As renewable energy penetration continues to rise, the demand for coordinated optimization of decentralized source-load-storage. Virtual power plant (VPP) addresses this need by aggregating and coordinating diverse resources. However, effective mechanisms for multi-agent VPP coordination remain limited. To address this, this paper proposes a distributed robust optimization strategy for multi-energy VPP clusters in high-altitude regions. This strategy combines a dual-norm uncertainty set with a Nash bargaining mechanism to coordinate multi-agent interactions and mitigate operational risks. First, addressing the integrated planning-operation problem for multi-energy VPPs, a two-stage distributed robust optimization model is established. The dual-norm uncertainty set characterizes scenario probability uncertainties. Second, aiming to minimize interaction costs, a bargaining model is proposed based on Nash bargaining theory. Finally, case studies are conducted on three multi-energy VPPs. Results demonstrate that the proposed optimization strategy effectively enhances overall system revenue, environmental benefits, and source-load matching capability while ensuring fair competition among VPPs.

Cloud service providers (CSPs) must manage cloud resources efficiently to optimize resource utilization and increase user application efficiency at the lowest possible cost. The increased resource reservations result in higher resource usage costs, but they improve user application needs' performance. This paper developed a hybrid evolutionary algorithm and a unique dynamic load-balancing architecture to get better load balancing (LB) and effective resource allocation (RA). Initially, compute Virtual Machine (VM) loads and cluster them using a self-adaptive evolutionary algorithm-based clustering technique. Hybrid optimization methods like Owl Optimization Algorithm (OOA) and Wild Goose Optimization (WGO) are used for load computation. Then, it introduces a multi-stage technique for optimal task allocation with Deep Reinforcement Learning (DRL) and metaheuristics. The DRL assigns tasks to underloaded VMs, and then evolutionary techniques are used to refine the assignments. The suggested approach has shown improved efficiency and scalability by providing a suitable solution to the problems associated with LB and job scheduling in Cloud Computing (CC) contexts. The developed model gained better results in makespan, energy consumption, and task prioritization.

Container virtualization technology is increasingly being used in the development of fault-tolerant clusters with high availability and low request processing latency. In designing highly reliable clusters, a key task is the structuralparametric model-oriented synthesis which takes into account the impact of the number of deployed containers on performance, request processing latency, and system reliability. Justifying the choice of solutions to ensure high cluster reliability currently requires the development of reliability models for recoverable container virtualization clusters during reconfiguration, considering the migration of virtual containers. The basis for decisions to ensure high cluster availability is the development of models for a recoverable cluster during reconfiguration, taking into account the migration of virtual containers. The novelty of the proposed Markov model of a cluster lies in considering a two-stage recovery of its operability, determining the impact of the number of containers to be migrated during reconfiguration — both before and after the physical recovery of failed servers — on cluster reliability. Two options for container migration during cluster recovery are considered. In the first scenario, during the physical recovery phase of a failed server, container migration to a functional server does not occur, while in the second scenario it does. In the second stage of reconfiguration, following the physical recovery of a failed server, container migration takes place, allowing for either an increase or decrease in the number of containers deployed on them. Based on the proposed Markov models of cluster reliability with container virtualization, an evaluation of its readiness coefficient is provided, and the influence of the number of containers loaded during migration at the two reconfiguration stages on system reliability is determined. The proposed Markov models of cluster reliability with container virtualization are aimed at justifying design decisions for organizing and restoring cluster operability after server failures, considering the impact of container migration implementation options on system availability. Future research will analyze the impact of container migration options on both cluster availability and request processing latency at the two considered reconfiguration stages.

