Synthesis, crystal structure and Hirshfeld analysis of a novel supra-molecular compound [Co(tsc)][Co(cit)](NO)·4HO.

A new cobalt complex, bis-[tris-(amino-thio-urea)cobalt(III)] bis-[2-(carb-oxy-methyl)-2-hy-droxy-butane-dioato]cobalt(II) tetra-nitrate tetra-hydrate, [Co(CHNS)][Co(CHO)](NO)·2HO, designated as [Co(tsc)][Co(cit)](NO)·4HO, was synthesized. Two crystallographically independent cobalt centers are present. In the first, the central metal atom is chelated by three thio-semicarbazide ligands in a bidentate fashion whereas the second, positioned on a crystallographic inversion center, is hexa-coordinated by two citrate anions in a distorted octa-hedral geometry.

GazeCapsNet: A Lightweight Gaze Estimation Framework.

Gaze estimation is increasingly pivotal in applications spanning virtual reality, augmented reality, and driver monitoring systems, necessitating efficient yet accurate models for mobile deployment. Current methodologies often fall short, particularly in mobile settings, due to their extensive computational requirements or reliance on intricate pre-processing. Addressing these limitations, we present Mobile-GazeCapsNet, an innovative gaze estimation framework that harnesses the strengths of capsule networks and integrates them with lightweight architectures such as MobileNet v2, MobileOne, and ResNet-18.

Optimized Lightweight Architecture for Coronary Artery Disease Classification in Medical Imaging.

: The early and accurate detection of Coronary Artery Disease (CAD) is crucial for preventing life-threatening complications, particularly among athletes engaged in high-intensity endurance sports. This demographic faces unique cardiovascular risks, as prolonged and intense physical exertion can exacerbate underlying CAD conditions. Studies indicate that while athletes typically exhibit enhanced cardiovascular health, this demographic is not immune to Coronary Artery Disease (CAD) risks.

A lightweight attention-driven YOLOv5m model for improved brain tumor detection.

Brain tumors are regarded as one of the most lethal, devastating, and aggressive diseases, significantly reducing the life expectancy of affected individuals. For this reason, in pursuit of advancing brain tumor diagnostics, this study introduces a significant enhancement to the YOLOv5m model by integrating an Enhanced Spatial Attention (ESA) layer, tailored specifically for the analysis of magnetic resonance imaging (MRI) brain scans. Traditional brain tumor detection methods, heavily reliant on expert interpretation of MRI, are fraught with challenges such as high variability and the risk of human error.

Cross-Modal Transformer-Based Streaming Dense Video Captioning with Neural ODE Temporal Localization.

Dense video captioning is a critical task in video understanding, requiring precise temporal localization of events and the generation of detailed, contextually rich descriptions. However, the current state-of-the-art (SOTA) models face significant challenges in event boundary detection, contextual understanding, and real-time processing, limiting their applicability to complex, multi-event videos. In this paper, we introduce CMSTR-ODE, a novel Cross-Modal Streaming Transformer with Neural ODE Temporal Localization framework for dense video captioning.

RL-Cervix.Net: A Hybrid Lightweight Model Integrating Reinforcement Learning for Cervical Cell Classification.

Reinforcement learning (RL) represents a significant advancement in artificial intelligence (AI), particularly for complex sequential decision-making challenges. Its capability to iteratively refine decisions makes it ideal for applications in medicine, such as the detection of cervical cancer; a major cause of mortality among women globally. The Pap smear test, a crucial diagnostic tool for cervical cancer, benefits from enhancements in AI, facilitating the development of automated diagnostic systems that improve screening effectiveness.

Lightweight Deep Learning Framework for Accurate Detection of Sports-Related Bone Fractures.

Sports-related bone fractures are a common challenge in sports medicine, requiring accurate and timely diagnosis to prevent long-term complications and enable effective treatment. Conventional diagnostic methods often rely on manual interpretation, which is prone to errors and inefficiencies, particularly for subtle and localized fractures. This study aims to develop a lightweight and efficient deep learning-based framework to improve the accuracy and computational efficiency of fracture detection, tailored to the needs of sports medicine.

Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices.

The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight and efficient RetinaNet variant tailored for medical edge device deployment. The model reduces computational overhead while maintaining high detection accuracy by replacing the computationally intensive ResNet backbone with MobileNet and leveraging depthwise separable convolutions.

