S. M. Habibullah, Md Arafat Sikder, Nadia Islam Tanha, Bhanu Prakash Sah
Blockchain technology has emerged as a transformative force in various industries, including supply chain management within the automotive sector. This review examines the impact of blockchain on the automotive supply chain by analyzing 183 articles, focusing on its ability to enhance transparency, traceability, and efficiency. By providing a decentralized and immutable ledger, blockchain ensures real-time tracking of parts and components, thereby reducing the risk of counterfeit products and ensuring compliance with regulatory standards. The automation of transactions through smart contracts streamlines processes, reduces the need for intermediaries, and leads to substantial cost savings and faster delivery times. However, the implementation of blockchain also presents challenges such as scalability, interoperability with existing systems, high costs, and regulatory concerns. Addressing these challenges through future research and pilot projects is essential for unlocking the full potential of blockchain technology in revolutionizing supply chain management in the automotive industry. This review synthesizes current literature to provide a comprehensive understanding of both the benefits and challenges associated with blockchain implementation, highlighting its transformative potential and the necessary steps for successful adoption.
{"title":"A REVIEW OF BLOCKCHAIN TECHNOLOGY'S IMPACT ON MODERN SUPPLY CHAIN MANAGEMENT IN THE AUTOMOTIVE INDUSTRY","authors":"S. M. Habibullah, Md Arafat Sikder, Nadia Islam Tanha, Bhanu Prakash Sah","doi":"10.62304/jieet.v3i3.163","DOIUrl":"https://doi.org/10.62304/jieet.v3i3.163","url":null,"abstract":"Blockchain technology has emerged as a transformative force in various industries, including supply chain management within the automotive sector. This review examines the impact of blockchain on the automotive supply chain by analyzing 183 articles, focusing on its ability to enhance transparency, traceability, and efficiency. By providing a decentralized and immutable ledger, blockchain ensures real-time tracking of parts and components, thereby reducing the risk of counterfeit products and ensuring compliance with regulatory standards. The automation of transactions through smart contracts streamlines processes, reduces the need for intermediaries, and leads to substantial cost savings and faster delivery times. However, the implementation of blockchain also presents challenges such as scalability, interoperability with existing systems, high costs, and regulatory concerns. Addressing these challenges through future research and pilot projects is essential for unlocking the full potential of blockchain technology in revolutionizing supply chain management in the automotive industry. This review synthesizes current literature to provide a comprehensive understanding of both the benefits and challenges associated with blockchain implementation, highlighting its transformative potential and the necessary steps for successful adoption.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"11 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141266386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-04DOI: 10.62304/ijmisds.v1i3.164
Bhanu Prakash Sah, Nadia Islam Tanha, Md Arafat Sikder, S. M. Habibullah
This systematic literature review examines the integration of Industry 4.0 and Lean technologies in manufacturing, a topic of growing importance as industries seek to enhance efficiency and competitiveness. By analyzing 156 peer-reviewed journal articles, conference papers, and industry reports published between 2010 and 2023, this review identifies vital themes, benefits, challenges, and gaps in the literature. Industry 4.0, characterized by IoT, big data analytics, artificial intelligence (AI), and machine learning (ML), offers significant potential for improving real-time data collection, process automation, and advanced analytics. When integrated with Lean manufacturing principles, which focus on waste reduction and continuous improvement, these technologies can lead to more efficient operations, better quality control, and faster response times. However, the review also highlights several challenges, including high initial costs, the need for a skilled workforce, and the complexity of integrating new technologies with existing systems. Despite these challenges, numerous case studies and best practices demonstrate the successful implementation of these integrated approaches, providing valuable insights for future research and practical applications. This review concludes with recommendations for addressing the identified gaps and leveraging the synergies between Industry 4.0 and Lean technologies to achieve operational excellence in manufacturing.
