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We present a nonapproximate computational method for generating G$G$‐optimal designs in response surface methodology (RSM) settings using Gloptipoly, a global polynomial optimizer. Traditional approaches use a grid approximation for computing a candidate design's G$G$‐score. Gloptipoly can find the global optimum of high‐order polynomials thus making it suitable for computing a design's G$G$‐score, that is, its maximum scaled prediction variance, which, for second‐order models, is a quartic polynomial function of the experimental factors. We demonstrate the efficacy and performance of our method through comprehensive application to well‐published examples, and illustrate, for the first time, its application to generating G$G$‐optimal designs supporting models of order greater than 2. This work represents the first non‐approximate computational approach to solving the G$G$‐optimal design problem. This advancement opens new possibilities for finding G$G$‐optimal designs beyond second‐order RSM models. [ABSTRACT FROM AUTHOR]

This research paper explores the innovative integration of green plant landscape design with advanced computer image processing in cloud, grid, and cluster computing environments to enhance urban air quality purification. The study begins by highlighting the critical need for improving air quality in urban areas, considering the rising levels of pollution and its impact on public health and the environment. The methodology involves the use of sophisticated image processing techniques to analyze various sensors on air quality measures and plant species their effectiveness in air purification, facilitated by the computational power of cloud, grid, and cluster computing. A diverse range of green plants was selected, and their air purification capabilities were assessed through a series of computer-simulated models. These models were developed using complex algorithms to predict the plants' performance in real-world urban settings. The research uniquely combines landscape architecture with technology, emphasizing the role of green spaces in urban areas for environmental sustainability. The results demonstrate that certain plant species are more effective than others in purifying urban air. The study provides a comprehensive ranking of these plants based on their purification capabilities, growth requirements, and suitability for various urban landscapes. The paper concludes by proposing practical guidelines for urban landscape designers and policymakers, recommending the strategic incorporation of specific green plants in urban areas to maximize air purification. Additionally, it highlights the potential of leveraging advanced computing technologies in environmental research and urban planning. This research contributes to the fields of environmental science, urban planning, and computer science by showcasing how multidisciplinary approaches can address pressing environmental issues. It opens avenues for further research in the optimization of urban green spaces using advanced computing techniques. The results demonstrate that certain plant species are more effective than others in purifying urban air. The study provides a comprehensive ranking of these plants based on their purification capabilities, growth requirements, and suitability for various urban landscapes. The paper concludes by proposing practical guidelines for urban landscape designers and policymakers, recommending the strategic incorporation of specific green plants in urban areas to maximize air purification. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive review of the primary distribution design of an advanced network control system, emphasizing its evolution from initial requirements to practical applications. The system solves complex problems of power management by combining real-time data analysis, intelligent decision making for resource allocation, rapid fault correction, remote monitoring and complex optimization methods, all aimed at ensuring stable and safe operation of the power grid. Its performance is geared towards fast response, efficient data processing and synchronous processing tasks, ensuring smooth operation even under heavy workloads. Security is enhanced through strict protocols, encryption methods, and controlled access systems. The system is divided into four layers-data collection, communication, decision-making and application management-using innovative tools such as Kalman filters and deep Q networks. The research showcases the integrated network command system's prowess, achieving an average response time of 0.27 s, 98.5% dispatching accuracy, and 83.2% resource utilization, evidencing exceptional performance. It excels under various tests, including managing high loads with minimal accuracy loss, rapidly adapting to changes with a hydro model response time of 0.22 s, efficiently integrating renewables at 78.0% efficiency, and proving resilient in peak hours, affirming its capability to bolster grid efficiency, reliability, and integration of renewable energy resources. By outlining these specific achievements, this case study not only illustrates the complex design of the system, but also highlights its great potential for improving grid resilience and efficiency, attracting a wide audience interested in the future of energy management. [ABSTRACT FROM AUTHOR]

