Visual inspection for illicit items in X-ray images using Deep Learning

Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours practically make it a Big Data problem. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage object detectors. However, no comparative experimental assessment of the various relevant DNN components/methods has been performed under a common evaluation protocol, which means that reliable cross-method comparisons are missing. This paper presents exactly such a comparative assessment, utilizing a public relevant dataset and a well-defined methodology for selecting the specific DNN components/modules that are being evaluated. The results indicate the superiority of Transformer detectors, the obsolete nature of auxiliary neural modules that have been developed in the past few years for security applications and the efficiency of the CSP-DarkNet backbone CNN.Comment: arXiv admin note: substantial text overlap with arXiv:2305.0193

Green Dental Environmentalism among Students and Dentists in Greece

Ηuman sustainability in dental enterprises, as in every workplace, is connected to air and water quality, eco-friendly and naturally designed working spaces, and the culture of the 4Rs. The purpose of this study was to assess pro-environmental behavior, as well as knowledge of preferences for circular economies and green building construction, among a sample of dental students and dentists in Greece. We further assessed the factors influencing their choices. Students (N1 = 93) and dentists (N2 = 126) filled in e-questionnaires from April to December 2022. The data revealed that both students and dentists lack knowledge about the circular economy (N1 = 67.74%, N2 = 68.25%), EU regulations on amalgam disposal (N1 = 64.51%, N2 = 58.73%), and plastic recycling (N1 = 76.34%, N2 = 76.98%); meanwhile, they do recycle at home (N1 = 80.64%, N2 = 82.54%) and have participated in voluntary environmental initiatives (N1 = 58.06%, N2 = 66.66%). Gender influences the importance of factors related to green dental practices, with women students being more likely to agree that increased costs for network changes (p = 0.02) and poor wastewater management (p = 0.01) are significant. Students from urban areas are more likely to give positive answers to questions related to the lack of state financial support (p = 0.02), low levels of green design in buildings (p = 0.03), the negligible direct financial benefits of green dental offices (p = 0.04), the negligible reputational benefits of green dental offices (p = 0.02), and the lack of continuing education training seminars on green dentistry (p = 0.05). For dentists, no significant relationships were observed, except for a weak positive relationship for the increases in costs due to changes related to utility networks (p = 0.08), while increases in waste energy (p = 0.12) and the waste of dental materials (p = 0.19) seemed significant only for dentists in urban areas. Women dentists were more likely to answer positively regarding wasting energy (p = 0.024) and the use of unapproved disinfection products (p = 0.036). The findings contribute ideas and solutions for green dental practice buildings and sustainable behaviors through educational activities and regarding the social aspects of factors such as age, experience in dentistry, gender, and urbanism. This study also provides a basis for future multi-disciplinary research on dental quality assurance, the psychology of environmentalism, economics, and behavioral science in dentistry