N. Sarah Arden, Adam C. Fisher, Katherine M. Tyner
et al.
Over the last two centuries, medicines have evolved from crude herbal and botanical preparations into more complex manufacturing of sophisticated drug products and dosage forms. Along with the evolution of medicines, the manufacturing practices for their production have advanced from small-scale manual processing with simple tools to large-scale production as part of a trillion-dollar pharmaceutical industry. Today's pharmaceutical manufacturing technologies continue to evolve as the internet of things, artificial intelligence, robotics, and advanced computing begin to challenge the traditional approaches, practices, and business models for the manufacture of pharmaceuticals. The application of these technologies has the potential to dramatically increase the agility, efficiency, flexibility, and quality of the industrial production of medicines. How these technologies are deployed on the journey from data collection to the hallmark digital maturity of Industry 4.0 will define the next generation of pharmaceutical manufacturing. Acheiving the benefits of this future requires a vision for it and an understanding of the extant regulatory, technical, and logistical barriers to realizing it.
Oluwafunmike O. Elumilade, Ibidapo Abiodun Ogundeji, Godwin Ozoemenam Achumie
et al.
Financial data analytics has become a critical tool for businesses seeking to drive growth, enhance fraud prevention, and mitigate risks in dynamic markets. By leveraging large datasets, advanced algorithms, and real-time analytics, organizations can make more informed financial decisions, improve operational efficiency, and enhance compliance with regulatory frameworks. This review explores how financial data analytics contributes to business growth by improving revenue forecasting, identifying market trends, and optimizing financial planning. Companies can leverage predictive models and artificial intelligence to gain competitive advantages through better risk assessment and investment decision-making. Fraud prevention is another key area where financial data analytics plays a transformative role. Machine learning algorithms, anomaly detection systems, and real-time transaction monitoring help identify and prevent fraudulent activities before they cause significant financial losses. Businesses and financial institutions can use automated risk-scoring models to strengthen security in banking, payments, and investment transactions. Risk mitigation in financial markets is also enhanced through data analytics. By employing predictive modeling, scenario analysis, and stress testing, businesses can assess potential market fluctuations and develop strategies to minimize financial exposure. Moreover, analytics-driven regulatory compliance mechanisms improve transparency and reporting, ensuring adherence to legal and industry standards. Despite its advantages, financial data analytics faces challenges such as data privacy concerns, integration with legacy systems, and the need for skilled professionals. However, emerging technologies, including blockchain, AI, and decentralized finance (DeFi), present new opportunities for strengthening financial security and business resilience. This review concludes that financial data analytics is a vital asset for modern businesses, offering strategic insights that drive profitability, enhance fraud detection, and strengthen risk management. Companies must continue to invest in data-driven solutions to stay competitive in an increasingly digital financial landscape. Keywords: Financial data, Business growth, Fraud prevention, Markets.
The purpose of the study is to identify the development of approaches to organize the formation of management elements of mining industry development in the Arctic conditions. Methods. The study used data obtained through the content analysis of a large number of scientific publications, practical materials, expert opinions, regulations and company reports. Such methods as scientific generalization, classification, economic analysis, expertise and statistics were applied in the course of the work. In addition to the traditional methods, the study included the interdisciplinary approach. Results. The article considers the issues of organizing interaction between mining enterprises and the state in developing socio-economic infrastructure in the Arctic using the case study of the Chukotka Autonomous District. The dynamics of changes in the production volumes of mining companies has been analyzed, the factors influencing the efficiency of mineral and raw materials enterprises and their influence on the growth rate of the regional economy have been considered. The measures of state support of enterprises are investigated, the mechanisms to develop the support settlements in the Arctic, approaches to the creation of their master plans are analyzed. The possibilities of the system approach to the formation of clusters for the development of the region's economic potential in linking industrial facilities with the development of infrastructure and social sphere have been studied. Measures to support family-run business as one of the mechanisms of strengthening the human resources potential of the Arctic zone of the region are justified.