In the present study, the spatio-temporal variation of the seismic b-value in the vicinity of the Kopili fault and its surrounding area has been analysed using the unified and homogenous earthquake catalog of historical and instrumental (1950–2021) earthquake events. The study region is subdivided into 16 equisized square grids of 1° × 1° dimension, and the b-value is computed for each grid using the maximum likelihood method. The spatial distribution of the b-value varies from 0.58 to 1.14. The Kolmogorov–Smirnov (K-S) test has been conducted to check the significance of the spatial-temporal and depth-wise distributions of the b-value. The epicentral location of April 28th, 2021, lies in the low-b-value square grid. Likewise, the temporal b-value curve shows a decreasing trend before the occurrence of the April 28th, 2021 earthquake. The mean return period of the April 28th, 2021earthquake and the most probable maximum annual magnitude earthquake are also computed for this region. Meanwhile, the spatial associations and anomalous patterns between the b-value and factors like seismic moment or energy release and focal depth are assessed, as they contribute to a more comprehensive understanding of the seismicity in this area. The antithetical relationship between the b-value and seismic moment or energy release is established. While variation in b-value with depth provides new insights, low b-values are linked to the top of the crust, which could mean that the crust is uniform and that a lot of stress is building up. [ABSTRACT FROM AUTHOR]

Numerical radiation-hydrodynamics (RHD) for non-relativistic flows is a challenging problem because it encompasses processes acting over a very broad range of time-scales, and where the relative importance of these processes often varies by orders of magnitude across the computational domain. Here, we present a new implicit–explicit method for numerical RHD that has a number of desirable properties that have not previously been combined in a single method. Our scheme is based on moments and allows machine-precision conservation of energy and momentum, making it highly suitable for adaptive mesh refinement applications; it requires no more communication than hydrodynamics and includes no non-local iterative steps, making it highly suitable for massively parallel and Graphics Processing Unit (GPU)-based systems where communication is a bottleneck; and we show that it is asymptotically accurate in the streaming, static diffusion, and dynamic diffusion limits, including in the so-called asymptotic diffusion regime where the computational grid does not resolve the photon mean-free path. We implement our method in the GPU-accelerated RHD code quokka and show that it passes a wide range of numerical tests. [ABSTRACT FROM AUTHOR]

With the rapid construction and implementation of smart grid, lots of studies have been conducted to explore how to ensure the security of information privacy. At present, most privacy-preserving data aggregation schemes in smart grid achieve privacy data protection through homomorphically encrypted data aggregation. However, these data aggregation schemes tend to rely on a trusted third party (TTP), and fail to efficiently handle the case of a meter failure. Besides, they are less flexible for overall user management, and resistance to collusion attacks needs to be improved. In this paper, we propose an efficient and robust privacy-preserving data aggregation scheme without TTP, called EFTA. Overall, the scheme eliminates the reliance on a TTP, combines with Shamir threshold secret sharing scheme to increase overall fault tolerance, supports flexible and dynamic user management, and effectively defends against entity initiated collusion attacks. According to security and performance analysis results, the scheme proposed in this paper meets the multiple security requirements of smart grid, and is more efficient in terms of overall overhead compared to the existing privacy-preserving data aggregation schemes. [ABSTRACT FROM AUTHOR]

Abstract Grid computing emerged as a powerful computing domain for running large-scale parallel applications. Scheduling computationally intensive parallel applications such as scientific, commercial etc., computational grids is a NP-complete problem. Many researchers have proposed several task scheduling algorithms on grids based on formulating and solving it as an optimization problem with different objective functions such as makespan, cost, energy etc. Further to address the requirements/demands/needs of the users (lesser cost, lower latency etc.) and grid service providers (high utilization and high profitability), a task scheduler needs to be designed based on solving a multi-objective optimization problem due to several trade-offs among the objective functions. In this direction, we propose an efficient multi-objective task scheduling framework to schedule computationally intensive tasks on heterogeneous grid networks. This framework minimizes turnaround time, communication, and execution costs while maximizing grid utilization. We evaluated the performance of our proposed algorithm through experiments conducted on standard, random, and scientific task graphs using the GridSim simulator.

The ATLAS experiment at CERN relies on a Worldwide Distributed Computing Grid infrastructure to support its physics program at the Large Hadron Collider. ATLAS has integrated cloud computing resources to complement its Grid infrastructure and conducted an R&D program on Google Cloud Platform. These initiatives leverage key features of commercial cloud providers: lightweight configuration and operation, elasticity and availability of diverse infrastructures. This paper examines the seamless integration of cloud computing services as a conventional Grid site within the ATLAS workflow management and data management systems, while also offering new setups for interactive, parallel analysis. It underscores pivotal results that enhance the on-site computing model and outlines several R&D projects that have benefited from large-scale, elastic resource provisioning models. Furthermore, this study discusses the impact of cloud-enabled R&D projects in three domains: accelerators and AI/ML, ARM CPUs and columnar data analysis techniques. [ABSTRACT FROM AUTHOR]

