Integration Of Technologies Research Articles

Pothole Detection Model for Road Safety using Computer Vision and Machine Learning

<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Potholes pose significant threats to vehicular movement, causing damage to vehicles and risking the safety of drivers and pedestrians. The escalating issue of potholes has led to substantial financial losses for vehicle owners and drivers. Traditional methods of pothole detection are impractical, necessitating an innovative approach. The study focuses on implementing a detection system capable of accurately identifying potholes, empowering vehicles to adapt their speed or halt to prevent damage. The transformative solution presented in this research leverages cutting-edge technologies, specifically computer vision and machine learning, aiming to enhance road safety and streamline maintenance efforts. By addressing the interdependence of modern civilization on road networks, the Pothole Detection Model promises improved road safety, efficient maintenance practices, and the emergence of an era in intelligent transportation systems. The integration of technology into transportation infrastructure highlights the proactive measures needed to combat road imperfections, ensuring a safer and more efficient road network for the benefit of society.</p></td></tr></tbody></table></div>

Strategic approaches to crisis management: The global challenges of the pandemic on the Turkish container shipping industry

The Covid-19 pandemic has posed significant challenges to the sector, and Türkiye’s strategic location plays a crucial role in crisis management. This study aims to explore the repercussions of the pandemic on the sector and propose appropriate solutions. To identify the most effective solutions for prioritizing the challenges posed by Covid-19, 53 sector experts participated in the study. The Stepwise Weight Assessment Ratio Analysis method was used to weigh the criteria representing these challenges. The solutions were ranked using the Weighted Aggregated Sum Product Assessment and Combined Compromise Solution methods. The results indicate that the primary challenge is the increased shipping cost, followed by the prolongation of the transport time, global-scale impacts, and port congestion. The most appropriate solution identified was alternative vessel loading, followed by mixed-mode transportation usage, reservation guarantees, long-term contracts with shipping operators, integrated logistics services, container purchases, and technological integration

Heritage Building Information Modelling (HBIM) for Heritage Conservation: Framework of Challenges, Gaps, and Existing limitations of HBIM

This study investigates the pivotal role of Heritage Building Information Modelling (HBIM) in conservation of cultural heritage buildings. Utilizing a systematic literature review and in-depth analysis of 59 studies within 2013-23 period, the study underscores HBIM's capacity to document, analyze, and manage heritage structures. It delineates the workflow from data acquisition through laser scanning and photogrammetry, to the modeling of historic fabric, highlighting the integration of digital technologies in conservation practices. The paper identifies key challenges in HBIM adoption, including technical limitations, the need for specialized skills, and the complexity of accurately modeling historical details. It proposes avenues for future research focused on enhancing data acquisition techniques, improving the interoperability of HBIM with other digital tools, and developing standards for the effective documentation of heritage sites. The findings advocate for a collaborative approach, leveraging HBIM to foster interdisciplinary partnerships between architects, historians, and conservationists, for sustainable preservation of cultural heritage.

Integration of the beam-down technology into SOLATOM’s linear Fresnel solar field: design and experimental results

The beam-down linear Fresnel solar field (BDLFR) is a concentrating solar technology that can irradiate linearly a heat flux on a ground receiver. This technology is developed for thermally processing high-density materials up to temperatures of 150–300 °C, such as asphalt aggregates. Here, the linear beam-down technology has been scaled-up and tested. SOLATOM’s commercial linear Fresnel reflector has been re-adapted to a BDLFR. This demonstrator loop is located in Universitat Politècnica de València (39.47° N, 0.37° W) and consists of ten linear mirrors 50 cm wide, curved along the transversal axis, and oriented clockwise 18° from the East-West axis. Different beam-down configurations were studied for the design conditions, and once the design was established, it was built using ten flat 15 cm wide mirrors and coupled to a SOLATOM XL module. The modified solar field was tested on day 269 and the obtained heat flux was measured, achieving a peak of 4100 W/m2 for a direct irradiance of 640 W/m2, in two adjacent normal distributions due to misalignments and errors in the installation of beam-down. This was contrasted with Monte Carlo ray-tracing simulations, and with proper mirror alignment it is expected to achieve 7750 W/m2 under similar conditions.

