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This paper aims to introduce a systematic computing and analytical procedure that is applied to the co-author network to identify the temporal evolution and growth of research collaborations in the area of e-governance. The empirical analysis of the temporal co-author network can trace the emerging authors and knowledge bursts over time.

The study applied social network theory to trace the author collaboration patterns in the domain of e-governance. Analysis of the co-author network using micro and macro parameters was done to trace the temporal evolution of the author collaborations.

E-governance is a multi-disciplinary research domain split over streams of management, politics, information technology and electronics. Hence, research collaborations play a significant role in its advancement. The knowledge sharing between individual authors, institutions and groups through research collaborations, resulting in extensive sharing of data, equipment and research methods, has boosted research activities and development in e-governance. In this paper, the authors systematically analyse the current scenario of research collaborations in the area of e-governance using co-author network to estimate its impact on the advancement of the field. The authors also analysed the temporal evolution of the co-author networks, which show remarkable growth of research collaborations in the domain of e-governance from the year 2000.

The co-author network analysis is only a proxy measure for the analysis of research collaborations. The names of the authors and the university affiliations used in the article are as retrieved from the research repository of Scopus. The degree, citations and other parameters related with authors have scope only within the environment of the co-author network used in the analysis. The criteria used in the study is limited to the degree of research collaborations and the number of co-authored publications in the giant component of the co-author network.

Institutions, authors and governments can trace and select suitable topics and choose research groups of co-authors over the world for future research collaborations in e-governance. The knowledge about the emerging and most discussed topics gives an overview of the global research trends of e-governance.

The study identified the evolution of creative collaborations in e-governance in the global perspective. The methodology introduced here is helpful to detect the proficient and productive author collaborations and the spectrum of related e-governance research topics associated with them. As the author collaborations can be mapped to the institutional and country-level collaborations, the information is helpful for researchers, institutions and governments to establish the best collaborations in e-governance research based on the author proficiency, collaboration patterns and research topics as per the requirements.

The paper introduces a novel research methodology using temporal analysis of co-author network to identify the evolution of research patterns and the associated research topics.

The purpose of this paper is to examine the impact of affiliate marketing strategies as a tool for increasing customers' engagement and vulnerability over financial services. This is attempted by examining the connection between affiliate marketing factors and customers' brand engagement and vulnerability metrics.

The authors developed a three-staged methodological context, based on the 7 most known centralized payment network (CPN) firms' website analytical data, which begins with linear regression analysis, followed by hybrid modeling (agent-based and dynamic models), so as to simulate brand engagement and vulnerability factors' variation in a 180-day period. The deployed context ends by applying the cognitive modeling method of producing heatmaps and facial analysis of CPN websites to the selected 47 vulnerable website customers, for gathering more insights into their brand engagement.

Throughout the simulation results of the study, it becomes clear that a higher number of backlinks and referral domains tend to increase CPN firms' brand-engaged and vulnerable customers.

From the simulation modeling process, the implication for backlinks and referral domains as factors that enhance website customers' brand engagement and vulnerability has been highlighted. A higher number of brand-engaged website customers could mean that vulnerable categories of customers would be impacted by CPNs' affiliate marketing. Improving those customers' knowledge of the financial services utility is of utmost importance.

The outcomes of the research indicate that online banking service providers can increase their customers' engagement with their brands by adopting affiliate marketing techniques. To avoid the increase in customers' vulnerability, marketers should aim to apply affiliate marketing strategies to domains relevant to the provided financial services.

The paper's outcomes provide a new approach to the literature, where the website customer's brand engagement comes out as a valuable metric for estimating online banking sector customers' vulnerability.

The purpose of this study is to experimentally investigate the trends in shock wave Mach number that were observed when different diaphragm material combinations were used in the small-scale shock tube.

A small-scale shock tube was designed and fabricated having a maximum Mach number production capacity to be 1.5 (theoretically). Two microphones attached in the driven section were used to calculate the shock wave Mach number. Preliminary tests were conducted on several materials to obtain the respective bursting pressures to decide the final set of materials along with the layered combinations.

According to the results obtained, 95 GSM tracing paper was seen to be the strongest reinforcing material, followed by 75 GSM royal executive bond paper and regular 70 GSM paper for aluminium foil diaphragms. The quadrupled layered diaphragms revealed a variation in shock Mach number based on the position of the reinforcing material. In quintuple layered combinations, the accuracy of obtaining a specific Mach number was seen to be increasing. Optimization of the combinations based on the production of the shock wave Mach number was carried out.

The shock tube was designed taking maximum incident shock Mach number as 1.5, the experiments conducted were found to achieve a maximum Mach number of 1.437. Thus, an extension to further experiments was avoided considering the factor of safety.

The paper presents a detailed study on the effect of change in the material and its position in the layered diaphragm combinations, which could lead to variation in Mach numbers that are produced. This could be used to obtain a specific Mach number for a required study accurately, with a low-cost setup.

