Search Results (1,504)

Multimodal forecasting is increasingly needed to maintain energy levels, storage capacity, and compute efficiency in off-grid, renewable-powered cloud environments. Variable sensor quality, uncertain interactions with renewable energy, and rapidly changing weather patterns make real-time forecasting difficult. Current transformer, GNN, and CNN systems suffer from sensor noise instability, multimodal temporal–spectral correlation issues, and challenges in the interpretability of operational decision-making. In this research, Q-RCANeX, a quantum-guided residual convolutional attention network for off-grid cloud infrastructures, estimates battery state of charge, renewable energy sources, and microgrid efficiency to overcome these restrictions. The system uses a Hybrid Quantum–Bayesian Evolutionary Optimizer, quantum feature embedding, temporal–spectral attention, residual convolutional encoding, and signal decomposition preprocessing. These parameters reinforce features, reduce noise, and align forecasting behavior with microgrid dynamics. Q-RCANeX obtains 98.6% accuracy, 0.992 AUC, and 0.986 R³ values for REAF, WGF, SOC-F, and EEIF forecasting tasks, according to a statistical study. Additionally, it determines inference latency to 4.9 ms and model size to 18.5 MB. Even with 20% of sensor data missing or noisy, the model outperforms 12 state-of-the-art baselines and maintains 96.8% accuracy using ANOVA, Wilcoxon, Nemenyi, and Holm tests. The findings indicate that the forecasting framework has high accuracy, clarity, and resilience to failures. This makes it useful for real-time, off-grid management of renewable cloud microgrids. Full article

Dynamic task planning for heterogeneous platforms across land, sea, air, and space is essential for achieving integrated situational awareness, yet current systems suffer from limited spatiotemporal coverage and inefficient resource scheduling. To address these challenges, we propose a novel mission planning method that integrates spatiotemporal segmentation with Deep Reinforcement Learning (DRL). The approach establishes a multidimensional spatiotemporal decomposition model to break down complex observation scenarios into manageable subtasks, while incorporating a unified accessibility–visibility computation framework that accounts for Earth curvature, platform dynamics, and sensor constraints. Using a Spatio-Temporal Adaptive Scheduling Network (STAS-Net) algorithm optimized with a multi-objective reward function covering mission completion rate, temporal coordination, and residual detection capacity, the method enables intelligent coordination of heterogeneous platforms. Experimental results across small-, medium-, and large-scale scenarios demonstrate that the proposed framework consistently achieves high target coverage (up to 98.4% in small-scale and 89.7% in large-scale tasks), with a reduction in coverage loss that is only about half of that exhibited by greedy and genetic algorithms as task scale expands. Moreover, STAS-Net maintains low planning time (as low as 9.5 s in small-scale and only 18.3 s in large-scale scenarios) and high resource utilization (reaching 86.8% under large-scale settings), substantially outperforming both baseline methods in scalability and scheduling efficiency. The framework not only establishes a solid theoretical foundation but also provides a practical and feasible solution for enhancing the overall performance of multi-platform cooperative observation systems. Full article

This study presents a comprehensive decomposition analysis of waste-to-energy (WtE) in India through a systematic literature review (SLR) employing the PRISMA guidelines. The findings underscore the immense potential of WtE technologies in addressing India’s escalating municipal solid waste (MSW) generation amid rapid urbanization while simultaneously contributing to sustainable energy production and circular economy goals. The thematic analysis reveals four key themes: global trends in MSW generation, MSW as an alternative energy source, WtE approaches within a circular economy framework, and the impact of India’s urban expansion on MSW generation. Despite significant potential, India’s current WtE initiatives face substantial challenges, including inadequate waste segregation, policy gaps, public resistance, technological limitations, and insufficient financial investment. To effectively harness WtE technologies, strategic efforts must focus on robust policy implementation, indigenous technology advancement tailored to India’s waste characteristics, fostering public–private partnerships, and enhancing community engagement to mitigate public concerns. Future research should aim to quantify the economic, environmental, and social impacts of localized WtE interventions to guide scalable solutions. This study contributes valuable insights to policymakers, urban planners, and stakeholders aiming to transition India toward sustainable waste management and energy systems. Full article