The objective of this study is to identify dual-target inhibitors against EGFR/c-Met through virtual screening, dynamic simulation, and biological activity evaluation. This endeavor is aimed at overcoming the challenge of drug resistance induced by L858R/T790M mutants. Active structures were gathered to construct sets of drug molecules. Next, property filtering was applied to the drug structures within the compound library. Active compounds were then identified through virtual screening and cluster analysis. Subsequently, we conducted MTT antitumor activity evaluation and kinase inhibition assays for the active compounds to identify the most promising candidates. Furthermore, AO staining and JC-1 assays were performed on the selected compounds. Ultimately, the preferred compounds underwent molecular docking and molecular dynamics simulation with the EGFR and c-Met proteins, respectively. The IC50 of T13074 was determined as 2.446 μM for EGFRL858R/T790M kinase and 7.401 nM for c-Met kinase, underscoring its potential in overcoming EGFRL858R/T790M resistance. Additionally, T13074 exhibited an IC50 of 1.93 μM on the H1975 cell. Results from AO staining and JC-1 assays indicated that T13074 induced tumor cell apoptosis in a concentration-dependent manner. Notably, the binding energy between T13074 and EGFR protein was found to be -90.329 ± 16.680 kJ/mol, while the binding energy with c-Met protein was -139.935 ± 17.414 kJ/mol. T13074 exhibited outstanding antitumor activity both in vivo and in vitro, indicating its potential utility as a dual-target EGFR/c-Met inhibitor. This suggests its promising role in overcoming EGFR resistance induced by the L858R/T790M mutation.

This article delves into the innovative application of Docker containers as High-Performance-Computing (HPC) environments, presenting the construction and operational efficiency of virtual container clusters. The study primarily focused on the integration of Docker technology in HPC, evaluating its feasibility and performance implications. A portion of the research was devoted to developing a virtual container cluster using Docker. Although the first Docker-enabled HPC studies date back several years, the approach remains highly relevant today, as modern AI-driven science demands portable, reproducible software stacks that can be deployed across heterogeneous, accelerator-rich clusters. Furthermore, the article explores the development of advanced distributed applications, with a special emphasis on Machine Learning (ML) algorithms. Key findings of the study include the successful implementation and operation of a Docker-based cluster. Additionally, the study successfully showcases a Python application using ML for anomaly detection in system logs, highlighting its effective execution in a virtual cluster. This research not only contributes to the understanding of Docker’s potential in distributed environments but also opens avenues for future explorations in the field of containerized HPC solutions and their applications in different areas.

The key role in achieving high reliability, security, fault tolerance, and low latency of query service in distributed systems (including cloud computing) is played by the consolidation of data processing and storage resources in clusters, the efficiency of which increases with the use of virtual machine technologies and container virtualization. The complexity of building queuing models for container virtualization systems is caused by the fact that the intensity of query execution in each container is associated with the dynamic division of shared resources between active (performing functional tasks) containers and the costs of supporting all containers deployed in the VM, including inactive containers waiting for service requests to be sent to them. The reduction in service intensity in each container due to shared resource allocation depends on many factors that are difficult to investigate. For clusters with container virtualization, this article provides an approximate boundary estimate of the average request waiting time and the probability of timely service. When building an analytical model, each container is represented as a separate single-channel queuing system with an infinite queue and the simplest input stream. The key feature of the proposed virtual cluster model is the estimation of upper, lower, and average bounds for the potential service intensity reduction in containers, resulting from the allocation of a node's limited computing resources among them. This depends on the number of deployed containers and the dynamically varying count of active containers, which is influenced by the input stream intensity. The study demonstrates the existence of an optimal number of containers per node, minimizing the average request processing time or maximizing the probability of timely request execution. The proposed models can be applied to the structural and parametric optimization of clusters with pipelined virtualization, including in the case of scaling and reconfiguration adaptive to traffic changes by disconnecting or connecting some of the deployed containers depending on changes in the load in the system.

