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Search Results (1,129)

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16 pages, 979 KiB  
Article
Reinforcement Learning Model for Optimizing Bid Price and Service Quality in Crowdshipping
by Daiki Min, Seokgi Lee and Yuncheol Kang
Systems 2025, 13(6), 440; https://doi.org/10.3390/systems13060440 - 5 Jun 2025
Abstract
Crowdshipping establishes a short-term connection between shippers and individual carriers, bridging the service requirements in last-mile logistics. From the perspective of a carrier operating multiple vehicles, this study considers the challenge of maximizing profits by optimizing bid strategies for delivery prices and transportation [...] Read more.
Crowdshipping establishes a short-term connection between shippers and individual carriers, bridging the service requirements in last-mile logistics. From the perspective of a carrier operating multiple vehicles, this study considers the challenge of maximizing profits by optimizing bid strategies for delivery prices and transportation conditions in the context of bid-based crowdshipping services. We considered two types of bid strategies: a price bid that adjusts the RFQ freight charge and a multi‑attribute bid that scores both price and service quality. We formulated the problem as a Markov decision process (MDP) to represent uncertain and sequential decision-making procedures. Furthermore, given the complexity of the newly proposed problem, which involves multiple vehicles, route optimizations, and multiple attributes of bids, we employed a reinforcement learning (RL) approach that learns an optimal bid strategy. Finally, numerical experiments are conducted to illustrate the superiority of the bid strategy learned by RL and to analyze the behavior of the bid strategy. A numerical analysis shows that the bid strategies learned by RL provide more rewards and lower costs than other benchmark strategies. In addition, a comparison of price-based and multi-attribute strategies reveals that the choice of appropriate strategies is situation-dependent. Full article
(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)
16 pages, 605 KiB  
Article
Kriging-Variance-Informed Multi-Robot Path Planning and Task Allocation for Efficient Mapping of Soil Properties
by Laurence Roberts-Elliott, Gautham P. Das and Grzegorz Cielniak
Robotics 2025, 14(6), 77; https://doi.org/10.3390/robotics14060077 - 31 May 2025
Viewed by 150
Abstract
One of the most commonly performed environmental explorations is soil sampling to identify soil properties of agricultural fields, which can inform the farmer about the variable rate treatment of fertilisers in precision agriculture. However, traditional manual methods are slow, costly, and yield low [...] Read more.
One of the most commonly performed environmental explorations is soil sampling to identify soil properties of agricultural fields, which can inform the farmer about the variable rate treatment of fertilisers in precision agriculture. However, traditional manual methods are slow, costly, and yield low spatial resolution. Deploying multiple robots with proximal sensors can address this challenge by parallelising the sampling process. Yet, multi-robot soil sampling is under-explored in the literature. This paper proposes an auction-based multi-robot task allocation that efficiently coordinates the sampling, coupled with a dynamic sampling strategy informed by Kriging variance from interpolation. This strategy aims to reduce the number of samples needed for accurate mapping by exploring and sampling areas that maximise information gained per sample. The key innovative contributions include (1) a novel Distance Over Variance (DOV) bid calculation for auction-based multi-robot task allocation, which incentivises sampling in high-uncertainty, nearby areas; (2) integration of the DOV bid calculation into the cheapest insertion heuristic for task queuing; and (3) thresholding of newly created tasks at locations with low Kriging variance to drop those unlikely to offer significant information gain. The proposed methods were evaluated through comparative simulated experiments using historical soil compaction data. Evaluation trials demonstrate the suitability of the DOV bid calculation combined with task dropping, resulting in substantial improvements in key performance metrics, including mapping accuracy. While the experiments were conducted in simulation, the system is compatible with ROS and the ‘move_base’ action client to allow real-world deployment. The results from these simulations indicate that the Kriging-variance-informed approach can be applied to the exploration and mapping of other soil properties (e.g., pH, soil organic carbon, etc.) and environmental data. Full article
(This article belongs to the Special Issue Autonomous Robotics for Exploration)
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13 pages, 590 KiB  
Article
KRM-II-81, a β3-Preferring GABAA Receptor Potentiator, Blocks Handling-Induced Seizures in Theiler’s Murine Encephalomyelitis Virus-Infected Mice
by Dishary Sharmin, Kamal P. Pandey, Lalit K. Golani, Sepideh Rezvanian, Md Yeunus Mian, Janet L. Fisher, Arnold Lippa, James M. Cook, Daniel P. Radin, Jodi L. Smith, Jeffrey M. Witkin, Hana Shafique and Rok Cerne
Future Pharmacol. 2025, 5(2), 25; https://doi.org/10.3390/futurepharmacol5020025 - 30 May 2025
Viewed by 504
Abstract
Background: The GABAA receptor (GABAAR) potentiator, KRM-II-81, is being developed as a novel antiseizure medication with reduced potential for sedation, tolerance development, and abuse liability. Although KRM-II-81 has been shown to provide antiseizure protection against a broad array of seizure induction paradigms, [...] Read more.
