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Industrial machine tools are both performance assets and environmental hotspots over their long service lives. Maintenance is traditionally optimized to safeguard availability, quality and cost. However, maintenance choices also determine the energy consumption, footprints, component duration and end-of-life pathways. In this study, we present a decision framework to compare performance-only maintenance (POM) with sustainability-aware maintenance (SAM) for machine tools. The framework integrates degradation and Remaining Useful Life (RUL) estimation, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC). Outcomes are summarized with a Sustainable Maintenance Balance (SMB) index. We test the proposed approach on a horizontal machining center for aluminum, validated by running a Monte Carlo simulation over a 1000 h functional unit. Across empirical data and simulation, SAM—compared to POM—demonstrated an ability to improve availability, reduces downtime and scrap, and lower total LCC while cutting carbon emissions. The proposed method is proposed as readily deployable in real plants, supporting robust sustainable-production decisions. Full article

We present the ML-CALMO framework, which integrates machine learning with queueing theory for last-mile delivery optimization under dynamic conditions. The system combines Long Short-Term Memory (LSTM) demand forecasting, Convolutional Neural Network (CNN) traffic prediction, and Deep Q-Network (DQN)-based routing with theoretical stability guarantees. Evaluation on modern benchmarks, including the 2022 Multi-Depot Dynamic VRP with Stochastic Road Capacity (MDDVRPSRC) dataset and real-world compatible data from OSMnx-based spatial extraction, demonstrates measurable improvements: 18.5% reduction in delivery time and +8.9 pp (≈12.2% relative) gain in service efficiency compared to current state-of-the-art methods, with statistical significance (p < 0.01). Critical limitations include (1) computational requirements that necessitate mid-range GPU hardware, (2) performance degradation under rapid parameter changes (drift rate > 0.5/min), and (3) validation limited to simulation environments. The framework provides a foundation for integrating predictive machine learning with operational guarantees, though field deployment requires addressing identified scalability and robustness constraints. All code, data, and experimental configurations are publicly available for reproducibility. Full article

Enhancing ultra-capacitor (UC) utilization and mitigating battery stress are pivotal for improving the energy management efficiency and service life of hybrid energy storage systems (HESSs). Conventional energy management strategies (EMSs), however, rely on fixed parameters and therefore struggle to allocate power flexibly or reduce battery degradation. This paper proposes a VMD-LSTM-based EMS that incorporates auto-tuning weight and constraint to address these limitations. First, a VMD-LSTM predictor was proposed to improve the velocity and road gradient prediction accuracy, thus leading an accurate power demand for EMS and enabling real-time parameter adaptation, especially in the nonlinear area. Second, the model predictive controller (MPC) was adopted to construct the EMS by solving a multi-objective problem using quadratic programming. Third, a combination of rule-based and fuzzy logic-based strategies was introduced to adjust the weights and constraints, optimizing UC utilization while alleviating the burden on batteries. Simulation results show that the proposed scheme boosts UC utilization by 10.98% and extends battery life by 19.75% compared to traditional MPC. These gains underscore the practical viability of intelligent, optimizing EMSs for HESSs. Full article

In saline soil and alpine regions of northwest China, fiber-reinforced recycled aggregate concrete (FR-RAC) beams are subjected to coupled degradation from a chloride–sulfate composite salt attack and freeze–thaw cycling. Existing studies predominantly focus on natural aggregate concrete in freshwater environments or single-salt solutions, with limited documentation on the shear performance of FR-RAC beams after freeze–thaw exposure in chloride–sulfate composite salt solutions. To investigate the durability degradation patterns of FR-RAC beams in Xinjiang’s saline soil regions, two exposure environments (pure water and 5% NaCl + 2.0% Na₂SO₄ composite salt solution) were established. Shear performance tests were conducted on nine groups of FR-RAC beams after 0–175 freeze–thaw cycles, with measurements focusing on failure modes, cracking loads, and ultimate shear capacities. The results revealed that under composite salt freeze–thaw conditions: after 100 cycles, the cracking load and shear capacity of tested beams decreased by 39.8% and 22.2%, respectively, compared to unfrozen specimens representing reductions 29.6% and 82.0% greater than those in freshwater environments; at 175 cycles, cumulative damage intensified, with total reductions reaching 56.8% (cracking load) and 36.1% (shear capacity). A shear capacity degradation prediction model for FR-RAC beams under composite salt freeze–thaw coupling was developed, accounting for concrete strength attenuation and interfacial bond degradation. Model validation demonstrated excellent agreement between predicted and experimental values, confirming its robust applicability. Full article

