Showing papers in "Cogent engineering in 2022"

Industry 4.0: Clustering of concepts and characteristics

Abstract: Abstract The Fourth Industrial Revolution, also known as Industry 4.0, stems from the rapid advancement of digital technologies such as the Internet of Things and Cyber-Physical Production Systems. It has the potential to weave positive changes to firms and impact organizational structure layers. Therefore, it provides an impetus for the collaboration of factories, suppliers, and customers. Nevertheless, due to the difference of Industry 4.0 vision among companies, there is a lack of unified perception and approach of its implementation roadmap. Therefore, many firms in both developed and developing countries that step in the way of digital transformation encounter not only organizational, technological, and operational challenges but are also compelled to cope with a large deal of confusion. Hence, this paper aims to identify the main concepts, characteristics, and technology enablers related to Industry 4.0 to provide stakeholders with a clear understanding of this paradigm. It then clusters and matches the derived concepts and characteristics associated with Industry 4.0. Further, the paper provides an analysis of how these clusters are supported by technology enablers of Industry 4.0, as well as managerial implications.

Performance improvement of mode division multiplexing free space optical communication system through various atmospheric conditions with a decision feedback equalizer

Abstract: Abstract Free space optics (FSO) systems use the atmosphere as a propagation medium. However, a common problem is atmospheric turbulence, including fog, rain, and haze that emerges between the transmitter and the receiver from time to time. These adverse weather conditions impose power loss on the optical signal, producing distortion and degrading bit error rate (BER) and throughput. To reduce the effect of atmospheric turbulence, this paper proposes a decision feedback equalizer (DFE) with minimum mean square error (MMSE) in mode division multiplexing (MDM) for the FSO system. The DFE with varying tap counts is investigated. The MMSE algorithm is utilized to optimize both the feedforward and feedback filter coefficients of the DFE. The proposed system consists of four parallel 2.5 Gbps channels that use Hermite-Gaussian (HG) modes. The results show that the DFE equalization scheme successfully transmits 10 Gbps over 40 m, 800 m, 1400 m, and 2 km in medium fog, medium rain, medium haze, and clear weather. Performance is analyzed in terms of BER and eye diagrams and compared with the traditional model. Based on BER and eye diagram results, DFE improves the outdoor FSO system immunity to distortion in medium fog, medium haze, medium rain, and clear weather while maintaining high throughput and desired low BER.

Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs

Abstract: Abstract With web-based education and Technology Enhanced Learning (TEL) assuming new importance, there has been a shift towards Massive Open Online Courses (MOOC) platforms owing to their openness and flexible “on-the-go” nature. The previous decade has seen tremendous research in the field of Adaptive E-Learning Systems but work in the field of personalization in MOOCs is still a promising avenue. This paper aims to discuss the scope of said personalization in a MOOC environment along with proposing an approach to build a knowledge-based recommendation system that uses multiple domain ontologies and operates on semantically related usage data. The recommendation system employs cluster-based collaborative filtering in conjunction with rules written in the Semantic Web Rule Language (SWRL) and thus is truly a hybrid recommendation system. It has at its core, clusters of learners which are segregated using predicted learning style in accordance with the Felder Silverman Learning Style Model (FSLSM) through the detection of tracked usage parameters. Recommendations are made to the granularity of internal course elements along with learning path recommendation and provided general learning tips and suggestions. The study is concluded with an observed positive trend in the learning experience of participants, gauged through click-through log and explicit feedback forms. In addition, the impact of recommendation is statistically analyzed and used to improve the recommendations.

Grid connected photovoltaic system impression on power quality of low voltage distribution system

Abstract: Abstract To ensure the global energy demands and decarbonize the production of electricity, the expanded utilization of solar photovoltaics (PV) as a renewable energy resource has been increasing in recent decades, principally with the feasibility to be integrated with the conventional power grid. However, supplying clean power from PV grid-connected systems is often hampered by power quality (PQ) disturbances caused by the intermittent nature of solar radiation and other factors related to the grid, converters, and connected loads. To prevent deterioration of the power quality of the system, these disturbances must be mitigated. This paper technically studies some of these PQ issues, that is, the current total harmonic distortion (THD) which causes harmful effects on the whole connected power system and the linked loads. The case study works on a 5.5 kW grid-connected rooftop PV power system established at Benha Faculty of Engineering, Egypt, with the assistance of an installed weather station that boosts the validation of the research results. All aspects regarding the aforementioned small plant are presented including description and simulation of the whole system, review of current THD problems occurring at the point of common coupling (PCC), and a review of other disturbances observed by connected meters. A detailed examination of four techniques for harmonic mitigation, namely the on-off technique, LCL filter, active power filter, and hybrid active power filter is presented with a final comparison to assess the merits and demerits of each one. This research achieved a current harmonic limitation of 1.5%.