Background: The GABAA receptor (GABAAR) potentiator, KRM-II-81, is being developed as a novel antiseizure medication with reduced potential for sedation, tolerance development, and abuse liability. Although KRM-II-81 has been shown to provide antiseizure protection against a broad array of seizure induction paradigms, seizures induced by viral vectors have not been previously studied. GABAARs with specific α subunit compositions have been studied in relation to the reduced side-effect liability of KRM-II-81; however, the role of β subunit composition has yet to be determined. Methods: In the present study, KRM-II-81 was studied against handling-induced seizures in Theiler’s murine encephalomyelitis virus (TMEV)-infected mice. Results: An intracerebral infusion of TMEV on day 0 increased the cumulative seizure burden in mice when assessed for handling-induced seizures on days 3–7. KRM-II-81 (15 mg/kg, p.o., bid) nearly completely suppressed seizures in TMEV-infected mice over the course of daily treatments. The number of the most severe seizures (stage 5, tonic/clonic seizures) in the mice was suppressed to zero by KRM-II-81. Although the selectivity of KRM-II-81 for GABAAR α2/3 receptor subtypes might imbue KRM-II-81 with a reduced side-effect liability, other mechanisms are possible, and the potentiation of β1-containing GABAARs has been implicated in inducing sedation. The role of β subunit composition has yet to be determined for KRM-II-81. In electrophysiological studies with cells transfected with αxβ1γ2 or αxβ3γ2, KRM-II-81 preferentially potentiated GABA responses in cells containing β3 subunits in α2/3-containing GABAARs. Conclusions: The present findings confirm the robust antiseizure activity of KRM-II-81, now extended to a virus-induction model, and suggest a possible role of reduced β1-potentiation in the low side-effect profile of KRM-II-81. Full article
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20 pages, 1882 KiB  
Article
Optimal Bidding Strategies for the Participation of Aggregators in Energy Flexibility Markets
by Gian Giuseppe Soma, Giuseppe Marco Tina and Stefania Conti
Energies 2025, 18(11), 2870; https://doi.org/10.3390/en18112870 - 30 May 2025
Viewed by 217
Abstract
The increasing adoption of Renewable Energy Sources (RESs), due to international energy policies mainly related to the decarbonization of electricity production, raises several operating issues for power systems, which need “flexibility” in order to guarantee reliable and secure operation. RESs can be considered [...] Read more.
The increasing adoption of Renewable Energy Sources (RESs), due to international energy policies mainly related to the decarbonization of electricity production, raises several operating issues for power systems, which need “flexibility” in order to guarantee reliable and secure operation. RESs can be considered examples of Distributed Energy Resources (DERs), which are typically electric power generators connected to distribution networks, including photovoltaic and wind systems, fuel cells, micro-turbines, etc., as well as energy storage systems. In this case, improved operation of power systems can be achieved through coordinated control of groups of DERs by “aggregators”, who also offer a “flexibility service” to power systems that need to be appropriately remunerated according to market rules. The implementation of the aggregator function requires the development of tools to optimally operate, control, and dispatch the DERs to define their overall flexibility as a “market product” in the form of bids. The contribution of the present paper in this field is to propose a new optimization strategy for flexibility bidding to maximize the profit of the aggregator in flexibility markets. The proposed optimal scheduling procedure accounts for important practical and technical aspects related to the DERs’ operation and their flexibility estimation. A case study is also presented and discussed to demonstrate the validity of the method; the results clearly highlight the efficacy of the proposed approach, showing a profit increase of 10% in comparison with the base case without the use of the proposed methodology. It is evident that quantitatively more significant results can be obtained when larger aggregations (more participants) are considered. Full article
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18 pages, 555 KiB  
Article
Strategic Bidding to Increase the Market Value of Variable Renewable Generators in New Electricity Market Designs
by Hugo Algarvio and Vivian Sousa
Energies 2025, 18(11), 2848; https://doi.org/10.3390/en18112848 - 29 May 2025
Viewed by 147
Abstract
Electricity markets with a high share of variable renewable energy require significant balancing reserves to ensure stability by preserving the balance of supply and demand. However, they were originally conceived for dispatchable technologies, which operate with predictable and controllable generation. As a result, [...] Read more.
