Search Results (128,267)

The extensive application of lithium-ion batteries (LIBs) in electronic devices, electric vehicles, and related applications has significantly enhanced the quality of spent LIBs. As a critical component of LIBs, cathode materials contain substantial amounts of valuable metals (e.g., lithium, cobalt, nickel, and manganese), and their efficient recovery offers significant environmental and economic advantages. Owing to its simple operating conditions, effective impurity removal, and high reaction efficiency, pyrometallurgical roasting has become an important approach for recycling spent LIB cathode materials. This review focuses on pyrometallurgical roasting technologies for prior lithium extraction from spent LIB cathodes. By examining the structural characteristics of different cathode materials and their property variations during recycling, the fundamental principles and characteristics of pyrometallurgical roasting are clarified. The applications of roasting-based prior lithium extraction in LIB recycling are systematically reviewed, covering conventional processes, emerging high-efficiency roasting routes, and other advanced strategies for prior lithium extraction. Finally, the development trends of pyrometallurgical roasting technologies for spent LIB cathode materials are discussed, with the objectives of supporting technological advancement in LIB recycling and facilitating the establishment of a more sustainable development framework for the battery industry. Full article

The running reliability of rolling bearings depends on the effective lubrication state, and poor lubrication will induce abnormal vibration. Therefore, vibration-based fault diagnosis is an important means to evaluate the health of bearings through vibration characteristics. However, the lack of fault samples in actual working conditions seriously restricts the generalization ability and accuracy of an intelligent diagnosis model. A novel few-shot diagnosis method integrating multi-layer feature fusion and adaptive similarity measurement is proposed. This method adopts a meta-learning framework to simulate sample scarcity through numerous N-way K-shot diagnostic tasks. An efficient feature extractor with a cross-task feature stitching mechanism is designed to fuse features from support and query sets. To overcome the limitation of fixed-distance metrics in existing meta-learners, a learnable similarity scheduler adaptively generates optimal pseudo-distance functions. In particular, a multi-layer feature fusion strategy is introduced to compute adaptive similarities at multiple network depths, which significantly enhances feature robustness against operational variations. Experimental results demonstrate the method achieves stable diagnostic accuracy above 90% under extremely few-shot conditions and maintains over 90% accuracy when transferring from laboratory-simulated faults to natural operational faults, validating its strong potential for practical industrial applications where annotated fault data is scarce. Full article

Textile industry wastewater poses a significant environmental challenge due to the presence of persistent dyes. Cationic dyes are characterized by resistance to the conventional coagulation method. The appropriate properties and combination of chemicals guarantee an effective removal process. This study explains the effect of modification of methylene blue solution by the addition of a natural biopolymer—hydrolyzed tannic acid (TA). The study assumed that a combination of tannic acid, methylene blue and polyaluminum chloride would provide a synergistic effect and significantly improve the coagulation and sediment filtration process. Coagulation tests were carried out for a range of methylene blue concentrations. The optimal arrangement of solution components and coagulant doses was selected and tested. Over 95% dye removal efficiency was achieved. The maximum dye removal efficiency was determined to be 5 mg/mg Al at pH = 5.0. Based on the analysis of UV-VIS spectroscopy, FTIR and electrokinetic potential, changes in the solutions of tannin-modified dyes and their effect on the precipitation of flocs and the nature of sorption were determined. The main phenomena affecting the removal mechanism are discussed. The results indicate that tannic acid can serve as a sustainable coagulant aid, supporting the development of technologies for treating cationic-dye-laden wastewater. Full article