{"title":"THE INTEGRATION OF INDUSTRY 4.0 AND LEAN TECHNOLOGIES IN MANUFACTURING INDUSTRIES: A SYSTEMATIC LITERATURE REVIEW","authors":"Bhanu Prakash Sah, Nadia Islam Tanha, Md Arafat Sikder, S. M. Habibullah","doi":"10.62304/ijmisds.v1i3.164","DOIUrl":"https://doi.org/10.62304/ijmisds.v1i3.164","url":null,"abstract":"This systematic literature review examines the integration of Industry 4.0 and Lean technologies in manufacturing, a topic of growing importance as industries seek to enhance efficiency and competitiveness. By analyzing 156 peer-reviewed journal articles, conference papers, and industry reports published between 2010 and 2023, this review identifies vital themes, benefits, challenges, and gaps in the literature. Industry 4.0, characterized by IoT, big data analytics, artificial intelligence (AI), and machine learning (ML), offers significant potential for improving real-time data collection, process automation, and advanced analytics. When integrated with Lean manufacturing principles, which focus on waste reduction and continuous improvement, these technologies can lead to more efficient operations, better quality control, and faster response times. However, the review also highlights several challenges, including high initial costs, the need for a skilled workforce, and the complexity of integrating new technologies with existing systems. Despite these challenges, numerous case studies and best practices demonstrate the successful implementation of these integrated approaches, providing valuable insights for future research and practical applications. This review concludes with recommendations for addressing the identified gaps and leveraging the synergies between Industry 4.0 and Lean technologies to achieve operational excellence in manufacturing.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"9 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141265410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.62304/ijmisds.v1i3.160
Tonmoy Barua, Sunanda Barua
This study explores the transformative impact of integrating data analytics and information systems on enhancing efficiency in the financial services industry. The research highlights significant improvements in operational efficiency, risk management, and customer satisfaction through detailed case studies of JPMorgan Chase, Allstate Insurance, BlackRock, and Bank of America. The findings reveal that AI-driven analytics tools at JPMorgan Chase led to a 30% reduction in fraud-related losses and a 20% increase in customer satisfaction. Through predictive analytics, Allstate Insurance achieved a 40% reduction in claims processing time and a 25% improvement in underwriting accuracy. BlackRock reported a 35% increase in portfolio returns due to machine learning and predictive analytics. In comparison, Bank of America experienced a 22% increase in customer retention and a 15% rise in satisfaction through data-driven CRM systems. These outcomes underscore the critical role of advanced data analytics and information systems in driving innovation and operational excellence in financial services. The study emphasises the importance of continuous technological advancements and strategic implementation to maximise the benefits of these tools in the industry.
{"title":"REVIEW OF DATA ANALYTICS AND INFORMATION SYSTEMS IN ENHANCING EFFICIENCY IN FINANCIAL SERVICES: CASE STUDIES FROM THE INDUSTRY","authors":"Tonmoy Barua, Sunanda Barua","doi":"10.62304/ijmisds.v1i3.160","DOIUrl":"https://doi.org/10.62304/ijmisds.v1i3.160","url":null,"abstract":"This study explores the transformative impact of integrating data analytics and information systems on enhancing efficiency in the financial services industry. The research highlights significant improvements in operational efficiency, risk management, and customer satisfaction through detailed case studies of JPMorgan Chase, Allstate Insurance, BlackRock, and Bank of America. The findings reveal that AI-driven analytics tools at JPMorgan Chase led to a 30% reduction in fraud-related losses and a 20% increase in customer satisfaction. Through predictive analytics, Allstate Insurance achieved a 40% reduction in claims processing time and a 25% improvement in underwriting accuracy. BlackRock reported a 35% increase in portfolio returns due to machine learning and predictive analytics. In comparison, Bank of America experienced a 22% increase in customer retention and a 15% rise in satisfaction through data-driven CRM systems. These outcomes underscore the critical role of advanced data analytics and information systems in driving innovation and operational excellence in financial services. The study emphasises the importance of continuous technological advancements and strategic implementation to maximise the benefits of these tools in the industry.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"37 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141270372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zihad Hasan Joy, Md Mahfuzur Rahman, A. Uzzaman, Md Abdul Ahad Maraj
