Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning

Inland waterways are critical for freight movement, but limited means exist for monitoring their performance and usage by freight-carrying vessels, e.g., barges. While methods to track vessels, e.g., tug and tow boats, are publicly available through Automatic Identification Systems (AIS), ways to track freight tonnages and commodity flows carried on barges along these critical marine highways are non-existent, especially in real-time settings. This paper develops a method to detect barge traffic on inland waterways using existing traffic cameras with opportune viewing angles. Deep learning models, specifically, You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and EfficientDet are employed. The model detects the presence of vessels and/or barges from video and performs a classification (no vessel or barge, vessel without barge, vessel with barge, and barge). A dataset of 331 annotated images was collected from five existing traffic cameras along the Mississippi and Ohio Rivers for model development. YOLOv8 achieves an F1-score of 96%, outperforming YOLOv5, SSD, and EfficientDet models with 86%, 79%, and 77% respectively. Sensitivity analysis was carried out regarding weather conditions (fog and rain) and location (Mississippi and Ohio rivers). A background subtraction technique was used to normalize video images across the various locations for the location sensitivity analysis. This model can be used to detect the presence of barges along river segments, which can be used for anonymous bulk commodity tracking and monitoring. Such data is valuable for long-range transportation planning efforts carried out by public transportation agencies, in addition to operational and maintenance planning conducted by federal agencies such as the US Army Corp of Engineers

Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning

The proper enforcement of motorcycle helmet regulations is crucial for ensuring the safety of motorbike passengers and riders, as roadway cyclists and passengers are not likely to abide by these regulations if no proper enforcement systems are instituted. This paper presents the development and evaluation of a real-time YOLOv5 Deep Learning (DL) model for detecting riders and passengers on motorbikes, identifying whether the detected person is wearing a helmet. We trained the model on 100 videos recorded at 10 fps, each for 20 seconds. Our study demonstrated the applicability of DL models to accurately detect helmet regulation violators even in challenging lighting and weather conditions. We employed several data augmentation techniques in the study to ensure the training data is diverse enough to help build a robust model. The proposed model was tested on 100 test videos and produced an mAP score of 0.5267, ranking 11th on the AI City Track 5 public leaderboard. The use of deep learning techniques for image classification tasks, such as identifying helmet-wearing riders, has enormous potential for improving road safety. The study shows the potential of deep learning models for application in smart cities and enforcing traffic regulations and can be deployed in real-time for city-wide monitoring

Skin color, cultural capital, and beauty products: An investigation of the use of skin fairness products in Mumbai, India

The use of skin fairness products that frequently contain toxic ingredients is associated with significant adverse health side effects. Due to the high prevalence of use in Asian and African countries, skin fairness product use is recognized as a growing public health concern. The multi-million-dollar skin fairness product industry has also been criticized for perpetuating racism and social inequalities by reinforcing beliefs about the benefits of skin fairness for cultural capital. No quantitative studies have assessed people’s beliefs about fairness and reasons for using or not using these products in India, one of the largest global markets for skin fairness products. The current study explored skin fairness product use among 1,992 women and men aged 16–60 years in the city of Mumbai, India using a self-report questionnaire. A total of 37.6% of the sample reported currently using skin fairness products, with women being two times more likely to use these products. Among current users, 17% reported past experiences of adverse side effects, and “Media/TV/Adverts” were the most common prompts for using fairness products, followed by “Friends” and “Family.” Men were significantly more likely than women to endorse beliefs about fairness being more attractive and were more likely to perceive family and peers as viewing fairness as beneficial for cultural capital. There were no differences between women and men currently using products in their desire to look as fair as media celebrities. Among non-users, women were significantly more likely than men to report concerns about product efficacy and side effects as reasons for non-use, while men were significantly more likely to report socioeconomic reasons for non-use. Implications of these findings are discussed in light of growing public health concerns about the use of fairness products, and potential for advocacy and public health interventions to address the use of skin fairness products

Metal and nonmetal doped semiconductor photocatalysts for water treatment

PhD. (Chemistry)Please refer to full text to view abstrac

A comparative analysis on levels of mercury in human scalp hair of students from different locations

Abstract: This research was carried out to assess the levels of accumulation of total mercury (Hg-T) in human scalp hair samples from selected students. Thirty seven (37) human scalp hair samples were collected from students of the Kwame Nkrumah University of Science and Technology whilst on campus and analysed for total mercury (Hg-T) concentrations by cold vapour atomic absorption spectrometry. The least concentration, 0.007±0.001 :/g was measured in a sample from a male student. The highest concentration, 5.535±0.133 :g/g was measured in a sample from a female student.5.4% of the population had Hg-T concentrations above the WHO, 1990 limit of 2 :g/g based on fish consumption. 94.6% of the population studied however measured Hg-T concentrations below the WHO limit. In general, the concentrations measured in female students were higher compared to concentrations in male students. The mean concentration of Hg-T in female students was 1.417±0.037 :g/g compared to 0.600±0.001 :g/g for male students. The higher concentrations measured in female students may be attributed to the application of Hg containing cosmetics aside environmental exposures

Sulfur/Gadolinium-Codoped TiO2 Nanoparticles for Enhanced Visible-Light Photocatalytic Performance

A series of S/Gd3+-codoped TiO2 photocatalysts were synthesized by a modified sol-gel method. The materials were characterized by X-ray diffraction (XRD), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), UV-visible diffuse reflectance spectroscopy, scanning electron microscopy (SEM)/energy-dispersive X-ray spectroscopy (EDX), and transmission electron microscopy (TEM)/energy-dispersive spectroscopy (EDS). Laboratory experiments with Indigo Carmine chosen as a model for organic pollutants were used to evaluate the photocatalytic performance of S/Gd3+-codoped TiO2 under visible-light with varying concentrations of Gd3+ ions in the host material. XRD and Raman results confirmed the existence of anatase phase TiO2 with particle size ranging from 5 to 12 nm. Codoping has exerted a great influence on the optical responses along with red shift in the absorption edge. S/Gd3+-codoped TiO2 showed significant visible-light induced photocatalytic activity towards Indigo Carmine dye compared with S-TiO2 or commercial TiO2. TiO2-S/Gd3+ (0.6% Gd3+) degraded the dye (ka = 5.6 × 10−2 min−1) completely in 50 min