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Unexpected instances have posed challenges to production lines over the last few years. The latest COVID-19 global epidemic is one notable example. In addition to its social impact, the virus has destroyed the traditional industrial production system. Industry 4.0 requires adapting to changing prerequisites with adaptability. However, the next movement, Industry 5.0, has emerged in recent years. Industry 5.0 takes a more coordinated approach than Industry 4.0, with increased collaboration among humans and machines. With a human-centered strategy, Industry 5.0 improves Industry 4.0 for greater sustainability and resilience. The concept of Industry 4.0 is the interconnection via cyber-physical systems. Industry 5.0, also associated with systems enabled by Industry 4.0, discusses the relationship between "man and machine," called robots or cobots. This paper discusses the industry 5.0 possibilities, the restrictions, and future analysis potentials. Industry 5.0 is a new paradigm change that tends to bring negotiated settlement because it places less prominence on technology and assumes that the possibilities for advancement are predicated on collaboration between humans and machines. This paper aims to examine the potential implementations of Industry 5.0. Once the current progress and problem were discovered, the previous research on the investigated topic was reviewed, research limitations were found, and the systematic analysis procedure was developed. The classifications of industry 5.0 and the sophisticated technology required for this industry revolution are the first subjects of discussion. There is additional discussion of the application domains enabled by Industry 5.0, such as healthcare, supply chain, production growth, cloud industrial production, and so on. The research also included challenges and problems investigated in this paper to understand better the issues caused by organizations among some robotic systems and individuals on the production lines. [ABSTRACT FROM AUTHOR]

The disruptions caused by the COVID-19 pandemic have forced many companies in the logistics sector to innovate, or even transform their business and underlying processes. Closing borders, limited supply and manpower, and continuous changes in regulations challenged many logistics firms to innovate. This study analyzes 5098 abstracts of logistics articles using text mining to identify and to quantify the changes in logistics trends and innovations before and during the COVID-19 pandemic, and if these trends and innovations were accelerated by the COVID-19 pandemic. Results indicate that (1) resiliency is an ongoing trend in logistics and has shown increasing importance during the COVID-19 pandemic; (2) there appears to be acceleration in digitalization trend in logistics based on emerging focus on blockchain, Internet of Things, data, drones, robots, and unmanned vehicles during COVID-19 pandemic, and (3) there seems to be no evidence of acceleration in sustainability due to COVID-19 despite an observed shift in sustainability trends in terms of bioenergy and biofuel before COVID-19 pandemic to low-carbon, hydrogen and electric vehicles during COVID-19 pandemic. This paper recommends logistics firms, especially Small and Medium Enterprises (SMEs), to analyze their readiness to adopt digitalization in terms of data, resources, and technology via, e.g., the use of a maturity scan, to contribute to sustainable and resilient logistics and to make sure that they remain competitive and future-proof. Policy makers can provide support to these SMEs by providing information, funding, and template solutions. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) has spread quickly around the globe. COVID-19 has affected the education sector due to partial or complete lockdowns that were implemented throughout the world between 2019 and 2022. This pandemic severely affected the education sectors in developing countries such as Pakistan. All the educational institutions in Pakistan turned to online education. However, the education sector lacked the teaching experts, digital experts, the Internet of Things (IoT), and resources needed for online education. The shift from traditional to online education has created many challenges for developing countries during a pandemic such as COVID-19, for example, access to the IoT. This paper aims to introduce the factor model (F model), which will provide guidelines for the government and universities for minimizing the deficiencies related to online education. The F-model will identify all the factors that affect the performance and guide the user about their importance. This will allow the user to resolve that issue and improve the performance of their department or institution. Thus, the F model will benefit the education sector by mitigating the challenges related to online education. The F model is not only confined to online education but can be operated in the fields of science and industry for data extraction and the calculation of results. First, the data is collected physically and online through a student survey related to the challenges of online education during a pandemic. The data extraction and the calculation of the results are carried out using the F model. The results of the survey are alarming and the government has a lot of work to do to improve online education using the IoT. According to the F model, the government should take serious action to improve the performance of students, teachers, and all education sectors not only during the COVID-19 pandemic but also for possible future pandemics. [ABSTRACT FROM AUTHOR]