The integration of machine learning (ML) and big data analytics within smart healthcare systems represents a transformative advancement in medical services, emphasizing efficiency, accuracy, and patient-centered care. This paper investigates the application of these advanced technologies in real-time disease detection, showcasing their potential to revolutionize healthcare delivery. Smart healthcare systems leverage a multitude of technological components, including Internet of Things (IoT) devices, sensors, and artificial intelligence (AI), to enable continuous monitoring and diagnostics. This real-time monitoring facilitates prompt interventions and treatment adjustments, which is particularly advantageous for managing chronic conditions and acute illnesses where timely responses are critical to improving patient outcomes. Despite the evident benefits, traditional healthcare infrastructures face significant challenges such as delays in diagnosis due to manual processes, inefficient data handling resulting in data silos, and limited interoperability between different healthcare providers, leading to worsened health outcomes and increased healthcare costs. The integration of ML and big data analytics offers promising solutions to these challenges. ML algorithms can process vast amounts of healthcare data to identify patterns and predict outcomes with high accuracy, such as recognizing early signs of diseases like cancer or diabetes from medical images or electronic health records (EHRs). Big data analytics complements ML by providing the necessary infrastructure to handle and process large volumes of health data, enabling the collection, storage, and analysis of structured data from EHRs, unstructured data from clinical notes, and real-time data from wearable devices. By integrating these technologies, healthcare providers can gain deeper insights into patient health trends and outcomes, leading to more informed decision-making and better patient management. This study employs a qualitative research design, focusing on five genuine case studies: the Mayo Clinic's predictive analytics for heart disease, Cleveland Clinic's use of ML for cancer diagnosis, Kaiser Permanente's diabetes management program, Johns Hopkins Hospital's sepsis detection system, and Mount Sinai Health System's genomic data analysis. Each case study is chosen for its relevance and comprehensive data, detailing the specific healthcare environment and context. This paper interprets these findings in the broader context of smart healthcare systems and existing literature, emphasizing the importance of these technologies in modernizing healthcare and addressing inefficiencies. The challenges encountered during integration, such as data privacy concerns and interoperability issues, are examined along with implemented solutions.
在智能医疗系统中整合机器学习（ML）和大数据分析代表着医疗服务的变革性进步，强调效率、准确性和以患者为中心的护理。本文研究了这些先进技术在实时疾病检测中的应用，展示了它们彻底改变医疗服务的潜力。智能医疗系统利用多种技术组件，包括物联网（IoT）设备、传感器和人工智能（AI），实现持续监测和诊断。这种实时监测有助于及时干预和调整治疗方案，这对于管理慢性病和急性病尤其有利，因为及时应对对于改善患者预后至关重要。尽管好处显而易见，但传统的医疗基础设施仍面临着巨大挑战，如人工流程导致诊断延误、数据处理效率低下导致数据孤岛，以及不同医疗服务提供商之间的互操作性有限，从而导致健康状况恶化和医疗成本增加。ML 与大数据分析的整合为应对这些挑战提供了前景广阔的解决方案。人工智能算法可以处理海量医疗保健数据，以高精度识别模式和预测结果，例如从医学影像或电子健康记录（EHR）中识别癌症或糖尿病等疾病的早期征兆。大数据分析技术是对 ML 的补充，它提供了处理大量医疗数据的必要基础设施，能够收集、存储和分析电子病历中的结构化数据、临床笔记中的非结构化数据以及可穿戴设备中的实时数据。通过整合这些技术，医疗服务提供者可以更深入地了解患者的健康趋势和结果，从而做出更明智的决策和更好的患者管理。本研究采用定性研究设计，重点关注五个真实案例研究：梅奥诊所的心脏病预测分析、克利夫兰诊所使用 ML 进行癌症诊断、凯撒医疗集团的糖尿病管理项目、约翰霍普金斯医院的败血症检测系统以及西奈山医疗系统的基因组数据分析。每一个案例研究都因其相关性和全面的数据而被选中，详细介绍了特定的医疗环境和背景。本文在智能医疗系统和现有文献的大背景下解读了这些发现，强调了这些技术在医疗现代化和解决低效问题方面的重要性。本文还探讨了整合过程中遇到的挑战，如数据隐私问题和互操作性问题，以及已实施的解决方案。
