Search Results (4,598)

Natural gas plays a critical role in integrated energy systems. In this context, the present study proposes an optimization model for the electricity–gas coupling system, grounded in the theory of interdependent networks. By integrating network topology parameters with real-time operational metrics, the model substantially enhances system robustness and adaptability. To quantify nodal vulnerability and importance, the study introduces two novel evaluation indicators: the Electric Potential–Closeness Fusion Indicator (EPFI) for power networks and the Pressure Difference–Closeness Comprehensive Indicator (PDCI) for natural gas systems. Leveraging these indicators, three coupling paradigms—assortative, disassortative, and random—are systematically constructed and analyzed. System resilience is assessed through simulation experiments incorporating three attack strategies: degree-based, betweenness centrality-based, and random node removal. Evaluation metrics include network efficiency and the variation in the size of the largest connected subgraph under different coupling configurations. The proposed framework is validated using a hybrid case study that combines the IEEE 118-node electricity network with a 20-node Belgian natural gas system, operating under a unidirectional gas-to-electricity energy flow model. Results confirm that the disassortative coupling configuration, based on EPFI and PDCI indicators, exhibits superior resistance to network perturbations, thereby affirming the effectiveness of the model in improving the robustness of integrated energy systems. Full article

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1,030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

Internet of Things (IoT) applications and services have transformed the way people interact with their environment, enhancing comfort and quality of life. Additionally, Machine Learning (ML) approaches show significant promise for detecting intrusions in IoT environments. However, the high dimensionality, class imbalance, and complexity of network traffic—combined with the dynamic nature of sensor networks—pose substantial challenges to the development of efficient and effective detection algorithms. In this study, a multi-objective metaheuristic optimization approach, referred to as MOOIDS-IoT, is integrated with ML techniques to develop an intelligent cybersecurity system for IoT environments. MOOIDS-IoT combines a Genetic Algorithm (GA)-based feature selection technique with a multi-objective Particle Swarm Optimization (PSO) algorithm. PSO optimizes convergence speed, model complexity, and classification accuracy by dynamically adjusting the weights and thresholds of the deployed classifiers. Furthermore, PSO integrates Pareto-based multi-objective optimization directly into the particle swarm framework, extending conventional swarm intelligence while preserving a diverse set of non-dominated solutions. In addition, the GA reduces training time and eliminates redundancy by identifying the most significant input characteristics. The MOOIDS-IoT framework is evaluated using two lightweight models—MOO-PSO-XGBoost and MOO-PSO-RF—across two benchmark datasets, namely the NSL-KDD and CICIoT2023 datasets. On CICIoT2023, MOO-PSO-RF obtains 91.42% accuracy, whereas MOO-PSO-XGBoost obtains 98.38% accuracy. In addition, both models perform well on NSL-KDD (MOO-PSO-RF: 99.66% accuracy, MOO-PSO-XGBoost: 98.46% accuracy). The proposed approach is particularly appropriate for IoT applications with limited resources, where scalability and model efficiency are crucial considerations. Full article

To address the small-sample training bottleneck and inadequate convergence efficiency of Deep Reinforcement Learning (DRL)-based communication anti-jamming methods in complex electromagnetic environments, this paper proposes a Generative Adversarial Network-enhanced Deep Q-Network (GA-DQN) anti-jamming method. The method constructs a Generative Adversarial Network (GAN) to learn the time–frequency distribution characteristics of short-period jamming and to generate high-fidelity mixed samples. Furthermore, it screens qualified samples using the Pearson correlation coefficient to form a sample set, which is input into the DQN network model for pre-training to expand the experience replay buffer, effectively improving the convergence speed and decision accuracy of DQN. Our simulation results show that under periodic jamming, compared with the DQN algorithm, this algorithm significantly reduces the number of interference occurrences in the early communication stage and improves the convergence speed, to a certain extent. Under dynamic jamming and intelligent jamming, the algorithm significantly outperforms the DQN, Proximal Policy Optimization (PPO), and Q-learning (QL) algorithms. Full article

