An appropriate artificial intelligence technique for plastic materials recycling using bipolar dual hesitant fuzzy set.

Plastic recycling has become more important than ever as the globe struggles with growing environmental issues. This research explores the significant environmental impact of recycling plastic and its growing relevance. The pervasive material known as plastic presents a complex risk to both human health and ecosystems in contemporary life.

An ultra-sensitive surface plasmon resonance biosensor with PtSe and BlueP/WS heterostructure.

This paper presents the design and simulation of a surface plasmon resonance (SPR) biosensor using a Platinum diselenide (PtSe) and Blue Phosphorus/tungsten disulfide (BlueP/WS) heterostructure for biosensing protocols. The simulation is done by using a finite element method (FEM) based COMSOL Multiphysics software. The performance of the SPR biosensor is then optimized for obtaining maximum sensitivity, quality factor, detection accuracy, and low limit of detection (LOD).

Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI.

The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields.

An Integrated Smart Pond Water Quality Monitoring and Fish Farming Recommendation Aquabot System.

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality.

Time-dependent localized patterns in a predator-prey model.

Numerical continuation is used to compute solution branches in a two-component reaction-diffusion model of Leslie-Gower type. Two regimes are studied in detail. In the first, the homogeneous state loses stability to supercritical spatially uniform oscillations, followed by a subcritical steady state bifurcation of Turing type.

COVID-19 symptom identification using Deep Learning and hardware emulated systems.

The COVID-19 pandemic disrupted regular global activities in every possible way. This pandemic, caused by the transmission of the infectious Coronavirus, is characterized by main symptoms such as fever, fatigue, cough, and loss of smell. A current key focus of the scientific community is to develop automated methods that can effectively identify COVID-19 patients and are also adaptable for foreseen future virus outbreaks.

Corrigendum to:"Deep learning based suture training system" [Surgery Open Science 15 (2023) 1-11/1].

[This corrects the article DOI: 10.1016/j.sopen.

Corrigendum: Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks.

[This corrects the article DOI: 10.3389/fdata.2023.

Corrigendum: Anemia detection through non-invasive analysis of lip mucosa images.

[This corrects the article DOI: 10.3389/fdata.2023.

Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks.

Anemia is defined as a drop in the number of erythrocytes or hemoglobin concentration below normal levels in healthy people. The increase in paleness of the skin might vary based on the color of the skin, although there is currently no quantifiable measurement. The pallor of the skin is best visible in locations where the cuticle is thin, such as the interior of the mouth, lips, or conjunctiva.

Anemia detection through non-invasive analysis of lip mucosa images.

This paper aims to detect anemia using images of the lip mucosa, where the skin tissue is thin, and to confirm the feasibility of detecting anemia noninvasively and in the home environment using machine learning (ML). Data were collected from 138 patients, including 100 women and 38 men. Six ML algorithms: artificial neural network (ANN), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM) which are widely used in medical applications, were used to classify the collected data.

Deep learning based suture training system.

Background And Objectives: Surgical suturing is a fundamental skill that all medical and dental students learn during their education. Currently, the grading of students' suture skills in the medical faculty during general surgery training is relative, and students do not have the opportunity to learn specific techniques. Recent technological advances, however, have made it possible to classify and measure suture skills using artificial intelligence methods, such as Deep Learning (DL).

Transitions between dissipative localized structures in the simplified Gilad-Meron model for dryland plant ecology.

Spatially extended patterns and multistability of possible different states are common in many ecosystems, and their combination has an important impact on their dynamical behaviors. One potential combination involves tristability between a patterned state and two different uniform states. Using a simplified version of the Gilad-Meron model for dryland ecosystems, we study the organization, in bifurcation terms, of the localized structures arising in tristable regimes.

The Effect of Various Environmental Conditions on the Impact Damage Behaviour of Natural-Fibre-Reinforced Composites (NFRCs)-A Critical Review.

Studies into environmental conditions and their effects on the properties of renewable materials are gaining significant attention in the research field, particularly for natural fibres and their resultant composites. However, natural fibres are prone to water absorption because of the hydrophilic nature of the fibres, which affects the overall mechanical properties of natural-fibre-reinforced composites (NFRCs). In addition, NFRCs are based mainly on thermoplastic and thermosetting matrices, which could be used in automobile and aerospace components as lightweight materials.

Bandwidth Enhancement and Generation of CP of Yagi-Uda-Shape Feed on a Rectangular DRA for 5G Applications.

A wideband circularly polarized rectangular dielectric resonator antenna (DRA) fed by a single feeding mechanism has been studied theoretically and experimentally. The purpose of the study is to determine how adding a parasitic strip next to the flat surface metallic feed would affect various far- and near-field antenna characteristics. Initially, the basic antenna design, i.

Micro-Mechanical Investigation of Interfacial Debonding in Carbon Fiber-Reinforced Composites Using Extended Finite Element Method (XFEM) Approach.