Soil organic carbon (SOC) sequestration plays a vital role in sustaining soil productivity and mitigating climate change. Although biochar and charcoal-based fertilizers are known to enhance SOC sequestration, current understanding is predominantly derived from studies applying high doses. With the goal of elucidating the mechanisms through which long-term, low-dose biochar application influences SOC composition and stability, this study evaluated the long-term impacts of biochar and carbon-based fertilizers on SOC content, chemical structure, and microbial residual carbon assessed via amino sugar biomarkers. The following features are demonstrated by this study: (1) The application of biochar and carbon-based fertilizers significantly increased the contents of active organic carbon components (DOC, MBC, POC) and stable carbon components (MAOC, humic carbon) in the plow layer soil. Notably, the C50 treatment reduced the easily oxidizable organic carbon (EOC) content by 19.25% compared to the control. (2) Long-term application increased the relative abundance of aromatic functional groups in SOC, enhanced SOC decomposition resistance (as reflected by the F-index). Compared with NPK, the BBF treatment increased the F-index by 21.28% and 25.00% in the 0–20 cm and 20–40 cm soil layers. (3) The BBF treatment significantly increased both soil amino sugar content and the contribution of microbial residual carbon to SOC. Specifically, it elevated the levels of GluN, GalN, and MurN by 9.24% to 33.31% across soil layers. Fungal residual carbon constituted the dominant fraction across all treatments. In summary, the content and stability of SOC are enhanced by biochar and biochar-based fertilizers through synergistic mechanisms that involve altering its chemical composition and stimulating the accumulation of fungal residual carbon. Full article

Metal Injection Molding (MIM) is an established, high-precision manufacturing route for small, geometrically complex metallic components, integrating polymer injection molding with powder metallurgy. State-of-the-art feedstock systems, such as Catamold (polyacetal-based), enable catalytic debinding performed in furnaces operating under ultra-high-purity nitric acid atmospheres (>99.999%). The subsequent thermal stages pre-sintering and sintering are carried out in continuous controlled-atmosphere furnaces or vacuum systems, typically employing inert (N₂) or reducing (H₂) atmospheres to meet the specific thermodynamic requirements of each alloy. However, incomplete decomposition or secondary volatilization of binder residues can lead to progressive hydrocarbon accumulation within the sinering chamber. These contaminants promote undesirable carburizing atmospheres, which, under austenitizing or intercritical conditions, increase carbon diffusion and generate uncontrolled surface carbon gradients. Such effects alter the microstructural evolution, hardness, wear behavior, and mechanical integrity of MIM steels. Conversely, inadequate dew point control may shift the atmosphere toward oxidizing regimes, resulting in surface decarburization and oxide formation effects that are particularly detrimental in stainless steels, tool steels, and martensitic alloys, where surface chemistry is critical for performance. This review synthesizes current knowledge on atmosphere-induced surface deviations in MIM steels, examining the underlying thermodynamic and kinetic mechanisms governing carbon transport, oxidation, and phase evolution. Strategies for atmosphere monitoring, contamination mitigation, and corrective thermal or thermochemical treatments are evaluated. Recommendations are provided to optimize surface substrate interactions and maximize the functional performance and reliability of MIM-processed steel components in demanding engineering applications. Full article

This paper presents an integrated approach to the structural decomposition of concurrent control systems using exact transversal hypergraphs (XT-hypergraphs). The proposed method combines formal properties of XT-hypergraphs with invariant-based Petri net analysis to enable automatic partitioning of complex, concurrent specifications into deterministic and independent components. The approach focuses on preserving behavioral correctness while minimizing inter-component dependencies and computational complexity. By exploiting the uniqueness of minimal transversal covers, reducibility, and structural stability of XT-hypergraphs, the method achieves a deterministic decomposition process with polynomial-delay generation of exact transversals. The research provides practical insights into the construction, reduction, and classification of XT structures, together with quality metrics evaluating decomposition efficiency and structural compactness. The developed methodology is validated on representative real-world control and embedded systems, showing its applicability in deterministic modeling, analysis, and implementation of concurrent architectures. Future work includes the integration of XT-hypergraph algorithms with adaptive decomposition and verification frameworks to enhance scalability and automation in modern system design and integration with currently popular AI and machine learning methods. Full article