{"title":"INTEGRATING MACHINE LEARNING AND BIG DATA ANALYTICS FOR REAL-TIME DISEASE DETECTION IN SMART HEALTHCARE SYSTEMS","authors":"Zihad Hasan Joy, Md Mahfuzur Rahman, A. Uzzaman, Md Abdul Ahad Maraj","doi":"10.62304/ijhm.v1i3.162","DOIUrl":"https://doi.org/10.62304/ijhm.v1i3.162","url":null,"abstract":"The integration of machine learning (ML) and big data analytics within smart healthcare systems represents a transformative advancement in medical services, emphasizing efficiency, accuracy, and patient-centered care. This paper investigates the application of these advanced technologies in real-time disease detection, showcasing their potential to revolutionize healthcare delivery. Smart healthcare systems leverage a multitude of technological components, including Internet of Things (IoT) devices, sensors, and artificial intelligence (AI), to enable continuous monitoring and diagnostics. This real-time monitoring facilitates prompt interventions and treatment adjustments, which is particularly advantageous for managing chronic conditions and acute illnesses where timely responses are critical to improving patient outcomes. Despite the evident benefits, traditional healthcare infrastructures face significant challenges such as delays in diagnosis due to manual processes, inefficient data handling resulting in data silos, and limited interoperability between different healthcare providers, leading to worsened health outcomes and increased healthcare costs. The integration of ML and big data analytics offers promising solutions to these challenges. ML algorithms can process vast amounts of healthcare data to identify patterns and predict outcomes with high accuracy, such as recognizing early signs of diseases like cancer or diabetes from medical images or electronic health records (EHRs). Big data analytics complements ML by providing the necessary infrastructure to handle and process large volumes of health data, enabling the collection, storage, and analysis of structured data from EHRs, unstructured data from clinical notes, and real-time data from wearable devices. By integrating these technologies, healthcare providers can gain deeper insights into patient health trends and outcomes, leading to more informed decision-making and better patient management. This study employs a qualitative research design, focusing on five genuine case studies: the Mayo Clinic's predictive analytics for heart disease, Cleveland Clinic's use of ML for cancer diagnosis, Kaiser Permanente's diabetes management program, Johns Hopkins Hospital's sepsis detection system, and Mount Sinai Health System's genomic data analysis. Each case study is chosen for its relevance and comprehensive data, detailing the specific healthcare environment and context. This paper interprets these findings in the broader context of smart healthcare systems and existing literature, emphasizing the importance of these technologies in modernizing healthcare and addressing inefficiencies. The challenges encountered during integration, such as data privacy concerns and interoperability issues, are examined along with implemented solutions.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"38 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141270230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Janifer Nahar, Nourin Nishat, A. S. M. Shoaib, Qaium Hossain
This comprehensive review analyzes the impact of high-frequency trading (HFT) on market efficiency and stability, synthesizing insights from 50 peer-reviewed articles, industry reports, and regulatory documents. High-frequency trading, which leverages sophisticated algorithms and high-speed data networks, has significantly transformed financial markets. The review confirms that HFT enhances market efficiency by providing liquidity and facilitating rapid price discovery, contributing to tighter bid-ask spreads and lower transaction costs. However, it also highlights several challenges, including market fragmentation, increased volatility, and potential for market manipulation. The review examines how HFT can exacerbate market instability and systemic risks, as demonstrated by incidents like the 2010 Flash Crash. It underscores the importance of robust risk management practices and regulatory measures to mitigate these risks and enhance market resilience. While current regulatory frameworks have had some success, continuous adaptation is necessary to keep pace with rapid technological advancements. Additionally, the review points to the potential of AI and machine learning in improving market surveillance and risk management. Ultimately, the findings suggest that a balanced approach to regulation and innovation is crucial to maximizing the benefits of HFT while ensuring market integrity and stability.
