نشریه دستاوردهای نوین در برق،کامپیوتر و فناوری
پیاپی 9 (زمستان 1402)

The impact of climate change on the availability and diversity of wind and solar resources for renewable electricity generation was evaluated under recent scenarios of joint social and economic pathways. To do this, the Climate Research and Prediction Model was used to perform very high-resolution (≈1 km2) climate simulations for one current scenario and two future scenarios (SSP2-4.5 and SSP5-8.5). Modeled meteorological variables were then used to calculate potential future changes in wind power and solar photovoltaic energy for electricity generation for each climate change scenario compared to the present. The results show a sharp increase in wind speed during winter of +45% and a decrease of +45% during summer, with future seasonal generation differences for wind power of ≈100 kWh for both scenarios. For solar radiation, the biggest difference is during winter (+30% to +45% increase) with solar photovoltaic production varying from +10 kWh to +20 kWh, depending on the scenario and the season. Seasonal changes in wind speed and solar radiation imply a seasonal loss that could threaten the stability of future renewable electricity generation in the region and make decarbonization efforts more difficult. This study highlights the importance of a holistic approach in the assessment of the electricity generation system, as the seasonal variations and intermittency of some renewable energy sources can potentially be covered by others.

Decarbonization has been a worldwide trend, especially in the transportation industry. A pure battery electric ship (AES) may be the most flexible way to achieve ship decarbonisation in recent years. Compared to conventional ships, the initial investment cost is high and the energy surplus is low for battery-powered ships. Therefore, an energy management strategy that can minimize the cost of operation and ensure the safety of energy consumption is very important. In this paper, considering the dynamic price of electricity and environmental factors, a joint optimization method of ship speed and energy consumption for ships with internal batteries is proposed. In the first step, the energy consumption model is built by analyzing the interaction of the hull, engine and propeller. Then the speed and energy optimization model is proposed to minimize the ship operation cost. Finally, a neighborhood search differential evolution (NSDE) is adopted to solve the proposed complex nonlinear optimization problem to obtain the optimal sailing speed and movement time. A case study is then carried out with a real battery powered cargo ship. The results show that this method can provide a reference for ship operators to better manage AES with internal battery.

The practical efficiency of commercial m-Si and CIS PV modules measured in reality by our group was less than 15% due to the higher temperature of the PV modules, and the rest is more than 85% of the exhaust solar energy. Environmental Heating It was previously reported in 2020 that the principle of an environmentally friendly PV/T (photovoltaic/thermal) solar panel using m-Si PV module to use 71.3% of solar energy for electricity and 40°C water This panel was designed to confirm the principle, that is, it was an experimental PV/T solar panel. In this paper, a new environmentally friendly PV/T solar panel for use in BIPVT (photovoltaic) systems is presented. /integrated heating in the building) is proposed. The new panel uses a CIS PV module and all functions, including the heat exchanger using aluminum flat tubes, are housed in a panel box that is approximately the size of a simple CIS PV panel. PV/T Solar Panel Proposed 73.5 % of solar energy with 13.0% power generation efficiency and 60.5% heat collection efficiency in a 40°C hot water source in Yokohama, Japan. The efficiency is higher than the previous test panel. The proposed panel can also suppress heat radiation around 50°C even in the case of hot water of 60°C. The proposed PV/T solar panel can meet all residential thermal needs such as domestic hot water (DHW) and space heating or cooling using solar heat with less environmental heat load.

Installing decentralized resources in a network requires consideration of various agents and parameters. One important parameter in the installation of decentralized resources is the reduction of power losses in the network. In other words, these power plants must be installed in a way that not only provides the required energy for the network but also reduces losses in the network. In this research, a two-objective problem is defined, with the first objective being the reduction of network losses and the second objective being the improvement of network reliability. Various criteria have been defined to measure network reliability, and in this study, the LOLP criterion will be used. Another innovation considered in this research is the use of energy storage resources. Instead of using IEEE test network data, data from Ekbatan Town in Tehran is used in this research. The results show that by allocating a number of distributed generation units and batteries in the power network and using demand response programs, it is possible to manage power losses and improve the reliability of the power network.

This paper explores the possibility of using ChatGPT to develop advanced phishing attacks and automate large-scale deployments. We make ChatGPT implement the following parts of a phishing attack: 1) impersonate a target website, 2) integrate code to steal user information, 3) obfuscate code, 4) automatically deploy the website to your hosting provider, 5) register phishing domain, and 6) integrating the website with a reverse proxy. It will make the initial evaluation of automatically generated phishing kits practical. It highlights their rapid production and deployment process as well as the close similarity of the resulting pages to the intended website. More broadly, we show that recent advances in artificial intelligence confirm the potential dangers of its misuse in phishing attacks, which could lead to an increase in their prevalence and severity. This highlights the need for advanced countermeasures and defensive measures in artificial intelligence systems.

Molecular computing is an emerging Nanotechnology. Molecular computing is the base of designing and implementing molecular processors on a platform of biological macromolecules such as deoxyribonucleotide acid, ribonucleotide acid, and proteins [1]. DNA computing is a branch of molecular computing that uses DNA strands and chemical reactions for computation. DNA computing is the most successful molecular computing method in terms of scalability, simplicity of execution and implementation, design, and costs [2]. Recently, according to the features of the problem, many DNA computational models were proposed. The Self-assembly model consists of a lot of tiles that reacted parallel. this model has some advantages and disadvantages, for example, its massively parallel computing and regularity and vice versa uncontrollability tiles, high error rate, and large crystals on output (unscalable). This article proposed a design method for the self-assembly logic functions. Also, this method has advantages such as scalability, automatic design flow, and low-cost implementation. The proposed methodology can be a promising starting point for performing computations in parallel.