National Institute of Textile Engineering and Research

Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not; and from semantic perspective, MSE and PSNR are giving only absolute error; on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.

The use of composites in different sectors has become inevitable due to the enhancement in properties, reduction in the manufacturing cost and suitability to several applications. Among different classifications, polymeric composites are mainly focused on their use as structural components and the selection and composition of reinforcement play a vital role in determining the characteristics of the composite. Although composites are developed with man-made reinforcement in the beginning stage, in the present situation, natural reinforcements have proved excellent results in terms of properties. Hence, nowadays researches are mainly focused on the use of different natural fibers in different forms as reinforcements in a polymeric composite. This work presents a brief overview of the properties of natural fiber and natural fiber reinforced composites which is an emerging area in polymer science. Interests in natural fiber are reasonable due to the advantages of these materials compared to others, such as synthetic fiber composites, including low environmental impact and low cost and support their potential to be used. Moreover, the disadvantage of the synthetic and fiberglass as reinforcement, the use of natural fiber reinforced composite gained the attention of the young scientists, researchers, and engineers and are being exploited as a replacement for the conventional fiber such as glass, aramid, carbon, etc. Natural fibers have been proven alternative to synthetic fiber in transportation such as automobiles, railway coaches and aerospace, military, building, packaging, consumer products, and construction industries for ceiling paneling, partition boards, etc. However, in the development of these composites, some drawbacks have also emerged. In this paper, it has been tried to overview all of this together.

Software-Defined Networking (SDN) and Blockchain are leading technologies used worldwide to establish safe network communication as well as build secure network infrastructures. They provide a robust and reliable platform to address threats and face challenges such as security, privacy, flexibility, scalability, and confidentiality. Driven by these assumptions, this paper presents an optimized energy-efficient and secure Blockchain-based software-defined IoT framework for smart networks. Indeed, SDN and Blockchain technologies have proven to be able to suitably manage resource utilization and to develop secure network communication across the IoT ecosystem. However, there is a lack of research works that present a comprehensive definition of such a framework that can meet the requirements of the IoT ecosystem (i.e. efficient energy utilization and reduced end-to-end delay). Therefore, in this research, we present a layered hierarchical architecture for the deployment of a distributed yet efficient Blockchain-enabled SDN-IoT framework that ensures efficient cluster-head selection and secure network communication via the identification and isolation of rouge switches. Besides, the Blockchain-enabled flow-rules record keeps track of the rules enforced in the switches and maintains the consistency within the controller cluster. Finally, we assess the performance of the proposed framework in a simulation environment and show that it can achieve optimized energy-utilization, end-to-end delay, and throughput compared to considered baselines, thus being able to achieve efficiency and security in the smart network.

The paper highlights a stretchable, wash-durable and wearable smart textile-based Joule heater with high thermal stability.

Smart textiles are fabrics that have been designed and manufactured to include technologies that provide the wearer with increased functionality. These textiles have numerous potential applications, such as the ability to communicate with other devices, conduct energy, transform into other materials and protect the wearer from environmental hazards. Research and development towards wearable textile-based personal systems allowing e.g. health monitoring, protection and safety, and healthy lifestyle gained strong interest during the last few years. Smart fabrics and interactive textiles' activities include personal health management through integration, validation, and use of smart clothing and other networked mobile devices as well as projects targeting the full integration of sensors/ actuators, energy sources, processing and communication within the clothes to enable personal applications such as protection/safety, emergency and healthcare. This writing includes the origin and introduction of smart textile and integrated wearable electronics for sport wear, industrial purpose, automotive and entertainment applications, healthcare & safety,