{"title":"MARKET EFFICIENCY AND STABILITY IN THE ERA OF HIGH-FREQUENCY TRADING: A COMPREHENSIVE REVIEW","authors":"Janifer Nahar, Nourin Nishat, A. S. M. Shoaib, Qaium Hossain","doi":"10.62304/ijbm.v1i3.166","DOIUrl":"https://doi.org/10.62304/ijbm.v1i3.166","url":null,"abstract":"This comprehensive review analyzes the impact of high-frequency trading (HFT) on market efficiency and stability, synthesizing insights from 50 peer-reviewed articles, industry reports, and regulatory documents. High-frequency trading, which leverages sophisticated algorithms and high-speed data networks, has significantly transformed financial markets. The review confirms that HFT enhances market efficiency by providing liquidity and facilitating rapid price discovery, contributing to tighter bid-ask spreads and lower transaction costs. However, it also highlights several challenges, including market fragmentation, increased volatility, and potential for market manipulation. The review examines how HFT can exacerbate market instability and systemic risks, as demonstrated by incidents like the 2010 Flash Crash. It underscores the importance of robust risk management practices and regulatory measures to mitigate these risks and enhance market resilience. While current regulatory frameworks have had some success, continuous adaptation is necessary to keep pace with rapid technological advancements. Additionally, the review points to the potential of AI and machine learning in improving market surveillance and risk management. Ultimately, the findings suggest that a balanced approach to regulation and innovation is crucial to maximizing the benefits of HFT while ensuring market integrity and stability.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141273009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The convergence of big data, artificial intelligence (AI), and digital marketing analytics is revolutionizing the field of digital marketing. This paper explores the transformative effects of these technologies on marketing strategies, focusing on their capacity to enhance decision-making, optimize marketing operations, and personalize customer interactions. By integrating big data and AI with digital marketing analytics, businesses can unlock valuable insights from vast datasets, facilitating more targeted and effective marketing campaigns. This research reviews current literature and employs case studies to illustrate this technological integration's practical applications and benefits in various marketing contexts. The findings highlight a significant shift towards data-driven and AI-enhanced marketing approaches, which are proving to be critical in achieving competitive advantage and customer satisfaction in the digital age.
{"title":"EXPLORING THE CONFLUENCE OF BIG DATA, ARTIFICIAL INTELLIGENCE, AND DIGITAL MARKETING ANALYTICS: A COMPREHENSIVE REVIEW","authors":"Rafsan Mahi, Farin Alam, Mahmudul Hasan","doi":"10.62304/jieet.v3i3.159","DOIUrl":"https://doi.org/10.62304/jieet.v3i3.159","url":null,"abstract":"The convergence of big data, artificial intelligence (AI), and digital marketing analytics is revolutionizing the field of digital marketing. This paper explores the transformative effects of these technologies on marketing strategies, focusing on their capacity to enhance decision-making, optimize marketing operations, and personalize customer interactions. By integrating big data and AI with digital marketing analytics, businesses can unlock valuable insights from vast datasets, facilitating more targeted and effective marketing campaigns. This research reviews current literature and employs case studies to illustrate this technological integration's practical applications and benefits in various marketing contexts. The findings highlight a significant shift towards data-driven and AI-enhanced marketing approaches, which are proving to be critical in achieving competitive advantage and customer satisfaction in the digital age.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"12 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141273977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anup Nandi, Md. Mukter Hossain Emon, Md Ashraful Azad, H. M. Shamsuzzaman, Md Mahfuzur Rahman Enam
Designing a state-of-the-art PLC-based extrusion machine with a user-friendly HMI ensures seamless operation, enhancing Overall Equipment Effectiveness (OEE). This project focuses on automating an extrusion system with advanced technologies for optimized functionality and reliability. The architecture includes sophisticated components to boost productivity and product quality. Key aspects involve orderly control and synchronization of the extruder motor, feeder motor, lubrication pump, and vacuum pump for consistent performance with precise temperate profile. A significant innovation is the centralized blower system for machine temperature profile analysis and control, replacing individual controllers to enhance thermal management efficiency and ensure uniform temperature distribution. A high-low temperature alarm system alerts operators to deviations, maintaining process stability. Real-time data on current (Amps) and frequency (Hz) is displayed on the HMI from the inverter for monitoring and diagnostics. The system also features machine downline controlling capabilities for efficient management of downstream processes. Collectively, these innovations create a robust, efficient, and user-friendly extrusion machine that enhances OEE and product quality.
