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We recently established an impedance standard for the D-band, one of the 6G candidate frequencies. However, primary standards are difficult to use for routine calibration because they require multiple impedance standards and a lot of time to calibrate the vector network analyzer (VNA). Therefore, a transfer standard is needed to efficiently apply the calibration value and to propagate the uncertainty of the primary standard to a device under test (DUT). In this paper, we describe a design method for a transfer standard with small uncertainty even when an arbitrary DUT is measured. This is achieved by propagating the uncertainty of the primary standard to the transfer standard and then propagating it again to the uncertainty of the DUT. We developed a calibration kit that has low uncertainty over a wide frequency band, from 110 GHz to 170 GHz, and consists of waveguide offset shorts for ease of production. We also propose a method to minimize DUT uncertainty and a method to minimize “phase distance” to find the optimal length of the offset short. When using three offset shorts, an uncertainty similar to that of the short-open-load-thru (SOLT) calibration kit was obtained, and when using four offset shorts, an uncertainty comparable to the primary standard was obtained. Lastly, this paper examines the repeatability of measurements and reproducibility during the production process.

Reconfiguration of a distribution network is one of the main approaches to control and enhance distribution network indices, such as voltage profile and power losses. Distribution network operators perform reconfiguration for long-term or short-term periods based on network equipment and intended objectives. Long-term or static reconfiguration is suitable for traditional and modern networks with conventional switches. On the other hand, modern distribution networks that are equipped with one or more remote control switches can perform reconfigurations within short-term periods, to maximize predefined objectives. This paper presents a comprehensive review of recent literature on network reconfiguration. Reconfiguration methodologies are classified into five groups: classical methods, heuristic methods, metaheuristic methods, hybrid methods, and methods based on machine learning. The paper provides a general definition and comparison of the categories and discusses their application in dynamic and static reconfiguration. The paper introduces dynamic reconfiguration as the future challenges in smart and modern distribution networks and for the first time categorizes various methodologies in dynamic reconfiguration. The paper serves as a guide to assist engineers and researchers in selecting the most suitable methodology based on their system equipment and objectives.

The purpose of this paper is to review the scientific literature on direct load control (DLC) technology in the context of a smart grid (SG). This study synthesized and compared various articles from the literature with the aim of conducting a comprehensive analysis. This study aims to provide a detailed understanding of the landscape, recent advancements, and the relationships among various aspects within this field. The evaluation is conducted through a systematic literature review, examining recent advancements in the field. A survey of over 200 papers published between 1983 and 2022 is classified based on appliance information, aim/objective, constraint, management method and tool, implementation, and publication status. In the DLC application, the study identifies the type and population of appliances used in and the aim/objective and constraint considered. It also determines whether forecast, prediction, and estimation structures, optimization and control algorithms, and specific structures are employed. The practical aspect of the study examines various perspectives, including the modeling and presentation of the research. This involves identifying the validation methods, time period for implementation, solver, program, software, toolbox, and platform structures used during validation. Additionally, the characteristics of the database used for implementation and the location of the implementation are detailed. The publication year and the countries of the authors are also provided. Comparative relationships between these elements are presented based on the aforementioned information.

The agile eye is classified as a 3-degrees of freedom (DOF) type 3-RRR spherical parallel mechanism (SPM) designed to replicate the movement patterns seen in the human eye. The end organ exhibits a range of motion inside a cone of vision spanning 140°, with a twist tolerance of ±30°. Additionally, the mechanism can achieve angular velocities exceeding 1000 °/s and angular accelerations surpassing 20 °/ $\text{s}^{2}$ . The objective of this research is to conduct a comprehensive examination of the direct and inverse kinematics of the spatial parallel manipulator (SPM) on a manipulator. The purpose of doing a kinematic analysis on the manipulator is to enhance the design optimization process, accurately determine the dimensions of all components, and improve the functionality of the computer-aided design (CAD) system. This study aims to ensure the efficient operation of the SPM and maximize its available workspace. Additionally, this study enables the achievement of a high level of stiffness inside the workspace and the establishment of clearly comprehensible limits for the workspace. Moreover, it facilitates the precise control of the SPM. An evaluation is conducted to assess the efficacy of the existing technique by comparing its outputs with those obtained from a virtual reality simulation using the commercially available software CopeliaSim. The control mechanism described in the present paper demonstrates a surplus in the resolution of precision problems while ensuring competitiveness at a controlled cost. The implementation of the robot revealed the effectiveness of the design approach adopted by recording an error of no more than 1%.