Generally, software developers make errors during the distributed software development process; therefore, software testing delay is a significant concern. Some of the software mistakes are minor, but others may be costly or harmful. Since things can still go wrong—individuals encounter mistakes from time to time—there is a need to double-check any software we develop in a distributed environment. The current global pandemic, COVID-19, has exacerbated and generated new challenges for IT organizations. Many issues exist for distributed software testing that prevent the achievement of successful and timely risk reduction when several of the mechanisms on which testing is based are disrupted. The environment surrounding COVID-19 is quickly evolving on a daily basis. Moreover, the pandemic has exposed or helped to develop flaws in production systems, which obstruct software test completion. Although some of these issues were urgent and needed to be evaluated early during the distributed software development process, this paper attempts to capture the details that represent the current pandemic reality in the software testing process. We used a Fuzzy TOPSIS-based multiple-criteria decision-making approach to evaluate the distributed software testing challenges. The statistical findings show that data insecurity is the biggest challenge for successful distributed software testing. [ABSTRACT FROM AUTHOR]

The global urgency in response to the COVID-19 pandemic has catalyzed extensive research into discovering efficacious antiviral compounds against SARS-CoV-2. Among these, Nirmatrelvir (PF-07321332) has emerged as a promising candidate, exhibiting potent antiviral activity by targeting the main protease of SARS-CoV-2, and has been marketed in combination with ritonavir as the first oral treatment for COVID-19 with the name of PaxlovidTM. This review outlines the synthetic approaches to Nirmatrelvir, ranging from Pfizer's original method to newer, more sustainable strategies, such as flow chemistry strategies and multicomponent reactions. Each approach's novelty and contributions to yield and purification processes are highlighted. Additionally, the synthesis of key fragments comprising Nirmatrelvir and innovative optimization strategies are discussed. [ABSTRACT FROM AUTHOR]

This paper is divided into seven main parts. Its purpose is to review the literature to demonstrate the importance of developing bioengineering and global production of biomaterials to care for the level of healthcare in the world. First, the general description of health as a universal human value and assumptions of a long and healthy life policy is presented. The ethical aspects of the mission of medical doctors and dentists were emphasized. The coronavirus, COVID-19, pandemic has had a significant impact on health issues, determining the world's health situation. The scope of the diseases is given, and specific methods of their prevention are discussed. The next part focuses on bioengineering issues, mainly medical engineering and dental engineering, and the need for doctors to use technical solutions supporting medicine and dentistry, taking into account the current stage Industry 4.0 of the industrial revolution. The concept of Dentistry 4.0 was generally presented, and a general Bioengineering 4.0 approach was suggested. The basics of production management and the quality loop of the product life cycle were analyzed. The general classification of medical devices and biomedical materials necessary for their production was presented. The paper contains an analysis of the synthesis and characterization of biomedical materials supporting medicine and dentistry, emphasizing additive manufacturing methods. Numerous examples of clinical applications supported considerations regarding biomedical materials. The economic conditions for implementing various biomedical materials groups were supported by forecasts for developing global markets for biomaterials, regenerative medicine, and tissue engineering. In the seventh part, recapitulation and final remarks against the background of historical retrospection, it was emphasized that the technological processes of production and processing of biomedical materials and the systematic increase in their global production are a determinant of the implementation of a long and healthy policy. [ABSTRACT FROM AUTHOR]

The paper presents a comparison of two methods of manufacturing utility objects made of plastics, applied to the emerging immediate need in the field of quick provision of personal protective equipment for medical services. The traditional processing method, which is injection molding (IM), and a modern rapid prototyping method, which is fused filament fabrication (FFF) 3D printing, were compared in terms of unit costs and production possibilities at various timeframes. The paper presents the effects of launching two production processes of protective helmets (face shields) using the example of real cases implemented ad hoc during the epidemic development. The implementation of the protective helmet production project based on polyamide-6 processing showed the real possibilities of quickly launching the rapid production of protective equipment with the aid of mold injection technology. [ABSTRACT FROM AUTHOR]

In response to the urgent need for efficient pneumonia diagnosis—a significant health challenge that has been intensified during the COVID-19 era—this study introduces the RCGAN-CTL model. This innovative approach combines a coupled generative adversarial network (GAN) with relativistic and conditional discriminators to optimize performance in contexts with limited data resources. It significantly enhances the efficacy of small or incomplete datasets through the integration of synthetic images generated by an advanced RCGAN. Rigorous evaluations using a wide range of lung X-ray images validate the model's effectiveness. In binary classification tasks that differentiate between normal and pneumonia cases, RCGAN-CTL demonstrates exceptional accuracy, exceeding 99%, with an area under the curve (AUC) of around 95%. Its capabilities extend to a complex triple classification task, accurately distinguishing between normal, viral pneumonia, and bacterial pneumonia, with precision scores of 89.9%, 95.5%, and 90.5%, respectively. A notable improvement in sensitivity further evidences the model's robustness. Comprehensive validation underscores RCGAN-CTL's superior accuracy and reliability in both binary and triple classification scenarios. This advancement is pivotal for enhancing deep learning applications in medical diagnostics, presenting a significant tool in addressing the challenges of pneumonia diagnosis, a key concern in contemporary healthcare. [ABSTRACT FROM AUTHOR]