The role of soil microbial communities in soil organic matter (OM) decomposition, transformation, and the global nitrogen (N) and carbon (C) cycles has been widely investigated. However, a comprehensive understanding of how specific agricultural practices and OM inputs shape microbial-driven processes across different European pedoclimatic conditions is still lacking, particularly regarding their effectiveness in mitigating greenhouse gas (GHG) emissions. This systematic review synthesizes current knowledge on the biotic mechanisms underlying soil C sequestration and GHG reduction, emphasizing key microbial processes influenced by land management practices. A rigorous selection was applied, resulting in 16 eligible articles that addressed the targeted outcomes: soil microorganism biodiversity, including microbiome composition and other common Biodiversity Indexes, C sequestration and non-CO₂ GHG emissions (namely N₂O and CH₄ emissions), and N leaching. The review highlights that, despite some variations across studies, the application of OM enhances soil microbial biomass (MB) and activity, boosts soil organic carbon (SOC), and potentially reduces emissions. Notably, plant richness and diversity emerged as critical factors in reducing N₂O emissions and promoting carbon storage. However, the lack of methodological standardization across studies hinders meaningful comparison of outcomes—a key challenge identified in this review. The analysis reveals that studies examining the simultaneous effects of agricultural management practices and OM inputs on soil microorganisms, non-CO₂ GHG emissions, and SOC are scarce. Standardized studies across Europe’s diverse pedoclimatic regions would be valuable for assessing the benefits of OM inputs in agricultural soils. This would enable the identification of region-specific solutions that enhance soil health, prevent degradation, and support sustainable and productive farming systems. Full article

The rapid progress of perovskite solar cells (PSCs) has established them as a groundbreaking technology for sustainable energy. However, the sustainability of their lifecycle is still hindered by challenges related to material toxicity and end-of-life management. This review comprehensively assesses emerging recycling technologies, with a particular focus on their effectiveness in recovering perovskite compounds, transparent conductive oxides, and metallic contacts. Mechanical separation, solvent-based dissolution, thermal decomposition, and hybrid methods are compared in terms of recovery rates, purity levels, energy consumption, and scalability. Current challenges, such as the generation of secondary waste, the instability of recovered perovskites, and economic barriers, are critically analyzed alongside emerging solutions, including the use of non-toxic solvents, vacuum-assisted recovery, and the integration of closed-loop manufacturing. By evaluating lifecycle impacts and cost–benefit trade-offs, this work outlines pathways for transforming PSC waste into high-value secondary resources, thereby promoting both environmental sustainability and industrial competitiveness. Full article

The flow structure and unsteady evolution characteristics of dual impinging jets represent a flow problem of significant engineering importance in the aerospace field. Currently, there is a lack of systematic research on the unsteady characteristics and the underlying mechanisms of flow structure evolution in dual impinging jets across different velocity regimes. This study investigates a dual impinging jet configuration with a nozzle pressure ratio ranging from 1.52 to 2.77, an impingement spacing of 5d (where d is the nozzle exit diameter), and an inter-nozzle spacing of 10.42d. By employing Particle Image Velocimetry and Proper Orthogonal Decomposition, the evolution of the flow field structure from subsonic to supersonic conditions is systematically analyzed. The results demonstrate that the fountain motion is composed of an anti-symmetric oscillatory mode, a symmetric breathing mode, and an intermittent transport mode. The upper confinement plate obstructs the fountain motion to some extent, inducing unsteady oscillation modes. An increase in jet velocity enhances the upwash momentum of the fountain and raises the characteristic frequencies of its dynamic structures. This research elucidates the influence of jet velocity variation on the flow field structure, providing a theoretical basis for formulating flow control strategies in related engineering applications. Full article

Irregular wheel wear can significantly degrade wheel–rail interaction performance and, in severe cases, compromise the safety of high-speed trains. Accurate and timely monitoring of wheel wear is crucial for maintaining operational reliability. Existing monitoring methods often rely on high-end sensors or are sensitive to environmental disturbances, limiting their practical deployment. This study proposes a novel method for monitoring irregular wheel wear by analyzing the stator current spectrum of traction motors. Firstly, an electromechanical coupled model is developed by integrating the electric drive system with the vehicle–track dynamic model to capture the propagation of wear-induced excitation. The effect of polygonal wear on the stator current is investigated, revealing the presence of harmonic components coupled with the wear excitation frequency. To extract these features, a comb filter based on Variational Mode Decomposition (VMD) is introduced. The method effectively isolates wheel wear-related harmonics from existing electrical harmonics in the stator current signal. Simulation results demonstrate that the proposed approach can accurately detect harmonic features caused by polygonal wear, validating its applicability. This method provides a feasible and non-intrusive solution for wheel wear monitoring, offering theoretical support for condition-based maintenance of high-speed rail systems. Full article