Under the envisioned smart grid paradigm, there is an increasing demand for a fast, accurate, and efficient power flow solution for distribution system operation and control. Various solution techniques have been proposed, each with its own unique formulation, solution methodology, advantages, and drawbacks. Motivated by challenges associated with the integration of renewable distributed energy resources and electric vehicles into distribution systems and further by the speed and convergence limitations of existing tools, this paper presents a novel graph‐based power flow solution for smart grid's real‐time operation and control, named Flow‐AugmentationPF algorithm. The proposed method formulates a power flow problem as a network‐flow problem and solves it by using a maximum‐flow algorithm, inspired by the push‐relabel max‐flow technique. The performance of the proposed algorithm is tested and validated using several benchmark networks of different sizes, topologies, and parameters and compared against the most commonly used solution techniques and commercial software packages, namely PSS/E and PSCAD. The proposed formulation is simple, accurate, fast, yet computationally efficient, as it is based on matrix‐vector multiplication, and is also scalable, considering the formulation works as a graph‐based method, which, inherently, allows for parallel computation for added computational speed. [ABSTRACT FROM AUTHOR]

The ICTs industry is witnessing the rise of a groundbreaking technology known as cloud computing. This innovation is built upon the Internet, offering a virtual infrastructure for computers. This virtual setup encompasses databases, servers, storage, software, networking, processing power, office applications, and artificial intelligence. What sets cloud computing apart is its capacity to deliver services without the need for physical proximity to the underlying computer hardware. This approach brings with it numerous advantages over traditional computing methods, including grid computing. Also Cloud Computing introducing new challenges for environmental protection. This paper offers an extensive survey, overview and analysis of the fundamental concepts, historical evolution, components of cloud computing, diverse cloud services and categories, cloud integration, the merits of this technology, its focal areas, security considerations, and the implications of adopting cloud computing. This comprehensive analysis is based on the examination of more than 26 research papers and articles. The findings of this survey shed light on the transformation that the IT industry undergoes before and after embracing cloud computing. [ABSTRACT FROM AUTHOR]

We consider the problem of finding a "treasure" at an unknown point of an n-dimensional infinite grid, n ≥ 3 , by initially collocated finite automaton (FA) agents. Recently, the problem has been well characterized for 2 dimensions for deterministic as well as randomized FA agents, both in synchronous and semi-synchronous models (Brandt et al. in Proceedings of 32nd International Symposium on Distributed Computing (DISC) LIPCS 121:13:1–13:17, 2018; Emek et al. in Theor Comput Sci 608:255–267, 2015). It has been conjectured that n + 1 randomized FA agents are necessary to solve this problem in the n-dimensional grid (Cohen et al. in Proceedings of the 28th SODA, SODA '17, pp 207–224, 2017). In this paper we disprove the conjecture in a strong sense: we show that three randomized synchronous FA agents suffice to explore an n-dimensional grid for anyn. Our algorithm is optimal in terms of the number of the agents. Our key insight is that a constant number of FA agents can, by their positions and movements, implement a stack, which can store the path being explored. We also show how to implement our algorithm using: four randomized semi-synchronous FA agents; four deterministic synchronous FA agents; or five deterministic semi-synchronous FA agents. We give a different, no-stack algorithm that uses 4 deterministic semi-synchronous FA agents for the 3-dimensional grid. This is provably optimal in the number of agents and the exploration cost, and surprisingly, matches the result for 2 dimensions. For n ≥ 4 , the time complexity of the stack-based algorithms mentioned above is exponential in distance D of the treasure from the starting point of the agents. We show that in the deterministic case, one additional finite automaton agent brings the time down to a polynomial. We also show that any algorithm using 3 synchronous deterministic FA agents in 3 dimensions must travel beyond Ω (D 3 / 2) from the origin. Finally, we show that all the above algorithms can be generalized to unoriented grids. More specifically, six deterministic semi-synchronous FA agents are sufficient to locate the treasure in an unoriented n-dimensional grid. [ABSTRACT FROM AUTHOR]

