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Over the past few decades, machine learning (ML) has demonstrated significant advancements in all areas of human existence. Machine learning and deep learning models rely heavily on data. Typically, basic machine learning (ML) and deep learning (DL) models receive input data and its matching output. Within the model, these models generate rules. In a physics-guided model, input and output rules are provided to optimize the model’s learning, hence enhancing the model’s loss optimization. The concept of the physics-guided neural network (PGNN) is becoming increasingly popular among researchers and industry professionals. It has been applied in numerous fields such as healthcare, medicine, environmental science, and control systems. This review was conducted using four specific research questions. We obtained papers from six different sources and reviewed a total of 81 papers, based on the selected keywords. In addition, we have specifically addressed the difficulties and potential advantages of the PGNN. Our intention is for this review to provide guidance for aspiring researchers seeking to obtain a deeper understanding of the PGNN.

Abstract Skin cancer, one of the most dangerous cancers, poses a significant global threat. While early detection can substantially improve survival rates, traditional dermatologists often face challenges in accurate diagnosis, leading to delays in treatment and avoidable fatalities. Deep learning models like CNN and transfer learning have enhanced diagnosis from dermoscopic images, providing precise and timely detection. However, despite the progress made with hybrid models, many existing approaches still face challenges, such as limited generalization across diverse datasets, vulnerability to overfitting, and difficulty in capturing complex patterns. As a result, there is a growing need for more robust and effective hybrid models that integrate multiple architectures and advanced mechanisms to address these challenges. Therefore, this study aims to introduce a novel multi-architecture hybrid deep learning model called "RvXmBlendNet," which combines the strengths of four individual models: ResNet50 (R), VGG19 (v), Xception (X), and MobileNet (m), followed by "BlendNet" to signify their fusion into a unified architecture. The integration of these models is achieved through a synergistic combination of architectures, incorporating self-attention mechanisms using attention layers and adaptive content blocks. This study used the HAM10000 dataset to refine dermoscopic image preprocessing and enhance deep learning model accuracy. Techniques like OpenCV-based hair removal, min–max scaling, and adaptive histogram equalization were employed to improve image quality and feature extraction. A comparative study between the proposed hybrid "RvXmBlendNet" and individual models (CNN, ResNet50, VGG19, Xception, and MobileNet) demonstrated that "RvXmBlendNet" achieved the highest accuracy of 98.26%, surpassing other models. These results suggest that the system can facilitate earlier interventions, improve patient outcomes, and potentially lower healthcare costs by reducing the need for invasive diagnostic procedures.

Unmanned Aerial Vehicles (UAVs) have transformed the process of data collection and analysis in a variety of research disciplines, delivering unparalleled adaptability and efficacy. This paper presents a thorough examination of UAV datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains.

The integration of distributed energy resources (DERs) into the power grid has made it important for distribution systems to participate in frequency regulation. Regulatory authorities (e.g., Federal Electricity Regulatory Commission in the United States) are recommending that DERs participate in energy and reserve markets, and a mechanism is needed to facilitate this at the distribution level. Though a single distribution system may not have sufficient reserves for tertiary frequency regulation, stacked reserves from multiple distribution systems can be utilized in frequency regulation. This paper proposes a coalitional game theory-based approach for reserve allocation, enabling DERs to participate in tertiary frequency regulation. The proposed two-stage approach involves computing worthiness index (WI) and power loss reduction (PLR) characteristic functions in the first stage and equivalent Shapley values in the second stage. The Shapley values are then used to determine distribution factors for reserve allocation among DERs. We demonstrate the effectiveness of the proposed method through case studies on IEEE 13-node, IEEE 34-node, and IEEE 123-node distribution test systems. The method is shown to be effective in allocating reserves and determining active power set-points of DERs, and can enable efficient participation of DERs in tertiary frequency regulation.

