Volume-10, Issue-9, September 2024

Abstract

This report is an overview of some basic security associated services, that guard against risks to security of a system, using the ecommerce trading platform eBay February 2014 data breach, as case study. It covers some security policies, that not only detect the risks, but also outlines the conditions for a guaranteed protected system. Security procedures spot and stop incidents, therefore evaluating the security of a system demands an awareness of the procedures that implement the security policies. Correspondingly, basic knowledge of certain theories and trust, which lead to the risks and the extent to which they may likely be executed, is equally required. Human beings are the most vulnerable link in the security procedures of any system, and so should be taken into account while establishing policies (Matt Bishop, 2002).

Keywords: E-commerce security, Data breach, eBay data breach, Cybersecurity vulnerabilities, User information security.

References

Keywords: E-commerce security, Data breach, eBay data breach, Cybersecurity vulnerabilities, User information security.

Abstract

The article introduces a novel metaheuristic algorithm called the Worker Ant Optimization (WAO) algorithm. This algorithm is mathematically modeled based on five natural behaviors of worker ants: avoiding danger, foraging, approaching food, decomposing food, and transporting food. The performance of WAO was evaluated using 23 classical test functions and compared with results from seven well-known metaheuristic algorithms. Simulation results demonstrate that the WAO algorithm exhibits significant advantages in terms of convergence speed, avoidance of local optima, and optimization accuracy. To assess the effectiveness of WAO in practical applications, the method was applied to three classical engineering design problems, validating the engineering applicability of the WAO optimization algorithm. WAO effectively explores the decision space and performs well across various evaluation metrics, demonstrating its capability to effectively address challenges in practical applications.

Keywords: Classical test functions; Constrained optimization; Engineering constraint issues; Swarm optimization; WAO.

References

Keywords: Classical test functions; Constrained optimization; Engineering constraint issues; Swarm optimization; WAO.

Abstract

The rapid evolution of technology and the growth of internet networking have established the Internet of Things (IoT) as a central communication method between devices. The number of internet-connected devices is rising, especially in industrial settings, where they enhance the control and monitoring of production processes. IoT plays a critical role in continuously monitoring key industrial parameters, ensuring consistent and high-quality production. Specifically, IoT-based systems are instrumental in ensuring the reliable performance of motors, thereby boosting productivity. These systems are highly beneficial for industrial applications, including solar power plants, where they contribute to efficient electricity usage.

IoT's current capabilities extend beyond mere connectivity; they now link people and devices, paving the way for increased remote control via mobile applications. This development not only simplifies daily life but also empowers businesses by providing deeper insights into operations and customer preferences. Whether in home automation, healthcare, or industrial applications, IoT-enabled control and monitoring of motors significantly enhance operational productivity. By effectively tracking conditions based on predefined parameters, these systems can adjust motor speeds according to specific process requirements, thereby optimizing performance. Moreover, IoT's ability to collect and analyze vast amounts of data from motors allows for predictive maintenance, preventing issues such as motor overloading and improper handling. This leads to safer operations and extends the lifespan of the equipment. The integration of IoT in monitoring systems enables the storage, processing, and real-time analysis of data, providing actionable insights that reduce maintenance costs, improve reliability, optimize motor performance, and enhance the accuracy of failure predictions. Ultimately, the adoption of IoT in industrial environments not only ensures efficient and effective operations but also drives innovation and sustainability in various sectors.

Keywords: Internet of Things (IoT), Technology evolution, Control and monitoring, Motor performance, Mobile applications, Predictive maintenance, Failure prediction, Sustainability.

References

Keywords: Internet of Things (IoT), Technology evolution, Control and monitoring, Motor performance, Mobile applications, Predictive maintenance, Failure prediction, Sustainability.

