Sustainable performance through digital supply chains in industry 4.0 era: amidst the pandemic experience

Amidst the COVID-19 pandemic disruption, industry 4.0 technologies (I4TEs) and digital supply chains (DSCs) are reinforcing businesses to gain economic stability and agility to enrich their sustainable performance (S.P.). Survey methods have been deployed based on the constructs obtained from the literature. Data collection through a survey resulted in 202 valid responses. Confirmatory factor analysis (CFA) confirms the constructs and the mediating effect of the DSCs through partial least squares structural equation modeling (PLS-SEM). The study is among the few studies that examine the I4TE impact on DSCs and S.P. The results show that industry 4.0 technologies enhance the sustainable performance of firms. Results also show a complete mediation of DSCs on the inter-relationship between I4TEs and S.P. Those DSCs with I4TE inclusion can transform an organization’s strategic decision-making. For the authors, this study is the first of its kind. Although some of the literature explored different aspects of the concept of industry 4.0 and digitalizing supply chains, studies have yet to specifically evaluate the potential impacts of digital supply chains on sustainable performance. The novelty of DSCs is their support of firms in improving their preparedness, agility, and transparency to strengthen their sustainable performance. These DSCs will provide agile, collaboration, responsiveness, end-to-end visibility, and resilient supply chains to diminish supply risk and enrich preparedness and responsiveness to recuperate quickly from uncertainty amidst the pandemic. The study will help managers re-designing their strategic planning, resulting in new cost reduction and resilience models for supply chains. The study calls for firms to employ multiple DSCs once they have set clear strategic priorities. The overall findings of the work fill the literature gaps of studies in the digitalization of supply chains

Mediastinal tuberculosis in adult: case report

Mediastinal lymph node enlargement commonly seen in sarcoidosis, lung cancer, lymphoma and tuberculosis in children’s. Tuberculosis in adult mostly involve parenchyma of lung and very rarely involve mediastinal lymph nodes, here we report a 27-year-old male, non-diabetic, non-hypertensive, non-alcoholic and non-smoker who present with low grade fever and dry cough. Search for the cause of morbidity revealed him to be suffering from mediastinal tuberculosis. He was treated for tuberculosis with ATT

Automotive Movie Recommendation System based on Natural Language Processing

People are puzzled about which movie to watch these days because there are so many movies available on various OTT platforms. A recommender system would solve this problem by recommending the best movie to the user based on his genre, actor, director, and rating preferences. The cosine similarity principle would be used to guide the recommendation system. Apart from that, we will use the Tfidftransformer and count vectorizer from the sci-kit-learn library in Python in this work. In this study work, all of the approaches' constraints have been described. All of this work was done using datasets from several OTT platforms that were available on Kaggle

An effective method for extraction and polymerase chain reaction (PCR) amplification of DNA from formalin preserved tissue samples of snow leopard

Formalin-preserved biological samples obtained from endangered species are valuable in assessing genetic diversity. To make use of snow leopard samples preserved in formalin over a period of two to seven years, we optimized the method of extracting DNA from these samples. We used (a) phenol chloroform : isoamyl alcohol, (b) the Qiagen DNeasy Blood and Tissue Kit (Qiagen, Germany), (c) the Qiagen DNeasy Blood and Tissue Kit after treating the samples with NaOH for three days and (d) the Qiagen DNeasy Blood and Tissue Kit after treating the samples with phosphate buffered saline (PBS) for three days. The usefulness of the extracted DNA was assessed on the basis of mitochondrial (150 to 550 bp) and nuclear (95 to 229 bp) markers. There was no PCR amplification with the first two methods. The PCR amplification with the NaOH and PBS treatment had a success rate of 30 to 100% for both mitochondrial and nuclear markers. The PBS method is the best method for extraction of DNA from formalin-preserved samples of longer period (two to seven years) because of higher success rate in amplifying mitochondrial gene of ca. 550 bp (60%) than the NaOH method (28%). The overall amplification of microsatellite markers in such samples was also higher in samples treated with PBS (43 to 100%) than NaOH (0 to 100%). The PCR products obtained were confirmed through DNA sequencing to be of snow leopard origin. The optimized protocol will enable genetic studies to be conducted on tissue samples of other species that have been preserved in formalin. The protocol will be particularly useful for species that are elusive and from which it is difficult to collect fresh tissue samples.Keywords: Formalin, polymerase chain reaction (PCR), mtDNA, microsatellites, snow leopardAfrican Journal of Biotechnology Vol. 12(22), pp. 3399-340