Nowadays, the e-commerce industry is developing rapidly, and online shopping has become the mainstream consumption mode. Online shopping order data, online shopping user data, online shopping product preference data, online retail industry has produced a variety of data. Therefore, we can study the overall situation of China's online retail market, analyze online retail data, and deeply explore and predict the development of online retail industry. Therefore, a powerful online retail visual analysis system is needed to integrate e-commerce data and visually show the connection and value of data through rich and diverse dynamic interactive charts. This system can analyze and monitor the national network retail market and provide service and support for the economic development of network retail industry. From the perspective of the data analysis staff of the e-commerce center, this paper will use the unique e-commerce data of the platform, combined with emerging technologies and tools in the big data environment, propose a visual analysis scheme based on online retail data, and finally design a set of online retail visualization and analysis system to realize the dynamic interaction of visual large-screen charts. Diversity of data display and multidimensional analysis. .Data visualization libraries, such as Python's Matplotlib. Seaborn, or JavaScript's D3.js, are used to present the analyzed and processed data in the form of intuitive charts. This system can help internal analysts to understand the data more intuitively and accurately, analyze the data in multiple dimensions and granularity, predict the future trend of online retail, and dig the meaning of the value behind the data. Build a set of network retail visual analysis system with good security, stability and high availability.
Introduction The ubiquity of digital devices, the infrastructure of today, and the ever-increasing proliferation of digital products have dawned a new era, the era of big data (BD). This era began when the volume, variety, and velocity of data overwhelmed traditional systems that used to analyze and store that data. This precipitated a new class of software systems, namely, BD systems. Whereas BD systems provide a competitive advantage to businesses, many have failed to harness the power of them. It has been estimated that only 20% of companies have successfully implemented a BD project. Methods This study aims to facilitate BD system development by introducing Cybermycelium, a domain-driven decentralized BD reference architecture (RA). The artifact was developed following the guidelines of empirically grounded RAs and evaluated through implementation in a real-world scenario using the Architecture Tradeoff Analysis Method (ATAM). Results The evaluation revealed that Cybermycelium successfully addressed key architectural qualities: performance (achieving <1,000 ms response times), availability (through event brokers and circuit breaking), and modifiability (enabling rapid service deployment and configuration). The prototype demonstrated effective handling of data processing, scalability challenges, and domain-specific requirements in a large-scale international company setting. Discussion The results highlight important architectural trade-offs between event backbone implementation and service mesh design. While the domain-driven distributed approach improved scalability and maintainability compared to traditional monolithic architectures, it requires significant technical expertise for implementation. This contribution advances the field by providing a validated reference architecture that addresses the challenges of adopting BD in modern enterprises.
Abdulatif Ali Hussain, Ismael Khaleel, Tahsien Al-Quraishi
Big Data and Analytics mean an enormous and complex collection of very diverse information, which is processed with various technologies and methods to produce and deliver useful and valuable insights. Analytics is the science of using data, or information to extract useful and actionable insights, facts and knowledge from a collection of data it could be stated that Big Data Analytics is the best thing since every commercial data system ever built, although everybody with a more optimistic vision of technology would like to take note that there is a fine line where Everything Data crosses the boundary to something else, especially with regard to privacy and security of the world as we know it. Privacy and security are two distinct but closely related phenomena. Whereas privacy refers to the control over access to the individual, security refers to the stability or strength of controls designed to protect the individual’s privacy. There are many obvious considerations and obstacles when attempting to securely share data. During big data analytics, many invasive techniques such as data fusion, cross-correlation, and algorithm training are often conducted over shared data, which can lead to severe privacy leaks. This means that every enterprise, organization, and individual maintaining large data repositories are in danger of being breached. Our study teaches us that security, privacy, and ethical concerns in big data analytics do not exist in parallel to the business cycle, but must be wisely and ethically managed in coherence throughout all emerging processes of the big data and information systems.