Containers are recognized for their lightweight and virtualization efficiency, making them a vital element in modern application orchestration. In this context, the scheduler is crucial in strategically distributing containers across diverse computing nodes. This paper presents a novel two-stage container scheduling solution that addresses node imbalances and efficiently deploys containers. The proposed solution formulates the scheduling process as an optimization problem, integrating various objective functions and constraints to enhance server consolidation and minimize energy consumption. The confidentiality of migrated containers is ensured through encryption, and the associated costs are incorporated into the optimization constraints. This approach ensures security in container scheduling, considering container attributes as input features in our proposed attributes-based encryption model. By carefully selecting containers and destination nodes, this work seeks to establish balance within cloud-based clusters. This contributes to the improvement of container orchestration systems and their effectiveness in real-world scenarios. The proposed solution’s efficacy is demonstrated in its ability to efficiently deploy containers in multi-data center cloud environments and seamlessly migrate them between hosts within the same data center or across different data centers. Our results show optimal consolidation with a reduction in the number of running hosts, ranging from 4% to over 18%. Additionally, the solution promotes minimal total power consumption with savings ranging from 3.5 to 16.25 megawatts, while also ensuring balanced server loads.

Energy storage is a crucial technology for facilitating the integration of renewable energy sources (RES), such as wind and solar energy, into the electrical grid. The challenge of maintaining a balance between incoming and outgoing grid power can be effectively addressed by integrating energy storage technologies with inherently intermittent RES. A range of viable options for storing energy from RES currently exists, among which the Linear Electric Machine Gravity Energy Storage System (LEM-GESS) stands out as a promising choice. The LEM-GESS stores energy in a shaft using piston masses based on the concept of gravity. This paper presents the performance and cost analysis of different linear machines employed as the main drive units in a dry gravity energy storage system. Specifically, linear permanent magnet flux switching machine demonstrates the best performance in terms of overall system cost when considering a 20MW/10MWh system and optimizing for the minimum levelized cost of storage (LCOS). Noteworthy findings reveal that the LEM-GESS cost is highly sensitive to system efficiency, with factors such as material cost and power factor also influencing the LCOS. Designs with modest secondary height, low usage of copper and magnet materials on the primary of the LEM, and a high power factor are preferred to minimize the LCOS. In conclusion, the LEM-GESS with a permanent magnet machine drive option is a highly promising and cost-effective technology for supporting the integration of RES into the grid.

As a result of advancements in technology and population growth, there has been a significant rise in global electrical demand. Consequently, the integration of renewable sources such as photovoltaic (PV) systems into distribution systems has gained popularity as an effective solution to meet the increasing load requirements. This research paper introduces an optimized approach for allocating PV systems at various penetration levels, utilizing a powerful optimization algorithm known as the modified Reptile Search Algorithm (MRSA). MRSA is an enhanced version of the Reptile Search Algorithm (RSA) that addresses issues related to local optima stagnation and premature convergence by incorporating the disperse ambush strategy and proportional selection method. To assess the efficacy of the proposed optimizer, a comprehensive set of comparative experiments was conducted using the CEC’2020 test suite. The experimental results consistently demonstrate that the proposed technique outperforms alternative methods in terms of both convergence speed and accuracy. Additionally, the MRSA algorithm was employed to determine the optimal allocation of PV systems, with the total power loss serving as a single objective function while considering equality and inequality constraints. The IEEE 33-bus RDS was employed as the test system. The obtained results provide evidence that incorporating multiple PV systems yields superior outcomes compared to a single PV system at various penetration levels within the RDS. Furthermore, integrating PV systems at higher penetration levels yields better results than incorporating them at lower penetration levels.

