Search Results (96)

Traditional electricity market bidding typically focuses on unilateral structures, where independent energy storage units and flexible loads act merely as price takers. This reduces bidding motivation and weakens the balancing capability of regional power systems, thereby limiting the large-scale utilization of renewable energy. To address these challenges and support sustainable power system operation, this paper proposes a double-sided auction market strategy for heterogeneous multi-resource (HMR) participation based on multi-agent reinforcement learning (MARL). The framework explicitly considers the heterogeneous bidding and quantity reporting behaviors of renewable generation, flexible demand, and energy storage. An improved incentive mechanism is introduced to enhance real-time system power balance, thereby enabling higher renewable energy integration and reducing curtailment. To efficiently solve the market-clearing problem, an improved Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm is employed, along with a temporal-difference (TD) error-based prioritized experience replay mechanism to strengthen exploration. Case studies validate the effectiveness of the proposed approach in guiding heterogeneous resources toward cooperative bidding behaviors, improving market efficiency, and reinforcing the sustainable and resilient operation of future power systems. Full article

With increasing renewable energy integration, electricity spot markets exhibit high volatility and uncertainty, making it difficult to balance profit and risk. To address this challenge, this paper proposes Joint (Version 1.0), a multi-agent closed-loop framework that integrates forecasting, strategy, and feedback for coordinated decision-making. The Prediction Agent learns statistical patterns of price spreads to generate distributional forecasts, directional probabilities, and extreme-value indicators; the Strategy Agent adaptively maps these signals into executable bidding ratios through a hybrid mechanism; and the Feedback Agent incorporates settlement results for performance evaluation, CVaR-based risk control, and preference-driven optimization. These agents form a dynamic “forecast–strategy–feedback” loop enabling self-improving trading. Experimental results show that Joint achieves a monthly profit of 146,933.46 CNY with strong classification performance (Precision = 53.25%, Recall = 40.45%, AA = 56.05%, SWA = 57.36%), and the complete model in ablation experiments reaches 157,746.64 CNY, demonstrating the indispensable contributions of each component and confirming its robustness and practical value in volatile electricity spot markets. Full article

The recent synergistic explosion of artificial intelligence and the world of machines, in a bid to make them smarter entities as a result of the fourth industrial revolution, has resulted in the concept of chatbots, which have evolved over the years and gained heightened attention for the sustainability of most human corporations. Organisations are increasingly utilising chatbots to enhance customer engagement through the process of agent-based autonomous sensing, interaction, and enhanced service delivery. The current state of the art in chatbot technology is such that the system lacks the ability to conduct text-sensing in a bid to acquire new information or learn from the external world autonomously. This has limited the current chatbot systems to being system-controlled interactive agents, hence, strongly limiting their functionalities and posing a question on the purported intelligence. In this research, an integrated framework that combines the functionalities and capabilities of a chatbot and machine learning was developed. The integrated system was designed to accept new text queries from the external world and import them into the knowledge base using the SQL (Structured Query Language) syntax and MySQL workbench (version 8.0.44). The search engine and decision-making cluster was built in the Python (version 3.12.7) coding environment with the learning process, solution adaptation, and inference, anchored using a reinforcement machine learning approach. This mode of chatbot operation, with an interactive capacity, is known as the mixed controlled system mode, with a viable human–machine system interaction. The smart chatbot was assessed for efficacy using performance metrics (response time, accuracy) and user experience (usability, satisfaction). The analysis further revealed that several self-governed chatbots deployed in most corporate organisations are system-controlled and significantly constrained, hence lacking the ability to adapt or filter queries beyond their predefined databases when users employ diverse phrasing or alternative terms in their interactions. Full article

