Global Industry Research Articles

Stochastic Game Network-inspired intelligent framework for quality assessment in logistic industry

The widespread utilization of IoT-Fog-Cloud computing has revolutionized customer-oriented service delivery aspects in the global logistics industry. With industrial globalization, the logistics industry is often challenged to address quality issues. Moreover, with improper product management and transportation, the logistics industry has to face considerable financial loss. Conspicuously, a novel Stochastic Game Network (SGN) is proposed in the current study to ensure enhanced service product quality in the logistics industry. Specifically, the smart logistic industry-based decision-making framework is presented by incorporating several parameters of product quality. In the proposed method, each IoT device acts as a game player with a preset action set and strategy. Moreover, each IoT sensor at 4 levels of the logistic industry including Production Management Sensor (IMS), Inventory Management Sensor (IMS), Transportation Management Sensor (TMS), and Delivery Management Sensor (DMS) is incorporated as a game player node. Quality parameters are analyzed using the proposed Bayesian Belief Model (BBM) technique for effective classification. Moreover, Game-theoretic decision-making is used to automate enhanced product quality management and control units in IoT-based logistic industries for effective analysis and customer-oriented service delivery. For validation purposes, experimental results are compared with state-of-the-art decision-making techniques. Based on the results, the proposed model has registered enhanced statistical measures in terms of Precision (92.98%), Specificity (93.78%), Sensitivity (96.78%), Reliability (96.54%), and Stability (78%). To assess the theoretical efficacy of the proposed strategy, mathematical analysis is performed to quantify customer satisfaction. Conclusively, the proposed model presents an effective mechanism for quality assurance by incorporating stochastic behavior in the logistics industry.

Applying Building Information Modelling (BIM) Technology in Pre-Tender Cost Estimation of Construction Projects: A Case Study in Iran

The pre-tender cost estimation serves as the foundation for determining the project cost in the early stages and is crucial for all parties involved in the tendering process. It is expected to be highly accurate. However, industry surveys have advocated that in the Iranian construction industry, the pre-tender estimated costs of construction projects are not sufficiently accurate during the bidding stage. Building information modelling (BIM) technology is a modern digital tool deployed in the construction industry that has seen substantial growth of application. It employs useful tools in different sectors and has extended its involvement in various stages of the project lifecycle, including feasibility studies, planning, design, construction, and operation and maintenance. This study examines and assesses the status of BIM development in Iran for its applicability in the pre-tender cost estimating process in construction projects. The findings of this study indicate that while there is a limited number of Iranian building projects’ BIM models that possess the required quality and capabilities for BIM-based cost estimation, the results obtained are more precise when compared to existing manual methods. Additionally, utilising BIM-based cost estimation significantly reduces the time required for this process by enhancing the speed of cost estimation operations. An innovative evaluation framework for assessing the quality and functionalities of BIM models is presented, which improves the accuracy of cost estimation before the bidding process in Iran’s construction industry. The research findings demonstrate the enhanced accuracy and effectiveness of BIM as compared to conventional approaches. It also suggests the incorporation of AI technology for model evaluation, which has the potential to become an established global industry standard.

Multimarket contact between partners and strategic alliance survival

AbstractResearch SummaryThe impact of multimarket contact (MMC) between partners on strategic alliance survival is unclear, even though recent studies have suggested that MMC increases the likelihood of alliance formation. Our study investigates this issue by integrating two mechanisms occurring between multimarket firms: mutual forbearance and technological resource imitation. We argue that MMC between partners deters opportunism in alliances via mutual forbearance, resulting in a positive effect on the likelihood of strategic alliance survival. We also suggest that the positive effect is weakened in two settings with higher risks of technological resource imitation: technological overlap between partners and the presence of R&D activities in an alliance. Evidence from strategic alliances in the global semiconductor industry supports these conclusions.Managerial SummaryRecent research has shown that firms encountering each other in multiple markets are more likely to form strategic alliances, but it is unclear whether these firms are likely to stick together. Our theory suggests that the threat of broad retaliation limits opportunism and increases the likelihood of alliance survival when partners encounter each other in multiple markets. Nonetheless, in settings where partners have similar technologies, or in alliances involving R&D activities, their ability and incentives to copy each other's technological resources offsets the positive effect of multimarket contact on alliance survival. We study strategic alliances in the global semiconductor industry and find evidence consistent with these arguments.