The use of robots to carry out activities in various fields has evolved over the years. Robots can move from one location to another according to a plan and have a good understanding of the environment to perform their assigned tasks. Robot movements are based on motion planning and control algorithms. This paper proposes a new hybrid motion planning and control algorithm called FAStep-Kinematic (Stepping Ahead Firefly algorithm and Kinematic equations). This algorithm includes the Firefly Stepping Ahead algorithm to avoid obstacles, the equation of motion to control the movement of the robot, and a method to detect obstacles. The algorithm starts with the equation of motion and switches to the stepping ahead firefly algorithm when an obstacle is detected. The stepping ahead firefly algorithm then plans the robot’s next step to avoid obstacles. The robot moves to this step. This procedure is repeated until the robot successfully avoids an obstacle and reaches its target. The obstacles used in this article are static and are known to robots. The hybrid algorithm will be used to navigate robots in a cluttered environment. The new algorithm’s effectiveness will be seen with an application to tractor-trailer robotic systems. The results show that the robots are able to reach their destination safely and using a shorter route. Additionally, this research compared the new hybrid algorithm’s performance with that of the ACO-Kinematic (Ant Colony Optimization and Kinematic equations) algorithm in terms of path length and convergence time. The analysis show that the hybrid algorithm is superior to the ACO-Kinematic algorithm. The proposed algorithm improves the path length by 0.37%, 9.22%, and 5.79% compared to the ACO-Kinematic algorithm in three different scenarios.

The development of efficient energy management for nanogrid (NG) systems, while reducing both the carbon dioxide (CO2) emissions and power generation cost, is achievable through the effective utilization of available energy sources. This paper proposes a multi-objective optimal energy management strategy for grid-connected NG systems, which incorporates PV arrays and battery storage devices (BSDs), to reduce operating costs and CO2 emission simultaneously over a 24-hour scheduling period. This strategy, which is based on the improved pelican optimization algorithm (IPOA), involves the development of a multi-objective optimization (MOA) equation with several constraints, while taking into account the Malaysian grid purchasing and selling prices. An innovative IPOA-derived technique is developed to facilitate the NG’s optimal energy management operation in multi-objective situations. The proposed algorithm is tested on three distinct scenarios to affirm its efficacy. It is assumed that (a) power exchange between the NG and the main grid is limitless, (b) power interchange between the NG and main grid has a predetermined limit and (c) operating at the maximum capacity of PV array. In order to demonstrate the effectiveness of the proposed algorithm, The outcomes of the simulation are juxtaposed with results obtained from the initial Pelican Optimisation Algorithm (POA), the Bat Algorithm, and the Improved Differential Evolutionary (IDE) Algorithm. The simulation reveals that the suggested IPOA algorithm exhibited the most economical performance and the lowest CO2 emissions. Moreover, in the second scenario, operational costs decreased by 9.5%, and CO2 emissions were reduced by 15%. Conversely, in Scenario 3, there was a 2% decrease in cost and 23% reduction in CO2 emissions as against the first scenario.

