Search Results (3,338)

Short-term passenger flow prediction provides critical data-driven support for optimizing resource allocation, guiding passenger mobility, and enhancing risk response capabilities in urban rail transit systems. To further improve prediction accuracy, this study proposes a hybrid SMA-VMD-Informer-BiLSTM prediction model. Addressing the challenge of error propagation caused by non-stationary components (e.g., noise and abrupt fluctuations) in conventional passenger flow signals, the Variational Mode Decomposition (VMD) method is introduced to decompose raw flow data into multiple intrinsic mode functions (IMFs). A Slime Mould Algorithm (SMA)-based optimization mechanism is designed to adaptively tune VMD parameters, effectively mitigating mode redundancy and information loss. Furthermore, to circumvent error accumulation inherent in serial modeling frameworks, a parallel prediction architecture is developed: the Informer branch captures long-term dependencies through its ProbSparse self-attention mechanism, while the Bidirectional Long Short-Term Memory (BiLSTM) network extracts localized short-term temporal patterns. The outputs of both branches are fused via a fully connected layer, balancing global trend adherence and local fluctuation characterization. Experimental validation using historical entry flow data from Weihouzhuang Station on Xi’an Metro demonstrated the superior performance of the SMA-VMD-Informer-BiLSTM model. Compared to benchmark models (CNN-BiLSTM, CNN-BiGRU, Transformer-LSTM, ARIMA-LSTM), the proposed model achieved reductions of 7.14–53.33% in $f_{mse}$, 3.81–31.14% in $f_{rmse}$, and 8.87–38.08% in $f_{mae}$, alongside a 4.11–5.48% improvement in $R^2$. Cross-station validation across multiple Xi’an Metro hubs further confirmed robust spatial generalizability, with prediction errors bounded within $f_{mse}$: 0.0009–0.01, $f_{rmse}$: 0.0303–0.1, $f_{mae}$: 0.0196–0.0697, and $R^2$: 0.9011–0.9971. Furthermore, the model demonstrated favorable predictive performance when applied to forecasting passenger inflows at multiple stations in Nanjing and Zhengzhou, showcasing its excellent spatial transferability. By integrating multi-level, multi-scale data processing and adaptive feature extraction mechanisms, the proposed model significantly mitigates error accumulation observed in traditional approaches. These findings collectively indicate its potential as a scientific foundation for refined operational decision-making in urban rail transit management, thereby significantly promoting the sustainable development and long-term stable operation of urban rail transit systems. Full article

While intermittent desaturations are a common occurrence in near-term and term infants, these may not be benign events with fluctuations in oxygen saturation associated with later neurodevelopmental impairment. Further, intermittent desaturation events do not necessarily result in intermittent hypoxia (IH). Polysomnography is the gold standard to diagnose intermittent desaturations in infants; however, it remains an expensive, inaccessible test. Therefore, overnight oximetry; an economical and more readily available test, is routinely used in this population. Overnight oximetry employs measurement of peripheral cutaneous arterial oxygen saturation (SpO₂) alone to inform clinical management of intermittent desaturations. Management strategies include discharging near-term and term infants on low-flow long-term oxygen therapy (LTOT), typically for upwards of six months. Oxygen saturation targets for neonates have been widely studied. However, interpretation of overnight oximetry is problematic due to a lack of established reference ranges with current approaches still based on limited evidence. This raises questions of the clinical relevancy of overnight oximetry in infants for diagnosing IH and the resultant need for LTOT. Given the association between IH and later neurodevelopmental impairment, concurrent measurement of cerebral tissue oxygen saturation by near-infrared spectroscopy (NIRS) may better identify those near-term and term infants in need of LTOT. Here we review the emerging evidence for the clinical use of cerebral NIRS and the relevance of overnight oximetry in identifying IH in near-term and term newborns and its potential role in identifying those requiring LTOT. Full article