Surveillance for highly pathogenic avian influenza A (H5N1) in a raptor rehabilitation center-2022.

An ongoing, severe outbreak of highly pathogenic avian influenza virus (HPAI) A H5N1 clade 2.3.4.4b has been circulating in wild and domestic bird populations throughout the world, reaching North America in 2021. This HPAI outbreak has exhibited unique characteristics when compared to previous outbreaks. The global distribution of disease, prolonged duration, extensive number of species and individual wild birds affected, and the large impact on the global poultry industry have all exceeded historical impacts of previous outbreaks in North America. In this study, we describe the results of HPAI surveillance conducted at The Raptor Center, a wildlife rehabilitation hospital at University of Minnesota (Saint Paul, MN, U.S.A.), from March 28th-December 31, 2022. All wild raptors admitted to the facility were tested for avian influenza viruses using polymerase chain reaction (PCR) testing. All non-negative samples were submitted to the United States Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) National Veterinary Services Laboratories for confirmatory HPAI testing and genetic sequencing. During the study period, 996 individual birds representing 20 different species were tested for avian influenza, and 213 birds were confirmed HPAI positive. Highly pathogenic avian influenza surveillance conducted at The Raptor Center contributed 75% of the HPAI positive raptor detections within the state of Minnesota, located within the Mississippi flyway, significantly augmenting state wildlife surveillance efforts. The viral genotypes observed in birds sampled at The Raptor Center were representative of what was seen in wild bird surveillance within the Mississippi flyway during the same time frame. Wildlife rehabilitation centers provide an opportune situation to augment disease surveillance at the human, wildlife and domestic animal interface during ongoing infectious disease outbreaks.

Saccharomyces cerevisiae oral immunization in mice using multi-antigen of the African swine fever virus elicits a robust immune response.

African swine fever virus (ASFV) is one of the most complex viruses. ASFV is a serious threat to the global swine industry because no commercial vaccines against this virus are currently available except in Vietnam. Moreover, ASFV is highly stable in the environment and can survive in water, feed, and aerosols for a long time. ASFV is transmitted through the digestive and respiratory tract. Mucosal immunity is the first line of defense against ASFV. Saccharomyces cerevisiae (SC), which has been certified by the U.S. Food and Drug Administration and has a generally recognized as safe status in the food industry, was used for oral immunization in this study. ASFV antigens were effectively expressed in recombinant SC strains with high DNA copy numbers and stable growth though surface display technology and chromosome engineering (δ-integration). The recombinant SC strains containing eight ASFV antigens-KP177R, E183L, E199L, CP204L, E248R, EP402R, B602L, and B646L- induced strong humoral and mucosal immune responses in mice. There was no antigenic competition, and these antigens induced Th1 and Th2 cellular immune responses. Therefore, the oral immunization strategy using recombinant SC strains containing multiple ASFV antigens demonstrate potential for future testing in swine, including challenge studies to evaluate its efficacy as a vaccine against ASFV.

An Examination of Nepal's Aviation Sectors: An Analysis of the Aviation Industry as an Expanding Economic Sector

This research paper is prepare using data from studies on the aviation sector in Nepal and how it affects the country's economy. It is acknowledged that Nepali aviation currently influences the country's economic growth in three ways: directly, indirectly, and inducedly. Even though fixed-wing aircraft are being employed less frequently in Nepal, their use in other nations, combined with the employment of helicopters, has helped to strengthen the country's aviation sector. This has also made a significant contribution to the nation's improving economic standing. This industry's growth and development seem to be happening at a snail's pace, despite the enormous potential of this business. Thus, the basis of this research paper is the explanation for the industry's delayed advancement, with a primary focus on its impact on the economy of the nation along with the data of accident occurred in recent ten years. The results of this study will be helpful for the global aviation industry in achieving rapid economic growth and decrease accidents.

Hotel industry of Ukraine in crisis conditions: challenges, trends, prospects

Hotel industry of Ukraine in crisis conditions: challenges, trends, prospects

Rhein kills Actinobacillus pleuropneumoniae, reduces biofilm formation, and effectively treats bacterial lung infections in mice.