Optimal instantaneous prediction of voltage instability due to transient faults in power networks taking into account the dynamic effect of generators

Abstract: Abstract Changes in consumption and changes in the structure of the system always occur in each power system. One of the effects of these changes can be the instability of the system voltage. When voltage is unstable, their performance is in conditions of power fluctuations after large errors occur. Determining the voltage stability of traditional methods is time consuming and does not have the necessary efficiency for instantaneous monitoring. In this paper, an index based on the changes of two indices of frequency deviation and frequency response of inertia in the time after the occurrence of perturbation is presented, which has the ability to detect the occurrence of instability and at the same time high speed timely estimation of voltage instability in the power system. In addition, this indicator has been used to determine the appropriate time to start load removal (voltage reduction load). All simulations are performed on the IEEE 33-bus network in DIgSILENT software, the results of which indicate that the proposed index has a very low computational load. Because the proposed method for instantaneous voltage instability prediction does not depend on the network structure and load model and does not require any threshold value. Therefore, the proposed index has a very low computational load. These advantages make the proposed method an interesting option for online and practical applications.

Enhanced thermal performance of tubular heat exchanger using triangular wing vortex generator

Abstract: Abstract Enhancement of heat transfer for turbulent flow in a circular tube heat exchanger using triangular wing vortex generators (TWVG) is presented using Computational Fluid Dynamics methodology. A detailed parametric analysis is carried out to evaluate the impact of non-dimensional base width (0.059–0.216), non-dimensional height (0.039–0.314) and different flow attack angle of TWVG (α = 22.5°, 45°, 67.5°, 90°, 112.5° and 135°) for Re = 6000–18,000. The results show that the TWVG provides considerable heat transfer improvement through two mechanisms such as flow impingement effect on the upstream side and vortex formation on the downstream side. The heat transfer is found to decrease with increasing flow attack angle. However, the friction factor is found to increase from α = 22.5° to α = 90° and drops for all α > 90° due to increased streamlined orientation of vortex generator to the air stream. Longitudinal vortices are formed for α = 22.5°, whereas transverse vortices are generated for all other flow attack angles used in the analysis which are found to have lower flow mixing effect as well as lower coverage of tube wall region. Greater height and base width of TWVG provides greater heat transfer and friction factor enhancement. The maximum enhancement in Nusselt number and friction factor is in the range of 2.61–2.96 and 6.54–8.1 respectively for b/D = 0.216 and h/D = 0.314. The maximum thermal enhancement factor is produced by the configuration having h/D = 0.235 and b/D = 0.059 and has a range of 1.34–1.63.

Student workload assessment for online learning: An empirical analysis during Covid-19

Abstract: Abstract Covid-19 has forced most educational institutions around the world to migrate to online learning in an emergency mode to protect students from the pandemic. This sudden migration to online learning has created multi-dimensional demands on students. Therefore, student workload needs to be measured during online learning. The purpose of this study is to measure the student workload from student perception by evaluating online learning in terms of Mental demand (MD), Physical demand (PD), Temporal demand (TD), Effort (EF), Performance (PE) and Frustration (FR). This study through a cross-sectional survey analysed 223 student’s workloads on six dimensions using a NASA -TLX scale. The study finds all six components of workload significant for student assessment during online learning. Besides, the NASA-TLX scale was tested using confirmatory factor analysis for its ability to assess student workload for online learning. This is the first study to assess the student workload for online learning and hence contributes to the theory of measurement of workload assessment for online learning. The educational institutions can use this study to measure the student workload assessment for various courses offered by them using this simple tool.

Deep learning model for detection of COVID-19 utilizing the chest X-ray images

Abstract: Abstract The COVID-19 pandemic has caused more than 200 million infected cases and 4 million deaths across the world. The pandemic has triggered a massive epidemic, with a significant effect on the health and lives of many people worldwide. Early detection of this disease is very important for maintaining social well-being. Generally, the RT-PCR test is a diagnosis method used for the detection of the COVID-19, yet it is not the only reliable diagnostic tool. In this study, we discuss the image-based modalities for the detection of coronavirus utilizing Deep Learning methodology, which is one of the most innovative technologies today and has proven to be an efficient solution for a number of medical conditions. Coronavirus affects the respiratory tract of individuals. One of the best ways is to identify this disease from chest radiography images. Early research demonstrated unique anomalies in chest radiographs of covid-positive patients. By using Deep Learning Multi-layered networks, we classified the chest images as covid positive or negative. The proposed model uses the dataset of patients infected with Coronavirus, in which the radiologist indicated multilobar involvements in the chest X-rays. A total of 6500 images have been considered for the study. The convolutional network (CNN) model was trained and a validation accuracy of 94% is obtained.

