Average Execution Time Research Articles

Perbandingan Integrasi Numerik Metode Simpson Tiga per Delapan dan Romberg Menggunakan Pemrograman Perl Hypertext Prepocessor

Difficult integral problems that cannot be solved analytically often require numerical approaches. PHP (Perl Hypertexr Prepocessor) programming has become one of the tools that can be used to solve numerical problems. This research aims to develop a numerical integration program using the Simpson 3/8 method and Romberg method on polynomial, exponential, and trigonometric functions with PHP programming, as well as to compare the efficiency of both methods based on relative error and execution time. Based on the implementation of the algorithms of both methods in PHP programming, the research results show that the average error for the Simpson 3/8 method and Romberg method are 0,013787% and 0,001356%, respectively. The comparison of errors between these two methods indicates that the Romberg method is 9,89% more accurate than the Simpson 3/8 method. However, in terms of program execution time, the Simpson 3/8 method requires less time compared to the Romberg method. The average execution time for the Simpson 3/8 method and Romberg method are 0,00117 seconds and 1,77811 seconds, respectively, and the comparison of execution time between these two methods shows that the Simpson 3/8 method is shorter than the Romberg method when using the same number of iterations.

Performance Comparison of Selected Filters in Fast Denoising of Oil Palm Hyperspectral Data

Usually, hyperspectral data captured from an airborne UAV or satellite contain some noise that can be severe in some channels. Often, channels that are badly affected by the noise are discarded. This is because the corrupted channels cannot be reclaimed by common filtering techniques, making important information in the affected channels different from those of field spectroscopy of similar wavelengths. In this study, a fast-denoising method is implemented on some channels of oil palm hyperspectral data that are badly affected by noise. The amount of noise is unknown, and it varies across the noisy channels from bad to severe. This is different from the data normally used by many studies, which are essentially clean data spiked with mild noise of known variance. The process starts by identifying which noisy channels to filter based on the level of the estimated noise in them. Then, filtering is conducted within each channel and across channels. Once the noise is removed, the improvement in signal-to-noise ratio (SNR) is calculated for each channel. The performance of Kalman, Wiener, Savitzky–Golay, wavelet, and cosine filters is tested in the same framework and the results are compared in terms of execution time, signal-to-noise ratio, and visual quality. The results show that the Kalman filter slightly outperformed the other filters. The proposed scheme was implemented using MATLAB R2023b running on an Intel i7 processor, and the average execution time was less than 1 second for each channel. To the best of our knowledge, this is the first attempt to filter real oil palm hyperspectral data containing speckle noise using a Kalman filter. This technique can be a useful tool to those working in the oil palm industry.

Dual-stage machine learning approach for advanced malicious node detection in WSNs

Within wireless sensor networks (WSNs), a multitude of vulnerabilities can arise, particularly those originating from malicious nodes (MNs), which lead to compromised data integrity, network stability, and critical application reliability. Although security and energy efficiency remain critical, current MN detection methods are resource-intensive and time-consuming, rendering them unsuitable for constrained WSNs. Although machine learning-based methods excel at detecting MNs, they often incur significant time overhead owing to extensive data transmission and coordination, leading to increased latency and energy consumption within the network. This study introduces DSMND, a novel dual-stage MN detection scheme that harnesses machine learning to enhance MN identification in WSNs. The initial stage uses dynamic threshold detection and decision-tree algorithms at the cluster head (CH) level. This adaptive detection process optimizes CH resource levels, feature counts, and threshold values for efficient MN identification. When thresholds are exceeded, the second stage activates on the server side, employing an advanced MN detection model that seamlessly integrates a hybrid convolutional neural network and a random forest classifier to boost detection accuracy. Leveraging SensorNetGuard, a dataset with diverse node and network features, further enhances reliability. Extensive analysis shows that our scheme achieves up to 99.5 % detection accuracy at the CH level and nearly 100 % at the server side. The average execution time is 124.63 ms, making it 97 % faster than conventional methods. Additionally, DSMND reduces CH power consumption by up to 70 % and extends network lifetime by 2.7 times compared to existing methods. These results confirm the effectiveness of our approach for real-time detection and mitigation of MNs within WSNs.

Topology-aware scalable resource management in multi-hop dense networks

The current society is becoming increasingly interconnected and hyper-connected. Communication networks are advancing, as well as logistics networks, or even networks for the transportation and distribution of natural resources. One of the key benefits of the evolution of these networks is to bring consumers closer to the source of a resource or service. However, this is not a straightforward task, particularly since networks near final users are usually shaped by heterogeneous nodes, sometimes even in very dense scenarios, which may demand or offer a resource at any given moment. In this paper, we present DEN2NE, a novel algorithm designed for the automatic distribution and reallocation of resources in distributed environments. The algorithm has been implemented with six different criteria in order to adapt it to the specific use case under consideration. The results obtained from DEN2DE are promising, owing to its adaptability and its average execution time, which follows a linear distribution in relation to the topology size.