Lithium-ion batteries are essential to electric vehicles, so it is crucial to continuously monitor and control their health. However, since today’s battery packs consist of hundreds or thousands of cells, monitoring all of them is challenging. Additionally, the performance of the entire battery pack is often limited by the weakest cell. Therefore, developing effective monitoring techniques that can reliably forecast the remaining time to depletion (RTD) of lithium-ion battery cells is essential for safe and efficient battery management. However, even in robust systems, this data can be lost due to electromagnetic interference, microcontroller malfunction, failed contacts, and other issues. Gaps in voltage measurements compromise the accuracy of data-driven forecasts. This work systematically evaluates how different voltage reconstruction methods affect the performance of recurrent neural network (RNN) forecast models trained to predict RTD through quantile regression. The paper uses experimental battery pack data based on the behavior of an electric vehicle under dynamic driving conditions. Artificial gaps of 500 s were introduced at the beginning, middle, and end of each discharge phase, resulting in over 4300 reconstruction cases. Four reconstruction methods were considered: a zero-order hold (ZOH), an autoregressive integrated moving average (ARIMA) model, a gated recurrent unit (GRU) model, and a hybrid unscented Kalman filter (UKF) model. The results presented here reveal that the UKF model, followed by the GRU model, outperform alternative reconstruction methods. These models minimize signal degradation and provide forecasts similar to the original past data signal, thus achieving the highest coefficient of determination and the lowest error indicators. The reconstructed signals were fed into LSTM and GRU RNNs to estimate RTD, which produced confidence intervals and median values for decision-making purposes. Full article

As a typical sucrose-accumulating fruit, longan commonly experiences sugar receding during on-tree preservation, leading to quality deterioration. To investigate the mechanism of sucrose degradation in longan fruit, we conducted genome-wide identification and analysis of key genes involved in sucrose synthesis and catabolism based on the ‘Shixia’ (SX) genome. The results revealed that longan contained 8 sucrose synthases (SUSs), 4 sucrose phosphate synthases (SPSs), and 26 invertases (INVs). Notably, members of the longan SUS, SPS, and cell wall invertase (CWINV) families all contained the motif 10 sequence, while cytoplasmic invertase (CINV) members exhibited diverse motif combinations. Similarity analysis revealed that sequence similarity was reliable only when the sequence lengths of the compared genes were comparable. Cis-acting elements and miRNA prediction showed that these genes were enriched in MYB elements and regulated by miR156/827/171. Additionally, the expansion of SUS, SPS, and INV genes was driven by segmental duplication events under purifying selection. Furthermore, the ‘Chuliang’ (CL) cultivar exhibited slower on-tree sucrose degradation than SX, with sucrose accounting for 72.2% of total sugars at maturity, which is 33.4% higher than SX. Enzyme activity assay during the sucrose decline stage revealed that SUS, SPS, and INV activities were generally higher in SX pulp than in CL. Furthermore, correlation analysis showed that the activities of AINV and A/N-INV were both significantly negatively correlated with TSS and sucrose content, respectively. Additionally, the expression of DlCWINV10 exhibited a negative correlation with TSS (p < 0.05) and sucrose content (r = −0.6, p = 0.07), suggesting that DlCWINV10 may play an important role in the sucrose degradation process. In summary, this study elucidates the characteristics of SUS, SPS, and INV gene families in longan and their potential roles in sucrose metabolism, providing a theoretical foundation for understanding the on-tree sucrose degradation mechanism. Full article

