Search Results (26)

The widespread adoption of electric vehicles (EVs) has posed significant challenges to the security of distribution grid loads. To address issues such as increased grid load fluctuations, rising user charging costs, and rapid load surges around midnight caused by uncoordinated nighttime charging of household electric vehicles in communities, this paper first models electric vehicle charging behavior as a Markov Decision Process (MDP). By improving the state-space sampling mechanism, a continuous space mapping and a priority mechanism are designed to transform the charging scheduling problem into a continuous decision-making framework while optimizing the dynamic adjustment between state and action spaces. On this basis, to achieve synergistic load forecasting and charging scheduling decisions, a forecast-augmented deep reinforcement learning method integrating Gated Recurrent Unit and Twin Delayed Deep Deterministic Policy Gradient (GRU-TD3) is proposed. This method constructs a multi-objective reward function that comprehensively considers time-of-use electricity pricing, load stability, and user demands. The method also applies a single-objective pre-training phase and a model-specific importance-sampling strategy to improve learning efficiency and policy stability. Its effectiveness is verified through extensive comparative and ablation validation. The results show that our method outperforms several benchmarks. Specifically, compared to the Deep Deterministic Policy Gradient (DDPG) and Particle Swarm Optimization (PSO) algorithms, it reduces user costs by 11.7% and the load standard deviation by 12.9%. In contrast to uncoordinated charging strategies, it achieves a 42.5% reduction in user costs and a 20.3% decrease in load standard deviation. Moreover, relative to single-objective cost optimization approaches, the proposed algorithm effectively suppresses short-term load growth rates and mitigates the “midnight peak” phenomenon. Full article

Currently, there remains a lack of systematic quantitative analysis of the acoustic impact mechanism of ancestral temple theatres in relation to their core function of opera performance. This paper takes the Zhaomutang—a typical ancestral temple theatre in northeast Jiangxi—as an example, and comprehensively uses on-site mapping, impulse response testing, and ODEON three-dimensional sound field simulation to conduct acoustic sensitivity analysis on five key spatial elements of the theatre. The results show that the theatre has a hierarchical sound field pattern along its depth, characterized by “high in the front, low in the rear, stronger on the sides and weaker in the middle”. The front patio and the Xiangtang support the clarity of Gan opera dialogue and the fullness of singing through early lateral reflections and moderate reverberation (EDT of 0.8–1.1 s, C₈₀ of 3.2–6.1 dB). However, the rear patio and the Qintang show apparent loudness deficiency (G of −1.5–3.2 dB) and lack of spatial immersion (LF₈₀ below 0.23). The most effective optimization comes from the reconstruction of the geometric relationship between performers and audience: moving the performers forward and appropriately raising the stage and audience area floor can significantly shorten the rear area EDT and increase C₈₀ and G; in contrast, the improvement in sound quality brought about by adding a patio cover and raising the gables is minimal, and the changes in various parameters are generally less than 1 JND. Based on this, the “schedule priority—reversible intervention” acoustic maintenance strategy for living heritage is proposed, and it is suggested that reversible reflective components be set in the side corridor to specifically enhance the sense of immersion in the rear area sound field. The study constructs a quantitative correlation framework of space, materials, and sound field, providing methodological support and parameter basis for the acoustic assessment and protective utilization of ancestral temple theatres. Full article

To address the efficiency and safety challenges arising from the mixed operation of autonomous and human-driven container trucks during the automation transformation of traditional container terminals, this study designed a simulation-based optimization framework for mixed vehicle scheduling. A spatio-temporal graph dynamic scheduling model was constructed, incorporating node capacity, arc capacity, and path constraints, to establish a multi-objective optimization model aimed at minimizing the maximum completion time of internal trucks and the average waiting time of external trucks. An improved NSGA-II algorithm was employed to generate task assignment solutions, which were evaluated using discrete-event simulation, integrating a dynamic programming-based yard block selection strategy for external trucks and a congestion-aware path planning algorithm. Experimental results demonstrate that the dynamic priority strategy effectively adapts to different traffic flow scenarios: under low external truck flow, the autonomous internal truck priority strategy reduces task completion time by 18% to 25%, while under high flow, the external truck priority strategy significantly decreases the average waiting time. The optimal configuration ratio between internal and external trucks was identified as approximately 1:2. This research provides a theoretical basis and decision support for enhancing terminal operational efficiency and automation transformation. Full article

