Search Results (2,848)

High-silica–alumina coal gangue is rich in kaolinite, quartz, and other mineral components. The potential for resource utilization is huge, but the silica–aluminate structure is highly stable, and it is difficult to achieve efficient dissociation and elemental enrichment using traditional extraction processes. This study selects typical high-silica–alumina coal gangue as the research object and systematically studies the rules of the physical phase transformation mechanism and ion migration behavior in the activation process of the sodium-based additives stage. In addition, a graded leaching and separation processing route is established, realizing the effective separation and extraction of silica–alumina. The key parameters were optimized using response surface methodology (RSM), obtaining the optimal activation conditions of 800 °C, 30 min, and an additives ratio of 0.8. Under these conditions, the highest dissolution rates of silica and alumina are 82.1% and 92.36%, respectively. Characterization techniques such as XRD, FTIR, and SEM reveal that the activation mechanism of coal gangue involves the decomposition of the aluminosilicate framework and the erosion of sodium ions. At the same time, the chemical bonding reorganization contributes to forming water-soluble sodium silicate (Na₂SiO₃) and insoluble nepheline (NaAlSiO₄), which significantly promotes the release of Si and Al. When the activation temperature is too high, the nepheline phase is transformed into amorphous glassy sodium aluminate and precipitated on the surface, which gradually encapsulates the sodium silicate. This encapsulation restricts dissolution pathways, thereby leading to system densification. Moreover, enhanced resistance to acid attack leads to a decrease in the dissolution rates of Si and Al. This study elucidates the mineral phase reconstruction and element migration mechanisms involved in sodium-based activation and presents a viable approach for the high-value utilization of coal gangue. Full article

Blending kaolin is an effective method to alleviate fouling and slagging during the combustion of Zhundong coal. The influence of blending kaolin with varying particle sizes on the adsorption behavior of alkali metal sodium and the ash fusion characteristics was studied using sodium capture experiments and ash fusion temperature tests. The results indicate that kaolin particle size is a critical factor influencing sodium retention. As the particle size decreases, the sodium retention rate increases accordingly. In the absence of kaolin, the sodium retention rate was only 28.03%. However, when 75–100 µm particles of kaolin were added, the retention rate increased to 43.49%. Further reducing the particle size to 20–63 µm resulted in an additional increase of 10.51%. Additionally, decreasing the kaolin particle size contributed to the noticeable increase in ash fusion temperatures. After 75–100 µm kaolin was blended, the DT and FT of the ash were 1137 °C and 1161 °C, respectively. With 20–63 µm kaolin, the DT increased by 42 °C, and the FT increased by 36 °C. This trend is attributed to the enhanced decomposition and transformation of sulfates in the ash, which promotes the formation of high-melting-point feldspar minerals such as anorthite and gehlenite. These findings provide important data support for understanding the influence of kaolin particle size on the ash fusion behavior during the combustion of Zhundong coal. Full article

Temperature and precipitation are the primary factors restricting litter decomposition in desert ecosystems. The desert ecosystems in Central Asia are ecologically fragile regions, and the climate shows a trend of “warm and wet” due to the regional climate change. However, the influencing mechanisms of warming and winter snow changes on litter decomposition are still poorly understood in desert ecosystems. Furthermore, the litter decomposition rate cannot be directly compared due to the large variations in litter quality across different ecosystems. Here, we simulated warming and altered winter snow changes in the field, continuously monitored litter decomposition rates of standard litter bags (i.e., red tea and green tea) and a dominant plant species (i.e., Erodium oxyrrhynchum) during a snow-cover and non-snow-cover period over five months. We found that warming and increased snow cover increased the litter decomposition rate of red tea, green tea, and Erodium oxyrhinchum, and had significant synergistic effects on litter decomposition. The effects of warming and winter snow changes on litter decomposition were more pronounced in April, when the hydrothermal conditions were the best. The decomposition rates of all three litter types belowground were higher than those on the soil surface, highlighting the important roles of soil microbes in accelerating litter decomposition. Furthermore, we found that warming and winter snow changes altered litter decomposition by influencing soil enzyme activities related to soil carbon cycling during the snow-cover period, while influencing soil enzyme activities related to soil phosphorus cycling during the non-snow-cover period. And, notably, decreased snow cover promoted soil enzyme activities during the snow-cover period. More interestingly, our results indicated that the decomposition rate (k) was the lowest, but the stability factor (S) was the highest in the Gurbantünggüt Desert based on the cross-ecosystem comparison using the “Tea Bag Index” method. Overall, our results highlighted the critical roles of warming and winter snow changes on litter decomposition. In future research, the consideration of relationships between litter decomposition and soil carbon sequestration will advance our understanding of soil carbon cycling under climate change in desert ecosystems. Full article