Energy-aware tasks offloading based on DQN in medical mobile devices

Offloading some tasks from the local device to the remote cloud is one of the important methods to overcome the drawbacks of the medical mobile device, such as the limitation in the execution time and energy supply. The challenges of offloading task is how to meet multiple requirement while keeping energy-saving. We classify tasks in the medical mobile device into two kinds: the first is the task that hopes to be executed as soon as possible, those tasks always have a deadline; the second is the task that can be executed anytime and always has no deadlines. Past work always neglects the energy consumption when the medical mobile device is charged. To the best of our knowledge, this paper is the first paper that focuses on the energy efficiency of charging from a power grid to a medical device during work. By considering the energy consumption in different locations, the energy efficiency during working and energy transmission, the available energy of and the battery, we propose a scheduling method based on DQN. Simulations show that our proposed method can reduce the number of un-completed tasks, while having a minimum value in the average execution time and energy consumption.

Enhancing Retina Images by Lowpass Filtering Using Binomial Filter.

This study presents a method to enhance the contrast and luminosity of fundus images with boundary reflection. In this work, 100 retina images taken from online databases are utilized to test the performance of the proposed method. First, the red, green and blue channels are read and stored in separate arrays. Then, the area of the eye also called the region of interest (ROI) is located by thresholding. Next, the ratios of R to G and B to G at every pixel in the ROI are calculated and stored along with copies of the R, G and B channels. Then, the RGB channels are subjected to average filtering using a 3 × 3 mask to smoothen the RGB values of pixels, especially along the border of the ROI. In the background brightness estimation stage, the ROI of the three channels is filtered by binomial filters (BFs). This step creates a background brightness (BB) surface of the eye region by levelling the foreground objects like blood vessels, fundi, optic discs and blood spots, thus allowing the estimation of the background illumination. In the next stage, using the BB, the luminosity of the ROI is equalized so that all pixels will have the same background brightness. This is followed by a contrast adjustment of the ROI using CLAHE. Afterward, details of the adjusted green channel are enhanced using information from the adjusted red and blue channels. In the color correction stage, the intensities of pixels in the red and blue channels are adjusted according to their original ratios to the green channel before the three channels are reunited. The resulting color image resembles the original one in color distribution and tone but shows marked improvement in luminosity and contrast. The effectiveness of the approach is tested on the test images and enhancement is noticeable visually and quantitatively in greyscale and color. On average, this method manages to increase the contrast and luminosity of the images. The proposed method was implemented using MATLAB R2021b on an AMD 5900HS processor and the average execution time was less than 10 s. The performance of the filter is compared to those of two other filters and it shows better results. This technique can be a useful tool for ophthalmologists who perform diagnoses on the eyes of diabetic patients.

A Performance Analysis of Hybrid and Columnar Cloud Databases for Efficient Schema Design in Distributed Data Warehouse as a Service

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions.

Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach

The cloud computing paradigm provides services to users in an on-demand fashion using high-speed Internet. This Internet-based computing paradigm provides resources on a rent basis without any fault. Virtual machine resource allocation is one of the challenging concerns in a cloud computing environment. The existing static, dynamic, and Meta-Heuristic approaches provide the solution to the virtual machine allocation problem. These techniques stuck with the local optimal solution. The slow convergence rate leads to the optimal solution locally and fails to provide the optimal solution Globally. This manuscript proposes a hybrid Spotted Hyena optimizer and artificial neural network, named the SHO-ANN technique, to provide a solution to the virtual machine assignment problem. The presented hybrid technique is evaluated and analyzed using performance metrics “Energy Consumption (Kwh) (8.54%), Host Utilization (24.8%), Average Execution Time(ms) (26.33%), SLA Violations (1.33%), and Number of Migrations (Counts) (19.73%)”. The spotted hyena optimizer is used to provide the vast data set to the ANN model for better accuracy. The hybrid approach provides an optimal solution globally with high convergence. The experimental results exhibit that the SHO-ANN outperforms the IqMc, SHO, and Genetic approaches using real workload scenarios and fabricated scenarios.

