing real operational sound samples of ICEs, labeled across 12 distinct fault subclasses. The methodology encompassed dataset construction, signal preprocessing using log-mel spectrograms, and the evaluation of several Machine Learning (ML) and DL models. Among the evaluated architectures, the proposed hybrid model, BiGRUT (Bidirectional GRU + Transformer), achieved the best performance, with an accuracy of 97.3%. This architecture leverages the multi-attention capability of Transformers and the sequential memory strength of GRUs, enhancing robustness in complex fault scenarios such as combined and mechanical anomalies. The results demonstrate the superiority of DL models over traditional ML approaches in acoustic-based ICE fault detection. Furthermore, the dataset and hybrid model introduced in this study contribute toward the development of scalable real-time diagnostic systems for sustainable and intelligent maintenance in transportation systems. Full article

Infections caused by Staphylococcus aureus pose significant public health challenges, particularly due to antibiotic-resistant strains like MRSA. In this context, Streptomyces, a genus known for producing natural antibiotics, emerges as a promising source for novel therapeutic agents. In this study, a bibliometric analysis of the scientific literature (2015–2024) on Streptomyces as antibiotic biofactories against S. aureus was performed, aiming to identify publication trends, collaborative networks, and emerging research areas. Using the Web of Science database, searches were performed with descriptors (“Streptomyces” AND “Staphylococcus aureus”), including original articles and reviews in English. Data were analyzed with VOSviewer and Biblioshiny to visualize collaborative networks, keyword co-occurrences, and trends. A total of 755 articles from 3705 authors were analyzed, highlighting significant collaboration (98.7%). Publications showed marked growth, particularly in Microbiology (21.7%), Pharmacology and Pharmacy (16.8%), and Biotechnology and Applied Microbiology (16.1%). China and India led in publication volume, whereas the United States exhibited the highest citation impact. Key emerging research topics include biosynthesis and metabolic optimization, antimicrobial activity and bioprospecting, mechanisms of antibiotic action and bacterial resistance, and genomic analyses. Research on Streptomyces for antibiotic production against S. aureus demonstrates continuous expansion and global interest, emphasizing the importance of international collaboration and multidisciplinary approaches. Future studies should intensify exploration of biodiverse environments, genetic engineering applications, and combinatorial strategies to effectively address antimicrobial resistance. Full article

Gallium nitride high electron mobility transistors (GaN HEMTs), characterized by their extremely high switching speeds and superior high-frequency performance, have demonstrated significant advantages, and gained extensive applications in fields such as aerospace and high-power-density power supplies. However, their unique internal architecture renders these devices highly sensitive to circuit parasitic parameters. Conventional circuit design methodologies often induce severe issues such as overshoot and high-frequency oscillations, which significantly constrain the realization of their high-frequency performance. To solve this problem, this paper investigates the nonlinear dynamic behavior of GaN HEMTs during switching transients by establishing an equivalent impedance model. Based on this model, a detailed analysis is implemented to elucidate the mechanism by which RC Snubber circuits influence the system’s resonance frequency and the amplitude at the resonant frequency. Through this analysis, an optimal RC Snubber circuit parameter is derived, enabling effective suppression of high-frequency oscillations during the switching transient of GaN HEMT. Experimental results demonstrate that the proposed design achieves a maximum reduction of 40% in voltage overshoot, shortens the ringing time to one-twentieth of the original value, and suppresses noise by 20 dB in the high-frequency range of 20 MHz to 30 MHz, thereby significantly enhancing the stability and reliability of circuit operation. Additionally, considering the heat dissipation requirements in high power density scenarios, this work optimizes the layout of devices, and heat sinks to maintain operational temperatures within safe limits, further mitigating the impact of parasitic parameters on overall system performance. Full article