A Blockchain Based Decentralized Identifiers for Entity Authentication in Electronic Health Records

Abstract: Abstract Over the past two decades, the fast pace of digitization in the healthcare ecosystem led to a phenomenal rise in the creation, storage and sharing of Electronic Health Records (EHRs) across the globe. However, the mechanism of authentication used for proving the identity of entities in EHRs is based on the identifiers issued by centralized identity providers (IDPs). It may lead to a single point of failure, loss of privacy and lack of interoperability. A new wave of decentralized identifiers (DIDs) and verifiable credentials(VCs) data modelled by blockchain has made it possible to achieve entity authentication in a decentralized manner. In this study, a blockchain-based framework with decentralized identifiers for patient authentication and consent management for EHR access using verifiable credentials is proposed. It describes the process of DID generation and authentication credential setup along with workflows for issuing and verifying credentials in the EHR ecosystem. The framework is implemented using Hyperledger Indy blockchain and Aries library. The study evaluates the performance of proposed workflows in terms of scalability, efficiency, resource utilization and conducts security analysis. Specifically, the outcome of this study can be used to realize the decentralized identity management and authentication in EHR systems.

Classification of paddy crop and weeds using semantic segmentation

Abstract: Abstract Weeds are unwanted plants in a farm field and have harmful effects on the crops. Sometimes rigorous weeds bring down the crop yield significantly, causing huge losses to farmers. A prevalent method of controlling weeds is the use of chemical herbicides. These herbicides are known to cause harmful effects on our environment. One of the ways to control the ill effects of herbicides is to follow the Site-Specific Weed Management (SSWM). Site-specific weed management is to use the right herbicide for the right amount on agricultural land. This paper investigates a semantic segmentation approach to classify two types of weeds in paddy fields, namely sedges and broadleaved weeds. Three semantic segmentation models such as SegNet, Pyramid Scene Parsing Network (PSPNet), and UNet were used in the segmentation of paddy crop and two types of weeds. Promising results with an accuracy over 90% has been obtained. We believe that this can be used to recommend suitable herbicide to farmers, thus contributing to site-specific weed management and sustainable agriculture.

Feasibility analysis of solar PV/biogas hybrid energy system for rural electrification in Ghana

Abstract: Abstract Globally, reliable access to electricity improves people’s well-being, provides quality education, and promotes good health. Greenhouse gas emissions associated with fossil fuel combustion have incited an intense interest in low-carbon technologies for power generation. This study analyses the prospect of utilising a solar PV/biogas/battery hybrid energy system to provide electricity for Ghana’s remote communities. The study goal is to utilise locally available renewable energy resources to achieve a cost-effective levelized cost of electricity (LCOE) and mitigate greenhouse gas emissions. Hybrid Optimisation of Multiple Energy Resources (HOMER) software was employed to model and analyse the hybrid energy system’s technical, economic, and environmental aspects. The findings indicate that PV/biogas/battery system perform better than PV/diesel/battery and diesel-only systems in terms of cost and emissions reductions. Also, the LCOE generated from the PV/biogas/battery system is around 0.256 $/kWh. However, this LCOE is only about 64% higher than the LCOE for Ghana’s household residents. The sensitivity test indicates that the PV/biogas/battery system is sensitive to discount rates and capital subsidies, making it attractive for future development. This attests that Ghanaian rural communities without electricity access and with substantial biomass potential are likely to be electrified when given the necessary attention. Moreover, this project could be a viable alternative to rural electrification in Ghana with proper investment support.

Techno-economic analysis of solar energy system for electrification of rural school in Southern Ethiopia

Abstract: To provide rural communities with low-cost electricity, innovative off-grid renewable energy producing techniques have emerged. The International Energy Agency estimates that around 45% of Ethiopia’s total population have access to electricity. Nearly 85% of Ethiopia’s urban population has access to public electricity, but this figure is only 29% for the rural population. This study examines the feasibility of using combined photovoltaic (PV)/diesel/battery systems to power a remote rural school in southern Ethiopia. The performance of various hybrid systems was assessed using techno-economic and environmental analyses, and the optimal solution was chosen using the Hybrid Optimization Model for Electric Renewable (HOMER) analytic tool. The evaluation criteria include net present cost (NPC), cost of energy (COE) and emissions. The results indicate that PV/DG/battery hybrid energy system (HES) with a 7.5 kW PV, 7.3 kW DG, 6.60 kW converter, and 11 units of batteries (case I) is the most feasible, optimized, cost-effective and environmentally friendly system among the systems considered. This system has a Net Present Cost (NPC) of $32,019 and a Cost of Energy (COE) of $0.254/kWh, as computed using current equipment values. The optimized system is also environmentally benign, emitting 793 kg of carbon dioxide per year, about 91% less than the PV/diesel combination (worst case IV). Furthermore, sensitivity analysis was performed to examine the impacts of altering factors such as solar radiation, fuel price, and battery minimum state of charge (SOCmin) on system cost and performance. We believe that the information given in this paper will shed light on the current state and future prospects for renewable energy deployment in Ethiopia, and also show that, if policymakers create the necessary investment environment, such projects can be a viable alternative to rural electrification.