In wireless sensor networks, improving the network lifetime is considered as the prime objective that needs to be significantly addressed during data aggregation. Among the traditional data aggregation techniques, cluster-based dominating set algorithms are identified as more effective in aggregating data through cluster heads. But, the existing cluster-based dominating set algorithms suffer from a major drawback of energy deficiency when a large number of communicating nodes need to collaborate for transferring the aggregated data. Further, due to this reason, the energy of each communicating node is gradually decreased and the network lifetime is also decreased. To increase the lifetime of the network, the proposed algorithm uses two sets: Dominating set and hit set.

The proposed algorithm uses two sets: Dominating set and hit set. The dominating set constructs an unequal clustering, and the hit set minimizes the number of communicating nodes by selecting the optimized cluster head for transferring the aggregated data to the base station. The simulation results also infer that the proposed optimized unequal clustering algorithm (OUCA) is greater in improving the network lifetime to a maximum amount of 22% than the existing cluster head selection approach considered for examination.

In this paper, lifetime of the network is prolonged by constructing an unequal cluster using the dominating set and electing an optimized cluster head using hit set. The dominator set chooses the dominator based on the remaining energy and its node degree of each node. The optimized cluster head is chosen by the hit set to minimize the number of communicating nodes in the network. The proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit set. The simulation result confirms that the proposed algorithm prolonging the lifetime of the network efficiently when compared with the existing algorithms.

The proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit sets. The simulation result confirms that the proposed algorithm is prolonging the lifetime of the network efficiently when compared with the existing algorithms.

Despite carbon reduction commitments, many constraints hinder the delivery of low-carbon buildings (LCBs) in high-rise high-density cities. The collaborative commitment of relevant stakeholders is vital to effectively address and mitigate these constraints. Hence, this study aims to comprehensively explore the required stakeholder collaboration attributes to address and mitigate the “common” constraints of delivering LCBs by focussing on several high-rise high-density cities.

A list of 21 “significant and common” constraints was identified through a systematic literature review followed by a questionnaire survey covering five economies (Hong Kong, Singapore, Australia, Qatar and the UAE). Nineteen influential stakeholders/stakeholder categories were identified through the literature, and their ability to influence the 21 constraints was mapped and identified through a two-round Delphi survey of 15 experienced professionals. The Delphi survey findings were analysed through social network analysis (SNA) methods to assess the stakeholder engagement and collaboration attributes.

The SNA results revealed the ability of stakeholders to influence the constraints, required collaborative stakeholder networks to address the constraints, significance of stakeholders according to the SNA centrality measures, core and periphery stakeholders and individual co-affiliation networks of core stakeholders.

While achieving the planned primary target of exploring stakeholder collaboration and their significance through SNA, this study also presents a useful sequential methodological approach for future researchers to conduct similar studies in different contexts. The findings also provide a foundation for accelerating the delivery of LCBs by strengthening stakeholder collaboration.

This paper aims to optimize feed moisture contents, barrel temperatures, blending ratios of maize and lupine for processing of protein-rich best quality extruded product using a twin-screw extruder.

A three-factor three-level response surface methodology by Box-Behnken Design was applied to evaluate the effect of selected processing conditions of blending ratios of lupine (10-20 per cent), barrel temperatures (120°C-150°C) and feed moisture content (14-18 per cent) on functional, nutritional and sensory characteristics of produced snack food.

The results of functional properties such as radial expansion ratio, bulk density, water absorption index, water solubility index observed as 0.71-1.2, 0.33-0.92 g/cc, 4.4-6.4 per cent and 10.2-15.1 per cent, respectively. The snack food showed the moisture 5.6-7.2 per cent, protein 8.1-18.1 per cent, fiber 1.6-2.7 per cent, ash 1.6-2.2 per cent and carbohydrate 64.8-81.4 per cent. The independent variables (lupine blending ratio, barrel temperature and feed moisture content) posed significant effects on expansion ration (p = 0.0030), bulk density (p = 0.0026), water absorption index (p = 0.0075) and water-solubility index (p = 0.0116). Higher blending ratio of lupine was increase in the bulk density and water solubility index, but decrease in expansion ratio and water absorption index of snack food. Higher feed moisture content was led to a reduction in expansion ratio and water-soluble index of snack food. Whereas, higher feed moisture contents was lead to rise in bulk density and water absorption index. Fiber (p = 0.0145), ash (p = 0.0343) and carbohydrate (p = 0.0001) contents were significantly depended on blending ratio. Blending of lupine 15.06 per cent, barrel temperature of 150 °C and feed moisture content of 14.0 per cent produced the snack food with desirability value of 72.8 per cent.