In response to the imperative to mitigate methane—one of the most potent greenhouse gases—this study proposes and tests an integrated workflow for designing methane drainage boreholes targeting post-mining areas in an active underground coal mine (Pniówek, Poland). The workflow combines the following: (1) forecasting methane emissions from goafs and active longwalls for 2024–2040; (2) 3D geological characterization (structural and lithofacies models); (3) selection and sealing of goaf zones; and (4) optimization of well placement, drilling, and performance evaluation of drainage boreholes, including an assessment of energy use from the recovered gas. Applying the method delineated priority capture zones and estimated recoverable rates under multiple scenarios. Preliminary field data from a borehole near seam 362/1 indicate stable methane inflow to the drainage system and a concomitant reduction in methane load within the ventilation network. The integrated design improves targeting efficiency and provides a quantitative basis for scheduling, risk management, and sizing of surface-to-underground infrastructure. The results suggest that systematic drainage of post-mining voids can enhance safety, limit fugitive emissions, and create opportunities for on-site power generation. The approach is transferable to other active mines with legacy workings, provided site-specific calibration and monitoring are implemented. Full article

Reasonable planning and scheduling in low-carbon parks is conducive to coordinating and optimizing energy resources, saving total system costs, and improving equipment utilization efficiency. In this paper, the optimization study of a distributed photovoltaic energy storage system considers the synergistic effects of the planning and operation phases. On the basis of the variable operating characteristics of the unit equipment and the complementary synergistic characteristics of the energy storage equipment, a two-layer optimization model combining planning and operation is adopted, with the minimum total cost and the minimum carbon emission content in the whole life cycle of the system as the optimization objectives and the upper layer of the planning equipment capacity and the configured capacity of each equipment in the system as the optimization variables, which are solved by using the multi-objective no-dominated-sorting genetic algorithm. The lower layer is the optimized operation mode, and the time-by-time operating capacity of each item of equipment is the optimization variable, which is solved by the interior point method. The upper layer optimization results are used as the constraint boundary conditions for optimization of the lower layer, and the lower layer optimization results provide feedback correction to the upper layer optimization results, which ultimately determine the energy system optimization scheme. The optimization results reflect that photovoltaic green power should be arranged in large quantities as a priority, and the synergistic effect of power and cold storage equipment on the system’s economy and low-carbon performance is positive. At the same time, by setting up four control scenarios of only cold storage, only electricity storage, no energy storage, and no two-tier optimization, the impacts of cold storage and electricity storage on the economic and environmental aspects of the system and the positive effect of mutual synergy are investigated, which concretely proves the validity of the two-tier optimization strategy, taking into account the operating characteristics of the equipment. Full article

The optimal dispatching of renewable energy power stations is particularly crucial in scenarios where the stations face energy rationing due to the large proportion of renewable energy integrated into the power system. In order to achieve safe, economical, and fair scheduling of renewable energy power stations, this paper proposes a two-stage scheduling framework. Specifically, in the initial stage, the maximum consumption space of renewable energy for the system can be optimized by optimizing the formulated safe-economic dispatch model. In the second stage, the fair allocation mechanism of renewable energy power stations is proposed based on the game-fairness empowerment approach. In order to obtain a comprehensive evaluation of renewable energy power stations, an evaluation index system is constructed considering equipment performance, output characteristics, reliability, flexibility, and economy. Subsequently, the cooperative game weighting method is proposed to rank the performance of renewable energy power stations as the basis for fair dispatching. Simulation results show that the proposed scheduling strategy can effectively ensure the priority of renewable energy power stations based on their comprehensive ranking, and improve the safety, economy, and fairness of power station participation in scheduling. Full article

Background/Objectives: Diagnosis of Autism Spectrum Disorder (ASD) relies on the observation of difficulties in social communication and interaction, plus the presence of repetitive and restrictive behaviors. The identification of neurological correlates of these symptoms remains a high priority for clinical research, and has the potential to increase the validity of diagnosis of ASD as well as provide greater understanding of how the autistic brain functions. This study focused on two neurological phenomena that have been previously associated with psychiatric disorders (alpha- and theta-wave asymmetry across the frontal region of the brain), and tested for their association with the major diagnostic criteria for ASD. Methods: A total of 41 male autistic youth underwent assessment with the Autism Diagnostic Observation Schedule (ADOS-2) and 3 min of eyes-closed resting EEG to collect alpha- and theta-wave data from right and left frontal brain sites. Results: Different associations were found for theta versus alpha asymmetry and the ADOS-2 subscales, across different brain regions responsible for a varying range of cognitive functions. In general, theta asymmetry was associated with conversation with others, sharing of enjoyment, and making social overtures, whereas alpha asymmetry was linked with making eye contact, reporting events to others, and engaging in reciprocal social communication. Specific brain regions involved are identified, as well as implications for clinical practice. Conclusions: Specific autism symptoms may be associated with selected brain region activity, providing a neurological basis for diagnosis and treatment. Full article