Under carbon emission constraints, to promote low-carbon transformation and achieve the aim of carbon peaking and carbon neutrality in the energy sector, this paper constructs an operational optimization model for the coordinated operation of a virtual power plant cluster (VPPC). Considering the resource characteristics of different virtual power plants (VPPs) within a cooperative alliance, we propose a multi-VPP interaction and sharing architecture accounting for electricity–carbon interaction. An optimization model for VPPC is developed based on the asymmetric Nash bargaining theory. Finally, the proposed model is solved using an alternating-direction method of multipliers (ADMM) algorithm featuring an improved penalty factor. The research results show that P2P trading within the VPPC achieves resource optimization and allocation at a larger scale. The proposed distributed ADMM solution algorithm requires only the exchange of traded electricity volume and price among VPPs, thus preserving user privacy. Compared with independent operation, the total operation cost of the VPPC is reduced by 20.37%, and the overall proportion of new energy consumption is increased by 16.83%. The operation costs of the three VPPs are reduced by 1.12%, 20.51%, and 6.42%, respectively, while their carbon emissions are decreased by 4.47%, 5.80%, and 5.47%, respectively. In addition, the bargaining index incorporated in the proposed (point-to-point) P2P trading mechanism motivates each VPP to enhance its contribution to the alliance to achieve higher bargaining power, thereby improving the resource allocation efficiency of the entire alliance. The ADMM algorithm based on the improved penalty factor demonstrates good computational performance and achieves a solution speed increase of 15.8% compared to the unimproved version.

As an essential platform for aggregating and coordinating distributed energy resources (DERs), the virtual power plant (VPP) has attracted widespread attention in recent years. With the increasing scale of VPPs, energy interaction and sharing among VPP clusters (VPPCs) have become key approaches to improving energy utilization efficiency and reducing operational costs. Therefore, studying the coordinated operation mechanism of VPPCs is of great significance. This paper proposes a two-stage coordinated operation model for VPPCs based on energy interaction to enhance the overall economic performance and coordination of the cluster. In the day-ahead stage, a cooperative operation model based on Nash bargaining theory is constructed. The inherently non-convex and nonlinear problem is decomposed into a cluster-level benefit maximization subproblem and a benefit allocation subproblem. The Alternating Direction Method of Multipliers (ADMM) is employed to achieve distributed optimization, ensuring both the efficiency of coordination and the privacy and decision independence of each VPP. In the intra-day stage, to address the uncertainty in renewable generation and load demand, a real-time pricing mechanism based on the supply–demand ratio is designed. Each VPP performs short-term energy forecasting and submits real-time supply–demand information to the coordination center, which dynamically determines the price for the next trading interval according to the reported imbalance. This pricing mechanism facilitates real-time electricity sharing among VPPs. Finally, numerical case studies validate the effectiveness and practical value of the proposed model in improving both operational efficiency and fairness.

To solve the problem of imbalanced resource load in virtual machine clusters, an energy-saving virtual machine scheduling algorithm based on cloud computing technology for resource management is proposed. In this paper, the current research status of cloud computing and virtual machine scheduling in cloud computing environments is analyzed, and the concept and characteristics, classification, application scenarios, and key technologies of cloud computing are elaborated. This paper innovatively designs a universal chromosome structure with regions to adapt to different data center server compositions and introduces adaptive mutation operators based on genetic algorithms to improve global search capabilities and optimize virtual machine scheduling schemes. In addition, by restricting the migration of virtual machines between homogeneous physical machines, the energy loss during the migration process can be reduced, and a more energy-efficient virtual machine physical machine mapping scheme can be further calculated. Finally, by collecting real data on virtual machine loads in reality, the algorithm proposed in this paper is experimentally validated using the CloudSim cloud computing simulation platform. The experimental results show that, in the same original configuration scheme, the migration times based on the greedy algorithm used by GA2ND are around 1000, while the migration times of GA1ST are between 200 and 500, indicating that the migration scheme of GA2ND requires fewer virtual machines than that of GA1ST. Therefore, the algorithm proposed in this paper can effectively reduce energy consumption while avoiding frequent migration of virtual machines, and the innovation in genetic algorithm optimization strategy improves the overall efficiency and stability of scheduling.