Background. Actinobacillus pleuropneumoniae, a member of the Pasteurellaceae family, is known for its highly infectious nature and is the primary causative agent of infectious pleuropneumonia in pigs. This disease poses a considerable threat to the global pig industry and leads to substantial economic losses due to reduced productivity, increased mortality rates, and the need for extensive veterinary care and treatment. Due to the emergence of multi-drug-resistant strains, Chinese herbal medicine is considered one of the best alternatives to antibiotics due to its unique mechanism of action and other properties. As a type of Chinese herbal medicine, Rhein has the advantages of a wide antibacterial spectrum and is less likely to develop drug resistance, which can perfectly solve the limitations of current antibacterial treatments.Methods. The killing effect of Rhein on A. pleuropneumoniae was detected by fluorescence quantification of differential expression changes of key genes, and scanning electron microscopy was used to observe the changes in A. pleuropneumoniae status after Rhein treatment. Establishing a mouse model to observe the treatment of Rhein after A. pleuropneumoniae infection.Results. Here, in this study, we found that Rhein had a good killing effect on A. pleuropneumoniae and that the MIC was 25 µg ml-1. After 3 h of action, Rhein (4×MIC) completely kills A. pleuropneumoniae and Rhein has good stability. In addition, the treatment with Rhein (1×MIC) significantly reduced the formation of bacterial biofilms. Therapeutic evaluation in a murine model showed that Rhein protects mice from A. pleuropneumoniae and relieves lung inflammation. Quantitative RT-PCR (Quantitative reverse transcription polymerase chain reaction is a molecular biology technique that combines both reverse transcription and polymerase chain reaction methods to quantitatively detect the amount of a specific RNA molecule) results showed that Rhein treatment significantly downregulated the expression of the IL-18 (Interleukin refers to a class of cytokines produced by white blood cells), TNF-α, p65 and p38 genes. Along with the downregulation of genes such as IL-18, it means that Rhein has an inhibitory effect on the expression of these genes, thereby reducing the activation of inflammatory cells and the production of inflammatory mediators. This helps reduce inflammation and protects tissue from further damage.Conclusions. This study reports the activity of Rhein against A. pleuropneumoniae and its mechanism, and reveals the ability of Rhein to treat A. pleuropneumoniae infection in mice, laying the foundation for the development of new drugs for bacterial infections.

Utilizing Deep Neural Networks for Chrysanthemum Leaf and Flower Feature Recognition

Chrysanthemums, a significant genus within the Asteraceae, hold a paramount position in the global floricultural industry, second only to roses in market demand. The proliferation of diverse chrysanthemum cultivars presents a formidable challenge for accurate identification, exacerbated by the abundance of varieties, intricate floral structures, diverse floret types, and complex genetic profiles. Precise recognition of chrysanthemum phenotypes is indispensable to navigating these complexities. Traditional methods, including morphology studies, statistical analyses, and molecular markers, have fallen short due to their manual nature and time-intensive processes. This study presents an innovative solution employing deep learning techniques for image-based chrysanthemum phenotype recognition. Leveraging machine learning, our system autonomously extracts key features from chrysanthemum images, converting morphological data into accessible two-dimensional representations. We utilized Support Vector Machine (SVM) and Multilayer Perceptron (MLP) algorithms to construct frameworks for processing image data and classifying chrysanthemum cultivars based on color, shape, and texture. Experimental results, encompassing 10 cultivars, 10 flower colors, and five flower shapes, consistently demonstrated recognition accuracy ranging from 79.29% up to 97.86%. This tool promises streamlined identification of flower traits, and we anticipate its potential for real-time identification enhancements in future iterations, promising advances in chrysanthemum cultivation and exportation processes. Our approach offers a novel and efficient means to address the challenges posed by the vast diversity within chrysanthemum species, facilitating improved management, breeding, and marketing strategies in the floricultural industry.

Strategic Challenges to the Eradication of African Swine Fever Genotype II in Domestic Pigs in North Italy.

