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Microbial fuel cell (MFC) has received much attention in the last decade as a promising technology to simultaneously generate electricity and decontaminate wastewater. This study aims to quantitatively review the published literature on MFC, published in the period of 1970–2020, based on the Web of Science (WoS) database. For the first time in literature, a comprehensive quantitative review of MFC has been conducted by employing the technique of bibliometric and content analyses. A total of 11,397 publications have been retrieved from WoS, out of which 81.6% are research articles. The evaluation in the field of MFC has been mapped in various categories, such as publication history, publication distribution, subject category distribution, leading journals, leading countries and leading organizations in MFC research. Additionally, content analysis has been conducted to unearth the research trends in MFC; and some hot research topics in MFC have been spotted. Results depict that the period 2011–2020 has been the most appreciating era for MFC research, as it contributed 87% of the total publications. Among the subject categories, energy fuel and microbiology lead with contributions of 26.5% for each, butthe overall growth of the energy fuel category in the last decade has been the highest. Out of 1,147 journals publishing MFC research, Bioresource Technology is the leading one; and countries like China, USA and India are the main hub of MFC research with 26.47%, 16.95% and 7.69% contributions in publications, respectively. The hottest topics in MFC research are nanoparticles, catalysts, air electrodes, graphene electrodes, power enhancement, air cathode and nitrogen removal. Moreover, major research areas are engineering, energy fuels and biotechnology with each contribution 26.5% of the total publications.

Microplastics are a growing concern in the environments and especially agricultural soil due to numerous negative impacts on the ecosystems and human health. This study aimed to investigate the properties of microplastics (including quantity, shape, size, and polymer types) in five samples (DA1-5) taken from agriculture soil in Dong Anh district, Hanoi using Nicolet iN10 MX Fourier transform infrared (µFTIR) microscope. The study found that the microplastic content in these soils ranging from 494 ± 292 items/kg dry weight to 1031 ± 379 items/kg dry weight. In terms of shape, microplastic fragments accounted for the majority of microplastics found in soil (65% - 86% of the total microplastics). Fifteen types of microplastic polymers were identified in the soil samples, with PET being the most common polymer of 38%, followed by urea-formaldehyde resin of 15%, and nylon of 13%. Microplastics with sizes ranging from 50-150 µm were the dominant group (39% - 57% of the total microplastics). This study provides an initial assessment of the presence of microplastics in the agricultural soil of the suburban area of Hanoi, highlighting the potential risk of contamination that may effect on environment and pose a threat to human health.

Shading the greenhouses is necessary in summer to reduce the solar radiation load. This however generates a considerable amount of thermal radiation heat load that needs to be removed via cooling systems. This study aimed to evaluate the effect of different shading configurations on the solar and thermal radiation in a greenhouse. Nets at four different locations were employed to shade the roof and side-walls of a polycarbonate, mechanically ventilated greenhouse. The spectral radiative properties of all these plastic materials were measured in short and long wave spectrum bands. The net solar and thermal radiations and air temperature were measured outside and inside two identical shaded and unshaded greenhouses. The results showed that external roof-shading is desirable, as it reduced the generated thermal radiation in the greenhouse by 21% and 15% during the day and night time, respectively and reduced the greenhouse air temperature during the day. The internal shading (roof and side walls) is undesirable, since it drastically increased the generated thermal radiation in the greenhouse by 147% and strongly increased the greenhouse air temperature during the day. Shading the side-walls is not recommended because it significantly reduces the transmitted solar radiation in the morning and afternoon (when the outside irradiance is low) and is useless at around noon when the outside irradiance is extremely high.

Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO2, and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.

This study examines the online teaching abilities and competencies needed to teach online courses in Saudi Arabia’s higher education system. As a result, the goal of this study was to examine and expand the technology acceptance model (TAM) to assess online teaching abilities and competences utilizing digital technologies in higher education during the COVID-19 epidemic. Therefore, it aimed to develop a new model to measure and explore critical factors that influence online teaching skills, competencies, and actual use of digital tools in higher education. The participants in the study were 350 lecturers at King Faisal University. The research data were analyzed using structural equation modeling (AMOS-SEM). The findings revealed that: (a) perceived ease of use and perceived usefulness on using digital tools during the COVID-19 pandemic has a direct positive impact on perceived teaching self-efficacy, perceived enjoyment, online teaching skills, and digital tools access; (b) perceived ease of use and perceived usefulness of using digital tools have a direct positive impact on lecturers’ attitude toward use and lecturers’ behavioral intention to use digital tools during the COVID-19 pandemic; and (c) perceived ease of use. As a consequence of the findings, a validated instrument was designed to assess and investigate crucial elements that impact lecturers’ real usage of digital technologies for teaching and learning in Saudi Arabia’s higher education.

The environmental control and management of municipal solid waste (MSW) dumping sites is considered one of the sensitive challenges faced by executive municipalities. This is especially true in Makkah due to the gradual increase in urban population and visitors, with an overall MSW generation of about one million tons per year. Consequently, the geo-environmental evaluation of the Kaakia dumping site shed light on the potential environmental threats, in terms of ambient air quality levels and meteorological parameters, in addition to geophysical inspection. An air quality survey discussed the major trends of ambient air pollutants (SO2, NO2, CO, O3, CH4, and PM10) downwind from the Kaakia dumping site. It indicated the presence of a significant increase in sporadic plumes of Methane concentration. The maximum hourly averages ranged between 22.9–26.6 µg/m3 for SO2, 44.4–64.0 µg/m3 for NO2, 0.86–1.38 mg/m3 for CO, 150.2–158.8 µg/m3 for O3, 5.09–5.9 ppm for CH4, and 955–994 µg/m3 for PM10. The ground penetrating radar (GPR) geophysical survey indicated the subsurface sequence of three geological layers, as confirmed by nearby bores of the investigated site: (1) a surface layer formed of alluvial sediments of sand, which were 2.5–3.1 m thick; (2) a second layer represented by sand and gravel, with a thickness of 4.6–6.5 m; and (3) a third layer equivalent to saturated alluvial sediments mixed with rock fragments that extended to a depth of 13 m. The signals of the GPR were attenuated at the base of the conducted profiles due to the percolation of generated leachate to the subsurface sequence and contaminated groundwater aquifer.

Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions.

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.