AHP based multi criteria decision analysis of success factors to enhance decision making in infrastructure construction projects

Abstract: Abstract The study presents analytical data-based multi-criteria approach of critical success factors of infrastructure construction projects analyzed in the Ethiopian construction industry. This multi-criteria technique helps to improve the decision capabilities and ultimate performance of construction processes in various low-income countries of the East African region. The aim of this paper is to establish a logical relationship and interdependencies of success-related factors for enhancing decision making for various project teams and identify priorities while taking into account all known construction organizational constraints. A structured hierarchical matrix was developed based on a pre-identified success-related factors, and initially evaluated by experienced professionals as part of a content validation of the survey. Different professionals working in various construction organizations in Ethiopia were invited to participate in the questionnaire survey. All the required data analysis, including sensitivity performance, was conducted through Expert Choice© 11. Further, Kendall’s coefficient of concordance was conducted to examine and compare multiple expert responses. Based on the findings, the top success-related factors that affect decision making in construction projects are Adequate Goals/Objectives, Consultant’s Competency, Prior Experience of Consulting Firms, Consulting Firm’s Willingness and Cooperation, and Financial Standing of Contractor. The results are based on their global priority weights in the hierarchical model. The findings highlighted that there is disagreement between the major stakeholders involved in the construction process. The contribution of the study is introducing a bench-marking multi-criteria decision analysis technique to enhance decision making in the Ethiopian infrastructure sector.

Evaluation water scarcity based on GIS estimation and climate-change effects: A case study of Thi-Qar Governorate, Iraq

Abstract: Abstract This work aims to evaluate water scarcity in Thi-Qar governorate, Iraq, based on GIS estimation, environmental data, climate-change effects, and detection of the changes in marshes over the last three decades (1991–2021). The methodology process included collecting and analysing the related data sets such as water quality indicators, surface water quantity, climatic data, and Landsat’s images. GIS-based data and spatial data were acquired from the USGS website. Arc GIS 10.4.1 software was used to create a hydrological analysis. The results showed that generally, in Iraq, the annual volume of water available per person is 1,390.95 m3/cap/year, which is lower than the threshold for water scarcity (1700 m3/cap/year). The average daily potable water per person in Thi-Qar governorate was 284 L/cap/day, lower than the general average daily potable water per person of Iraq (340 L/cap/day). Meanwhile, 6% of the months along 1998–2018 did not meet the water demands. Water quality tests exhibited some high amounts of pollutants in drinking water, e.g., biological pollution was recorded in 55% of the total number of annual samples. Landsat’s images illustrated a high variation in water areas of marshes over the selected period, whereas the highest marshes area was 1548.21 km2 in 1991 compared to the lowest area, 65.45 km2 found in 1999. To sum up, the research outcomes revealed that the study area faced a serious water scarcity, which had a negative impact on the local people. Also, this research offered a scientific view for the decision-makers to mitigate and manage the water scarcity problem.

Semantic interdisciplinary evaluation of image captioning models

Abstract: Abstract In our day-to-day life, synchronizing vision and language aspects plays a crucial role in solving various real-time challenges. Image captioning is one of them, and it aims to recognise objects, activities, and their relationships in order to provide a syntactically and semantically correct visual description. There are existing works of image captioning in various directions, such as news, fashion, art, and medical domains. The core architectural idea of image captioning is based on merging CNN, RNN, and transformer models. In practice, there are many conceivable combinations, and brute forcing all of them would take a long time. As we know, there is no work on interpreting image captioning models across various usecases. In this research article, we examine and analyze different image captioning models used across various domains, and multiple insights are extracted to determine the best combinational architecture for a new application without ignoring contextual semantics. We examined numerous designs and determined that LSTM is best for image captioning across several domains.