African swine fever (ASF) is a severe viral disease characterized by high lethality in suids and caused by the African Swine Fever Virus (ASFV). The ASF genotype I virus was introduced to Europe in 1957, marking the onset of the first European epidemic wave. In 2007, ASFV genotype II was detected in Georgia, affecting domestic pigs and wild boars before spreading to various European and extra-European countries, including Italy. The first case of ASFV in Italy was documented on 7 January 2022, in a wild boar in the Piedmont region. Since then, several ASFV-positive wild boar carcasses have been identified in the Piedmont and Liguria regions. By June 2023, ASFV had spread to Lombardy, one of the major pig-producing regions in northern Italy; the virus was first detected in early summer in wild boar carcasses. Two months later, it was diagnosed in a commercial pig farm as a consequence of the disease's spread amongst wild boars and an increase in the viral environmental load. This report aims to describe the features of ASFV domestic pig outbreaks that occurred in the Zinasco municipality (Lombardy) and the joint efforts to mitigate potential direct and indirect economic impacts on the Italian and global pig industry. The epidemiological investigation and the measures implemented, which were all performed according to national and European regulations, as well as exceptional ad hoc measures aimed at protecting the pig industry, are described in order to provide a practical and effective approach to combating ASF.

Assessment of the global healthcare industry during COVID-19 pandemic: A content analysis approach

Background Content analysis (CA) is an investigative research tool used in healthcare studies to identify imminent challenges while providing lead time for preparedness measures. The objective of this study is to report on the challenges confronting the global healthcare industry. Methods This study used the conventional CA “bottom-up” approach to a quantitative data. CA is a research technique that uses the qualitative research method and word counts to evaluate communication outcomes. A group of executive master’s in business administration (EMBA) and medical students championed the study. Using 13 periodicals as the data sources, researchers conducted online keyword searches for all articles, titles, and abstracts containing the word “healthcare”. The collected data was gathered from five international, four Middle Eastern, and four Lebanese periodicals from January 1, 2021 to December 31, 2021. Results CA results indicate remarkable domination of keywords “COVID-19 health impact” as a major worldwide theme. Significant differences were noticeable among subcategories but remained related to COVID-19 in particular, social responsibilities, and research and development. Topics such as comorbid illnesses, social responsibility, healthcare logistics, and the well-being of healthcare service providers were among the least covered topics. Conclusion Our findings emphasize the importance of global media and their coverage of healthcare issues on the types of healthcare topics (e.g., vaccines, drugs…) covered in the Lebanese periodicals in our study. Thus, global healthcare media are the main source for Lebanese periodicals reporting healthcare issues rather than the Lebanese healthcare authorities. Therefore, there is need for the development of a more efficient technology-driven healthcare management system. At the global level, there is a need to develop a step-by-step action-oriented strategic initiative to respond to emerging pandemics.

From Vulnerability to Resilience: How Japanese Restaurants in Australia Responded to the COVID-19 Pandemic

ABSTRACT Over the last two decades, Australia has led the global Japanese restaurant industry in growth. This article investigates how this burgeoning cross-cultural sector has navigated the COVID-19 pandemic. Through qualitative analysis, it reveals the vulnerabilities and resiliencies of Japanese restaurants in Australia, focusing on digital transformation, digital divide, labour and skill shortages, and institutional vulnerabilities. While some challenges align with the wider hospitality industry, distinctive issues specific to Japanese restaurants are identified. The study highlights how the pandemic amplified existing issues rather than creating new ones and examines how the distinct nature of Japanese restaurants, coupled with Australia’s political-economic landscape, influenced operator responses. Offering insights that transcend its Australian context, this study provides valuable information for both researchers and practitioners in the field.

Exploring Biochar with N-Fertilizer Effects on Soil CO2 Emissions and Physical-Chemical Properties as a Climate Change Mitigation Tool

Abstract The global agriculture industry is facing never before faced issues in the form of soil degradation, water scarcity, rising greenhouse gas emissions, and climate change. Among the possible remedies, applying biochar to the soil has drawn interest as a viable strategy. Although a great deal of literature has been written about the benefits and drawbacks of applying biochar initially, there is still a significant amount of research on the effects of using biochar repeatedly. This study seeks to address this gap by examining the varied effects of both the initial application (at rates of 0 t.ha−1, 10 t.ha−1, and 20 t.ha−1) and the reapplication of biochar (at rates of 0, 10, and 20 t.ha−1), especially when combined with different levels of nitrogen fertilizer (0, 108, and 162 kg.ha−1). The investigation focuses on soil properties and CO2 emissions from Haplic Luvisol in the temperate climate zone (Slovakia). The results showed that biochar generally improved soil properties, such as soil pH (KCl) (p <0.05), shifting it from acidic towards moderately acidic, and generally led to a decrease in ammonium (NH4 +) and nitrate (NO3 -) content. The second level of fertilization, combined with different biochar treatments, yielded the most efficient results in physical properties such as soil temperature, bulk density (BD), and soil water content (SWC) compared to control treatments without biochar. Biochar application contributed to the reduction of both average daily CO2 emissions and cumulative CO2 emissions during the study period (April – October) in 2022 compared to the control without biochar application.