Plasmodium parasites are the causative agents of malaria and can infect humans and other vertebrates, impacting socioeconomic development and causing significant health issues globally. Plasmodium falciparum causes the most severe type of infection, which can lead to chronic morbidity and other severe complications like anemia and cerebral malaria. The onset of infection is marked by the injection of sporozoites into the skin through the bite of a female Anopheles mosquito. This triggers a cascade of reactions elicited both by the host immune system in response to infection and by the parasite in a bid to evade the host immune system, survive, and replicate. The dynamics of this host–parasite relationship have prompted extensive research in an attempt to understand and exploit it in the fight against malaria. Thus, understanding the temporal and spatial dimensions of adaptation in host–parasite relationships is critical for forecasting parasite evolution and spread within and between host populations. One such relationship is the complex interplay between malaria and cellular aging processes. Understanding this dynamic will provide novel insights into the pathophysiology of the disease. This comprehensive review takes us on that journey by providing an overview of the interaction between the Plasmodium parasite and its host and the interplay between infection mechanisms, host immune response, and parasite evasion strategies, narrowing it down to how it affects cellular aging biomarkers and how this can be explored as a platform in the fight against the disease. Full article

Background: Scedosporium apiospermum is a filamentous fungus increasingly recognized as an opportunistic pathogen in immunocompromised hosts, though rare infections in immunocompetent individuals with structural lung disease have been reported. Its diagnosis and management remain challenging due to non-specific clinical presentation and intrinsic resistance to multiple antifungal agents. Case Presentation: We report the case of a 66-year-old immunocompetent woman with idiopathic bilateral non-cystic fibrosis bronchiectasis, who presented with subacute cough and increased sputum production. Chest high-resolution CT revealed new subsolid and ground-glass infiltrates superimposed on stable bronchiectatic changes. Bronchoalveolar lavage (BAL) cultures isolated S. apiospermum as the sole pathogen. The patient was treated with oral voriconazole (200 mg BID) for 4 weeks, followed by a 4-week course of aerosolized amphotericin B. Clinical and radiological improvement was observed, and no relapse occurred during follow-up. Discussion: This case highlights the potential for S. apiospermum to cause clinically relevant pulmonary infection in structurally abnormal but immunocompetent lungs. Non-CF bronchiectasis may facilitate fungal colonization due to impaired mucociliary clearance and chronic mucus retention. Combined antifungal therapy involving systemic voriconazole and inhaled amphotericin B (though not yet standardized) was employed based on clinical rationale and the available literature, resulting in favorable outcomes. Conclusions:S. apiospermum pulmonary infection, although rare in immunocompetent hosts with bronchiectasis, should be considered in cases of new or persistent infiltrates. Early recognition and individualized antifungal strategies, including the potential role of inhaled agents, may improve clinical outcomes. This case reinforces the importance of multidisciplinary collaboration in the management of complex fungal infections in chronic airway disease. Full article

The discovery of penicillin led to remarkable progress in the treatment of diseases and far-reaching advancements in novel antibiotics’ development and use. However, the uncontrolled use and abuse of antibiotics in subsequent years have led to the emergence of the antimicrobial resistance (AMR) crisis, which now threatens modern medicine. There is an increasing number of emerging and reemerging infectious diseases, which have worsened the state of AMR and pose a serious threat to global health. The World Health Organization (WHO) reports the inadequacy of the drug development pipeline to meet the needs of the pharmaceutical sector in the face of AMR, and this poses a significant challenge in the treatment of diseases. Natural products (NPs) represent a promising group of antibiotic alternatives that can potentially mitigate AMR, as they bypass the pharmacodynamics of traditional antibiotics, thereby making them immune to the mechanisms of AMR. NPs, including plant derivatives, bacteriophages, metals, antimicrobial peptides, enzymes, and immune modulators, as monotherapies or in synergism with existing antibiotics, are gaining attention in a bid to reconstruct the antibiotic pipeline. Harnessing these as antimicrobial agents to curb AMR can help to provide sufficient defence against these infectious pathogens. The current review provides a comprehensive overview of the state of AMR and the potential of the above-mentioned antibiotic alternatives. Additionally, we discuss progress made and research breakthroughs in the application of these alternative therapies in humans, exploring findings from clinical trials and experimental models. The review further evaluates the advancement in technology, interdisciplinary approaches to the formulation and utilisation of NPs, and collaborations in alternative drug development. The research gaps present in this ever-evolving field are highlighted and evaluated together with regulatory issues, safety concerns, and technical difficulties in implementation. Full article