ABSTRACTThe presence of numerous distributed power sources in distribution grids leads to a diverse array of controlled object points and significant uncertainties, thereby posing a series of challenges to the control and operation of distribution grids. Hence, this study proposes a virtual cluster partitioning model for active distribution networks using a quantum particle swarm optimization (QPSO) algorithm and sector search, aiming to achieve autonomy within clusters and coordination between clusters. First, the article proposes a sector search model that transforms the topological connections of the distribution network into mathematical expressions. This model simplifies the search for node locations and improves the algorithm's convergence speed. Building upon the traditional particle swarm optimization (PSO) algorithm, this study introduces the wave function and Schrödinger equation to enhance algorithm performance. By treating the vectors obtained from sector searches as particles, the proposed QPSO algorithm significantly improves both the search efficiency and global convergence in solving the virtual cluster partitioning model. Finally, case studies conducted on the modified PG&E 69‐node system demonstrated the proposed method's significant advantages. The method improved computational efficiency, with a cluster power supply rate over 0.6 and modularity above 0.7, ensuring balanced partitioning. The scalability and effectiveness of the proposed method were validated on an 85‐node system, achieving balanced cluster partitioning with high operational efficiency and adaptability.

When designing a virtual desktop infrastructure (VDI) for a university or inter-university cloud, developers must overcome many complex technical challenges. One of these tasks is estimating the required number of virtualization cluster nodes. Such nodes host virtual machines for users. These virtual machines can be used by students and teachers to complete academic assignments or research work. Another task that arises in the VDI design process is the problem of algorithmizing the placement of virtual machines in a computer network. In this case, optimal placement of virtual machines will reduce the number of computer nodes without affecting functionality. And this, ultimately, helps to reduce the cost of such a solution, which is important for educational institutions. The article proposes a model for estimating the required number of virtualization cluster nodes. The proposed model is based on a combined approach, which involves jointly solving the problem of optimal packaging and finding the configuration of server platforms of a private university cloud using a genetic algorithm. The model introduced in this research is universal. It can be used in the design of university cloud systems for different purposes-for example, educational systems or inter-university scientific laboratory management systems.

Two-stage scheduling optimization model and benefit allocation strategy for virtual power plant clusters aggregated by multidimensional information indicators

Wireless sensor network for fire detection with network coding to improve security and reliability

A significant evolution has occurred in the architectural and infrastructural domains of web applications over the past several years. Monolithic systems are gradually being superseded by microservices-based architectures, which are now considered the de facto standard for web application development owing to their inherent portability, scalability, and ease of deployment. Concurrently, the prevalence of this architecture has rendered it susceptible to specialized cyberattacks. While honeypots have proven effective in the past for gathering real-world attack data and uncovering attacker methods, their growing popularity has made them a specific target for cyberattacks. Traditional honeypots lack the flexibility of microservices architecture. Honeypots have proven effective in gathering authentic attack data and analyzing attacker tactics. The core idea that honey traps help identify malicious packets with minimal effort to remove incorrect alerts is preserved. In addition to identifying and documenting specific attack methods used by intruders, this system helps thwart attacks by creating realistic simulations of the actual systems and applications within the network. This effectively slows down and confuses attackers by making it difficult for them to gain access to real devices. This paper presents a groundbreaking approach to honeypot management within cybersecurity, utilizing virtual clusters and a microservice architecture to significantly improve the effectiveness of threat detection. To conduct our research, we initially surveyed the internet to pinpoint container and container management systems operating on standard ports that might be susceptible to attacks. The monitoring of the instrumented approach generated a massive dataset, enabling researchers to make significant inferences about the behavior and goals of malevolent users. We advocate for the implementation of honeypots on lightweight distribution orchestration tools installed on Ubuntu servers, situated behind a meticulously crafted gateway and operating on standard port configurations. In light of the scan outcomes, we recommend the deployment of honeypot orchestration on streamlined distributions. To better protect your systems based on our scan results, we recommend implementing honeypot orchestration for easier deployment and management. By deploying honeypots on lightweight operating systems, you can optimize resource usage and improve performance while maintaining essential capabilities. These capabilities include monitoring attack patterns on vulnerable systems and analyzing the security measures implemented by those responsible for managing exposed systems.