During the process of oil and gas drilling, due to the existence of pores or micro-cracks, drilling fluid is prone to invade the formation. Under the action of hydration expansion of clay in the formation and liquid pressure, wellbore instability occurs. In order to reduce the wellbore instability caused by drilling fluid intrusion into the formation, this study proposed a method of forming a dynamic hydrogen bond cross-linked network weak gel structure with modified nano-silica and P(AM-AAC). The plugging performance of the drilling fluid and the performance of inhibiting the hydration of shale were evaluated through various experimental methods. The results show that the gel composite system (GCS) effectively optimizes the plugging performance of drilling fluid. The 1% GCS can reduce the linear expansion rate of cuttings to 14.8% and increase the recovery rate of cuttings to 96.7%, and its hydration inhibition effect is better than that of KCl and polyamines. The dynamic cross-linked network structure can significantly increase the viscosity of drilling fluid. Meanwhile, by taking advantage of the liquid-phase viscosity effect and the physical blocking effect, the loss of drilling fluid can be significantly reduced. Mechanism studies conducted using zeta potential measurement, SEM analysis, contact angle measurement and capillary force assessment have shown that modified nano-silica stabilizes the wellbore by physically blocking the nano-pores of shale and changing the wettability of the shale surface from hydrophilic to hydrophobic when the contact angle exceeds 60°, thereby reducing capillary force and surface free energy. Meanwhile, the dynamic cross-linked network can reduce the seepage of free water into the formation, thereby significantly lowering the fluid loss of the drilling fluid. This research provides new insights into improving the stability of the wellbore in drilling fluids. Full article

Background/Objectives: Male infertility is a major health concern with a complex etiopathology, yet a substantial proportion of cases remain idiopathic. Mitochondrial dysfunction and non-coding RNA (ncRNA) deregulation have both been implicated in impaired spermatogenesis, but their interplay remains poorly understood. This study aimed to identify infertility-specific variants in ncRNAs that affect mitochondrial dynamics and homeostasis and to explore their roles. Methods: Whole-genome sequencing (WGS) was performed on genomic DNA samples from teratozoospermic, asthenozoospermic, oligozoospermic, and normozoospermic men. Variants uniquely present in infertile individuals and mapped to ncRNAs that affect mitochondrial dynamics were selected and prioritized using bioinformatics tools. An independent transcriptomic validation was conducted using RNA-sequencing data from testicular biopsies of men with non-obstructive azoospermia (NOA) to determine whether the ncRNAs harboring WGS-derived variants were transcriptionally altered. Results: We identified several infertility-specific variants located in lncRNAs known to interact with mitochondrial regulators, including GAS5, HOTAIR, PVT1, MEG3, and CDKN2B-AS1. Transcriptomic analysis confirmed significant deregulation of these lncRNAs in azoospermic testicular samples. Bioinformatic analysis also implicated the disruption of lncRNA–miRNA–mitochondria networks, potentially contributing to mitochondrial membrane potential loss, elevated reactive oxygen species (ROS) production, impaired mitophagy, and germ cell apoptosis. Conclusions: Our integrative genomic and transcriptomic analysis highlights lncRNA–mitochondrial gene interactions as a novel regulatory layer in male infertility, while the identified lncRNAs hold promise as biomarkers and therapeutic targets. However, future functional studies are warranted to elucidate their mechanistic roles and potential for clinical translation in reproductive medicine. Full article

With the popularization of electric vehicles and the continuous expansion of the electric vehicle market, the construction and management of charging facilities for electric vehicles have become important issues in research and practice. In some remote areas, the charging stations are idle due to low traffic flow, resulting in a waste of resources. Areas with high traffic flow may have fewer charging stations, resulting in long queues and road congestion. The purpose of this study is to optimize the location of charging stations and the number of charging piles in the stations based on the distribution of traffic flow, and to construct a bi-level programming model by analyzing the distribution of traffic flow. The upper-level planning model is the user-balanced flow allocation model, which is solved to obtain the optimal traffic flow allocation of the road network, and the output of the upper-level planning model is used as the input of the lower-layer model. The lower-level planning model is a generalized minimum cost model with driving time, charging waiting time, charging time, and the cost of electricity consumed to reach the destination of the trip as objective functions. In this study, an empirical simulation is conducted on the road network of Hefei City, Anhui Province, utilizing three algorithms—GA, GWO, and PSO—for optimization and sensitivity analysis. The optimized results are compared with the existing charging station deployment scheme in the road network to demonstrate the effectiveness of the proposed methodology. Full article