A literature survey on vaccine supply chain management amidst COVID-19: literature developments, future directions and open challenges for public health

This review aims to evaluate the existing literature on Vaccine Supply Chain Management (VSCM). All relevant articles between 2002 and 2022 were systematically collected. The retrieved articles were further analyzed using bibliometric data analysis techniques. The unit of analysis is research papers published from 2002 to 2022. Vaccine Supply Chain Management (VSCM) literature has gained prominence since early 2000 and has now become voluminous. A review is the first endeavour to provide a unified body of literature. This study contributes to the existing research through insights from the bibliometric analysis and critical measurement of the literature. The results show 4288 papers on VSCM in the last 20 years. The top five countries contributing to VSCM literature are the USA, France, China, the United Kingdom, and Switzerland. Supply chain, vaccine, immunization, and Vaccine Supply Chain Management are the high-frequency keywords in the area of VSCM. The research hotspots mainly focus on healthcare, drugs, and manufacturers. In light of the COVID-19 era, this review paper indicates the area of VSCM is diversified. This study is useful for policymakers and other stakeholders to understand the existing issues in VSCM. The research trends and patterns from the literature review of VSCM will help in designing AAA (agile, adaptive, and aligned) VSCM in the future from the viewpoint of public health. This study attempts to analyze existing works, trends, developments, and potential research directions

Credibility Evaluation of User-generated Content using Novel Multinomial Classification Technique

Awareness about the features of the internet, easy access to data using mobile, and affordable data facilities have caused a lot of traffic on the internet. Digitization came with a lot of opportunities and challenges as well. One of the important advantages of digitization is paperless transactions, and transparency in payment, while data privacy, fake news, and cyber-attacks are the evolving challenges. The extensive use of social media networks and e-commerce websites has caused a lot of user-generated information, misinformation, and disinformation on the Internet. The quality of information depends upon various stages (of information) like generation of information, medium of propagation, and consumption of information. Content being user-generated, information needs a quality assessment before consumption. The loss of information is also necessary to be examined by applying the machine learning approach as the volume of content is extremely huge. This research work focuses on novel multinomial classification (based on multinoulli distribution) techniques to determine the quality of the information in the given content. To evaluate the information content a single algorithm with some processing is not sufficient and various approaches are necessary to evaluate the quality of content.  We propose a novel approach to calculate the bias, for which the Machine Learning model will be fitted appropriately to classify the content correctly. As an empirical study, rotten tomatoes’ movie review data set is used to apply the classification techniques. The accuracy of the system is evaluated using the ROC curve, confusion matrix, and MAP

Detection of Pulmonary Embolism: Workflow Architecture and Comparative Analysis of the CNN Models

Machine learning has proven to be a practical medical image processing technique for pattern discovery in low-quality labelled and unlabeled datasets. Deep vein thrombosis and pulmonary embolism are both examples of venous thromboembolism, which is a key factor in patient mortality and necessitates prompt diagnosis by experts. An immediate diagnosis and course of treatment are necessary for the life-threatening cardiovascular condition known as pulmonary embolism (PE). In the study of medical imaging, especially the identification of PE, machine learning (ML) algorithms have produced encouraging results. This study's objective is to assess how well machine learning (ML) algorithms perform in identifying PE in computed tomography (CT) scans. A range of ML approaches were used to the dataset, including deep learning algorithms such as convolutional neural networks. The effectiveness of PE detection systems can be greatly enhanced by the use of cutting-edge methodologies like deep learning, which lowers the possibility of incorrect diagnoses and enables the quick administration of therapy to individuals who require it. This work contributes to the growing body of evidence that supports the use of ML in medical imaging and diagnosis. Future research should examine how these algorithms might be included into clinical workflows, resolving any potential implementation challenges, and making sure their adoption is done so in a secure and efficient way. In this study, we provide a thorough evaluation of three different models: the streamlined architecture MobileNetV2 with an accuracy of 96%, compared to other models like the Xception model with an accuracy of 91%, and the Efficientnet B5 model with an accuracy of 97%, after observation and process following