A covert communication channel facilitates direct data transfer between two parties through pre-communication knowledge agreements, ensuring secure and confidential transmission of information. However, the existing covert channels suffer from performance limitations, specifically in terms of throughput and speed. The encoding techniques employed in covert channels can be time-consuming and have limited data transfer capabilities. Furthermore, the ability of covert channels to handle files with different formats has not been sufficiently explored. This paper introduces a high-performance implementation of a covert channel that leverages Java exception handling during program execution. To optimize the covert channel’s performance, this research explores the use of several encoding methods, including ASCII, Byte, Hexadecimal, Base64, and Huffman coding. The proposed covert channel’s performance is evaluated and analyzed for various coding methods. To study the impact on performance, multiple file formats, including text, audio, and video, were used in the experiment. Experimental results showed that the hexadecimal coding method improves the throughput and decreases the time delay of the covert channel. This is attributed to its ability to minimize the number of tries before encountering an “Index Out of Bounds” exception. On the contrary, the Base64 method is found to be inefficient as it produces longer strings than the original inputs, resulting in increased time delays during data transfer. The best results are achieved when applying the hexadecimal method with Huffman coding. It takes 6241 milliseconds to transmit a 12.8-megabyte text file, with a throughput of 23116 bits per millisecond.

Electricity market clearing models are crucial for operational and investment planning in modern power systems. Two commonly used models for market clearing are the merit-order and unit commitment models. While the merit-order employs a snapshot approach and lacks precision in representing time-based variations in demand and generation, the unit commitment model incorporates crucial temporal constraints. This paper highlights the significance of temporal constraints in market clearing models, emphasizing the potential for significant errors in generation commitment if these constraints are not carefully considered. Using real data from the Irish electricity system, the impact of temporal constraints on intra-day market clearing is demonstrated, comparing with the merit-order rule with the unit commitment solution. The findings reveal 6% underestimation in market clearing prices when compared to unit commitment solutions. The minimum stable operating constraints of thermal units were identified as having the most substantial impact on the unit commitment solution. This study underscores the risk of significant errors in generation planning if temporal constraints are not adequately factored in, advocating for a more accurate approach for market clearing models.

Fuse saving protection is usually applied in distribution networks, through auto-reclosing, to prevent fuse burning under transient faults. The modern fault ride-through (FRT) standards, which require distributed generators (DGs) to remain connected during the fault, may lead to the miscoordination of recloser-fuse. This paper proposes a discrete coordinate-descent (DCD) approach that optimizes the FRT of DGs so that the coordination between recloser and fuse is preserved. First, the method performs a short-circuit calculation (SCC) considering the currents contributed by the DGs, according to the FRT standards of each country. Then, if a miscoordination between recloser-fuses is observed, it optimally scales down the DG currents, until the coordination is restored. The proposed method has simple implementation, low communication requirements and offers adequate voltage support during the fault. Simulations were carried out in the IEEE 13-bus and the IEEE 8500-node network to highlight the distinct features of the proposed approach against the state-of-the-art current limiting approaches.

Swarm-based intelligent optimization algorithms that employ the principles of collective behavior have been gaining traction as viable solutions in optimization research. One area of optimization is the Examination Timetabling Problem (ETP), which presents a significant challenge for many Higher Education Institutes (HEIs). This study proposes a novel approach for solving the Uncapacitated Examination Timetabling Problem (UETP), where a stepping-ahead mechanism is utilized with threshold acceptance in the Firefly Algorithm (FA). The proposed method improves exploration with the use of the stepping-ahead mechanism, while threshold acceptance allows for better exploitation of the search space. Initially, a neighborhood search mechanism is employed as the discretization of FA to improve solution quality, known as Kempe Chain-based neighborhoods. The proposed method is tested on seven UETP problems, with the results showing comparative performance to the best solutions available in the literature for the Toronto exam timetabling dataset. The selection of seven problems is made with exams totaling less than 400, this allows to create a manageable yet representative benchmark. The study further extends the experiment to a real-world dataset collected from an HEI. The use of a real-world dataset allows us to see the potential of the algorithm and at the same time evaluate its performance under realistic conditions and resource constraints. The proposed stepping-ahead mechanism has the potential for use in other domains, such as robotics and engineering. Overall, this paper presents a new methodology for solving the UETP that has the potential to offer superior results when compared to existing approaches.