Abstract

The Egrets Swarm Optimization Algorithm is a recently proposed heuristic algorithm that simulates the hunting behavior of egrets. To address the limitations of the original algorithm, such as insufficient development capability and decreased population diversity, a multi-strategy improved Egrets Swarm Optimization Algorithm is proposed. First, in the population initialization phase, Logistic chaotic mapping is introduced to generate chaotic sequences, enriching population diversity. Next, a dynamic perception factor is introduced to replace the step size factor in the idle strategy, which allows for more effective exploration and discovery of potential optimal solutions. Furthermore, to increase the breadth and depth of exploration, a crayfish foraging strategy is incorporated into the random walk strategy, and a roulette wheel strategy is added to the encircling mechanism to enhance the algorithm’s search ability and avoid ineffective actions. Additionally, a distribution estimation strategy and a new exploration and exploitation strategy based on whale spiral ascent are introduced to improve the overall efficiency and functionality of the algorithm. Finally, testing on 20 classical functions shows that the improved algorithm enhances optimization performance. The algorithm is also applied to solve engineering constraint problems, demonstrating its practicality.

Keywords: Egret Optimization Algorithm; Roulette Wheel Strategy; Distribution Estimation Strategy; Spiral Ascent Strategy; Crayfish Foraging Strategy

References

Keywords: Egret Optimization Algorithm; Roulette Wheel Strategy; Distribution Estimation Strategy; Spiral Ascent Strategy; Crayfish Foraging Strategy

Abstract

Agricultural products require suitable preservation methods for retention of their nutritional qualities and attributes. Sweet potatoes are underutilized due to cost implications of their preservation using modern storage techniques. Enzymatic discoloration of freshly cut potato flesh surfaces from exposure to air affects processed potato qualities adversely. While steeping/blanching pretreatment is deployed in curbing this challenge, varying the steeping/blanching and drying treatments can assist in arriving at the optimum process conditions for white-fleshed sweet potatoes. 4 kg of similarly sized and healthy white-fleshed sweet potato tubers obtained from Eke Awka market (Latitude 6.210528 °N and Longitude 7.072277 °E) in Anambra state, Nigeria, was washed with clean tap water, peeled and cut into 4 mm by 10 mm by 10 mm slices. The experiment was designed using the Response Surface Methodology (RSM) software. The chipped potatoes were divided into 24 samples of 100g each for the analysis. 19 samples were pretreated in water at temperature range of 11oC to 100oC and 5 samples were not pretreated (served as control). All the pretreated samples were held in the fluid for period of 2 mins to 18 mins as contained in the design matrix. Both the pretreated and control samples were grouped and dried in convective oven at set temperatures of 53 °C, 60 °C, 70 °C, 80 °C and 87 °C and weighed every 30 mins until there was no further weight loss at three consecutive readings. The dried sweet potato chips were grated into flour, for the determination of the proximate composition of the chips. Optimized drying and pretreatment conditions were analyzed from the results using RSM software. Result of the analysis showed significant increase in proximate composition of the samples with increase in both the steeping and drying temperatures and indicated significance of the model terms. The error analysis showed a high degree of correlation between the observed and the predicted values. From this work the optimal conditions predicted by the RSM model were blanching temperature of 70 oC, blanching time of 5 mins and drying temperature of 80 oC for 4 mm-thick 100g of sliced whitefleshed sweet potato chips to yield 2.40% Crude protein, 73.95% Carbohydrate, 2.54% Fiber, 4.31% Ash, 3.24% Fat and 9.86% Moisture content which served as a guide for high quality sweet potato chips production from freshly harvested whitefleshed sweet potato tubers. The dried potato chips can be grated into flour and used as the major ingredient for bread, noodles and cake making. The application of the outcome of the work will help in reduction of post-harvest losses of the crop.

Keywords: Sweet potato chips, Steeping/blanching pretreatment, Steeping temperature, Steeping time, Drying temperature.

References

Keywords: Sweet potato chips, Steeping/blanching pretreatment, Steeping temperature, Steeping time, Drying temperature.