In this paper, we study the existence and uniqueness of solutions for impulsive Atangana-Baleanu-Caputo ABC fractional integro-differential equations with boundary conditions. Schaefer’s fixed point theorem and Banach contraction principle are used to prove the existence and uniqueness results. An example is presented to illustrate the results.

In recent times, a pertinent effort has been made by researchers to seek for biodegradable and environmentally friendly reinforcements to manufacture composite-based products. Carbon footprints and emissions of greenhouse gases have been considerably minimized by the increased use of biomaterials lately. Consequentially, reinforcement of natural fibers in polymer matrices is widely preferred owing to the striking of a remarkable balance between cost and performance and ease of processing. However, these materials have their limitations in terms of poor fire resistance and retarded flammability behaviour which restricts the use of natural fiber polymer composites widely in industrial sectors. The end-of-life conditions favour the use of natural fibers as potential reinforcements. Besides physical, chemical, and mechanical properties, the thermal properties of the composites must be given equal importance as they widen the application background of natural fiber polymer composites. Thermal properties are also important in structural applications which include the assessment of capability of a composite to transfer temperature from end to end, material behaviour, stability at elevated temperatures, and load-carrying capability at high temperatures. Degradation of composite materials is considered to be important during their service life as the composites will be subjected to various outdoor environmental conditions. All these parameters are important for any functional composite during its service life. After their use, the problem of disposal of these materials arises which ends up in various environmental hazards such as pollution and landfills. Hence, the assessment of biodegradability of green composites is also important which mostly happens with the action of microorganisms. Therefore, it could be deduced that the understanding of the flammability and degradation of biocomposites along with their testing methods is important for developing biocomposites with better fire-resistant, thermal degradation, and biodegradation characteristics. Hence, this review focuses on the flame retardance behaviour, thermal stability, and degradation of plant-based natural fiber polymer composites during and after their service life.

A growing worldwide awareness of the need for environmental sustainability has increased the demand for fibers in all possible applications. Utilizing agrowaste biofiber from Cardiospermum halicababum that has been untreated/surface-processed to create biofiber-based brake friction composites is the primary focus of this research. A standard manual retting technique was used to extract these fibers, which were then subjected to alkali and silane treatments. The fibers were then tested for their physiochemical characteristics. A standard industrial manufacturing procedure produced brake friction composites in brake pad form using surface-processed / untreated Cardiospermum halicababum fibers. The brake pads' performance was compared to industry-standard commercial pads. As per SAE J 661, the Chase test evaluated their tribological performance. According to the findings, brake pads made from silane-treated Cardiospermum halicacabum fibers exhibited a higher friction coefficient than those made from alkali-treated and untreated fibers. The worn surface of the tested pads was examined using a scanning electron microscope, revealing distinctive characteristics such as plateau formations and plowing. The pads were ranked by considering different criteria using the Extensive Evaluation Method.

Control charts are generally used to detect mean process changes in continuous processes. However, counting procedures that characterize product quality are popular in production processes, such as the number of defects or the proportion of defective products. This research aims to investigate using the Modified Exponentially Weighted Moving Average combined with a nonparametric Sign Rank control chart, namely MEWMA-SR chart is a novel tool. Comparative analyses involving different run length measures are conducted to evaluate the proposed scheme against MEWMA, MEWMA-SR, and classical EWMA charts. The performance of the MEWMA-SR chart is assessed using Monte Carlo simulations based on its run-length profiles. It was found that the proposed combination control chart was effective in detecting changes better than other control charts along with presenting applications with real data. A study further validates the proposed chart’s practical utility through a case study analyzing COVID-19 mortality data.

The most likely materials for use in packaging are plastics. A lot of synthetic polymers are harming the environment. A plasticizer is required for all polymers to improve their characteristics and workability. The plasticizers come in liquid form and are also derived from fossil fuels, which are harmful to the environment. Producing functional and affordable biopolymer for packaging applications is a difficult task nowadays. The preparation of biofilm for packaging using biopolymer and bioplasticizer is the main aim of this work. The biopolymer poly L-lactic acid (PLA) is used, and the bio plasticizer is extracted from Pedalium murex plant. Chemical and mechanical methods are used to extract the plasticizer. Plasticization of polylactic acid biopolymer was done using the extracted plasticizer at additions of 1 %, 2 %, 3 %, 4 %, and 5 %. FT-IR spectroscopy, X-ray diffraction spectroscopy, and surface roughness values are used to characterise the prepared biofilms. Scanning electron spectroscopy pictures are utilised to evaluate the morphological orientation of the biofilms. Strawberries packed with biofilms are used to evaluate the barrier properties of biofilms using UV spectroscopy analysis. Thermal degradation behaviour is investigated using thermo gravimetric analysis. We examined the mechanical characteristics, such as tensile strength, elongation modulus, and elongation break percentage. The plasticizing effect of the plasticizer raises the elongation break percentage while decreasing the tensile strength and modulus. For 2 % plasticizer addition the elongation break increases and the tensile not much affected. To demonstrate biodegradability and microbial resistance, the soil degradation behaviour and antimicrobial activities were examined.