We consider two finite and disjoint sets of homogeneous robots deployed at the nodes of an infinite grid graph. The grid graph also comprises two finite and disjoint sets of prefixed meeting nodes located over the nodes of the grid. The objective of our study is to design a distributed algorithm that gathers all the robots belonging to the first team at one of the meeting nodes belonging to the first type, and all the robots in the second team must gather at one of the meeting nodes belonging to the second type. The robots can distinguish between the two types of meeting nodes. However, a robot cannot identify its team members. This paper assumes the strongest adversarial model, namely the asynchronous scheduler. We have characterized all the initial configurations for which the gathering problem is unsolvable. For the remaining initial configurations, the paper proposes a distributed gathering algorithm. Assuming the robots are capable of global-weak multiplicity detection , the proposed algorithm solves the problem within a finite time period. The algorithm runs in |$\Theta (dn)$| moves and |$O(dn)$| epochs, where |$d$| is the diameter of the minimum enclosing rectangle of all the robots and meeting nodes in the initial configuration, and |$n$| is the total number of robots in the system. [ABSTRACT FROM AUTHOR]

Grid computing is the aggregation of the power of heterogeneous, geographically distributed computing resources to provide high-performance computing. To benefit from the grid computing capabilities, effectual scheduling algorithms are primarily essential. This paper presents a GA-based approach, called Grid Workflow Tasks Scheduling Algorithm (GWTSA), for scheduling workflow tasks on grid services based on users' QoS (quality of service) constraints in terms of cost and time. For a given set of inter-dependent workflow tasks, it generates an optimal schedule, which minimizes the execution time and cost, such that the optimized time be within the time constraints (deadline) imposed by the user. In GWTSA, the workflow tasks are modeled as a DAG, which is divided, then the optimal sub-schedules of all task divisions are computed and used to obtain the execution schedule of the entire workflow. A GA-based technique is employed in GWTSA to compute the optimal execution sub-schedule for each branch division that consists of a set of sequential tasks. In this technique, the chromosome represents a branch division, where each gene holds the id of the service provider chosen to execute the corresponding task in the branch; and the fitness function is formulated as a multi-objective function of time and cost, this gives users the ability to determine their requirements if speed against cost or vice versa, by changing the weighting coefficients in the fitness function. The paper also exhibits the experimental results of assessing the performance of GWTSA with workflow samples of different sizes. [ABSTRACT FROM AUTHOR]

Cloud computing has received a resounding welcome. It was created following methodical and thorough study in web services, distributed computing, utility computing, and virtualization, and it offers several benefits, including lower costs, less space required, and easier management. These advantages bring in a significant number of new users to the cloud platform every day. In addition, because cloud computing is an Internet-based computing paradigm, it must deal with the issue of overwhelming demands through effective load-balancing. A very small number of studies only focus on load-balancing problems in cloud computing platforms, while the majority of load-balancing research is accessible in many domains, including parallel, distributed, and grid computing. Infrastructure as a Service (IaaS), Software as a Service, and Platform as a Service are the three basic categories under which cloud computing falls. For these models, there are notable differences in the load-balancing techniques used. This work compared the outcome with the current method and presented a hybrid agent-based load-balancing approach for the IaaS platform. [ABSTRACT FROM AUTHOR]

Summary: This work explores the performance of single‐ and multi‐GPU computing on state‐of‐the‐art NVIDIA‐ and AMD‐based server‐class hardware using various programming interfaces to accelerate a real‐world scientific application for solidification modeling based on the phase‐field method. The main computations of this memory‐bound application correspond to 20 stencils computed across grid nodes. We investigate the application's scalability for two basic schemes of organizing computation: without and with hiding data transfers behind computation, combined with using either peer‐to‐peer inter‐GPU data transfers through NVIDIA NVLink and AMD Infinity interconnects or communication over the PCIe and main memory. Among the studied programming interfaces is CUDA, HIP, and OpenMP Accelerator Model. While the first two are designed to write the codes for a specific hardware platform, OpenMP enables code portability between NVIDIA and AMD GPUs. The resulting performance is experimentally assessed on computing platforms containing NVIDIA V100 (up to 8 GPUs) and A100 (one GPU), as well as AMD MI210 (one device) and MI250 (up to 8 logical GPUs). [ABSTRACT FROM AUTHOR]

The original vision on what later became LifeWatch ERIC started about a quarter of a century ago in 1996. In those days, the promise of digital technologies entered biodiversity and ecosystem research. Not only by digitiing relevant information, but also with applications to process such data. While several (inter)national initiatives embarked on specific topics, there was also an idea that the upcoming view on grid computing provided attractive solutions for federated data sources, together with a strong computing capacity. This paper presents the history from conception to early actions, until actual preparations towards a research infrastructure on the European scale. [ABSTRACT FROM AUTHOR]