Review of cloud computing models in education and the unmet needs

<p>This article thoroughly examines recently proposed cloud computing (CC) models used within the higher educational institutions (HEI) field, scrutinizing their objectives, structures, and incorporated requirements. Each model's unique architecture and functionality are analyzed to understand their potential educational contributions. Beyond technical considerations, the study explores nuanced requirements essential for successful integration in educational settings. The review exposes diverse aims pursued by the models, such as enhanced scalability, collaborative learning, and resource management, emphasizing their capacity to reshape traditional educational paradigms. However, a notable gap emerges-the absence of cultural and requirement elicitation models within the frameworks. Despite growing cultural diversity and varied educational needs, most models lack components addressing cultural nuances and robust requirement elicitation. In conclusion, the paper identifies a pressing need for a transformative shift in developing CC models for education. The absence of dedicated cultural and requirement elicitation models challenges the holistic effectiveness of these frameworks. Future efforts should prioritize integrating culturally sensitive components and comprehensive requirement elicitation strategies to create adaptive, universally applicable, and inclusive CC educational environments. Addressing these gaps will pave the way for a nuanced and responsive integration of CC technologies in diverse educational settings.</p>

Fuzzy logic assessment of X-ray tube risks in robotic C-arm angiography: a failure mode and effect analysis study

This research examines the integration of robotic C-arm technology in angiography, a critical tool for treating cardiac conditions. The robotic C-arm, which includes an X-ray tube, is essential for scanning patients during procedures. The study also investigates the associated risks, specifically in Indonesian hospitals with cardiac facilities. Angiography is used to diagnose and treat heart disease by visualizing blood vessels and facilitating catheterization procedures. However, its mobility poses hazards and can impact the process. To address potential risks, failure mode and effect analysis (FMEA) is utilized. Traditionally, risk assessment using risk priority numbers (RPN) is conducted, but these may not accurately reflect failures due to complex evaluating processes. To overcome this limitation, fuzzy logic is employed, enhancing risk assessment accuracy. Through this approach, twenty-seven failure modes are identified across two brands, with ten major ones prioritized using fuzzy logic. These findings facilitate the development of preventive measures to mitigate future failures and enhance patient safety during angiography in hospitals. In conclusion, the study underscores the importance of robust risk management in medical equipment, particularly in dynamic environments. By integrating fuzzy logic into risk assessment, the study improves prioritization accuracy, enabling effective allocation of resources for preventive actions.

Aqua-stream: an IoT based smart water management system for sustainable living

<p class="03IJISAEAbstract">Aqua-stream, an innovative internet of things (IoT) enabled water management system, utilizes the power of long short-term memory (LSTM) networks, a sophisticated time-series forecasting machine learning technique with Kafka. Aqua-stream seamlessly integrates LSTM within the Kafka streaming architecture for efficient real-time data processing, ensuring quick responses to emerging water management needs. LSTM is employed for real-time anomaly detection, dynamically analyzing streaming data to prevent leaks through automated shut-off valves. The system’s comprehensive dashboard utilizes LSTM insights for live water quality analysis; adaptive scheduling based on individual preferences and personalized recommendations, enhancing cost-effective water management. This streamlined approach extends to the smart gardening system, where LSTM guides automation for optimal plant care incorporating sensors to monitor soil moisture, temperature, and sunlight levels. This system automatically adjusts watering and lighting to ensure optimal conditions for plant growth. Users can control and monitor their garden remotely via a smartphone, facilitating plant care while saving water and energy. Aqua-stream redefines home water management, offering a holistic solution that combines intelligent water conservation with smart gardening for a sustainable and connected living experience. Aqua-stream represents a seamless integration of LSTM-based machine learning and IoT technologies, offering an intelligent, yet simplified, solution for sustainable and connected living.</p>

