Resource Utilization Rate Research Articles

Recycling and Reuse of Spent LIBs: Technological Advances and Future Directions.

Recovering valuable metals from spent lithium-ion batteries (LIBs), a kind of solid waste with high pollution and high-value potential, is very important. In recent years, the extraction of valuable metals from the cathodes of spent LIBs and cathode regeneration technology are still rapidly developing (such as flash Joule heating technology to regenerate cathodes). This review summarized the studies published in the recent ten years to catch the rapid pace of development in this field. The development, structure, and working principle of LIBs were firstly introduced. Subsequently, the recent developments in mechanisms and processes of pyrometallurgy and hydrometallurgy for extracting valuable metals and cathode regeneration were summarized. The commonly used processes, products, and efficiencies for the recycling of nickel-cobalt-manganese cathodes (NCM/LCO/LMO/NCA) and lithium iron phosphate (LFP) cathodes were analyzed and compared. Compared with pyrometallurgy and hydrometallurgy, the regeneration method was a method with a higher resource utilization rate, which has more industrial application prospects. Finally, this paper pointed out the shortcomings of the current research and put forward some suggestions for the recovery and reuse of spent lithium-ion battery cathodes in the future.

Experimental study on the scaling law of the heat exchange tube surface in the process of low-temperature single-effect distillation of high-mineralized mine water

High-mineralized mine water, due to its characteristics such as high mineralization degree, will have scaling phenomena on the surface of the heat exchange tube during the desalination treatment process, resulting in a reduction in the water production efficiency of the equipment and affecting the normal operation. To reduce the cost of water production and improve the service life of distillation equipment, this paper independently designs and builds a low-temperature single-effect distillation high-mineralized mine water experimental system to study the influence of operating parameters (temperature, spray density, concentration ratio, and system pressure) on the scaling law on the surface of the heat exchange tube. The distribution and characteristics of scale deposits on the surface of the heat exchange tube are analyzed through the growth rate and microscopic morphology of the scale deposit. The results indicate that the scaling on the surface of the titanium plate is mainly in the needle-like and rod-like morphology of CaCO3 aragonite, and a small amount of CaSO4 and calcite are generated with the increase of temperature. It was found that within the range of spray density from 0.0509 kg/(m·s) to 0.0625 kg/(m·s), concentration ratio from 2.3 to 2.7, and system pressure at 36.3 kPa, it is beneficial to the evaporation heat transfer of the heat exchange tube and the reduction of scaling generation. At the same time, it is found that the scaling of the first row of heat exchange tubes is mainly within the range of circumferential angle from 0 to 100 degrees, and the scaling of the second row and the third row of heat exchange tubes is mainly within the range of circumferential angle from 110 to 170 degrees. The research results of this paper provide support for the design and operation of the low-temperature multi-effect treatment system for mine water in engineering, promote the process of zero discharge of high-mineralized mine water in Xinjiang, China, and improve the resource utilization rate of high-mineralized mine water.

Co-Frequency Interference Suppression of Integrated Detection and Jamming System Based on 2D Sparse Recovery

The integrated detection and jamming system employs integrated signals devoid of typical radar signal characteristics for detection and jamming. This allows for the sharing of resources such as waveform, frequency, time, and aperture, significantly enhancing the overall utilization rate of system resources. However, to achieve effective interference, the integrated waveform must overlap with the adversary radar signal within the frequency band. Consequently, the detection echoes are susceptible to the strong co-frequency direct wave generated by the adversary signals. This paper proposes a co-frequency direct wave interference suppression algorithm based on 2D generalized smoothed-l0 norm sparse recovery. The algorithm exploits a joint dictionary comprising our integrated signals and adversary signals, along with the sparsity of 2D range-Doppler maps. The direct solution of the sparse decomposition optimization problem, formulated for the entire echo matrix, enhances the target detection performance for integrated signals even in the presence of robust co-frequency direct wave interference. Furthermore, the proposed method achieves robustness to interference of varying intensities through the adaptive updating and adjustment of relevant parameters. The effectiveness of the proposed method is validated through simulation and experimental results.

