Guanidine dicycloamine-based analogs: green chemistry synthesis, biological investigation, and molecular docking studies as promising antibacterial and antiglycation leads.

Dicyandiamide (DCD) reacted with amino acids 1a-f to produce biguanides 2 and 4 and guanidine pyrazolones 3, 5, 6, 7, and 8, according to the reaction. DCD exhibited the following reactions: imidodicarbonimidicdiamide 9, diazocan-2-ylguanidine 10, methyl biguanidylthion 11, N-carbamothioylimidodicarbonimidicdiamide 12, 2-guanidinebenzoimidazole 13a, 2-guanidinylbenzoxazole 13b, and 2-guanidinylbenzothiazol 13c. These reactions were triggered by 6-amino caproic acid, thioacetamide, thiourea, o-aminophenol, o-aminothiophenol, and anthranilic acid, respectively. Compound 2 had the least antimicrobial activity, while compound 13c demonstrated the most antibacterial impact against all bacterial strains. Furthermore, in terms of antiglycation efficacy (AGEs), 12, 11, and 7 were the most effective AGE cross-linking inhibitors. Eight and ten, which showed a considerable inhibition on cross-linking AGEs, come next. Compounds 4 and 6 on the other hand have shown the least suppression of AGE production. The most promising antiglycation scaffolds 8, 11, and 12 in the Human serum albumin (HAS) active site were shown to be able to adopt crucial binding interactions with important amino acids based on the results of in silico molecular docking. The most promising antiglycation compounds 8, 11, and 12 were also shown to have better hydrophilicity, acceptable lipophilicity, gastrointestinal tract absorption (GIT), and blood-brain barrier penetration qualities when their physicochemical properties were examined using the egg-boiled method.

Visual-Based Children and Pet Rescue from Suffocation and Incidence of Hyperthermia Death in Enclosed Vehicles.

Over the past several years, many children have died from suffocation due to being left inside a closed vehicle on a sunny day. Various vehicle manufacturers have proposed a variety of technologies to locate an unattended child in a vehicle, including pressure sensors, passive infrared motion sensors, temperature sensors, and microwave sensors. However, these methods have not yet reliably located forgotten children in the vehicle. Recently, visual-based methods have taken the attention of manufacturers after the emergence of deep learning technology. However, the existing methods focus only on the forgotten child and neglect a forgotten pet. Furthermore, their systems only detect the presence of a child in the car with or without their parents. Therefore, this research introduces a visual-based framework to reduce hyperthermia deaths in enclosed vehicles. This visual-based system detects objects inside a vehicle; if the child or pet are without an adult, a notification is sent to the parents. First, a dataset is constructed for vehicle interiors containing children, pets, and adults. The proposed dataset is collected from different online sources, considering varying illumination, skin color, pet type, clothes, and car brands for guaranteed model robustness. Second, blurring, sharpening, brightness, contrast, noise, perspective transform, and fog effect augmentation algorithms are applied to these images to increase the training data. The augmented images are annotated with three classes: child, pet, and adult. This research concentrates on fine-tuning different state-of-the-art real-time detection models to detect objects inside the vehicle: NanoDet, YOLOv6_1, YOLOv6_3, and YOLO7. The simulation results demonstrate that YOLOv6_1 presents significant values with 96% recall, 95% precision, and 95% F1.

User Preference-Based Video Synopsis Using Person Appearance and Motion Descriptions.