This paper presents a novel warping based method to stitch videos from unstructured camera arrays. Our approach adopts a two-step energy optimization for video stitching. In the first step, we perform an initial stitching on keyframes, and then extract the boundary vertices and warped vertices as constraints for further optimization. In the second step, we design a global optimization to effectively propagate the stitching from the keyframe to other frames while ensuring the feature alignment, boundary regularity and temporal coherence. The optimization can be efficiently solved by a linear system, and the final stitching results are produced by warping and blending. Experimental results and comparisons show that our method can efficiently stitch multiple videos from unstructured camera arrays, and outperforms state-of-the-art methods.

An essential element of association rules is the strong confidence values that depend on the support value threshold, which determines the optimum number of datasets. The existing method for determining the support value threshold is carried out manually by trial and error; the user determines a support value such as 10%, 30%, or 60% according to their instincts. If the support value threshold is inappropriate, it produces useless frequent patterns, overburdens computer resources, and wastes time. The formula for predicting the maximum count of frequent patterns was 2n – 1, where $n$ is the number of distinct items in the dataset. This paper proposes a new SDFP-growth algorithm that does not require manual determination of the support threshold value. The SDFP-growth algorithm will perform dimensionality reduction on the original dataset that will generate level 1 and level 2 smaller datasets, thus automatically producing a dataset with an optimum amount of data with a minimum support value threshold. The proposed formula for predicting the maximum number of frequent patterns will become 2 $^{\vert A\vert }$ - 1, which is $\vert A \vert $ will always be smaller than $n$ . Experiments were performed on five various datasets, which reduced the number of data dimensions by more than 3% on the Level 1 dataset and more than 69% on the Level 2 dataset by maintaining the confidence value of the strong rules. In the execution time evaluated, we found an optimization of more than 2% on the level 1 dataset and more than 94% on the level 2 dataset.

The current state of Association Rule Mining (ARM) technology is heading towards a critical yet profitable direction. The ARM process uncovers numerous association rules, determining correlations between itemsets, forming building blocks that have led to revolutionary scientific discoveries. However, a high level of privacy is vital for protecting sensitive rules, raising privacy concerns. Researchers have recently highlighted challenges in the Privacy-Preserving Association Rule Mining (PPARM) field. Many studies have proposed workarounds for the PPARM dilemma by using metaheuristics. This paper conducts a systematic literature review on metaheuristic-based algorithms addressing PPARM challenges. It explores existing studies, providing insights into diverse metaheuristic approaches tackling PPARM problems. A detailed taxonomy is presented, offering a structured classification of metaheuristic-based algorithms specific to PPARM. This classification facilitates a nuanced understanding of the field by categorizing these algorithms into metaphor-based and non-metaphor-based groups, with a discussion of the nature of the representation schemes for each category identified in the survey. The review extends its analysis to encompass the latest applied approaches, highlighting the diversification of existing metaheuristic algorithms in the PPARM context. Moreover, common datasets and evaluation metrics identified from selected studies are documented to provide a deeper understanding of the methodological choices made by researchers in this domain. Finally, a discussion of existing challenges and potential future directions is presented. This review serves as a helpful guide that outlines previous research and presents potential future opportunities for metaheuristic-based algorithms in the context of PPARM.

This paper introduces an optimal bi-objective optimization methodology customized for microgrid systems, encompassing economic, technological, and environmental considerations. The framework portrays the objectives of an intelligent microgrid, aiming to minimize operational costs, CO2 emissions, peak-to-average ratio (PAR), and energy consumption while concurrently enhancing user comfort (UC). A scheduled power allocation strategy is formulated to efficiently cater to the energy needs of residential loads. The stochastic nature of wind and solar resources is characterized by modeling wind speed and solar radiation intensity using a beta probability density function (PDF). The non-dominated sorting genetic algorithm II (NSGA-II) is employed to address optimization challenges. A decision-making process is implemented to select the optimal solution from the non-dominated alternatives. The study presents three scenarios illustrating the optimal operational values for various parameters and energy consumption, providing a comprehensive analysis of the proposed algorithm’s efficacy. Leveraging the NSGA-II algorithm, coupled with renewable energy resources and optimal energy storage system scheduling, yielded significant reductions in overall expenses, PAR, CO2 emissions, user discomfort, and energy consumption. MATLAB simulations were conducted to substantiate the efficacy of our proposed approach. The obtained results underscore the effectiveness and productivity of our devised NSGA-II-based approach. Notably, the proposed algorithm demonstrated a substantial reduction in electricity costs by 19.0%, peak-to-average ratio (PAR) by 30.7%, and carbon emissions by 21.7% in scenario-3, as evidenced by a comparative analysis with the unscheduled case.