The influence of temperature on soil behavior has traditionally attracted attention for geotechnical engineers, especially in the design of engineering works and nuclear facilities located in regions with severe cold climates. This research emphasizes exploring how temperature variations affect essential soil properties that are significant for the resilience and long-term stability of geotechnical structures. For this reason, the influence of temperature on the soil’s mechanical and physical attributes was comprehensively evaluated. To achieve this, soil mixtures consisting of two blends prepared as 70% bentonite with 30% sand and 70% sand with 30% bentonite (70B30S and 70S30B) were exposed to temperatures ranging from –45 °C to +105 °C for durations of 24 and 48 h. The study examined how temperature variations affect the mechanical, physical, and mineralogical features of soil through consistency limit tests, direct shear tests, swelling pressure tests, and X-ray diffraction (XRD) analysis. It was observed that the internal friction angle (Φ) declined as temperature increased in both mixtures, particularly in specimens with higher sand content. Similarly, cohesion (c) values decreased with increasing temperature, more significantly in mixtures with higher bentonite content. Additionally, the consistency limits and swelling pressure decreased as temperature rose. This trend was evident in both mixtures. Swelling pressure results showed that from 20 °C to 105 °C, the pressure rose with temperature in bentonite-rich soils, while it decreased in sand-rich soils. Conversely, at subzero conditions (–10 to –45 °C), swelling pressure increased as temperature decreased in mixtures dominated by bentonite, while it dropped in those rich in sand. Full article

This study investigates how perceptions of economic inequality are associated with confidence in elections through the mechanism of subjective class identification. Whereas much existing research relies on objective indicators of inequality, this analysis emphasizes the importance of subjective perceptions for understanding political trust. Using cross-national survey data from the International Social Survey Programme, the findings showed that individuals who perceive greater inequality are more likely to identify with a lower social class, and this self-placement is, in turn, associated with lower trust in electoral outcomes. These results highlight a pathway through which inequality influences democratic legitimacy, operating not only through structural conditions but also through how individuals interpret their relative social position. By identifying this association, this study contributes to debates on inequality and democratic resilience and calls for greater attention to the subjective dimensions of inequality in efforts to safeguard electoral legitimacy. Full article

The enhancement effect and mechanism of porous frameworks on hydrogel thermal control performance are key factors in evaluating their engineering applications and performance improvements. This study investigates the enhancement mechanism of porous framework composite phase-change materials (CPCM) on hydrogel thermal control performance through multi-scale visualization comparison experiments. Results indicate that pure hydrogels, due to their dense internal structure, hinder water vapor escape, thereby impeding overall fluidity and mass transfer rates. The introduction of a porous framework significantly improves internal heat transfer and moisture transport pathways within the hydrogel, enabling smooth water vapor release during heating and preventing localized heat accumulation. Under 100 °C heating conditions, CPCM exhibited a 65% reduction in mass-specific dehydration rate compared to pure hydrogel, with a 25% lower temperature drop. Energy efficiency increased by 13.5% over hydrogel, while the coefficient of variation decreased by 34.1%, demonstrating superior thermal stability and temperature control capabilities. This study elucidates from a mechanistic perspective how porous frameworks regulate the thermal and mass transfer behaviors of hydrogels, providing a theoretical basis and experimental support for their advanced application and optimization in the thermal control systems of electronic devices. Full article

FMS-like tyrosine kinase 3 (FLT3) mutations in acute myeloid leukemia (AML) are associated with an increased risk of relapse and a poor prognosis. Several FLT3 inhibitors that have been developed demonstrated efficacy against the FLT3 tyrosine kinase domain and/or internal tandem duplication mutations. Nevertheless, remission rates for these agents remain in the range of 30~40% of patients, attributed to both primary and secondary mechanisms of resistance, with relapse rates varying from ~30 to 50%. The mechanisms underlying resistance to FLT3 inhibitors have been characterized, offering valuable insights that can guide the development of clinical trials aimed at discovering novel FLT3 tyrosine kinase inhibitors (TKIs) that can overcome resistance. Additionally, elucidating resistance signaling pathways may facilitate the identification of other TKIs, rational combination therapies or multiple targeted TKIs to address alternative pathways, potentially helping overcome resistance in AML patients with refractory clones. Full article