Fault tolerance approaches in distributed systems are essentially based on replication and checkpointing. Each of these approaches has its advantages and limitations. This paper has two objectives: first, it proposes a fault tolerance approach based on the nodes status of a distributed system. For this purpose, it defines 3 nodes status: safety, faulty and potentially faulty. With respect of classical node status (safety, faulty), it introduces a new status that we call potentially faulty. This last node allows to enhance the availability of a distributed system. Second, it discusses the efficiency of the proposed model on two types of architectures: virtual multi-node cluster and a physical multi-node cluster with WIFI connection. Experiments have showed that proposed approach increases the system performance throughput and its fault tolerance level.

BackgroundSleep disorders can be harmful to our health and treating them can also be expensive. Due to the widespread occurrence and impact of sleep disorders, it is valuable to investigate and study them from an epidemiological perspective. Therefore, this study aimed to determine the prevalence of sleep disorders among Iranian adults in 2022.MethodThis is a cross-sectional study that examines the prevalence of sleep disorders and problems in Iranian society. The participants were selected using a mixed sampling approach (utilizing virtual space and cluster sampling). A two-part package was used for evaluating participants sleep problems, which included a demographic profile form and the Holland Sleep Disorders Questionnaire. After collecting the data, appropriate statistical tests in SPSS version 25 were utilized for analysis.ResultsA total of 6013 questionnaires were fully filled out for this research, with participants answering the specific questions. The participants’ score on sleep disorders was 1.94, which means 44.1% of them suffer from sleep disorders. The results indicated that the prevalence of sleep disorders in this study was as follows: insomnia (35%), parasomnia (35.3%), circadian rhythm sleep disorder (38.4%), hypersomnia and excessive daytime sleepiness (39%), restless leg syndrome and leg movements during sleep (43%), and breathing disorders related to sleep (38.4%). The prevalence of sleep disorders among the study participants demonstrated a significant relationship with most of their demographic variables (P < 0.001). The Pearson correlation coefficient also revealed an inverse and significant relationship between the prevalence of sleep disorders in the study participants and their age, monthly household income, and BMI (P < 0.001).ConclusionOverall, the results depicted a relatively high prevalence of sleep disorders in the studied community. These findings emphasize the need for interventions to prevent and treat sleep disorders in society.

Methods of processing information transmitted through wireless networks with software development are investigated in the work. Innovative methods of data transmission such as optical technologies, quantum data transmission and wireless data transmission technologies are disclosed. It is noted that in the modern understanding, the concept of distributed computing defines the process of convergence (convergence) of distributed processing methods, such as GRID, cloud and fog computing, with the combination of virtual cluster systems (grid clusters, cloud clusters and fog clusters) into a single information communication and computing system . It is emphasized that, unlike cellular modems, ZigBee technology nodes have microcontrollers with a pre-installed operating system and flash memory, which allows solving simple computational tasks in real time before sending data. It is advisable to solve such tasks within the framework of a multi-agent approach, which will increase the efficiency of the use of sensor nodes and the entire sensor network. The advantages of the multi-agent technology of fog computing based on sensor nodes of the wireless network of the ZigBee standard are revealed. The method of multi-agent processing of sensory information and its main components are described. The architecture of the system of distributed sensor data processing is outlined, which includes 4 hardware and software levels: Terminal sensor nodes and controllers of measuring devices and automation devices that implement fuzzy calculations; Coordinators, sensor segment routers and cellular modems that collect, protect and transmit sensor data to the processing center; A data processing center that includes a cluster of servers for GRID calculations and a cloud data storage server; Client devices to access cloud storage, computing cluster servers, and distributed fog computing terminals. It is emphasized that indicators and forecast results can be stored on distributed sensor nodes or transmitted for accumulation in cloud storage for further extraction and intelligent processing in the GRID cluster of the data center.