Adenosine nucleotides and polyphosphates play a significant role in biochemistry, from participating in the formation of genetic material to serving as metabolic energy currency. In this study, we examine the stability and decomposition rates of adenosine phosphates—5′-AMP, 5′-ADP, and 5′-ATP (mentioned simply as AMP, ADP and ATP hereafter)—at temperatures of 22–25 °C, 50–55 °C, 70–75 °C, and 85–90 °C, at a pH of 4, over periods of 2 and 4 days, in both saltwater and ultrapure water, under unsealed and completely dried down conditions. We found that adenosine phosphates degrade rapidly under heat and dehydration, particularly at temperatures above 25 °C. Among the three compounds, AMP is the most stable, maintaining its integrity between 22 and 55 °C, whereas ATP begins to degrade at 22–25 °C and ADP is completely decomposed at temperatures above this range. Decomposition rates were analyzed using quantitative ³¹P-NMR, based on the detection of various phosphorus-containing species. AMP primarily hydrolyzed into phosphate, pyrophosphate and even formed 2′,3′-cAMP. In contrast, the condensed adenosine phosphates (ADP and ATP) hydrolyzed to AMP, phosphate, pyrophosphate, triphosphate, 5′-AMP and, in some cases, 2′,3′-cyclic adenosine monophosphate (2′,3′-cAMP). We also investigated the formation of these compounds in the presence of N-containing additives such as thiourea, urea, imidazole, and cyanamide at temperatures between 65 and 70 °C. Among these, cyanamide and urea were particularly effective in promoting the synthesis of adenosine monophosphates (AMPs) from phosphate and adenosine. The major products observed were 2′,3′,5′-AMPs and cyclic 2′,3′-AMPs. In some experiments, adenosine diphosphate (ADP) and dimeric nucleotide species were also detected. These findings suggest that moderately heated evaporating pools could facilitate the abiotic formation of AMPs. However, such environments would likely have been unsuitable for the long-term accumulation of these compounds due to continued degradation, though there would exist some level of these nucleotides at steady state. Full article

During operation, failures in a centrifugal pump’s rotary shaft seal—such as wear, deformation, or thermal cracking—can adversely affect system performance. This study utilizes both theoretical and experimental methods to investigate the vibration characteristics of centrifugal pumps under different rotary-shaft-seal conditions. Vibration signals are collected and processed using empirical mode decomposition (EMD) and autoregressive (AR) modeling to generate an EMD-AR spectrum. The results show that rotary-shaft-seal failure leads to decreases in both the head and efficiency of the centrifugal pump. For improved operation stability, centrifugal pumps should operate at or slightly above their design flow rates (Q_d), while avoiding low-flow conditions. Furthermore, the amplitude of the EMD-AR spectrum increases progressively as rotary-shaft-seal degradation worsens. Therefore, the EMD-AR spectrum provides a reliable diagnostic indicator for detecting rotary-shaft-seal damage. Full article