MIRA-CAP: Memory-Integrated Retrieval-Augmented Captioning for State-of-the-Art Image and Video Captioning.

Generating accurate and contextually rich captions for images and videos is essential for various applications, from assistive technology to content recommendation. However, challenges such as maintaining temporal coherence in videos, reducing noise in large-scale datasets, and enabling real-time captioning remain significant. We introduce MIRA-CAP (Memory-Integrated Retrieval-Augmented Captioning), a novel framework designed to address these issues through three core innovations: a cross-modal memory bank, adaptive dataset pruning, and a streaming decoder.

Dynamic Focus on Tumor Boundaries: A Lightweight U-Net for MRI Brain Tumor Segmentation.

Accurate segmentation of brain tumors in MRI scans is critical for diagnosis and treatment planning. Traditional segmentation models, such as U-Net, excel in capturing spatial information but often struggle with complex tumor boundaries and subtle variations in image contrast. These limitations can lead to inconsistencies in identifying critical regions, impacting the accuracy of clinical outcomes.

Lightweight Super-Resolution Techniques in Medical Imaging: Bridging Quality and Computational Efficiency.

Medical imaging plays an essential role in modern healthcare, providing non-invasive tools for diagnosing and monitoring various medical conditions. However, the resolution limitations of imaging hardware often result in suboptimal images, which can hinder the precision of clinical decision-making. Single image super-resolution (SISR) techniques offer a solution by reconstructing high-resolution (HR) images from low-resolution (LR) counterparts, enhancing the visual quality of medical images.

In Vitro conservation and genetic diversity analysis of rare species Ribes janczewskii.

Ribes janczewskii is a rare and valuable plant known for its resistance to spring frosts, pests, and diseases. It is used in hybridization to develop resistant currant varieties but is on the verge of extinction, listed in Kazakhstan Red Book. This study developed a micropropagation and slow-growth storage protocol for conservation.

Novelty Classification Model Use in Reinforcement Learning for Cervical Cancer.

Purpose: Cervical cancer significantly impacts global health, where early detection is piv- otal for improving patient outcomes. This study aims to enhance the accuracy of cervical cancer diagnosis by addressing class imbalance through a novel hybrid deep learning model.

Methods: The proposed model, RL-CancerNet, integrates EfficientNetV2 and Vision Transformers (ViTs) within a Reinforcement Learning (RL) framework.

Enhancing Medical Image Denoising with Innovative Teacher-Student Model-Based Approaches for Precision Diagnostics.

The realm of medical imaging is a critical frontier in precision diagnostics, where the clarity of the image is paramount. Despite advancements in imaging technology, noise remains a pervasive challenge that can obscure crucial details and impede accurate diagnoses. Addressing this, we introduce a novel teacher-student network model that leverages the potency of our bespoke NoiseContextNet Block to discern and mitigate noise with unprecedented precision.

Enhancing the Super-Resolution of Medical Images: Introducing the Deep Residual Feature Distillation Channel Attention Network for Optimized Performance and Efficiency.

In the advancement of medical image super-resolution (SR), the Deep Residual Feature Distillation Channel Attention Network (DRFDCAN) marks a significant step forward. This work presents DRFDCAN, a model that innovates traditional SR approaches by introducing a channel attention block that is tailored for high-frequency features-crucial for the nuanced details in medical diagnostics-while streamlining the network structure for enhanced computational efficiency. DRFDCAN's architecture adopts a residual-within-residual design to facilitate faster inference and reduce memory demands without compromising the integrity of the image reconstruction.

Peculiarities of occupational mental health care in kindergarten teachers.

The purpose of this paper is to quantify the factors that disrupt the mental health of kindergarten (KG) teachers. For this, the researchers conducted an electronic survey of preschool teachers ( = 587) on a popular educational platform with the Symptom Checklist-90-R and content analysis of interviews in practicing KG teachers ( = 105) with an open discussion of the main stressors during professional activities. Self-reports indicated that depression, interpersonal sensitivity, and anxiety were the main mental health symptoms.

Nutritional Evaluation of Buns Developed from Chickpea-Mung Bean Composite Flour and Sugar Beet Powder.

The research was aimed at developing recipes for buns studying the nutritional value of securities. In the work, an assortment of bakery products was developed from flour, composite mixtures of leguminous crops and dry powders of sugar beets. As a result, bakery products with useful properties and improved qualities were obtained.