Augmented Reality Application Selection Framework Using Spherical Fuzzy COPRAS Multi Criteria Decision Making

Abstract: Abstract One of the key aspects of digitalization brought by Industry 4.0 is driven by the Internet of Things. Perhaps, the most interesting component of digital transformation is Augmented Reality. Augmented reality is merging the digital and physical worlds in the same experience. This new technology is accepted and applications are initiated in various areas including manufacturing. The main usage area in manufacturing is planning, execution, and verification of the assembly and maintenance operations. Likewise, augmented reality is used in the training of inexperienced people and to guide them by remote expert support. However, the augmented reality application selection, especially for non-technology savvy manufacturing organizations is challenging. Detailed and time-consuming analyses are required to understand the key features to compare and select the most suitable augmented reality application. In this paper, the most critical nine augmented reality software selection criteria are identified. To help organizations to apply such criteria and make a selection that is most suitable for the enterprise, a multi criteria decision-making approach is suggested. The suggested method is based on a fuzzy spherical number. The complex proportional assessment method is used to calculate the rankings. Therefore, the suggested method is called SF-COPRAS. Moreover, the detailed definition of augmented reality features and terminologies is explained. Several augmented reality use-case scenarios are discussed for manufacturing organizations on their Industry 4.0 initiatives. This paper aims to guide decision makers on their augmented reality software selection journey. The offered framework aims to save time for investigating augmented reality solution features systematically and objectively.

Static structural analysis of the effect of change in femoral head sizes used in Total Hip Arthroplasty using finite element method

Abstract: Abstract The total hip prosthesis is considered one of the greatest advancements in orthopaedic surgery in the early 20th century. In hip implant replacement, the stem is inserted into the femur and femoral head, acetabular cup, and backing cup over the stem. This felicitates the normal range of motion and stability of an individual compared to the natural hip joint. Several different types of biomaterials are used in the total hip prosthesis. Where the combination of UHMWPE, CoCrMo alloy, and Ti-6Al-4 V alloy is widely used due to their superior mechanical properties over the others. In this work, these material combinations are used for the analysis with changes in the femoral head sizes from 24 mm to 48 mm to know the best size with the better material combination. Static structural analyses are carried out using Ansys R-19. Two sets of analyses are performed for two different material combinations between Ti-6Al-4 V, CoCr, and UHMWPE. The circular cross-shaped stem with change in femoral head sizes from 24 mm to 48 mm is used for analysis. The acetabular cup and backing cup thickness are kept constant throughout the study. The size of the acetabular cup and backing cup is considered 6 mm and 4 mm respectively. Loading and boundary conditions are considered as per the ASTM standards. Hip implant with Ti-6Al-4 V for stem and a 4 mm backing cup of CoCr and 6 mm acetabular cup of UHMWPE shown the least stress value of 203.48 MPa over all the other models which are used in the analysis. This work shows the analysis to know the effect of the change in femoral head size and also the change in material combinations used in hip implants. Further experimental work can be carried out to validate the results obtained in the current work.

Optimal allocation of multiple distributed generation units in power distribution networks for voltage profile improvement and power losses minimization

Abstract: Abstract Distributed Generation (DG) integration into an existing electrical distribution system plays a great role in combating power system profile problems associated with load growth, overloading, lowquality of supply and non-reliability. Hence, there is a need to improve the technical benefits of DG integration by optimal sitting and sizing in a power system network. These benefits can potentially defer the investments to be made to upgrade the assets of the distribution system, extend equipment maintenance intervals, reduce electrical power losses, and improve the voltage profile and reliability of the distribution system. The aim of this research is therefore to minimize power losses and improve voltage profile of Bahir Dar distribution network using different types of DGs (type-1 DG, type-2 DG and type-3 DG). An improved particle swarm optimization (IPSO)-based methodology is applied to optimally allocate and size the required DG units. It is found that the proposed algorithm is more effective in enhancing the performance of distributions systems when compared with conventional optimization techniques. In particular, the percentage reduction in real power loss is 55.73%, 73.1% and 54.098% while the percentage reactive power loss reduction is 55.102%, 73.46% and 57.14% for type-1, type-2 and type-3 DGs, respectively. Moreover, the minimum voltage is significantly improved and all bus voltages are maintained above the permissible limit. Generally, the proposed optimization algorithm is found to be more efficient and robust for the performance enhancement of a radial distribution system using multiple DG integrations.

A comprehensive review on coal fly ash and its application in the construction industry

Abstract: Abstract Coal fly ash (CFA) is a coal ignition buildup at thermal power plants, which has been viewed as a hazardous waste globally. The major problems with CFA are the large volume of land needed for its disposal and poisonous weighty metal sifted to groundwater. CFA has been considered a waste and water pollutant until recently; however, CFA is a helpful material and has shown its useful value, especially in the construction industry. This review paper aims to evaluate CFA properties and validate its utilization in the construction industry to save the planet from damages associated with its disposal. The current paper surveys the potential uses of CFA as a crude material in the construction industry in catalysis, soil stabilization and replacement, brick production, cement replacement highway embankment, bricks construction, material for soil replacement and stabilization, and dams, asphalt pavement, and road construction. This review was conducted through systematic consultation of mostly recent relevant literature with a few old publications to evaluate the efficiency of CFA utilization in the construction industry. Moreover, all the literature rated CFA as a suitable material for use in the construction industry. A major drawback of CFA usage in concrete is the slow early strength development. However, this can be taken care of by accelerating the admixtures in the concrete mix. Future research tends towards production of CFA with more improved features suitable for advanced construction technology as in 3D printing construction. Conclusively, CFA is recommended for use in the construction industry based on its performance success recorded from the research findings reviewed in this paper.