This study investigates the atmospheric corrosion behavior of zinc in tropical marine environments affected by hydrogen sulfide (H₂S), particularly from the decomposition of stranded Sargassum algae. Four exposure sites in Martinique with varying levels of H₂S and marine chlorides were selected. Gravimetric analysis showed that zinc thickness loss reached up to 45 µm after one year at the most impacted site (Frégate Est), compared to only 3–10 µm at less contaminated locations. This degradation level classifies the site as “extremely corrosive” according to ISO 9223. Electrochemical impedance spectroscopy (EIS) and linear polarization measurements revealed distinct corrosion behaviors. After 12 months of exposure, the polarization resistance and corrosion current density reached R_p = 916 Ω·cm² and I_corr = 28 µA·cm² at the Frégate Est site and R_p = 1835 Ω·cm² and I_corr = 6 µA·cm² at the Vauclin site. In H₂S-poor environments (Diamant, Vert-Pré, Vauclin), corrosion resistance increased over time due to the formation of protective layers such as hydrozincite and simonkolleite. In contrast, H₂S-rich environments favored the formation of sulfur-based compounds like elemental sulfur and zinc sulfide (ZnS), which exhibit poor protective properties and result in lower polarization resistance and higher corrosion current densities. Polarization curves confirmed a general decrease in anodic and cathodic currents over time, with less significant improvements in passivation at H₂S-impacted sites. The corrosion mechanism is influenced by both pollutant type and exposure duration. Overall, this study highlights the synergistic effect of H₂S and chlorides on accelerating zinc corrosion and underscores the need for adapted protection strategies in tropical coastal zones affected by Sargassum proliferation. Full article

Surface crack detection and temporal evolution analysis are fundamental tasks in remote sensing and photogrammetry, providing critical information for slope stability assessment, infrastructure safety inspection, and long-term geohazard monitoring. However, current unmanned aerial vehicle (UAV)-based crack detection pipelines typically treat spatial detection and temporal change analysis as separate processes, leading to weak geometric consistency across time and limiting the interpretability of crack evolution patterns. To overcome these limitations, we propose the Longitudinal Crack Fitting Network (LCFNet), a unified and physically interpretable framework that achieves, for the first time, integrated time-series crack detection and evolution analysis from UAV remote sensing imagery. At its core, the Longitudinal Crack Fitting Convolution (LCFConv) integrates Fourier-series decomposition with affine Lie group convolution, enabling anisotropic feature representation that preserves equivariance to translation, rotation, and scale. This design effectively captures the elongated and oscillatory morphology of surface cracks while suppressing background interference under complex aerial viewpoints. Beyond detection, a Lie-group-based Temporal Crack Change Detection (LTCCD) module is introduced to perform geometrically consistent matching between bi-temporal UAV images, guided by a partial differential equation (PDE) formulation that models the continuous propagation of surface fractures, providing a bridge between discrete perception and physical dynamics. Extensive experiments on the constructed UAV-Filiform Crack Dataset (10,588 remote sensing images) demonstrate that LCFNet surpasses advanced detection frameworks such as You only look once v12 (YOLOv12), RT-DETR, and RS-Mamba, achieving superior performance (mAP_50:95 = 75.3%, F1 = 85.5%, and CDR = 85.6%) while maintaining real-time inference speed (88.9 FPS). Field deployment on a UAV–IoT monitoring platform further confirms the robustness of LCFNet in multi-temporal remote sensing applications, accurately identifying newly formed and extended cracks under varying illumination and terrain conditions. This work establishes the first end-to-end paradigm that unifies spatial crack detection and temporal evolution modeling in UAV remote sensing, bridging discrete deep learning inference with continuous physical dynamics. The proposed LCFNet provides both algorithmic robustness and physical interpretability, offering a new foundation for intelligent remote sensing-based structural health assessment and high-precision photogrammetric monitoring. Full article

