Fabrication of a Superhydrophobic-Photodynamic Dual Antimicrobial Polyester Textile for Inhibiting Biofilm Formation in Medical Applications.

The problem of hospital-acquired infections arising from inadequate antimicrobial and antibiofilm performance in medical textiles is an increasingly urgent threat to public health. The dual strategy combining superhydrophobic surfaces with aPDT exhibits potent antibacterial efficacy and barely triggers the risk of antimicrobial resistance, but still encounters significant challenges, including intricate fabrication methods and narrow spectral absorption of single-photosensitizer (PS) systems. A superhydrophobic-photodynamic dual antimicrobial polyester fabric is developed herein for medical applications to address these challenges.

An enzymatic modification of sisal fiber macromolecules (cellulose/lignin) for spinnable fibers used in value-added textile applications.

Sisal fiber is one of the natural alternative materials with excellent mechanical and biodegradable properties. Despite this, raw sisal fibers have not found extensive application in manufacturing high-quality textiles for apparel and clothing because of their coarse texture and non-cellulosic impurities. This study investigates the chemical and enzyme degumming methods in producing spinnable textile-grade sisal fiber.

Correction: Sarwar et al. Evaluating Antibacterial Efficacy and Biocompatibility of PAN Nanofibers Loaded with Diclofenac Sodium Salt. 2021, , 510.

In the originally published manuscript [...

Unsupervised fake news detection on social media using hybrid Gaussian Mixture Model.

The rise of social media has revolutionized information dissemination, creating new opportunities but also significant challenges. One such challenge is the proliferation of fake news, which undermines the credibility of journalism and contributes to societal unrest. Manually identifying fake news is impractical due to the vast volume of content, prompting the development of automated systems for fake news detection.

Cotton/Protex knitted multifunctional fabrics for undergarments with antibacterial, flame-retardant and thermal comfort properties.

Knitted protective garments with reliable thermal and functional properties are the subject of intense research. However, achieving both attributes is quite challenging and necessary at the same time, especially when the fabrics are employed for undergarments. Hence, this research aims to develop fire protection clothing liners and undergarment fabrics integrating thermal comfort, fire resistance, antibacterial and antistatic properties.

Enhancing anti-pilling performance of wool knitted fabrics synergistic treatment with dopamine and silk sericin: a sustainable approach.

Pilling of wool knitted fabrics during wearing or washing has long been a persistent and challenging issue. Conventionally, most anti-pilling treatments are either environmentally unfriendly or cause substantial damage to the internal structure of wool. This study adopts dopamine cross-linking silk sericin to modify wool knitted fabrics.

A machine learning approach to wastewater treatment: Gaussian process regression and Monte Carlo analysis.

This study aimed to analyze the application of Gaussian Process Regression (GPR) modeling to improve the accuracy of degradation response predictions in wastewater treatment. Three crucial factors, , catalyst (CFA-ZnF), oxidant (HO), and pollutant (MB) concentration, were selected to evaluate their impact on the response variable (degradation) using the GPR model. The range of factors was 5-15 mg/100 mL for CFA-ZnF, 5-15 mM for HO, and 5-15 ppm for MB concentration.

Correction: Zahid et al. Fabrication and Characterization of Sulfonated Graphene Oxide-Doped Polymeric Membranes with Improved Anti-Biofouling Behavior. 2021, , 563.

In the original publication [...

Transmission dynamics and stability analysis of fractional HIV and AIDS epidemic model with antiretroviral therapy.

Worldwide populations have historically experienced serious issues from infectious diseases, requiring coordinated and inclusive prevention measures. HIV is one of the most hazardous of these as it attacks CD4 + cells, or T-cell lymphocytes, which are crucial to human immunity. To explore the variability of HIV/AIDS transmission, this study introduces a nonlinear stochastic mathematical model that incorporates a recovery compartment to account for hospitalized patients' progression to complete recovery and to better capture the intricate dynamics of disease transmission.

Towards efficient solutions: A novel approach to quadratic nonlinearity in boundary value problems.

The Newton method is a classical method for solving systems of nonlinear equations and offers quadratic convergence. The order of convergence of the Newton method is optimal as it requires one evaluation for the system of nonlinear equations and the second for the Jacobian. Many boundary value problems in nature have quadratic non-linearity and the corresponding system of nonlinear equations associated with their discrete formulation has constant 2nd-order Fréchet derivatives.

Statistical modelling and optimization of thermophysiological and tactile comfort properties of sports socks.

The thermo-physiological and tactile properties of socks were analysed by using hybrid yarns which were made by Polyethylene (PE) fibre with different fibres in variable ratios, using full-factorial experimental design. Socks samples were designed using hybrid yarns made with three blend ratios of polyethylene filament with cotton (Co), polyester (PET), polypropylene (PP), modal (Mo) and viscose (Vis) spun yarns. Statistical analysis was carried out according to response surface regression analysis (RSM).

Polyurethane/silk fibroin-based electrospun membranes for wound healing and skin substitute applications.

The importance of electrospun membranes for biomedical applications has increased, especially when it comes to skin regeneration and wound healing. This review presents the production and applications of electrospun membranes based on polyurethane (PU) and silk fibroin (SF) and highlights their benefits as a skin substitute. This review also highlights the electrospinning technique used to prepare nanofibers for these biomedical applications.

Enhancing Mechanical and Impact Properties of Flax/Glass and Jute/Glass Hybrid Composites Through KOH Alkaline Treatment.