The understanding of the social determinants factors of public acceptance towards the end of life vehicles

Abstract: Abstract Indonesia is one of the highest producers of the automotive industry in Southeast Asia. However, rapid progress in this sector directly poses a threat to the increase in End-of-Life Vehicles in Indonesia. Several policies and instruments have been implemented to reduce the high number of ELVs, such as raising vehicle taxes and requiring periodic emissions testing systems to determine vehicle eligibility. These initiatives, however, do not reduce the use of ELV vehicles in Indonesia. Indonesia did not win the ELV because of high rejection from the users. To date, no research has been done to determine public acceptance of ELV policies. This study aims to examine the public acceptance of ELV management, particularly to gauge information about people’s knowledge, attitude, social influence, and institutional trust as mediation variables. A cross-sectional survey was conducted involving 243 respondents from JABODETABEK (Jakarta, Bandung, Depok, Tangerang, Bogor, and Bekasi). After passing satisfactory reliability and validity tests, the hypothesized research model was estimated using structural equation modelling. The study found that knowledge, attitude, social influence, and institutional trust all had a significant influence on public acceptance. The result also indicated that institutional trust variables serve as effective mediators. The proposed model is a good model (overall R2 = 0.703, F = 58.2, p = 0.00). The implication of this study suggested that policymakers should consider that implementing ELV-related policies is not only a solution to the automotive cycle and environmental health but also must address individual differences by taking into the factors forming one’s acceptance.

Investigation on material variants and fabrication methods for microstrip textile antennas: A review based on conventional and novel concepts of weaving, knitting and embroidery

Abstract: Abstract Wearable electronics sector is a fast-growing industry due to the rapid progress gained by developing textile-based conductive materials and conductive yarns. The demand for wireless communication in smart textiles has been increasing progressively, and therefore textile antennas will create potential benefits for wireless applications. Microstrip patch antennas play a major role in the field of textile antennas due to their low profile, conformal nature, compatible dimensions, and manufacturing feasibility. The performance of the microstrip patch antenna depends on the antenna dimensions, fabrication techniques and materials, which determine the antenna input impedance, gain, radiation efficiency, and bandwidth. The selection of correct materials, fabrication methods, and topology are very crucial for textile antennas. The heterogeneities in the textile material affect the quality factor and hence degrade the antenna performance. This article reviews conventional and novel fabrication techniques and material variants of each antenna component based on knitting, weaving, and embroidery in order to provide background information and application ideas for designing and developing microstrip patch antennas.

Implementation of Sustainable Reforms in the Indian Automotive Industry: From Vehicle Emissions Perspective

Abstract: Abstract The scenario of the Automobile Industry has changed, not only in India but also for the world as a whole. Rise in demand for more sustainable mobility and vehicles with increased usage of renewable energies have given rise to a revolution. This revolution has not only been affecting mankind for better implementation of resources but has also shown tremendous greener and cleaner effects on the flora and fauna of the land as a whole. There has been electrification of vehicles, addition of superior systems to generate lesser harmful effluents and also changing the guidelines of emissions to cleaner and more sustainable ones. The purpose of this paper is to review and state the existence of different pieces of literature for the transition from Bharat Stage-IV (BS-IV) to Bharat Stage-VI (BS-VI). Much has been written about the transition to BS-VI, the difficulties, and the positive effects on vehicle reliability, economy and the environment. In particular, this work explores the parallels between BS-VI and the corresponding Euro norms and their sequence of implementation. The aforesaid transition has increased the number of oil refineries in India that produce BS-VI-compliant fuels, keeping in mind the increased need for it. This paper explores the current state of the literature and throws light on many of the questions regarding this significant transition. The authors have tried to study the ongoing issues including the stress of inventory management of diesel-run passenger cars, the different refinements required to process and manufacture the BS-VI-compliant vehicles, people's reaction on the changes brought in by the issuing bodies with respect to the norms, to name a few. The focus of the research has been on Bharat Stage emission norms, their implementation challenges and automobile pollution in India to name a few. The authors also discuss the needs, advantages and challenges faced by the industry, producers and customers and conclude that this change is one of the reasons for the decline in car sales in India as it may affect the buying power of the customers.