Natural hydrogen is generated by fairly deep processes and/or in low-permeability rocks. In such contexts, fluids circulate mainly through the network of faults and fractures. However, hydrogen flows from these hydrogen-generating layers can reach sedimentary rocks with more typical permeability and porosity, allowing H₂ flows to spread out rather than be concentrated in fractures. In that case, three different H₂ transport modes exist: advection (displacement of water carrying dissolved gas), diffusion, and free gas Darcy flow. Numerical models have been run to compare the efficiency of these different modes and the pathway they imply for the H₂ in a sedimentary basin with active aquifers. The results show the key roles of these aquifers but also the competition between free gas flow and the dissolved gas displacement which can go in opposite directions. Even with a conservative hypothesis on the H₂ charge, a gaseous phase exists at few kilometers deep as well as free gas accumulation. Gaseous phase displacement could be the faster and diffusion is neglectable. The modeling also allows us to predict where H₂ is expected in the soil: in fault zones, eventually above accumulations, and, more likely, due to exsolution, above shallow aquifers. Full article

The synthesis of “green ammonia” from “green hydrogen” represents a critical pathway for renewable energy integration and industrial decarbonization. This study investigates the green ammonia synthesis process using an axial–radial fixed-bed reactor equipped with three catalyst layers. A simplified two-dimensional physical model was developed, and a multiscale simulation approach combining computational fluid dynamics (CFD) with physics-informed neural networks (PINNs) employed. The simulation results demonstrate that the majority of fluid flows axially through the catalyst beds, leading to significantly higher temperatures in the upper bed regions. The reactor exhibits excellent heat exchange performance, ensuring effective preheating of the feed gas. High-pressure zones are concentrated near the top and bottom gas outlets, while the ammonia mole fraction approaches 100% near the bottom outlet, confirming superior conversion efficiency. By integrating PINNs, the prediction accuracy was substantially improved, with flow field errors in the catalyst beds below 4.5% and ammonia concentration prediction accuracy above 97.2%. Key reaction kinetic parameters (pre-exponential factor k₀ and activation energy Ea) were successfully inverted with errors within 7%, while computational efficiency increased by 200 times compared to traditional CFD. The proposed CFD–PINN integrated framework provides a high-fidelity and computationally efficient simulation tool for green ammonia reactor design, particularly suitable for scenarios with fluctuating hydrogen supply. The reactor design reduces energy per unit ammonia and improves conversion efficiency. Its radial flow configuration enhances operational stability by damping feed fluctuations, thereby accelerating green hydrogen adoption. By reducing fossil fuel dependence, it promotes industrial decarbonization. Full article

We propose an advanced electronic nose based on SE-RelationNet for COPD diagnosis with limited breath samples. The model integrates residual blocks, BiGRU layers, and squeeze–excitation attention mechanisms to enhance feature-extraction efficiency. Experimental results demonstrate exceptional performance with minimal samples: in 4-way 1-shot tasks, the model achieves 85.8% mean accuracy (F1-score = 0.852), scaling to 93.3% accuracy (F1-score = 0.931) with four samples per class. Ablation studies confirm that the 5-layer residual structure and single-hidden-layer BiGRU optimize stability (h_F1-score ≤ 0.011). Compared to SiameseNet and ProtoNet, SE-RelationNet shows superior accuracy (>15% improvement in 1-shot tasks). This technology enables COPD detection with as few as one breath sample, facilitating early intervention to mitigate lung cancer risks in COPD patients. Full article

