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The textile industry includes processes such as the design, manufacture, and distribution of textiles, fabrics, and clothing, all of which result in the production of large amounts of waste. Among the most serious issues to be concerned about is the presence of synthetic dyes, heavy metals, and toxic chemicals in the wastewater. Nanotechnology has emerged as a cutting-edge technology that has demonstrated exceptional capabilities in the treatment of wastewater. Nanoparticles outperform other new technologies in terms of producing superior results, owing to their large surface area and other diverse characteristics. As a new approach to dye removal from wastewater, nanopowders and carbon nanotubes can be purified, functionalized, and used as an absorption material to remove dyes from the wastewater. An investigation into the nanotechnologies in the treatment of textile wastewater is the subject of this review. Following a brief introduction to nanomaterials, synthesis, different types of adsorptions, and the development of nanoparticles towards the remediation of dyes in textile effluent are discussed. Moreover, it brings together the most recent breakthroughs in nanotechnology for dye adsorption in textile industry effluent.

In the present study, hazardous heavy metal ion hexavalent chromium (Cr(VI)) was removed from aqueous solution by using Indion GS-300 (IGS-300), strong base anion exchange resin. The process parameters for the removal of Cr(VI) were optimized using response surface methodology (RSM) approach. Procured resin was analyzed by various techniques like FTIR, and FESEM associated with elemental analysis which provided functional groups and surface structure of the adsorbents. Various batch adsorption experiments were conducted by varying parameters such as Cr(VI) concentrations from 5 mg/L to 45 mg/L, 2 to 10 pH, IGS-300 resin dosage between 0.38 and 1.88 g/L, and temperature of 20–40 °C with 90 min fixed contact time. Fixed time was determined from preliminary study of the present work. The maximum adsorption capacity of IGS-300 resin was found 294.11 mg/g and 98.20% of removal achieved with optimum conditions of 4 pH, 1.50 g/L adsorbent dosage, 15 mg/L Cr(VI) concentration and 35 °C temperature. The experimental data was found with best fitted Freundlich Isotherm and pseudo second order kinetic model. Regeneration study was also done on the adsorbed resins using different solution includes water, 0.1 M HCl, and 0.1 M NaOH. Up to 3 cycles of 0.1 M NaOH treatment, resin showed >50% Cr(VI) removal in aqueous solution whereas water and HCl were found less effective on third cycle. Therefore, this study found that IGS-300 resin is more efficient adsorbent for the removal of Cr(VI) from aqueous solutions and possessing highly significant regeneration capacity.

Cycloarenes such as kekulenes, septulenes and extended kekulenes are of considerable interest as they are precursors to holey nanographene with pores of variable sizes with interesting electronic and magnetic properties. These systems have also been of interest due to aromaticity, ring currents arising from π\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi $$\end{document}-electrons and intriguing topological properties. This study’s primary objective is to determine the topological properties including entropies through analytical expressions of degree-based entropy metrics for two well-known classes of tessellations of kekulenes. The various topological indices and entropies obtained here to shed light on the underlying topological connectivities and molecular structural features. It is shown that the rhomboidal tessellations exhibit greater entropies compared to triangular tessellations. Applications of the developed techniques to machine learning of NMR and ESR spectra of these tessellations are pointed out.

Herein, Cu-rGO/ZnO ternary nanocomposite powder was synthesised by hydrothermal method. Analytical techniques such as XRD, FTIR, UV-Vis, SEM-EDAX, BET and XPS were used to study the morphology and structural properties of obtained nanocomposite. The photocatalytic activity of prepared nanocomposite was assessed by degradation of bromophenol blue in aqueous media. The catalytic results demonstrated that photocatalyst (0.1g) exhibited 86.2% bromophenol blue dye degradation efficiency with kinetic rate constant of 3.401x10 − 2 min − 1 . Antibacterial activity of nanocomposite was evaluated using agar well diffusion technique. The experimental results revealed that Cu-rGO/ZnO exhibited significant antibacterial activity against Escherichia coli, Staphylococcus aureus, Enterococcus faecium and Pseudomonas aeruginosa.

The study showed the ability to synthesize environmentally friendly silver nanoparticles (AgNPs) using extracts from Wrightia tinctoria seeds and Acacia chundra stems. Surface plasmon resonance peaks in the UV-Vis absorption spectra of both plant extracts verified AgNP synthesis. The structural and morphological properties of the AgNPs were investigated using analytical techniques such as XRD, FTIR, TEM, and EDAX. The AgNPs have an FCC crystalline structure, according to XRD study, and their sizes range from 20 to 40 nm, according to TEM images. Based on the results, these plant extracts have been identified as suitable bioresources for AgNP production. The study also showed that both AgNPs had significant levels of antibacterial activity when tested on four different microbial strains using the agar-well diffusion method. The bacteria tested included two Gram-positive strains (Staphylococcus aureus and Micrococcus luteus) and two Gram-negative strains (Proteus vulgaris and Escherichia coli). Furthermore, the AgNPs were found to have a significant anticancer effect on MCF-7 cell lines, suggesting that they may be useful in therapeutic applications. Overall, this research highlights the potential of the plant extracts considered as a source for synthesizing eco-friendly AgNPs with potential applications in medicine and other fields.