Electricity markets with a high share of variable renewable energy require significant balancing reserves to ensure stability by preserving the balance of supply and demand. However, they were originally conceived for dispatchable technologies, which operate with predictable and controllable generation. As a result, adapting market mechanisms to accommodate the characteristics of variable renewables is essential for enhancing grid reliability and efficiency. This work studies the strategic behavior of a wind power producer (WPP) in the Iberian electricity market (MIBEL) and the Portuguese balancing markets (BMs), where wind farms are economically responsible for deviations and do not have support schemes. In addition to exploring current market dynamics, the study proposes new market designs for the balancing markets, with separate procurement of upward and downward secondary balancing capacity, aligning with European Electricity Regulation guidelines. The difference between market designs considers that the wind farm can hourly bid in both (New 1) or only one (New 2) balancing direction. The study considers seven strategies (S1–S7) for the participation of a wind farm in the past (S1), actual (S2 and S3), New 1 (S4) and New 2 (S5–S7) market designs. The results demonstrate that new market designs can increase the wind market value by 2% compared to the optimal scenario and by 31% compared to the operational scenario. Among the tested approaches, New 2 delivers the best operational and economic outcomes. In S7, the wind farm achieves the lowest imbalance and curtailment while maintaining the same remuneration of S4. Additionally, the difference between the optimal and operational remuneration of the WPP under the New 2 design is only 22%, indicating that this design enables the WPP to achieve remuneration levels close to the optimal case. Full article
(This article belongs to the Special Issue New Approaches and Valuation in Electricity Markets)
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21 pages, 1873 KiB  
Article
Pharmacological Evaluation of Angelica keiskei Extract: Molecular Interaction Analysis in Hepatocellular Carcinoma
by Alka Ashok Singh, Minseok Song and Gun-Do Kim
Curr. Issues Mol. Biol. 2025, 47(6), 401; https://doi.org/10.3390/cimb47060401 - 29 May 2025
Viewed by 208
Abstract
Hepatocellular carcinoma (HCC), the most prevalent primary liver cancer, is the most significant cause of cancer-related death globally, with limited treatment options, including surgical resection, liver transplantation, ablation, chemoembolization, immunotherapy, and radiation. Angelica keiskei, a plant that is rich in chalcones and [...] Read more.
Hepatocellular carcinoma (HCC), the most prevalent primary liver cancer, is the most significant cause of cancer-related death globally, with limited treatment options, including surgical resection, liver transplantation, ablation, chemoembolization, immunotherapy, and radiation. Angelica keiskei, a plant that is rich in chalcones and flavonoids, has demonstrated interesting anticancer properties. This study assesses the pharmacological effects of Angelica keiskei extract on HepG2 cells in order to investigate its efficacy as a therapeutic intervention for HCC. Using in vitro cell culture models, HepG2 cells were treated with different doses of the extract, and its cytotoxic and apoptotic effects were studied. GC-MS analysis revealed the presence of several bioactive compounds, including DDMP, which are likely involved in the observed effects. The MTT assay revealed a considerable, dose-dependent reduction in cell viability, with higher dosages causing notable morphological alterations. An antibody apoptotic array indicated significant changes in apoptotic proteins, specifically IGFBP1, BAD, and Bid. Cluster heatmaps, volcano plots, STRING analysis, Voom-mean variance trends, Glimma plots, and PCA were used to obtain an understanding of the molecular interactions involved. These results suggest that Angelica keiskei extract can cause apoptosis in HepG2 cells, with DDMP appearing as a potentially significant contributor. However, more experimental validation is required to determine the precise molecular mechanisms driving these favorable effects and their clinical implications in HCC. Full article
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10 pages, 1235 KiB  
Article
Cost-Efficient Active Transfer Learning Framework for Object Detection from Engineering Documents
by Yu-Ri Han, Donghyun Park, Young-Suk Han and Jae-Yoon Jung
Processes 2025, 13(6), 1657; https://doi.org/10.3390/pr13061657 - 25 May 2025
Viewed by 265
Abstract
Recently, engineering companies have started to digitise documents in image form to analyse their meaning and extract important content. However, many engineering and contract documents contain different types of components such as texts, tables, and forms, which often hinder accurate interpretation by simple [...] Read more.
