As non-renewable conventional fossil fuel sources are depleting day by day, researchers are continually finding new ways of producing and utilizing alternative, renewable, and reliable fuels. Due to conventional technologies, the environment has been degraded seriously, which profoundly impacts life on earth. To reduce the emissions caused by running the compression ignition engines, waste cooking oil (WCO) biodiesel is one of the best alternative fuels locally available in all parts of the world. Different study results are reviewed with a clear focus on combustion, performance, and emission characteristics, and the impact on engine durability. Moreover, the environmental and economic impacts are also reviewed in this study. When determining the combustion characteristics of WCO biodiesel, the cylinder peak pressure value increases and the heat release rate and ignition delay period decreases. In performance characteristics, brake-specific fuel consumption increases while brake-specific energy consumption, brake power, and torque decrease. WCO biodiesel cuts down the emissions value by 85% due to decreased hydrocarbon, SO2, CO, and smoke emissions in the exhaust that will effectively save the environment. However, CO2 and NOx generally increase when compared to diesel. The overall economic impact of production on the utilization of this resource is also elaborated. The results show that the use of WCO biodiesel is technically, economically, environmentally, and tribologically appropriate for any diesel engine.

The rise in energy requirements and its shortfall in developing countries have affected socioeconomic life. Communities in remote mountainous regions in Asia are among the most affected by energy deprivation. This study presents the feasibility of an alternate strategy of supplying clean energy to the areas consisting of pristine mountains and forest terrain. Southeast Asia has a much-diversified landscape and varied natural resources, including abundant water resources. The current study is motivated by this abundant supply of streams which provides an excellent environment for run-of-river micro vertical axis water turbines. However, to limit the scope of the study, the rivers and streams flowing in northern areas of Pakistan are taken as the reference. The study proposes a comprehensive answer for supplying low-cost sustainable energy solutions for such remote communities. The suggested solution consists of a preliminary hydrodynamic design using Qblade, further analysis using numerical simulations, and finally, experimental testing in a real-world environment. The results of this study show that the use of microturbines is a very feasible option considering that the power generation density of the microturbine comes out to be approximately 2100 kWh/year/m2, with minimal adverse effects on the environment.

The generation and use of energy are significant contributors to CO2 emissions. Globally, approximately 30% to 40% of all energy consumption can be directly or indirectly linked to buildings. Nearly half of energy usage in buildings is linked to maintaining the thermal comfort of the inhabitants. Therefore, finding solutions that are not only technically but also economically feasible is of utmost importance. Though much research has been conducted to address this issue, most solutions are still costly for developing countries to implement practically. This study endeavors to find a less expensive yet straightforward methodology to achieve thermal comfort while conserving energy. This study takes a broader view of multiple habitat-related CO2 emission issues in developing regions and describes a hybrid solution to address them. New technologies and innovative concepts are being globally examined to benefit from the considerable potential of PCMs and their role in thermal energy storage (TES) applications for buildings. The current study numerically investigates the thermal response of a hybrid building envelope consisting of PCM and local organic waste materials for low-cost low-energy buildings. The local organic waste materials used are those whose disposal is usually done by burning, resulting in an immense amount of greenhouse gases. In the first phase, different waste materials are characterized to determine their thermophysical properties. In the second phase, a low-cost, commonly available PCM calcium chloride hexahydrate, CaCl2·6H2O, is integrated with a brick and corn husk wall to enhance the thermal storage in the building envelope to minimize energy consumption. Temperature distribution plots are primarily used for analysis. The results show a marked improvement in thermal comfort by maintaining a maximum indoor temperature of 27 °C when construction is performed with a 6% corn husk composite material embedded with the PCM, while under similar conditions, the standard brick construction maintained a 31 °C indoor temperature. It is concluded that the integration of the PCM layer with the corn husk wall provides an adequate solution for low-cost and low-energy buildings.

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Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.