Security in Internet of Drones: A Comprehensive Review

Abstract: Abstract Unmanned Aerial Vehicles (UAVs), also called drones, are used for various applications with two basic classifications: civilian and military drones. Civilian drones are used for various applications like construction site monitoring, natural disaster area monitoring, agriculture, etc. Military drones are used for applications like monitoring a country’s border, the transmission of information about the intruders to Ground Control Station (GCS), or to any other server which has been designed for the purpose. Before the real-time deployment of the Internet of Drones (IoD), the feasibility and efficiency of a proposed UAV framework should be tested in an open-source UAV simulator or any network simulator. AirSim simulator is an open-source UAV simulator that has different features like ease of development, efficient motion capture, efficient obstacle detections and collision detections, use of different sensor models, and physics models. Hence, a review has been done on recent UAV frameworks which have been designed and tested using the AirSim simulator. Since a vast amount of data is being transmitted between IoD devices in this era, there is a need for designing secure IoD communication frameworks, with the least compromise in the performance of the designed frameworks. Hence, an extensive review has been done on different secure IoD communication frameworks which have used different cryptography concepts like key agreement, authentication, encryption and decryption, integrity, blockchain, digital signatures, and have implemented their proposed frameworks in real-time, or by using network simulators. The common network simulators that have been used for simulating secure IoD frameworks/mechanisms are NS3 and OMNeT++.

Textile additive manufacturing: An overview

Abstract: Abstract Additive manufacturing (3D-printing) is a rapidly emerging technology grouped under the heading of Industry 4.0 and revolutionises the way products are created. One emerging area is textiles (apparel) where additive manufacturing allows the rapid fabrication of products that are not easily produced using traditional manufacturing approaches. In this review, we provide an overview of recent developments in textile additive manufacturing highlighting this exciting and rapidly growing research area and offer insights into the future perspectives of this promising and innovative technology.

Physical, thermal, mechanical, sound absorption and vibration damping characteristics of natural fiber reinforced composites and hybrid fiber reinforced composites: A review

Abstract: Abstract Adaptation of alternative and novel materials in place of traditional metals and alloys is getting higher attention in all domains of life. Tailored or customized fiber reinforced composite materials offer multiple advantages such as lighter systems with adequate strength, cost-effectiveness, and easier handling during fabrication and service time. In this review paper, attempts are made to comprehensively look into property improvement, fabrication, and characterization followed by researchers for natural fiber reinforced, natural fiber-synthetic fiber reinforced and hybrid natural fibers reinforced polymer-based composites. Composites having natural fibers as reinforcements had improved sound absorption and vibration damping properties. A combination of natural and synthetic fiber was found to be ideal for obtaining improved mechanical properties.

Estimation of global solar radiation using sunshine-based models in Ethiopia

Abstract: Abstract Solar energy is one of the renewable energy sources that can be used to solve Ethiopia’s current energy problems. However, global solar radiation data for the country are either not available at all levels or recorded for only a few years at some locations. In this study, monthly mean daily global solar radiation (H) over the horizontal surface at 15 sites in Ethiopia was calculated using sunshine hour-based models, such as the Angstrom-Prescott model (AP), the Louche model (LO), and the Glover-McCulloch Estimation Model (GM). For the performance of the proposed model, statistical error analysis and geospatial results were performed to ensure the validity of the model used. The validation results show that the H estimates from all stations agree well with the measured data from all models. Therefore, the proposed model can be used to predict global solar radiance. However, among the other models, the GM model performed the best H estimates for all sites, with an average r of 0.9574, an average RMSE of 0.6017, an average MPE of 0.00335, and an average MBE of −0.7555, respectively. Therefore, the GM model is suitable for estimating global insolation for the entire country. The highest measured solar radiation value (from NASA) was recorded in February (6.89 kWh/m2/day). Likewise, the highest estimated global solar irradiance in February was observed using the three empirical models, with values of 7.55, 7.12, and 7.47 kWh/m2/day for the AP, LO, and GM models, respectively. The lowest measured and calculated radiation values were recorded in July due to the country’s highest cloud cover and rainy season. The results also show that the estimated H in Ethiopia ranges from 3.45 6.89 kWh/m2/day (July) to 7.47 6.89 kWh/m2/day (February) based on the GM model calculations, with an annual average of 5.83 kWh/m2/day (average across all sites). This study can be utilized in the design, analysis and performance evaluation of solar energy potential, which is gaining significant attention from the Ethiopian government in the advocacy of a green economy and reducing greenhouse gas emissions as one of its political agendas to meet the Sustainable Development Goal (SDG).

