This study optimized the extraction, purification, and structural chemical characterization of polysaccharides from fruitless wolfberry bud tea (FWP), and evaluated their antioxidant activities against H2O2-induced oxidative damage in SH-SY5Y cells. Crude FWP was obtained by ultrasonic-assisted water extraction followed by ethanol precipitation. An orthogonal experiment was conducted to optimize decolorization using D301G macroporous resin, achieving a decolorization rate of 74%, a polysaccharide retention rate of 85%, and a protein removal rate of 61%. Two main purified polysaccharide fractions, FWP-1 (52.3 kDa) and FWP-2 (9.95 kDa), were isolated by DEAE-52 and Sephadex G-150 chromatography. Structural analysis revealed that FWP-1 was a neutral heteropolysaccharide rich in glucose and galactose, while FWP-2 was an acidic polysaccharide with a high content of galacturonic acid. In H2O2-induced SH-SY5Y cells, both polysaccharides significantly enhanced cell viability, increased superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH) levels, reduced lactate dehydrogenase (LDH) leakage and malondialdehyde (MDA) content, scavenged excessive reactive oxygen species (ROS), and maintained mitochondrial membrane potential. FWP-2 exhibited stronger ROS-scavenging capacity than FWP-1. This study established reliable methods for the purification and characterization of FWP, and verified their potential as natural antioxidants against neuronal oxidative injury.

Accurate wind power prediction during ramp events remains challenging due to wind speed volatility. This study proposes a hybrid forecasting framework combining improved variational mode decomposition (VMD), a novel ramp factor (RF), and the Informer model. First, a dynamic adaptive VMD method is employed to filter noise and identify abrupt wind speed changes. Subsequently, a similar period matching algorithm, enhanced by the RF and wind speed similarity coefficients, captures historical convergence features. Finally, the Informer network fuses these features with NWP data. Experimental results demonstrate that the proposed method significantly outperforms existing models in accuracy during ramp events, enhancing grid stability.

This study investigates methane leakage and diffusion from a buried high-pressure natural gas pipeline (8 MPa, 1000 mm diameter) using CFD simulations with the DES turbulence model. Based on homogeneous and layered soil models, the influences of soil porosity (0.46 to 0.54), particle size (10 μm to 100 μm), and soil stratification on the spatial and temporal characteristics of methane diffusion are systematically explored. The simulation results show that (1) methane diffuses from the leak hole to the surrounding soil in an ellipsoidal pattern, with the fastest diffusion speed along the pipeline’s axial direction. (2) In homogeneous soil, within the range of soil parameter values considered in this study, the absolute changes in risk assessment indices (FDR, GDR) caused by soil particle size were more significant; whereas the relative percentage changes in risk assessment indicators caused by soil porosity were more pronounced. (3) In layered soil, the permeability contrast between adjacent layers creates the permeability discontinuity interface effect. When a fine-grained or low-porosity layer overlies a coarse-grained layer, the upper layer acts as a hydraulic barrier, prolonging FDT from 130 s to 354 s while promoting significant horizontal spread at the interface. Conversely, a coarse-grained or high-porosity upper layer accelerates vertical breakthrough. These findings provide a scientific basis for risk assessment, monitoring site optimization, and emergency response planning, particularly in regions with heterogeneous stratified soils.

Horizontal wells in bottom-water reservoirs are highly susceptible to water coning during production. Consequently, accurately evaluating the water-control performance of inflow control valves (ICVs) is critical for optimizing completion strategies. Conventional semi-analytical models often struggle to capture the transient dynamics of multiphase flow, while standard numerical reservoir simulators fail to explicitly resolve the complex geometries of completion hardware. To address these limitations, this study proposes a multiscale composite modeling framework tailored for bottom-water reservoirs. At the near-well scale, a semi-analytical model is developed to characterize wellbore hydraulics and the pressure drops induced by ICV completions. At the reservoir scale, a numerical model is employed to simulate multiphase fluid transport, with the two scales coupled via cross-scale pressure field mapping. Validation against NETool software under steady-state conditions confirms the physical consistency of the near-well model in determining zonal flow allocation. Comparisons with conventional equivalent well numerical models demonstrate that the proposed composite model offers superior resolution of ICV-induced flow redistribution, yielding distinct production performance profiles. Furthermore, the integration of a Particle Swarm Optimization (PSO) algorithm enables the dynamic optimization of ICV settings. Results indicate that this composite framework provides a robust theoretical and computational basis for designing and evaluating intelligent water-control completions in bottom-water reservoirs.

