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With the ongoing development of new power systems, the integration of new energy sources is facing increasingly daunting challenges. The collaborative operation of shared energy storage systems with distribution networks and microgrids can effectively leverage the complementary nature of various energy sources and loads, enhancing energy absorption capacity. To address this, a shared energy storage capacity allocation method based on a Stackelberg game is proposed, considering the integration of wind and solar energy into distribution networks and microgrids. In this approach, a third-party shared energy storage investor acts as the leader, while distribution networks and microgrids serve as followers. The shared energy storage operator aims to maximize annual revenue, plan shared energy storage capacity, and set unit capacity leasing fees. Upon receiving pricing, distribution networks and microgrids aim to minimize annual operating costs, determine leased energy storage capacity, and develop operational plans based on typical daily scenarios. Distribution networks and microgrids report leasing capacity, and shared energy storage adjusts leasing prices, accordingly, forming a Stackelberg game. In the case study results, the annual cost of MGs decreased by 29.63%, the annual operating cost of the ADN decreased by 11.25%, the cost of abandoned light decreased by 60.77%, and the cost of abandoned wind decreased by 27.79% to achieve the collaborative optimization of operations. It is proven that this strategy can improve the economic benefits of all parties and has a positive impact on the integration of new energy.

This study evaluates the effect of climate change on reference evapotranspiration (ET0), which is one of the most important variables in water resources management and irrigation scheduling. For this purpose, daily weather data of 30 Iranian weather stations from 1981 and 2010 were used. The HadCM3 statistical model was applied to report the output subscale of LARS-WG and to predict the weather information by A1B, A2, and B1 scenarios in three periods: 2011–2045, 2046–2079, and 2080–2113. The ET0 values were estimated by the Ref-ET software. The results indicated that the ET0 will rise from 2011 to 2113 approximately in all stations under three scenarios. The ET0 changes percentages in the A1B scenario during three periods from 2011 to 2113 were found to be 0.98%, 5.18%, and 12.17% compared to base period, respectively, while for the B1 scenario, they were calculated as 0.67%, 4.07%, and 6.61% and for the A2 scenario, they were observed as 0.59%, 5.35%, and 9.38%, respectively. Thus, the highest increase of the ET0 will happen from 2080 to 2113 under the A1B scenario; however, the lowest will occur between 2046 and 2079 under the B1 scenario. Furthermore, the assessment of uncertainty in the ET0 calculated by the different scenarios showed that the ET0 predicted under the A2 scenario was more reliable than the others. The spatial distribution of the ET0 showed that the highest ET0 amount in all scenarios belonged to the southeast and the west of the studied area. The most noticeable point of the results was that the ET0 differs from one scenario to another and from a period to another.

Inherent hazards such as landslides pose a threat to human life and may inflict significant harm on the surrounding ecosystem. For planning, controlling, and avoiding landslide situations to minimize damages, a landslide susceptibility map is necessary. As a consequence of this, the current research makes use of a methodical approach and upgraded algorithms to identify and forecast locations that are susceptible to landslides. When it comes to problems associated with landslides, standard optimization techniques have been used quite a bit. This study presents a novel approach to the development of an artificial neural network (ANN) in the Iranian region of Kurdistan by using the cuckoo optimization algorithm (COA) and the SailFish optimizer (SFO) as metaheuristic approaches. In order to maximize the computational properties of these algorithms and depict a new kind of swarm intelligence, a multi-layer perceptron (MLP) neural network is used in the synthesis process. The findings of the landslide hazard maps were checked and compared using actual landslide sites. There were 1072 landslides shown on the inventory map. There was a 70:30 split between training and testing locations at random. Model input was narrowed down to 16 different landslide qualifying variables, namely elevation, slope aspect, slope angle, NDVI, distance to fault, plan curvature, profile curvature, rainfall, distance from river, distance to road, SPI, STI, TRI, TWI, land use, and geology. All of these parameters were considered to be important in determining the likelihood of a landslide occurring. The area under the curve (AUC) criterion was used to evaluate the accuracy of the probabilistic models that were put into use. Incidentally, the calculated comparable AUCs were as follows: 0.797, 0.789, 0.784, 0.779, 0.763, 0.758, 0.749, 0.740, 0.725, and 0.716 for COA-MLP, and 0.719, 0.695, 0.682, 0.675, 0.671, 0.670, 0.662, and 0.650 for SFO-MLP. The greatest hybrid model for forecasting landslide detection corresponds to the COA-MLP model, and it has a swarm size of four hundred people. As a consequence, the findings demonstrated that these two models had an effective performance for ANN-MLP optimization. Taking into consideration this evaluation, the hybrid models that were provided are trustworthy for the modeling of landslide susceptibility. As a result, the map of vulnerability that was developed can be utilized for hazardous design and increased planners' knowledge of dangerous locations.