Antithrombin testing and treatment in pregnancy: Their real-world relationship to clinical outcomes

BackgroundAntithrombin (AT) deficiency is a severe thrombophilia associated with increased rates of maternal morbidity, mortality, and greater healthcare resource utilization during pregnancy and postpartum. MethodsTwo large U.S. healthcare databases were queried for women aged 15–44 with delivery-related encounters: Cerner Real-World Data (CRWD, 01/01/2000–12/31/2021) and Premier Healthcare Database (PHD, 01/01/2016–01/01/2019). Individuals receiving cardiopulmonary bypass were excluded. Three cohorts were created: 1) Individuals who had AT levels tested any time between 9-months pre- through 3-months post-delivery (CRWD Test Cohort); 2) individuals prescribed AT concentrate (ATc) within 1-year pre- or 1-year post-delivery in CRWD (CRWD Medication Cohort); and 3) the same criteria as 2) applied to PHD (PHD Medication Cohort). ResultsThere were 5411 individuals in the CRWD Test Cohort, 13 in the CRWD Medication Cohort and 38 in the PHD Medication Cohort. Demographic and baseline clinical characteristics were similar across cohorts. AT level testing occurred pre-delivery in 47.9 % of the CRWD Test Cohort and 23.1 % of the CRWD Medication Cohort. ATc was administered during the delivery hospitalization to 0.1 %, 23.1 % and 50.0 % of the CRWD Test, CRWD Medication, and PHD Medication Cohorts, respectively. Across cohorts, 5.4–7.9 % of individuals experienced thrombosis during the delivery-related encounter. Mean (SD) total costs for delivery through 1-year post-delivery were $190,894 ($276,893) with $123,763 ($177,122) of total costs related to abnormal coagulation. ConclusionOpportunities exist to enhance the care of pregnant individuals with low AT levels throughout pregnancy, aiming for optimal maternal outcomes.

Navigation Resource Allocation Algorithm for LEO Constellations Based on Dynamic Programming

Navigation resource allocation for low-earth-orbit (LEO) constellations refers to the optimal allocation of navigational assets when the number and allocation of satellites in the LEO constellation have been determined. LEO constellations can not only transmit navigation enhancement signals but also enable space-based monitoring (SBM) for real-time assessment of GNSS signal quality. However, proximity in the frequencies of LEO navigation signals and SBM can lead to significant interference, necessitating isolated transmission and reception. This separation requires that SBM and navigation signal transmission be carried out by different satellites within the constellation, thus demanding a strategic allocation of satellite resources. Given the vast number of satellites and their rapid movement, the visibility among LEO, medium-earth-orbit (MEO), and geostationary orbit (GEO) satellites is highly dynamic, presenting substantial challenges in resource allocation due to the computational intensity involved. Therefore, this paper proposes an optimal allocation algorithm for LEO constellation navigation resources based on dynamic programming. In this algorithm, a network model for the allocation of navigation resources in LEO constellations is initially established. Under the constraints of visibility time windows and onboard transmission and reception isolation, the objective is set to minimize the number of LEO satellites used while achieving effective navigation signal transmission and SBM. The constraints of resource allocation and the mathematical expression of the optimization objective are derived. A dynamic programming approach is then employed to determine the optimal resource allocation scheme. Analytical results demonstrate that compared to Greedy and Divide-and-Conquer algorithms, this algorithm achieves the highest resource utilization rate and the lowest computational complexity, making it highly valuable for future resource allocation in LEO constellations.

Production of polysaccharides from Angelica sinensis by microbial fermentation

Angelica sinensis polysaccharides are important active ingredients and biological resources in traditional Chinese medicine. Properly changing the fermentation conditions of microorganisms may alter the yield of fermentation products. Based on single factor test results, three factors with great influence on the yield of Angelica sinensis polysaccharides produced by Aspergillus niger were optimized: initial pH value, fermentation time, and culture temperature. According to the box Behnken central combination principle, a response surface analysis scheme with three factors and three levels was designed, and the yields of active polysaccharide of Angelica sinensis were taken as the response value to optimize the fermentation process. The results of response surface analysis showed that under the optimized fermentation temperature of 30 °C, cultivation time of 8 days, and initial pH value of 5, the predicted yield of active polysaccharides from Angelica sinensis was 15.5%, while the actual value was 15.35%, which was 0.11% lower than the predicted value. This indicated that using response surface analysis to optimize the fermentation conditions of Angelica sinensis polysaccharides was reasonable and feasible. This method can effectively improve the biological resource utilization rate of Angelica sinensis polysaccharides.