Nanomaterials in recycled aggregates concrete applications: mechanical properties and durability. A review

Abstract: Abstract The use of recycled aggregates concrete (RAC) contributes effectively to reduce CO2 emissions from concrete manufacturing process while also protecting natural resources by utilizing existing available concrete as an aggregates source for a new one. Studies on the behaviour of RAC have revealed negative effects on concrete strength and microstructure development, resulting in deterioration of mechanical and durability properties. As a result, numerous practical studies have been implemented to enhance the RAC properties using various treatment techniques such as chemical, physical and heating treatments. However, most of these techniques are ineffective compared to conventional concrete applications due to poor mechanical performance of RAC, insufficient environmental requirements, and prolonged treatment times. Recently, the use of nanomaterials has been given significant concern in RAC research. Their nano-sized particles can help to reduce micropores formation by acting as a filling agent to produce a high-density microstructure, thereby enhancing the mechanical properties and durability of RAC. This had led to a wide range of studies being published on improving RAC properties by using nanomaterials. However, relatively few literatures had been conducted on the effects of different types of nanomaterials on the performance of RAC exposed to various types of loads and various external environmental impacts. Besides, the conditions used by authors in these literatures limit comparisons, and in some cases contradictory findings are observed. Thus, this paper aims to bridge the knowledge gap between researchers. This would allow the potential of nanotechnology in innovations to be applied in appropriate areas of RAC applications to benefit the general public good. This paper aims to provide a critical review and comprehensive conclusions on the performance of nano-modified RAC under external loads, environmental impacts and other various conditions. The effects of nanomaterials on the compressive, tensile, and flexural strength of RAC are discussed. The nanomaterials considered are nano-SiO2, nano-CaCO3, nano-TiO2, nano-Clay, nano-Al2O3, and nano-Carbon. Durability characteristics including water absorption, chloride penetration, fire exposure, abrasion resistance, acid and carbonation diffusions are extensively discussed. Microstructure characteristics using SEM, XRD, EDS, and micro-hardness of nano-modified RAC are addressed as well.

Effects of active microbreaks on the physical and mental well-being of office workers: A systematic review

Abstract: Abstract Risk factors associated with sedentary work and prolonged sitting time can be detrimental to office workers’ health and productivity. Recent literature introduced the concept of active microbreaks and their benefits to sedentary workers. The purpose of this study was to better define active microbreaks and to determine the evidence behind utilizing active microbreaks at work, through a qualitative synthesis of the literature in a systematic review. A comprehensive systematic search was conducted using primarily ergonomics, medicine and allied health databases, in addition to grey literature (CINAHL, Google Scholar, PubMed, and ScienceDirect) and respective ergonomics journals. Six interventional controlled trials (232 total participants) met the inclusion criteria and qualified for the inclusion in this review. The quality of the reviewed articles was deemed to be moderate to high according to the utilized assessment scales. The results of this review may support the use of short active microbreaks (2–3 minutes of light intensity exercises every 30 minutes) due to the observed physical and mental health benefits without negative impact on productivity in the workplace.

Public attitude towards autonomous vehicles before and after crashes: A detailed analysis based on the demographic characteristics

Abstract: Autonomous vehicles (AVs) have the potential to offer a large number of benefits such as reducing the energy consumed and reducing the anxiety of the drivers. On the other side, the degree to which AVs will be adopted mainly depends on the public attitude and acceptance of this emerging technology. Over the last few years, AVs got involved in multiple accidents with different levels of severity. These accidents were widely covered in the media, creating a debate about the safety of this technology and discouraging people from adopting this new technology even if it offers a safer environment. In this study, a questionnaire survey was conducted to understand the impact of accidents involving AVs on the public perception of this technology for respondents with different demographic characteristics (age, gender, education, income, and prior knowledge about AVs). The results show the most negative shift in the attitude occurs for respondents who are older, female, and have no prior knowledge about AVs or their incidents. Additionally, the results shed light on the importance of educating the public about AVs in order to guarantee the highest level of acceptance. Finally, the findings of this paper can help AVs developer, policymakers, and transport planning agencies in understating the public attitude after accidents in order to react properly to avoid discouraging people from adopting AVs.

IoT-Based Real-Time updating multi-layered learning system applied for a special care context during COVID-19

Abstract: Abstract In response to the COVID-19 pandemic and the need for increased research, this study aimed to develop a real-time learning system to provide infection control for residential special care contexts and in doing so, explored different crowdsourcing technologies, spatial usages, and data processing methods within the scope of smart health-care systems and environments. Experiments were conducted in the selected special care indoor environment, which was fitted with sensors and Internet of Things devices, from which generated data were used to train Convolutional Neural Networks, Long-Short Term Memory, and Binary Layered Long-Short Term Memory neural networks. Sequential neural networks were multi-layered and configured in tandem and from these, the real-time updating learning system was developed. The system monitors the user activity and environmental data and predicts critical cases to send alerts to caregivers. Findings showed that stacking neural networks over one another increases the efficiency in updating the training data of real-time learning system. Overall, the study concludes that the developed real-time learning system is lightweight, fast, and efficient for infection control and special care at the private scale and can be multiplied at multiple nodes of larger networks of smart health services and environments.