Significant changes in society were emphasized as being required to achieve Sustainable Development Goals, a need which was further intensified with the emergence of the pandemic. The prospective society should be directed towards sustainable development, a process in which technology plays a crucial role. The proposed study discusses the technological potential for attaining the Sustainable Development Goals via disruptive technologies. This study further analyzes the outcome of disruptive technologies from the aspects of product development, health care transformation, a pandemic case study, nature-inclusive business models, smart cities and villages. These outcomes are mapped as a direct influence on Sustainable Development Goals 3, 8, 9 and 11. Various disruptive technologies and the ways in which the Sustainable Development Goals are influenced are elaborated. The investigation into the potential of disruptive technologies highlighted that Industry 5.0 and Society 5.0 are the most supportive development to underpin the efforts to achieve the Sustainable Development Goals. The study proposes the scenario where both Industry 5.0 and Society 5.0 are integrated to form smart cities and villages where the prospects of achieving Sustainable Development Goals are more favorable due to the integrated framework and Sustainable Development Goals’ interactions. Furthermore, the study proposes an integrated framework for including new age technologies to establish the concepts of Industry 5.0 and Society 5.0 integrated into smart cities and villages. The corresponding influence on the Sustainable Development Goals are also mapped. A SWOT analysis is performed to assess the proposed integrated approach to achieve Sustainable Development Goals. Ultimately, this study can assist the industrialist, policy makers and researchers in envisioning Sustainable Development Goals from technological perspectives.

With the growing consumer demand in the electronics field, sustainable and effective cooling approaches are imperative to maximize operational efficiency. Heat pipes shave a major consideration in the field of heat transfer in a modern era of miniaturization of equipment. In current trends, the proportion of custom-designed electronic chips is increasing, given the space constraints of the application. Additionally, the use of nanofluids in heat pipes has drawn considerable attention because of their exceptional performance in heat transfer. This research is proposed primarily to investigate the effect of nanofluids on the performance of the partially flattened heat pipe. Here, the evaporator portion forms flat shape which is mostly suitable for fixing easily in electronic circuits. The remaining portions, such as the adiabatic and condenser, are left as circular. This work also covers the development of flattened heat pipes and analyzes their performance. Pure water, Titanium Oxide (TiO2), and Aluminum Oxide (Al2O3)-water-based nanofluids have been used in this research as working fluids. The heat transfer analysis on the customized partially flattened heat pipe was performed, and the results have been compared with fully flattened and circular heat pipes. The heat transfer parameters, such as the heat transfer coefficient and thermal resistance, have been determined from the heat input, evaporator temperature, and condenser temperature for various inclination angles including 0°, 45°, and 90° with the heat input varied between 50–300 W. The results have shown that the flattened heat pipe performed better with Al2O3 nanofluid at an inclination angle of 45° at all of the heat inputs and provided better thermal resistance compared with the other combinations. At 45°, the resistance of the heat pipe was reduced by 2% and 8% with Al2O3 nanofluid compared with water and TiO2 nanofluid. Furthermore, the heat transfer coefficient was found higher by 4 W/m2-K and 4.6 W/m2-K with Al2O3 and gives better results in terms of resistance and heat transfer coefficient.

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The inclusion of photovoltaics (PV) in electric power supply systems continues to be a significant factor in global interest. However, solar power exhibits intermittent uncertainty and is further unpredictable. Accurate solar generation prediction and efficient utilization are mandatory for power distribution management and demand-side management. Peak demand management and reducing energy costs can be effectively tackled through the implementation of a reliable solar power forecasting system and its efficient utilization. In this regard, the proposed work is related to efficiently managing solar PV power and optimizing power distribution using an enhanced reinforced binary particle swarm optimization (RBPSO) technique. This DSM (demand-side management) strategy involves utilizing a forecast of solar PV generation for the upcoming day and adjusting the consumption schedule of the load to decrease the highest energy demand. The proposed approach improves user comfort by adjusting the non-interruptible and flexible institutional load through clipping and shifting techniques. To evaluate the effectiveness of this approach, its performance is assessed by analyzing the peak demand range and PAR (peak-to-average ratio). It is then compared to the conventional genetic algorithm to determine its effectiveness. Simulation results obtained using MATLAB show that the PAR peak demand before DSM was found to be 1.8602 kW and 378.06 kW, and after DSM, it was reduced to 0.7211 kW and 266.54 kW. This indicates a 29% reduction in Peak demand and performance compared to the conventional genetic algorithm (GA).