Internet addiction is one of the most growing addictive behaviors worldwide, especially among university students affecting their physical and mental health negatively. During COVID-19, accessing online books, completing assignments, and online assessments are highly recommended by universities, teachers, and students. The Internet has increasingly become a vital part of our lives, and distant online classes increase the dependency of students on the Internet. The aim of this study is to assess the level of internet addiction among medical or non-medical students in Tanta University, Egypt. This was a cross-sectional study conducted during October and November 2020. It included 373 students from the faculty of medicine and 373 non-medical students from the faculty of science. The validated (IAT-20) was used to assess the level of Internet addiction among these students. A total of 51.7% of medical students were found severe internet addicts and 43.4% of them were possible addicts compared to only 11.3% of non-medical students who were found severely addicted to the Internet and 68.9% of them were possible addicts with statistically significant difference between them. Female students of both colleges were found addicted to the Internet than males. Female medical and non-medical students suffered from severe Internet addiction more than non-medical and male ones. Appropriate awareness creation intervention strategies should be implemented to decrease the level of Internet addiction among university students and to reduce its harmful and negative consequences.

Many alternative fuel technologies have been studied for the transport sector to increase its sustainability while reducing costs, greenhouse gases (GHG), and air pollution emissions. Nevertheless, conventional diesel is still the predominant fuel for heavy-duty trucks. Road freight transport consumes 25% of the world’s energy and is responsible for emissions with local health impacts and the global greenhouse effect. In this context, this paper reviewed items from 2015 to 2020 to analyze the technologies available for the road freight transport regarding pollutant and GHG emissions. Results are presented in two parts: first quantitatively, quantitative data was extracted from reviewed papers and statistically treated and, second, qualitatively through a comparative chart, which shows the impact on air pollutants from the use of a different type of fuels. In general, papers are mostly concerned with particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbons (HC) emissions due to its impact on public health, with a low number of papers covering GHG emissions. The trade-off between different fuels and how this process can impact emissions, sometimes increasing or decreasing specific pollutants, is discussed. According to the analyzed papers, the main characteristics that affect the pollutant emissions are, in general, the fuel oxygen content and the combustion chamber temperature.

This work reports the production of activated carbons (AC) from bitter orange peel by using ZnCl₂ or H₃PO₄ as activating agent at two different carbonization temperatures (450 °C and 550 °C). Surface morphology analyses of produced bitter orange activated carbons (BOACs) were done by SEM. The surface areas of the produced BOACs, functional groups on the AC surfaces and compositions, were determined by using BET, FT-IR, and TGA analyses. Furthermore, their application for the removal of methylene blue (MB) was studied. The parameters affecting adsorption, such as pH and temperature, were evaluated. BOAC produced with ZnCl₂ at 550 °C, exhibits the highest surface area (1450.6 m² g⁻¹), whereas BOAC produced with ZnCl₂ at 450 °C yielded with the highest adsorption capacity (108.9 m² g⁻¹). Langmuir isotherm shows a good fit for the BOACs. Adsorption kinetics were studied and follows pseudo-second-kinetic model. The thermodynamic parameter studies indicate that the adsorption process is endothermic and spontaneous.

The population structure of Barbodes carnaticus species was studied using conventional (based on body morphometrics and meristic) and image-based analysis (truss network system) methods. The study was carried out with four stocks, namely Karnataka (KA) and Tamil Nadu (TN) stocks from the River Cauvery, Kerala (KE) stock from the River Chalakudy and farm-reared stock (CI) from Central Institute of Freshwater Aquaculture, Bangalore. A total of 27 morphometric, 9 meristic and 30 truss measurements were used in the study for the stock structure. Fifteen landmarks were used to generate 30 truss distance measurements. The principal component analysis (PCA), factor analysis (FA), discriminant function analysis (DFA) and cluster analysis (CA) were deployed to determine the variation using both the conventional and truss variables. Variations (86.9%) among the morphometric characters were explained by five principal components, while four principal components explain 96.01% of the variation among the truss distances. DFA using conventional method correctly classified 100% of the original grouped classes of the KA, KE and CI and 93.8% of TN stocks. The DFA employed with truss distance was classified into the stocks CI, KA, KE and TN, and the values are 100, 89.1, 8.6 and 6.1%, respectively. Factor analysis based on truss morphometry showed that factor one is related to body shape and factor two is related to head shape. Two clusters were identified in both the conventional and the truss distance analysis. Truss distance-based cluster showed that the KE and CI stocks are similar compared to the TN stock. In contrary, morphometry-based cluster showed the KE and TN stocks are similar compared to CI stock. The multivariate analysis showed that the farm-reared stock (CI) is different from the wild stocks (KA, KE and TN). This study explained that the combination of the conventional and image-based truss network analysis helps to discriminate various stocks of B. carnaticus. Based on the PCA, bilinear data models were generated using R 3.5.3 software for predicting the stock of each individual. Stock discrimination of this species was mainly due to the geographic isolation, river ecology and temperature variations. The stocks of B. carnaticus are highly exploited from the studied rivers, and the species is an important candidate for species diversification to enhance aquaculture production. Within stock variations are found to be minimum in the present morphometric study, hence the gene pool identification and marker study are required for better understanding of the stocks. This stock structure study may help to develop conservation programmes for this endemic species through a more scientific approach.