Functional nanocomposites have emerged as a transformative class of materials for advanced energy and electronic applications due to their ability to integrate multiple functionalities within engineered nanoscale architectures. This review provides a comprehensive analysis of the fundamental principles governing nanocomposite behavior, including classification frameworks, commonly employed nanofillers, and critical structure–property relationships. Emphasis is placed on interfacial interactions, dispersion quality, percolation phenomena, and anisotropic effects that dictate electrical, thermal, mechanical, and electrochemical performance. State-of-the-art synthesis and fabrication strategies—ranging from solution-based and melt-processing techniques to vapor-phase deposition and additive manufacturing—are systematically examined in relation to microstructural control and scalability. The multifunctional properties of nanocomposites are critically evaluated, highlighting their relevance in energy storage systems, energy conversion technologies, flexible electronics, sensors, and electromagnetic interference shielding. Key challenges, including nanofiller agglomeration, interfacial compatibility, long-term stability, cost, and sustainability considerations, are discussed alongside emerging solutions. Finally, future perspectives focusing on next-generation nanofillers, AI-assisted materials design, and sustainable manufacturing pathways are outlined, providing a roadmap for the rational development and industrial translation of high-performance multifunctional nanocomposites. The scope of this review is deliberately focused on materials-level structure–process–property relationships in functional nanocomposites, rather than on detailed device-level electronic design or application-specific electromechanical implementations.

Industry 4.0 is transforming the way companies manufacture, improve, and distribute products, moving toward fast, intelligent, and flexible manufacturing, which will bring about fundamental changes in enterprises’ production capabilities. The Flexible Job Shop Scheduling Problem (FJSP) allows a single job to be divided into multiple operations, each of which can be processed on multiple machines. Due to its high flexibility and complexity, traditional scheduling methods are difficult to meet the needs of dynamic production. Dispatching rules struggle to effectively perceive the global precedence relationships among jobs and the distribution of machine workloads; metaheuristic approaches suffer from slow iterative convergence; existing deep reinforcement learning methods often employ a single policy network to handle both operation sequencing and machine assignment in a coupled manner, which tends to cause training instability and slow convergence. This paper proposes a deep reinforcement learning model that integrates Multi-Proximal Policy Optimization (MPPO) and Dual Attention Network (DAN) to address the FJSP. The model uses the operation message attention block and machine message attention block of DAN to capture the dependency relationships between operations and the dynamic competitive relationships between machines, respectively, and extract deep features. At the same time, MPPO designs dual actor networks to handle operation sequencing and machine assignment decisions separately, and combines a centralized critic to optimize the policy. This balances exploration and exploitation and improves training stability. Experiments are conducted based on the SD1 and SD2 datasets. In FJSP instances of four scales, the model is compared with PPO-DAN, PPO-HGNN, traditional scheduling rules, and OR-Tools. The results show that the algorithm reduces makespan by up to 4.2% on SD1 and 10.1% on SD2. Moreover, it achieves better performance than traditional scheduling rules. Its comprehensive performance is superior to that of the comparison methods, verifying its effectiveness and practical application potential in solving the FJSP.

Materials derived from renewable and recycled resources offer a promising route toward more sustainable thermoset composites. In this study, waste poly(ethylene terephthalate) (PET) was depolymerized by glycolysis with propylene glycol to obtain a glycolysate, and subsequently polycondensed with biobased propylene glycol, maleic anhydride, and trimethylolpropane diallyl ether to synthesize biobased UV-curable unsaturated polyester resin (UV-bUPR). The composites were prepared with acryloyl-modified Kraft lignin (KrL-A) as a reactive bio-filler using a dual-curing approach, in which rapid UV curing was followed by thermal/redox post-curing to improve conversion and network homogeneity. The structure of the synthesized resin and composites was confirmed by FTIR and NMR spectroscopy. Mechanical properties were evaluated by tensile testing and hardness measurements, while morphology and fracture behavior were analyzed by scanning electron microscopy. The unmodified lignin decreased tensile performance due to limited compatibility with the polyester matrix and the formation of interfacial defects and agglomerates. In contrast, KrL-A exhibited improved dispersion and stronger filler–matrix interactions, resulting in superior mechanical performance. The most pronounced effect of lignin modification was observed at 15 wt.% filler loading, where the tensile strength reached 27.83 MPa, compared with 13.91 MPa for the corresponding unmodified system. The developed composites also showed improved sustainability, assessed through the E-factor, due to the combined use of recycled PET and renewable lignin.

