With the extensive penetration of distributed renewable energy and self-interested prosumers,the emerging power market tends to enable user autonomy by bottom-up control and distributed *** paper is devoted to solving...
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With the extensive penetration of distributed renewable energy and self-interested prosumers,the emerging power market tends to enable user autonomy by bottom-up control and distributed *** paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer(P2P)energy sharing among prosumers.A novel cloud-edge-based We-Market is presented,where the prosumers,as edge nodes with independent control,balance the electricity cost and thermal comfort by formulating a dynamic household energy management system(HEMS).Meanwhile,the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility *** the cloud center,a distributed convergence bidding(CB)algorithm based on consistency criterion is developed,which promotes faster and fairer bidding through the interactive iteration with the edge ***,the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing *** results show the effectiveness and practicability of the proposed We-Market,which achieves 15%cost reduction with shorter running *** analysis indicates better scalability,which is more suitable for largerscale We-Market implementation.
Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion...
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Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.
Source code vulnerability detection is essential for software security, but identifying subtle vulnerabilities in code is challenging. While Transformer-based pre-trained models have achieved success in this area, the...
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The oxygen evolution reaction(OER)is a crucial step in metal-air batteries and water splitting technologies,playing a significant role in the efficiency and achievable heights of these two ***,the OER is a four-step,f...
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The oxygen evolution reaction(OER)is a crucial step in metal-air batteries and water splitting technologies,playing a significant role in the efficiency and achievable heights of these two ***,the OER is a four-step,four-electron reaction,and its slow kinetics result in high overpotentials,posing a *** address this issue,numerous strategies involving modified catalysts have been proposed and proven to be highly *** these strategies,the introduction of strain has been widely reported because it is generally believed to effectively regulate the electronic structure of metal sites and alter the adsorption energy of catalyst surfaces with reaction ***,strain has many other effects that are not well known,making it an important yet unexplored *** on this,this review provides a detailed introduction to the various roles of strain in *** better explain these roles,the review also presents the definition of strain and elucidates the potential mechanisms of strain in OER based on the d-band center theory and adsorption volcano ***,the review showcases various ways of introducing strain in OER through examples reported in the latest literature,aiming to provide a comprehensive perspective for the development of strain ***,the review analyzes the appropriate proportion of strain introduction,compares compressive and tensile strain,and examines the impact of strain on *** the review offers prospects for future research directions in this emerging field.
Accurately predicting submarine positions is critical to ensure safe underwater navigation, especially in complex and dynamic marine environments. Traditional methods, such as the Kalman Filter, have been widely used ...
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We consider the multiobjective optimization problem for the resource allocation of the multiagent network, where each agent contains multiple conflicting local objective functions. The goal is to find compromise solut...
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We consider the multiobjective optimization problem for the resource allocation of the multiagent network, where each agent contains multiple conflicting local objective functions. The goal is to find compromise solutions minimizing all local objective functions subject to resource constraints as much as possible, i.e., the Pareto optimums. To this end, we first reformulate the multiobjective optimization problem into one single-objective distributed optimization problem by using the weighted Lppreference index,where the weighting factors of all local objective functions are obtained from the optimization procedure so that the optimizer of the latter is the desired Pareto optimum of the former. Next, we propose novel predefined-time algorithms to solve the reformulated problem by time-based generators. We show that the reformulated problem is solved within a predefined time if the local objective functions are strongly convex and smooth. Moreover, the settling time can be arbitrarily preset since it does not depend on the initial values and designed parameters. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed algorithms.
Some researchers use GCL on code graphs to realize code defects prediction in an self-supervised manner. While graph contrastive learning is a popular self-supervised method for function level code defects prediction,...
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Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, ...
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Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.
The effect of aging treatment on the microstructure, mechanical properties, wear resistance, biocorrosion behavior, antibacterial properties and cytocompatibility of Ti-5Cu-2Fe alloys was investigated in this paper to...
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The effect of aging treatment on the microstructure, mechanical properties, wear resistance, biocorrosion behavior, antibacterial properties and cytocompatibility of Ti-5Cu-2Fe alloys was investigated in this paper to reveal the potential application as a biomedical implant. The results showed that the aging at 773 K induced martensitic phase transformation with very fine martensitic laths and the presence of intertwined dislocation cells as well as the precipitation of Ti2Cu phase, which resulted high hardness, flexural strength, elastic modulus, and good wear resistance. The aging at higher temperature, 873 K, led to the formation of equiaxial α-phase and wider lamellar α-phase, and increase in the number and size of α-phase and Ti2Cu phases. As a result, the hardness,strength, wear resistance, and corrosion resistance were reduced but the plasticity was significantly *** aging treatment provided Ti-5Cu-2Fe alloy with strong antibacterial properties and a good *** is suggested that the mechanical properties could be controlled by the aging treatment depending on the application requirement.
Herein,a hot cracking initiation criterion based on the characteristics of solidification liquid film and the microstructure was proposed,which integrated both the mechanical and non-mechanical factors during *** crit...
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Herein,a hot cracking initiation criterion based on the characteristics of solidification liquid film and the microstructure was proposed,which integrated both the mechanical and non-mechanical factors during *** criterion also took the effect of the shrinkage volume of the solid-liquid two-phase in the mushy zone,the flow behavior of the liquid film and the microstructure on the feeding behavior into ***,the effect factors of hot cracking initiation such as alloy composition,microstructure,mold design and process condition were included in this criterion,and it could quantitatively calculate whether hot cracks occurred under a certain state or not during *** criterion was utilized to predict whether hot cracks occurred in Al-4.0 wt%Cu alloy in different initial solidification states or not,which was consistent with the experimental results and verified its *** to the criterion expression,Vfeeding*was related with five effect factors includingη,ΔP*,l*,r*and n,in which r*and n were in positive correlation with Vfeeding*whileη,ΔP*and l*were in negative correlation with that,which provided a good instructive significance for mold design,process optimization and composition and microstructure regulation of alloys and simultaneously further enriched the mechanism and influencing factors of hot cracking ***,a multiscale simulation method for calculating the characteristic parameters of hot tearing behavior during solidification was also provided in this study.
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