This study investigates the development of mechanically reprocessed poly(lactic acid) (rPLA) films reinforced with rice husk (RH) and rice husk biochar (RHB) to evaluate their processing behavior, key functional properties, and disintegration under composting conditions. rPLA was produced from PLA through an additional processing cycle to simulate the valorization of industrial PLA waste, while composites containing 1 and 3 wt.% RH or RHB 500 µm sized particles were manufactured by melt extrusion followed by a compression molding process. Reprocessing increased the melt flow index and decreased intrinsic viscosity and viscosimetric molecular weight, evidencing the occurrence of chain scission during mechanical reprocessing. The addition of RH slightly restricted melt flow and promoted higher surface hydrophilicity, whereas RHB showed a filler-loading-dependent effect on melt flow and increased surface hydrophobicity at low content, consistent with its carbonized and less polar nature. Both RH and RHB promote a nucleating effect, with increased crystallinity in RHB-containing films, and tensile tests showing that filler incorporation mainly reduced ductility compared with unfilled rPLA, while stiffness and strength was maintained or exhibited more moderate variations. Despite these contrasting trends in surface properties and thermo-mechanical performance, all formulations achieved complete disintegration within 21 days under composting conditions at laboratory scale level. Overall, RH and RHB provide a viable route to valorize agro-industrial residues in rPLA films and to tune structure–property relationships within the circular economy framework. Full article

Financial fraud is still one of the most important threats to the financial industry, causing enormous economic losses and mounting difficulties for conventional fraud detection systems. The systems tend to face challenges in dealing with the rising amount of transactional data, the problem of class imbalance, and the continually changing nature of fraudulent activity. In order to solve these problems, in this research a cloud hybrid framework for detecting fraud using Long Short-Term Memory (LSTM) networks, Autoencoders, and Cat Swarm Optimization (CSO) is suggested. The purpose of the suggested framework is to provide improved detection performance and flexibility on a benchmark financial dataset, with a design intended to support scalability in real-time applications. The framework uses the Credit Card Fraud Detection Dataset from Kaggle, which consists primarily of numerical features, including anonymized variables (V1–V28), along with time and amount. The LSTM networks learn the sequential relationships of transactions, while Autoencoders learn to detect anomalies in the data unsupervised. CSO is used to optimize key hyperparameters of the hybrid model, including the learning rate (0.0001–0.01), batch size (32–128), number of LSTM layers (1–3), number of hidden units per layer (16–128), dropout rate (0.1–0.5), and fusion weights (0–1 for each weight, with the sum constrained to 1) between the LSTM and Autoencoder outputs. In addition, CSO is applied for feature subset selection and threshold tuning to further enhance model performance. Preprocessing is performed on the data, including normalization and feature scaling prior to model training. The suggested framework has a 96.2% accuracy, 94.6% precision, 97.9% recall, 96.2% F1-score, and 0.97 AUC-ROC, showing improved performance compared to CNN-based and LSTM-CNN models under the evaluated conditions. However, since no multiple experiments were conducted to verify the robustness, the results should be interpreted as indicative rather than definitive. The framework exhibits competitive fraud detection performance on the evaluated benchmark dataset, particularly in handling class imbalance. In a simulated environment configured to mimic cloud-like conditions, the framework achieved inference latency between 15 and 30 ms, GPU utilization between 60% and 70%, and a data transfer volume of approximately 1.5 GB per day, suggesting its potential for deployment in cloud-based fraud detection systems. The framework indicates immense potential for cloud deployment, with a robust solution for preventing financial fraud. The proposed framework demonstrates the potential of integrating sequential modeling, anomaly detection, and metaheuristic optimization within a unified and cloud-oriented architecture, providing a more comprehensive approach compared to conventional hybrid models. Full article