The receding globalization has reshaped the logistics industry, while the additional pressure of the COVID-19 pandemic has posed new difficulties and challenges as has the pressure towards sustainable development. Achieving the synergistic development of economic, social, and environmental benefits in the logistics industry is essential to achieving its high-quality development. Therefore, we propose a data-driven calculation, evaluation, and enhancement method for the synergistic development of the composite system of economic, social, and environmental benefits (ESE-B) of the logistics industry. Based on relevant data, the logistics industry ESE-B composite system sequential parametric index system is then constructed. The Z-score is applied to standardize the original index data without dimension, and a collaborative degree model of logistics industry ESE-B composite system is constructed to estimate the coordinated development among the subsystems of the logistics industry's ESE-B system. The method is then applied to the development of the logistics industry in Anhui Province, China from 2011 to 2020. The results provide policy recommendations for the coordinated development of the logistics industry. This study provides theoretical and methodological support for the sustainable development aspects of the logistics industry. [ABSTRACT FROM AUTHOR]

The COVID-19 global pandemic is still affecting the world, even considering vaccine applications in most countries, especially due to new variant outbreaks and the possibility that they may present immunological escape. Therefore, mass testing is relevant in infection monitoring and restriction policy evaluations, making low-cost and easy-to-use tests essential. Serological tests might also be useful in monitoring immune response after vaccination. The present work proposes a less-expensive ELISA test route, using a scanner instead of a spectrophotometer and using the saturation of the image as a surrogate for the absorbance of each sample. Images from multiple experiments were selected and correlated with their spectrophotometric absorbance. ELISA plate images were digitized by a simple table scanner and, then, preprocessed using Hue, Saturation, Value (HSV) transformation, aiming to determine which correlates best with the obtained absorbance. Saturation correlated better with absorbance, and the experiments presented R 2 consistently above 90% between absorbance and the square of saturation. The new methodology showed similar accuracy, sensitivity, and specificity to the original method, all metrics ranging between 90% and 100% in most cases. An open-source software was also designed to analyze the images, perform the diagnosis, and generate reports. [ABSTRACT FROM AUTHOR]