Sustainable mobility perspectives: exploring the impact of UTAUT2 model on fostering electric vehicle adoption in India

PurposeThe global automobile industry is striving towards a sustainable future. Emerging countries including India are gearing up for the revolution. Considering the key role of customer acceptance in the success of any technological shift, the study endeavors to ascertain the catalysts accelerating the adoption of Electric Two-Wheelers (E2W) in India by leveraging an extended Unified Theory of Acceptance and Use of Technology-2 model. The same would assist Electric Vehicle (EV) stakeholders in directing their efforts toward pivotal aspects having the potential to significantly bolster E2W penetration.Design/methodology/approachData was collected using convenience sampling technique from 1,254 electric two-wheeler owners across four Indian states and analyzed using Structural Equation Modelling.FindingsPerformance Expectancy, Price Value and Hedonic Motivation have a significant influence on purchase intention leading to actual buying behavior. Effort Expectancy, Social Influence, habit value and facilitating conditions were insignificant. Pro-Environmental Approach and Government Support significantly impact adoption intention and behavior respectively in addition to model predictors thus supporting the study’s novelty. Purchase intention proved to influence Actual Buying Behavior. Synergized efforts of EV stakeholders towards performance innovation, cost-effectiveness, improved infrastructure and information diffusion on sustainability and user-friendliness could aid in achieving transition to green mobility.Originality/valueThe study predominantly intends to address the intention–behavior gap related to electric two-wheelers in India. Also, two additional constructs, government support and pro-environmental approach, were incorporated resulting in a novel research framework that aims to test their nuanced ability to impact the model predictors.

Development of a duplex recombinase polymerase amplification-lateral flow strip assay with 1 min of DNA extraction for simultaneous identification of pork and chicken ingredients

Meat adulteration is a challenge faced by the global food industry. However, existing protein-based methods or DNA-based polymerase chain reactions are time-consuming and require specialist devices. Therefore, a rapid and on-site assay is urgently needed for identifying meat species. In this study, we developed a duplex recombinase polymerase amplification-lateral flow strip (dRPA-LFS) assay coupled with a rapid DNA extraction method (utilizing the UiO-66 coated stick as the DNA isolation device, and the isolation can be completed in 1 minute, including lysis, washing, and desorption in three steps) for the identification of pork and chicken ingredients. Targeted genes, the pork (Porcine) mitochondrial ND2 gene and chicken (Gallus gallus) cytochrome B gene, were designed with primers and probes. The whole dRPA-LFS detection procedure can be finished within 18.5 minutes (including 1 minute of rapid DNA extraction, 15 minutes of dRPA amplification, and 2.5 minutes of LFS semi-quantitative detection by the naked eye), and the result can also be quantified by ImageJ software within 15 minutes. No positive amplification was observed in beef, lamb, duck, rabbit, goose, ostrich, and horse meat DNA. This assay is sensitive and can detect as low as 0.1 % (wt %) of pork and chicken in simulated adulterated meat mixtures. This assay was successfully applied to the authentication identification of 30 commercial beef and lamb products.

Resilience assessment of trade network in copper industry chain and the risk resistance capacity of core countries: Based on complex network