Insecure and portable devices in the smart city’s Internet of Things (IoT) network are increasing at an incredible rate. Various distributed and centralized platforms against cyber attacks have been implemented in recent years, but these platforms are inefficient due to their constrained levels of storage, high energy consumption, the central point of failure, underutilized resources, high latency, etc. In addition, the current architecture confronts the problems of scalability, flexibility, complexity, monitoring, managing and collecting of IoT data, and defend against cyber threats. To address these issues, the authors present a distributed and decentralized blockchain-software-defined networking (SDN)-based energy-aware architecture for IoT in smart cities. Thus, SDN is continuously observing, controlling, and managing IoT devices activities and detects possible attacks in the network; blockchain provides adequate security and privacy against cyber attacks, and reduces the central point of failure issues; network function virtualization (NFV) is used to saving energy, load balancing, as well as increasing the lifetime of the entire network. Also, we introduce a cluster head selection (CHS) algorithm to reduce the energy consumption in the presented model. Finally, we analyze the performance using various parameters (e.g., throughput, response time, gas consumption, and communication overhead) and demonstrate the result that provides higher throughput, lower response time, and lower gas consumption than existing works for smart cities.

This study deals with the determination of new natural fibers extracted from the Corypha taliera fruit (CTF) and its characteristics were reported for the potential alternative of harmful synthetic fiber. The physical, chemical, mechanical, thermal, and morphological characteristics were investigated for CTF fibers. X-ray diffraction and chemical composition characterization ensured a higher amount of cellulose (55.1 wt%) content and crystallinity (62.5%) in the CTF fiber. The FTIR analysis ensured the different functional groups of cellulose, hemicellulose, and lignin present in the fiber. The Scherrer's equation was used to determine crystallite size 1.45 nm. The mean diameter, specific density, and linear density of the CTF fiber were found (average) 131 μm, 0.86 g/cc, and 43 Tex, respectively. The maximum tensile strength was obtained 53.55 MPa for GL 20 mm and Young's modulus 572.21 MPa for GL 30 mm. The required energy at break was recorded during the tensile strength experiment from the tensile strength tester and the average values for GL 20 mm and GL 30 mm are 0.05381 J and 0.08968 J, respectively. The thermal analysis ensured the thermal sustainability of CTF fiber up to 230 °C. Entirely the aforementioned outcomes ensured that the new CTF fiber is the expected reinforcement to the fiber-reinforced composite materials.

In recent years, there has been increasing interest in developing green biocomposite for industrial wastewater treatment. In this study, prawn-shell-derived chitosan (CHT) and kaolinite rich modified clay (MC) were used to fabricate biocomposite beads with different compositions. Prepared composite beads were characterized by FTIR, and XRD, and SEM. The possible application of the beads was evaluated primarily by measuring the adsorption efficiency in standard models of lead (II) and methylene blue (MB) dye solution, and the results show a promising removal efficiency. In addition, the composites were used to remove Cr (VI), Pb (II), and MB from real industrial effluents. From tannery effluent, 50.90% of chromium and 39.50% of lead ions were removed by composites rich in chitosan and 31.50% of MB was removed from textile effluent by a composite rich in clay. Moreover, the composite beads were found to be activated in both acidic and basic media depending on their composition, which gives a scope to their universal application in dye and heavy metal removal from wastewater from various industries.

Despite the growing knowledge and commitment to climate change, carbon dioxide (CO2) emissions continue to rise dramatically throughout the planet. In recent years, the consequences of climate change have become more catastrophic and have attracted widespread attention globally. CO2 emissions from the energy industry have lately been highlighted as one of the world’s most pressing concerns for all countries. This paper examines the relationships between CO2 emissions, electrical energy consumption, and gross domestic product (GDP) in Bangladesh from 1972 to 2019 in the first section. In this purpose, we applied the fully modified ordinary least squares (FMOLS) approach. The findings indicate that CO2 emissions, electrical energy consumption, and GDP have a statistically significant long-term cointegrating relationship. Developing an accurate CO2 emissions forecasting model is crucial for tackling it safely. This leads to the second step, which involves formulating the multivariate time series CO2 emissions forecasting challenges considering its influential factors. Based on multivariate time series prediction, four deep learning algorithms are analyzed in this work, those are convolution neural network (CNN), CNN long short-term memory (CNN–LSTM), long short-term memory (LSTM), and dense neural network (DNN). The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to analyze and compare the performances of the predictive models. The prediction errors in MAPE of the CNN, CNN–LSTM, LSTM, and DNN are 15.043, 5.065, 5.377, and 3.678, respectively. After evaluating those deep learning models, a multivariate polynomial regression has also been employed to forecast CO2 emissions. It seems to have nearly similar accuracy as the LSTM model, having a MAPE of 5.541.

