Surface Thermo-Dynamic Characterization of Poly (Vinylidene Chloride-Co-Acrylonitrile) (P(VDC-co-AN)) Using Inverse-Gas Chromatography and Investigation of Visual Traits Using Computer Vision Image Processing Algorithms

Exploring diabetes through the lens of AI and computer vision: Methods and future prospects

Early diagnosis and timely initiation of treatment plans for diabetes are crucial for ensuring individuals' well-being. Emerging technologies like artificial intelligence (AI) and computer vision are highly regarded for their ability to enhance the accessibility of large datasets for dynamic training and deliver efficient real-time intelligent technologies and predictable models. The application of AI and computer vision techniques to enhance the analysis of clinical data is referred to as eHealth solutions that employ advanced approaches to aid medical applications. This study examines several advancements and applications of machine learning, deep learning, and machine vision in global perception, with a focus on sustainability. This article discusses the significance of utilizing artificial intelligence and computer vision to detect diabetes, as it has the potential to significantly mitigate harm to human life. This paper provides several comments addressing challenges and recommendations for the use of this technology in the field of diabetes. This study explores the potential of employing Industry 4.0 technologies, including machine learning, deep learning, and computer vision robotics, as effective tools for effectively dealing with diabetes related aspects.

Toxicological assessment of divalent ion-modified ZnO nanomaterials through artificial intelligence and in vivo study

The nanotechnology-driven industrial revolution widely relies on metal oxide-based nanomaterial (NM). Zinc oxide (ZnO) production has rapidly increased globally due to its outstanding physical and chemical properties and versatile applications in industries including cement, rubber, paints, cosmetics, and more. Nevertheless, releasing Zn2+ ions into the environment can profoundly impact living systems and affect water-based ecosystems, including biological ones. In aquatic environments, Zn2+ ions can change water properties, directly influencing underwater ecosystems, especially fish populations. These ions can accumulate in fish tissues when fish are exposed to contaminated water and pose health risks to humans who consume them, leading to symptoms such as nausea, vomiting, and even organ damage. To address this issue, safety of ZnO NMs should be enhanced without altering their nanoscale properties, thus preventing toxic-related problems. In this study, an eco-friendly precipitation method was employed to prepare ZnO NMs. These NMs were found to reduce ZnO toxicity levels by incorporating elements such as Mg, Ca, Sr, and Ba. Structural, morphological, and optical properties of synthesized NMs were thoroughly investigated. In vitro tests demonstrated potential antioxidative properties of NMs with significant effects on free radical scavenging activities. In vivo, toxicity tests were conducted using Oreochromis mossambicus fish and male Swiss Albino mice to compare toxicities of different ZnO NMs. Fish and mice exposed to these NMs exhibited biochemical changes and histological abnormalities. Notably, ZnCaO NMs demonstrated lower toxicity to fish and mice than other ZnO NMs. This was attributed to its Ca2+ ions, which could enhance body growth metabolism compared to other metals, thus improving material safety. Furthermore, whether nanomaterials' surface roughness might contribute to their increased toxicity in biological systems was investigated utilizing computer vision (CV)-based AI tools to obtain SEM images of NMs, providing valuable image-based surface morphology data that could be correlated with relevant toxicology studies.

Surface Thermal Behavior and RT CO Gas Sensing Application of an Oligoacenaphthylene with p-Hydroxyphenylacetic Acid Composite

The current work describes room-temperature gas sensing performances using an oligoacenaphthylene (OAN)/p-hydroxyphenylacetic acid (p-HPA) composite. Based on inverse gas chromatography (IGC), the London dispersive surface energy γ_s ^d is calculated by using 14 representative models. Even when the γ_s ^d values of both OAN and the OAN/p-HPA composite are decreased as the temperature increases, the surface of OAN shows a higher value than that of the composite. The Gibbs surface free energy values of both are decreased with an increasing temperature. In our results, higher Lewis basic characters are observed in OAN and the OAN/p-HPA composite and the OAN/p-HPA surface exhibits a higher basicity compared to OAN. Because of the presence of phenolic groups in the OAN/p-HPA composite, the more important basic character drives a significant CO gas sensing ability with a sensitivity of 8.96% and good cycling stability as compared to the pristine counterparts. It is expected that the current study sheds light on a new pathway to exploring polymer composite materials for futuristic diverse and multiple applications, including IGC and gas sensor applications.

Evaluation of the surface properties of 4-(Decyloxy) benzoic acid liquid crystal and its use in structural isomer separation

The selectivity of 4-(Decyloxy) benzoic acid (DBA) liquid crystal in surface adsorption region (303.2–328.2 K) and thermodynamic region (423.2 – 433.2 K) was investigated by inverse gas chromatography at infinite dilution (IGC-ID). The selectivity parameters of the structural isomer series named butyl acetate, butyl alcohol, and amyl alcohol series were calculated for the DBA using IGC-ID technique. Additionally, the surface properties including dispersive surface energy (gS D), free energy (DGA S), enthalpy (DHA S), and acidity-basicity constants were calculated with net retention volumes obtained from IGC-ID experiment results. When the DHA S and DGA S are constants, DBA surface was found to be an acidic character (KD/KA @ 0.89).

Vision-Based Tactile Sensor Mechanism for the Estimation of Contact Position and Force Distribution Using Deep Learning

This work describes the development of a vision-based tactile sensor system that utilizes the image-based information of the tactile sensor in conjunction with input loads at various motions to train the neural network for the estimation of tactile contact position, area, and force distribution. The current study also addresses pragmatic aspects, such as choice of the thickness and materials for the tactile fingertips and surface tendency, etc. The overall vision-based tactile sensor equipment interacts with an actuating motion controller, force gauge, and control PC (personal computer) with a LabVIEW software on it. The image acquisition was carried out using a compact stereo camera setup mounted inside the elastic body to observe and measure the amount of deformation by the motion and input load. The vision-based tactile sensor test bench was employed to collect the output contact position, angle, and force distribution caused by various randomly considered input loads for motion in X, Y, Z directions and RxRy rotational motion. The retrieved image information, contact position, area, and force distribution from different input loads with specified 3D position and angle are utilized for deep learning. A convolutional neural network VGG-16 classification modelhas been modified to a regression network model and transfer learning was applied to suit the regression task of estimating contact position and force distribution. Several experiments were carried out using thick and thin sized tactile sensors with various shapes, such as circle, square, hexagon, for better validation of the predicted contact position, contact area, and force distribution.