Blast furnace (BF) ironmaking remains one of the most efficient countercurrent processes; however, achieving further CO₂ emission reductions through conventional methods is increasingly challenging. Currently, BF ironmaking emits approximately 2.33 tonnes of fossil-derived CO₂ per tonne of crude steel cast. Integrating briquettes composed of biochar and in-plant fines into the BF process offers a promising short- to medium-term strategy for lowering emissions. This approach enables efficient recycling of fine residues and the substitution of fossil reductants with bio-based alternatives, thereby improving productivity while reducing energy and carbon intensity. This study investigates the reduction behavior of (i) biochar mixed with pellet fines, (ii) various in-plant residues individually, and (iii) briquettes composed of biochar and in-plant fines. The reduction rate of biochar–pellet fine mixtures was found to depend on biochar type, with pyrolyzed pine sawdust exhibiting the highest reactivity, and pyrolyzed contorta wood chips the lowest. A correlation between reduction rate and the alkali index of each char was established, although other factors such as char origin and physical properties also influenced reactivity. The effect of biochar addition (0, 5, and 10 wt.%) on the reduction of steelmaking residues was also studied. In general, biochar enhanced the reduction degree and shifted the reaction onset to lower temperatures. The produced briquettes maintained high mechanical integrity during and after reduction, regardless of biochar origin. Thermogravimetric and XRD analyses revealed that mass loss initiates with the dehydroxylation of cement phases and release of volatiles, followed by carbonate decomposition and reduction of higher oxides above 500 °C. At temperatures ≥ 850 °C, the remaining iron oxides were further reduced to metallic iron. Full article

Agriculture is fundamental to food security and environmental sustainability. Advancing its holistic ecological transformation can stimulate socioeconomic progress while fostering human–nature harmony. Utilizing provincial data from mainland China (2013–2022), this research establishes a multidimensional evaluation framework across four pillars: agricultural ecology, low-carbon practices, modernization, and productivity enhancement. Through comprehensive assessment, we quantify China’s low-carbon green agriculture (LGA) development trajectory and conduct comparative regional analysis across eastern, central, and western zones. As for methods, this study employs multiple econometric approaches: LGA was quantified using the TOPSIS entropy weight method at the first step. Moreover, multidimensional spatial–temporal patterns were characterized through ArcGIS spatial analysis, Dagum Gini coefficient decomposition, Kernel density estimation, and Markov chain techniques, revealing regional disparities, evolutionary trajectories, and state transition dynamics. Last but not least, Tobit regression modeling identified driving mechanisms, informing improvement strategies derived from empirical evidence. The key findings reveal the following: 1. From 2013 to 2022, LGA in China fluctuated significantly. However, the current growth rate is basically maintained between 0% and 10%. Meanwhile, LGA in the vast majority of provinces exceeds 0.3705, indicating that LGA in China is currently in a stable growth period. 2. After 2016, the growth momentum in the central and western regions continued. The growth rate peaked in 2020, with some provinces having a growth rate exceeding 20%. Then the growth rate slowed down, and the intra-regional differences in all regions remained stable at around 0.11. 3. Inter-regional differences are the main factor causing the differences in national LGA, with contribution rates ranging from 67.14% to 74.86%. 4. LGA has the characteristic of polarization. Some regions have developed rapidly, while others have lagged behind. At the end of our ten-year study period, LGA in Yunnan, Guizhou and Shanxi was still below 0.2430, remaining in the low-level range. 5. In the long term, the possibility of improvement in LGA in various regions of China is relatively high, but there is a possibility of maintaining the status quo or “deteriorating”. Even provinces with a high level of LGA may be downgraded, with possibilities ranging from 1.69% to 4.55%. 6. The analysis of driving factors indicates that the level of economic development has a significant positive impact on the level of urban development, while the influences of urbanization, agricultural scale operation, technological input, and industrialization level on the level of urban development show significant regional heterogeneity. In summary, during the period from 2013 to 2022, although China’s LGA showed polarization and experienced ups and downs, it generally entered a period of stable growth. Among them, the inter-regional differences were the main cause of the unbalanced development across the country, but there was also a risk of stagnation and decline. Economic development was the general driving force, while other driving factors showed significant regional heterogeneity. Finally, suggestions such as differentiated development strategies, regional cooperation and resource sharing, and coordinated policy allocation were put forward for the development of LGA. This research is conducive to providing references for future LGA, offering policy inspirations for LGA in other countries and regions, and also providing new empirical results for the academic community. Full article