Efficacy of cellulose acetate-based membranes for wastewater treatment has been critically evaluated. With the aim to improve efficiency, future prospects and research direction of CA based membranes are also discussed in the review.

With the improvement of living standards, everybody is very much conscious about the environmental protection and health safety. Natural dyes have attracted more attention to the industry due to exhibiting better biodegradability and more compatibility with the environment. Characteristic colours that are gathered from common assets can be categorized as either plant, creature, mineral, or microbial colours and can be used for colouring a wide range of regular filaments. Late examination shows that it can likewise be utilized to colour a portion of the manufactured filaments too. Normal colours are not just utilized in the shading of material filaments, they are also utilized for food, prescriptions, handiwork articles, and leather preparing. Extraction and purification play a vital role in the processing of natural dyes. There are different types of extraction process currently available for these natural dyes, such as solvent extraction, aqueous extraction, enzymatic extraction and fermentation, extraction with microwave or ultrasonic energy, supercritical fluid extraction, and alkaline or acid extraction. All these extraction processes have their own advantages as well as some drawbacks depending on the parameters that need to be maintained during the extraction process. Appropriate extraction can be beneficial for specific types of such dyes. In this paper, the classification, characteristics, extraction methods, and the application of natural dyes are introduced in an organized manner.

The ultraviolet (UV) radiation is increasing in atmosphere due to the continuous increasing of global warming throughout the globe, which causes a negative impact on the human skin. The aim of this work is to produce an ecofriendly fabric having functional properties of UV protection & antimicrobial using herbal synthesized colloidal solution of silver nanoparticles (H-AgNPs). This solution was synthesized with Tulsi (Ocimum tenuiflorum) extract as a reducing and capping agent. The cotton woven sample was developed with ten layers of colloidal solution of H-AgNPs using anionic surfactant sodium dodecyl sulfate (SDS) through pad-dry-cure method. Besides, another sample developed with the mixture of natural oils and the same concentration of H-AgNPs colloidal solution. Treated samples were subjected to color strength (K/S value) measurement, color fastness test, scanning electron microscopy (SEM) analysis, ultra-violet protection factor (UPF) test and antimicrobial test for evaluating their different functional & aesthetic performances. Testing data reveals that the treated sample has higher color strength of 0.95 compared to the untreated 0.04 and excellent all-round color fastness ratings of 4–5 for wash, 6–7 for light, 4 for dry rubbing & 3–4 for wet rubbing in contrast to the untreated one. SEM analysis confirmed that the H-AgNPs successfully deposited on cotton fibre surface. X-ray diffraction (XRD) experiment revealed the mean crystal size of H-AgNPs about 23 ± 3 nm. On the other hand, tensile strength increased by 6N & elongation 3–4% in both warp and weft direction. Moreover, less UV transmission of 1.04% & 3.12% for UV-B and UV-A respectively represents outstanding UV protection. In addition, antimicrobial test depicts the zone of inhibition for gram-positive S.aureus 9.5± 0.5 mm & gram-negative E.coli 10.5± 0.9 mm, respectively. The presence of phytochemicals of Tulsi extract, natural oils, and chemical compounds of cellulosic cotton has been further analyzed by Fourier transform infrared spectroscopy (FTIR) spectrum investigation.