Growing global supply chain uncertainties significantly threaten China’s forage import security. The evolving characteristics of the global forage trade network directly impact the stability of China’s supply. This study constructs a directed, weighted trade network based on global forage products trade data (2000–2024). Using complex network analysis methods, it systematically analyzes the network’s topological structure and evolutionary patterns, with a focus on their impact on China’s import security. The study addresses the following questions: What evolutionary patterns does the global forage trade network exhibit in terms of its topological structure? How does the evolution of this network impact the import security of forage products in China, specifically regarding supply chain stability and risk resilience? The research findings indicate the following: (1) From 2000 to 2024, the total volume of global forage products trade increased by 48.17%, primarily driven by forage products excluding alfalfa meal and pellets, which accounted for an average of 82.04% of volume annually. Additionally, the number of participating countries grew by 21.95%. (2) The global forage products trade network follows a power–law distribution, characterized by increasing network density, a clustering coefficient that initially declines and then rises, and a shortening of the average path length. (3) The core structure of the global forage products trade network shows an evolutionary trend of diffusion from core nodes in North America, Oceania, and Asia to multiple core nodes, including those in North America, Oceania, Europe, Africa, and Asia. (4) China’s forage products trade network displays distinct phase characteristics; however, imports face significant risks from high supply chain dependency and exposure to international price fluctuations. Based on these conclusions, it is recommended that China actively expands trade relations with potential product-exporting countries in Africa, encouraging enterprises to “go global.” Additionally, China should establish a three-dimensional supply chain security system, comprising maritime, land, and storage components, to enhance risk resistance and import safety. Full article

This study presents the development and evaluation of an automated ultraviolet-C irradiation system for maize seed treatment, emphasizing disinfection performance, environmental control, and vision-based monitoring. The system features dual 8-watt ultraviolet-C lamps, sensors for temperature and humidity, and an air extraction unit to regulate the microclimate of the chamber. Without air extraction, radiation stabilized within one minute, with internal temperatures increasing by 5.1 °C and humidity decreasing by 13.26% over 10 min. When activated, the extractor reduced heat build-up by 1.4 °C, minimized humidity fluctuations (4.6%), and removed odors, although it also attenuated the intensity of ultraviolet-C by up to 19.59%. A 10 min ultraviolet-C treatment significantly reduced the fungal infestation in maize seeds by 23.5–26.25% under both extraction conditions. Thermal imaging confirmed localized heating on seed surfaces, which stressed the importance of temperature regulation during exposure. Notable color changes ($\Delta E>2.3$) in treated seeds suggested radiation-induced pigment degradation. Ultraviolet-C intensity mapping revealed spatial non-uniformity, with measurements limited to a central axis, indicating the need for comprehensive spatial analysis. The integrated computer vision system successfully detected seed contours and color changes under high-contrast conditions, but underperformed under low-light or uneven illumination. These limitations highlight the need for improved image processing and consistent lighting to ensure accurate monitoring. Overall, the chamber shows strong potential as a non-chemical seed disinfection tool. Future research will focus on improving radiation uniformity, assessing effects on germination and plant growth, and advancing system calibration, safety mechanisms, and remote control capabilities. Full article

With the rapid evolution of artificial intelligence technologies in power systems, data-driven time-series forecasting has become instrumental in enhancing the stability and reliability of power systems, allowing operators to anticipate demand fluctuations and optimize energy distribution. Despite the notable progress made by current methods, they are still hindered by two major limitations: most existing models are relatively small in architecture, failing to fully leverage the potential of large-scale models, and they are based on fixed nonlinear mapping functions that cannot adequately capture complex patterns, leading to information loss. To this end, an LLM-Empowered Kolmogorov–Arnold frequency learning (LKFL) is proposed for time series forecasting in power systems, which consists of LLM-based prompt representation learning, KAN-based frequency representation learning, and entropy-oriented cross-modal fusion. Specifically, LKFL first transforms multivariable time-series data into text prompts and leverages a pre-trained LLM to extract semantic-rich prompt representations. It then applies Fast Fourier Transform to convert the time-series data into the frequency domain and employs Kolmogorov–Arnold networks (KAN) to capture multi-scale periodic structures and complex frequency characteristics. Finally, LKFL integrates the prompt and frequency representations through an entropy-oriented cross-modal fusion strategy, which minimizes the semantic gap between different modalities and ensures full integration of complementary information. This comprehensive approach enables LKFL to achieve superior forecasting performance in power systems. Extensive evaluations on five benchmarks verify that LKFL sets a new standard for time-series forecasting in power systems compared with baseline methods. Full article