Fungal mycelial pellets (MPs) exhibit high biomass-loading capacity; however, their application in wastewater treatment is constrained by structural fragility and the risk of environmental dispersion. To overcome these limitations, a dual-crosslinked polyvinyl alcohol–alginate gel (10% PVA, 2% sodium alginate) embedding strategy was developed and stabilized using 2% CaCl₂ and saturated boric acid. This encapsulation enhanced the tensile strength of MPs by 499% (310.4 vs. 62.1 kPa) and improved their settling velocity by 2.3-fold (1.12 vs. 0.49 cm/s), which was critical for stability under turbulent bioreactor conditions. Following encapsulation, the specific oxygen uptake rates (SOURs) of three fungal strains (F557, Y3, and F507) decreased by 30.3%, 54.8%, and 48.3%, respectively, while maintaining metabolic functionality. SEM revealed tight adhesion between the gel layer and both surface and internal hyphae, with the preservation of porous channels conducive to microbial colonization. In sequential-batch reactors treating sulfamethoxazole (SMX)-contaminated wastewater, gel-encapsulated MPs combined with acclimated sludge consistently achieved 72–75% SMX removal efficiency over six cycles, outperforming uncoated MPs (efficiency decreased from 81.2% to 58.7%) and pure gel–sludge composites (34–39%). The gel coating inhibited hyphal dispersion by over 90% and resisted mechanical disintegration under 24 h agitation. This approach offers a scalable and environmentally sustainable means of enhancing MPs’ operational stability in continuous-flow systems while mitigating fungal dissemination risks. Full article

Data-driven modeling methods have been preliminarily applied in the development of tight-gas reservoirs, demonstrating unique advantages in post-fracturing productivity prediction. However, most of the established predictive models are “black-box” models, which provide productivity predictions based on a set of input parameters without revealing the internal prediction mechanisms. This lack of transparency reduces the credibility and practical utility of such models. To address the challenges of poor performance and low trustworthiness of “black-box” machine learning models, this study explores a data-driven approach to “black-box” predictive modeling by integrating ensemble learning with interpretability methods. The results indicate the following: The post-fracturing productivity prediction model for tight-gas reservoirs developed in this study, based on ensemble learning, achieves a goodness of fit of 0.923, representing a 26.09% improvement compared to the best-performing individual machine learning model. The stacking ensemble model predicts post-fracturing productivity for horizontal wells more accurately and effectively mitigates the prediction biases of individual machine learning models. An interpretability method for the “black-box” ensemble learning-based productivity prediction model was established, revealing the ranked importance of factors influencing post-fracturing productivity: reservoir properties, controllable operational parameters, and rock mechanics. This ranking aligns with the results of orthogonal experiments from mechanism-driven numerical models, providing mutual validation and enhancing the credibility of the ensemble learning-based productivity prediction model. In conclusion, this study integrates mechanistic numerical models and data-driven models to explore the influence of various factors on post-fracturing productivity. The cross-validation of results from both approaches underscores the reliability of the findings, offering theoretical and methodological support for the design of fracturing schemes and the iterative advancement of fracturing technologies in tight-gas reservoirs. Full article