This study aimed to create a high flame retardant, load bearing and thermally stable epoxy composite for various engineering applications. To achieve this, cashew nut shell de-oiled cake derived biosilica and satin weaved sunn hemp fibre was utilized as reinforcements along with epoxy resin. The biosilica is prepared using thermo-chemical process, whereas the satin weaved sunn hemp fibre was made using hand loom. Furthermore, the composites were manufactured by the hand lay-up method and characterized in line with the ASTM standards. According to the results, the lowest value of combustion rate is approximately 11.93 mm/min for the composite designation RSB3. The addition of sunn hemp fibre and 3.0 vol. % of biosilica composite (RSB2) yielded a tensile strength of 164 MPa, flexural strength of 204 MPa and modulus of 6.21 and 6.82 GPa, respectively. It is noted that the thermal conductivity improved marginally when the biosilica is added into the composite. Furthermore, adding cashew nut shell biosilica decreases the specific wear rate of the composites. Such natural fibre composite with high resistances to heat as well as mechanical load and thermal stability could be employed in automobiles, industries making consumer and electronic goods as well as sports applications.

With the advent of Technology in the healthcare industry, there is a growth in revenue generation. Patients, doctors, insurance companies, and other entities are the key players in Healthcare Industry. Further effective and accurate back workplace method is urgently needed towards maintaining poise among the growing number of patients and the paperwork required for development plus insurance prerogatives, among other things. Hence, this concerns progressive mechanisation elucidations such as Robotic Process Automation (RPA) being able to assist healthcare organisations in increasing effective proficiency, lowering expenses, and reducing the risk of human error once handling data such as doctor credentialing, staffing, and patient fitness, as well as medical record maintenance, payables and denial recover, and patient scheduling.

This article provides an overall view of Robotic Process Automation (RPA) evolution and its use cases in the IT Industrial sector. In recent times RPA has evolved and democratized all sectors of the economy for better and efficient production and usage of products, processes, and services. The chapter shows the reasons for the flourishment of the technology. The evolution of industrial sectors from steam engines to unattended automatic robots taking business intelligence decisions are driven by one major quotient i.e. automation. The chapter comprises the entire journey that RPA has taken to be now one of the affordable and efficient solutions out of all. The ultimate aim of every corporation is to generate revenue and to achieve it through the different processes that happen around it. Each of the processes involves different user personas and applications and thus humans act as the binding chains and deliver the end product or service to the consumer. As humans cannot handle complex computations and tedious monotonous tasks, automation minimizes these tasks and makes their work easier. This article also covers the sectors where automation is being a critical savior for industries. It also talks about the pros and cons of automation over a period and how it could be a disruptive technology in the upcoming years.

The brain tumor is the most common destructive and deadly disease. In general, various imaging modalities such as CT, MRI and PET are used to evaluate the brain tumor. Magnetic resonance imaging (MRI) is a prominent diagnostic method for evaluating these tumors. Gliomas, due to their malignant nature and rapid development, are the most common and aggressive form of brain tumors. In the clinical routine, the method of identifying tumor borders from healthy cells is still a difficult task. Manual segmentation takes time, so we use a deep convolutional neural network to improve efficiency. We present a combined DNN architecture using U-net and MobilenetV2. It exploits both local characteristics and more global contextual characteristics from the 2D MRI FLAIR images. The proposed network has encoder and decoder architecture. The performance metrices such as dice loss, dice coefficient, accuracy and IOU have been calculated. Automated segmentation of 3D MRI is essential for the identification, assessment, and treatment of brain tumors although there is significant interest in machine-learning algorithms for computerized segmentation of brain tumors. The goal of this work is to perform 3D volumetric segmentation using BraTumIA. It is a widely available software application used to separate tumor characteristics on 3D brain MR volumes. BraTumIA has lately been used in a number of clinical trials. In this work, we have segmented 2D slices and 3D volumes of MRI brain tumor images.

This article addresses a design of an apposite system which provides a supportive hand for hearing and speaking challenged person to expediently communicate with normal people. Normally, a sign language is adopted by them for their communication which needs an interpreter to convert into user’s understandable language. The proposed system is used for converting the sign language into voice and text and vice versa. The idea of the proposed project is to come up with a device that captures the gestures and converts it to voice output as well as in text output and also to capture the voice by speech recognition module and convert it to corresponding sign language by displaying on a screen with the help of various elements like microphone, camera, sign language database and display unit. For the general-purpose indoor implementation, a facial expression recognition system can also be additionally included.