During the last decade, surveillance cameras have spread quickly; their spread is predicted to increase rapidly in the following years. Therefore, browsing and analyzing these vast amounts of created surveillance videos effectively is vital in surveillance applications. Recently, a video synopsis approach was proposed to reduce the surveillance video duration by rearranging the objects to present them in a portion of time. However, performing a synopsis for all the persons in the video is not efficacious for crowded videos. Different clustering and user-defined query methods are introduced to generate the video synopsis according to general descriptions such as color, size, class, and motion. This work presents a user-defined query synopsis video based on motion descriptions and specific visual appearance features such as gender, age, carrying something, having a baby buggy, and upper and lower clothing color. The proposed method assists the camera monitor in retrieving people who meet certain appearance constraints and people who enter a predefined area or move in a specific direction to generate the video, including a suspected person with specific features. After retrieving the persons, a whale optimization algorithm is applied to arrange these persons reserving chronological order, reducing collisions, and assuring a short synopsis video. The evaluation of the proposed work for the retrieval process in terms of precision, recall, and F1 score ranges from 83% to 100%, while for the video synopsis process, the synopsis video length compared to the original video is decreased by 68% to 93.2%, and the interacting tube pairs are preserved in the synopsis video by 78.6% to 100%.

Light-Weight Deep Learning Techniques with Advanced Processing for Real-Time Hand Gesture Recognition.

In the discipline of hand gesture and dynamic sign language recognition, deep learning approaches with high computational complexity and a wide range of parameters have been an extremely remarkable success. However, the implementation of sign language recognition applications for mobile phones with restricted storage and computing capacities is usually greatly constrained by those limited resources. In light of this situation, we suggest lightweight deep neural networks with advanced processing for real-time dynamic sign language recognition (DSLR). This paper presents a DSLR application to minimize the gap between hearing-impaired communities and regular society. The DSLR application was developed using two robust deep learning models, the GRU and the 1D CNN, combined with the MediaPipe framework. In this paper, the authors implement advanced processes to solve most of the DSLR problems, especially in real-time detection, e.g., differences in depth and location. The solution method consists of three main parts. First, the input dataset is preprocessed with our algorithm to standardize the number of frames. Then, the MediaPipe framework extracts hands and poses landmarks (features) to detect and locate them. Finally, the features of the models are passed after processing the unification of the depth and location of the body to recognize the DSL accurately. To accomplish this, the authors built a new American video-based sign dataset and named it DSL-46. DSL-46 contains 46 daily used signs that were presented with all the needed details and properties for recording the new dataset. The results of the experiments show that the presented solution method can recognize dynamic signs extremely fast and accurately, even in real-time detection. The DSLR reaches an accuracy of 98.8%, 99.84%, and 88.40% on the DSL-46, LSA64, and LIBRAS-BSL datasets, respectively.

More efficient estimators of the area under the receiver operating characteristic curve in paired ranked set sampling.

Receiver operating characteristic is a beneficial technique for evaluating the performance of a binary classification. The area under the curve of the receiver operating characteristic is an effective index of the accuracy of the classification process. While nonparametric point estimation has been well-studied under the ranked set sampling, it has received little attention under ranked set sampling variations. In order to set out to fill this gap, this article deals with the problem of estimating the area under the curve of the receiver operating characteristic based on paired ranked set sampling. New estimators of the area under the curve of the receiver operating characteristic based on paired ranked set sampling are proposed. Using the information supported by the concomitant variable, the additional area under the curve of the receiver operating characteristic estimators based on ranked set sampling as well as paired ranked set sampling are also introduced. It is shown either theoretically or numerically that the proposed estimators are consistent under the perfectness situation. It emerges that the concomitant-based estimators are shown to be superior to their competitors provided that the perfect assumption is not sharply violated. In contrast, kernel-based estimators are significantly superior relative to their rivals regardless of the quality of ranking. Finally, the application of the proposed procedures is also demonstrated by using empirical datasets in the context of medicine.

On non-negative estimation of variance components in mixed linear models.

Alternative estimators have been derived for estimating the variance components according to Iterative Almost Unbiased Estimation (IAUE). As a result two modified IAUEs are introduced. The relative performances of the proposed estimators and other estimators are studied by simulating their bias, Mean Square Error and the probability of getting negative estimates under unbalanced nested-factorial model with two fixed crossed factorial and one nested random factor. Finally the Empirical Quantile Dispersion Graph (EQDG), which provides a comprehensive picture of the quality of estimation, is depicted corresponding to all the studied methods.