Background/Objectives: Dipeptidyl peptidase-4 (DPP-4) inhibitors are antidiabetic agents that regulate blood glucose by preventing the degradation of active incretin hormones. Although clinically effective, this drug class is associated with adverse effects, creating the need for new molecular scaffolds with improved safety and efficacy. Methods: We evaluated the antihyperglycemic activity of β-aminohydrazine and β-amino-N-acylhydrazone derivatives (LASSBio-2123, 2125, 2129, and 2130) using a combined in vivo and in silico approach. Male C57BL/6 mice underwent glucose tolerance tests (GTT) and dexamethasone-induced insulin resistance protocols. Hepatic and skeletal muscle glycogen levels, as well as GLUT4 mRNA expression, were quantified. In silico studies included ADMET predictions and molecular docking analyses against aldose reductase and glucokinase enzymes. MTT was performed on the pancreatic cell line MIN6 (Mus musculus). Results: Among the compounds tested, LASSBio-2129 demonstrated the most promising profile, with favorable ADMET parameters, metabolic stability, and high docking affinity for aldose reductase and glucokinase. In vivo, LASSBio-2129 (10 mg/kg, i.p.) reduced blood glucose, increased hepatic and muscle glycogen storage, and upregulated GLUT4 mRNA expression in skeletal muscle. Additionally, LASSBio-2129 improved insulin sensitivity in the dexamethasone-induced insulin resistance model, with effects comparable to sitagliptin. Conclusions: The combined pharmacological, docking, and ADMET analyses identified LASSBio-2129 as aldose reductase inhibitor candidate and glucokinase activator. Its ability to improve glucose tolerance, enhance glycogen storage, and increase GLUT4 expression highlights its potential as a promising molecule for the treatment of type 2 diabetes mellitus. Full article

This study aims to evaluate the restoration effect of artificially mixed-sown grasslands by investigating the characteristics of plant communities and soil fungal communities in long-term (22-year-established) artificial grasslands under six Poaceae mixture combinations. The experiment took mixed-sown grasslands of grass species established in 2002 on the Qinghai–Tibet Plateau as the research object. It employed ITS gene high-throughput sequencing technology to construct a fungal community distribution map and combined it with FUNGuild (Functional Guilds of Fungi) functional predictions to analyze fungal species abundance, structural diversity, molecular co-occurrence networks, and functional characteristics. By integrating Mantel test and RDA (redundancy analysis), we identified key environmental factors driving soil microbial community structure in mixed-sown grasslands and revealed the plant–soil–microbe interaction mechanisms in a Poaceae mixture grassland. The results showed that the HC treatment (a mixture of three grass species) significantly enhanced plant biomass and soil nutrient accumulation. In 2023 and 2024, its aboveground biomass increased by 66.14% and 60.91%, respectively, compared to the HA treatment (monoculture). Soil organic matter increased by 52.32% and 48.35%, while electrical conductivity decreased by 48.99% and 51.72%, respectively. The fungal community structure improved under the HD treatment (a mixture of four grass species), with an increased abundance of the dominant phylum Ascomycota and a 14.44% rise in the Shannon index compared to the HA treatment. The network complexity under the HF treatment (a mixture of six grass species) increased (with edge numbers reaching 494), while the functional abundance of plant pathogen was significantly lower than that under the HA treatment. Mantel test and RDA revealed that SEC (soil electrical conductivity) was significantly positively correlated with pH, while both exhibited negative correlations with other plant and soil physicochemical indicators. Moreover, SEC emerged as the core factor driving fungal community assembly. Mixed sowing of three to four grass species effectively regulated soil electrical conductivity, simultaneously enhancing plant biomass, soil nutrients, and fungal community diversity, representing an optimal strategy for artificial restoration of degraded grasslands. Full article

Bottom sediments play a central role in regulating contaminant dynamics in aquatic systems. They act as both storage sites and reactive zones where contaminants undergo transformation, sequestration, or remobilization. Contaminants primarily enter sediments through anthropogenic activities, including agricultural runoff, industrial effluents, wastewater discharge, urban runoff, and mining operations. This review focuses on six major contaminant groups, including nutrients, heavy metals, pharmaceutical residues, pesticides, polycyclic aromatic hydrocarbons, and microplastics, and examines the mechanistic processes that govern their fate in sediments. The main mechanisms includesorption–desorption on minerals and organic materials, sedimentation, and redox processes that regulate metal immobilization and sulfide formation. The persistence and mobility of contaminants are also influenced by synergistic or antagonistic interactions among pollutants, microbial transformation of organic compounds, and oxidative degradation of microplastics by reactive oxygen species. Contaminants can affect benthic communities by causing toxic effects and oxygen depletion. They also may alter microbial and macrofaunal populations and contribute to bioaccumulation and biomagnification. Ultimately, these insights are important for predicting contaminant behavior and assessing ecological risks, which directly informs the development of effective environmental monitoring programs and sustainable sediment remediation strategies for the long-term protection of aquatic ecosystems. Full article