This research investigates the influence of potassium hydroxide (KOH) treatment on the mechanical, flexural, and impact properties of flax/glass and jute/glass hybrid composites. Hybrid composite materials have been developed, incorporating natural fibers that are both treated and untreated by KOH, with glass fiber within an epoxy matrix. Natural fibers, such as flax and jute, were chemically treated using different KOH concentrations and immersion times specific to each fiber type.

A multifunctional nursing pad for lactating mothers.

Lactating mothers commonly use nursing pads to prevent milk from leaking and avoid staining their clothes. This research aims to develop a nursing pad that consists of a dual-layer structure: viscose fabric with three different grams per square meter (GSM) as an absorbent inner layer and a polyethylene laminated outer layer to provide barrier properties. The developed composite was further treated with varying concentrations (5%,10%, and 15%) of an antimicrobial finish to protect against skin infections and odor caused by the milk.

Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment.

Worldwide, coronary heart disease (CHD) is a leading cause of mortality, and its early prediction remains a critical challenge in clinical data analysis. Machine learning (ML) offers valuable diagnostic support by leveraging healthcare data to enhance decision-making and prediction accuracy. Although numerous studies have applied ML classifiers for heart disease prediction, their contributions often lack clarity in addressing key challenges.

Flexible and Stretchable Strain Sensor Integrated with a Supercapacitor Based on Coaxial Wet-Spun Polyurethane/Polypyrrole@poly(3,4-ethylenedioxythiophene)/Single-Walled Carbon Nanotubes Fibers.

Stretchable strain sensors integrated with supercapacitor functions have drawn significant attention due to their wide potential applications. In this study, a composite fiber with a core-shell structure is fabricated via a coaxial wet spinning method to enhance the comprehensive properties. The fiber uses thermoplastic polyurethane (TPU) and polypyrrole (PPy) as the shell layer and poly(3,4-ethylenedioxythiophene) (PEDOT) and single-walled carbon nanotubes (SWCNTs) as the core layer.

An eco-friendly microwave assisted recycling of cellulosic fabric dyed with reactive red CL4BN and reactive black PLS.

The study was aimed to retain the quality of recycled cotton fabric by microwave-assisted color stripping of faulty dyed cotton and waste of garments industry. Microwave assisted method reduces the chemical consumption and coverts stripping time from hours to seconds. Additionally, this is an eco-friendly approach as it also reduces the chemical wastes into environment due to much less usage of chemicals in this process compare to conventional stripping methods.

Graphene quantum dots reduce oxidation behavior and mechanical damage of epoxy resin irradiated by γ-rays.

Epoxy resin is a widely used polymer, but it generates highly reactive free radicals under gamma ray irradiation, which reduces its structural integrity. Compared with traditional carbon nanomaterials such as carbon nanotubes and graphene oxide, graphene quantum dots (GQDs) exhibit excellent radiation resistance due to their zero-dimensional high specific surface area, strong ability to remove free radicals through surface defects, and real-time oxidation monitoring based on fluorescence. This study explores the role of graphene quantum dots in mitigating radiation damage and the microscopic mechanisms behind their protective effects.

Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems.

Skin cancer is among the most prevalent types of malignancy all over the global and is strongly associated with the patient's prognosis and the accuracy of the initial diagnosis. Clinical examination of skin lesions is a key aspect that is important in the assessment of skin disease but comes with some drawbacks mainly with interpretational aspects, time-consuming and healthare expenditure. Skin cancer if detected early and treated in time can be controlled and its deadly impacts arrested completely.

Eco-friendly hemp woven (two-dimensional and three-dimensional) fabrics for tear and puncture protection; an approach for comfort and ergonomics.

. Two-dimensional (2D) woven fabrics are inefficient in exhibiting viable thermal comfort and protective function in high-tech applications that govern a need for three-dimensional (3D) weaves. Such 3D fabrics offer superior performance over 2D fabrics; however, no study has reported the simultaneous comparison of thermal and protective performance properties of 2D and 3D structures with equal thread densities.

Preserving Privacy of Internet of Things Network with Certificateless Ring Signature.

With the rapid development of quantum computers and quantum computing, Internet of Things (IoT) networks equipped with traditional cryptographic algorithms have become very weak against quantum attacks. This paper focuses on the privacy-preserving problem in IoT networks and proposes a certificateless ring signature (CLRS) scheme. This CLRS is constructed with lattice theories, which show promising advantages in resisting quantum attacks.

A multi-scale CNN with atrous spatial pyramid pooling for enhanced chest-based disease detection.

We introduce a sophisticated deep-learning model designed for the early detection of COVID-19 and pneumonia. The model employs a convolutional neural network-integrated with atrous spatial pyramid pooling. The atrous spatial pyramid pooling mechanism enhances the convolutional neural network model's ability to capture fine and large-scale features, optimizing detection accuracy in chest X-ray images.

Sustainable extraction of phytochemicals from Mentha arvensis using supramolecular eutectic solvent via microwave Irradiation: Unveiling insights with CatBoost-Driven feature analysis.

The present study revealed the higher extraction potential of sustainable choline chloride (ChCl) and ethylene glycol (EG) based deep eutectic solvent (DES) from Mentha arvensis via microwave irradiation. The categorical boosting (CatBoost) machine learning model was applied to optimize the extraction process against time (4-8 min), microwave power (160-320 W), and biomass quantity (1-2.0 g/10 mL) with DES.

Taguchi Grey relational analysis (GRA) based multi response optimization of flammability, comfort and mechanical properties in station suits.

Fire fighters' stations suits are expected to have adequate flame resistance for protection of the wearer. At the same time the comfort of wearing is essential to prevent fatigue and to maintain the efficiency during long working hours. Therefore, it is essential to balance these multi responses in a scientific manner.