Corporate sustainability has emerged as a critical strategic imperative for organizations seeking to mitigate their environmental impacts amid escalating climate pressures and growing stakeholder demands. This study examines corporate strategies aimed at reducing environmental footprints—including circular economy models, energy efficiency measures, and digitalization—and investigates how young adults perceive and evaluate corporate sustainability commitments, with particular emphasis on greenwashing skepticism. A cross-sectional quantitative survey was administered to 150 university students and young professionals aged 18–25 years in Spain. Data were analyzed using descriptive statistics, analysis of variance (ANOVA), and linear regression to examine the influence of prior sustainability knowledge, academic background, age, and sectoral context on perceived corporate sustainability commitment, greenwashing perception, and willingness to consume sustainable products. The findings reveal that prior sustainability knowledge significantly and positively predicts higher evaluations of corporate environmental commitment, while age and academic background—particularly among students in Economics and Business—are associated with heightened greenwashing skepticism. Perceived corporate sustainability commitment is found to exert a significant positive influence on sustainable consumption intention, and production-intensive sectors are consistently perceived as more environmentally harmful than service-oriented industries. These findings underscore the importance of transparent, credible, and verifiable sustainability strategies in building legitimacy and trust among younger generations, and contribute to the growing literature on stakeholder perceptions of corporate environmental responsibility. Full article

This study reveals the internal mechanism of the synergetic evolution of financial support, technological innovation, and industrial upgrading from the perspective of system synergy. It aims to provide a theoretical basis and reference for promoting benign interactions among these elements, thereby driving high-quality economic development. During the research process, an evaluation indicator system was constructed based on China’s industrial development data, utilizing the entropy method to determine indicator weights and the Haken model to analyze synergy effects. In a methodological innovation, this study identifies the system’s order parameters to derive the potential function. Through this approach, it systematically analyzes the dynamic evolution characteristics and synergetic mechanisms of the composite system. The research results indicate that the three systems have formed a mutually promoting and closely coupled compound synergetic mechanism, rather than following a single linear transmission path. The overall synergy level presents a medium-to-low development trend, following an asymmetric U-shaped evolution trajectory that first decreases and then slowly recovers. Furthermore, the degree of synergy exhibits an inverse relationship with the volatility of the subsystems, suggesting that the stability of synergy is highly susceptible to external forces and remains in a state of dynamic flux. Full article

Industrial maintenance and predictive diagnostics constitute fundamental pillars of modern manufacturing that prevent equipment failures, minimize operational downtime, and optimize maintenance costs across diverse industrial environments. Vibration-based fault classification plays an important role in industrial operations, necessitating highly sophisticated diagnostic methodologies. This research addresses these industrial imperatives through a comprehensive investigation of novel hybrid deep learning architectures for vibration-based fault classification. This study introduces a strategic integration of Quadratic Neural Networks (QNNs), which demonstrate superior non-linear feature extraction capabilities on a vibration signal compared to traditional convolutional approaches. A systematic evaluation of seven sophisticated architectures establishes a clear performance hierarchy, with QuCNN-LSTM-Transformer emerging as the optimal model achieving 99.26% average accuracy. All proposed models demonstrate excellence, with test accuracies consistently surpassing 95% across all evaluated scenarios. The data analyzed is emprical utilizing sensor data collected from an experimental rig and shows exceptional performance consistency on CWRU and HUST datasets. This investigation establishes a new paradigm in intelligent diagnostics, offering functional guidance and definitive analysis of hybrid architectures that advance industrial fault classification applications. Full article

This study investigates the structural misalignment between official wage benchmarks and actual market wages in the Republic of Korea to establish an independent, forestry-specific wage system essential for labor sustainability. Historically, the Republic of Korea forestry project costs have relied on construction industry benchmarks, leading to a “diverging hypothesis” where official rates fail to reflect the specialized risks and technical skills required in forest operations. To address this, a comprehensive wage survey was conducted in 2025 across 13 specialized forestry occupations. Utilizing a sampling frame of 7555 sites, 1044 units were selected via stratified sampling with square-root proportional allocation, ensuring a relative standard error (RSE) of 2.5%. The findings reveal that market wages consistently exceed construction benchmarks by 4.5% to 41.0%. The most significant disparities were observed in leadership and mechanized roles, reflecting substantial “risk–responsibility” and “skill premiums”. Furthermore, the study identifies a structural shift toward risk-transfer strategies, such as stumpage sales, in response to the Serious Accidents Punishment Act (SAPA). These results underscore the urgent need for a specialized wage framework to ensure safety and long-term resilience. Ultimately, such institutional refinement is a prerequisite for securing the high-quality human capital necessary for a sustainable circular bioeconomy. Full article