This paper aims to investigate a Nash equilibrium (NE)-seeking approach for the aggregative game problem of second-order multi-agent systems (MAS) with uncontrollable malicious players, which may cause the decisions of global players to become uncontrollable, thereby hindering the ability of normal players to reach the NE. To mitigate the influence of malicious players on the system, a malicious player detection and disconnection (MPDD) algorithm is proposed, based on the fixed-time convergence method. Subsequently, a predefined-time distributed NE-seeking algorithm is presented, utilizing a time-varying, time-based generator (TBG) and state-feedback scheme, ensuring that all normal players complete the game problem within the predefined time. The convergence properties of the algorithms are analyzed using Lyapunov stability theory. Theoretically, the aggregative game problem with malicious players can be solved using the proposed algorithms within any user-defined time. Finally, a numerical simulation of electricity market bidding verifies the effectiveness of the proposed algorithm. Full article

The large-scale integration of renewable energy and electric vehicles(EVs) into power distribution systems presents complex operational challenges, particularly in coordinating market mechanisms with grid stability requirements. This study proposes a new dispatching method based on dynamic electricity prices to coordinate the relationship between the market and the physical characteristics of the power grid. The proposed approach introduces a multi-agent transaction model incorporating voltage regulation metrics and network loss considerations into market bidding mechanisms. For EV integration, a differentiated scheduling strategy categorizes vehicles based on usage patterns and charging elasticity. The methodological innovations primarily include an enhanced scheduling algorithm for coordinated optimization of renewable energy and energy storage, and a dynamic coordinated optimization method for EV clusters. Implemented on a modified IEEE test system, the framework demonstrates improved voltage stability through price-guided energy storage dispatch, with coordinated strategies effectively balancing peak demand management and renewable energy utilization. Case studies verify the system’s capability to align economic incentives with technical objectives, where time-of-use pricing dynamically regulates storage operations to enhance reactive power support during critical periods. This research establishes a theoretical linkage between electricity market dynamics and grid security constraints, providing system operators with a holistic tool for managing high-renewable penetration networks. By bridging market participation with operational resilience, this work contributes actionable insights for developing interoperable electricity market architectures in energy transition scenarios. Full article

The deepening operation of the carbon emission trading market has reshaped the cost–benefit structure of the power generation side. In the process of participating in the market quotation, power suppliers not only need to calculate the conventional power generation cost but also need to coordinate the superimposed impact of carbon quota accounting on operating income, which causes the power suppliers a multi-time-scale decision-making collaborative optimization problem under the interaction of the carbon market, power market, and coal market. This paper focuses on the multi-market-coupling decision optimization problem of thermal power suppliers. It proposes a collaborative bidding decision framework based on a multi-agent deep deterministic policy gradient (MADDPG). Firstly, aiming at the time-scale difference of multi-sided market decision making, a decision-making cycle coordination scheme for the carbon–electricity–coal coupling market is proposed. Secondly, upper and lower optimization models for the bidding decision making of power suppliers are constructed. Then, based on the MADDPG algorithm, the multi-generator bidding scenario is simulated to solve the optimal multi-generator bidding strategy in the carbon–electricity–coal coupling market. Finally, the multi-scenario simulation based on the IEEE-5 node system shows that the model can effectively analyze the differential influence of a multi-market structure on the bidding strategy of power suppliers, verifying the superiority of the algorithm in convergence speed and revenue optimization. Full article