A franchise business is a contractual relationship in which the franchisor and franchisee should cooperate to promote sustainable growth of their franchise entities. However, it is still unclear whether the relationship between franchisees and franchisors is a principal–agent or a business partner sharing a business goal. Thus, this study is a first attempt to investigate the relationship between franchisees and franchises using metafrontier and bootstrap DEA from the perspective of efficiency. We measured the efficiency of coffee franchises in Korea, which have grown rapidly in recent years despite the COVID-19 pandemic. Based on the bootstrap DEA results, there was a statistically significant difference in efficiency between franchisors and franchisees under the variable return-to-scale assumption. While the main cause of inefficiency in premium coffee chains is attributed to scale inefficiency, most franchisees have pure technological inefficiency. Thus, coffee franchisees can improve the operational efficiency by adjusting their business scale and reallocating service resources. This study demonstrates tailored operational plans to improve the operational efficiency of premium and mainstream coffee franchises and offers strategic initiatives to decrease the difference in efficiency between franchisors and franchisees. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic broke out and the global logistics industry suffered severe losses; therefore, the Fuzzy FMEA-AHP (Fuzzy Failure Mode and Effects Analysis-Analytic Hierarchy Process) method is proposed to analyze the failure reasons of the logistics system in the COVID-19 pandemic. In this article, we have made an optimization on the basis of the FMEA method: the fuzzy is integrated into the FMEA algorithm, referred to as F-RPWN (fuzzy risk priority-weighted number). Meanwhile, the AHP is used to determine the weights of risk indicators. In this article, we consider new logistics failures, such as the failure modes and failure reasons of the logistics system under the COVID-19 pandemic. There are 12 failures that have been determined, and relevant preventive and corrective measures have been recommended to cut off the path of failure propagation and reduce the impact of failures. In addition, the proposed method can help logistics firms, their supply chain partners, and customers with risk management issues during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Medical materials are the most important, fundamental resources necessary for emergency relief of major infectious disease disasters. The scientific and optimal allocation of emergency medical materials is the key to reducing casualties and losses in epidemic regions, and to improving the effectiveness and efficiency of rescue operations. In response to the cross-border characteristics of major infectious diseases, the imbalance of material storage, and the differences of supply across regions, a multi-objective optimization model for a multi-regional collaborative allocation of emergency medical materials was developed. Then, an improved adaptive genetic algorithm (IAGA) was designed and applied to solve the proposed model. Finally, a case study of the collaborative response to the COVID-19 epidemic in the Yangtze River Delta of China was conducted for model verification. The results show that collaborative allocation can improve the material satisfaction rate at demand points, especially under peak demand pressure during the early stage of the response, and can meet all material needs at all demand points in the shortest possible amount of time. The proposed model can achieve the effective integration and mutual sharing of emergency materials across regions, and improve the efficiency of emergency material utilization and rescue efforts. The material allocation scheme considers the difference coefficients in different regions, which is conducive to enhancing the flexibility of decision-making and the practical applicability of collaborative allocation operations. A comparative analysis of the algorithms shows that the proposed IAGA is an effective method for managing large-scale multi-regional emergency material allocation optimization problems, as it has higher solving efficiency, better convergence, and stronger stability. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has boosted the growth of the online food delivery (OFD) market in every corner of the world. In Vietnam, the food delivery service is rising rapidly and opening a large gateway of opportunities for numerous OFD platforms, also making it a competing business market in this country. Thus, to keep up with the ever-changing market dynamics, there are numerous measures and dimensions for the OFD entrepreneurs to take into consideration towards sustainable development. This paper's objective is to evaluate major OFD companies in Vietnam based on a comprehensive set of criteria, which are social and environmental criteria (healthy and safety, information security, and environmental impact), economic criteria (delivery cost, operational capability, and risk management), service quality (order fulfillment, delivery speed, convenience of payment, online/offline service level, and customer feedback), and technology (web design, real-time tracking systems, and marketing techniques). To achieve this objective, this work proposes a multi-criteria decision-making (MCDM)-based framework combining the fuzzy analytic hierarchy process (FAHP) and the weighted aggregated sum product assessment (WASPAS). The FAHP is used to generate criteria weights in which fuzzy set theory is applied to translate the linguistic evaluation statements of experts. Then, WASPAS is used to rank the OFD companies against the selected criteria. The evaluation criteria that have obtained maximum weight priority in the FAHP analysis are "convenience of payment", "delivery speed", "online service level", "order fulfillment", and "delivery cost". From the final ranking of WASPAS, Foody is today the best performing OFD player in Vietnam regarding the selected criteria, followed by GrabFood and Now. The proposed methodology can be an accurate and robust evaluation model for the industry, while the managerial implications of this study provide significant materials for decision-makers in the OFD market in improving their businesses towards sustainable development. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has further highlighted the need for antimicrobial surfaces, especially those used in a healthcare environment. Textiles are the most difficult surfaces to modify since their typical use is in direct human body contact and, consequently, some aspects need to be improved, such as wear time and filtration efficiency, antibacterial and anti-viral capacity, or hydrophobicity. To this end, several techniques can be used for the surface modification of tissues, being magnetron sputtering (MS) one of [hose that have been growing in the last years to meet the antimicrobial objective. The current state of the art available on textile functionalisation techniques, the improvements obtained by using MS, and the potential of diamond-like-carbon (DLC) coatings on fabrics for medical applications will be discussed in this review in order to contribute to a higher knowledge of functionalized textiles themes. [ABSTRACT FROM AUTHOR]

The global coronavirus pandemic continues to restrict public life worldwide. An effective means of limiting the pandemic is vaccination. Messenger ribonucleic acid (mRNA) vaccines currently available on the market have proven to be a well-tolerated and effective class of vaccine against coronavirus type 2 (CoV2). Accordingly, demand is presently outstripping mRNA vaccine production. One way to increase productivity is to switch from the currently performed batch to continuous in vitro transcription, which has proven to be a crucial material-consuming step. In this article, a physico-chemical model of in vitro mRNA transcription in a tubular reactor is presented and compared to classical batch and continuous in vitro transcription in a stirred tank. The three models are validated based on a distinct and quantitative validation workflow. Statistically significant parameters are identified as part of the parameter determination concept. Monte Carlo simulations showed that the model is precise, with a deviation of less than 1%. The advantages of continuous production are pointed out compared to batchwise in vitro transcription by optimization of the space–time yield. Improvements of a factor of 56 (0.011 µM/min) in the case of the continuously stirred tank reactor (CSTR) and 68 (0.013 µM/min) in the case of the plug flow reactor (PFR) were found. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has motivated the rapid development of numerous vaccines that have proven effective against SARS-CoV-2. Several of these successful vaccines are based on the adenoviral vector platform. The mass manufacturing of these vaccines poses great challenges, especially in the context of a pandemic where extremely large quantities must be produced quickly at an affordable cost. In this work, two baseline processes for the production of a COVID-19 adenoviral vector vaccine, B1 and P1, were designed, simulated and economically evaluated with the aid of the software SuperPro Designer. B1 used a batch cell culture viral production step, with a viral titer of 5 × 1010 viral particles (VP)/mL in both stainless-steel and disposable equipment. P1 used a perfusion cell culture viral production step, with a viral titer of 1 × 1012 VP/mL in exclusively disposable equipment. Both processes were sized to produce 400 M/yr vaccine doses. P1 led to a smaller cost per dose than B1 ($0.15 vs. $0.23) and required a much smaller capital investment ($126 M vs. $299 M). The media and facility-dependent expenses were found to be the main contributors to the operating cost. The results indicate that adenoviral vector vaccines can be practically manufactured at large scale and low cost. [ABSTRACT FROM AUTHOR]