The evolution and effectiveness of China’s marine carbon sink fishery policies

The marine carbon sink fishery (MCSF) is an innovative and sustainable development model that integrates carbon sinks with fisheries. The Chinese government has actively promoted this concept by issuing a series of related policies. This study systematically reviewed China’s MCSF policies using both quantitative and qualitative analyses to examine their evolution, characteristics, and effectiveness. Through a comprehensive analysis of 135 MCSF policies issued by various levels of government in China’s coastal areas from 1985 to 2023, three developmental stages were identified: the initial exploration period (1985−2009), the rapid development period (2010−2015), and the optimization and adjustment period (2016−2023). The characteristics of policy evolution were mainly reflected in three aspects. First, the issuance power and functioning power of policies exhibited a growing trend. Second, the promulgators of policies were mainly local governments, with a trend towards diversification. Third, support for scientific research and skills development has gradually been strengthened. The results of a fixed-effects model showed that policy power had a significant positive impact on the development of MCSFs, with high-intensity policies promoting their healthy growth. In the future, newly formulated policies should prioritize establishing a multi-channel, multi-level funding system, advancing the integration of digital technology, enhancing the carbon trading mechanism, and establishing a comprehensive management platform for smart MCSFs.

A review of practices and digital technology integration in reading instruction and suggestions for the Philippines

A review of practices and digital technology integration in reading instruction and suggestions for the Philippines

Enhancing Carbon Trading Mechanisms through Innovative Collaboration: Case Studies from Developing Nations

Against a backdrop of global climate change mitigation efforts, carbon trading has emerged as a critical mechanism, yet developing countries often lack the necessary infrastructure and collaborative frameworks. The research investigates how intermediaries facilitate stakeholders and employ innovative practices to foster effective carbon trading markets. Methodologically, it employs a qualitative approach, conducting in-depth case studies of four intermediaries through 32 semi-structured interviews and archival document. Findings underscore the pivotal role of digital platforms in enabling real-time trading, rigorous standardization processes to ensure market credibility, and multi-stakeholder engagement strategies that promote inclusive participation. This research contributes to the theoretical understanding of carbon trading by illustrating how digital platforms and technological integration enhance resource acquisition and allocation. It emphasizes the significance of standardization and verification processes in building trust among stakeholders, crucial for effective supply chain collaboration. Practically, the study highlights the benefits of these approaches in enhancing market efficiency and transparency. It also underscores the importance of multi-stakeholder engagement strategies and strategic alliances for creating resilient and inclusive carbon trading markets, offering actionable insights for stakeholders and policymakers alike.

Consumer Attitudes towards AI-based Financial Advice: Insights for Decision Support Systems (DSS) and Technology Integration

As financial technology (fintech) rapidly transforms the financial services landscape, the integration of artificial intelligence (AI) in providing financial advice becomes a focal point. This study delves into the intricate dynamics of AI-based financial advice adoption, focusing on technology integration, decision support systems, and the mediating role of perceived utility. The primary purpose of this research is to unravel the complex relationships between technology integration, Decision Support System (DSS), perceived utility, and their collective influence on consumer attitudes toward AI-based financial advice and technology adoption. Adopting a cross-sectional design, we target the Chinese population and utilize an online questionnaire for data collection. A sample size of 259 participants is determined using the rule of thumb technique, with random sampling ensuring representativeness. Data analysis will be conducted using the AMOS software, allowing for an in-depth examination of the relationships between variables. The study findings demonstrate how integrating technology enhances customers' perceptions and acceptance of it, especially when it comes to data security and compatibility. The study also demonstrates how customer attitudes and the uptake of AI-driven financial aid are significantly impacted by DSS aspects like decision transparency and predictive analytics precision. The study is unique because of its cross-cultural approach and perceived value as a mediating component. While this study focuses on the Chinese population, the researchers acknowledge the importance of cultural differences in shaping user perceptions and behaviors. While the findings may offer valuable insights into the Chinese context, caution is advised in generalizing the results to other cultural contexts.