The engagement of emerging market participants in electricity markets exerts dual influences on price formation mechanisms and operational dynamics. To quantify the impacts on locational marginal prices and stakeholders’ economic interests when EV aggregators (EVAs), cloud energy storage operators (CESSOs), and load aggregators (LAs) collectively participate in market competition, this study develops a bi-level game-theoretic framework for market equilibrium analysis. The proposed architecture comprises two interdependent layers: The upper-layer Stackelberg game coordinates strategic interactions among EVA, LA, and CESSO to mitigate bidding uncertainties through cooperative mechanisms. The lower-layer non-cooperative Nash game models competition patterns to determine market equilibria under multi-agent participation. A hybrid solution methodology integrating nonlinear complementarity formulations with genetic algorithm-based optimization was developed. Extensive numerical case studies validate the methodological efficacy, demonstrating improvements in solution optimality and computational efficiency compared to conventional approaches. Full article

Background: While Bacillus Calmette-Guérin (BCG) remains the first-line therapy for high-risk bladder cancer, 30–40% of patients develop treatment resistance necessitating radical cystectomy, some are not suitable candidates for this procedure. This underscores the critical need for novel therapeutic approaches. Emerging clinical evidence has increasingly supported the therapeutic potential of oncolytic viruses in bladder cancer treatment. Based on this clinical foundation, we investigated the anti-tumor effects of KD01, a novel type 5 recombinant oncolytic adenovirus previously developed by our team engineered to express truncated BID (tBID), in bladder cancer. Methods: The cytotoxic effects and anti-tumor efficacy of KD01 were systematically evaluated across human bladder cancer cell lines, and cell death pathways were investigated by RNA sequencing and validated. Combination therapy studies with cisplatin employed cytotoxic testing. In the final stage, the safety of KD01 bladder instillation was evaluated. Results: KD01 induced bladder cancer cell death through multiple mechanisms, including oncolysis, immunogenic cell death, and mitochondrial apoptosis. At higher doses, KD01 combined with cisplatin synergistically inhibited cancer cell proliferation and induced apoptosis. Additionally, KD01 amplified damage-associated molecular patterns (DAMPs) release and immune activation; the combination with cisplatin further enhanced the process. Safety evaluations showed favorable tolerance to intravesical perfusion with KD01. Conclusions: The dual action of KD01 in directly killing tumor cells and activating anti-tumor immunity underscores its potential as a therapeutic agent. These findings highlight the preclinical efficacy and safety of KD01, informing the design of clinical trials. Full article

Background and Objective: Mammea siamensis (MS) is a Thai herb used in traditional medicine. Previous studies have reported the antiproliferative effects of its constituents in various cancer cell lines. However, the effects of MS extract on cytotoxicity and molecular mechanisms of apoptosis induction in HCT116 colon cancer cells have not been fully explored. Methods and Results: The cytotoxic effect of MS extract on HCT116 cells was assessed using the MTT assay. MS extract increased cytotoxicity in a concentration-dependent manner. It also induced nuclear morphological changes and disrupted the mitochondrial membrane potential (ΔΨm), as assessed by Hoechst 33342 and JC-1 staining, respectively. These findings indicated that MS extract induced apoptosis, which was further confirmed by flow cytometry showing an increase in the sub-G1 phase. To investigate the expression of signaling proteins, Western blot analysis was conducted. The results showed that MS extract activated caspase activity (caspase-8, -9, and -7) and inhibited PARP activity. Additionally, MS extract upregulated pro-apoptotic proteins (tBid, Bak, and cytochrome c) while downregulating anti-apoptotic proteins (Bcl-2 and Bcl-xL). Mechanistic studies revealed that MS extract activated MAPK pathways while inactivating the PI3K/Akt/NF-κB and GSK-3β/β-catenin pathways. Notably, MS extract also inhibited V-ATPases, as evaluated by acridine orange staining and Western blot analysis. Conclusions: Our findings suggest that MS extract induces apoptosis via the activation of both intrinsic and extrinsic pathways associated with the key signaling pathways. Therefore, MS extract shows potential as a therapeutic agent for colon cancer. Full article