Structural Damage Identification Using Autoencoders: A Comparative Study

Structural health monitoring (SHM) ensures the safety and reliability of civil infrastructure. Autoencoders, as unsupervised learning models, offer promise for SHM by learning data features and reducing dimensionality. However, comprehensive studies comparing autoencoder models in SHM are scarce. This study investigates the effectiveness of four autoencoder-based methodologies, combined with Hotelling’s T2 statistical tool, to detect and quantify structural changes in three civil engineering structures. The methodologies are evaluated based on computational costs and their abilities to identify structural anomalies accurately. Signals from the structures, collected by accelerometers, feed the autoencoders for unsupervised classification. The latent layer values of the autoencoders are used as parameters in Hotelling’s T2, and results are compared between classes to assess structural changes. Average execution times of each model were calculated for computational efficiency. Despite variations, computational cost did not hinder any methodology. The study demonstrates that the best fitting model, VAE-T2, outperforms its counterparts in identifying and quantifying structural changes. While the AE, SAE, and CAE models showed limitations in quantifying changes, they remain relevant for detecting anomalies. Continuous application and development of these techniques contribute to SHM advancements, enabling the increased safety, cost-effectiveness, and long-term durability of civil engineering structures.

Distributed economic model predictive control for the joint energy dispatch of wind farms and run-of-the-river hydropower plants

This study addresses the energy dispatch problem of a virtual power plant (VPP) acting as a price-tacker in the day-ahead electricity market. The VPP comprises wind farms and a cascade of run-of-the-river hydropower plants. Even if the storage capacity of the cascade is limited, it can still be exploited to compensate the variability of wind. This implies dispatching the water reservoirs near to real-time, while accounting for complex constraints and various sources of uncertainty. To this aim, we present a control strategy based on economic model predictive control (MPC), which is then decomposed using the auxiliary problem principle. As a distinctive feature, the proposed algorithm is fully-distributed, i.e. no central coordinator is required. Compared to centralized MPC, the distributed algorithm brings a ∼10% reduction in the average execution time of the controller. Moreover, the joint operation of hydropower and wind is shown to enhance the economic value of both assets.

Improved RSA with Enhanced Security on STM32 @ 84MHz

Enhanced computations resulted in the evolution of cyber-physical systems (CPS). These systems are highly vulnerable to security threats. This work addresses the security concerns for a reliable CPS in the Internet of Things (IoT) through an improvised RSA with enhanced security features. The proposed method renders data throughput encryption at 11.67[Formula: see text]Kbps with an overall average execution time of 0.02[Formula: see text]s. The proposed algorithm exhibits the unique feature of being asymmetric, lightweight & enhanced security owing to randomization (average entropy of 0.98). The performance metrics of the proposed method are compared with other state-of-art techniques. This algorithm is implemented on commercially competent architecture STM32F401RET6 with cortex M4 processor running @ 84[Formula: see text]MHz. The proposed encryption method exhibits great features thus making it a potential real-world solution.

Topic Modeling for Scientific Articles: Exploring Optimal Hyperparameter Tuning in BERT

Topic modeling has emerged as a successful approach to uncovering topics from textual data. Various topic modeling techniques have been introduced, ranging from traditional algorithms to those based on neural networks. In this research, we explore advanced topic modeling techniques, including BERT-based approaches, to enhance the analysis of scientific articles. We first investigate a widely used Latent Dirichlet Allocation (LDA) model and then explore the capabilities of BERT, to automatically uncover latent topics within scientific papers. The goal of this study is to identify the optimal hyperparameter setting for BERT-based topic modeling of scientific articles. We conduct experiments across several scenarios involving combinations of word embedding, dimension reduction, and clustering methods. The results were analyzed based on the coherence values, average execution time, number of topics generated, visualization through the inter-topic distance map, and the top-N-words of each topic. Our findings suggest that combination of RoBERTa for word embedding, PCA for dimension reduction, and K-Means for clustering yields superior results among the tested scenarios. Further evaluation of BERT-based topic modeling is necessary to validate these findings and explore its applications in various academic and industrial contexts. The implications of these advanced techniques could significantly streamline the process of staying updated with scientific literature, potentially revolutionizing research methodologies across disciplines.

A Hybrid Genetic Algorithm for Ground Station Scheduling Problems

In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite–ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks.