To enhance the sustainability of lignocellulosic biomass utilization process, a significant attention is given to a high-value lignin byproduct of this process. Herein, antimicrobial, antioxidant, and physicochemical properties of lignin in black liquor extracted from sugarcane leaf (SCL) for levulinic acid (LA) and hydrochar production were investigated. The lignin was extracted from SCL through an alkaline pretreatment process using 5–25 wt% of either NaOH or KOH at 120–160 °C. Disk diffusion susceptibility tests, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) revealed that the SCL lignin is effective against Gram-positive bacteria (S. aureus) rather than Gram-negative bacteria (E. coli and S. typhimurium). Different alkaline reagents marginally affected the antimicrobial and antioxidant activities of the lignin. Py-GC/MS analysis results determined that the SCL lignin contained mainly p-hydroxyphenyl (H) and guaiacyl (G) with a small amount of syringyl (S) lignin. Furthermore, the structural alterations and linkages of SCL lignin during alkaline pretreatment were examined using 2D-HSQC NMR, providing valuable insights into the transformation processes. The conversion of delignified SCL to LA through a catalytic hydrothermal process using various acid catalysts indicated that the alkaline pretreatment could enhance LA yield with a maximum yield of 33.4 wt%. Additionally, fuel property characterization of the hydrochar co-product from LA production determined that the hydrochar can be used as a coal substitute.

The hemp-reinforced polypropylene composite was produced by the twin-screw extrusion in the presence of polypropylene-graft-maleic anhydride (PP-g-MA) and silane as coupling agents and subsequence by the compression molding. The hemp fiber was obtained by sodium hydroxide treatment prior to the twin-screw extruder. The PP/Hemp composite was analyzed using a universal testing machine, scanning electron microscope, differential scanning calorimetry, X-ray diffraction, and Fourier transform infrared spectroscopy. The mechanical properties of the PP/MA/Hemp demonstrated a better result than that of PP/Si/Hemp in most hemp contents. The SEM images of the PP/MA/Hemp exhibited excellent interfacial bonding in PP/MA/Hemp30 without fiber pullouts. However, the fiber breakage and some fiber pullout with gaps around the fiber of PP/Si/Hemp indicate the poor bonding of the PP matrix. The functional group of the PP/Hemp composite showed the –OH cellulose result peak of the reaction between hemp fibers and polypropylene.

Wearable devices are paramount in health monitoring applications since they provide contextual information to identify and recognize human activities. Although sensor-based human activity recognition (HAR) has been thoroughly examined, prior studies have yet to definitively differentiate between symmetric and asymmetric motions. Determining these movement patterns might provide a more profound understanding of assessing physical activity. The main objective of this research is to investigate the use of wearable motion sensors and deep convolutional neural networks in the analysis of symmetric and asymmetric activities. This study provides a new approach for classifying symmetric and asymmetric motions using a deep residual network incorporating channel and spatial convolutional block attention modules (CBAMs). Two publicly accessible benchmark HAR datasets, which consist of inertial measurements obtained from wrist-worn sensors, are used to assess the model’s efficacy. The model we have presented is subjected to thorough examination and demonstrates exceptional accuracy on both datasets. The ablation experiment examination also demonstrates noteworthy contributions from the residual mappings and CBAMs. The significance of recognizing basic movement symmetries in increasing sensor-based activity identification utilizing wearable devices is shown by the enhanced accuracy and F1-score, especially in asymmetric activities. The technique under consideration can provide activity monitoring with enhanced accuracy and detail, offering prospective advantages in diverse domains like customized healthcare, fitness tracking, and rehabilitation progress evaluation.

The problem of trapdoor stability is a crucial problem in geotechnical engineering. This study is the first to introduce novel neural network-based metaheuristic models for the stability prediction of 3D rectangular trapdoors in anisotropic and nonhomogeneous clays. However, no researcher has considered such trapdoor problems in the past. In this study, the dataset is obtained by using finite element limit analysis (FELA). The proposed hybrid machine learning models based on artificial neural networks (ANNs) and various types of optimization algorithms (OAs), namely, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competition algorithm (ICA), and shuffled complex evolution (SCE), are also proposed in this paper by undergoing rigorous optimization to ensure accuracy and efficiency in capturing the intricate dynamics of the stability investigations from the models. The performance of the proposed ANN-based models is assessed using several performance metrics, regression plots, and rank analysis. The proposed ANN-SCE model outperforms the other proposed models in predicting trapdoor stability, where the ANN-SCE model achieved the highest rank, with a score of 58, followed by the ANN-ALO (47), ANN-ICA (33), and ANN-ACO (22) models. The proposed neural network-based metaheuristic models deliver precise and effective forecasts of trapdoor stability to make informed decisions concerning road design and mitigation tactics, ultimately improving the robustness of infrastructure facing geotechnical challenges.