Digital Transformation in Talent Acquisition: An Investigation into the Integration of Big Data Analytics and Management Information Systems for Optimal Hiring Strategies

The primary goal was to empirically investigate the relationships amongst Big Data Analytics (BDA), Management Information System (MIS), optimal hiring strategies, leadership support, and talent acquisition process. This research aimed to fill crucial gaps in the literature by examining how technology adoption and leadership support influence the formulation and effectiveness of optimal hiring strategies, finally impacting talent acquisition results in the Chinese business landscape. Quantitative research was employed, targeting experts and decision-makers in human resource departments across numerous industries in China. The research used a random sampling approach to acquire facts through an online questionnaire, examining responses from 168 participants. Regression and mediation analyses, facilitated by SPSS, were applied to discover the relationships and mechanisms among BDA, MIS, optimal hiring strategies, leadership support, and talent acquisition success. The results discovered a significant and positive relationship between BDA and MIS with the optimal hiring techniques. Moreover, optimal hiring techniques have proven an advantageous association with talent acquisition success. The research similarly showed the mediating role of optimal hiring strategies in translating the advantages of BDA and MIS into talent acquisition success. Leadership support emerged as a moderator, amplifying the relationship between optimum hiring techniques and talent acquisition success. This study contributes empirical proof to the underexplored intersection of leadership support, technology, and talent hiring strategies. The findings offer practical insights for companies aiming to navigate the complexities of recruitment, emphasizing the significance of strategic decision-making, technology integration, and management endorsement in achieving talent acquisition success.

Ensuring product authenticity and traceability with blockchain in supply chains

This review paper explores the efficacy of blockchain technology in enhancing product authenticity and traceability within supply chains. With the complexity of global supply chains increasing, the risk of counterfeit products and inefficiencies in tracking origins is escalating. This paper synthesizes existing literature to outline the current landscape of blockchain applications in supply chains, focusing on the decentralized and immutable capabilities of the technology that promise to revolutionize traditional practices. The core of the review involves a comprehensive analysis of documented implementations of blockchain in various industries, assessing their impact on enhancing transparency, reducing fraud, and improving overall supply chain integrity. We critically examine case studies and existing research to identify patterns of success and common challenges faced by organizations adopting blockchain solutions. Findings highlight that blockchain technology not only supports better traceability and authentication processes but also fosters a more transparent environment for transactions, significantly deterring the proliferation of counterfeit goods. Additionally, the review points out the technological and organizational hurdles in adopting blockchain, such as the need for standardization and the resistance to change in legacy systems. The paper concludes that blockchain technology holds substantial promise for addressing critical issues in supply chain management. However, achieving its full potential requires overcoming significant adoption challenges and fostering collaborative efforts for standardization across industries. This review calls for ongoing research into scalable blockchain applications and strategic partnerships to drive the technology's integration into mainstream supply chain operations.

Integrating 4IR technologies in microgrid development: Implications for sustainable economic growth in rural areas of Nigeria

Integrating Fourth Industrial Revolution (4IR) technologies in microgrid development offers significant potential to drive sustainable economic growth in rural areas of Nigeria. This paper explores how advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain can be leveraged to enhance the efficiency, reliability, and scalability of microgrids, which are critical for providing reliable electricity in underserved regions. The integration of 4IR technologies can optimize energy generation and distribution, enabling real-time monitoring, predictive maintenance, and decentralized energy management. These innovations not only improve the operational efficiency of microgrids but also facilitate the creation of new economic opportunities by powering local industries, supporting agricultural productivity, and enabling digital services in rural areas. Furthermore, the deployment of 4IR-enhanced microgrids can significantly reduce the carbon footprint of energy systems in rural communities by promoting the use of renewable energy sources. This contributes to Nigeria's broader sustainability goals and aligns with global efforts to combat climate change. The study also examines the socio-economic impacts of microgrid development, including job creation, improved quality of life, and the empowerment of local communities through increased access to electricity. The findings suggest that integrating 4IR technologies in microgrid development can play a pivotal role in addressing energy poverty, fostering economic resilience, and promoting inclusive growth in rural Nigeria. This research underscores the need for supportive policies, investment in technological infrastructure, and capacity-building initiatives to fully realize the potential of 4IR technologies in transforming rural energy systems. The implications for sustainable economic growth are profound, offering a pathway for rural areas to leapfrog into a more prosperous and sustainable future.