This paper proposes a rolling bearing fault diagnosis method based on HFMD and a dual-branch parallel network, aiming to address the issue of diagnostic accuracy being compromised by the disparity in data quality across different source domains due to sparse feature separation in rolling bearing acoustic signals. Traditional methods face challenges in feature extraction, sensitivity to noise, and difficulties in handling coupled multi-fault conditions in rolling bearing fault diagnosis. To overcome these challenges, this study first employs the HawkFish Optimization Algorithm to optimize Feature Mode Decomposition (HFMD) parameters, thereby improving modal decomposition accuracy. The optimal modal components are selected based on the minimum Residual Energy Index (REI) criterion, with their time-domain graphs and Continuous Wavelet Transform (CWT) time-frequency diagrams extracted as network inputs. Then, a dual-branch parallel network model is constructed, where the multi-scale residual structure (Res2Net) incorporating the Efficient Channel Attention (ECA) mechanism serves as the temporal branch to extract key features and suppress noise interference, while the Swin Transformer integrating multi-stage cross-scale attention (MSCSA) acts as the time-frequency branch to break through local perception bottlenecks and enhance classification performance under limited resources. Finally, the time-domain graphs and time-frequency graphs are, respectively, input into Res2Net and Swin Transformer, and the features from both branches are fused through a fully connected layer to obtain comprehensive fault diagnosis results. The research results demonstrate that the proposed method achieves 100% accuracy in open-source datasets. In the experimental data, the diagnostic accuracy of this study demonstrates significant advantages over other diagnostic models, achieving an accuracy rate of 98.5%. Under few-shot conditions, this study maintains an accuracy rate no lower than 95%, with only a 2.34% variation in accuracy. HFMD and the dual-branch parallel network exhibit remarkable stability and superiority in the field of rolling bearing fault diagnosis. Full article

Straw return is widely adopted to promote agricultural sustainability, but it can also increase cadmium (Cd) bioavailability in contaminated paddy soils, potentially leading to higher Cd accumulation in rice grains. Although numerous studies have investigated straw incorporation, the specific differences between early- and late-season straw return regarding Cd dynamics within double-cropping rice systems remain inadequately characterized. To address this knowledge gap, we conducted a two-year field experiment comparing early-rice (ER) and late-rice (LR) straw return, complemented by controlled pot experiments simulating ER (ER-S, ER-CK; July–September 2023) and LR (LR-S, LR-CK; December 2022–March 2023) straw incorporation. The results revealed that the Total-Cd exhibited an upward trend following both ER and LR straw incorporation. The ER treatment caused a rapid yet short-lived increase in CaCl₂-extractable Cd (CaCl₂-Cd) concentration, peaking around 60 days following straw return and exhibiting a 28.83% increase compared to the LR treatment. In contrast, the LR treatment induced a slower but more prolonged Cd release, with CaCl₂-Cd concentration peaking around 210 days and exhibiting a 34.89% increase relative to the ER treatment. Additionally, at the late-rice stage, grain Cd concentration in the ER treatment increased by 23.64% relative to the LR treatment. In the subsequent year, grain Cd concentrations in the LR treatment increased significantly by 32.12% to 45.08% compared to the ER treatment for both early- and late-rice crops. These differences were attributed to variations in straw decomposition rates, soil pH, and redox potential between warm, aerobic summer–autumn conditions and cooler, anaerobic winter–spring conditions. This suggests that returning late-rice straw constitutes an elevated hazard to soil health and rice safety compared to early-rice straw return. Full article