Fossil fuels are crucial to the global energy supply, with pipelines being a vital transportation method. However, these vital assets are highly susceptible to pitting corrosion, an insidious form of degradation that can lead to catastrophic failures. Unlike uniform corrosion, which represents a symmetric form of material loss, pitting corrosion is a highly asymmetric and localized phenomenon. The inherent complexity and asymmetry of this process make its prediction a significant challenge. To address this, this study presents SSA-CNN-Attention, a deep learning model specifically designed to analyze the complex, nonlinear interactions among environmental factors. The model employs a Convolutional Neural Network (CNN) to extract local features, while a crucial attention mechanism allows it to asymmetrically weight the importance of these features, enhancing its ability to recognize intricate interactions. Additionally, the Sparrow Search Algorithm (SSA) optimizes the model’s hyperparameters for improved accuracy and stability. Furthermore, a post hoc interpretability analysis using the LIME framework validates that the model’s learned feature relationships are consistent with established corrosion science, revealing how the model accounts for the asymmetric influence of key variables. The experimental results demonstrate that the proposed model reduces mean squared error (MSE) by 61.3% and mean absolute error (MAE) by 26.6%, while improving the coefficient of determination (R²) by 28.2% compared to traditional CNNs. These findings highlight the model’s superior performance in predicting a fundamentally asymmetric process and provide valuable insights into the underlying corrosion mechanisms. Full article

In battery energy storage systems, accurately estimating battery capacity and state of health is crucial to ensure satisfactory operation and system efficiency and reliability. However, these tasks present particular challenges under irregular charge–discharge conditions, such as those encountered in renewable energy integration and electric vehicles, where heterogeneous cycling accelerates degradation. This study introduces a hybrid deep learning framework to address these challenges. It combines convolutional layers for localized feature extraction, bidirectional recurrent units for sequential learning and a temporal attention mechanism. The proposed hybrid deep learning model, termed CBATE-Net, uses ensemble averaging to improve stability and emphasizes degradation-critical intervals. The framework was evaluated using voltage, current and temperature signals from four benchmark lithium-ion cells across complete life cycles, as part of the NASA dataset. The results demonstrate that the proposed method can accurately track both smooth and abrupt capacity fade while maintaining stability near the end of the life cycle, an area in which conventional models often struggle. Integrating feature learning, temporal modelling and robustness enhancements in a unified design provides the framework with the ability to make accurate and interpretable predictions, making it suitable for deployment in real-world battery energy storage applications. Full article

The precise prediction of the remaining useful life (RUL) of lithium-ion batteries is of great significance for improving energy management efficiency and extending battery lifespan, and it is widely applied in the fields of new energy and electric vehicles. However, accurate RUL prediction still faces significant challenges. Although various methods based on deep learning have been proposed, the performance of their neural networks is strongly correlated with the hyperparameters. To overcome this limitation, this study proposes an innovative approach that combines the Alpha evolutionary (AE) algorithm with a deep learning model. Specifically, this hybrid deep learning architecture consists of convolutional neural network (CNN), time convolutional network (TCN), bidirectional long short-term memory (BiLSTM) and multi-scale attention mechanism, which extracts the spatial features, long-term temporal dependencies, and key degradation information of battery data, respectively. To optimize the model performance, the AE algorithm is introduced to automatically optimize the hyperparameters of the hybrid model, including the number and size of convolutional kernels in CNN, the dilation rate in TCN, the number of units in BiLSTM, and the parameters of the fusion layer in the attention mechanism. Experimental results demonstrate that our method significantly enhances prediction accuracy and model robustness compared to conventional deep learning techniques. This approach not only improves the accuracy and robustness of battery RUL prediction but also provides new ideas for solving the parameter tuning problem of neural networks. Full article