MXenes, a rapidly emerging family of two-dimensional transition metal carbides and nitrides, have attracted considerable attention in recent years for their potential in next-generation energy storage technologies. In solid-state batteries (SSBs), they combine metallic-level conductivity (>10³ S cm⁻¹), adjustable surface terminations, and mechanical resilience, which makes them suitable for diverse functions within the cell architecture. Current studies have shown that MXene-based anodes can deliver reversible lithium storage with Coulombic efficiencies approaching ~98% over 500 cycles, while their use as conductive additives in cathodes significantly improves electron transport and rate capability. As interfacial layers or structural scaffolds, MXenes effectively buffer volume fluctuations and suppress lithium dendrite growth, contributing to extended cycle life. In solid polymer and composite electrolytes, MXene fillers have been reported to increase Li⁺ conductivity to the 10⁻³–10⁻² S cm⁻¹ range and enhance Li⁺ transference numbers (up to ~0.76), thereby improving both ionic transport and mechanical stability. Beyond established Ti-based systems, double transition metal MXenes (e.g., Mo₂TiC₂, Mo₂Ti₂C₃) and hybrid heterostructures offer expanded opportunities for tailoring interfacial chemistry and optimizing energy density. Despite these advances, large-scale deployment remains constrained by high synthesis costs (often exceeding USD 200–400 kg⁻¹ for Ti₃C₂T_x at lab scale), restacking effects, and stability concerns, highlighting the need for greener etching processes, robust quality control, and integration with existing gigafactory production lines. Addressing these challenges will be crucial for enabling MXene-based SSBs to transition from laboratory prototypes to commercially viable, safe, and high-performance energy storage systems. Beyond summarizing performance, this review elucidates the mechanistic roles of MXenes in SSBs—linking lithiophilicity, field homogenization, and interphase formation to dendrite suppression at Li|SSE interfaces, and termination-assisted salt dissociation, segmental-motion facilitation, and MWS polarization to enhanced electrolyte conductivity—thereby providing a clear design rationale for practical implementation. Full article

The harsh conditions of the space environment necessitate advanced materials capable of withstanding extreme temperature fluctuations and ultraviolet (UV) radiation. While epoxy-based composites are widely utilized in aerospace due to their favorable strength-to-weight ratio, they are prone to degradation, especially under prolonged high-energy UV-C exposure. This study investigated the mechanical and chemical stability of epoxy composites reinforced with nanosilica at 0, 2, 5, and 10 wt% before and after UV-C irradiation. Dynamic mechanical analysis (DMA) revealed that increased nanosilica content enhanced the storage modulus below the glass transition temperature (Tg) but reduced both Tg and the damping factor. Following UV-C exposure, all samples showed a decrease in storage modulus and Tg; however, composites with higher nanosilica content maintained better property retention. Frequency sweeps corroborated these findings, indicating improved instantaneous modulus but accelerated relaxation with increased nanosilica. Fourier-transform infrared (FTIR) spectroscopy of UV-C-exposed samples demonstrated significant oxidation and carboxylic group formation in neat epoxy, contrasting with minimal spectral changes in nanosilica-modified composites, signifying improved chemical resistance. Overall, nanosilica incorporation substantially enhances the thermomechanical and oxidative stability of epoxy composites under simulated space conditions, highlighting their potential for more durable performance in low Earth orbit applications. Full article

Atmospheric turbulence severely limits the stability and reliability of free-space optical (FSO) uplinks by inducing wavefront distortions and random intensity fluctuations. This study investigates the use of reinforcement learning (RL) with beacon-based feedback for adaptive beam shaping in a spatial light modulator (SLM)-controlled FSO link. The RL agent dynamically adjusts phase patterns to maximize received signal strength, while the beacon channel provides turbulence estimates that guide the optimization process. Experiments under low, moderate, and high turbulence levels demonstrate that incorporating beacon feedback can enhance link stability in severe conditions, reducing signal variability and suppressing extreme fluctuations. In low-turbulence scenarios, the performance is comparable to non-feedback operation, whereas under high turbulence, beacon-assisted control consistently achieves lower coefficients of variation and improved bit error rate (BER) performance. Under high turbulence replay experiments—where the best-performing RL-learned phase patterns are reapplied without learning—further show that configurations trained with feedback retain robustness, even without real-time turbulence measurements under high turbulence. These results highlight the potential of integrating contextual feedback with RL to achieve turbulence-resilient and stable optical uplinks in dynamic atmospheric environments. Full article