Corporate carbon disclosure has become increasingly important in China’s transition toward sustainability and low-carbon development, yet existing research often focuses on isolated determinants and relies mainly on linear empirical models. Using 48,187 observations of Chinese A-share firms from 2012 to 2024, this study identifies the key predictors of corporate carbon disclosure. It develops an interpretable machine learning model and compares its predictive performance with that of linear regression, LASSO, decision tree, random forest, support vector machine, GBDT, and XGBoost. The results show that ensemble methods outperform linear models in both in-sample and out-of-sample predictions. GBDT delivers the best out-of-sample performance, with an R² of 0.5191, suggesting that nonlinear relationships and interaction effects matter in predicting corporate carbon disclosure. The key factors identified are firm size, media attention, environmental policy intensity, market concentration, and executive financial background. The heterogeneity tests show that regulatory and governance factors are more important for firms in heavily polluting industries, state-owned firms, and firms in central and western China, whereas market factors are more important for firms in eastern China, private firms, and firms in less polluting industries. Overall, the paper provides new evidence on the prediction of corporate carbon disclosure and offers practical implications for regulators and firms seeking to improve their sustainability-related disclosure practices. Full article

The rapid growth of the digital economy has transformed the tourism industry, yet the industrial linkages and environmental impacts of this integration remain underexplored. This study employs an input–output framework to examine the interactions between the digital economy and tourism and their carbon footprint effects in China. Multi-year digital economy–tourism input–output tables for 2017, 2018, 2020, 2022, and 2023 are constructed using sectoral disaggregation and the RAS updating method. Results indicate increasing integration, with tourism more dependent on the digital economy sectors and both industries exerting the strongest influence on the secondary sector. The digital economy shows a gradual shift from hardware manufacturing to information services. Structural decomposition analysis and structural path analysis reveal that technological progress significantly reduces emissions, whereas population growth drives increases. These findings offer empirical evidence for guiding digital–tourism integration and supporting low-carbon strategies in the tourism sector. Full article

Against the backdrop of the full implementation of “antibiotic ban” and “zinc restriction” policies in livestock and poultry breeding, and the growing consumer demand for safe livestock and poultry products, the development of natural and efficient green feed additives has become crucial for the sustainable development of the animal husbandry industry. Curcumin, a natural polyphenolic compound extracted from the rhizome of Curcuma longa L., has attracted extensive attention in poultry production due to its various biological activities and safety. This paper thoroughly reviews the chemical structure and physicochemical properties of curcumin, and elaborates on its core molecular mechanisms of action, which mainly involve the regulation of nuclear factor erythroid 2-related factor 2 (Nrf2)/antioxidant response element (ARE), nuclear factor-κB (NF-κB), peroxisome proliferator-activated receptor γ (PPAR-γ), and mitogen-activated protein kinase (MAPK) pathways to exert antioxidant, anti-inflammatory, antibacterial, immunomodulatory and lipid metabolism regulatory effects. It further clarifies the practical application value of curcumin in major poultry species including broilers, laying hens, ducks and quails, showing that curcumin can significantly improve poultry production performance, optimize meat and egg quality, protect intestinal health, and enhance the ability of poultry to resist stress and diseases. Meanwhile, the review notes curcumin’s current application limitations (low bioavailability, poor stability, unclear standardized dosage, and high industrialization cost) and proposes targeted future research directions to address these issues. In conclusion, curcumin is a promising green feed additive alternative to antibiotics, and its large-scale and standardized application in poultry production will effectively promote the green, healthy and sustainable development of the poultry industry. Full article