Current methods of detecting bogie gearbox faults mainly depend on manual judgment, which leads to inaccurate fault identification. In this study, a fault diagnosis model is proposed based on a pied kingfisher optimizer-improved complete ensemble empirical mode decomposition with adaptive noise (PKO-ICEEMDAN), improved multi-scale weighted permutation entropy (IMWPE), and a starfish optimization algorithm optimizing a least-squares support vector machine (SFOA-LSSVM). Firstly, the acceleration signals of a bogie gearbox under six different working conditions were extracted through experiments. Secondly, the acceleration signals were decomposed by ICEEMDAN optimized by PKO to obtain the intrinsic mode function (IMF). Thirdly, IMFs with rich fault information were selected to reconstruct the signals according to the double screening criteria of both the correlation coefficient and variance contribution rate, and the IMWPE of the reconstructed signals was extracted. Finally, IMWPE as a feature vector was input into LSSVM optimized by the SFOA for fault diagnosis and compared with various models. The results show that the average accuracy of the training data of the proposed model was 99.13%, and the standard deviation was 0.09, while the average accuracy of the testing data was 99.44%, and the standard deviation was 0.12. Thus, the effectiveness of the proposed fault diagnosis model for the bogie gearbox was verified. Full article

Global ambitions for net-zero emissions and resource circularity are propelling industry from linear “make-use-dispose”models toward closed-loop value creation. Remanufacturing, which aims to restore end-of-life products to a “like-new” condition, plays a central role in this transition. However, its stochastic inputs and complex, multi-stage processes pose significant challenges to traditional production planning methods. This study delivers an integrated overview of remanufacturing scheduling by combining a systematic bibliometric review of 190 publications (2005–2025) with a critical synthesis of modelling approaches and enabling technologies. The bibliometric results reveal five thematic clusters and a 14% annual growth rate, highlighting a shift from deterministic, shop-floor-focused models to uncertainty-aware, sustainability-oriented frameworks. The scheduling problems are formalised to capture features arising from variable core quality, multi-phase precedence, and carbon reduction goals, in both centralised and cloud-based systems. Advances in human–robot disassembly, vision-based inspection, hybrid repair, and digital testing demonstrate feedback-rich environments that increasingly integrate planning and execution. A comparative analysis shows that, while mixed-integer programming and metaheuristics perform well in small static settings, dynamic and large-scale contexts benefit from reinforcement learning and hybrid decomposition models. Finally, future directions for dynamic, collaborative, carbon-conscious, and digital-twin-driven scheduling are outlined and investigated. Full article

Conventional rail transit train running gear fault diagnosis mainly depends on routine maintenance inspections and manual judgment. However, these approaches lack robustness under complex operational environments and elevated noise levels, rendering them inadequate for real-time performance and the rigorous accuracy standards demanded by modern rail transit systems. Furthermore, many existing deep learning–based methods suffer from inherent limitations in feature extraction or incur prohibitive computational costs when processing multivariate time series data. This study represents one of the early efforts to introduce the TimesNet time series modeling framework into the domain of fault diagnosis for rail transit train running gear. By utilizing an innovative multi-period decomposition strategy and a mechanism for reshaping one-dimensional data into two-dimensional tensors, the framework enables advanced temporal-spatial representation of time series data. Algorithm validation is performed on both the high-speed train running gear bearing fault dataset and the multi-mode fault diagnosis datasets of gearbox under variable working conditions. The TimesNet model exhibits outstanding diagnostic performance on both datasets, achieving a diagnostic accuracy of 91.7% on the high-speed train bearing fault dataset. Embedded deployment experiments demonstrate that single-sample inference is completed within 70.3 ± 5.8 ms, thereby satisfying the real-time monitoring requirement (<100 ms) with a 100% success rate over 50 consecutive tests. The two-dimensional reshaping approach inherent to TimesNet markedly enhances the capacity of the model to capture intrinsic periodic structures within multivariate time series data, presenting a novel paradigm for the intelligent fault diagnosis of complex mechanical systems in train running gears. The integrated human–machine interaction system includes a comprehensive closed-loop process encompassing detection, diagnosis, and decision-making, thereby laying a robust foundation for the continued development of train running gear predictive maintenance technologies. Full article