The inhibition mechanism and binding properties of four phenolic acids (ferulic acid (FA), p-coumaric acid (CA), gallic acid (GA), and protocatechuic acid (PA)) on xanthine oxidase (XOD) were investigated. All four phenolic acids acted via a mixed inhibition pattern, mainly influencing the hydrophobic regions and secondary conformation of XOD through hydrophobic bonding and hydrophobic association. Molecular dynamics simulations exhibited that the complexes of XOD with FA and CA revealed smaller radii of gyration (Rg) and solvent-accessible surface areas (SASA), along with lower variability in root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF), collectively indicating greater structural stability. FA, CA, and PA significantly reduced uric acid (UA) concentration in the 25–100 μM range. Although GA only reduced UA levels in cell models at 25 μM, this effect was likely due to its larger polar surface area, which limits cellular uptake. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation suggested that these phenolic acids have potential for development. Full article

The Lower Cretaceous Jiufotang Formation in the Fuxin Basin contains a proven petroleum system. However, its southern part remains underexplored due to limited drilling and fragmentary sedimentary studies. To address this issue, we conducted detailed sedimentological logging of the two typical outcrop sections, Pijiagou and Tanjiagou. Field observations, petrographic data, and grain-size analysis were integrated to decipher hydrodynamic conditions, calibrate microfacies associations, and reconstruct the sedimentary evolution through facies stacking pattern analysis. The results show that the Jiufotang Formation predominantly consists of calcareous fine-grained clastic rocks, with poorly sorted sandstones indicative of low-energy conditions. Sediment transport mechanisms include both traction and turbidity currents, with suspension being predominant. The succession records a depositional transition from fan-delta to lacustrine environments. Two subfacies, fan-delta front and shore-shallow lacustrine, were identified and subdivided into seven microfacies: subaqueous distributary channels, interdistributary bays, subaqueous levees, mouth bars, muddy shoals, sandy shoals, and carbonate shoals. The sedimentary evolution reflects an initial lacustrine transgression followed by regression, interrupted by multiple lacustrine-level fluctuations. The alternating depositional pattern of lacustrine and deltaic facies has formed complete source-reservoir-seal assemblages in the Jiufotang Formation in the study area, making it a potential favorable target for hydrocarbon accumulation. Full article

Tactical production–distribution planning in paper manufacturing involves hierarchical decision-making under hybrid uncertainty, where aleatory randomness (demand fluctuations, machine variations) and epistemic uncertainty (expert judgments, market trends) simultaneously affect operations. Existing approaches fail to address the bi-level nature under hybrid uncertainty, treating production and distribution decisions independently or using single-paradigm uncertainty models. This research develops a bi-level dependent-chance goal programming framework based on uncertain random theory, where the upper level optimizes distribution decisions while the lower level handles production decisions. The framework exploits structural symmetries through machine interchangeability, symmetric transportation routes, and temporal symmetry, incorporating symmetry-breaking constraints to eliminate redundant solutions. A hybrid intelligent algorithm (HIA) integrates uncertain random simulation with a Reinforcement-Learning-enhanced Arithmetic Optimization Algorithm (RL-AOA) for bi-level coordination, where Q-learning enables adaptive parameter tuning. The RL component utilizes symmetric state representations to maintain solution quality across symmetric transformations. Computational experiments demonstrate HIA’s superiority over standard metaheuristics, achieving 3.2–7.8% solution quality improvement and 18.5% computational time reduction. Symmetry exploitation reduces search space by approximately 35%. The framework provides probability-based performance metrics with optimal confidence levels (0.82–0.87), offering 2.8–4.5% annual cost savings potential. Full article

This study aimed to evaluate the effect of seasonal dynamics of phytoplankton biomass in the Bay of Gdansk (Southern Baltic Sea, Poland) on its usability for anaerobic digestion. Biomass samples were collected between May and October (2023–2024) for quantitative, taxonomic, and chemical analyses as well as for anaerobic digestion in mesophilic periodical bioreactors. Study results demonstrated substantial seasonal variations in the taxonomic composition of phytoplankton, with green algae and dinoflagellates prevailing in the spring, cyanobacteria in the summer, and diatoms in the autumn. These fluctuations were also reflected in the chemical composition of the biomass and its anaerobic digestion efficiency. The highest methane yield of 270 ± 13 mL CH₄/g VS and its highest production rate reaching 32.5 ± 1.6 mL CH₄/g VS·d were recorded in August, i.e., in the period of cyanobacteria predominance with the maximal contents of TOC (51.4 ± 2.1% TS), sugars (599 ± 42 mg/g TS), and lipids (126 ± 13 mg/g TS) in the biomass. In contrast, the lowest biomethanation efficiency was determined in October under diatom prevalence. A strong correlation was found between taxonomic, structural, and chemical properties of the substrate, and anaerobic digestion efficiency. This study’s findings underscore the enormous potential of phytoplankton biomass from summer blooms for energy production as a crucial element of sustainable management of coastal ecosystems and the circular economy. Full article