{"title":"DEVELOPING AN EXTRUDER MACHINE OPERATING SYSTEM THROUGH PLC PROGRAMMING WITH HMI DESIGN TO ENHANCE MACHINE OUTPUT AND OVERALL EQUIPMENT EFFECTIVENESS (OEE)","authors":"Anup Nandi, Md. Mukter Hossain Emon, Md Ashraful Azad, H. M. Shamsuzzaman, Md Mahfuzur Rahman Enam","doi":"10.62304/ijse.v1i3.157","DOIUrl":"https://doi.org/10.62304/ijse.v1i3.157","url":null,"abstract":"Designing a state-of-the-art PLC-based extrusion machine with a user-friendly HMI ensures seamless operation, enhancing Overall Equipment Effectiveness (OEE). This project focuses on automating an extrusion system with advanced technologies for optimized functionality and reliability. The architecture includes sophisticated components to boost productivity and product quality. Key aspects involve orderly control and synchronization of the extruder motor, feeder motor, lubrication pump, and vacuum pump for consistent performance with precise temperate profile. A significant innovation is the centralized blower system for machine temperature profile analysis and control, replacing individual controllers to enhance thermal management efficiency and ensure uniform temperature distribution. A high-low temperature alarm system alerts operators to deviations, maintaining process stability. Real-time data on current (Amps) and frequency (Hz) is displayed on the HMI from the inverter for monitoring and diagnostics. The system also features machine downline controlling capabilities for efficient management of downstream processes. Collectively, these innovations create a robust, efficient, and user-friendly extrusion machine that enhances OEE and product quality.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"26 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141273292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Miraz Uddin Ahmed, Md. Iqbal Hossain, Md Abdul Ahad Maraj, Mst. Mohona Islam
This study aimed to assess the bacteriological quality and antibiotic resistance of ready-to-eat street foods sold in various locations across Dhaka City. Eight samples were collected from different vendors and analyzed for the presence of foodborne pathogens and their resistance to antibiotics. The findings revealed significant contamination with E. coli, Klebsiella spp., Pseudomonas spp., Vibrio spp., and Staphylococcus aureus. Total aerobic counts (TAC) ranged from 4.6 × 10⁵ to 9.5 × 10⁷ CFU/g, exceeding acceptable limits set by the International Commission for Microbiological Specifications for Foods (ICMSF). The total coliform count and Enterobacteriaceae count also showed alarmingly high levels. Antibiotic susceptibility tests indicated widespread resistance, particularly to Penicillin G, which was ineffective against all isolates. The results underscore the urgent need for improved food safety practices, regular inspections, and vendor education to mitigate the public health risks associated with street-vended foods in Dhaka City.
这项研究旨在评估达卡市不同地点出售的即食街头食品的细菌学质量和抗生素耐药性。研究人员从不同商贩处采集了 8 个样本，分析其中是否存在食源性病原体及其对抗生素的耐药性。结果显示，大肠杆菌、克雷伯氏菌属、假单胞菌属、弧菌属和金黄色葡萄球菌污染严重。总需氧菌落总数（TAC）介于 4.6 × 10⁵ 至 9.5 × 10⁷ CFU/g 之间，超过了国际食品微生物规范委员会（ICMSF）规定的可接受限值。总大肠菌群和肠杆菌科细菌的数量也高得惊人。抗生素敏感性测试表明，耐药性十分普遍，尤其是对青霉素 G 的耐药性，青霉素 G 对所有分离物均无效。这些结果突出表明，迫切需要改进食品安全操作、定期检查和对摊贩进行教育，以降低达卡市街头贩卖食品对公众健康造成的风险。
{"title":"MICROBIAL HAZARDS IN STREET FOODS: A COMPREHENSIVE STUDY IN DHAKA, BANGLADESH","authors":"Miraz Uddin Ahmed, Md. Iqbal Hossain, Md Abdul Ahad Maraj, Mst. Mohona Islam","doi":"10.62304/ijhm.v1i3.158","DOIUrl":"https://doi.org/10.62304/ijhm.v1i3.158","url":null,"abstract":"This study aimed to assess the bacteriological quality and antibiotic resistance of ready-to-eat street foods sold in various locations across Dhaka City. Eight samples were collected from different vendors and analyzed for the presence of foodborne pathogens and their resistance to antibiotics. The findings revealed significant contamination with E. coli, Klebsiella spp., Pseudomonas spp., Vibrio spp., and Staphylococcus aureus. Total aerobic counts (TAC) ranged from 4.6 × 10⁵ to 9.5 × 10⁷ CFU/g, exceeding acceptable limits set by the International Commission for Microbiological Specifications for Foods (ICMSF). The total coliform count and Enterobacteriaceae count also showed alarmingly high levels. Antibiotic susceptibility tests indicated widespread resistance, particularly to Penicillin G, which was ineffective against all isolates. The results underscore the urgent need for improved food safety practices, regular inspections, and vendor education to mitigate the public health risks associated with street-vended foods in Dhaka City.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141272937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Poet Daloar is a prominent Bengali poet. He is known as the "Poet of the Masses" because his poetry reflects the thoughts and emotions of ordinary Bangladeshis. Daloar's work is filled with patriotism, especially evident during the Bangladesh Liberation War. His poems and songs depict the love for the country and its people. He believed that poetry has the power to influence and inspire people. Daloar's work, infused with socialist ideals, calls for equality and justice, drawing inspiration from global leaders like Mandela and Lenin. Despite personal hardships, his writings remained a steadfast source of patriotic fervor. Daloar's legacy endures through his poems and songs, which continue to resonate with themes of national pride and the fight for human rights. Daloar's transformation played a significant role in both national and international platforms.