The applicability of biochar as a coarse aggregate substitute in concrete to increase sustainability and multifunctionality was investigated. Biochar, a porous carbon-rich byproduct from biomass pyrolysis, was incorporated at various replacement ratios (5–20%) under four water-to-binder (w/b) conditions (0.25–0.40). The key physical, mechanical, thermal, and microstructural properties, including the unit weight, porosity, compressive strength, flexural strength, and thermal conductivity, were evaluated via SEM and EDS analyses. The results revealed that although increasing the biochar content reduced the mechanical strength, it significantly improved the thermal insulation performance because of the porous structure of the biochar. At low w/b ratios and 5–10% biochar content, sufficient mechanical properties were retained, indicating a viable design range. Higher replacement ratios (>15%) led to excessive porosity, reduced hydration, and impaired durability. This study quantitatively analyzed the interproperty correlations, confirming that the strength and thermal performance are closely linked to the internal matrix density and porosity. These findings suggest that biochar-based concrete has potential for use in thermal energy storage systems, high-temperature insulation, and low-carbon construction. The low-carbon effect is achieved both by sequestering stable carbon within the concrete matrix and by partially replacing cement, thereby reducing CO₂ emissions from cement production. Moreover, the results highlight a strong correlation between increased porosity, enhanced thermal insulation, and reduced strength, thereby offering a solid foundation for sustainable material design. In particular, the term ‘high temperature’ in this context refers to exposure conditions above approximately 200~400 °C, as reported in previous studies. However, this should be considered as a potential application to be validated in future experiments rather than a confirmed outcome of this study. Full article

This study investigated the structural behavior of slab-on-grade (SOG) specimens constructed using two materials: conventional concrete reinforced with steel mesh and high-ductility fiber-reinforced cement composites (HDFRCC) containing 1.2% polyethylene (PE) fiber without steel reinforcement. The compressive strengths of conventional concrete and HDFRCC were 37 MPa and 54 MPa, respectively. The average flexural tensile strength of HDFRCC was 3.9 MPa at first cracking and 9.7 MPa at peak load. Punching shear tests were performed under three loading configurations: internal (center), edge, and corner loading. Crack patterns and load–displacement responses were analyzed for both material types. Under center loading, the experimentally measured load-bearing capacities were 174.52 kN for conventional concrete and 380.82 kN for HDFRCC, with both materials exhibiting reduced capacities under edge and corner loading. Analytical predictions demonstrated close agreement with the experimental results for conventional concrete but significantly underestimated the load capacity of HDFRCC SOG. This discrepancy is attributed to the strain-hardening and crack-bridging mechanisms inherent in HDFRCC, which contribute to enhanced strength beyond conventional analytical predictions. In terms of failure mode, the conventional concrete SOG exhibited the expected flexural failure. In contrast, the HDFRCC SOG experienced either flexural failure or a combination of flexural and punching failure, in contradiction to the analytical prediction of exclusive punching shear failure. These findings indicate that the punching shear resistance of the HDFRCC SOG is substantially higher than predicted. Full article

In lithium-ion batteries, the fracture of active particles that are under stress is a key cause of battery aging, which leads to a reduction in active materials, an increase in internal resistance, and a decay in battery capacity. A coupled electrochemical–thermal–mechanical model was established to study the concentration and stress distributions of negative electrode particles under different charging rates and ambient temperatures. The results show that during charging, the maximum lithium-ion concentration occurs on the particle surface, while the minimum concentration appears at the particle center. Moreover, as the temperature decreases, the concentration distribution of negative electrode active particles becomes more uneven. Stress analysis indicates that when charging at a rate of 1C and 0 °C, the maximum stress of particles at the negative electrode–separator interface reaches 123.7 MPa, while when charging at 30 °C, the maximum particle stress is 24.3 MPa. The maximum shear stress occurs at the particle center, presenting a tensile stress state, while the minimum shear stress is located on the particle surface, showing a compressive stress state. Finally, to manage the stress of active materials in lithium-ion batteries while charging for health maintenance, this study uses a DNN (Deep Neural Network) to predict the maximum shear stress of particles based on simulation results. The predicted indicators, MAE (Mean Absolute Error) and RMSE (Root Mean Square Error), are 0.034 and 0.046, respectively. This research is helpful for optimizing charging strategies based on the stress of active materials in lithium-ion batteries during charging, inhibiting battery aging and improving safety performance. Full article