This research proposes a quantitative cavitation-coefficient identification method based on a VMD-SWO-BiLSTM model, addressing the current limitations of existing machine-learning-based cavitation diagnostics, which mainly classify cavitation stages rather than identify the cavitation coefficient as a continuous quantity. Pressure fluctuation signals under various cavitation conditions are decomposed using Variational Mode Decomposition (VMD), and sample entropy is extracted to represent signal complexity. The Spider Wasp Optimization (SWO) algorithm optimizes the hyperparameters of a BiLSTM network, forming a composite VMD-SWO-BiLSTM framework. Multi-point input features from unsteady internal flow simulations and experimental measurements are used for model training and validation. Results show that the proposed model outperforms conventional BiLSTM, achieving mean absolute percentage error below 5% based on multi-point pressure signals. Validation with eight experimental datasets yields a maximum absolute error of 0.006 and a maximum percentage error of 4.7%. The proposed method can be applied to online monitoring and intelligent diagnosis of cavitation in axial flow pumps. Full article

In order to investigate the microstructure evolution and the degradation mechanism of SiC fiber in a high-temperature oxidative environment, the SiC fiber was thermally exposed at temperature up to 1600 °C in air. The morphologies of the surface and fracture surface were characterized by scanning electron microscopy. The consisting phase and crystallite size were analyzed by X-ray diffractometer. The mechanical properties of SiC fiber was characterized by a single-fiber tensile test technique. It was found that an obvious grain coarsening occurred at temperature above 1400 °C. A visible silica layer was formed at 1300 °C, and the morphology of silica layer was dependent on the exposure temperature. At 1400 °C, fiber surface formed a thick silica layer with cracks, while the silica layer exhibited a multilayered structure at 1600 °C. As for the tensile strength of fiber, it firstly decreased to about 1 GPa at 1200 °C, then the strength was maintained at 1400 °C. After thermal exposure at 1500 °C and 1600 °C, the strength decreased again. The degradation of mechanical properties was attributed to the grain coarsening and the decomposition of amorphous phase in fiber. Particularly, the decomposition of amorphous phase would damage the structure integrity of fiber. The current work would provide a valuable reference for research and application of SiC fiber. Full article

To avoid dependence on conventional raw materials, global emphasis has been placed on obtaining alternative plant celluloses and the chemical synthesis of cellulose. The use of synthetically derived cellulose as a precursor for cellulose nitrates (NCs) is currently absent in global practice, which underscores the undoubted relevance of this research. Cellulose nitrate (NC) was synthesized in a 138% actual yield by nitration of synthetic cellulose (SC)—a new type of cellulose—prepared by electropolymerization from an aqueous glucose solution in the presence of catalytic tungsten–vanadium heteropolyacid of the 1–12 series with the chemical formula H₆[PW₁₀V₂O₄₀]: a nitrogen content of 11.83%, a viscosity of 198 mPa·s, a high solubility of 91% in an alcohol–ether solvent, and an ash content of 0.05%. SEM provided a general concept of the morphological structure of SC and SC-derived NC. The initial SC consisted of flat, curly fibers with a smooth surface approximately 10–20 μm wide, with no aggregation observed. The fibers of SC-derived NC had a cylindrical shape with a diameter of up to 25 μm and a rough surface. FT-IR spectroscopy revealed that SC and SC-derived NC have the main functional groups characteristic of classical cellulose (3346, 2901, 1644, 1429, 1162, and 1112 cm⁻¹) and nitrate esters of cellulose (1650, 1278, 832, 747, and 689 cm⁻¹), respectively. For the first time, a full-profile analysis discovered that SC is made up of the monoclinic phase of cellulose Iβ with an antiparallel chain arrangement. SC with a crystallinity index (CrI) of 81–86% was shown to undergo amorphization upon nitration, with the CrI declining to 17% and the crystallite sizes decreasing from 44 × 62 × 59 × 94 Å to 29 × 62 × 26 × 38 Å. Coupled TGA/DTA revealed that SC exhibits a high-temperature endothermic peak of decomposition of 374 °C, with a weight loss of 84%. The thermostable SC-derived NC exhibits a high onset temperature of intense decomposition of 200 °C and an exothermic peak of 208 °C, with a weight loss of 88%, and is characterized by a high specific heat of decomposition of 7.74 kJ/g. This study provides new insights into the functionalization of SC with a high degree of polymerization, expanding the classical concepts of cellulose nitration. Full article