The Cyrillo-Methodian heritage is a common phenomenon of European value, promoting religious tolerance, cultural equality, and understanding of European cultural history and identity. The virtual cluster KYRILLOMETHODIKON developed at the Cyrillo-Methodian Research Centre (CMRC) at BAS is part of the Centre of Excellence "Heritage BG" (BG05M2OP001-1.001-0001 Project), aiming to provide structured knowledge on this phenomenon and its manifestations. The digital repository offers resources for research on the first stage of Slavic Christian culture with an accent on its Old Bulgarian part, reflecting the multidisciplinary research paradigm of the Centre: studies in medieval literature, linguistics, history, theology, art history, and anthropology. To ensure the educational socialisation of the research in these disciplines, a Scientific and Educational Interdisciplinary Centre for Cyrillo-Methodian studies was created within the framework of KYRILLOMETHODIKON, sharing educational tools for a wide range of audiences, including children, school students, undergraduates, and doctoral candidates.

Introduction. Integrated entrepreneurship creates special advantages for the development of small and medium-sized businesses in knowledge-intensive industries, which are manifested in the formation of a suitable business environment and provision of infrastructure (innovative, production, financial, transport, telecommunications, etc.), which was previously available only to large corporate organizations closed type. This opens access to participation in the real sector of the economy for a large number of the most active subjects of innovative and economic activity – developers of new products, private entrepreneurs and owners of free financial capital.The hypothesis of the scientific research consists in the research of scientific-methodical approaches to the assessment of structural properties regarding the development of potential and strengthening of cluster interaction within corporations with the aim of obtaining competitive advantages and a synergistic effect.The purpose of the study is to analyze methodological approaches to the assessment of structural properties of integrated business structures.The methodology of scientific research is scientific-methodical approaches to the assessment of structural properties of integrated business structures. Modeling of the organization of intellectual management of the integration of business entities of partners; clustering for the development of existing potential within corporations in order to obtain competitive advantages.Conclusions. The development of integrated entrepreneurship through the creation of virtual enterprises, corporations, clusters and networks of suppliers is today the focus of theoretical developments that have great practical significance, as they reflect the changing conditions of the functioning of organizations in the business environment.Integration processes in entrepreneurship are focused on more effective use of all types of resources (scientific and technical, production, raw materials, financial) with the use of the latest technologies and methods of highly productive business activities, which lead to the emergence of various forms of vertical and horizontal association of business entities .Keywords: business environment; integrated business structures; integrated processes; integrated entrepreneurship; corporatization; clustering; association; entrepreneurship; synergy.

SummaryTo address the unbalanced resource load of a virtual machine cluster, the author proposes an energy‐saving virtual machine scheduling algorithm based on resource management cloud computing technology. This article analyzes the current cloud computing and virtual machine scheduling research in the cloud computing environment. It discusses the concept, characteristics, classification, application scenarios, and key cloud computing technologies. A genetic algorithm is used to solve the problem of high energy consumption in the data center. The test results show that in the same original configuration scheme, the migration times based on the greedy algorithm adopted by GA2ND are about 1000, and the migration times of GA1ST are between 200 and 500. The GA2ND migration scheme requires fewer virtual machines. In the result analysis, the experiments compare the proposed algorithms—DVFS, IMC, GA1ST, and GA2ND—with a focus on energy consumption and virtual machine migration. Notably, DVFS serves as a reference for energy efficiency, IMC represents the proposed algorithm without genetic optimization, GA1ST denotes the genetic algorithm under a heterogeneous model, and GA2ND signifies the enhanced genetic algorithm introduced in this article. The comparison aims to assess the energy efficiency and virtual machine migration performance of each algorithm in the context of a simulated cloud computing environment. Therefore, the algorithm proposed in this article can effectively reduce energy consumption and avoid frequent migration of virtual machines.