Copper is a critical mineral for the energy transition, facing significant risks of supply disruptions. To explore the copper industry chain's resistance to such risks, this article constructs a trade network for the global copper industry chain based on complex network methods, utilizing 2022 data on copper ores and concentrates, blister copper, refined copper, copper semis, and copper waste and scrap. It assesses the industry's resilience from both static and dynamic perspectives, and analyzes the risk resistance capacity of core countries. The results indicate that: (1) in the copper industry chain, copper semis, copper waste and scrap exhibit a relatively high level of overall resilience, but copper ores and concentrates, blister copper, and refined copper display a lower level of overall resilience. This indicates that the upstream of the copper industry chain is more susceptible to external factors than that of the downstream. (2) Targeted factors such as economic sanctions and trade disputes have a more serious impact on the copper industry chain than random factors such as natural disasters and transportation interruptions. Meanwhile, targeted factors have a stronger impact on the transmission efficiency and connectivity of the network. (3) There are significant communities in the copper industry chain, with some core countries playing an important role in connecting the network. In terms of connectivity, influence, and control, developed countries represented by the United States and the United Kingdom are stronger than the other two groups; emerging developed countries like Japan and South Korea exhibit lower propagation and intermediary capacities; emerging industrialized countries represented by Brazil and South Africa fall somewhere between the first two. Additionally, China and India demonstrate relatively important influence within the copper industry chain. Finally, this article provides policy recommendations for maintaining the stability of the copper industry chain and enhancing its resilience.

A Survey to Assess the Current Status of Structured Benefit-Risk Assessment in the Global Drug and Medical Device Industry.

This industry survey was conducted to gain insight into the ways structured Benefit-Risk assessment (sBRA) of medical products is approached across drug or medical device developing companies, including frameworks and methods that are currently used and areas where future work is being planned. A survey containing 28 questions covering five key areas of sBRA was set-up and shared with representatives from the participating companies. Each company was asked to complete a single survey response including inputs across the company's multidisciplinary key representatives involved in benefit-risk assessment. Of the 26 participating companies, 21 (81%) are conducting sBRA. Considering these 21 qualitative frameworks were used by almost every company (19, 90%), while only 12 (57%) have used a quantitative method. Many companies have sBRA training (17, 81%), document templates (16,76%), Standard Operating Procedures (SOPs)/checklists (13, 62%), and /or best practice manuals/examples (12,57%) available. Considering all 26 companies Software tools (15, 58%) and BR planning documents (11,42%) were identified as areas into which many companies intend to put effort. The industry survey confirmed a wide usage of sBRA by many companies involved in research and development. Nevertheless, sBRA is evolving and several future opportunities like the implementation of visualization tools were identified by the representatives of the pharmaceutical companies. Finally, challenges like the cross-functional comprehension of the added value of sBRA are still seen.

Towards Sustainable Packaging Solutions: A Comprehensive Analysis of Industry Trends and Innovations

The demand for sustainable packaging solutions has surged in recent years, driven by heightened environmental awareness and regulatory pressures. This paper provides a thorough examination of global and Indian packaging industries, focusing on trends, innovations, and challenges in the pursuit of sustainability. Through case studies and examples, it explores how companies are adopting eco-friendly practices to meet consumer expectations and contribute to a greener future.

Asia Pacific and the global maritime industry: Hierarchies, vulnerabilities, solidarities

Asia Pacific and the global maritime industry: Hierarchies, vulnerabilities, solidarities

Blockchain-based resale and leasing of pre-owned medical equipment

Medical equipment plays a crucial role in the global healthcare industry, towards the delivery of high-quality medical care and contributing to positive patient outcomes. The traditional method of handling pre-owned medical equipment often leads to improper disposal of functional devices in landfills, compelling healthcare providers to invest in costly brand-new equipment. Several proposals have been put forth to establish trading platforms that facilitate the resale of pre-owned medical equipment. However, existing solutions are characterized by a centralized approach, and face challenges related to traceability, certification, system se-curity, and equipment reliability. In this paper, we propose a blockchain-based solution that is capable of orchestrating the resale, leasing, and auctioning of pre-owned medical equipment. Blockchain smart contracts are utilized to automate the resale and leasing process, register stakeholders with authenticity, transfer and verify ownership, and maintain public reputation scores of entities. A comprehensive overview of the architectural components, sequence diagrams, and algorithms that define the operations of our solution is provided. In addition, we develop and test a working implementation of the smart contracts to verify the feasibility of our proposed solution in terms of execution cost and code security. Through a discussion of the proposed solution's key components and concepts, we showcase our solution's ability to fulfill the specified requirements and attain the main objectives. Moreover, we evaluate and compare our solution with existing works in terms of capabilities and robustness, and explore potential adaptations and extensions of our architecture to allow for a wide range of additional use cases. Our smart contract code has been made publicly available on GitHub.