The scientific article is devoted to the study of strategic management of transport companies in the context of digital transformation. Digitalization is an important factor in increasing business efficiency, but its implementation is accompanied by numerous challenges that affect management processes and the competitiveness of companies. The author solves a number of tasks, namely: analyzes the key challenges of digital transformation for transport companies, in particular the lack of qualified personnel, the need for significant investments in technology and internal resistance to change; explores the possibilities of using big data, artificial intelligence, process automation and digital platforms to improve management efficiency; assesses the prospects for implementing scenario and resource-oriented approaches in the strategic management of transport companies. The study confirms that the lack of a clear digital transformation strategy is one of the main barriers for enterprises in the transport sector. Fragmented implementation of digital solutions without a comprehensive strategic approach leads to a loss of competitive advantages and low efficiency of technological changes. Successful digitalization involves deep integration of innovations into all levels of a company's activities, from operational processes to corporate culture. The scientific article proposes a conceptual model of strategic management of digital changes in the transport industry, which includes the adaptation of business models, the use of a flexible approach to management (Agile management), the implementation of digital platforms and predictive analytics tools. Particular attention is paid to cybersecurity issues, since digital transformation is accompanied by risks of data loss and threats of unauthorized access to critical systems. The study concludes that a comprehensive approach to strategic management of digital changes is necessary, which will allow transport companies to increase the efficiency of logistics processes, reduce operating costs and ensure long-term competitiveness in a dynamic business environment.
Abstract This article offers a new interpretation of the coming of state ownership in aluminium-related big businesses in Norway. It shows that the Norwegian aluminium business of the late 1930s and the 1940s was undertaken by a Scandinavian business elite fully capable of filling capital requirements after the war. This elite had, however, entangled itself in the German war effort in Norway mainly by supporting the building of new aluminium plants under the German occupiers’ control. This left it morally vulnerable to the increasing emphasis during the war on aluminium as a strategic metal. The Allied war effort—especially evident in US attitudes—had come to see the cartelized aluminium industry of the 1930s as working against the national interest by impacting national production capacity in a negative way. The Allies bombed the major new plant in Norway in 1943, and after the war the US acted restrictively toward Norwegian capital assets in the US. By pursuing ownership after 1945, the Norwegian state performed strategic ownership roles in large corporations, thereby also protecting these entities from the possible wrath of the US against private owners.
The convergence of artificial intelligence with China's manufacturing sector marks the dawn of a new digital era with profound implications for productivity, innovation, and global trade. The emergence of artificial intelligence is redefining how factories operate in China, bringing unparalleled efficiency and agility. Powered by AI, the promise of smart manufacturing is more than just an incremental advance; It represents a fundamental shift in manufacturing towards intelligent, connected and autonomous systems. In the current situation, AI has begun to be deployed in many manufacturing areas in China, focusing on process optimization, supply chain management, product development and after-sales service. Factories are becoming smarter, using big data analytics and machine learning algorithms to improve decision-making and operational efficiency across all aspects. At the same time, such rapid development will raise questions about the collapse of the low-skilled labor market and information safety. This essay also puts forward some solutions in turn.
O. Alzaabi, Khawla Al Mahri, Mounir El Khatib
et al.
Abstract Big data analysis allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or used. Companies can use advanced analytics techniques such as text analysis, machine learning, predictive analytics, data mining, statistics, and natural language processing to gain new insights and insights from previously untapped data sources independently or with existing enterprise data. Significant challenges are still in how to deal with this data and maximize their use, so large data have become the clock that has become the most national, regional, and international institutions, and a broad range of database management, methodologies and measures that can be adopted for the exploitation of large data in all areas of life. This paper investigates the effects of big data analytics on project risk management with examples from Healthcare sector in UAE. Inclusive research has been done by searching approximately more than 20 references resulted in a literature review studied the effect of implementing data analytics in business, technology, industry, and society businesses aspects. A research methodology has been done by interviewing professionals from healthcare field investigating further the role of Data analytics in analyzing and managing data in healthcare, its benefits in predicting risks and improving healthcare outcomes and future insights. This research’s result reveals that collecting data and applying data analytics even in businesses or healthcare represent an important kind of digital transformation. An obvious finding from this research is that business intelligence and data analytics has been implemented widely in UAE healthcare sector by both government and privet sectors resulting in opportunities that develop and emphasize positive changes to this sector.
hojatollah Paknahad, hamed dehghanan, Ali Naghavi
et al.