Industrial decarbonisation requires the large-scale integration of renewable energy into energy-intensive processes traditionally characterised by limited flexibility, high heat demands, and strong dependence on fossil fuels. In this context, energy storage, encompassing thermal and electrical storage as well as hydrogen as an energy carrier, emerges as a key enabling solution to reconcile variable renewable supply with industrial process demands. This paper proposes a dynamic techno-economic framework linking sectoral energy profiles to storage sizing and economic performance in industrial renewable integration. Storage technologies are assessed with hydrogen emerging as a long-duration buffer and a solution for decarbonising high-temperature heat. A representative industrial plant with 5 GWh/year energy demand and an 80%/20% thermal-to-electric load split is analysed under increasing solar-to-load ratios (20–60%), with storage technologies evaluated both individually and in hybrid configurations. Results demonstrate that hybrid battery–hydrogen configurations systematically outperform single-technology solutions. Yearly energy cost reductions reach 16.6–33.8% at 20% renewable penetration, 30.0–49.6% at 40%, and 43.4–62.8% at 60%, with advantages over the best standalone option increasing on average from 13.5% to 28.0% as renewable availability rises. Overall, the study identifies scale-dependent feasibility thresholds and highlights small and medium-sized industrial plants as the most actionable deployment context under current technological and market conditions.

Servo valves are critical components in hydraulic control systems; their performance directly affects the accuracy and reliability of systems used in aerospace and construction machinery. In service, micron-scale solid contaminants in hydraulic oil tend to deposit within the narrow clearances between spool and sleeve, causing spool sticking and accelerated wear that degrade system stability and lifetime. This study combines fluid–particle coupling analysis, numerical simulation, and experiments to examine particle motion and migration in representative valve-like flow fields. A force model for particles in viscous hydraulic oil is derived from fluid- and particle-dynamics principles, and two-dimensional CFD–DPM models are constructed for laminar, jet-like, and swirling flow conditions. Parametric simulations explore the influence of flow velocity, particle size, and particle density on particle trajectories and displacement. Results indicate that particle size has the strongest effect on migration behavior, with particle displacement increasing from 0.35% to 30.65% in laminar flow, from 2.31% to 67.08% in jet-like flow, and from 1.93% to 145.09% in swirling flow. Fluid velocity also significantly affects particle displacement, while particle density has a relatively minor influence. Swirling flow produces the largest displacement, followed by jet-like and laminar flow. Finally, a Particle Image Velocimetry (PIV)–style experimental platform on scaled models is used to validate key simulation trends. Findings clarify dominant mechanisms of particle contamination in servo valves and offer guidance for gap optimization and anti-contamination design.

Arsenic-contaminated groundwater is a major environmental concern, particularly in northern Argentina. Here, Microbacterium oxydans AE038-20, isolated from arsenic-rich groundwater, was investigated to elucidate its tolerance and transformation capacity. Growth assays showed that the strain tolerates inorganic arsenic [As(III), As(V)] and methylarsenite [MAs(III)] without significant inhibition. Speciation analyses revealed progressive oxidation of As(III) to As(V), reaching near-complete conversion after 10 days. Similarly, MAs(III) was fully oxidized to MAs(V). Genome sequencing identified ars-related determinants, including arsR, arsC, putative arsenite efflux systems, and arsP, supporting detoxification via arsenate reduction and arsenite efflux. Proteomic analyses confirmed the expression of proteins related to arsenic resistance, oxidative stress response, and metal transport. However, no canonical arsenite oxidases were detected at either the genomic or proteomic level. Despite this, M. oxydans AE038-20 exhibited clear arsenic oxidation activity. The detection of pigment-associated proteins and in vitro oxidation assays suggest an alternative mechanism potentially mediated by redox-active pigments. These findings highlight an alternative pathway for arsenic transformation in environmental bacteria.