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The growing environmental awareness throughout the world has triggered a shift toward developing environmentally friendly materials from renewable resources. This work aims to develop needle punched nonwoven fabrics from textile waste for thermal insulation application and also to analyze the influence of porosity and air permeability of the nonwovens on thermal insulation performance. The needle punched nonwovens were manufactured using comber noil, silk cocoon waste, and polyester/cotton flat strip waste. Also, a nonwoven has been developed by blending comber noil with silk cocoon waste (50/50%). It was found that the nonwoven developed from 100% silk cocoon waste has better thermal insulation value (0.201 m2K/W) compared to 100% comber noil and polyester/cotton flat strip waste. It was also found that blending silk cocoon fiber with comber noil improves the thermal insulation of the nonwovens. 50/50 silk cocoon/ comber noil nonwoven has a good thermal insulation value (0.212 m2K/W) among the developed nonwovens. There is an increase in thermal insulation observed with an increase in porosity of nonwovens. The test results showed that all the developed nonwovens have excellent thermal insulation performance and are comparable with that of conventional insulation materials.

Thermal resistance of cold-weather clothing is achieved by layering and filling. In this study, kapok/polypropylene blended (70/30, 60/40, and 50/50) needle-punched nonwovens were manufactured with 100 and 200 g/m2 areal density. Chicken feather fibers were used as fillers for developing quilted composite structures (QCS). To produce a QCS, 100 and 200 g/m2 nonwovens were individually used as the top and bottom layers, and the middle layer was filled with chicken feather fibers in 100, 200, 350, 550, 600, and 850 g by weights. The structures were characterized for thermal properties, and it was noted that 70/30 kapok/polypropylene nonwovens in 100 g/m2 set had the highest thermal resistance between 0.5901–0.6461 °C m2/W. Thermal resistance is greatly influenced by kapok, chicken feather fiber quantity in the assembly along with the areal density (1000 g/m2) of the QCS. Results show that the developed QCS has higher thermal insulation and can be used for cold-weather apparel and sleeping bag. [ABSTRACT FROM AUTHOR]

This article explores the use of borax and citric acid as flame retardants for cotton fabrics. It discusses the importance of flame retardants in ensuring the safety of textile materials and preventing fires. The article explains the mechanisms of combustion and various methods used to suppress it. Traditional flame retardants for cotton fabrics have drawbacks like toxicity and the generation of corrosive gases, so alternative options like polyphosphates and nitrogenous-based agents are being explored. The article presents a study that demonstrates the effectiveness of borax and citric acid in reducing the char length of cotton fabrics. The addition of a crosslinker further enhances the flame retardancy. The study aims to improve the fire safety of cotton fabrics, which have lower flame resistance compared to synthetic fibers. [Extracted from the article]

In this study, open-end spun yarns have been produced from recycled cotton fibres by blending with r-PET fibres in three blend ratios (80/20, 70/30, and 60/40). The linear density of the yarn is maintained at 20Ne. Tensile strength, elongation at break, unevenness (CVm%), and hairiness of the produced yarns have been measured. It is found that the yarn manufactured with 60/40 recycled cotton/r-PET blend ratio has better yarn quality index value as compared to other blend ratios. The single jersey fabric has been developed from the 60/40 recycled cotton/r-PET yarn. Bursting strength, pilling resistance, and dimensional stability of the developed fabric are studied and compared with the commercial fabric. It is found that the properties of the developed fabric are comparable with commercial fabric and can be used to produce knit top garments, such as T-shirts and polo shirts. [ABSTRACT FROM AUTHOR]

This research aims to explore a sustainable way of utilizing pineapple leaf fibres (PALF) to develop flushable wipes with a higher disintegration percentage. The suitable fibre extraction method has been determined, and the extracted PALF is subjected to steam-explosion using a sodium hydroxide (NaOH) solution with varying NaOH concentration, pressure, and time. Flushable wipes are manufactured using the wet-laid nonwoven technique from steam-exploded pineapple leaf microfibres. From SEM analysis, it is observed that the fibre bundles are separated into individual micro-sized fibres (3–4 μm) after steam explosion. The effect of process parameters in steam explosion treatment on disintegration percentage with respect to shaking speed and time has been explored and then compared with commercial products. The result indicates that the micro-fibres obtained by steam explosion of PALF with 1.5% NaOH at 25 psi for 60 min demonstrate excellent flushability properties (~96.67% disintegration), which exceeds the commercial product by 27.62%. This suggests a potential to meet the EDANA/INDA flushability standards, making it a viable good flushable wipe that won't cause drainage clog. This research concludes that the steam-exploded PALF is found to be beneficial in the hygiene and health care segments for the production of emerging and essential flushable wipes, taking advantage of its unique property of fibrillation on steam explosion. [ABSTRACT FROM AUTHOR]