This work is aimed at giving emphasis on the present pollution condition in dyeing & printing industries of Bangladesh due to different textile pollutant. Though the economy of our country is increasing day by day due to medium and small-scale industrial activities, the toxic waste discharge is contributing severe pollution to the environment by this dyeing & printing industry. The liquid & solid effluents from different industries are causing a major destruction to the environment, ecology, agriculture, aquaculture and public health since the development of textile industries in the country. So, it is high time to give a break to the pollution and time it out gradually to save the river system. It had become a prerequisite to take required steps to mitigate the pollution problems in each industrial establishment, particularly at dyeing and printing industries that are discharging massive amount of liquid effluent to the rivers every day. Here various types of pollution which is created by the textile dyeing & printing industries are discussed and some suggestions including raw materials purchasing, Eco-friendly dyeing etc. are point out for mitigation.

Breast cancer diagnosis from biopsy tissue images conducted manually by pathologists is costly, time-consuming, and disagreements among specialists. Nowadays, the advancement of the Computer-Aided Diagnosis (CAD) system allows pathologists to identify breast cancer more reliably and quickly.For this reason, interest in CAD-based deep learning models has been increased significantly. In this study, we propose a “MultiNet” framework based on the transfer learning concept to classify different breast cancer types using two publicly available datasets that include 7909 and 400 microscopic breast images, respectively. The proposed “MultiNet” framework is designed to provide fast and accurate diagnostics for breast cancer with binary classification (benign and malignant) and multi-class classification (benign, in situ, invasive, and normal). In the proposed framework, features from microscopy images are extracted using three well-known pre-trained models, including DenseNet-201, NasNetMobile, and VGG16. The extracted features are then fed into the concatenate layer, making a robust hybrid model. The proposed framework yields an overall classification accuracy of 99% in classifying two classes. It also achieves 98% classification accuracy in classifying four classes. Such promising results will provide the opportunity to use “MultiNet” framework as a diagnostic model in clinics and health care.

Disposal of reactive dye contaminants in surface waters causes serious health risks to the aquatic living bodies and populations adjacent to the polluted water sources. This study investigated the applicability of banana peels to remediate water contamination with reactive dyes used in the textile industry. A set of batch experiments was conducted using a standard dye solution to determine optimum adsorption parameters, and these parameters were used for the removal of dyes from actual wastewater. Fitting experimental data into the isotherm and kinetic models suggested monolayer dye adsorption with chemisorption rate-limiting step. The maximum adsorption found from modeling results was 28.8 mg/g. Fourier transformed infrared (FTIR) spectra revealed the existence of hydroxyl, amine and carboxylic groups, contributing to high adsorption of dye molecules onto the adsorbent surface. About 93% of the dyes from the standard solution were removed at optimum conditions (pH—7.0, initial dye concentration—100 mg/L, contact time—60 min, and adsorbent dose—0.5 g) while this value was 84.2% for industrial textile wastewater. This difference was mainly attributed to the composition difference between the solutions. However, the removal efficiency for actual wastewater is still significant, indicating the high potentiality of banana peel removing dyes from textile effluent. Furthermore, desorption studies showed about 95% of banana peel can be recovered with simple acid-base treatment.

Condominium network refers to intra-organization networks, where smart buildings or apartments are connected and share resources over the network. Secured communication platform or channel has been highlighted as a key requirement for a reliable condominium which can be ensured by the utilization of the advanced techniques and platforms like Software-Defined Network (SDN), Network Function Virtualization (NFV) and Blockchain (BC). These technologies provide a robust, and secured platform to meet all kinds of challenges, such as safety, confidentiality, flexibility, efficiency, and availability. This work suggests a distributed, scalable IoT-SDN with Blockchain-based NFV framework for a smart condominium (DistB-Condo) that can act as an efficient secured platform for a small community. Moreover, the Blockchain-based IoT-SDN with NFV framework provides the combined benefits of leading technologies. It also presents an optimized Cluster Head Selection (CHS) algorithm for selecting a Cluster Head (CH) among the clusters that efficiently saves energy. Besides, a decentralized and secured Blockchain approach has been introduced that allows more prominent security and privacy to the desired condominium network. Our proposed approach has also the ability to detect attacks in an IoT environment. Eventually, this article evaluates the performance of the proposed architecture using different parameters (e.g., throughput, packet arrival rate, and response time). The proposed approach outperforms the existing OF-Based SDN. DistB-Condo has better throughput on average, and the bandwidth (Mbps) much higher than the OF-Based SDN approach in the presence of attacks. Also, the proposed model has an average response time of 5% less than the core model.