Games provide an excellent testing ground for machine learning and artificial intelligence, offering diverse environments with strategic challenges and complex decision-making scenarios. This study seeks to design a self-learning artificial intelligent agent capable of playing the trick-taking stage of the popular card game Thousand, known for its complex bidding system and dynamic gameplay. Due to the game’s vast state space and strategic complexity, other artificial intelligence approaches, such as Monte Carlo Tree Search and Deep Counterfactual Regret Minimisation, are infeasible. To address these challenges, the enhanced version of the REINFORCE policy gradient algorithm is proposed. Introducing a score-related parameter $\beta$ designed to guide the learning process by prioritising valuable games, the proposed approach enhances policy updates and improves overall learning outcomes. Moreover, leveraging the off-policy experience replay, along with the importance weighting of behavioural policy, enhanced training stability and reduced model variance. The proposed algorithm was applied to the trick-taking stage of the popular game Thousand Schnapsen in a two-player setup. Four distinct neural network models were explored to evaluate the performance of the proposed approach. A custom test suite of selected deals and tournament evaluations was employed to assess effectiveness. Comparisons were made against two benchmark strategies: a random strategy agent and an alpha-beta pruning tree search with varying search depths. The proposed algorithm achieved win rates exceeding 65% against the random agent, nearly 60% against alpha-beta pruning at a search depth of 6, and 55% against alpha-beta pruning at the maximum possible depth. Full article

Spectrum auctions in international mobile telecommunications (IMT) are a representative method for selling the right to transmit signals within a specific band of electromagnetic waves to communication service providers (CSPs); it is important to design a fair spectrum auction that can benefit both government and auction bidders. The government should reduce the burden of maintenance costs by setting a reasonable initial price and selling it to bidders at the highest price they can afford. However, due to the complex auction rules and decision-making process, not many studies has been conducted on how to select an appropriate initial price for the auction. This study aims at introducing a novel simulation modeling approach to develop a spectrum auction policy for international mobile telecommunications (IMT) using agent-based simulation (ABS), which involves three telecommunications service provider types (i.e., the Aggressive bidder, the Moderate bidder, and the Conservative bidder) and the auction environment of IMT in South Korea. In particular, the proposed approach adopts the exponential utility theory to model the behavior of auction bidders and identify the optimal initial bid price. The devised ABS model is calibrated to the IMT spectrum auction conducted in 2018 in South Korea, and the best initial pricing policy identified (i.e., $85.24 million per spectrum block) regarding a sustainable market environment for existing service providers (i.e., 10 blocks for the Aggressive bidder, 10 blocks for the Moderate bidder, and 8 blocks for the Conservative bidder). The proposed approach will be beneficial to both government agencies and auction bidders under fair competition in the IMT market. Full article

Given the accelerating global movement towards decarbonization, the importance of promoting renewable energy (RE) adoption and ensuring efficient transactions in energy markets is increasing worldwide. However, renewable energy sources, including photovoltaic (PV) systems, are subject to output fluctuations due to weather conditions, requiring large-scale backup power to balance supply and demand. This makes trading electricity from large-scale PV systems connected to the existing grid challenging. To address this, peer-to-peer (P2P) energy markets where individual prosumers can trade excess power within their local communities have been garnering attention. This study introduces a simulator for P2P energy trading, designed to account for the diverse behaviors and objectives of participants within a market mechanism. The simulator incorporates two risk aversion parameters: one related to transaction timing, expressed through order prices, and another related to forecast errors, managed by adjusting trade volumes. This allows participants to customize their trading strategies, resulting in more realistic analyses of trading outcomes. To explore the effects of these risk aversion settings, we conduct a case study with 120 participants, including both consumers and prosumers, using real data from household smart meters collected on sunny and cloudy days. Our analysis shows that participants with higher aversion to transaction timing tend to settle trades earlier, often resulting in unnecessary transactions due to forecast inaccuracies. Furthermore, trading outcomes are significantly influenced by weather conditions: sunny days typically benefit buyers through lower settlement prices, while cloudy days favor sellers who execute trades closer to their actual needs. These findings demonstrate the trade-off between early execution and forecast error losses, emphasizing the simulator’s ability to analyze trading outcomes while accounting for participant risk aversion preferences. Full article