It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid's stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method. [ABSTRACT FROM AUTHOR]

Machine learning (ML) methods are applied to optimal resource control for Network Powered by Computing Infrastructure (NPC)—a new generation computing infrastructure. The relation between the proposed computing infrastructure and the GRID concept is considered. It is shown how ML methods applied to computing infrastructure control make it possible to solve the problems of computing infrastructure control that did not allow the GRID concept to be implemented in full force. As an example, the application of multi-agent optimization methods with reinforcement learning for network resource management is considered. It is shown that multi-agent ML methods increase the speed of distribution of transport flows and ensure optimal NPC network channel load based on uniform load balancing; moreover, such control of network resources is more effective than a centralized approach. [ABSTRACT FROM AUTHOR]

In a 3D voxel-based digital urban model - or digital urban twin, the semantics of each cell is typically visualized using RGB values to give a realistic impression. However, due to measurement uncertainties or incompleteness, some voxel occupancy might be less likely than others. This paper suggests computing an occupancy grid map using an inverse sensor model and embodying the obtained occupancy probabilities into the urban digital twin.This article then presents visualization of additional information, in this case the uncertainty in space occupancy, in a 3D voxel-based urban model. To this end, a concept is proposed, in which static visual variables are complemented by dynamics. It suggests to utilize the visual variables transparency, size, and color, supplemented by animation. The main idea is to communicate occupancy uncertainty information and enhance its perception via visual variables while visually inspecting a digital twin in an animation mode, observing the occupancy uncertainty without losing the perception of the urban environment. [ABSTRACT FROM AUTHOR]

Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications. Nevertheless, existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets, such as preservation and server infrastructure, in a limited number of large-scale worldwide data facilities. Optimizing the deployment of virtual machines (VMs) is crucial in this scenario to ensure system dependability, performance, and minimal latency. A significant barrier in the present scenario is the load distribution, particularly when striving for improved energy consumption in a hypothetical grid computing framework. This design employs load-balancing techniques to allocate different user workloads across several virtual machines. To address this challenge, we propose using the twin-fold moth flame technique, which serves as a very effective optimization technique. Developers intentionally designed the twin-fold moth flame method to consider various restrictions, including energy efficiency, lifespan analysis, and resource expenditures. It provides a thorough approach to evaluating total costs in the cloud computing environment. When assessing the efficacy of our suggested strategy, the study will analyze significant metrics such as energy efficiency, lifespan analysis, and resource expenditures. This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing. The proposed work demonstrates notable improvements of 12.15%, 10.68%, 8.70%, 13.29%, 18.46%, and 33.39% for 40 count data of nodes using the artificial bee colony-bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Lévy-based whale optimization algorithm, respectively. [ABSTRACT FROM AUTHOR]

Owing to the enormous advancement in miniature hardware, modern smart mobile devices (SMDs) have become computationally powerful. Mobile crowd computing (MCC) is the computing paradigm that uses public-owned SMDs to garner affordable high-performance computing (HPC). Though several empirical works have established the feasibility of mobile-based computing for various applications, there is a lack of comprehensive coverage of MCC. This paper aims to explore the fundamentals and other nitty–gritty of the idea of MCC in a comprehensive manner. Starting with an explicit definition of MCC, the enabling backdrops and the detailed architectural layouts of different models of MCC are presented, along with categorising different types of MCC based on infrastructure and application demands. MCC is compared extensively with other HPC systems (e.g. desktop grid, cloud, clusters and supercomputers) and similar mobile computing systems (e.g. mobile grid, mobile cloud, ad hoc mobile cloud, and mobile crowdsourcing). MCC being a complex system, various design requirements and considerations are extensively analysed. The potential benefits of MCC are meticulously mentioned, with special discussions on the ubiquity and sustainability of MCC. The issues and challenges of MCC are critically presented in light of further research scopes. Several real-world applications of MCC are identified and propositioned. Finally, to carry forward the accomplishment of the MCC vision, the future prospects are briefly elucidated. [ABSTRACT FROM AUTHOR]

Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud computing employ a moderate design, wherein computational resources like storage and servers are housed in a few sizable worldwide data centers. System reliability, efficiency, and low latency are all goals of virtual machine (VM) placement. Load balancing has emerged as a crucial challenge for attaining energy efficiency in a fictitious grid computing architecture where a variety of users' workloads are distributed across several virtual machines. We propose a more effective optimization technique known as the twin fold moth flame algorithm. This algorithm considers multiple constraints, including computation time, stability, and placement cost. The proposed model's effectiveness will be evaluated based on relocation costs, reaction times, and stability assessments. The most significant gains of the presented work are 4.24%, 9.73%, 11.10%, 28.83%, 7.63%, and 10.62% for 20 count data of nodes for artificial bee colony–bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Lévy-based whale optimization algorithm, respectively. [ABSTRACT FROM AUTHOR]

Atmospheric environmental issues have evolved from point source pollution to regional pollution, leading to controlling specific air pollutant emissions. A-value method has been found suitable for estimating large-scale atmospheric environmental capacity rather than small-scale, resulting in the inaccuracy of developing air pollution control strategy. This study proposed a grid computing method based on the CALPUFF modelling system and GIS spatial analysis tool. The meteorological data from the MM5 model were used to simulate the spatial distribution of air pollutants. The meteorological flow field data was used to simulate the ventilation coefficient. The A value was revised with the simulated to achieve accurate results of atmospheric environment capacity. The credibility was verified by applying this method to Fengtai District, Beijing, China. The research area was divided into small partitions via the ArcGIS spatial analysis tool. The simulation results agreed well with the observation data from actual monitoring stations, even for the PM10 concentration with the most significant error (MRE: 7.05%−13.28%, RMSE: 11.62–17.89, R2: 0.84–0.90). The GIS spatial analysis tools were applied to match the underlying surface types and overcome the restrictions of administrative boundary management. The study proposed four schemes to achieve differentiated air pollutant emission reduction and develop suitable control strategies. Furthermore, this method can be applied on different scales of natural geographic boundaries and realize the precise spatial management of atmospheric environmental capacity. [ABSTRACT FROM AUTHOR]

There is a rapid increase in power demand which has led to an increase in the need for smaller grids all over the world. The microgrid is such a small power supply system that consists of loads, distributed energy resources, and energy storage systems. With the development of the internet, the power sector has started the development of the newest type of grids, involving a smart technology called smart grids. Smart grids have got smart integration and advanced storage, along with dynamic optimization of grid operation and resources, digital information and control, distributed resources, demand-side resources, energy efficiency resources, and smart metering systems. The developments in intelligent controllers reflect in power flow management also. This paper gives an idea of how to achieve power flow management through a tariff-based Fuzzy Logic Controller (FLC). The control algorithm used here is the k factor-based Icosϕ algorithm, which decides the amount of power shared by microgrids and the grid based on the load demand. The power forecasting of the microgrid with a solar source is performed using ANN. The regression plot and the error histogram obtained show the accuracy of the ANN controller. The simulation of power flow management of the smart microgrid system is tested and analyzed and the proposed system is compared with other existing research. The hardware implementation of the FLC controller with the Arduino interface which is analogous to real-time measurement is explained in detail to compare the simulation results obtained. [ABSTRACT FROM AUTHOR]

Control vector parameterization method is the most commonly used numerical computation method in solving dynamic programming problems. However, to balance the expected trajectory and the computational cost, this method faces difficulties in dividing the optimal discretization time grid. In this paper, we propose an effective control approach for non-uniform adaptive grid division. By analyzing the slope change trend of the control parameter, time nodes are adaptively refined by merging time grids with gentle slopes to remove unnecessary time nodes and adding time nodes to time grids with steep slopes to improve function approximation accuracy. Eventually, we obtain an adaptive time grid division method under which the trajectory of discretization control parameter is more approximate to the optimal control trajectory. Under the condition of intuitive slope information, the proposed method can achieve better performance with fewer optimization parameters and shorter computation time. Finally, the proposed method is applied to solve a classic optimal control problem and the obtained results are compared with the traditional control vector parameterization method. By comparison through the example, our proposed method can overcome the contradiction between approximation accuracy and computational cost in control vector parameterization method and improve the optimization efficiency. [ABSTRACT FROM AUTHOR]