In metallurgy, the quenching process often induces changes in certain material properties, such as hardness and ductility, through the rapid cooling of a workpiece in water, gas, oil, polymer, air, or other fluids. Given that lithium-ion batteries operate under relatively benign conditions, conventional rapid cooling does not significantly affect the property variations in their internal electrode materials during normal use. However, thermal runaway presents an exception due to its dramatic temperature fluctuations from room temperature to several hundred degrees Celsius. In this study, we investigated NCM811 cathodes in 18,650 batteries subjected to transient thermal runaway followed by rapid cooling using several advanced analytical techniques. The results reveal a phenomenon characterized by intergranular cracking within NCM811 cathode materials when exposed to rapid cooling during transient thermal runaway. Furthermore, lithium-ion cells utilizing reused NCM-182.4 electrodes in fresh electrolyte demonstrate a reversible capacity of 231.4 mAh/g after 30 cycles at 0.1 C, highlighting the potential for reusing NCM811 cathodes in the lithium-ion battery recycling process. These findings not only illustrate that NCM811 particles may experience intergranular cracking when subjected to rapid cooling during transient thermal runaway, but also the rapidly cooled NCM811 electrodes exhibit potential for reuse. Full article

Inadequate screening of features and insufficient extraction of multi-source time-series data potentially result in insensitivity to historical noise and poor extraction of features for PM_2.5 concentration prediction models. Precipitable water vapor (PWV) data obtained from the Global Navigation Satellite System (GNSS), along with air quality and meteorological data collected in Suzhou city from February 2021 to July 2023, were employed in this study. The Spearman correlation analysis and Random Forest (RF) feature importance assessment were used to select key input features, including PWV, PM₁₀, O₃, atmospheric pressure, temperature, and wind speed. Based on RF, Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP) algorithms, four PM_2.5 concentration prediction models were developed using sliding window and fusion algorithms. Experimental results show that the root mean square error (RMSE) of the 1 h PM_2.5 concentration prediction model using the RF-LSTM fusion algorithm is 4.36 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, while its mean absolute error (MAE) and mean absolute percentage error (MAPE) values are 2.63 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$ and 9.3%. Compared to the individual LSTM and MLP algorithms, the RMSE of the RF-LSTM PM_2.5 prediction model improves by 34.7% and 23.2%, respectively. Therefore, the RF-LSTM fusion algorithm significantly enhances the prediction accuracy of the 1 h PM_2.5 concentration model. As for the 2 h, 3 h, 6 h, 12 h, and 24 h PM_2.5 prediction models using the RF-LSTM fusion algorithm, their RMSEs are 5.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 6.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 9.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 12.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and 15.3 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and their corresponding MAPEs are 13.8%, 18.3%, 28.3%, 38.2%, and 48.2%, respectively. Their prediction accuracy decreases with longer forecasting time, but they can effectively capture the fluctuation trends of future PM_2.5 concentrations. The RF-LSTM PM_2.5 prediction models are efficient and reliable for early warning systems in Suzhou city. Full article

Ducted fan flying robots with robotic arms can perform physical interaction tasks in complex environments such as indoors. However, the coupling effects between the aerial platform, the robotic arm, and physical environment pose significant challenges for the robot to accurately approach and stably contact the target. To address this problem, we propose a unified control framework for a ducted fan flying robot that encompasses both flight planning and physical interaction. This contribution mainly includes the following: (1) A nonlinear model predictive control (NMPC)-based trajectory optimization controller is proposed, which achieves accurate and smooth tracking of the robot’s end effector by considering the coupling of redundant states and various motion and performance constraints, while avoiding potential singularities and dangers. (2) On this basis, an easy-to-practice hierarchical control framework is proposed, achieving stable and compliant contact of the end effector without controller switching between the flight and interaction processes. The results of experimental tests show that the proposed method exhibits accurate position tracking of the end effector without overshoot, while the maximum fluctuation is reduced by up to 75.5% without wind and 71.0% with wind compared to the closed-loop inverse kinematics (CLIK) method, and it can also ensure continuous stable contact of the end effector with the vertical wall target. Full article