Research on the Investment Mode and Path of China’s Construction Waste Resourceization Industry

At present, China’s construction waste accounts for about 40% of the total municipal solid waste, with a large stock, a large increment, and a low rate of resource utilization, which is still a big gap compared with developed countries. However, the market prospect of the construction waste industry is relatively broad, the reuse value of the construction waste is high, so these resources should be fully utilized. This paper presents an in-depth analysis of projects in the construction waste resourceization industry, examining the industrial chain, development history, current status, investment and financing modes, and case studies. It proposes targeted strategies for companies to focus on the upstream and downstream segments of the construction industry’s value chain and explores the implementation of investment opportunities in construction waste resourceization.

Research on Solidification Layer Detection in Coal Transportation Process Based on Improved YOLOv8 Algorithm

Railway transportation is one of the main modes of long-distance coal transportation, and it inevitably causes environmental pollution during the transportation process. In order to improve the environment along the railway and increase the utilization rate of coal resources, this paper proposes a detection algorithm for the scattered coal solidification layer during transportation based on the YOLOv8 model and designs an intelligent recognition model suitable for coal solidification layer detection devices by combining fluorescence detection methods. Through experimental analysis, we found that the model meets the requirements of practical detection and can play a crucial role in environmental protection, with high practical application value.

Application of image processing technology based on field programmable gate array in mechanical part inspection

Introduction: In the field of industrial manufacturing, accurate inspection of mechanical components such as gears and bearings is of Paramount importance. However, the traditional mechanical testing methods are often disturbed by human factors, which not only affects the stability of the test results, but also leads to low efficiency and large error. In order to solve these problems, this research focuses on developing a new edge detection model.Methods: A novel edge detection model based on field-programmable gate array image processing technology was used in this study. The model uses adaptive threshold multi-directional edge detection technology to identify the edge features of mechanical gears and bearings, aiming at improving the precision of detection.Results and Discussion: After performance verification, the running time of the model was controlled within 11 s, and the detection error was limited to less than 9%. Compared with the control group and the experimental group, their performance was superior. Further analysis data show that the detection accuracy of this model is as high as 0.9004, its internal resource utilization rate is 88%, and the detection rate is as high as 91%, which are better than the comparison model.Conclusion: The proposed test model not only significantly improves the efficiency and accuracy of the test, but also fully meets the requirements of the test. This new edge detection model has potential application value in industrial manufacturing field, and provides a new solution for industrial manufacturing quality inspection.

Logistics distribution scheduling algorithm based on artificial intelligence

In order to improve the quality of urban logistics distribution services and meet customer needs to the greatest extent, improve the utilization rate of service resources and reduce the cost of logistics and distribution services, the author proposes an urban logistics distribution scheduling algorithm based on artificial bee colony. Uncertain dynamic customer demands often occur during the execution of logistics distribution services, resulting in the inability to achieve optimal cost by executing the distribution service according to the original plan, in response to such problems with dynamic customer needs, the author conducted a problem analysis, the corresponding mathematical model is established and transformed into a static problem for rescheduling. The result shows: Using the scheduling algorithm to solve the example 100 times, when the vehicle 1 and vehicle 3 can meet their own load, they can serve customers with new needs, which can make the delivery cost the lowest. In the case of a small number of customers, the algorithm can achieve high accuracy, and when the number of customers reaches a certain scale, the stability of the algorithm will decrease slightly. ConclusionThe algorithm achieves the purpose of promptly responding to changes in customer demand and quickly adjusting distribution services.

High-performance convolutional neural network emulation via FPGA-integrated memristive circuitry

Although memristors address the potential data flow and memory access bottlenecks in traditional von Neumann architectures by moving computational operations into the storage elements, thereby improving computational efficiency and reducing energy consumption, the current costs of memristors are relatively high, and they face issues such as low reliability and short lifespan. Therefore, this paper proposes a simulation circuit model based on FPGA, which can provide a low-cost solution for rapid prototyping design and exploration of the design space. The memristor simulation circuit is deployed on the Xilinx ZYNO-7000 FPGA XC7Z020 with a resource utilization rate of less than 1%, and it has been successfully integrated and verified. Furthermore, this paper also constructs a convolutional neural network for MNIST image recognition using a memristor model. With a basis of 512 parameters, it achieves an accuracy of 90.22%.