With the rapid development of renewable energy, the proportion of small hydropower as a clean energy in the distribution network (DN) is increasing. However, the randomness and intermittence of small hydropower has brought new challenges to the operation of DN; especially, the problems of increasing network loss and reactive voltage exceeding the limit have become increasingly prominent. Aiming at the above problems, this paper proposes a reactive power optimization control method for DN with hydropower based on an improved discrete particle swarm optimization (PSO) algorithm. Firstly, this paper analyzes the specific characteristics of small hydropower and establishes its mathematical model. Secondly, considering the constraints of bus voltage and generator RP output, an extended minimum objective function for system power loss is established, with bus voltage violation serving as the penalty function. Then, in order to solve the following problems: that the traditional discrete PSO algorithm is easy to fall into local optimization and slow convergence, this paper proposes an improved discrete PSO algorithm, which improves the global search ability and convergence speed by introducing adaptive inertia weight. Finally, based on the IEEE-33 buses distribution system as an example, the simulation analysis shows that compared with GA optimization, the line loss can be reduced by 3.4% in the wet season and 13.6% in the dry season. Therefore, the proposed method can effectively reduce the network loss and improve the voltage quality, which verifies the effectiveness and superiority of the proposed method. Full article

Rotating compressors are foundational in various industrial processes, particularly in the oil-and-gas sector, where reliable fault detection is crucial for maintaining operational continuity. While Graph Attention Network (GAT) frameworks are widely available, this study advances the state of the art by introducing a Bayesian GAT method specifically tailored for vibration-based compressor fault diagnosis. The approach integrates domain-specific digital-twin simulations built with Rotordynamic software (1.3.0), and constructs dual adjacency matrices to encode both physically informed and data-driven sensor relationships. Additionally, a hybrid forecasting-and-reconstruction objective enables the model to capture short-term deviations as well as long-term waveform fidelity. Monte Carlo dropout further decomposes prediction uncertainty into aleatoric and epistemic components, providing a more robust and interpretable model. Comparative evaluations against conventional Long Short-Term Memory (LSTM)-based autoencoder and forecasting methods demonstrate that the proposed framework achieves superior fault-detection performance across multiple fault types, including misalignment, bearing failure, and unbalance. Moreover, uncertainty analyses confirm that fault severity correlates with increasing levels of both aleatoric and epistemic uncertainty, reflecting heightened noise and reduced model confidence under more severe conditions. By enhancing GAT fundamentals with a domain-tailored dual-graph strategy, specialized Bayesian inference, and digital-twin data generation, this research delivers a comprehensive and interpretable solution for compressor fault diagnosis, paving the way for more reliable and risk-aware predictive maintenance in complex rotating machinery. Full article

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations. Full article

Camellia oleifera, a woody oilseed species endemic to China, often experiences growth constraints due to seasonal drought. This study investigates the coordinated regulation of photosynthetic traits, stomatal behavior, and hormone responses during drought–rehydration cycles in two cultivars with contrasting drought resistance: ‘CL53’ (tolerant) and ‘CL40’ (sensitive). Photosynthetic inhibition resulted from both stomatal and non-stomatal limitations, with cultivar-specific differences. After 28 days of drought, the net photosynthetic rate (P_n) declined by 26.6% in CL53 and 32.6% in CL40. A stable intercellular CO₂ concentration (C_i) in CL53 indicated superior mesophyll integrity and antioxidant capacity. CL53 showed rapid P_n recovery and photosynthetic compensation post-rehydration, in contrast to CL40. Drought triggered extensive stomatal closure; >98% reopened upon rehydration, though the total stomatal pore area remained reduced. Abscisic acid (ABA) accumulation was greater in CL40, contributing to stomatal closure and P_n suppression. CL53 exhibited faster ABA degradation and gibberellin (GA₃) recovery, promoting photosynthetic restoration. ABA negatively correlated with P_n, transpiration rate (T_r), stomatal conductance (G_s), and C_i, but positively with stomatal limitation (L_s). Water use efficiency (WUE) displayed a parabolic response to ABA, differing by cultivar. This integrative analysis highlights a coordinated photosynthesis–stomata–hormone network underlying drought adaptation and informs selection strategies for drought-resilient cultivars and precision irrigation. Full article