The charge/discharge operation of the prosumer’s energy storage and the energy interaction between prosumers and MGs are chaotic from the overall point of the MG’s operation. It causes considerable resource waste and reduces the overall benefits of the MG with multi-prosumers. Therefore, a game theory-based optimal scheduling strategy for the MG with multi-prosumers combined into a PRCO is proposed in this paper. According to the prosumers’ complementary characteristics of ES utilization and energy production, prosumers can be integrated into the PRCO to obtain energy reciprocity by sharing ES with an ordered charge–discharge operation. Meanwhile, to improve the collaboration of prosumers and the overall efficiency of the MG, a game scheduling model is established with the MG as the leader and the PRCO as the follower. The ToU price incentive policy is implemented in the MG to maximize the operational benefits and reduce the difference between the valley and peak load. Meanwhile, the PRCO responds to the price policy and implements an ordered charge–discharge strategy of ES to optimize each member’s energy scheduling strategy and minimize the total costs. The PRCO revenues are distributed to prosumers based on the Shapley value method. The uniqueness and existence of Stackelberg equilibrium in the game model are proved. The simulations of a community MG show that the ordered charge–discharge operation of ES is achieved and the overall benefits of the system are improved.

Terminal sliding mode controller method is introduced to enhance the regulation performance of the hydraulic turbine governing system (HTGS). For the purpose of describing the characteristics of controlled system and deducing the control rule, a nonlinear mathematic model of hydraulic turbine governing system with bifurcated penstocks (HTGSBF) under control input saturation is established, and the input/output state linearization feedback approach is used to obtain the relationship between turbine speed and controller output. To address the control input saturation problem, an adaptive assistant system is designed to compensate for controller truncation. Numerical simulations have been conducted under fixed point stabilization and periodic orbit tracking conditions to compare the dynamic performances of proposed terminal sliding mode controllers and conventional sliding mode controller. The results indicate that the proposed terminal sliding mode controllers not only have a faster response and accurate tracking results, but also own a stronger robustness to the system parameter variations. Moreover, the comparisons between the proposed terminal sliding mode controllers and current most often used proportional-integral-differential (PID) controller, as well its variant NPID controller, are discussed at the end of this paper, where the superiority of the terminal sliding mode controllers also have been verified.

This paper investigates a novel flexible job-shop scheduling problem, where the machines have batch-processing capacity, but incompatible jobs cannot be processed in a batch (FJSPBI) simultaneously. This problem has wide applications in discrete manufacturing, especially in chemical and steel casting industries. For the first time, in this study, a 3-indexed mixed-integer linear programming (MILP) model is proposed, which can be efficiently and optimally solved by commercial solvers for small-scale problems. In addition, an improved large neighborhood search (LNS) algorithmic framework with an optimal insertion and tabu-based components (LNSIT) is proposed, which can achieve high-quality solutions for a large-scale FJSPBI in a reasonable time. A perturbation strategy and an optimal insertion strategy are then additionally embedded to improve the exploitation and exploration ability of the algorithm. The proposed model and algorithm are tested on numerous existing benchmark instances without the incompatibility characteristics, and on newly generated instances of the FJSPBI. The experimental results indicate the effectiveness of the proposed MILP model and the algorithm, including the proposed strategies, and the optimal insertion strategy can significantly reduce the computational burden of the LNS algorithm. The comparison results further verify that the proposed LNSIT can directly solve the specific flexible job-shop batch scheduling problem without incompatibility, with better results than existing methods, especially for large-scale instances. Additionally, the impacts of a wide range of characteristics, including batch capacity, incompatibility rate, instance scale, and machine processing rate, on the performance of the LNSIT and the scheduling results are analyzed and presented.