Reducing power consumption is a pivotal challenge in 5G millimeter wave (mmWave) networks due to the density of the base stations (BSs) in these networks. In this paper, we focus on the joint user and power allocation problem in 5G mmWave networks, aiming to minimize power consumption while maintaining the user Quality of Service (QoS), considering the BSs switching on/off strategy. Initially, we formulate the problem as an Integer Linear Program (ILP), aiming to obtain the optimal solution. Due to the NP-hardness of the problem, we propose a Genetic Algorithm (GA)-based heuristic strategy. Extensive simulations are conducted to evaluate the performance of the GA. The obtained results demonstrate the efficiency of the proposed GA in providing close to optimal solutions, even in scenarios with a large number of users, when compared to the ILP. Additionally, the proposed GA outperforms the benchmark algorithms in terms of network power consumption, network throughput, energy efficiency (EE), and the number of switched-off BSs. Moreover, when comparing the running time of the different methods, the proposed GA shows a significantly reduced time compared to the optimal solution obtained by the ILP method. At the same time, it requires a running time close to the running times of the benchmark solutions. Further insights from power consumption patterns in residential and office areas affirm the consistent energy savings achieved by the proposed GA, emphasizing its applicability in real-world scenarios. In our case, the proposed GA yields energy savings up to 22.34% and 33.12% in residential and office areas, respectively. These findings underscore the practicality and efficiency of our proposed GA in optimizing power consumption and enhancing EE in mmWave networks.

This paper proposes a novel method for estimating the inertia, damping, and mechanical power variation of power systems using short-term phasor measurement data during transient events. The method applies a linear least square optimization technique to the generator swing equation and utilizes a pair of sliding windows to capture the dynamic changes of the system. The method can estimate the inertia under different types of disturbances in real-time and track the inertia changes caused by secondary disturbances. The method is validated using simulations in the IEEE New England 10-machine system and compared with existing methods.

As optimization problems become more complicated and extensive, parameterization becomes complex, resulting in a difficult task requiring significant amounts of time and resources. In this paper we propose a heuristic search algorithm we call MCSA (Montecarlo-Clustering Search Algorithm), which is based on Montecarlo sampling, and a clustering strategy involving two techniques. Our objective is to apply MCSA, an inherently stochastic method, to address optimization problems. To assess its performance, we conducted an evaluation using classical benchmark optimization functions. Additionally, we leveraged the CEC2017 benchmark suite to comprehensively evaluate the algorithm, highlighting the pivotal role of the Exploration stage in our methodology. Subsequently, we extended our methodology to tackle a practical combinatorial problem, the Knapsack problem. This NP-Hard problem holds significant real-world applications in resource allocation, scheduling, planning, logistics, and more. Our contributions lie in parameterizing the Knapsack Problem to align with MCSA’s parameters for reference indicators adjustment and achieving high-quality solutions, surpassing 90% in comparison to exhaustive methods such as branch and bound.

This paper presents novel operational laws for $p,q,r-$ spherical fuzzy sets ( $p,q,r-$ SFSs) by harnessing the Dombi t-norm (DTN) and t-conorm (DTCN). These laws serve as the foundation for a set of aggregation operators (AOs) designed to consolidate $p,q,r-$ spherical fuzzy ( $p,q,r-$ SF) information. Additionally, a multi-criteria decision-making (MCDM) method is outlined for addressing practical decision-making (DM) challenges. To demonstrate the application of the proposed approach, a numerical example is offered. Furthermore, we conducted a comparative study to validate the efficacy of the suggested approach. Finally, we discuss both the advantages and limitations of this innovative approach.