Task scheduling and resource management are critical for improving system performance and enhancing consumer satisfaction in distributed computing environments. The dynamic nature of tasks and the environments creates new challenges for schedulers. To solve this problem, researchers developed fuzzy-based scheduling algorithms. Fuzzy logic is ideal for decision-making processes since it has a low computational complexity and processing power requirement. Motivated by the extensive research efforts in the distributed computing and fuzzy applications, we present a review of high-quality articles related to fuzzy-based scheduling algorithms in grid, cloud, and fog published between 2005 and June 2023. This paper discusses and compares fuzzy-based scheduling schemes based on merits and demerits, evaluation techniques, simulation environments, and important parameters. We begin by introducing distributed environments, and scheduling process followed by their surveys. This study has summarized several domains where fuzzy logic is used in distributed systems. More specifically, the basic concepts of fuzzy inference system and motivations of fuzzy theory in scheduler are addressed smoothly. A fuzzy-based scheduling algorithm employs fuzzy logic in different ways (e.g., calculating fitness functions, assigning tasks to fog/cloud nodes, and clustering tasks or resources). Finally, open challenges and promising future directions in fuzzy-based scheduling are identified and discussed. [ABSTRACT FROM AUTHOR]

A large amount of high-value data are stored in smart grid data centers, but since the resource diversity of a single data center is limited, data sharing becomes especially important in conducting an effective data mining process. However, traditional data-sharing models often use centralized schemes without authentication of the shared objects, making it difficult to establish trust relationships and ensure data privacy. This makes it difficult to break through the problem of data islands. To solve the above problems, this paper proposes a blockchain-based data-sharing incentive model for edge smart grid scenarios. First, the model uses blockchain and proxy re-encryption technology to achieve the security and traceability of the smart grid data-sharing process. Secondly, the data-sharing incentive algorithm is designed using game theory to maximize the willingness of data owners to share data. Finally, the model in this paper is compared and analyzed with other existing data-sharing models, and the designed performance test shows that the scheme in this paper has significant advantages over the other literature schemes in terms of functionality and computational overhead, and the increase in costs is not significant, and the model can meet the requirements for large-scale data sharing in edge smart grid scenarios. [ABSTRACT FROM AUTHOR]

This paper highlights the importance of efficient task distribution and robust fault tolerance in network systems. It emphasizes the limitations of relying on a fixed resource quantity and proposes task replication as a solution to improve data availability. The introduced algorithm dynamically determines the optimal number of replicas based on network history, response time, and joint probability of successful servers, aiming to minimize task failure rates. The algorithm's advancements in grid scheduling lie in optimal resource management, fault-aware job placement, adaptability to changing conditions, and efficient fault tolerance through redundancy planning. The algorithm outperforms three other algorithms, showcasing significant enhancements. [ABSTRACT FROM AUTHOR]

Load balancing is critical to managing server resources efficiently and ensuring optimal performance in distributed systems. The weighted round robin (WRR) algorithm is commonly used to allocate incoming requests among servers based on their assigned weights. However, static weights may not reflect the changing demands of servers, leading to imbalanced workloads. To address this issue, this study proposes a dynamic mechanism for assigning weights to servers in the WRR algorithm based on the data rate and incorporates the least connection approach for the best result. The dynamic mechanism considers each server's real-time data rate, representing its current load. Servers with higher data rates are assigned higher weights to attract a larger share of incoming requests, while those with lower data rates receive lower weights to manage their loads effectively. This dynamic weight assignment allows the algorithm to adapt to varying workloads and achieve better load balancing across servers. To further refine the distribution of requests, the least connection approach is employed to handle tie-breaking situations and for more fairness in distributing the loads. The proposed algorithm is a hybrid of data rate and the least connection, it is evaluated through simulations and real-world experiments. The results demonstrate its superiority in achieving improved load balance compared to other algorithms, such as round-robin RR and traditional static-weight WRR algorithms. By dynamically adjusting weights based on data rate and employing the least connection approach, the algorithm optimizes server resource usage, minimizes response times, and enhances overall system performance in distributed environments. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the power grid, the forms of faults are becoming increasingly complex, with a wide range of impacts and long maintenance cycles, posing increasingly severe challenges for power grid operators. In the current power system (PS), due to system limitations, there is a large amount of data, and the current computing system is limited by hardware and computing power, making it difficult to satisfy the requirements of real-time computing, comprehensive analysis, and expansion. This paper analyzed the technical basis of the knowledge graph (KG) of power grid operation and maintenance (O&M), and clarified the key role of model driving in the current large-scale smart grid (SG), which can effectively improve the efficiency and reliability of data processing in the SG. This paper also analyzed the model-driven PG O&M KG platform, and analyzed the role of particle swarm optimization (PSO) algorithm in PG operation and fault maintenance. After applying the PSO algorithm in this article, in the experimental results section, the fault rate of Transformer 5 was 2.1% lower than that of Transformer 4. The knowledge mapping model of grid O&M and the particle swarm algorithm in this paper can significantly reduce the failure rate of the grid, which has wide extension value. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused significant harm globally, prompting us to prioritize prevention measures. Effective hand-washing is one of the most critical and straightforward measures that can help prevent the spread of this virus. Medical staff's hands are considered a major source of hospital infection. Effective hand-washing can prevent up to 30% of diarrhea-related illnesses, which is crucial in preventing nosocomial infections (Tartari et al. in Clin Microbiol Infect 23(9):596–598, 2017). This paper proposes an electronic-based real-time hand-washing identification framework called Alpha Hand Washing (ALPHA HW). The system uses camera-based identification, edge computing, and deep learning to automatically identify correct hand-washing behaviors, thereby facilitating effective hand-washing (Bertasius et al. in: Computer vision and pattern recognition, 2015). We achieved an accuracy of 78.0% mAP and a speed of 52 FPS in detecting scenes using specific monitoring datasets in hospitals by constructing the complex recognition system into a grid computing problem. Leveraging edge computing, our system achieves real-time identification with low memory consumption and high-efficiency computation. Alpha HW presents scientific and financial values in epidemic prevention and control that can facilitate popularization to reduce virus spread (Bewley et al. in 2016 IEEE international conference on image processing, 2016). [ABSTRACT FROM AUTHOR]

