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Defective parts in manufacturing is a serious issue faced by every manufacturer. Even after proper care in design, material selection and manufacturing of product, there exists a defective part. The purpose of this paper is to explore the quality of the manufacturing, and find the use of effective quality tools to reduce the part defect rate in an electrical parts manufacturing unit, thereby, reducing the replaced cost of defective parts.

With the help of quality initiatives, like total quality management (TQM) and Lean Six Sigma (LSS), the firms can produce quality product in each stage of production. The paper focuses on the primary data collected from the XYZ electric manufacturer.

The main finding of this case analysis is that by the effective use of quality tools, the defective part return rate can be reduced, because of which the firm can observe reduction in replaced cost of almost INR24 lakh. In addition, 10A switch part contributes more in replacement cost. Further, it adds to the 35 percent of the overall part rejection.

The study is more focused on particular type of switch product and can extend to other types of products. In addition, the analysis reveals the results of only 88 percent of the defective products.

The study provides results of the improved quality by effective use of quality tools and discusses the different types of defects in the electrical parts manufacturing. Introducing TQM and LSS to manufacturing can reduce the customer return rate to 1,300 parts per million (PPM) and even to 1,000 PPM in future.

The paper discusses the quality issues in the electrical manufacturer. Moreover, the case analysis briefs effective ways to improve the product quality and reduce the rejection rate.

The perception of a situation or reality determines how one responds and awareness is the first step towards understanding, knowing or recognizing it. The majority of the public and the police may be familiar with the phrase ‘cybercrime’, but all of them are fully informed of the nature and scope of these crimes, as well as of the cybercriminals and cyber victims, which has an impact on how they see these issues. This study's main goal was to examine the perception and awareness of cybercrime among undergraduate youth studying in BBA or BCA courses. In this study, we discovered that young people's responses to cybercrime mostly depend on their perceptions of it and their awareness level. To accomplish the study's objective, a thorough examination of existing literature was undertaken. Primary data of 200 students were collected through Google Forms. Percentile analysis, correlation analysis and t-test are done to test the hypotheses. The results of this study may help college administrators better comprehend the mindset of today's youth as they develop laws and policies aimed at reducing cybercrime among students. The results of this study show that the youngsters surveyed have high levels of awareness and a good perception.

The purpose of this paper is to classify the given image as indoor or outdoor with higher success rate by mixing various features like brightness, number of straight lines, number of Euclidean shapes and recursive shapes.

For annotating an image, it is very easy, if the image is categorized as indoor or outdoor. Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object.

This work is carried out on the standard image data sets. The data sets are Microsoft Research Cambridge (MRC) object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly.

Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object. This work is carried out on the standard image data sets. The data sets are MRC object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly.

Given the unique nature of business events tourism, this paper evaluates the forecasting performance of various models using search query data (SQD) to forecast convention attendance.

This research uses monthly and quarterly business event attendance data from both the U.S. (Las Vegas) and China (Macau) markets. Using SQD as the input, we evaluated and compared the cutting-edge forecasting models including Prophet and Long Short-Term Memory (LSTM).

The study reveals that Prophet outperforms complex neural network models in forecasting business event tourism demand. Keywords related to convention facilities, conventions or exhibitions, and transportation are proven to be useful in forecasting business travel demand.

Prophet is an accessible forecasting model for event-tourism practitioners, especially useful in the volatile business event tourism sector. Using verified search keywords in models helps understand traveler motivations and aids event planning.

Our study is among the first to empirically evaluate the performance of forecasting models for business travel demand. In comparison with other mainstream forecasting models, our study extends the scope to examine both the U.S. and Chinese markets.

Nowadays, firms are keen on improving the quality culture in the organizations. The proven success of Lean and Six Sigma has given rise to the synergetic Lean Six Sigma (LSS) approach that has been catching fire in the past one decade. However, there exists a gap between the understanding and implementation of LSS in the organizations, especially in the emerging economies. Taking this as a valuable opportunity, the purpose of this paper is to present a development of LSS training module.

This study starts with a literature review of LSS to reinforce the understanding of the research subject in scope of manufacturing sector. Then, an online questionnaire was designed and used to collect responses from 181 companies located in the Indian sub-continent. Subsequently, the results obtained from the survey were analyzed using COARSE approach.

This study reveals two key findings and associated contributions. First, it was found that the overall awareness of LSS within the responded manufacturing firms is about 70.4 percent. Second, there is no single standard training module that exists in any of the sampled firms to cater to their quality programs. Hence, in order to improve the LSS awareness which could subsequently help managers as a resource for creating an efficient workplace, this paper presents a structured LSS training framework.

Although this paper presents the importance of LSS and associated awareness level among the responded firms, more empirical evidence is required to generalize the model findings. Second, this study is scoped to firms that work out of the Indian sub-continent, and this provides a future opportunity to expand the scope of this research toward a global study for a comparison between emerging and developed economies. Third, this study is limited to manufacturing firms and hence paves an opportunity to research on a similar theme in services context as well.

Before embarking on an LSS journey, an organization can use the LSS training module proposed in this study to assess the employee awareness on LSS. Furthermore, organizations that already have a mature LSS practice can incorporate the LSS training module for periodic evaluation of the employees for effective change management.

The training module presented in this paper is the original contribution by the authors. This is no association to any single identifiable organization or associated funding. The direct practical implication of its application in real time is the value that managers could derive from the proposed LSS training framework.