Optimizing Hyperparameters for Enhanced LSTM-Based Prediction System Performance

This research paper explores the application of deep learning and supervised machine learning algorithms, specifically Long Short-Term Memory (LSTM), for stock market prediction. The study focuses on the closing prices of three companies - Tata Steel, Apple, and Powergrid - using a dataset sourced from Yahoo Finance. Performance evaluation of the LSTM model employed RMSE, MAPE, and accuracy metrics, along with hyperparameter calibration to determine the optimal model parameters. The findings indicate that a single-layer LSTM model outperformed a multilayer LSTM model across all companies and evaluation metrics. Furthermore, a comparison with existing research demonstrated the superiority of the proposed model. The study emphasizes the effectiveness of LSTM models for stock price prediction, underscores the significance of proper hyperparameter tuning for optimal performance, and concludes that a single-layer LSTM model can yield superior results compared to a multilayer model

Accelerating retail supply chain performance against pandemic disruption: adopting resilient strategies to mitigate the long-term effects

Purpose: COVID-19 has disrupted global supply chains, revealing dreadful gaps and exposing vulnerabilities. Retailers are challenged to tackle risks and organise themselves to fit into the ‘new normal’ scenario. This global outbreak has established a volatile environment for supply chains; it has raised the question of survival in the market, forcing organisations to rethink resilient strategies to be adopted for the post pandemic situation to mitigate the long-term effects of this virus. This study explores the priorities for Retail Supply Chains (RSCs) to align their business operations and strategies for the post pandemic world. Design/methodology/approach: This study has utilised integrated Full Consistency Model (FUCOM) – Best Worst Method (BWM) methods for assessment of RSCs to enhance their business performance irrespective of pandemic disruptions. The FUCOM has been employed to identify the priorities of determinants enhancing business performance, whereas RSC strategies are evaluated using the BWM method. Finding: The current study identifies ‘Collaboration Efficiency’ as the main criterion for accelerating the performance of RSCs in a dynamic social environment. Also, the study concludes that ‘Order Fulfilment’ and ‘Digital RSCs’ are the most appropriate resilient business strategies to mitigate the long-term effects. Research limitations/implications: Supply-demand balancing is a challenging task at the moment, but highly significant for the future. The pandemic disruptions have placed intense pressure on retailers to deliver products as per consumers’ changing behaviours towards the purchase of essentials and other products. Hence, ‘Order Fulfilment’ and ‘Digitisation” strategies should be adopted for meeting customer requirements and to ensure sustainability in the post pandemic business world. Originality/value: This work sets out a comprehensive framework which will be helpful for accelerating RSCs performance against pandemic disruption by adopting resilient strategies to mitigate the long-term effects

Assessing supply chain innovations for building resilient food supply chains: an emerging economy perspective

Food waste reduction and security are the main concerns of agri-food supply chains, as more than thirty-three percent of global food production is wasted or lost due to mismanagement. The ongoing challenges, including resource scarcity, climate change, waste generation, etc., need immediate actions from stakeholders to develop resilient food supply chains. Previous studies explored food supply chains and their challenges, barriers, enablers, etc. Still, there needs to be more literature on the innovations in supply chains that can build resilient food chains to last long and compete in the post-pandemic scenario. Thus, studies are also required to explore supply chain innovations for the food sector. The current research employed a stepwise weight assessment ratio analysis (SWARA) to assess the supply chain innovations that can develop resilient food supply chains. This study is a pioneer in using the SWARA application to evaluate supply chain innovation and identify the most preferred alternatives. The results from the SWARA show that ‘Business strategy innovations’ are the most significant innovations that can bring resiliency to the food supply chains, followed by ‘Technological innovations.’ The study provides insights for decision makers to understand the significant supply chain innovations to attain resilience in food chains and help the industry to survive and sustain in the long run