A smart stick with an external camera and a Convolutional Neural Network (CNN) algorithm helps visually impaired people navigate their environment more autonomously. Users are able to more easily and confidently traverse their environment on their own because to the system's ability to recognise items in front of them and offer audio output to identify them. A Raspberry Pi computer, the Tensor Flow library, the Coco Dataset, and a Pi camera are used to perform the suggested fix. With a quick response time, minimal power consumption, and a lightweight, foldable form that is simple for users to handle and use, the smart stick is made with the user in mind. The proposed approach could, in general, considerably enhance the standard of living of individuals who are blind by boosting their mobility and autonomy. The smart stick's external camera takes pictures of the things the user is looking at, which are subsequently processed using the CNN algorithm. This algorithm can identify many items, including people, automobiles, and barriers, and it can voice-output the information to the user so they can recognise it. This makes it easier for users to move around and avoid potential dangers. The camera's photos can be processed effectively uses of the by using the Coco Dataset, which increases the algorithm's precision in detecting and classifying various objects. [ABSTRACT FROM AUTHOR]

The (Z)-N-(thiazol-2-yl)-4-((thiophene-2-ylmethylene)amino)benzenesulfonamide (TH2TH) were synthesized. The geometry was optimized by density functional theory (DFT) method. The titled compound week interaction and main binding areas confirmed by topological analysis. The simulated UV–Vis spectrum was calculated by TD-DFT method using IEFPCM solvation model. The HOMO-LUMO, molecular electrostatic potential, and nonlinear optical properties were carried out in the DFT method using gas phase. The pharmacological properties and toxicity were carried out using Swiss-ADME online tools, this result shows titled compound good drug-likeness properties. The molecular docking done using autodock software, against 5LOF protein. [ABSTRACT FROM AUTHOR]

In this work, nonwoven oil sorbent from nettle fiber was developed by changing the hydrophilic surface of nettle fiber to a hydrophobic surface by dodecycltrimethoxysialne (DTMS) treatment. Change in the surface structure of the fiber due to DTMS treatment was confirmed by SEM analysis. Fourier transform infrared analyses showed clear evidence of successful incorporation of DTMS on nettle fiber. The oil sorption capacities of diesel oil and crude oil were 31.39 and 41.89 g/g, respectively, which were found to be considerably higher than commercial oil sorbents. The pseudo first order and pseudo second order kinetic equations were employed to obtain the sorption rate constants and equilibrium sorption capacity. The results showed that the process fits the pseudo second-order kinetic model better for oil sorption than the pseudo first-order kinetic model. Also, the sorption capacities of the unmodified and modified samples were estimated by analyzing the isotherm data using Langmuir and Freundlich adsorption isotherm models. The results show that fitting via the Langmuir isotherm model produced a higher correlation coefficient than fitting via the Freundlich isotherm model. DTMS treated nettle nonwoven fabrics also showed good buoyancy characteristics under both static and dynamic conditions. Due to high oil sorption ability, reusability, and environmental responsiveness, the developed nonwoven oil sorbent can be considered as a potential substitute for organic synthetic fiber for oil spill removal applications. [ABSTRACT FROM AUTHOR]

Now-a-days, the demand for malware detection has been increased to protect the system from malware crash and to keep organization's data safe and secure. IoT devices are the nonstandard computing devices that connect wirelessly to a network and have the ability to transmit data. As IoT devices becomes more functional, the security became one major factor. More attacks have been targeted towards IOT devices and the security is not guaranteed. Hence several models have been developed to overcome these malware attacks. In order to get the better of, the model for detecting malware detection using behavior graphs is developed. In this approach, Behavior-Based Framework is used to extract the behavior graphs using API Calls and the output is obtained using the feature extraction and certain classifiers. The suggested model seeks to decrease the amount of characteristics and to convey the features succinctly at the highest level. Accuracy of the model is 99.1%. [ABSTRACT FROM AUTHOR]