Marine pollution is becoming more and more serious, especially in coastal areas. Because of the sequestration and consequent accumulation of pollutants in sediments (mainly organic compounds and heavy metals), marine environment restoration cannot exempt from effective remediation of sediments themselves. It has been well proven that, after entering into the seawater, these pollutants are biotransformed into their metabolites, which may be more toxic than their parent molecules. Based on their bioavailability and toxic nature, these compounds may accumulate into the living cells of marine organisms. Pollutants bioaccumulation and biomagnification along the marine food chain lead to seafood contamination and human health hazards. Nowadays, different technologies are available for sediment remediation, such as physicochemical, biological, and bioelectrochemical processes. This paper gives an overview of the most recent techniques for marine sediment remediation while presenting sediment-based microbial fuel cells (SMFCs). We discuss the issues, the progress, and future perspectives of SMFC application to the removal of hydrocarbons and metals in the marine environment with concurrent energy production. We give an insight into the possible mechanisms leading to sediment remediation, SMFC energy balance, and future exploitation.

This study focuses on the Dombrovska pit lake, near the city of Kalush in Ukraine, which is a former potassium salt mine filled with brine and freshwater. The water level is still increasing and as a result the salinity is decreasing. We analyzed the benthic fauna communities and the genome instability by assessing the rearrangements in the polytene chromosomes of Chironomus salinarius and the physicochemical parameters of the near-bottom water (pH, conductivity, mineralization, major ions, NO₃⁻, NH₄⁺, metals Cd, Pb, Cu, Mn, and Fe) and sediment (pH, organic matter and metals Cd, Pb, Cu, Zn, Mn, and Fe) at four sites. The water mineralization ranged from 17.3 to 26.2 g dm⁻³ which are classified as mesohaline and polyhaline waters, respectively. The biodiversity of the benthic fauna was low, and the dominant species was C. salinarius. The density of C. salinarius varied spatially and changed from 637 ind./m² at a depth of 5 m to 8167 ind./m² at a depth of 2.5 m. The genome instability was analyzed by examining the structural and functional changes in the salivary gland chromosomes of C. salinarius. The exposure of C. salinarius damaged the chromosomes and the activities of key structures, such as the Balbiani ring and nucleolar organizer, were partially or completely suppressed.

Vietnam has witnessed a rapid increase in national-level CO₂ emissions due to rising urbanization, economic expansion, export growth, and industrial development. Moreover, to support the ambitious economic growth targets, reliance on and consumption of fossil fuels are increasing by each passing year. With this circumstance, this study aims to analyze the key drivers of CO₂ emissions in Vietnam from 1990 to 2016 using the Kaya identity and decomposition method. Following this approach, CO₂ emissions have been decomposed into five effect categories comprising population, affluence, energy intensity, fuel mix, and emission intensity. As per the results, CO₂ emissions in Vietnam were mainly driven by rising affluence (58.5%) and changing fuel mix (33.2%) which have resulted from improved living standards, rapid industrial development, and higher fossil fuel consumption. Moreover, population (13.8%) and emission intensity (3.1%) exhibited a relatively lower impact on CO₂ emissions during 1990–2016. However, energy intensity (− 8.7%) was the only negative driver which has resulted in the slowdown of carbon emissions in Vietnam. Based on the analysis of energy policy development, the share of renewable energy resources was still quite low in the national energy mix with higher reliance on traditional fossil fuels (mainly coal and petroleum). Therefore, to make a transition towards low-carbon economic growth, significant improvements in energy efficiency and emission intensity are necessary together with national energy mix restructuring for low-carbon economic growth.