In cognitive radio networks, channel assembling allows secondary users (SUs) to expand network capacity and improve spectrum utilization. Scheduling strategies only based on heterogeneous service classification cannot guarantee the delivery priority of vital elastic services in special scenarios such as emergency rescue. Therefore, a priority scheduling strategy with reserved queue (Ps-rq) is proposed in this work. A static factor is defined to classify SUs into elastic services and real-time services based on message type, while a dynamic factor is defined to differentiate high-priority elastic services based on information validity, message correlation and message size. The high-priority users in the interrupted elastic services are placed in the reserved queue to ensure its services. Accordingly, the scheduling algorithm and the dynamic channel access process is presented. A continuous-time Markov chain analysis is conducted and all possible transition states, trigger events, transition rates and transition conditions of the system starting from a general state are derived. Furthermore, evaluation indexes of system performance are obtained. Study cases and simulation results prove that the proposed strategy can enhance network capacity, reduce blocking probability and forced termination probability for secondary users, and notably enhance the performance of high-priority elastic services. In addition, we analyze the characteristics of Ps-rq through a comprehensive comparison with four other schemes. The experiment proves that the Ps-rq strategy can effectively improve the service quality of the vital elastic services on the basis of providing fair scheduling. Full article

This study integrates genetic algorithms with simulation programs, applying the genetic algorithm’s (GA) fitness calculation within the simulation to reduce complexity and significantly improve the efficiency of the optimization process. Additionally, the simulation introduces the concept of “Field Leveling” (FL), utilizing a push–pull strategy to explore more space for absorbing and utilizing unnecessary spillage for energy generation, thereby maximizing electricity production and ensuring optimal reservoir scheduling. Two methods are provided, namely the field-leveling genetic algorithms GAFL1 and GAFL2. GAFL1 involves only pushing and does not include a push–pull process; thus, it cannot optimize spillage. On the other hand, GAFL2 implements a complete push–pull strategy, continuously exploring additional space to absorb and utilize unnecessary spillage. Both GAFL1 and GAFL2 achieved reasonable results; specifically, compared to SQP, GAFL1 improved firm yield by 8.3%, spillage increased by 2.2 times, and total energy decreased by 1.2%. GAFL2, building on the basis of GAFL1, effectively reduces spillage under all hydrological conditions without affecting the highest priority of stable output. However, the impact of reducing spillage on energy generation is not consistent; in wet and dry years, reducing spillage increases energy generation. However, in normal years, a reduction in spillage corresponds with decreased energy generation. Full article

Industry 4.0, with its focus on flexibility and customizability, is pushing in the direction of wireless communication in future smart factories, in particular, massive multiple-input-multiple-output (MIMO) and its future evolution of large intelligent surfaces (LIS), which provide more reliable channel quality than previous technologies. At the same time, network slicing in 5G and beyond systems provides easier management of different categories of users and traffic, and a better basis for providing quality of service, especially for demanding use cases such as industrial control. In previous works, we have presented solutions for scheduling industrial control traffic in LIS and massive MIMO systems. We now consider the case of dynamic slicing in the radio access network, where we need to not only meet the stringent latency and reliability requirements of industrial control traffic, but also minimize the radio resources occupied by the network slice serving the control traffic, ensuring resources are available for lower-priority traffic slices. In this paper, we provide mixed-integer programming optimization formulations for radio resource usage minimization for dynamic network slicing. We tested our formulations in numerical experiments with varying traffic profiles and numbers of nodes, up to a maximum of 32 nodes. For all problem instances tested, we were able to calculate an optimal schedule within 1 s, making our approach feasible for use in real deployment scenarios. Full article

In the process of the integrated scheduling of multi-variety and small-batch complex products, the process structure and attribute characteristics are often ignored, which affects the overall scheduling effect. Aiming at solving this problem, a heuristic integrated scheduling algorithm (HIS-IDA) based on the improved Dijkstra algorithm is proposed. The algorithm takes the processing time of the process itself as the path value of the preceding and the following adjacent processes. Firstly, the improved Dijkstra algorithm prioritized the scheduling of the process sequence with long longitudinal paths and realized the “longitudinal optimization” of the integrated scheduling. Secondly, the layer priority strategy is used to shorten the interval time of process processing and realize the “horizontal optimization” of integrated scheduling. On the basis of “vertical and horizontal optimization”, the idle time of the equipment is further reduced by using the process priority strategy of the leaf node, and the “idle optimization” of the integrated scheduling is realized, so as to optimize the overall effect of the integrated scheduling. The effectiveness and superiority of the algorithm are proved using comparison analysis. Full article