The Monkeypox outbreak has emerged as a pressing global health challenge, evidenced by rising cases across nations. Individuals afflicted exhibit diverse dermatological symptoms that risk further transmission via contamination. Our study assessed the efficacy of three modified transfer learning models (M-VGG16, M-ResNet50, M-ResNet101) alongside vision transformers (ViT) across four investigations. We achieved high accuracy in discriminating Monkeypox cases, with M-VGG16 achieving 88%, 76%, and 77% accuracy in Studies One, Two and Four and M-ResNet50 achieving 89% in Study Three. To comprehend triggers for Monkeypox onset, we utilized Local Interpretable Model-Agnostic Explanations (LIME) to explain predictions visually. LIME alignments underscored our models' high accuracy, correlating with segmented identification of infected regions. Further, we implemented Federated Learning on decentralized data to evaluate generalization capabilities. Blending established deep learning with emerging decentralized learning and explanation techniques is vital in improving predictive accuracy and elucidating Monkeypox intricacies amid the persisting global outbreak. Our study emphasizes the continued relevance of pioneering techniques while introducing new approaches to address this major health challenge.

Plain English summary Eating disorders, maladaptive eating behaviors, and low diversified diets are major public health issues amid the COVID-19 pandemic among university students. The current study aimed to address these issues by investigating Orthorexia Nervosa (ON), food addiction (FA), and dietary diversity. Four-thousand-and-seventy-six students completed an online questionnaire. It was found that being underweight was associated with ON. On the other hand, being overweight/obese was associated with having FA. Individuals who were former smokers and who had poor social interaction were significantly associated with having Orthorexia Nervosa. 1st-year university students are more likely to have FA compared to 5th-year/master’s students. Active smokers were more likely to be food addicted. Students who had FA had less guilty feelings about violating food rules. Participants who had FA consumed more organ meats and eggs. Students who were from upper socio-economic status were more likely to have higher dietary diversity. Nutritional awareness programs focusing on DD and healthy eating habits should be implemented for students.

Background: Due to Covid-19 impacted disruption, Bangladesh government had temporarily shut all the educational institutes down in March 2020 though it was prolonged until September 2021. After continuous concern of UNICEF, when government considered about reopening of schools for in-person classes amid the pandemic, many parents were found to be worried regarding the status of school preparedness. Objective: The purpose of this study is to investigate compliance status of school preparedness as per UNICEF's guidelines and its effect on children's schooling decision during the pandemic. Methods: The study considers 30 primary schools and 310 parents by employing a simple random sampling technique in Dhaka city. Along with the descriptive analysis, principal component analysis (PCA) and a logistic regression model have been executed to come up the goal of the study. Results: The findings explore that classroom density, level of preparedness on health and hygiene, social distancing and awareness, reporting and referral mechanism, communication with school authority, and school arrival on foot have a positive association with the likelihood of parents’ decision for sending their children back to school. On the other hand, monthly expenditure and distance from school to home have a negative relationship with the likelihood on sending children to in-person schooling. Conclusion: The level of school preparedness greatly affects the parent's decision for children's physical schooling during pandemic. So, to keep mental stability of the children amidst any pandemic, the authorities should take care on school preparedness to ensure physical schooling of the children.

Over the past few decades, machine learning (ML) has demonstrated significant advancements in all areas of human existence. Machine learning and deep learning models rely heavily on data. Typically, basic machine learning (ML) and deep learning (DL) models receive input data and its matching output. Within the model, these models generate rules. In a physics-guided model, input and output rules are provided to optimize the model's learning, hence enhancing the model's loss optimization. The concept of the physics-guided neural network (PGNN) is becoming increasingly popular among researchers and industry professionals. It has been applied in numerous fields such as healthcare, medicine, environmental science, and control systems. This review was conducted using four specific research questions. We obtained papers from six different sources and reviewed a total of 81 papers, based on the selected keywords. In addition, we have specifically addressed the difficulties and potential advantages of the PGNN. Our intention is for this review to provide guidance for aspiring researchers seeking to obtain a deeper understanding of the PGNN. [ABSTRACT FROM AUTHOR]