Advancements in material science have allowed us to exploit the potential of new era for aircraft production. High-performance composites and alloys have allowed us to improve the performance and durability of aircraft, but they have become more and more precious with time. These materials can provide significant advantages in use but are costly, energy-intensive to produce, and their recovery and reuse has become a critical step to be addressed. Accordingly, a new approach in which end-of-life aircrafts represent unconventional mines rather than a disposal challenge is becoming increasingly relevant, providing access to high-value strategic raw materials and aligning with circular economy principles including European Green Deal and the United Nations Sustainable Development Goals. The complexity of dismantling and processing hybrid structures composed of metal alloys, ceramics, and advanced composites requires multiple approaches able to integrate chemical, mechanical, and thermal recovery routes. Accordingly, this review critically discusses the state of the art of the routes of end-of-life aircraft treatments, evaluating the connections between technology and regulation, and positions material recycling and reuse as central pillars for advancing sustainability in aerospace. Furthermore, this review provides a comprehensive reference for addressing the technical, economic, and policy challenges of waste management in aviation, contributing to broader goals of resource circularity and environmental preservation set forth by international sustainability agendas. Full article

A fundamental challenge in lithium-ion battery charging is the inherent trade–off between charging speed and battery health. Fast charging tends to accelerate battery degradation, while slow charging extends downtime and intensifies range anxiety, heightening concerns over inadequate driving range during operation. This contradiction has become a key bottleneck restricting the advancement of electric vehicles. In response to the limitations of conventional charging strategies and optimization methods, which typically intensify this trade–off, this study proposes a novel two–stage fast charging optimization strategy for lithium–ion batteries. The proposed method first introduces a hybrid clustering algorithm that combines the canopy algorithm with bisecting K–means to achieve adaptive SOC staging. This staging is guided by the nonlinear characteristics of the internal resistance with respect to the state of charge (SOC), allowing for a data–driven division of charging phases. Following staging, a closed–loop optimization framework is developed. A wavelet neural network (WNN) is employed to precisely capture and approximate the nonlinear characteristics of the charging process for performance prediction, upon which a multi–strategy enhanced multi–objective particle swarm optimization (MOPSO) algorithm is applied to efficiently search for Pareto–optimal solutions that balance charging time and ohmic loss. In addition, an active learning mechanism is incorporated to refine the WNN using selectively sampled data iteratively, thereby improving prediction accuracy and the robustness of the optimization process. Experimental results demonstrate that when the SOC reaches 70%, the proposed method shortens the charging time by 12.5% and reduces ohmic loss by 31% compared with the conventional constant current–constant voltage (CC–CV) strategy, effectively achieving a balance between charging efficiency and battery health. Full article

While material extrusion via fused filament fabrication (FFF) offers design flexibility and rapid prototyping, its practical use in engineering is limited by mechanical challenges, including residual stresses, geometric distortions, and potential interlayer debonding. These issues arise from the dynamic thermal profiles during FFF, including temperature gradients, non-uniform hardening, and rapid thermal cycling, which lead to uneven internal stress development depending on fabrication parameters and object topology. These problems can compromise the structural integrity and mechanical properties of FFF parts, especially when the load-bearing capacity and geometric accuracy are critical. This study focuses on polylactic acid (PLA) due to its widespread application in engineering. It introduces a computational framework for coupled thermo-mechanical simulations of the FFF process using ABAQUS (Version 2020) finite element software. A key innovation is an automated subroutine that converts G-code into a time-resolved event series for finite element activation. The simulation framework explicitly models the sequential stages of printing, cooling, and detachment, enabling prediction of adhesive loss and post-process warpage. A transient thermal model evaluates the temperature distribution during FFF, providing boundary conditions for a mechanical simulation that predicts residual stresses and warping. Uniquely, the proposed model incorporates the detachment stage, enabling a more realistic and experimentally validated prediction of warpage and residual stress release in FFF-fabricated components. Although the average deviation between predicted and measured displacements is about 10.6%, the simulation adequately reflects the spatial distribution and magnitude of warpage, confirming its practical usefulness for process optimization and design validation. Full article