Protein malnutrition is one of the major problems in child development in under developed countries including Ethiopia. Maize is a top producer in the country but least appreciated for cost. Lupine is one of the undervalued produce consumed in Ethiopia after boiling. Still data on the utilization of maize and lupine in the extruded snack preparation was very limited. Optimization of moisture and barrel temperatures for this snack was not reported clearly yet.

Purpose: Progression is an unpreventable reality of presence, and banking is no exclusion. Cash transformers and moneylenders from times gone past are great agents today. Cash held in trust became store taking, and money advancing became credit making; over an extended time, banks transformed into a need, and the occupation of banks, transformed into a critical piece of monetary reality. Banks’ turn of events and headway has been mind-boggling, with the latest frenzy being intuitive media banking. The chapter additionally framed the amazing open doors and dangers for banks because of the presentation of innovations and how banks are making the most of the open doors and endeavouring to cure the risks. The financial area in India is a lifesaver for the country. Indian banks could become the fifth most prominent on earth by 2020 and the third most prominent by 2025.

Methodology: This study has given auxiliary information. Furthermore, it’s gathered from the holding bank of India concerning utilisation by various banks. Which utilised graphic review including mean mode middle.

Finding: After the review, we find that sight, sound, and green banking have become fantastic assets for the baking area. During COVID-19, the utilisation of mixed media expanded in contrast to a year ago.

Significance: The review featured sight and sound and green banking sealed spine in COVID -19 and is utilised expanded after this pandemic.

The purpose of this paper is to report the electrochemical verification of anticorrosive properties of NiZn ferrites as pigments in protective coatings and evaluation of their microwave absorbing properties.

Ferrites, represented by formula Ni_xZn_(1−x)Fe₂O₄, where x=0, 0.2, 0.4, 0.6, 0.8, 1.0, were synthesized using a ceramic method. The samples of ferrites were examined by X‐ray diffraction and SEM. Immersion tests in ferrite extracts, combined with electrochemical tests using techniques such as polarization curves, linear polarization and electrochemical impedance spectroscopy (EIS) were involved to determine the protective action provided by the ferrites. Epoxy coatings containing 10 vol.% of the ferrite were investigated using EIS. Experimental verification is provided of the microwave attenuation capability of coatings containing the investigated ferrites. Reflection loss, measured in the frequency range of 6.5‐15 GHz, exhibited maximum attenuation at a level of 8‐16 dB.

The results obtained suggest that NiZn ferrites possess active anticorrosive properties and support inhibition of the cathodic reaction by scale deposition at the metal substrate in an alkaline environment created by the pigments and the cathodic reaction. Additionally, they can serve as a microwave suppressor.

NiZn pigments can be used in coatings to serve simultaneously as an active anticorrosion, non‐toxic pigment and a material protecting against electromagnetic interference problems.

The paper provides information regarding the anticorrosive properties of NiZn ferrites as pigments for protective coatings and also microwave absorbing materials. An attempt was made to elucidate the potential mechanism of anticorrosive action of these pigments.

The purpose of this paper is to review Lean Six Sigma (LSS) literature and report different definitions, demographics, methodologies and industries.

This paper highlights various definitions by different researchers and practitioners. A total of 235 research papers has been reviewed for the LSS theme, research methodology adopted, type of industry, author profile, country of research and year of publication.

From the review, four significant LSS classifications were identified that deal with the spread of LSS in different industries followed by observation for classification.

LSS is a strategy for success, but it did not examine its presence in various Industries. From this paper, readers can understand the quantum of its spread before implementing LSS. For academicians, it will be a comprehensive list of papers for research.

This paper reviews 235 research papers for their year, author profile, research methodology and type of industry. Various characteristics of LSS definitions and their theme are also reviewed.

Extraction of leaf region from the plant leaf images is a prerequisite process for species recognition, disease detection and classification and so on, which are required for crop management. Several approaches were developed to implement the process of leaf region segmentation from the background. However, most of the methods were applied to the images taken under laboratory setups or plain background, but the application of leaf segmentation methods is vital to be used on real-time cultivation field images that contain complex backgrounds. So far, the efficient method that automatically segments leaf region from the complex background exclusively for black gram plant leaf images has not been developed.

Extracting leaf regions from the complex background is cumbersome, and the proposed PLRSNet (Plant Leaf Region Segmentation Net) is one of the solutions to this problem. In this paper, a customized deep network is designed and applied to extract leaf regions from the images taken from cultivation fields.

The proposed PLRSNet compared with the state-of-the-art methods and the experimental results evident that proposed PLRSNet yields 96.9% of Similarity Index/Dice, 94.2% of Jaccard/IoU, 98.55% of Correct Detection Ratio, Total Segmentation Error of 0.059 and Average Surface Distance of 3.037, representing a significant improvement over existing methods particularly taking into account of cultivation field images.

In this work, a customized deep learning network is designed for segmenting plant leaf region under complex background and named it as a PLRSNet.