During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate.

In energy, sensor, and biomedical applications, borophene is considered as an emerging and promising material. However, since bulk boron exhibits rather intricate spatial structures and multiple chemical properties, the synthesis of borophene is yet to be considered as a challenging issue. A large amount of theoretical work has been conducted to characterize the properties of borophene incorporating the possible experimental methods. Unfortunately, synthesis of borophene and their properties in experimentation do not comply the theoretical expectations within the desired level. There are some methods that have been used to synthesize borophene but these techniques also have some limitations such as complexity and expensive. Due to this reason, the seeking of new methods continues even now a day. In this research, a novel method has been proposed. This method is nearly similar to the electrochemical exfoliation process of graphene production. Nevertheless, graphite is conductive at ambient temperature, whereas boron is almost insulating at low temperature. Therefore, a new design is explored with the boron attached heating coil so that it can act as a conductive material with increasing temperature and confirmed the synthesis of borophene from boron by electrochemical exfoliation process. The quality and crystallographic structure of anisotropic borophene with the change of temperature will also be followed by this method. Apart from the pure boron rod, the sintering process is utilized under different aspects to develop the boron structure under optimum conditions. The crystallographic structure of boron can be changed by the sintering process under different operating and processing parameters. The synthesis of borophene from various crystallographic structures of boron will provide the new insight of borophene for its use in large scales. This new method of borophene synthesis is practically conducted at a limited level, still needs more analysis by using advanced characterization techniques. The Raman spectrum of fabricated borophene is evaluated and stability of this borophene is tested by using zeta potential. This method can be considered as a most promising and potential method in comparison with the other available techniques.

The brain, which comprises the cerebrum, cere-bellum, and brainstem and is covered by the skull, is a very complex and intriguing organ in the human body. Stroke is the world's second-leading cause of mortality; as a result, it requires prompt treatment to avoid brain damage. Early detection of a brain stroke can help to prevent or lessen the severity of the stroke, which can lower death rates. Using machine learning algorithms to identify risk variables is a promising method. This paper proposed a model that included a methodology to achieve an accurate brain stroke forecast. The efficient data collection, data pre-processing, and data transformation methods have been applied to provide reliable information for our proposed model to be successful. A “brain stroke dataset” was employed to build up the model. The standardization technique is used to standardize data. In the training and testing procedure, Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) classifiers are applied. The performance of each classifier has been estimated by adopting performance evaluation metrics such as accuracy, sensitivity (SEN), error rate, false-positive rate (FPR), false-negative rate (FNR), root mean square error, and log loss. Based on the outcome while using the RF classifier, we can determine that our proposed model provided the maximum accuracy, which was 95.30%.

3D printing is a popular nonconventional manufacturing technique used to print 3D objects by using conventional and nonconventional materials. The application and uses of 3D printing are rapidly increasing in each dimension of the engineering and medical sectors. This article overviews the multipurpose applications of 3D printing based on current research. In the beginning, various popular methods including fused deposition method, stereolithography 3D printing method, powder bed fusion method, digital light processing method, and metal transfer dynamic method used in 3D printing are discussed. Popular materials utilized randomly in printing techniques such as hydrogel, ABS, steel, silver, and epoxy are overviewed. Engineering applications under the current development of the printing technique which include electrode, 4D printing technique, twisting object, photosensitive polymer, and engines are focused. Printing of medical equipment including artificial tissues, scaffolds, bioprinted model, prostheses, surgical instruments, COVID-19, skull, and heart is of major focus. Characterization techniques of the printed 3D products are mentioned. In addition, potential challenges and future prospects are evaluated based on the current scenario. This review article will work as a masterpiece for the researchers interested to work in this field.