Diesel-based vehicles are prevalent in underground mines all over the world; however, an electric revolution is beginning to unfold in the mining industry. Mines operating with diesel fleets require extensive ventilation due to toxic gas and heat emissions from diesel engines, and these ventilation costs increase as mines continue to dig deeper for depleting resources. Electric mining vehicles have zero emissions and emit less heat, meaning ventilation costs decrease. Furthermore, unlike diesel trucks, electric trucks can capture regenerative braking energy on downhill segments. Recent battery advancements have allowed electric mining trucks to become cost-competitive with their diesel counterparts when all system-level advantages are considered. However, there is presently a lack of knowledge in designing an optimal electric mining fleet and charging infrastructure, compared to the many decades of “best practices” available for diesel fleets. To fill this gap, this paper proposes an optimization algorithm for battery electric fleets in underground mines which provides insights into the design of electric fleets. This work identifies critical input design parameters, presents a battery optimization algorithm, and uses detailed vehicle models to simulate mining productivity and truck energy use. The main algorithm considers ventilation costs, charger infrastructure costs, fleet labor costs, truck energy costs, and truck capital costs (including battery lifetime) to obtain an overall cost-per-tonne output metric. The algorithm is tested on a variety of scenarios, and some key findings are that fast-charging with optimal battery sizes results in the lowest ${\$}$ /tonne and this result decreases as fast-charging levels increase to 450 kW.

Performance modelling of a deep learning application is essential to improve and quantify the efficiency of the model framework. However, existing performance models are mostly case-specific, with limited capability for the new deep learning frameworks/applications. In this paper, we propose a generic performance model of an application in a distributed environment with a generic expression of the application execution time that considers the influence of both intrinsic factors/operations (e.g. algorithmic parameters/internal operations) and extrinsic scaling factors (e.g. the number of processors, data chunks and batch size). We formulate it as a global optimisation problem and solve it using regularisation on a cost function and differential evolution algorithm to find the best-fit values of the constants in the generic expression to match the experimentally determined computation time. We have evaluated the proposed model on three deep learning frameworks (i.e., TensorFlow, MXnet, and Pytorch). The experimental results show that the proposed model can provide accurate performance predictions and interpretability. In addition, the proposed work can be applied to any distributed deep neural network without instrumenting the code and provides insight into the factors affecting performance and scalability.

Electrical equipment is prone to different types of Partial Discharge (PD) failures that are varying between minor and severe level. In this paper, Three developed models for Convolution Neural Network (CNN) are proposed to detect and classify four different partial discharge types which are arcing, corona discharge, tracking, looseness as well as healthy equipment situation. Notably, the resulting models exhibited an impressive overall accuracy of more than 94%, which is particularly significant considering the inherent presence of noise in the real-world samples obtained as representative field failures. These findings underscore the robustness and effectiveness of the CNN models in accurately identifying PDs, despite the intricate challenges associated with real-world data.