As a representative form of extreme weather, typhoons inflict widespread and systemic damage, posing a severe threat to the livestock industry. The stress they induce, typhoon stress (TS), is an unavoidable and complex environmental challenge that severely disrupts the ovarian function of Wenchang chickens. In this preliminary study, we employed a two-group comparison design (n = 6 per group) integrating behavioral observations, serum biochemical assays, histopathological examinations, and molecular analyses (qPCR, 16S rDNA sequencing, and transcriptome sequencing) to explore the role of the microbiota–gut–brain–ovarian axis (MGBOA) in this process. The findings revealed that TS markedly reduced water intake and locomotor activity, while it elevated serum corticosterone (CORT) and oxidative stress markers. It also induced shifts in gut microbiota composition, including a decrease in Bacteroides and an increase in Escherichia–Shigella. Furthermore, TS compromises duodenal intestinal barrier integrity, as evidenced by downregulation of the tight junction proteins TJP1 and CLDN1, structural damage to intestinal villi, and a reduced villus-to-crypt ratio. In the hypothalamus, VIP mRNA expression was upregulated, while GHSR expression was downregulated; the expression of the tight junction protein CLDN5 was also reduced. In the ovary, reproductive potential was suppressed, manifested by a reduction in follicle number and downregulation of STAR expression. Ovarian transcriptome analysis highlighted enrichments in pathways associated with inflammation (e.g., Toll-like receptor signaling) and lipid metabolism (e.g., PPAR signaling). These results support the hypothesis that TS impairs egg production via the MGBOA, providing preliminary mechanistic insights into how environmental stressors might disrupt animal productivity through MGBOA-mediated pathways. Full article

Rising energy costs and strict CO₂ traceability regulations create demand for monitoring energy and CO₂ emissions in manufacturing. This paper presents a framework for modelling component-wise energy models with deployable accuracy. In many factories, power metres log data at a sampling rate of 1–2 Hz, so short start-up peaks of components are underestimated. Manufacturers want to exploit this information to support operational decisions, such as peak shaving and optimising energy contract costs. To enable data-driven decisions with limited measurement infrastructure, energy models must extrapolate component behaviour from sparse data. The framework is based on power measurements in accordance with ISO 14955-3, ensuring that the load characteristics required for subsequent modelling are known. The measurements are then segmented, and regressions are fitted for each segment. As a case study considering the mist extractors of two different machine tools, the proposed segmentation achieved determination coefficients (R²) of up to 0.94 in the complex ramp-up phase. The resulting models are compact, interpretable, and suited for energy monitoring on edge devices. The contribution is a reproducible framework for delivering peak-aware, component-level energy models from low-frequency industrial power metre data. Full article

Deciphering the coloration mechanism of natural-colored cocoons in the domestic silkworm (Bombyx mori) is of great importance for the green and sustainable development of the sericulture industry. In this study, 14 silkworm strains were selected for studying differences in the coloration mechanism of diverse cocoon colors, and the present research carried out integrated investigations from three aspects: pigment content, differences in gene expression levels, and gene structural variation. The results demonstrated that pigment accumulation presented distinct tissue-specific and strain-specific characteristics. The middle silk gland (MSG) acts as the primary locus for pigment deposition: silkworm strains forming yellow or red cocoons accumulate carotenoids at high levels in this tissue, whereas those producing green cocoons show abundant flavonoid enrichment here. Analysis of gene expression profiles indicated that the expression patterns of core transporter genes are highly correlated with the spatial distribution of pigments. The expression level of CBP gene in the MSG is over 10-fold higher than that in the midgut (MG) among yellow/red cocoon strains. The pivotal glycosylation gene UGT86 displayed remarkably elevated expression in the MSG relative to other tissues across all green cocoon silkworm strains. The CBP gene acts as a core regulatory factor governing the transport of carotenoid pigments, and notable disparities existed in the coding region of the gene among silkworm strains with different cocoon colors. In contrast to yellow and red cocoon strains, the transcription start site of CBP gene is displaced in silkworm varieties that form green or white cocoons. In summary, this study clarified the expression patterns and variations in key pigment deposition-related genes at the population level for the first time and provided data references for the study of the biological basis and coloration mechanism of diverse cocoon colors. Full article