It is an enormous challenges and strategies in cross-cultural. As a result of the analysis in artificial intelligent, it isn’t a primary language for analysis teams. In society analysis, translation provides an additional challenge and strategy. In society analysis, the interpretation and comprehension of which suggest of data is concentrate. The aim of this text is to supply an overview of the interpretation methodology and explore variety of the challenges, like difficulties notice associate degree applicable translator, and also the importance of communication between the investigator and also the translator. A developing commercial center for dialect benefit on and rapidly developing innovation has introduced in an awfully unused wave of T&I computer program bundle advancement. In spite of interpretation tools’ wide choice of applications conjointly the current blast of AI, interpretation memory and interpretation tools’ machine learning calculations are expelled from palatable in giving dialect arrangements. Tragically, relate degree overreliance in this innovation. By dissecting cases wherever computational phonetics gave erroneous recommendations, this content points to investigate the association between rising innovation and antiquated expertness in T&I. The author believes that innovation and expertness aren’t in an awfully zero-sum amusement, and innovation doesn’t deny interpreters of work openings, in any case or maybe their expository aptitudes, fundamental considering, and stylish interests.

Communication is an essential need for every person in society. This socializing can be in audio, video and text forms. Gestures are the natural expressions of communication to facilitate a specific meaning. These gestures are combined with facial expressions to form a tool for the speech impaired and the hearing impaired which is known as Sign language. It varies according to the country’s native language as American Sign Language, British Sign Language, Japanese Sign Language, Indian Sign Language, etc. The researches in the field of SL recognition have been increased tremendously in the last 10 decades. This paper mainly aims at developing a Human Machine Interface based on gestures. Indian Sign Language is a visual-gestural language used to bridge the gap of differences within society and speech and hearing impaired, exclusion of translators and independent expressiveness. This system is designed with a wearable glove utilizing ten flex sensors and two accelerometers to recognize the words in the sign language vocabulary. The classified results are sent to voice module, where the voice corresponding to the gesture is played back through a speaker. The results of the first version of glove without accelerometers had an accuracy of 74.12%. The accuracy was improved to 97.2% in the second version by the placement of accelerometers over the back side of palm on both hands. Both the versions were verified with datasets varying with gender and signer. The proposed glove excels the existing gloves on constraints of sign misclassification, expenses and others related to image-based gesture recognition. Future extensions of this glove would be modification for other country’s sign language recognition or gesture-based controlled devices.

Power losses (PL) are one of the most—if not the most—vital concerns in power distribution networks (DN). With respect to sustainability, distribution network reconfiguration (DNR) is an effective course of action to minimize power losses. However, the optimal DNR is usually a non-convex optimization process that necessitates the employment of powerful global optimization methods. This paper proposes a novel hybrid metaheuristic optimization (MO) method called the chaotic golden flower algorithm (CGFA) for PL minimization. As the name implies, the proposed method combines the golden search method with the flower pollination algorithm to multiply their benefits, guarantee the best solution, and reduce convergence time. The performance of the algorithm has been evaluated under different test systems, including the IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus systems and the smart city (SC) network, each of which includes distributed-generation (DG) units and energy storage systems (ESS). In addition, the locations of tie-switches in the DN, which used to be considered as given information in previous studies, are assumed to be variable, and a branch-exchange adaption is included in the reconfiguration process. Furthermore, uncertainty analysis, such as bus and/or line fault conditions, are studied, and the performance of the proposed method is compared with other pioneering MO algorithms with minimal standard deviations ranging from 0.0012 to 0.0101. The case study of SC is considered and the obtained simulation results show the superiority of the algorithm in finding higher PL reduction under different scenarios, with the lowest standard deviations ranging from 0.012 to 0.0432.

Life-threatening cancer is prevalent over the globe. According to statistics, most people are diagnosed with cancer in the later stages, even though cancer can be prevented and cured in early stages. The goal of this research is to diagnose a breast cancer is either benign or malignant, as well as to forecast the likelihood of a cancer recurrence even after a course of therapy has been completed. Despite the fact that many machine learning algorithms have resulted in strong predictions, the accuracy in the early stages of categorization is not up to the expected level. Deep learning (DL), a higher degree of machine learning, can forecast breast cancer types and recurrences. Classifiers were built using a deep neural network (DNN) that used Principal Component Analysis (PCA) to choose features. Different machine learning techniques are compared with the proposed system’s accuracy. Early-stage breast cancer prediction is more accurate with the DNN-based method. The clinical management system will benefit from the proposed system since it will aid in identification of cancer at an early stage and the subsequent provision of appropriate therapy.