The interface debonding in carbon fiber-reinforced polymers is analyzed and evaluated using the extended finite element method (XFEM). In order to accurately evaluate the bonding properties between fibers and matrix, different tests were carried out, including the multiple tests for different orientations to study longitudinal, transversal, and shear properties of unidirectional carbon fiber-reinforced composites. Extensive experimentation has been performed in all the different groups and categories with different dimensions and parameters in order to ascertain the values of strength and the prediction of the damage to the structure.

A Novel Design and Development of a Strip-Fed Circularly Polarized Rectangular Dielectric Resonator Antenna for 5G NR Sub-6 GHz Band Applications.

In this article, a rectangular dielectric resonator antenna (RDRA) with circularly polarized (CP) response is presented for 5G NR (New Radio) Sub-6 GHz band applications. A uniquely shaped conformal metal feeding strip is proposed to excite the RDRA in higher-order mode for high gain utilization. By using the proposed feeding mechanism, the degenerate mode pair of the first higher-order, i.

Electrically Tunable Lens (ETL)-Based Variable Focus Imaging System for Parametric Surface Texture Analysis of Materials.

Electrically tunable lenses (ETLs) are those with the ability to alter their optical power in response to an electric signal. This feature allows such systems to not only image the areas of interest but also obtain spatial depth perception (depth of field, DOF). The aim of the present study was to develop an ETL-based imaging system for quantitative surface analysis.

Unprecedented Impacts of Aviation Emissions on Global Environmental and Climate Change Scenario.

There has been a continuously growing trend in international commercial air traffic, with the exception of COVID-19 crises; however, after the recovery, the trend is expected to even sharpen. The consequences of released emissions and by-products in the environment range from human health hazards, low air quality and global warming. This study is aimed to investigate the role of aviation emissions in global warming.

Unified framework for localized patterns in reaction-diffusion systems; the Gray-Scott and Gierer-Meinhardt cases.

A recent study of canonical activator-inhibitor Schnakenberg-like models posed on an infinite line is extended to include models, such as Gray-Scott, with bistability of homogeneous equilibria. A homotopy is studied that takes a Schnakenberg-like glycolysis model to the Gray-Scott model. Numerical continuation is used to understand the complete sequence of transitions to two-parameter bifurcation diagrams within the localized pattern parameter regime as the homotopy parameter varies.

Exploration of a New Source of Sustainable Nanomaterial from the Koh-e-Suleiman Mountain Range of Pakistan for Industrial Applications.

The present study aimed to explore a new source of montmorillonite and to develop an extraction and purification protocol for its isolation from raw clay samples acquired from the Koh-e-Suleiman mountain range in Pakistan. The process involved the collection of raw clay from the source, identification and quantification of montmorillonite. Granulometric extraction and purification protocols increased the montmorillonite content from 21.

Plasticiser-Free 3D Printed Hydrophilic Matrices: Quantitative 3D Surface Texture, Mechanical, Swelling, Erosion, Drug Release and Pharmacokinetic Studies.

Hydroxypropyl methyl cellulose, HPMC, a hydrophilic polymer, is widely used for the development of extended release hydrophilic matrices and it is also considered as a good contender for the fabrication of 3D printing of matrix tablets. It is often combined with plasticisers to enable extrusion. The aim of the current project was to develop plasticizer-free 3D printed hydrophilic matrices using drug loaded filaments prepared via HME to achieve an in vitro (swelling, erosion and drug release) and in vivo (drug absorption) performance which is analogous to hydrophilic matrix tablets developed through conventional approaches.

Advances in diamond nanofabrication for ultrasensitive devices.

This paper reviews some of the major recent advances in single-crystal diamond nanofabrication and its impact in nano- and micro-mechanical, nanophotonics and optomechanical components. These constituents of integrated devices incorporating specific dopants in the material provide the capacity to enhance the sensitivity in detecting mass and forces as well as magnetic field down to quantum mechanical limits and will lead pioneering innovations in ultrasensitive sensing and precision measurements in the realm of the medical sciences, quantum sciences and related technologies.

Nano-Magnetic Resonance Imaging (Nano-MRI) Gives Personalized Medicine a New Perspective.

This paper reviews some of the major and most recent advances in nanoscale-magnetic resonance imaging (nano-MRI) for personalized medicine (PM). Nano-MRI may drastically expand the capabilities of the traditional magnetic resonance images (MRI), down to the nanometer scale and possibly, in the near future, at the atomic scale. Nano-MRI is potentially able to observe structures which cannot be seen using today's molecular imaging, with sensitivities of many billions of times better than MRI as currently used in hospitals, for example.

Effect of boundary conditions on the evolution of lattice strains in a polycrystalline austenitic stainless steel.

The effect of boundary conditions (constant load, constant strain and elastic follow-up) on lattice strain evolution during creep in a polycrystalline austenitic stainless steel was studied using in situ neutron diffraction at 550 °C. The lattice strains were found to remain constant under constant load control. However, under constant strain and elastic follow-up control, the lattice strains relaxed the most in the elastically softest lattice plane {200} and the least in the elastically stiffest lattice plane {111}.