Recently, engineering companies have started to digitise documents in image form to analyse their meaning and extract important content. However, many engineering and contract documents contain different types of components such as texts, tables, and forms, which often hinder accurate interpretation by simple optical character recognition. Therefore, document object detection (DOD) has been studied as a preprocessing step for optical character recognition. Given the ease of acquiring image data, reducing annotation time and effort through transfer learning and active learning has emerged as a key research challenge. In this study, a cost-efficient active transfer learning (ATL) framework for DOD is presented to minimise the effort and cost of time-consuming image annotation for transfer learning. Specifically, three new sample evaluation measures are proposed to enhance the sampling performance of ATL. The proposed framework performed well in ATL experiments of DOD for invitation-to-bid documents. In the experiments, the DOD model was trained on only half of the labelled images, but, in terms of the F1-score, it achieved a similar performance as a DOD model trained on all labelled images. In particular, one of the proposed sampling measures, ambiguity, showed the best sampling performance compared to existing measures, such as entropy and uncertainty. The efficient sample evaluation measures proposed in this study are expected to reduce the time and effort required for ATL. Full article
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30 pages, 5334 KiB  
Article
Optimal Multi-Area Demand–Thermal Coordination Dispatch
by Yu-Shan Cheng, Yi-Yan Chen, Cheng-Ta Tsai and Chun-Lung Chen
Energies 2025, 18(11), 2690; https://doi.org/10.3390/en18112690 - 22 May 2025
Viewed by 236
Abstract
With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the [...] Read more.
With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the power system. This paper aims to design a demand bidding (DB) mechanism to collaborate between customers and suppliers on demand response (DR) to prevent the risks of energy shortage and realize energy conservation. The concurrent integration of the energy, transmission, and reserve capacity markets necessitates a new formulation for determining schedules and marginal prices, which is expected to enhance economic efficiency and reduce transaction costs. To dispatch energy and reserve markets concurrently, a hybrid approach of combining dynamic queuing dispatch (DQD) with direct search method (DSM) is developed to solve the extended economic dispatch (ED) problem. The effectiveness of the proposed approach is validated through three case studies of varying system scales. The impacts of tie-line congestion and area spinning reserve are fully reflected in the area marginal price, thereby facilitating the determination of optimal load reduction and spinning reserve allocation for demand-side management units. The results demonstrated that the multi-area bidding platform proposed in this paper can be used to address issues of congestion between areas, thus improving the economic efficiency and reliability of the day-ahead market system operation. Consequently, this research can serve as a valuable reference for the design of the demand bidding mechanism. Full article
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20 pages, 8580 KiB  
Article
Enhancing Fairness and Efficiency in PV Energy Curtailment: The Role of East–West-Facing Bifacial Installations in Radial Distribution Networks
by Francis Maina Itote, Ryuto Shigenobu, Akiko Takahashi, Masakazu Ito and Ghjuvan Antone Faggianelli
Energies 2025, 18(10), 2630; https://doi.org/10.3390/en18102630 - 20 May 2025
Viewed by 290
Abstract
Electricity market reforms and decreasing technology costs have propelled residential solar PV growth leading distribution network operators to face operational challenges including reverse power flows and voltage regulation during peak solar generation. Traditional mono-facial south-facing PV systems concentrate production at midday when demand [...] Read more.