Orchestrating scheduling, grouping and parallelism to enhance the performance of distributed stream computing system

In a big data stream computing environment, the arrival rate of data streams usually fluctuates over time, posing a great challenge to the elasticity of system. The performance of stream computing system is crucial, especially when dealing with unbounded and fluctuating data streams. Most prior studies have primarily focused on one or two aspects to enable elasticity, often lacking prompt and comprehensive performance optimization. This limitation could lead to a tuning bottleneck, preventing the system’s performance from consistently reaching its optimal state. Additionally, many stream computing systems are not intelligently adaptive in real time due to the challenges of manual parameter reconfiguration for fluctuating streams. To better address these issues, we propose a framework named Sgp-Stream, which orchestrates scheduling, grouping and parallelism (Sgp). To enhance the system performance. We conduct the following research: (1) Running experiments to evaluate the impact of different factors such as scheduling, grouping and parallelism on system performance. Results show that factors at a single level usually have an upper limit on tuning system performance, and better overall performance can be achieved by coordinating multi-level factors. (2) Establishing quantitative models for stream application that consider computational cost and communication cost, multi-dimensional featured data stream, data center resources, and latency & throughput performance. (3) Demonstrating the effectiveness of the proposed runtime-aware data stream grouping based on smooth weighted polling, elastic adaptive scheduling based on Linear Deterministic Greedy and elastic scaling strategy based on Gradient Descent in Sgp-Stream, for continuous performance optimization.(4) Evaluating the application latency, throughput and resource utilization objectives using a real-world elastic stream computing system and twitter data set. Experimental results show that, compared to existing state-of-the-art works, the proposed Sgp-Stream outperforms them by reducing latency by 26%–48%, improving throughput by 14%–20%, and increasing resource utilization rate by 15%–21%, especially under increasing data stream input rates.

Optimizing CNN inference speed over big social data through efficient model parallelism for sustainable web of things

The rapid development of artificial intelligence and networking technologies has catalyzed the popularity of intelligent services based on deep learning in recent years, which in turn fosters the advancement of Web of Things (WoT). Big social data (BSD) plays an important role during the processing of intelligent services in WoT. However, intelligent BSD services are computationally intensive and require ultra-low latency. End or edge devices with limited computing power cannot realize the extremely low response latency of those services. Distributed inference of deep neural networks (DNNs) on various devices is considered a feasible solution by allocating the computing load of a DNN to several devices. In this work, an efficient model parallelism method that couples convolution layer (Conv) split with resource allocation is proposed. First, given a random computing resource allocation strategy, the Conv split decision is made through a mathematical analysis method to realize the parallel inference of convolutional neural networks (CNNs). Next, Deep Reinforcement Learning is used to get the optimal computing resource allocation strategy to maximize the resource utilization rate and minimize the CNN inference latency. Finally, simulation results show that our approach performs better than the baselines and is applicable for BSD services in WoT with a high workload.

Study on triaxial compression performance and damage characteristics of fiber-reinforced ecological matrix cementing gangue gypsum fill material.

Polypropylene fiber was equally mixed into alkali-activated slag fly ash geopolymer in order to ensure the filling effect of mine goaf and improve the stability of cemented gangue paste filling material with ecological matrix. Triaxial compression tests were then conducted under various conditions. The mechanical properties and damage characteristics of composite paste filling materials are studied, and the damage evolution model of paste filling materials under triaxial compression is established, based on the deviatoric stress-strain curve generated by the progressive failure behavior of samples. Internal physical and chemical mechanisms of the evolution of structure and characteristics are elucidated and comprehended via the use of SEM-EDS and XRD micro-techniques. The results show that the fiber can effectively improve the ultimate strength and the corresponding effective stress strength index of the sample within the scope of the experimental study. The best strengthening effect is achieved when the amount of NaOH is 3% of the mass of the solid material, the amount of fiber is 5‰ of the mass of the solid material, and the length of the fiber is about 12 mm. The action mode of the fiber in the sample is mainly divided into single-grip anchoring and three-dimensional mesh traction. As the crack initiates and develops, connection occurs in the matrix, where the fiber has an obvious interference and retardation effect on the crack propagation, thereby transforming the brittle failure into a ductile failure and consequently improving the fracture properties of the ecological cementitious coal gangue matrix. The theoretical damage evolution model of a segmented filling body is constructed by taking the initial compaction stage end point as the critical point, and the curve of the damage evolution model of the specimen under different conditions is obtained. The theoretical model is verified by the results from the triaxial compression test. We concluded that the experimental curve is in good agreement with the theoretical curve. Therefore, the established theoretical model has a certain reference value for the analysis and evaluation of the mechanical properties of paste filling materials. The research results can improve the utilization rate of solid waste resources.