Given the significant impact of the UN 2015 agenda on sustainable development, the search for eco-friendly and efficient energy solutions has intensified. The renewable based hybrid systems have emerged as feasible solution in this context. This paper explores the potential of utilizing a hybrid energy system (HES) from a distinctive perspective — addressing the challenges of load shedding at the distribution level. The HES configuration in this investigation includes photovoltaic (PV) array, wind turbines (WT), battery storage unit (BSU) and diesel generator system. The research involves simulations, optimization, and sensitivity analysis for a residential community in the southwestern part of Pakistan, which frequently experiences load shedding. Grasshopper optimization algorithm (GOA) is applied to simultaneously minimize the levelized electricity cost (LEC), payback period (PBP), and loss of power supply probability (LPSP). Simulations compare different HES configurations based on the estimation of local renewable energy potential and the load shedding schedule of the utility company. The optimal solution, with $N_{PV}=110$ , $N_{WT}=2$ , and $N_{BSU}=16$ , was selected, resulting in a minimum LEC of 6.64 cents/kWh and a PBP of 7.4 years. These results are validated using the particle swarm optimization algorithm (PSO). The performance of HES is benchmarked against conventional solutions, such as standalone diesel generators, battery-based uninterruptible power supplies (UPS), and combinations of generators and UPS. The findings demonstrate the significant superiority of HES over conventional solutions in terms of reduced LEC, shorter PBP and reduced carbon emissions. Finally, the sensitivity analysis examines the impact of varying component prices, feed-in tariff rates, meteorological conditions, and load demand variations on the LEC and PBP of the HES.

This paper investigates the mathematical modelling of cybercrime attacks on multiple devices connected to the server. This model is a very successful way for cybercrime, bio-mathematics, and artificial intelligence to investigate and comprehend the behaviour of mannerisms with harmful intentions in a computer system. In this computational model, we are studying the factors (i.e., computer viruses, disease infections, and cyberattacks) that affect connected devices. This compartmental model, SEIAR, represents the various hardware utilised during the cyberattack. The letters S, E, I, A, and R are used to represent different stages or groups of individuals in epidemiological models, helping to understand the spread and control of infectious diseases. The dynamics of the previous model are determined by a series of differential equations. The dynamics of the preceding model are determined by a system of differential equations. Numerical solutions of the model are calculated using backpropagated Levenberg-Marquardt algorithm (BLMA) and a specific optimization algorithm known as the Levenberg-Marquardt algorithm (LMA). Reference solutions were obtained by using the Runge-Kutta algorithm of order 4 (RK-4). The backpropagated Levenberg-Marquardt algorithm (BLMA), commonly known as the damped least-squares (DLS) method. Subsequently, we endeavor to analyze the surrogate solutions obtained for the system and determine the stability of our approach. Moreover, we aim to ascertain fitting curves to the target solutions with minimum errors and achieve a regression value of 1 for all the predicted solutions. The outcome of our simulations ensures that our approach is capable of making precise predictions concerning the behavior of real-world phenomena under varying circumstances. The testing, validation, and training of our technique concerning the reference solutions are then used to determine the accuracy of the surrogate solutions obtained by BLMA. Convergence analysis, error histograms, regression analysis, and curve fitting were used for each differential equation to examine the robustness and accuracy of the design strategy.

Human action recognition has become one of the main topics in the computer vision field due to its high demand and competitiveness in real-world applications. The main goals of human action recognition are to improve classification accuracy and reduce computational complexity. Previous studies have mainly used two approaches: the hand-crafted feature extraction approach and the deep learning approach. The hand-crafted approach is simple, which confers it with an added advantage in terms of computational complexity. However, this method is low in accuracy. Conversely, the deep learning approach achieves high accuracy even for complex datasets, but it suffers in terms of computational complexity and long training time as it needs to process huge datasets during training. Other approaches include the use of pre-trained deep learning networks to fuse both methods. In this paper, we will introduce a combination of pre-trained convolutional neural networks (CNN) to extract features, an improved Fisher vector (iFV) codebook, and an optimized support vector machine SVM to achieve improved human action recognition. We leveraged three pre-trained CNNs, namely, Inception-ResNet-v2, NASNet-Large, and Xception, to extract the features. Then, we applied the improved Fisher vector codebook to encode them. We subsequently trained the codebook using SVM for classification and re- adjusted the SVM weights using five different optimization techniques, which are SGD, Adadelta, ADAM, Adamax, and Nadam. To evaluate the performance, we utilized UCF101 and HMDB51 datasets. The results demonstrate that the accuracy and computational complexity of our approach are comparable to state-of-the-art techniques.