With the advent of the first pandemic wave of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the question arises as to whether the spread of the virus will be controlled by the application of preventive measures or will follow a different course, regardless of the pattern of spread already recorded. These conditions caused by the unprecedented pandemic have highlighted the importance of reliable data from official sources, their complete recording and analysis, and accurate investigation of epidemiological indicators in almost real time. There is an ongoing research demand for reliable and effective modeling of the disease but also the formulation of substantiated views to make optimal decisions for the design of preventive or repressive measures by those responsible for the implementation of policy in favor of the protection of public health. The main objective of the study is to present an innovative data-analysis system of COVID-19 disease progression in Greece and her border countries by real-time statistics about the epidemiological indicators. This system utilizes visualized data produced by an automated information system developed during the study, which is based on the analysis of large pandemic-related datasets, making extensive use of advanced machine learning methods. Finally, the aim is to support with up-to-date technological means optimal decisions in almost real time as well as the development of medium-term forecast of disease progression, thus assisting the competent bodies in taking appropriate measures for the effective management of the available health resources. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (COVID-19) poses a severe threat to public health officials all around the world. The early COVID-19 outbreak in South Korea displayed significant spatial heterogeneity. The number of confirmed cases increased rapidly in the Daegu and Gyeongbuk (epicenter), whereas the spread was much slower in the rest of Korea. A two-patch mathematical model with a mobility matrix is developed to capture this significant spatial heterogeneity of COVID-19 outbreaks from 18 February to 24 March 2020. The mobility matrix is taken from the movement data provided by the Korea Transport Institute (KOTI). Some of the essential patch-specific parameters are estimated through cumulative confirmed cases, including the transmission rates and the basic reproduction numbers (local and global). Our simulations show that travel restrictions between the epicenter and the rest of Korea effectively prevented massive outbreaks in the rest of Korea. Furthermore, we explore the effectiveness of several additional strategies for the mitigation and suppression of Covid-19 spread in Korea, such as implementing social distancing and early diagnostic interventions. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To date, there have been more than 10 million reported cases, more than 517,000 deaths in 215 countries, areas or territories. There is no effective antiviral medicine to prevent or treat COVID-19. Natural products and traditional medicine products with known safety profiles are a promising source for the discovery of new drug leads. There is increasing number of publications reporting the effect of natural products and traditional medicine products on COVID-19. In our review, we provide an overview of natural products and their derivatives or mimics, as well as traditional medicine products, which were reported to exhibit potential to inhibit SARS-CoV-2 infection in vitro, and to manage COVID-19 in vivo, or in clinical reports or trials. These natural products and traditional medicine products are categorized in several classes: (1) anti-malaria drugs including chloroquine and hydroxychloroquine, (2) antivirals including nucleoside analogs (remdesivir, favipiravir, β-D-N4-hydroxycytidine, ribavirin and among others), lopinavir/ritonavir and arbidol, (3) antibiotics including azithromycin, ivermectin and teicoplanin, (4) anti-protozoal drug, emetine, anti-cancer drug, homoharringtonine, and others, as well as (5) traditional medicine (Lian Hua Qing Wen Capsule, Shuang Huang Lian Oral Liquid, Qingfei Paidu Decoction and Scutellariae Radix). Randomized, double-blind and placebo-controlled large clinical trials are needed to provide solid evidence for the potential effective treatment. Currently, drug repurposing is a promising strategy to quickly find an effective treatment for COVID-19. In addition, carefully combined cocktails need to be examined for preventing a COVID-19 pandemic and the resulting global health concerns. [ABSTRACT FROM AUTHOR]