Self-healing coatings can replace conventional coatings and are capable of self-healing and continuing to protect the substrate after coating damage. In this study, two types of self-healing resins were synthesized as coatings: Type-A via Diels–Alder crosslinking of furfuryl-modified diglycidyl ether bisphenol A with bismaleimide, and Type-B through epoxy blending/curing to form a semi-interpenetrating network. FTIR and Raman spectroscopy confirmed the formation of Diels–Alder (DA) bonds, while GPC tests indicated incomplete monomer conversion. Both resins were applied to glass and wood substrates, with performance evaluated through TGA, colorimetry (ΔE), gloss analysis, and scratch-healing tests (120 °C/30 min). The results showed that Type-A resins had a higher healing efficiency (about 80% on glass substrates and 60% on wood substrates), while Type-B resins had a lower healing rate (about 65% on glass substrates and 55% on wood substrates). However, Type-B is more heat-resistant, has a slower decomposition rate between 300 and 400 °C, higher gloss retention, and less color difference (ΔE) between wood and glass substrates. The visible light transmission of Type-B (74.14%) is also significantly higher. Full article

To enhance the accuracy and precision of fault diagnosis and location for the collector lines in wind farms under complex operating conditions, an intelligent combined method based on CNN-LSTM and ICEEMDAN-PE-improved wavelet threshold denoising is proposed. A wind power plant model is established using the PSCADV46/EMTDC software. In response to the issue of indistinct fault current signal characteristics under complex fault conditions, a hybrid fault diagnosis model is constructed using CNN-LSTM. The convolutional neural network is utilized to extract the local time-frequency features of the current signals, while the long short-term memory network is employed to capture the dynamic time series patterns of faults. Combined with the improved phase-mode transformation, various types of faults are intelligently classified, effectively resolving the problem of fault feature extraction and achieving a fault diagnosis accuracy rate of 96.5%. To resolve the problem of small fault current amplitudes, low fault traveling wave amplitudes, and difficulty in accurate location due to noise interference in actual wind farms with high-resistance grounding faults, a combined denoising algorithm based on ICEEMDAN-PE-improved wavelet threshold is proposed. This algorithm, through the collaborative optimization of modal decomposition and entropy threshold, significantly improves the signal-to-noise ratio and reduces the root mean square error under simulated conditions with injected Gaussian white noise, stabilizing the fault location error within 0.5%. Extensive simulation results demonstrate that the fault diagnosis and location method proposed in this paper can effectively meet engineering requirements and provide reliable technical support for the intelligent operation and maintenance system of a wind farm. Full article

Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in machine learning and signal decomposition, challenges remain in high-dimensional data handling and parameter optimization. This study mitigates these constraints by introducing an innovative enhanced prediction framework that integrates the optimal complete ensemble empirical mode decomposition with adaptive noise (OCEEMDAN) method and a strategically optimized combination weight prediction model. The grey wolf optimizer (GWO) is employed to autonomously modify the noise parameters of OCEEMDAN, while the zebra optimization algorithm (ZOA) dynamically fine-tunes the weights of predictive models—Bi-LSTM, GRU, and FNN. The proposed methodology exhibits enhanced prediction accuracy and robustness through simulation experiments on exchange rate data (EUR/USD, GBP/USD, and USD/JPY). This research improves the precision of exchange rate forecasts and introduces an innovative approach to enhancing model efficacy in volatile financial markets. Full article