Plastics are becoming a pervasive pollutant in every environmental matrix, particularly in the aquatic environment. Due to increased plastic usage and its impact on human and aquatic life, microplastic (MP) pollution has been studied extensively as a global issue. The production of MP has been linked to both consumer and commercial practices. There is a significant amount of MP's that must be removed by wastewater treatment plants before they can be bioaccumulated. Many researchers have recently become interested in the possibility of eliminating MPs in wastewater treatment plants (WWTP). Many studies have analyzed MP's environmental effects, including its emission sources, distribution, and impact on the surrounding environment. The effectiveness of their removal by various wastewater treatment technologies requires a critical review that accounts for all these methods. In this review, we have covered the most useful technologies for the removal of MP during WWTP. The findings of this review should help scientists and policymakers move forward with studies, prototypes, and proposals for significant remediation impact on water quality.

Mg-based and Zn-based biodegradable materials have the potential to become the next-generation implant materials to treat bone diseases, because of their desired degradation and mechanical properties. This article reviews the status of these implant materials. The required properties of biodegradable materials such as biodegradability, mechanical properties, and biocompatibility for performance evaluation were briefly discussed. The influence of fabrication techniques, microstructure, alloying elements, and post-processing techniques on the properties of Mg and Zn-based materials was addressed. The degradation mechanism by dissolution, oxidation, and interaction with human body cells was discussed. The biocompatibility of Mg and Zn-based biodegradable materials was analyzed. The significance of in vitro and in vivo biocompatibility testing was highlighted, emphasizing the superiority of in vivo results over cell line studies. This article identifies the many Mg and Zn-based biodegradable materials and summarizes the key findings.

The field of electrical energy is moving through many rapid changes and continuous development, especially with the huge boom in the field of artificial intelligence (AI) and the presence of renewable energy systems (RESs). Simultaneously with this development, it was necessary to have energy storage systems such as super capacitors and batteries. This necessity increased with sudden changes in climate or loads that affect the output of generation systems such as solar and wind energy systems. The main goal of this study is to focus on supercapacitors as a future solution with high reliability. It studies all the various factors and operational modes such as the principle of operation, installation, types and modeling of supercapacitors. The study also focused on the employment of supercapacitors in diverse applications, especially in microgrids with their various systems including control and energy management strategies for these systems and investigating the integration between AI, supercapacitors and microgrids and the outcome of this integration. Besides, this research intends to a proposal for a hybrid energy storage systems (HESS) suitable for different loads, including solar cells as main source with supercapacitors and batteries. In addition, it proposes the energy management system (EMS) as well as appropriate control system, to solve the problems of utilities and microgrids and meet the power demand of loads for different operating conditions.

Fish protein hydrolysates (FPH) are inherently unstable in their liquid form, necessitating either freezing or dewatering for stabilization. Gentle methods such as freeze concentration can be used to remove water, this can be achieved by freezing water in solution by decreasing the bulk temperature below freezing point and separating pure ice crystals from concentrated solution. This approach serves as an alternative to techniques like evaporation and reverse osmosis for concentrating solutions that have high water content, significant nutritional value, and thermolabile compounds. This is crucial as many bioactive compounds degrade when exposed to elevated temperatures. Another notable advantage of this technology is its potential to reduce energy consumption by up to 40% when integrated into the FPH drying process. Although this technology is currently industrialized primarily for juices, it can achieve concentrations of up to 60°Brix and manage viscosities up to 400 mPa.s. Numerous studies have been dedicated to enhancing design and processes, leading to a 35% reduction in the system's capital cost and a 20% reduction in energy consumption. Moreover, freeze concentration can synergize with other concentration techniques, creating more efficient hybrid processes. This review aims to introduce freeze concentration as a superior option for preserving fish protein hydrolysates, enhancing their stability, and maintaining their nutritional and bioactive qualities.

Tractors are manufactured without air-conditioned cabins in Pakistan. This leads to thermal discomfort for tractor operators working under direct solar exposure. Therefore, this study aimed to design and install an air-conditioned cabin on a tractor. Experiments were undertaken to evaluate the installed cabin performance under two scenarios i.e., conventional (S–I) and enhanced (S-II) air distribution. Computational fluid dynamics (CFD) simulations were used to analyze airflow and calculate thermal comfort indices. The results showed that the air-conditioned cabin attained optimum thermal conditions under the enhanced air distribution scenario (S-II). In this scenario, the inside cabin temperature was an average of 27.4 °C, compared with 30.4 °C in S–I. The relative humidity remained similar in both scenarios, around 53 %. The temperature difference between the cabin and the ambient environment was 11.09 °C in S-II, aligning with the thermal comfort conditions outlined in ISO 14269–2. Furthermore, the CFD simulations showed a predicted mean vote (PMV) index of 0.61 and the percentage people dissatisfied (PPD) index of 26.5 %. These results also confirm the provision of optimum thermal conditions for operator inside the cabin. The simulations also demonstrated good agreement with experimental data, with a small difference in air temperature (2 °C) and relative humidity (5.8 %). In the light of these findings, this study recommends installation of air-conditioned cabin on tractors with enhanced air distribution (S-II) in Pakistan to improve thermal comfort of operators.

Enormous consumption of fossil fuel resources has risked energy accessibility in the upcoming years. The price fluctuation and depletion rate of fossil fuels instigate the urgent need for searching their reliable substitute. The current study tries to address these issues by presenting butanol as a replacement for gasoline in SI engines at various speeds and loading conditions. The emission and performance parameters were ascertained for eight distinct butanol-gasoline fuel blends. The oxygenated butanol substantially increases engine efficiency and boosts power with lower fuel consumption. The carbon emissions were also observed to be lower in comparison with gasoline. Furthermore, the Artificial Intelligence (AI) approach was used in predicting engine performance running on the butanol blends. The correlation coefficients for the data training, validation, and testing were found to be 0.99986, 0.99942, and 0.99872, respectively. It was confirmed that the ANN predicted results were in accordance with the established statistical criteria. ANN was paired with Response Surface Methodology (RSM) technique to comprehend the influence of the sole design parameters along with their statistical interactions controlling the responses. Similarly, the R² value of responses in case of RSM were close to unity and mean relative errors (MRE) were confined under specified range. A comparative study between ANN and RSM models unveiled that the ANN model should be preferred. Therefore, a joint utilization of the RSM and ANN can be more effective for reliable statistical interactions and predictions.