Elemental compositions of water, and surface and subsurface soils indicate characterization and mobilization of elements within soil profiles. The chemical variations of groundwater (n = 21) and surface and subsurface soils (n = 18 each) of the Polpithigama area were examined to determine elemental spatial variation in the area. Physico-chemical parameters (pH, electrical conductivity (EC), dissolved oxygen (DO), oxygen reduction potential (ORP)) and NO₃⁻, PO₄²⁻, Mn, Fe, and F were analyzed for the water samples in the field using field test kits. Soil samples were analyzed using X-ray fluorescence for 22 major and trace elements. Iron, Mn, NO₃⁻, and PO₄²⁻ concentrations in water are lower than the threshold values of WHO. The fluoride content was increased with high elevation even within a short distance. The fluoride and phosphate are highly variable between the soil layers and are preferably retained in the surface soil. The vertical variation is resulted by intensive weathering under tropical conditions, mobility of ions, variation of elements, and variation of soil constituents such as different contents of organic matter and clay content consisting of high total sulfur (TS) contents. Migration of some ions to the groundwaters of the area is possible due to high mobility of elements and thus the compositions of some elements show variations in the surface and subsurface soils.

Rapid environmental microorganism (EM) classification under microscopic images would help considerably identify water quality. Because of the development of artificial intelligence, a deep convolutional neural network (CNN) has become a major solution for image classification. Three popular CNNs, referred to as ResNet50, Vgg16, and Inception-v3, were transferred to identify the EM images present on the Environmental Microorganism Dataset (EMDS), and EMAD was the small dataset, which only has 294 EM images with 21 EM classes. Besides data augmentation, optimizing the fully connected layer of CNN, i.e., both optimally fine-tuned neuron number and dropout rate, was adopted to enhance the performance produced by CNN. The discussions on the causes of the accuracy improved by optimization are also provided. The results showed that the Inception-v3 model obtained 84.9% of the accuracy and performed better than the other two famous CNNs. Also, the implement of data augmentation enhanced the performance of Inception-v3 on EMDS. To add to that, the optimized Inception-v3 model archived 90.5% of the accuracy, and this result demonstrated the improvement effect obtained by using genetic algorithm (GA) to optimize the fully connected layer of the Inception-v3. Therefore, the optimize Inception-v3 with data augmentation process obtained the accuracy of 92.9% and improved almost 21% higher than that obtained from the famous Vgg16. In addition, the optimized Inception-v3 would need less neurons, when compared with that of the optimized Vgg16 possibly. This optimized Inception-v3 could provide a solution to the EM classification in microscope with a digital camera system.

A novel periodical mixed-integer mathematical model in the field of fresh agri-food product’s distribution (tomato product for instance) is developed in this paper. Green topics and two significant features of fresh agricultural products, namely freshness and ripeness, are added to the inventory routing problem (IRP) with simultaneous pickup and delivery for perishable products. The objective function tends to optimize the system total interest. In this model, traditional costs such as transportation and holding costs are considered besides up-to-date points such as expired products and customer’s dissatisfaction costs. To compute the customer’s dissatisfaction costs, the level of deviation from ideal quality should be measured to specify the biological postharvest behavior of fresh products. To determine the fair pricing, the patterns of quality decay have been applied. Considering the environmental effects and recycling requirements of expired crops, the reverse logistics notion has been applied to collect and reuse the wasted products. The level of greenhouse gas (GHG) emission has been controlled to reduce the harmful impressions of this gas and protect the environment. In the optimization procedure, the system total costs are developed by assuming fuzzy quality levels and fuzzy holding costs. Also, a bipolar approach has been applied for fuzzy programming. Finally, a numerical example besides sensitivity analysis and managerial insights is presented. Results show that remarking fuzzy parameters lead to lower profit and different routing and transmission. Also, applying less pollutant vehicles and increasing plant’s delivery levels can be noted to reach a green environment and higher level of profit.