A Review of CNN-Based Object Recognition and Tracking Systems for Visually Impaired People

Visually impaired persons (VIPs) face significant challenges in navigating their environments, impacting their independence and quality of life. This paper reviews recent advancements in assistive technologies aimed at enhancing mobility and safety for VIPs. It explores various systems, including wearable devices, object detection technologies, and navigation aids, highlighting their functionalities, limitations, and potential future develop- ment. The integration of machine learning and sensor technologies has led to innovative solutions that provide realtime feedback and improved spatial awareness for users. VIPsneed assistance in performing daily tasks such as object detection, obstacle recognition,and navigation, particularly in indoor and outdoor environments. The paper emphasizes the importance of protecting and ensuring the safety of VIPs. Devices typically use sensors like infrared, ultrasonic, and imagery to gather environmental data, which is then processed by machine learning techniques. Users receive feedback via auditory or vibratory means. The paper provides a comprehensive comparative analysis of assistive devices, discussing their key attributes, challenges, and limitations, along with a score-based evaluation to help select appropriate devices for specific needs

Optimizing food transportation processes for grocery stores across the US

In the rapidly evolving landscape of food logistics, optimizing transportation processes for grocery stores across the United States has become paramount to enhancing operational efficiency and ensuring the delivery of high-quality, perishable goods. This article examines the multifaceted challenges inherent in food transportation, including logistical complexities, environmental concerns, and rising operational costs. It highlights the importance of data-driven strategies, such as route optimization and machine learning algorithms, to improve delivery times and reduce waste. Additionally, the integration of advanced technologies, such as Internet of Things (IoT) sensors for temperature monitoring and automation in warehouse management, is explored to enhance the cold chain and ensure food safety. Moreover, the article emphasizes the critical role of strategic partnerships with third-party logistics providers and suppliers in streamlining operations and enhancing overall performance. Through in-depth case studies of successful grocery chains, it illustrates effective practices that have led to significant improvements in food transportation processes. The regulatory landscape surrounding food transportation is also analysed, underscoring compliance with safety standards and sustainability initiatives. Finally, the article looks ahead to future trends, including the potential impact of autonomous vehicles, blockchain technology, and sustainable practices on the food supply chain. By providing a comprehensive overview of best practices and innovative solutions, this article aims to equip grocery store operators and stakeholders with actionable insights to optimize their food transportation processes, ultimately leading to improved customer satisfaction and reduced environmental impact.

Smart Machine Learning Analysis and Blockchain Healthcare Platform

The Smart Machine Learning Medical Analysis and Blockchain Healthcare Platform (SMLMAP) represents a transformative approach to medical diagnostics and data management by integrating advanced machine learning algorithms with blockchain technology. This platform is designed to accurately diagnose seven critical diseases, achieving impressive diagnostic outcomes across various conditions, including diabetes, breast cancer, heart disease, kidney disease, liver disease, malaria, and pneumonia. SMLMAP employs Random Forest algorithms for text data analysis and Convolutional Neural Networks (CNNs) for image data, ensuring rapid and precise diagnostic results. In addition to enhancing diagnostic capabilities, SMLMAP addresses significant privacy and security concerns inherent in healthcare data management. The integration of blockchain technology facilitates secure, transparent, and tamper-proof storage of patient records, empowering individuals with greater control over their medical information. This dual approach not only improves the accuracy of diagnoses but also fosters trust between patients and healthcare providers by safeguarding sensitive data against unauthorized access. Key functionalities of SMLMAP include the ability to schedule doctor appointments conveniently through the platform, ensuring that patients can receive timely medical attention. Additionally, the application provides users with a weekly healthcare magazine, delivering valuable information on health tips, disease management, and wellness advice to promote informed health decisions. By leveraging the strengths of machine learning and blockchain, SMLMAP paves the way for a more efficient, accurate, and secure healthcare ecosystem. This innovative platform not only enhances the quality of medical diagnostics but also represents a significant step forward in addressing the critical challenges of data privacy and security in the healthcare sector. The findings underscore the potential of SMLMAP to revolutionize patient care and data management in the evolving landscape of healthcare technology.