Track and Field Teaching Based on Computer Network Resources

Track and field teaching has always been an important part in school physical education (PE). With the deepening of curriculum reform and the continuous growth of IT, universities have gradually broken the old instructional mode, and have set up online teaching platforms and developed new instructional modes. How to integrate modern teaching and learning theory into the new teaching technology platform is the requirement of the times and the inevitable theme of the current PE reform. In this article, the track and field instructional resources under the platform of instructional resources management are studied, and the classification mining algorithm is used to mine and analyze the students’ interest data, so as to find out the rules and patterns of users’ access to instructional resources, thus further optimizing the allocation of users’ access to instructional resources, improving the efficiency of users’ access to instructional resources and the utilization rate of instructional resources. Experiments show that the improved collaborative filtering (CF) algorithm based on deep learning is superior to the other two algorithms in recommendation error, and the error is reduced by 10.69% compared with the traditional CF algorithm.

MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing

With the increasing popularity of the Internet of Things (IoT), the proliferation of computation-intensive and timing-dependent applications has brought serious load pressure on terrestrial networks. In order to solve the problem of computing resource conflict and long response delay caused by concurrent application service applications from multiple users, this paper proposes an improved edge computing timing-dependent, task-offloading scheme based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that aims to shorten the offloading delay and improve the resource utilization rate by means of resource prediction and collaboration among multiple agents to shorten the offloading delay and improve the resource utilization. First, to coordinate the global computing resource, the gated recurrent unit is utilized, which predicts the next computing resource requirements of the timing-dependent tasks according to historical information. Second, the predicted information, the historical offloading decisions and the current state are used as inputs, and the training process of the reinforcement learning algorithm is improved to propose a task-offloading algorithm based on MADDPG. The simulation results show that the algorithm reduces the response latency by 6.7% and improves the resource utilization by 30.6% compared with the suboptimal benchmark algorithm, and it reduces nearly 500 training rounds during the learning process, which effectively improves the timeliness of the offloading strategy.

Multi-objective green vehicle scheduling problem considering time window and emission factors in ship block transportation

Logistics distribution is one of the main sources of carbon dioxide emissions at present, and there are also such distribution problems in the shipbuilding process. With the increasing attention paid to environmental problems, how to effectively reduce the energy consumption of block transportation and improve the utilization rate of resources in the factory is the key problem that China’s shipbuilding industry needs to solve at present. This article considers the time windows for block transportation tasks, as well as the self-loading constraints of different types of flat cars, and establishes an optimization model that minimizes the empty transport time and energy consumption of the flat cars as the optimization objective. Then, an Improved Genetic Whale Optimization Algorithm is designed, which combines the cross and mutation ideas of genetic algorithms and proposes a whale individual position updating mechanism under a mixed strategy. Furthermore, the performance and computational efficiency of the algorithm are verified through comparative analysis with other classical optimization algorithms on standard test examples. Finally, the shipyard’s block transportation example proves that the energy-saving ship block transportation scheduling method can effectively improve the efficiency of shipbuilding enterprise’s block transportation and reduce the energy consumption in the block transportation process. It proves the engineering practicality of the green dispatching method proposed in this paper, which can further provide a decision-making method for shipyard managers.

Training mode of university dance performers based on sequential quadratic programming algorithm

Dance performance is an art form, which needs to cultivate students’ dance skills, artistic accomplishment and stage performance ability. Sequential quadratic programming algorithm is an optimization algorithm that can be used to solve complex optimization problems. In this paper, Sequential Quadratic Programming (SQP) is applied to explore the training mode of dance performers in colleges to help dance performers develop the optimal training plan and program. Aiming at the problems existing in the training mode of dance performance talents in colleges, this paper put forward an optimization method based on SQP algorithm, and implemented its optimization scheme in actual colleges. In the planning of dance performance talent training mode, particle swarm optimization (PSO) is used to optimize SQP algorithm, so that it can have higher planning efficiency. This paper studied the goal and index system of the training of dance performance talents in colleges, generated a personalized training program, and further improved the scientific and practical effectiveness of the training mode. This paper investigated the current situation of dance performance talent training in several dance schools in a certain province of China. The survey data include practical curriculum planning, teachers’ teaching philosophy and teaching content. Combined with SQP algorithm, the teaching and training program is optimized. After evaluation, it can be concluded that the SQP algorithm optimized by PSO shows good stability and accuracy. It can calculate the optimal solution of the cultivation scheme, and when calculating the optimal solution, the running time of the Central Processing Unit (CPU) was only 5.6 s, which can further improve the efficiency of the planning. Finally, through the satisfaction and resource utilization test, it can be found that the number of people who are very satisfied with the teaching content of the dance performance talent training program optimized by SQP increased from 38.4% to 52.4%. After optimization, the average utilization rate of teaching resources reached 88.1%. It can be concluded that SQP algorithm can provide scientific basis for dance education institutions to improve the training mode of dance talents. This can help dance education institutions better improve the training mode, and improve the overall quality and dance skills of dance students.

