Water desalination and purification using desalination units powered by solar panels

One of the problems of the south area in Iraq is the reduced of quantity and the lack of quality of the supplied water, especially in the remote areas. This problem is caused due to many reasons. One of these reasons is the decrease of supplied electricity, which reflected on the performance of water pumping and desalination stations. This paper presenting a project that presented to municipality of Al-Nasiriya city to overcome the problem of the lack in quantity and quality of the supplied water to some villages that remote from the center of the city, through the use of complex modules that consists of small renewable power station with desalination unit. The project goes through some stages starting from collecting the data that related to the aim of the project like; sun radiation level, wind speed, dust quantity, quality and quantity of the presented water, and the type of activity in the area. The collected data were analyzed and evaluated and then the decision comes to execute three complex modules in three locations, powered by small solar energy unit in each. The operation of these modules gives good results, where they offers an acceptable quality with sufficient quantity of water and this an encourage results to populate this experiment in remote areas

Harnessing the potential of ligninolytic enzymes for lignocellulosic biomass pretreatment

Abundant lignocellulosic biomass from various industries provides a great potential feedstock for the production of value-added products such as biofuel, animal feed, and paper pulping. However, low yield of sugar obtained from lignocellulosic hydrolysate is usually due to the presence of lignin that acts as a protective barrier for cellulose and thus restricts the accessibility of the enzyme to work on the cellulosic component. This review focuses on the significance of biological pretreatment specifically using ligninolytic enzymes as an alternative method apart from the conventional physical and chemical pretreatment. Different modes of biological pretreatment are discussed in this paper which is based on (i) fungal pretreatment where fungi mycelia colonise and directly attack the substrate by releasing ligninolytic enzymes and (ii) enzymatic pretreatment using ligninolytic enzymes to counter the drawbacks of fungal pretreatment. This review also discusses the important factors of biological pretreatment using ligninolytic enzymes such as nature of the lignocellulosic biomass, pH, temperature, presence of mediator, oxygen, and surfactant during the biodelignification process

Covid-19 mortality risk prediction using small dataset of chest x-ray images

COVID-19 outbreak ravaged the whole world starting from the early part of 2020. The rapid spread of the pandemic accounts for the major reason the world was thrown into panic mode and pervasive confusion. However, COVID-19’s greatest strength is its virility but its severity on an individual is mostly ambiguous, which is dependent on the particular individual. This, combined with the increasingly limited capacity of the global healthcare infrastructure warrants some mechanism that can predict the prognosis of an individual to better determine if the patient would require hospital resources or be better treated as an outpatient. The lack of such a mechanism leads to suboptimal utilization of valuable hospital resources leading to unnecessary loss of life. However, often at the onset of a pandemic such as it was experienced during the outbreak of COVID-19, ample and appropriately labelled dataset to build accurate deep learning models to assist in this respect was limited. In this vein, frantic efforts were made to acquire dataset to train deep learning models for the stated objectives, unfortunately only a small dataset from a single source was available at the time of the study. Consequently, deep learning models based on the ResNet-18 architecture were trained on a small dataset of chest X-rays of patients infected with COVID-19 to predict mortality risk. The models exhibit considerable accuracy with high sensitivity. The appropriateness of the techniques proposed in this study for predictive modelling maybe particularly suited when only small datasets are available especially at the onset of similar pandemics. From existing literature, models with low complexity such as ResNet perform better with small dataset. Hence, this study utilised ResNet-18 as the baseline to evaluate the performance of other popular models on small datasets. The performance of the baseline models based on ResNet-18 with an accuracy of 0.89 compared favourably with those of the several other models including AlexNet, MobileNetV3, EfficientNetV2, SwinTransformer, and ConvNeXt using the same datasets and similar parameters

Molecular modeling investigation on mechanism of diazinon pesticide removal from water by single- and multi-walled carbon nanotubes

In this study, the mechanism of diazinon adsorption on single-walled carbon nanotubes (SWNTs), as well as multi-walled carbon nanotubes (MWNTs), was investigated using molecular modelling. Determination of the lowest energy sites of different types of carbon nanotubes (CNTs) was demonstrated. The adsorption site locator module was used for this purpose. It was found that the 5-walled CNTs are the best MWNTs for diazinon elimination from water due to their higher interactions with diazinon. In addition, the adsorption mechanism in SWNT and MWNTs was determined to be wholly adsorption on the lateral surface. It is because the geometrical size of diazinon molecules is larger than the inner diameter of SWNT and MWNTs. Furthermore, the contribution of diazinon adsorption on the 5-wall MWNTs was the highest, for the lowest diazinon concentration in the mixture