Fluid flow prediction in clastic heterogeneous reservoirs is a universal issue, especially when diagenetic development supplants structural and depositional controls. We consider this issue in the Middle Miocene Belayim Formation of the Gulf of Suez, a principal syn-rift reservoir where extreme, diagenetically induced pore system heterogeneity thwarts production. Although fault compartmentalization is understood as creating first-order traps, sub-seismic diagenetic controls on permeability anisotropy and reservoir within these traps are not restricted. This study uses a comprehensive set of petrophysical logs (ray gamma, resistivity, density, neutrons, sonic) of four key wells in the western field of Tawila (Tw-1, Tw-3, TW-4, TN-1). We apply an integrated workflow that explicitly derives permeability from petrophysical logs and populates it within a seismically defined structural framework. This study assesses diagenetic controls over reservoir permeability and fluid flow. It has the following primary objectives: (1) to characterize complicated diagenetic assemblage utilizing sophisticated petrophysical crossplots; (2) to quantify the role of shale distribution morphologies in affecting porosity effectiveness utilizing the Thomas–Stieber model; (3) to define hydraulic flow units (HFUs) based on pore throat geometry; and (4) to synthesize these observations within a predictive 3D reservoir model. This multiparadigm methodology, involving M-N crossplotting, Thomas–Stieber modeling, and saturation analysis, deconstructs Tawila West field reservoir complexity. Diagenesis that has the potential to destroy or create reservoir quality, namely the general occlusion of pore throats by dispersed, authigenic clays (e.g., illite) and anhydrite cement filling pores, is discovered to be the dominant control of fluid flow, defining seven unique hydraulic flow units (HFUs) bisecting the individual stratigraphic units. We show that reservoir units with comparable depositional porosity display order-of-magnitude permeability variation (e.g., >100 mD versus <1 mD) because of this diagenetic alteration, primarily via pore throat clogging resulting from widespread authigenic illite and pore occupation anhydrite cement, as quantitatively exemplified by our HFU characterization. A 3D model depicts a definitive NW-SE trend towards greater shale volume and degrading reservoir quality, explaining mysterious dry holes on structurally valid highs. Critically, these diagenetic superimpressions can replace the influence of structural geometry on reservoir performance. Therefore, we determine that a paradigm shift from a highly structured control model to an integrated petrophysical and mineralogical approach is needed. Sweet spot prediction relies upon predicting diagenetic facies distribution as a control over permeability anisotropy. Full article

This paper presents a fixed-time fault-tolerant control strategy based on an improved long short-term memory network for dynamic positioning of unmanned marine vehicles subject to signal quantization, disturbances, and input saturation. Firstly, an improved long short-term memory network optimized by an adaptive mixed-gradient algorithm is developed to accurately estimate external disturbances. Secondly, a fixed-time extended state observer is designed to rapidly predict thruster faults. Subsequently, within a fixed-time control framework, a novel terminal sliding-mode surface incorporating signal quantization parameters is constructed. In addition, a dynamic uniform quantization strategy with tunable sensitivity is introduced to effectively alleviate the performance degradation induced by quantization errors. Based on this, a fixed-time fault-tolerant controller is constructed. Finally, simulation results and comparative experiments are provided to demonstrate the effectiveness of the proposed control scheme. Full article

Photovoltaic systems are a core component of eco-friendly energy technologies and are now widely utilized across the world for power generation. However, solar modules that are continuously exposed to the external environment experience gradual performance degradation, which results in significant power loss and operational problems. Existing aging diagnostic methods such as current–voltage curve analysis and electroluminescence/photoluminescence testing have limitations in terms of real-time monitoring, quantitative evaluation, and applicability to large-scale power plants. To address these challenges, this study proposes a novel degradation detection method that utilizes the first-order derivative of the voltage–power curve of solar modules to extract key features. This method can estimate the number of degraded solar modules within a string and the degree of degradation, enabling early detection of subtle changes in electrical characteristics. In this study, we developed an AI model based on long short-term memory to classify normal and abnormal states and predict aging status, thereby supporting monitoring and early diagnosis. The model architecture was designed to reflect the characteristics of solar power systems, adopting a relatively shallow network due to the time-series data not being excessively long and the feature changes being clear. This design effectively mitigates the issues of overfitting and gradient vanishing, thereby positively contributing to the stability of model training. The training and validation results of the proposed long short-term memory model were verified through MATLAB simulations, confirming its effectiveness in learning and convergence. Full article