Mobile dermatology applications (apps) created for the purpose of educating students and trainees present convenient supplemental learning opportunities. Before these apps can be widely utilized, there must be a method to assess educational objectives, quality, comprehensiveness of content, evidence-based accuracy, user-friendly design, and potential for bias. Herein, an established rubric was used to conduct a graded review of apps spanning general dermatology, skin cancer, and cosmetics, with an additional emphasis on affordability and accessibility for the user.
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http://dx.doi.org/10.12788/cutis.0127 | DOI Listing |
J Oral Rehabil
January 2025
Departamento de Odontologia Restauradora, Faculdade de Odontologia de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brazil.
Background: Previous research has highlighted the multifactorial nature of awake bruxism (AB), including its associations with stress, anxiety and other psychological factors. Dispositional mindfulness, known for its benefits in enhancing emotional regulation and reducing stress, has not yet been thoroughly investigated in association with AB.
Objective: This study aimed to investigate whether levels of dispositional mindfulness predict the efficacy of ecological momentary intervention (EMI) in reducing the frequency of AB behaviours.
Nutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
View Article and Find Full Text PDFPolymers (Basel)
January 2025
State Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.
Vibration sensors are integral to a multitude of engineering applications, yet the development of low-cost, easily assembled devices remains a formidable challenge. This study presents a highly sensitive flexible vibration sensor, based on the piezoresistive effect, tailored for the detection of high-dynamic-range vibrations and accelerations. The sensor's design incorporates a polylactic acid (PLA) housing with cavities and spherical recesses, a polydimethylsiloxane (PDMS) membrane, and electrodes that are positioned above.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea.
This study presents the fabrication of a sustainable flexible humidity sensor utilizing chitosan derived from mealworm biomass as the primary sensing material. The chitosan-based humidity sensor was fabricated by casting chitosan and polyvinyl alcohol (PVA) films with interdigitated copper electrodes, forming a laminate composite suitable for real-time, resistive-type humidity detection. Comprehensive characterization of the chitosan film was performed using Fourier-transform infrared (FTIR) spectroscopy, contact angle measurements, and tensile testing, which confirmed its chemical structure, wettability, and mechanical stability.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China.
With the proliferation of mobile terminals and the rapid growth of network applications, fine-grained traffic identification has become increasingly challenging. Methods based on machine learning and deep learning have achieved remarkable results, but they heavily rely on the distribution of training data, which makes them ineffective in handling unseen samples. In this paper, we propose AG-ZSL, a zero-shot learning framework based on traffic behavior and attribute representations for general encrypted traffic classification.
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