Disposal of biodegradable waste of seashells leads to an environmental imbalance. A tremendous amount of wastes produced from flourishing shell fish industries while preparing crustaceans for human consumption can be directed towards proper utilization. The review of the present study focuses on these polysaccharides from crustaceans and a few important industrial applications. This review aimed to emphasize the current research on structural analyses and extraction of polysaccharides. The article summarises the properties of chitin, chitosan, and chitooligosaccharides and their derivatives that make them non-toxic, biodegradable, and biocompatible. Different extraction methods of chitin, chitosan, and chitooligosaccharides have been discussed in detail. Additionally, this information outlines possible uses for derivatives of chitin, chitosan, and chitooligosaccharides in the environmental, pharmaceutical, agricultural, and food industries. Additionally, it is essential to the textile, cosmetic, and enzyme-immobilization industries. This review focuses on new, insightful suggestions for raising the value of crustacean shell waste by repurposing a highly valuable material.

Data security during internet transmission is the main concern. Therefore, protecting the data has become the focus among the researchers. In this paper, key frame selection process is developed to identify the frame with less error rate. The Region of Interest (ROI) and Non-Region of Interest (NROI) are identified using the similarity of key frame and water mark image. Based on the similarity, even and odd pixels data embedding procedures are applied. An improved data hiding technique is proposed using the Exclusively-OR (XOR) and flipping segmentation technique to conceal data inside the ROI. Finally, ROI alone is encrypted using Cellular Automata (CA) and full video is encrypted using Advance Encryption Standard (AES). The comparative analysis is implemented with various steganography techniques to demonstrate the proposed work offers better imperceptibility and capacity. Moreover, this method is secure against attacks.

Pulmonary emphysema is a main part of chronic obstructive pulmonary disease and lung cancer. Though, quantitative emphysema severity prediction is essential in patients with unclear lung cancer categories. Thus, diagnosis of pulmonary emphysema at the early stage is more significant and could save human life. Moreover, non-invasive positive pressure ventilation is a life-saving method that focuses on reducing the complexities in patients. When failure occurs in non-invasive positive pressure ventilation, there are more chances for mortality, which shows the significance of rational diagnosis. The delay in endotracheal intubation is avoided by developing different approaches, which leads to more demand. This paper plans to develop an enhanced system for pulmonary emphysema diagnosis using deep learning-based segmentation and classification. Initially, the detection model considers pre-processing with the help of Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filtering. Moreover, lung segmentation is the major part of pulmonary emphysema diagnosis. Here, the enhanced U-Net model is utilized for lung segmentation by considering the multi-objective function with the developed algorithm. Then, the feature extraction is done using the local tri-directional weber pattern and local directional pattern descriptors. The extracted features are classified by the heuristically improved deep neural network based on a new algorithm, which will optimally diagnose the severity of pulmonary emphysema. Both segmentation and classification will be enhanced by proposing a new Electric Fish-based Grey Wolf Optimization (EF-GWO). Here, various performance metrics, like accuracy, precision, specificity, etc., on the public dataset by comparing with other models. The accuracy of the EF-GWO with E-UNet is 96%. The accuracy of the suggested developed EF-GWO based on HI-DNN is 97%. Hence, it verifies the superior performance of the recommended pulmonary emphysema disease diagnosis method with improved segmentation and classification techniques compared to other existing methods.

Currently, the demand for vibration damping, lightweight and environmentally friendly material is increasing in automotive and aerospace sectors. Due to this quest, the use of eco-friendly fibrous material has gained importance for its use as a reinforcement in polymeric matrix composite. Therefore, in this present investigation, woven jute fiber mats or layers were added to pure polyester resin to form various composite samples, using compression molding technique. Five different samples were fabricated: neat polyester resin plate and 2–5 woven jute/polyester composites, denoted as NPRP, 2WJPC, 3WJPC, 4WJPC and 5WJPC samples, respectively. The natural frequencies and viscoelastic behaviours of the various samples were examined by free vibration test. From the free vibration test, both natural frequencies and damping factors were obtained. From the results obtained, it was evident that 4WJPC sample exhibited the maximum natural frequencies of 32.96, 231.9 and 659.2 Hz under modes I, II and III, respectively. Also, the natural frequency of 4WJPC sample was 40% higher than that of NPRP. Therefore, it was evident that the addition of woven jute fiber mat has a significant and good influence on the composite natural frequency. Comparison between experimental and theoretical analysis was carried out and found closely related with each other. Applicably, woven jute fiber mat reinforced polyester composite can be used as a vibration absorbing material (damper), low cost and efficient engineering structure.