The rapidly increasing number of delta Scuti stars with regular patterns among their pulsation frequencies necessitates modelling tools to better understand the observations. Further, with a dozen identified modes per star, there is potential to make meaningful inferences on stellar structure using these young δ Sct stars. We compute and describe a grid of >200 000 stellar models from the early pre-main sequence (pre-MS) to roughly one-third of the MS lifetime, and calculate their pulsation frequencies. From these, we also calculate asteroseismic parameters and explore how those parameters change with mass, age, and metal mass fraction. We show that the large frequency separation, Δ ν , is insensitive to mass at the zero-age main sequence. In the frequency regime observed, the Δ ν we measure (from modes with n ∼ 5–9) differs from the solar scaling relation by ∼13 per cent. We find that the lowest radial order is often poorly modelled, perhaps indicating that the lower order pressure modes contain further untapped potential for revealing the physics of the stellar interior. We also show that different nuclear reaction networks available in mesa can affect the pulsation frequencies of young δ Sct stars by as much as 5 per cent. We apply the grid to five newly modelled stars, including two pre-MS stars each with 15+ modes identified, and we make the grid available as a community resource. [ABSTRACT FROM AUTHOR]

This study presents a novel framework for the predictive maintenance of electric grid infrastructure, leveraging an Adaptive Predictive Maintenance Optimization (APMO) algorithm. Our comprehensive system architecture integrates IoT sensors and drone surveillance, harnessing grid computing for data processing and machine learning for analytics. The APMO algorithm, underpinned by reinforcement learning, dynamically refines maintenance schedules, enhancing operational efficiency. Results from simulated sensor data exhibit a negligible correlation, indicating a multi-faceted approach to predictive analytics. Specifically, the Health Index distribution showed a wide range of 0.7 to 1.0, while the electrical current maintained a stable mean at 10 A. Further, the APMO algorithm demonstrated a promising improvement in maintenance efficiency from 40% to 75% over a year. This research introduces a scalable, robust system for grid management, paving the way for smarter infrastructure maintenance. [ABSTRACT FROM AUTHOR]

The impact of photovoltaic (PV) systems on distribution networks in advances of cloud, grid, and cluster computing was annoying in the study. Additional PV systems are explained with their working principles. Furthermore, A clustering-based model was developed to reduce the error in the calculation. The weather conditions were classified into different synoptic types. Prediction-based models were introduced after classifying the weather conditions in different optic types. For the movie, it was noted that conditions play a Vital role in predicting the output produced by a PV system. Moreover, a coherent discussion about the key elements regarding the impact of PV systems on Distribution Networks for Cloud, Grid, and Cluster Computing is done. Thus, through the discussion, an empirical knowledge-based analysis was presented. [ABSTRACT FROM AUTHOR]