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To address the growing demand for sustainable agriculture practices, new technologies to boost crop productivity and soil health must be developed. In this research, we propose designing and building an agricultural rover capable of autonomous vegetable harvesting and soil analysis utilizing cutting-edge deep learning algorithms (YOLOv5). The precision and recall score of the model was 0.8518% and 0.7624% respectively. The rover uses robotics, computer vision, and soil sensing technology to perform accurate and efficient agricultural tasks. We go over the rover's hardware and software, as well as the soil analysis system and the tomato ripeness detection system using deep learning models. Field experiments indicate that this agricultural rover is effective and promising for improving crop management and soil monitoring in modern agriculture, hence achieving the UN's SDG 2 Zero Hunger goals.

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

Advanced Encryption Standard (AES) is the most secured ciphertext algorithm that is unbreakable in a software platform's reasonable time. AES has been proved to be the most robust symmetric encryption algorithm declared by the USA Government. Its hardware implementation offers much higher speed and physical security than that of its software implementation. The testability and hardware Trojans are two significant concerns that make the AES chip complex and vulnerable. The problem of testability in the complex AES chip is not addressed yet, and also, the hardware Trojan insertion into the chip may be a significant security threat by leaking information to the intruder. The proposed method is a dual-mode self-test architecture that can detect the hardware Trojans at the manufacturing test and perform an online parametric test to identify parametric chip defects. This work contributes to partitioning the AES circuit into small blocks and comparing adjacent blocks to ensure self-referencing. The detection accuracy is sharpened by a comparative power ratio threshold, determined by process variations and the accuracy of the built-in current sensors. This architecture can reduce the delay, power consumption, and area overhead compared to other works.

Bangladesh faced a substantial growth in primary energy demand in the last few years. According to several studies, energy generation is not the only means to address energy demand; efficient energy management practices are also very critical. A pertinent contribution in the energy management at the industrial sector ensures the proper utilization of energy. Energy management and its efficiency in the textile industries of Bangladesh are studied in this paper. The outcomes demonstrate several barriers to energy management practices which are inadequate technical cost-effective measures, inadequate capital expenditure, and poor research and development. However, this study also demonstrates that the risk of high energy prices in the future, assistance from energy professionals, and an energy management scheme constitute the important drivers for the implementation of energy efficiency measures in the studied textile mills. The studied textile industries seem unaccustomed to the dedicated energy service company concept, and insufficient information regarding energy service companies (ESCOs) and the shortage of trained professionals in energy management seem to be the reasons behind this. This paper likewise finds that 3−4% energy efficiency improvements can be gained with the help of energy management practices in these industries.