Evaluating communication capability across diverse groups for disaster medicine: A time-tracking method for functional exercises

Recent experiences in disaster medicine in Japan have shown the importance of preventing post-disaster health deterioration among citizens during the evacuation and recovery phases. The effective involvement of various medical, social welfare, and nursing organizations is necessary to mitigate these health problems. This study explored the effectiveness of a time-tracking evaluation method for functional exercises in analyzing the communication and joint task-processing capabilities of various medical and social welfare organizations in response to a citywide heavy-rain disaster. Each exercise inject was analyzed in advance to create a task-processing chain according to the emergency medical response plan. The actions of the exercise players were then compared with the expected tasks in the processing chain. The exercise created overload for some player groups. The completion rates and average execution times of the tasks were compared across task types and player groups, indicating the potential weaknesses of the current emergency operation plan. Responses of local medical associations to similar exercise injects were compared to analyze the range of diversity in responses and the merits of past exercise participation. Although the results of this time-tracking evaluation need to be carefully interpreted by combining the results regarding the quality of players' exercise actions, this method provides a new perspective for evaluating emergency operation plans through functional exercises.

Whale Optimization for Cloud-Edge-Offloading Decision-Making for Smart Grid Services.

As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services' computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.

Dynamic microservice placement in multi-tier Fog networks

Fog computing extends Cloud-like infrastructure to the Edge, advancing Industrial Internet-of-Things (IIoT) applications in which parts of business-oriented enterprise systems, decoupled via fine-grained microservices, can be deployed in Fog networks in proximity to sensor devices. It boosts the responsiveness to IIoT events significantly where timely decision-making and actions are required, particularly for problematic trends and anomalies. However, microservice placement is a non-trivial problem in IIoT settings, given the dynamic occurrence of different IIoT events and variable resource needs of microservices against resource-constrained, heterogeneous, and distributed Fog devices. The cost of matching and deploying microservices in real-time, highly dynamic, and contentious settings can easily exceed the benefit of just-in-time Edge deployments. Current studies largely focus on managing the microservice placement from Cloud servers and offloading to the Cloud when Fog devices are resource-deficient. Yet, there is a lack of research focusing on efficient Fog resource use and managing the placement at the edge to improve the responsiveness. The complexity increases when multiple inter-dependent microservices with similar priorities are required to be placed on the same Fog devices. To mitigate this, we develop a tiered framework for dynamic microservice placement, in which costly resource decision-making for microservices is dedicated to Master Fog devices and Citizen Fog devices are exclusively assigned to executing microservice requests. Firstly, we implement a priority-based algorithm in each Master Fog device to identify the high-priority Edge-required microservices. Secondly, we sort the Fog devices according to their resource availability and place the microservices based on their priority and dependencies. Thirdly, we implement the strategies for microservice placement, scaling and request escalation in each Master Fog, managing a small number of Citizen Fog devices. These exempt the Citizen Fog devices from common resource and communication management responsibilities and enable them to allocate more resources to Edge-required microservices with a higher number of instances. Finally, we implement and evaluate our framework in a simulated Fog environment. The results outperform state-of-the-art frameworks in terms of efficient resource utilisation, resulting in reduced Cloud dependency by one-third compared to other approaches and average application execution time by 65–70%.

Extraction of laser stripe centerlines from translucent optical components using a multi-scale attention deep neural network

The precise extraction of laser stripe centerlines is critical for line-laser 3D scanning systems. However, conventional methods relying on threshold segmentation and morphological operations face significant challenges when confronted with pervasive optical phenomena, including specular reflection, scattering, and bleeding, which are commonly observed in translucent optical components. These methods typically require complex preprocessing procedures and often yield poor precision in centerline extraction. In this paper, we introduce a novel learning-based approach, complemented by a meticulously curated dataset, explicitly designed to address these challenges. Our proposed method leverages a multi-scale attention U-Net-like architecture, initially tasked with the segmentation of laser stripes from the complex background environment. Subsequently, it employs the Steger algorithm for the precise extraction of laser stripe centerlines. The experimental results, obtained by comprehensively evaluating real-world captured images, clearly demonstrate the effectiveness of our deep neural network combined with the Steger algorithm. This combined approach exhibits exceptional accuracy even when challenged by the interferences from specular reflection, scattering, and bleeding artifacts. Specifically, our method achieves a mean intersection over union (mIoU) of 84.71% for the laser stripe detection task, accompanied by a mean square error (MSE) of 10.371 pixels. Also, the average execution time for the centerline extraction task is notably efficient at 0.125 s.