{"title":"Patriotism in the Poetry and Songs of Poet Daloar","authors":"","doi":"10.62304/ijass.v1i1.156","DOIUrl":"https://doi.org/10.62304/ijass.v1i1.156","url":null,"abstract":"Poet Daloar is a prominent Bengali poet. He is known as the \"Poet of the Masses\" because his poetry reflects the thoughts and emotions of ordinary Bangladeshis. Daloar's work is filled with patriotism, especially evident during the Bangladesh Liberation War. His poems and songs depict the love for the country and its people. He believed that poetry has the power to influence and inspire people. Daloar's work, infused with socialist ideals, calls for equality and justice, drawing inspiration from global leaders like Mandela and Lenin. Despite personal hardships, his writings remained a steadfast source of patriotic fervor. Daloar's legacy endures through his poems and songs, which continue to resonate with themes of national pride and the fight for human rights. Daloar's transformation played a significant role in both national and international platforms.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"49 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141107539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. S. M. Shoaib, Nourin Nishat, Muniroopesh Raasetti, Imran Arif
Integrative machine learning approaches have emerged as essential tools in the analysis of multi-omics data in cancer research, offering significant advancements in understanding complex biological systems. This review emphasizes recent progress in these techniques, highlighting their ability to manage the complexity and heterogeneity of multi-omics datasets, which include genomics, transcriptomics, proteomics, and metabolomics. By effectively integrating these diverse data types, machine learning approaches provide unprecedented insights into cancer mechanisms, facilitating the discovery of novel biomarkers and therapeutic targets. The review evaluates various machine learning methods, discussing their respective strengths and limitations in the context of cancer research. It also explores potential future directions for research, underscoring the need for continued methodological innovation and interdisciplinary collaboration to fully harness the power of integrative machine learning in advancing cancer treatment and personalized medicine.
{"title":"INTEGRATIVE MACHINE LEARNING APPROACHES FOR MULTI-OMICS DATA ANALYSIS IN CANCER RESEARCH","authors":"A. S. M. Shoaib, Nourin Nishat, Muniroopesh Raasetti, Imran Arif","doi":"10.62304/ijhm.v1i2.149","DOIUrl":"https://doi.org/10.62304/ijhm.v1i2.149","url":null,"abstract":"Integrative machine learning approaches have emerged as essential tools in the analysis of multi-omics data in cancer research, offering significant advancements in understanding complex biological systems. This review emphasizes recent progress in these techniques, highlighting their ability to manage the complexity and heterogeneity of multi-omics datasets, which include genomics, transcriptomics, proteomics, and metabolomics. By effectively integrating these diverse data types, machine learning approaches provide unprecedented insights into cancer mechanisms, facilitating the discovery of novel biomarkers and therapeutic targets. The review evaluates various machine learning methods, discussing their respective strengths and limitations in the context of cancer research. It also explores potential future directions for research, underscoring the need for continued methodological innovation and interdisciplinary collaboration to fully harness the power of integrative machine learning in advancing cancer treatment and personalized medicine.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"47 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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