Oil spills are environmental pollution events that occur due to natural disasters or human activities, resulting in a liquid petroleum hydrocarbon release in the environment, especially into the marine ecosystem. Once oil spills happen, they cause detrimental consequences to the environment, living organisms, and humans. Although there are increasing oil and gas activities in the Arctic region, which is abundant with undiscovered oil and gas resources, the harsh environmental conditions of the region, such as the ice coverage, cold temperatures, long periods of darkness, and its remoteness, pose significant challenges to managing the risk of accidental oil spills in ice-infested waters. In this paper, a bibliometric analysis has been applied to study the global work on oil spill research in ice-infested waters. The paper aims to present an overview of the available oil spill response methods in ice-infested waters, identify the current trends of the research on oil spills in ice-infested waters, and determine the challenges with the future research directions based on the bibliometric analysis. The analysis includes a total number of 77 articles that have been published in this research field which were available in the Scopus database, involving 193 authors from 17 countries dating from 1960 to September 2022. During the bibliometric analysis, the top five most productive authors and countries as well as the most cited publications on oil spills in ice-infested waters have been identified; the authors' cooperation network and the cooperation network between the countries in oil spills research in ice-infested waters have been created; a co-citation analysis and a terms analysis have been performed to identify the popular terms and topics. For future directions, it is recommended for researchers (1) to study real oil spills as much as possible to obtain a good overview through replication under different situations; (2) to develop a new technique for the careful examination and management of the potential risks; (3) to study oil separation from the recovered oil-ice mixture.
Currently, many deep learning models are being used to classify COVID-19 and normal cases from chest X-rays. However, the available data (X-rays) for COVID-19 is limited to train a robust deep-learning model. Researchers have used data augmentation techniques to tackle this issue by increasing the numbers of samples through flipping, translation, and rotation. However, by adopting this strategy, the model compromises for the learning of high-dimensional features for a given problem. Hence, there are high chances of overfitting. In this paper, we used deep-convolutional generative adversarial networks algorithm to address this issue, which generates synthetic images for all the classes (Normal, Pneumonia, and COVID-19). To validate whether the generated images are accurate, we used the k-mean clustering technique with three clusters (Normal, Pneumonia, and COVID-19). We only selected the X-ray images classified in the correct clusters for training. In this way, we formed a synthetic dataset with three classes. The generated dataset was then fed to The EfficientNetB4 for training. The experiments achieved promising results of 95% in terms of area under the curve (AUC). To validate that our network has learned discriminated features associated with lung in the X-rays, we used the Grad-CAM technique to visualize the underlying pattern, which leads the network to its final decision.
In this study, Enzalutamide (ENZ) loaded Poly Lactic-co-Glycolic Acid (PLGA) nanoparticles coated with polysarcosine and d-α-Tocopheryl polyethylene glycol 1000 succinate (TPGS) were prepared using a three-step modified nanoprecipitation method combined with self-assembly. A three-factor, three-level Box-Behnken design was implemented with Design-Expert® software to evaluate the impact of three independent variables on particle size, zeta potential, and percent entrapment efficiency through a numeric optimization approach. The results were corroborated with ANOVA analysis, regression equations, and response surface plots. Field emission scanning electron microscopy and transmission electron microscope images revealed nanosized, spherical polymeric nanoparticles (NPs) with a size distribution ranging from 178.9 ± 2.3 to 212.8 ± 0.7 nm, a zeta potential of 12.6 ± 0.8 mV, and entrapment efficiency of 71.2 ± 0.7 %. The latter increased with higher polymer concentration. Increased polymer concentration and homogenization speed also enhanced drug entrapment efficiency. In vitro drug release was 85 ± 22.5 %, following the Higuchi model (R2 = 0.98) and Fickian diffusion (n