The impact of the built environment on the ridership of ride-hailing results depends on the spatial grid scale. The existing research on the demand model of ride-hailing ignores the modifiable areal unit problem (MAUP). Taking Chengdu as an example, and taking the density of pick-ups and drop-offs as dependent variables, 12 explanatory variables were selected as independent variables according to the “5D” built environment theory. The nugget–sill ratio (NSR) method and optimal parameter-based geographical detector (OPGD) model were used to determine the optimal grid scale for the aggregation of the built environment variables and the ridership of ride-hailing. Based on the optimal grid scale, the optimal data discretization method of the explanatory variables was determined by comparing the results of the geographic detector under different discretization methods (such as the natural break method, k-means clustering method, equidistant method, and quantile method); we utilized the geographic detector model to explore the relative importance and the interactive impacts of the explanatory variables on the ridership of ride-hailing under the optimal grid scale and optimal data discretization method. The results indicated that: (1) the suggested grid scale for the aggregation of the built environment and ride-hailing ridership in Chengdu is 1100 m; (2) the optimal data discretization method is the quantile method; (3) the floor area ratio (FAR), distance from the nearest subway station, and residential POI (point of interest) density resulted in a relatively high importance of the explanatory variable that affects the ridership of ride-hailing; and (4) the interactions of the diversity index of mixed land use ∩ FAR, distance to the nearest subway station ∩ FAR, transportation POI density ∩ FAR, and distance to the central business district (CBD) ∩ FAR made a higher contribution to ride-hailing ridership than the single-factor effect of FAR, which had the highest contribution compared with the other explanatory variables. The proposed grid scale can provide the basis for the partitioning management and scheduling optimization of ride-hailing. In the process of adjusting the ride-hailing demand, the ranking results of the importance and interaction of the built-environment explanatory variables offer valuable references for formulating the priority renewal order and proposing a scientific combination scheme of the built-environment factors. Full article

Giving priority to the development of public transit is an important way to achieve efficient, convenient, safe, comfortable, economic, reliable, green and low-carbon sustainable development. In view of the highly dispersed and regular passenger flow, demand responsive transit is an important complementary means for traditional public transport to improve passenger satisfaction. However, high operating costs and low load factor will have a bad impact on the operation of public transport and reduce passenger satisfaction. In this work, firstly, by analyzing the demand frequency of historical travel stations, the stations with high demand are extracted by time periods as high probability travel points; On this basis, a dynamic vehicle dispatching optimization model is established, and the static vehicle dispatching is carried out with the goal of minimizing the running mileage of the bus system; Finally, based on the initial static route and the later real-time travel demand, the accurate dynamic planning algorithm is used to optimize the dynamic route with the goal of minimizing the change of the system mileage, so as to achieve timely response to the demand. The results show that the two-phase scheduling optimization model based on the station extraction strategy can provide a reasonable real-time vehicle scheduling and route optimization scheme, improve the utilization rate of vehicles and the passenger load factor, and provide a theoretical basis and application guidance for actual vehicle scheduling. Full article

As one of the power auxiliary services, peak shaving is the key problem to be solved in the power grid. With the rapid development of DGs, the traditional peak shaving scheduling method for centralized adjustable energy is no longer applicable. Thus, this paper proposes two-layer optimization methods of allocating the peak shaving task for DGs. Layer 1 mainly proposes four evaluation indexes and the peak shaving priority sequence can be obtained with modified TOPSIS, then the DG cluster’s task is allocated to the corresponding DGs. On the basis of dynamic evaluation and the self-renewal mechanism, layer 2 proposes a peak shaving optimization model with dynamic constraints which assigns peak shaving instructions to each cluster. Finally, the effectiveness of the method is verified by using the real DGs data of a regional power grid in China based on the MATLAB simulation platform. The results demonstrate that the proposed methods can simply the calculation complexity by ranking the DGs in the peak shaving task and update the reliable aggregate adjustable power of each cluster in time to allocate more reasonably. Full article

The number of Electric vehicle (EV) users is expected to increase in the future. The driving profile of EV users is unpredictable, necessitating the design of charging scheduling protocols for EV charging stations servicing multiple EVs. A large EV charging load affects the grid in terms of peak load demand. Electric vehicle charging stations with solar panels can help to reduce the grid impact of EV charging events. With reference to the increasing number of EVs, new technology needs to be developed for charging station and management to create a stable system for users, and electric utilities. The load of a total EV charge can affect the grid, degrading quality and system stability. In this paper, a charging station scheduling strategy is proposed based on the game theoretic approach. In the proposed strategy, with respect to the grid load demand minimization, charging stations have scheduled EV charging times to prevent sudden peak load on the grid the proposed game theory strategy is sudden peak load on the grid. The proposed game theory strategy is defined on the basis of priority so that both grid operators and EV users can maximize their profit by setting priorities for charging and discharging. This work provides a strategy for grid peak load minimization. Full article