Association between immigration status and total knee arthroplasty outcomes in Ontario, Canada: a population-based matched cohort study.

Immigrants and refugees face unique challenges navigating the health care system to manage severe arthritis, because of unfamiliarity, lack of awareness of surgical options, or access. The purpose of this study was to assess total knee arthroplasty (TKA) uptake, surgical outcomes, and hospital utilization among immigrants and refugees compared with Canadian-born patients. We included all adults undergoing primary TKA from January 2011 to December 2020 in Ontario. Cohorts were defined as Canadian-born or immigrants and refugees. We assessed change in yearly TKA utilization for trend. We compared differences in 1-year revision, infection rates, 30-day venous thromboembolism (VTE), presentation to emergency department, and hospital readmission between matched Canadian-born and immigrant and refugee groups. We included 158 031 TKA procedures. A total of 11 973 (7.6%) patients were in the immigrant and refugee group, and 146 058 (92.4%) patients were in the Canadian-born group. The proportion of TKAs in Ontario performed among immigrants and refugees nearly doubled over the 10-year study period (p < 0.001). After matching, immigrants were at relatively lower risk of 1-year revision (0.9% v. 1.6%, p < 0.001), infection (p < 0.001), death (p = 0.004), and surgical complications (p < 0.001). No differences were observed in rates of 30-day VTE or length of hospital stay. Immigrants were more likely to be discharged to rehabilitation (p < 0.001) and less likely to present to the emergency department (p < 0.001) than Canadian-born patients. Compared with Canadian-born patients, immigrants and refugees have favourable surgical outcomes and similar rates of resource utilization after TKA. We observed an underutilization of these procedures in Ontario relative to their proportion of the population. This may reflect differences in perceptions of chronic pain or barriers accessing arthroplasty.

Adaptive Random Mac Strategy for IoT Security Through Network Forensics Investigation

In the rapidly evolving landscape of digital systems and networks, ensuring robust cyber security measures is of paramount importance. This study delves into the evaluation of a proposed security method's efficiency through a comprehensive comparative analysis conducted under diverse security breach simulations. The objective is to discern the method's effectiveness in safeguarding sensitive information and maintaining system integrity.The research methodology involves the selection of representative security breach scenarios, each emulating distinct attack vectors and potential vulnerabilities. A benchmark security protocol, widely acknowledged for its reliability, serves as a reference for the proposed method's assessment. A series of controlled breach simulations are executed, simulating real-world cyber threats, ranging from sophisticated malware intrusions to social engineering attacks. Key performance metrics, such as response time, resource utilization, and data recovery rates, are meticulously recorded and analyzed for both the benchmark and proposed methods. Furthermore, the impact on system functionality and user experience is evaluated to comprehend the trade-off between heightened security and seamless user interaction. The experiments are conducted across multiple system architectures to account for variations in hardware and software configurations. The findings reveal nuanced insights into the proposed method's efficiency under distinct breach scenarios. It showcases commendable resilience against certain attack vectors while potentially exposing limitations in others. The comparative analysis provides a comprehensive overview of the method's overall efficacy and enables the identification of scenarios where enhancements might be necessary. The performance of the suggested approach under various security breach simulations is empirically demonstrated in this paper, adding to the body of current knowledge. The insights gained from this research aid in refining the method's design and implementation, bolstering its potential to address the dynamic and evolving landscape of cyber security threats. As digital systems continue to play an increasingly integral role in our lives, the significance of such evaluations cannot be overstated, ensuring that our vital digital infrastructure remains secure and resilient.