Electricity market reforms and decreasing technology costs have propelled residential solar PV growth leading distribution network operators to face operational challenges including reverse power flows and voltage regulation during peak solar generation. Traditional mono-facial south-facing PV systems concentrate production at midday when demand may be low, leading to high curtailment, especially for downstream households. This study proposes vertically installed east–west-facing bifacial PV systems (BiE and BiW), characterized by two energy peaks (morning and evening), which are better aligned with residential demand and alleviate grid constraints. Using load flow simulations, the performance of vertical bifacial configurations was compared against mono-facial systems across PV capacities from 1 to 20 kW. Fairness in curtailment was evaluated at 10 kW using Jain’s fairness index, the Gini index, and the Curtailment index. Simulation results show that BiE and BiW installations, especially at higher capacities, not only generate more energy but also are better at managing curtailment. At 10 kW, BiE and BiW increased bid energies by 815 kWh and 787 kWh, and reduced curtailed energy by 1566 kWh and 1499 kWh, respectively. These findings highlight the potential of bifacial PV installations in mitigating curtailment and improving fairness in energy distribution, supporting the demand for residential PV systems. Full article
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21 pages, 4151 KiB  
Article
Research on Resource Consumption Standards for Highway Electromechanical Equipment Based on Monte Carlo Model
by Linxuan Liu, Wei Tian, Xiaomin Dai and Liang Song
Sustainability 2025, 17(10), 4640; https://doi.org/10.3390/su17104640 - 19 May 2025
Viewed by 238
Abstract
The increasing complexity of highway electromechanical systems has created a critical need to improve the accuracy of resource consumption standards. Traditional deterministic methods often fail to capture inherent variability in resource usage, resulting in significant discrepancies between budget estimates and actual costs. To [...] Read more.
The increasing complexity of highway electromechanical systems has created a critical need to improve the accuracy of resource consumption standards. Traditional deterministic methods often fail to capture inherent variability in resource usage, resulting in significant discrepancies between budget estimates and actual costs. To address this issue for a specific device, this study develops a probabilistic framework based on Monte Carlo simulation, using manual barrier gate installation as a case study. First, probability distribution models for key parameters were established by collecting and statistically analyzing field data. Next, Monte Carlo simulation generated 100,000 pseudo-observations, yielding mean labor consumption of 1.08 workdays (SD 0.29), expansion bolt usage of 6.02 sets (SD 0.97), and equipment shifts of 0.20 (SD 0.10). Comparison with the “Highway Engineering Budget Standards” (JTG/T 3832-2018) revealed deviations of 1% to 4%, and comparison with market bid prices showed errors below 2%. These results demonstrate that the proposed method accurately captures dynamic fluctuations in resource consumption, aligning with both national norms and actual tender data. In conclusion, the framework offers a robust and adaptable tool for cost estimation and resource allocation in highway electromechanical projects, enhancing budgeting accuracy and reducing the risk of cost overruns. Full article
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21 pages, 5821 KiB  
Article
An Incentive Mechanism Based on Lottery for Data Quality in Mobile Crowdsensing
by Xinyu Hu, Shengjie Sun, Zhi Lv and Jiaqi Liu
Mathematics 2025, 13(10), 1650; https://doi.org/10.3390/math13101650 - 18 May 2025
Viewed by 172
Abstract
Mobile Crowdsensing (MCS) leverages smart devices within sensing networks to gather data. Given that data collection demands specific resources, such as device power and network bandwidth, many users are reluctant to participate in MCS. Therefore, it is essential to design an effective incentive [...] Read more.