The study aims to critically evaluate the Kochi Water Metro as a model for sustainable urban transportation and extract valuable lessons for policymakers and cities seeking similar initiatives. The study employs a qualitative data-driven approach, utilising a longitudinal case study design, open-ended interview questions, direct observations and qualitative content analysis. The Kochi Water Metro has emerged as a transformative urban mobility initiative, enhancing connectivity while simultaneously reducing congestion on roads. The project's sustainable practices have minimised its carbon footprint, contributing to environmental sustainability. Economic benefits have accrued through increased tourism, employment opportunities and improved local livelihoods. The project's adaptability to local conditions and changing needs further underscores its sustainability. Limited data from the project's early years necessitate further longitudinal analysis. The focus on benefits may overlook potential challenges, warranting broader investigation. Reliance on qualitative data may restrict generalisability. The Kochi Water Metro serves as a beacon for sustainable and equitable transportation solutions. It serves as a blueprint for policymakers, providing practical lessons in project implementation, especially its integration with existing modes enhances overall urban mobility. Policymakers should consider replicating elements such as electric boats and integrated networks, while emphasising environmental sustainability, accessibility and community engagement. The comprehensive evaluation provides valuable insights into the project's tangible impacts and replicable elements. The innovative approach blends sustainable practices, operational resilience and community engagement, serving as a model for cities seeking sustainable, equitable and efficient transportation solutions.

Corrosion-producing microorganisms have different physiology and include sulfate-reducing bacteria, iron oxidizers and magnesium oxidizers. Biocorrosion has been seen in various industries, especially the petrochemicals and oil industries. One proposal to solve this problem is the use of bacteriophages to treat the bacteria-caused corrosion. The aims of this study were isolation and identification of corrosion-producing bacteria from petroleum pipeline corrosion as well as finding their specific bacteriophages for phage therapy purposes.

The sample pipes with the corrosion were obtained from the Gandomkar petroleum pipeline station, Chaharmahal and Bakhtiari, Iran. For screening the corrosion-producing bacteria, the rusted pipe samples were cultured in a selective culture medium, manganese agar. The purified individual colonies were subjected to molecular examinations. For isolating bacteriophages from silversmithing workshops wastewater in Isfahan, whole plate titration methods and transmission electron microscopy were used to isolate and detect phages.

The cultivation of corrosion-based material on manganese agar after 18 hours incubation at 30°C resulted in the isolation of cream-colored colonies. The microscopic examinations showed Gram-negative coccobacilli. Based on molecular examinations, the isolated bacteria were identified as Stenotrophomonas maltophilia strain PBM-IAUF-2 with Genebank accession number of KU145278.1. The found bacteriophage was related to the Siphoviridae family of phages.

This paper is the first report of isolation and identification of corrosion-producing bacteria and its specific lytic phages from Gandomkar petroleum pipeline station, Chaharmahal and Bakhtiari, Iran. The biological procedures for preventing the microbial corrosion could be an asset and considered as a potential in the petroleum and industrial microbiology. Phage therapy is considered as one of the economical methods for reducing the biocorrosion.

The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community.

The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses.

The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases.

The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.

The issues of women empowerment and gender equality have gained the increased attention of scholars and policymakers in Western societies. Gender diversity and the professional participation of women are increasingly acknowledged as transversal drivers for economic development. However, in less developed countries, research and evidence are still accumulating. Thus, this study aims to explore actors and factors empowering female talent to work and achieve managerial positions and run their businesses in two countries with patriarchal social and cultural norms, Saudi Arabia and Kazakhstan. Based on the qualitative interview data collected from 15 female managers and entrepreneurs working in the healthcare sector, we explore the conditions under which women can start their businesses and get promoted to managerial positions in the organizations. Our findings indicate that individual agencies and structural factors in female talent capacity building and empowering women to achieve higher hierarchical positions in organizations form together important dynamics that foster more inclusive practices and internalized schemes. Furthermore, the findings also demonstrate the importance of female talent empowerment in achieving gender diversity in managerial positions in healthcare organizations. Hence, by stating that increased female talent participation in the upper-echelons of the organization and entrepreneurship contributes to the decent employment of women in countries with male-dominated social and cultural norms and promotes the more inclusive and sustainable economic growth of these countries, our research contributes to United Nations (UN) Sustainable Development Goals (SDG) #5.5, #8.5 and #10.2.

The purpose of this paper is to use the Twitter Search Network of the Apache NodeXL data discovery tool to extract over 5,000 data from Twitter accounts that twitted, re-twitted or commented on the hashtag, #NigeriaDecides, to gain insight into the impact of the social media on the politics and administration of developing countries.

Several algorithms like the Fruchterman-Reingold algorithm, Harel-Koren Fast Multiscale algorithm and the Clauset-Newman-Moore algorithms are used to analyse the social media metrics like betweenness, closeness centralities, etc., and visualize the sociograms.

Results from a typical application of this tool, on the Nigeria general election of 2015, show the social media as the major influencer and the contribution of the social media data analytics in predicting trends that may influence developing economies.

With this type of work, stakeholders can make informed decisions based on predictions that can yield high degree of accuracy as this case. It is also important to stress that this work can be reproduced for any other part of the world, as it is not limited to developing countries or Nigeria in particular or it is limited to the field of politics.

Increasingly, during the 2015 general election, citizens have taken over the blogosphere by writing, commenting and reporting about different issues from politics, society, human rights, disasters, contestants, attacks and other community-related issues. One of such instances is the #NigeriaDecides network on Twitter. The effect of these showed in the opinion polls organized by the various interest groups and media houses which were all in favour of GMB.

The case study the authors took on the Nigeria’s general election of 2015 further strengthens the fact that the developing countries have joined the social media race. The major contributions of this work are that policy makers, politicians, business managers, etc. can use the methods shown in this work to harness and gain insights from Big Data, like the social media data.