Owing to its natural and rich advantages, exploration of solar energy technology has become increasingly popular in recent years to counter the growing crude oil prices. However, its universal adoption is still limited, not only due to environmental restrictions but also due to lower overall efficiency. Rankine cycle is optimised to conduct 4-E (Exergy, Energy, Economic and Ecological) analysis. Furthermore, three sets (R-113, R-11, and R-1233zd) of refrigerants are prioritised and ranked on the basis of 4-E analysis as outcomes. The contemporary study addressed all critical factors and explains the impact of solar irradiance, mass flow rate of molten salt and steam, turbine inlet pressure, and turbine inlet temperature which are eventually weighed and prioritised using combined multi-criteria decision making (MCDM) techniques. The energy efficiency, exergetic efficiency, power/ cost of electricity, and ecological emissions are taken as the indicators of the combined cycle, respectively. The energy efficiency of the hybrid system is improved to 75.07% after including cogeneration cycle, with an increment of 54.58%. In comparison to conventional thermal powerplant setups, the power/cost of electricity and ecological efficiency have been reduced by 68% and upgraded by 16%, correspondingly. Direct normal radiation is the most critical factor followed by turbine inlet temperature. Further, the result indicates that maximum exergy destruction that occurs in the central receiver declines to 39.92%, followed by heliostat and steam turbine which was 27% and 9.32% respectively. In conclusion, the hybrid cycle can furnish cheaper electricity, with lower carbon imprint in sustainable manner with better efficiency.

Agriculture plays a vital role in the economic life cycle of an agrarian country and considers the backbone of the economy. It supplies not just raw food materials, but also a vast number of job opportunities. Therefore, modern technology is required in agriculture to increase productivity. Wireless Sensor Networks (WSN) could be used to monitor climatic parameters in an agriculture field, such as the acidity level of soil, soil moisture, humidity, light, and so on. Climate variables have a significant impact on crop growth, quality, and productivity. These factors contribute to increased agricultural productivity in terms of both quantity and quality. WSN, on the other hand, has security risks such as impersonation, alteration, interference, and interception all of which have negative effects on crop production and other agricultural activities. The primary issues for agricultural WSN are hence privacy preservation and security enhancement. In this paper, we proposed a privacy-preserving and efficient key agreement framework for smart agriculture monitoring systems by using elliptic curve cryptography and hash function. The proposed framework is secure against various security assaults and provides secure communication in smart agriculture monitoring systems. We demonstrate the accuracy of the proposed protocol for mutual authentication and key exchange using BAN logic, and we also simulate the security correctness of the encrypted proposed framework using the well-known security verification Scyther tool. Using the ROR model, we formalize the security of the proposed system. Further, we provide a comparison based on security features, computation, and communication overheads comparison between the proposed protocol and similar protocols in the same context. Hence, when compared to other similar protocols in the same environment, the proposed protocol provides superior security and efficiency than other existing protocols. For the practical implementation of smart agriculture monitoring systems, the proposed protocol is better than comparable protocols.

Integrating nanoparticles in waste oil-derived biodiesel can revolutionize its performance in internal combustion engines, making it a promising fuel for the future. Nanoparticles act as combustion catalysts, enhancing combustion efficiency, reducing emissions, and improving fuel economy. This study employed a comprehensive approach, incorporating both quantitative and qualitative analyses, to investigate the influence of selected input parameters on the performance and exhaust characteristics of biodiesel engines. The focus of this study is on the potential of using oils extracted from food waste that ended up in landfills. The study's results are analysed and compared with models created using intelligent hybrid prediction approaches including adaptive neuro-fuzzy inference system, Response surface methodology-Genetic algorithm, and Non sorting genetic algorithm. The analysis takes into account engine load, blend percentage, nano-additive concentration, and injection pressure, and the desired responses are the thermal efficiency and specific energy consumption of the brakes, as well as the concentrations of carbon monoxide, unburned hydrocarbon, and oxides of nitrogen. Root-mean-square error and the coefficient of determination were used to assess the predictive power of the model. Comparatively to Artificial Intelligence and the Response Surface Methodology-Genetic Algorithm model, the results provided by NSGA-II are superior. This is because it achieved a pareto optimum front of 24.45 kW, 2.76, 159.54 ppm, 4.68 ppm, and 0.020243% for Brake Thermal Efficiency, Brake Specific Energy Consumption, Oxides of nitrogen, Unburnt Hydro Carbon, and Carbon monoxide. Combining the precision of ANFIS's prediction with the efficiency of NSGA-optimization II's gives a reliable and thorough evaluation of the engine's settings. The qualitative assessment considered practical aspects and engineering constraints, ensuring the feasibility of applying the parameters in real-world engine applications.

A sustainable transport system is one which offers economical solutions and causes no/little damage to the environment. We usually categorize transport into three sections - roads, airways and water. Such systems need to be set up in order to have a transportation medium or ecosystem with minimum environmental impact. Public buses are an excellent example of sustainable road transportation. They can accommodate a large number of people economically and help ease the financial burden on individuals. Unfortunately, the majority of public buses are powered by non-renewable energy sources such as diesel. This results in the release of hazardous pollutants, leading to a degraded environment. Although the cost of using this form of transportation is relatively affordable, it nonetheless has an adverse effect on the environment. Therefore, it cannot be considered sustainable in that regard. We are constantly pushing the boundaries of innovation by introducing new technologies such as IoT, AI, Blockchain and 5G into our smart and sustainable transportation system. This way we can ensure its sustainability from all angles. The impact of transportation on our society is undeniable- both in terms of economic, social and environmental implications. To ensure a better quality of life for all, we must shift to smart and sustainable transport solutions. This research seeks to present a clever and eco-friendly transport system that brings economic and social advantages for developing countries.