Mobile Crowdsensing (MCS) leverages smart devices within sensing networks to gather data. Given that data collection demands specific resources, such as device power and network bandwidth, many users are reluctant to participate in MCS. Therefore, it is essential to design an effective incentive mechanism to encourage user participation and ensure the provision of high-quality data. Currently, most incentive mechanisms compensate users through monetary rewards, which often leads to users requiring higher prices to maintain their own profits. This, in turn, results in a limited number of users being selected due to platform budget constraints. To address this issue, we propose a lottery-based incentive mechanism. This mechanism analyzes the users’ bids to design a winning probability and budget allocation model, incentivizing users to lower their pricing and enhance data quality. Within a specific budget, the platform can engage more users in tasks and obtain higher-quality data. Compared to the ABSEE mechanism and the BBOM mechanism, the lottery incentive mechanism demonstrates improvements of approximately 47–74% in user participation and 14–66% in platform profits. Full article
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23 pages, 2449 KiB  
Article
Bi-Level Game-Theoretic Bidding Strategy for Large-Scale Renewable Energy Generators Participating in the Energy–Frequency Regulation Market
by Ran Gao, Shuyan Hui, Bingtuan Gao and Xiaofeng Liu
Energies 2025, 18(10), 2604; https://doi.org/10.3390/en18102604 - 17 May 2025
Viewed by 317
Abstract
The proportion of grid-connected renewable energy, represented by wind and photovoltaic power, continues to rise. The intermittence and volatility of the power output of renewable energy bring serious challenges to the secure and stable operation of the power system. Adopting a market-based approach [...] Read more.
The proportion of grid-connected renewable energy, represented by wind and photovoltaic power, continues to rise. The intermittence and volatility of the power output of renewable energy bring serious challenges to the secure and stable operation of the power system. Adopting a market-based approach to promote the active participation of producers in frequency regulation and other auxiliary service markets besides the energy market is the only way to comprehensively solve the problems of power system security, stability, and economic benefits. Therefore, for the future bidding decision scenario of large-scale renewable energy generators participating in the energy–frequency regulation market, a bi-level game-theoretic bidding model based on mean-field game and non-cooperative game theory is proposed. The inner level is a mean-field game among large-scale renewable energy generators of the same type, and the outer level is a non-cooperative game among different types of generators. A combination of fixed-point iteration and finite-difference method is employed to solve the proposed bi-level bidding decision model. Case analysis indicates that the proposed model can effectively realize the bidding decision optimization for large-scale renewable energy generators in the energy–frequency regulation market. Furthermore, in comparison to traditional proportional bidding model, the proposed model enables renewable energy generators to secure higher profits in the energy–frequency regulation market. Full article
(This article belongs to the Section A: Sustainable Energy)
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31 pages, 4090 KiB  
Article
Day-Ahead Electricity Price Forecasting for Sustainable Electricity Markets: A Multi-Objective Optimization Approach Combining Improved NSGA-II and RBF Neural Networks
by Chunlong Li, Zhenghan Liu, Guifan Zhang, Yumiao Sun, Shuang Qiu, Shiwei Song and Donglai Wang
Sustainability 2025, 17(10), 4551; https://doi.org/10.3390/su17104551 - 16 May 2025
Viewed by 210
Abstract
The large-scale integration of renewable energy into power grids introduces substantial stochasticity in generation profiles and operational complexities due to electricity’s non-storable nature. These factors cause significant fluctuations in day-ahead market prices. Accurate price forecasting is crucial for market participants to optimize bidding [...] Read more.
The large-scale integration of renewable energy into power grids introduces substantial stochasticity in generation profiles and operational complexities due to electricity’s non-storable nature. These factors cause significant fluctuations in day-ahead market prices. Accurate price forecasting is crucial for market participants to optimize bidding strategies, mitigate renewable curtailment, and enhance grid sustainability. However, conventional methods struggle to address the nonlinearity, high-frequency dynamics, and multivariate dependencies inherent in electricity prices. This study proposes a novel multi-objective optimization framework combining an improved non-dominated sorting genetic algorithm II (NSGA-II) with a radial basis function (RBF) neural network. The improved NSGA-II algorithm mitigates issues of population diversity loss, slow convergence, and parameter adaptability by incorporating dynamic crowding distance calculations, adaptive crossover and mutation probabilities, and a refined elite retention strategy. Simultaneously, the RBF neural network balances prediction accuracy and model complexity through structural optimization. It is verified by the data of Singapore power market and compared with other forecasting models and error calculation methods. These results highlight the ability of the model to track the peak price of electricity and adapt to seasonal changes, indicating that the improved NSGA-II and RBF (NSGA-II-RBF) model has superior performance and provides a reliable decision support tool for sustainable operation of the power market. Full article
(This article belongs to the Special Issue Recent Advances in Smart Grids for a Sustainable Energy System)
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19 pages, 1974 KiB  
Systematic Review
Outcomes of Different Regimens of Rivaroxaban and Aspirin in Cardiovascular Diseases: A Network Meta-Analysis
by Mohammed Maan Al-Salihi and Adnan I. Qureshi
J. Clin. Med. 2025, 14(10), 3437; https://doi.org/10.3390/jcm14103437 - 14 May 2025
Viewed by 274
Abstract
Background/Objectives: Rivaroxaban is widely used to prevent thrombotic events in cardiovascular diseases (CVD). While various doses and combinations with aspirin have been evaluated across CVD subtypes, the optimal regimen remains unclear. This network meta-analysis aims to identify the most effective and safe rivaroxaban [...] Read more.