Analyze the characteristic quantity difference between the transformer sympathetic inrush current and the internal fault current inside it depending on the Hilbert-huang transform and extract the new type Hilbert-huang criterion for the recognition of the sympathetic inrush current according to the transformed wave form features. Set up the transformer simulating models through PACAD for the sympathetic inrush current and internal fault current to extract their IMF component; identify the sympathetic inrush current and the internal fault current based on HHT criterion; verify whether HHT criterion can identify the sympathetic inrush current wave form and the transformer internal fault current correctly. The criterion has targeted feature with self-adaption ability.

Antibiotic residues in the marine environment pose a serious threat to ecosystems and human health, and there is an urgent need to develop efficient and selective pollution control technologies. Molecular imprinting technology (MIT) provides a new idea for antibiotic pollution control with its specific recognition and targeted removal ability. However, traditional titanium dioxide (TiO2) photocatalysts have limited degradation efficiency and lack of selectivity for low concentrations of antibiotics. This paper reviews the preparation strategy and modification means of molecularly imprinted TiO2 (MI-TiO2) and its composites and systematically explores its application mechanism and performance advantages in marine antibiotic wastewater treatment. It was shown that MI-TiO2 significantly enhanced the selective degradation efficiency of antibiotics such as tetracyclines and sulfonamides through the enrichment of target pollutants by specifically imprinted cavities, combined with the efficient generation of photocatalytic reactive oxygen species (ROS). In addition, emerging technologies such as magnetic/electric field-assisted catalysis and photothermal synergistic effect further optimized the recoverability and stability of the catalysts. This paper provides theoretical support for the practical application of MI-TiO2 in complex marine pollution systems and looks forward to its future development in the field of environmental remediation.

Ovarian cancer has a grim prognosis due to its delayed detection and limited therapeutic options. Emerging evidence suggests that the microbiome–metabolome axis may contribute to tumor progression, yet its role in ovarian cancer remains unclear. To address this gap, we performed a multiomics profiling of ovarian tumors and matched nonmalignant tissues, identifying a tumor‐associated dysbiosis coupled with metabolic reprogramming. Tumor tissues exhibited reduced overall α‐diversity compared with controls, while progressive compositional shifts were observed with increasing tumor grade. Correlation analyses further mapped a clinical microbe–metabolite network revealing coordinated association patterns across tumor features. Functionally, vaginal administration of Lactobacillus modestly attenuated tumor growth and promoted M1‐like macrophage polarization in an orthotopic ovarian cancer model. These findings highlight a functional microbiome–metabolome–immunity axis in ovarian cancer and suggest that restoring commensal taxa may offer a strategy to reprogram the tumor immune microenvironment.

With increasing attention paid by the state to the popularization of science for earthquake prevention and disaster reduction, the construction of natural disaster monitoring and early warning system, and the modernization of natural disaster prevention and control system and capacity, great progress has been made in China on public understanding of the earthquake prevention. The 5.1 magnitude earthquake occurred in Tangshan city on July 12, 2020 was an aftershock of the great Tangshan earthquake in 1976, and has drawn great attention and discussion. Based on this event, this study carried out science popularization and field research on relevant earthquake disaster knowledge, by conducting a questionnaire survey on earthquake disaster knowledge in Lunan District of Tangshan and the suburb of Fengnan District, and interviewing the Tangshan Earthquake Agency. Through the data cross analysis of the questionnaire results, the study provides a general estimate of the public understanding of the earthquake, which