In the contemporary epoch, China’s Belt and Road Initiative (BRI) has disrupted the cross-border trade and logistics encapsulating Asia and Europe. However, its environmental impact is controversial and remains to be a great concern. This study is having an aim to address the prominent research gaps by scrutinizing the environmental implications of supply chain innovations among the five selected BRI countries. These countries include Pakistan, Russia, China, Kazakhstan, and Vietnam. The study incorporates the panel data analysis and respective environmental indicators in order to analyze the impacts of logistics efficiency, regulatory quality, and infrastructure on emissions and energy use. The study has unearthed that the improved logistics performance result sin the reduced levels of emissions, however, the regulatory differences remain a challenge for the creation of a unified environmental standard across the member countries. Deep insights have been crafted for the policy makers to help them balance the trade growth without compromising the environmental sustainability, by developing improved governance frameworks. Future researchers are recommended to further explore the regional collaboration to harmonize the environmental standards. Only achieving the said harmony will result in fostering the sustainable practices across diverse regulatory landscapes. The study offers a comprehensive approach towards development of understanding of environmental outcomes in terms of trade and infrastructure development in BRI nations. Hence, the study opens the endeavors for the sustainable international trade.

The production, optimization, radiation mutagenesis and purification of extracellular xylanase enzyme produced by some fungal species isolated from three soil sites located in Jeddah governorate, Saudi Arabia were studied. Ten fungal species (Alternaria alternata, Aspergillus candidus, Aspergillus ochrochus, Aspergillus flavus, Botrytis cinerea, Fusarium roseum, Penicillium chrsogenum, Penicillium italicum, Penicillium canescens and Rhizopus microsporus) constituting 188 colonies were isolated. The highest significant value of extracellular xylanase enzyme (0.60 unit/ml) was shown in the culture filtrate of the Aspergillus candidus. Optimization of some nutritional and physical factors in order to intensify the production of A. candidus extracellular xylanase was carried out. The highest productivity of A. candidus extracellular xylanase (0.60 and 0.62 unit/ml) occurred on xylan and peptone as carbon and organic nitrogen sources, respectively. The optimum pH and temperature for extracellular xylanase activities were 9.0 and 55 °C, respectively. The effect of ultraviolet radiation on A. candidus xylanase indicated that the maximum stimulation of A. candidus xylanase (1.20 unit/ml) was attained on exposure to UV after 50 min. The radio-stimulated xylanase from the most xylanase producing organism (A. candidus) was purified to homogeneity by salting out with ammonium sulphate, dialysis and passage through chromatography resins (Sephadex G-100 column and Diethylaminoethyl sephadex column). The irradiated purified xylanase enzyme resulted in 1.301 fold of purification over the crude extract, exhibited a specific activity of 2.258 unit/mg with the recovery of 90.5 %. Test for the purity by Sodium dodecyl sulfate polyacrylamide gel electrophoresis technique (SDS-PAGE) resulted in a single protein band of the pure irradiated xylanase. Studying factors affecting the activity of the irradiated purified xylanase was carried out. The optimum reaction temperature and pH for maximum purified xylanase activities (1.82 and 2.10 unit/ml) were 60°C and 8, respectively. Thermostability of the irradiated purified A. candidus xylanase showed that it was stable (35% loss of activity) a 60°C after 60 min. exposure time.

Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.