Background/Objectives: Rivaroxaban is widely used to prevent thrombotic events in cardiovascular diseases (CVD). While various doses and combinations with aspirin have been evaluated across CVD subtypes, the optimal regimen remains unclear. This network meta-analysis aims to identify the most effective and safe rivaroxaban regimens, with or without aspirin, for patients with CVD. Methods: A systematic search of PubMed, Scopus, Cochrane Library, and Web of Science identified randomized-controlled trials (RCTs) assessing rivaroxaban, with or without aspirin, in CVD. Key outcomes included thromboembolic, hemorrhagic, and mortality events. A frequentist network meta-analysis (MetaInsight tool) was performed, using aspirin monotherapy as the reference. Subgroup analyses for coronary artery disease (CAD) were conducted. Results: Seven RCTs were included. Rivaroxaban 2.5 mg twice daily (“bis in die” (BID)) with aspirin showed the most significant venous thromboembolism (VTE) prevention (RR = 0.61, 95% CI [0.43–0.86]) but had the highest major bleeding risk (RR = 1.58, 95% CI [1.26–2]). Rivaroxaban 5 mg BID with aspirin showed the lowest myocardial infarction risk (RR = 0.78). Higher doses (20 mg BID) with aspirin were associated with an increased fatal bleeding risk (RR = 7.14, 95% CI [2.83–17.98]). Rivaroxaban 5 mg BID monotherapy had the highest hemorrhagic stroke risk (RR = 2.7, 95% CI [1.31–5.58]). In CAD, rivaroxaban 2.5 mg BID plus aspirin offered the lowest all-cause mortality (RR = 0.76, 95% CI [0.63–0.93]). Conclusions: Rivaroxaban 2.5 mg BID plus aspirin reduces VTE and lowers mortality in CAD but carries higher bleeding risks. Optimal regimen selection requires a careful risk–benefit balance. Full article
(This article belongs to the Section Cardiovascular Medicine)
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24 pages, 3105 KiB  
Article
Aggregation Method and Bidding Strategy for Virtual Power Plants in Energy and Frequency Regulation Markets Using Zonotopes
by Jun Zhan, Mei Huang, Xiaojia Sun, Zuowei Chen, Yubo Zhang, Xuejing Xie, Yilin Chen, Yining Qiao and Qian Ai
Energies 2025, 18(10), 2458; https://doi.org/10.3390/en18102458 - 10 May 2025
Viewed by 282
Abstract
Aggregating and scheduling flexible resources through virtual power plants (VPPs) is a key measure used to improve the flexibility of new power systems. To maximize the regulation potential of flexible resources and achieve the efficient, unified scheduling of flexible resource clusters by VPPs, [...] Read more.
Aggregating and scheduling flexible resources through virtual power plants (VPPs) is a key measure used to improve the flexibility of new power systems. To maximize the regulation potential of flexible resources and achieve the efficient, unified scheduling of flexible resource clusters by VPPs, this study proposed a flexible resource aggregation method for VPPs and a bidding strategy for participation in the electricity and frequency regulation markets. First, considering the differences in the grid frequency regulation demand across periods, an improved zonotope approximation method was adopted to internally approximate the feasible region of flexible resources, thereby achieving the efficient aggregation of feasible regions. On this basis, the aggregation model was applied to the optimization model for VPPs, and a day-ahead double-layer bidding model of VPPs participating in the electricity and frequency regulation markets was proposed. The upper layer optimizes the bidding strategies to maximize the VPP revenue, while the lower layer achieves joint market clearing with the goal of maximizing social welfare. Finally, case studies were undertaken to validate the effectiveness of the proposed method. Full article
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