Chest radiographs (X-rays) combined with Deep Convolutional Neural Network (CNN) methods have been demonstrated to detect and diagnose the onset of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). However, questions remain regarding the accuracy of those methods as they are often challenged by limited datasets, performance legitimacy on imbalanced data, and have their results typically reported without proper confidence intervals. Considering the opportunity to address these issues, in this study, we propose and test six modified deep learning models, including VGG16, InceptionResNetV2, ResNet50, MobileNetV2, ResNet101, and VGG19 to detect SARS-CoV-2 infection from chest X-ray images. Results are evaluated in terms of accuracy, precision, recall, and f- score using a small and balanced dataset (Study One), and a larger and imbalanced dataset (Study Two). With 95% confidence interval, VGG16 and MobileNetV2 show that, on both datasets, the model could identify patients with COVID-19 symptoms with an accuracy of up to 100%. We also present a pilot test of VGG16 models on a multi-class dataset, showing promising results by achieving 91% accuracy in detecting COVID-19, normal, and Pneumonia patients. Furthermore, we demonstrated that poorly performing models in Study One (ResNet50 and ResNet101) had their accuracy rise from 70% to 93% once trained with the comparatively larger dataset of Study Two. Still, models like InceptionResNetV2 and VGG19's demonstrated an accuracy of 97% on both datasets, which posits the effectiveness of our proposed methods, ultimately presenting a reasonable and accessible alternative to identify patients with COVID-19.

This paper scrutinized hydraulic fracturing applications mainly in the United States with regard to both groundwater and surface water contamination with the purpose of bringing forth objective analysis of research findings. Results from previous studies are often unconvincing due to the incomplete database of chemical additives; after and before well-founded water samples to define the change in parameters; and specific sources of water pollution in a particular region. Nonetheless, there is a superior chance of both surface and groundwater contamination induced by improper and less monitored wastewater disposal and management practices. This report has documented systematic evidence for total dissolved solids, salinity, and methane contamination regarding drinking water correlated with hydraulic fracturing. Methane concentrations were found on an average rate of 19.2 mg/L, which is 17 times higher than the acceptance rate and the maximum value was recorded as 64.2 mg/L near the active hydraulic fracturing drilling and extraction zones than that of the nonactive sites (1.1 mg/L). The concentration of total dissolved solids (350 g/L) was characterized as a voluminous amount of saline wastewater, which was quite unexpectedly high. The paper concludes with plausible solutions that should be implemented to avoid further contamination.

Facial recognition (FR) in unconstrained weather is still challenging and surprisingly ignored by many researchers and practitioners over the past few decades. Therefore, this paper aims to evaluate the performance of three existing popular facial recognition methods considering different weather conditions. As a result, a new face dataset (Lamar University database (LUDB)) was developed that contains face images captured under various weather conditions such as foggy, cloudy, rainy, and sunny. Three very popular FR methods—Eigenface (EF), Fisherface (FF), and Local binary pattern histogram (LBPH)—were evaluated considering two other face datasets, AT&T and 5_Celebrity, along with LUDB in term of accuracy, precision, recall, and F1 score with 95% confidence interval (CI). Computational results show a significant difference among the three FR techniques in terms of overall time complexity and accuracy. LBPH outperforms the other two FR algorithms on both LUDB and 5_Celebrity datasets by achieving 40% and 95% accuracy, respectively. On the other hand, with minimum execution time of 1.37, 1.37, and 1.44 s per image on AT&T,5_Celebrity, and LUDB, respectively, Fisherface achieved the best result.

Face recognition (FR) in an unconstrained environment, such as low light, illumination variations, and bad weather is very challenging and still needs intensive further study. Previously, numerous experiments on FR in an unconstrained environment have been assessed using Eigenface, Fisherface, and Local binary pattern histogram (LBPH) algorithms. The result indicates that LBPH FR is the optimal one compared to others due to its robustness in various lighting conditions. However, no specific experiment has been conducted to identify the best setting of four parameters of LBPH, radius, neighbors, grid, and the threshold value, for FR techniques in terms of accuracy and computation time. Additionally, the overall performance of LBPH in the unconstrained environments are usually underestimated. Therefore, in this work, an in-depth experiment is carried out to evaluate the four LBPH parameters using two face datasets: Lamar University data base (LUDB) and 5_celebrity dataset, and a novel Bilateral Median Convolution-Local binary pattern histogram (BMC-LBPH) method was proposed and examined in real-time in rainy weather using an unmanned aerial vehicle (UAV) incorporates with 4 vision sensors. The experimental results showed that the proposed BMC-LBPH FR techniques outperformed the traditional LBPH methods by achieving the accuracy of $65\%$, $98\%$, and $78\%$ in 5_celebrity dataset, LU dataset, and rainy weather, respectively. Ultimately, the proposed method provides a promising solution for facial recognition using UAV.

Numerous micropollutants, especially endocrine-disrupting compounds (EDCs), can pollute natural aquatic environments causing great concern for human and ecosystem health. While most of the conversation revolves around estrogen and androgen, glucocorticoids (GCs) are also prevalent in natural waters. Despite the fact that GCs play a crucial role in both inflammatory and immunologic development activities, they are also detected in natural waters and considered as one of the EDCs. Although many researchers have mentioned the adverse effect of GCs on aquatic organisms, a complete management technology to remove these pollutants from surface and coastal waters is yet to be established. In the current study, six glucocorticoids (prednisone, prednisolone, cortisone, cortisol, dexamethasone, and 6R-methylprednisolone) have been selected according to their higher detection frequency in environmental waters. The concentration of selected GCs ranged from 0.05 ng/L to 433 ng/L and their removal efficiency ranged from 10% to 99% depending on the water source and associated removal technologies. Although advanced technologies are available for achieving successful removal of GCs, associated operational and economic considerations make implementation of these processes unsustainable. Further studies are necessary to resolve the entry routes of GCs compounds into the surface water or drinking water permanently as well as employ sustainable detection and removal technologies.

Distribution of the water flow path and residence time (HRT) in the hyporheic zone is a pivotal aspect in anatomizing the transport of environmental contaminants and the metabolic rates at the groundwater and surface water interface in fluvial habitats. Due to high variability in material distribution and composition in streambed and subsurface media, a pragmatic model setup in the laboratory is strenuous. Moreover, investigation of an individual streamline cannot be efficiently executed in laboratory experiments. However, an automated generation of water flow paths, i.e., streamlines in the hyporheic zone with a range of different streambed configurations could lead to a greater insight into the behavior of hyporheic water flow. An automated approach to quantifying the water flow in hyporheic zone is developed in this study where the surface water modeling tool, HER-RAS, and subsurface water flow modelling code, MIN3P, are coupled. A 1m long stream with constant water surface elevation of 2 cm to generate hydraulic head gradients and a saturated subsurface computational space with the dimensions of x:y:z = 1:0.1:0.1 m is considered to analyze the hyporheic exchange. Response in the hyporheic streamlines and residence time due to small-scale changes in the gravel-sand streambed were analyzed. The outcomes of the model show that the size, shape, and distribution of the gravel and sand portions have a significant influence on the hyporheic flow path and HRT. A high number and length of the hyporheic flow path are found in case of the highly elevated portion of gravel pieces. With the increase in the base width of gravel pieces, the length of hyporheic flow path and HRT decreases. In the case of increased amounts of gravel and sand portions on the streambed, both the quantity and length of the hyporheic flow path are reduced significantly.