Context and purpose. The aim of this study was to analyze and prioritize indicators of cooperation based on trust in the cooperative sector of an urban economy from the perspective of members of consumer cooperatives in Lorestan province.Methodology/approach. This study was conducted based on a quantitative approach using survey method. The research population consisted of members of the top consumer cooperatives in Lorestan province, including members of the board of directors, inspectors, CEOs, members and shareholders of 11 consumer cooperatives. For this purpose, a questionnaire was distributed and collected among 385 people using stratified sampling method. Data were analyzed confirmatory factor analysis.Findings and conclusions. The results of confirmatory factor analysis showed that all components have an acceptable factor loading in relation to the latent variables of the research. The results of Friedman test showed that the priority of the effect of each research structure in the cooperation model based on the trust of Iranian cultural cooperatives is different. Indicators, in order of impact, from high to low, including effectiveness and efficiency, customer orientation and satisfaction, resources and financial status, trust requirements, prospects, cooperative pathology, rules and regulations, interaction and cooperation, diverse services and the requirements for forming a cooperative were identified.Originality. The current research was conducted in order to prioritize the identified behavioral, organizational and structural factors that affect trust-based cooperation in the cooperative sector. In this regard, the present study tries to direct the attention of the policy makers and planners of the country's cooperative economy to the identified indicators and their priority.
This research was carried out with the aim of analyzing the situation of rural cooperative companies in Kermanshah province and based on the social network analysis methodology to identify the weaknesses and strengths of the companies by comparing the top companies with the normal ones. Its statistical population includes members of the board of directors, CEO and inspectors and experts of 20 rural cooperative companies in Kermanshah province. After in-depth library studies, the main tool for collecting the required data and information was a questionnaire made by the researcher, Findings of correlation coefficient of organizational dynamics (r =0.31), sense of belonging and empathy (r =0.28), behavioral style (r =0.35), social level (r =0.32), social trust (r =0.30), social participation (r =0.32) and social relations (r =0.37) with the cooperatives’ performance were positive and meaningful. The results of variance analysis showed that these variables had a significant effect on the performance of cooperatives and were able to predict about 59.6% of the performance changes of both groups of cooperatives under study. Also, the effect of the mentioned variables on the performance of the company was different between successful and unsuccessful cooperatives. The current research had a different view on the category of rural cooperatives. It analyzed the structure of the social network as a new topic in these institutions and takes into account its current situation. In addition, it compares the state of relations and social network in two superior or successful and unsuccessful cooperative groups in the studied area.
In Industry 4.0, the rapid development of ICT has transformed various industries, including the Internet of Things and big data analytics, which introduces the concept of IIoT (Industrial Internet of Things). IIoT depends on data connectivity infrastructures, analytics, humans, and intelligent assets that store and transmit data to work. The IIoT network's large number of components also increases the possibility of security issues. The integration of Business Intelligence (BI) in Industrial Internet of Things (IoT) systems has exposed critical data to security threats. Existing security measures are inadequate, necessitating research into more robust solutions. This research aims to enhance the security of BI in Industrial IoT by leveraging blockchain technology. The objective is to design and implement a blockchain-based security framework to safeguard sensitive industrial data. The study presents a novel blockchain-enhanced security system for Industrial IoT, effectively safeguarding BI data. The evaluation demonstrates improved data integrity, confidentiality, and traceability, highlighting the potential of blockchain as a robust security solution in industrial settings. This paper also shows that integrating IIoT, Blockchain, and business intelligence can improve a firm's performance, which needs various types of data collection from different sources. Blockchain's essential qualities that greatly impact IIoT's security flaws are decentralization, immutability, and tamper-proof. This is why IIoT is a field in which blockchain technology is very important. The implications are going to be beneficial for smart industries in order to develop next-generation secure IIoT.