PMRNA: Parameter matching of realtime and non‐realtime applications for resource provisioning in fog‐integrated cloud

SummaryFog computing, an emerging technology, extends Cloud computing services to the network's edge in the proximity of the application request. This extension yields improvement in Bandwidth (BW) utilization, faster responses to Real‐Time (RT) and Internet of Things (IoT) requests, and the provision of the heterogeneous resource services. While extensive work has been conducted on resource allocation for RT and Non‐Real‐Time (NRT) requests separately in Fog as well as Cloud computing, there is limited focus on resource provisioning for mixed RT and NRT requests in the Fog‐integrated Cloud (FiC) environment. Moreover, the majority of the existing provisioning methods primarily consider parameters from the system's perspective, overlooking crucial user aspects such as deadline and request size. To address the gap, this work introduces a resource provisioning method named “Parameter Matching of Realtime and Non‐Realtime Applications (PMRNA),” which considers user parameters and resource information, gathered by the broker. The performance evaluation of the proposed model is done in CloudSim, using various combinations of RT and NRT requests along with diverse Fog and Cloud resource configurations. Evaluation metric includes average Execution Time (ET), average Waiting Time (WT), average Turn Around Time (TAT), and resource utilization. The experimental results demonstrate a significant reduction in both average WT and average TAT for the diverse pool of RT and NRT requests in the FiC compared to the Cloud‐only environment.

Нормативы по обслуживанию лабораторных животных в питомниках зоолаборантами

The foundation of all research using laboratory animals is to meet the basic needs of any living organism. The personnel responsible for the life support of laboratory animals belongs to category “A”. The purpose of our research was to create an approach for rationing the working day of zoo laboratory laboratory animals. Based on the goal, the following tasks were set: To characterize the work tasks of zoolaborants depending on the type of laboratory animals being serviced. Determine the time of manipulations performed by zoolaborants. Establish standards for the maintenance of laboratory animals for 1 employee for 1 work shift. As a result of the study, the main work tasks of zoo assistants serving mice, rats, gerbils, hamsters, guinea pigs, rabbits, dwarf pigs, ferrets, cats, dogs, Javanese macaques and common igrunks were characterized. Differences in labor tasks have been established depending on the type of maintenance of laboratory animals (cages or boxes). When servicing rodents, rabbits and ferrets contained in cages that require replacement, the work tasks of zoolaborants are divided into: preparation of clean cells for replacement, cell replacement, feeding, watering, cleaning/general cleaning and disinfection. When servicing cats, dogs, dwarf pigs that are kept in boxes, labor tasks are cleaning the box, feeding and watering. For primates (Javanese macaques and common monkeys) kept in stationary cages that do not require cell replacement, the work tasks of zoo laboratory assistants are cleaning the cage, feeding and wate­ring. The average execution time of each labor task by a zoolaborant has been established, depending on the type of laboratory animals served. The standards of service of animals for 1 employee for 1 work shift are calcula­ted. The data obtained make it possible to plan the number of zoolaborants and the production rate depending on the type and number of animals served. Together, the above factors can influence the formation of calculations of the full-time (normative) number of employees involved in the maintenance of laboratory animals when crea­ting a nursery or when introducing a new type of test systems.

Automated target placement for VMAT lattice radiation therapy: enhancing efficiency and consistency

Objective. An algorithm was developed for automated positioning of lattice points within volumetric modulated arc lattice radiation therapy (VMAT LRT) planning. These points are strategically placed within the gross tumor volume (GTV) to receive high doses, adhering to specific separation rules from adjacent organs at risk (OARs). The study goals included enhancing planning safety, consistency, and efficiency while emulating human performance. Approach. A Monte Carlo-based algorithm was designed to optimize the number and arrangement of lattice points within the GTV while considering placement constraints and objectives. These constraints encompassed minimum spacing between points, distance from OARs, and longitudinal separation along the z-axis. Additionally, the algorithm included an objective to permit, at the user’s discretion, solutions with more centrally placed lattice points within the GTV. To validate its effectiveness, the automated approach was compared with manually planned treatments for 24 previous patients. Prior to clinical implementation, a failure mode and effects analysis (FMEA) was conducted to identify potential shortcomings. Main results. The automated program successfully met all placement constraints with an average execution time (over 24 plans) of 0.29 ±0.07 min per lattice point. The average lattice point density (# points per 100 c.c. of GTV) was similar for automated (0.725) compared to manual placement (0.704). The dosimetric differences between the automated and manual plans were minimal, with statistically significant differences in certain metrics like minimum dose (1.9% versus 1.4%), D5% (52.8% versus 49.4%), D95% (7.1% versus 6.2%), and Body-GTV V30% (20.7 c.c. versus 19.7 c.c.). Significance. This study underscores the feasibility of employing a straightforward Monte Carlo-based algorithm to automate the creation of spherical target structures for VMAT LRT planning. The automated method yields similar dose metrics, enhances inter-planner consistency for larger targets, and requires fewer resources and less time compared to manual placement. This approach holds promise for standardizing treatment planning in prospective patient trials and facilitating its adoption across centers seeking to implement VMAT LRT techniques.