Review of penetration and impact of utility solar installation in developing countries: policy and challenges

This review examines the penetration and impact of utility-scale solar installations in developing countries, focusing on the policy landscape and challenges associated with their adoption. Solar energy has emerged as a vital component of the global shift towards renewable energy, offering substantial potential for alleviating energy poverty and fostering sustainable development in regions with abundant solar resources. Despite these opportunities, the penetration of utility-scale solar in developing countries faces several barriers. These include financial constraints, inconsistent policy frameworks, technological challenges, and social acceptance issues. This review explores the current status of solar energy adoption, highlighting regional trends and key countries such as Kenya, India, and South Africa, which have made significant strides in solar deployment. It assesses the economic, social, and environmental impacts of utility solar projects, emphasizing their role in job creation, electrification, and climate change mitigation. However, challenges such as high initial costs, limited access to financing, weak grid infrastructure, and regulatory bottlenecks continue to impede broader adoption. Policy and regulatory support are critical to overcoming these challenges. This review outlines the national and international frameworks that influence solar energy penetration, as well as the incentives, subsidies, and financing mechanisms available. Recommendations include streamlining regulatory processes, improving access to finance through innovative models, and enhancing grid infrastructure. Finally, the review discusses future trends, including advancements in solar technology, energy storage, and smart grid integration, which could accelerate the growth of solar installations in developing countries. The findings underscore the need for comprehensive strategies that address policy, financial, and social challenges to fully realize the potential of solar energy in these regions.

A SYSTEMATIC LITERATURE REVIEW ON THE DEVELOPMENT TRENDS IN MANAGEMENT ACCOUNTING RESEARCH

Background. The emergence of digital tools, such as data analytics and automation, enhances decision-making, aligns with sustainability, and supports strategic goals, especially in the context of achieving Sustainable Development Goals (SDGs) Research Purpose. The study aims to provide a comprehensive analysis of the developmental trends in management accounting by systematically reviewing recent literature. This review focuses on identifying key trends, existing gaps, and emerging themes to establish a framework for future research in this evolving field? ResearchMethod. Using a systematic literature review (SLR) approach, the research includes journal articles, conference proceedings, and reports published over the past decade. Articles were selected based on relevance to management accounting and sorted by topic, findings, and theoretical contributions.? Findings. Key trends in management accounting include the integration of digital technology (e.g., AI, blockchain) for efficiency, accuracy, and improved decision-making, alongside a strong focus on environmental sustainability. The study also found significant interest in sustainability management practices, particularly regarding their role in meeting SDGs and enhancing corporate performance ?Conclusion. Management accounting is increasingly shaped by digitalization and sustainability imperatives. Technologies like AI and blockchain are reshaping data accuracy and efficiency, while sustainability practices encourage responsible business practices.

AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics

This paper explores the transformative role of artificial intelligence (AI) and predictive analytics in enhancing operational efficiency within healthcare supply chains. By leveraging AI-driven business analytics, healthcare organizations can optimize inventory management, improve demand forecasting, and streamline supply chain processes. The study presents a comprehensive review of recent advancements, challenges, and opportunities in the integration of AI technologies, focusing on their application in various healthcare contexts. Through systematic analysis of existing literature, the findings emphasize the significance of adopting AI and predictive analytics for effective decision-making, cost reduction, and improved service delivery in healthcare. The research highlights the need for organizations to embrace digital transformation and foster a collaborative approach in the implementation of AI-driven solutions to enhance overall supply chain resilience.