The purpose of legal education is to cultivate professional legal talents, let them master the framework of the current legal system, use legal reasoning, and solve practical problems according to legal logic. In the context of big data (BD), artificial intelligence (AI) technology helps collect a large amount of information and process the information centrally, making the calculation process much simpler. Therefore, we apply AI to the education industry, design a law teaching system based on AI, and conduct a teaching effect test experiment. The experiment shows that the average grade of the class using the artificial intelligence system for law teaching is higher than that of the class using the traditional teaching method, and the grades increase faster, verifying the feasibility of the artificial intelligence teaching system in legal education.
Tourism is an important strategic pillar industry of the national economy, and transportation is the foundation and prerequisite for the development of tourism. In recent years, comprehensive transportation system has been constantly improved, the integrated development of transportation and tourism has become a new trend. By using big data survey and analysis technology, this paper analyzes the characteristics of different types of tourism transportation in the Yangtze River Delta region, evaluates the accessibility of urban tourism transportation, and explores the relationship between traffic and tourism development. Moreover, the project optimizes the productivity distribution, promotes the development of tourism economy in a large range, and provides a more scientific decision basis for planning and investment of regional transportation project. In this paper, the big data analysis and means adopted in the analysis can provide certain reference for other subjects.
Ali Houshangi, Azam Rezaee, farshid Eshraghi
et al.
This study aimed to evaluate the Factors Affecting the Technical Efficiency of Agricultural Production Cooperatives in Golestan Province by using the data envelopment analysis method with a product-oriented approach and constant and variable returns to scale. The required data was obtained in the field and by completing a questionnaire and interviewing the managers of 36 agricultural production cooperatives active in Golestan province in 2020. To evaluate the technical efficiency, the Tobit regression model has also been used to investigate the factors affecting performance. The results show that the average technical efficiency of the surveyed cooperatives under the conditions of fixed returns and variable returns to the scale is almost equal; Therefore, agricultural production cooperatives in Golestan province are faced with a high level of scale efficiency. The results show that 69% of companies have fixed returns, 25% have descending returns and 6% have upward returns to the scale of production. The results of the study of factors affecting technical efficiency also indicate that the age variable has a negative and significant effect on performance. Also, the asset variable has a positive and significant effect on efficiency. The average values of technical and managerial efficiency show that it is possible to increase the output by 13% with the same amount of input. It is necessary to increase the efficiency of production cooperatives by supporting young people and using them in the field of production and providing the necessary funds for companies.
AbstractWith the rapid growth in the amount of video data, efficient video indexing and retrieval methods have become one of the most critical challenges in multimedia management. For this purpose, Content-Based Video Retrieval (CBVR) is nowadays an active area of research. In this article, a CBVR system providing similar videos from a large multimedia dataset based on query video has been proposed. This approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key frames for rapid browsing and efficient video indexing. The proposed method has been implemented on both single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments were performed using various benchmark action and activity recognition datasets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to previous studies.
A. Bhuiyan, Md. Jafor Ali, Norhayah Zulkifli
et al.
In the recent decade, the term Industry 4.0 or Fourth Industrial Revolution is a common buzzword represents the adoption of disruptive digital technologies (Internet of things, Big Data, 3D printing, Cloud computing, Autonomous robots, Virtual reality, Augmented reality, Self-driving car, Cyber-physical system, Artificial intelligence, Smart sensors, Nanotechnology, Drones, and Biotechnology, etc.) in the production process which is transforming the manufacturing units into smart factories and experiencing a great change in the global value chain. Moreover, these revolutionary digital technologies have a profound impact on the economy, growth, globalization, governments, international trade, global supply chain, and human capital transformation, etc. The present review study aims to explore the impact, challenges, and opportunities of the fourth industrial revolution based on empirical findings specially and extensively in the context of Bangladesh. The study finds regardless of having enormous potentiality, the application of the fourth industrial revolution is far lagging for some challenges i.e. lack awareness, insufficient capital, lack of infrastructure, lack of skilled human capital, and some socio-economic challenges. This review paper will also develop conceptual links with the relevant aspect of strategic planning and application of key industry 4.0 technologies and help to formulate future policy guidelines regarding opportunities, application, and strategic decision making for the fourth industrial revolution in Bangladesh.