Robust Data Management Research Articles

Comparative Analysis of Human and Artificial Intelligence Planning in Production Processes

Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning.

Effective integration of COVID-19 vaccination with routine immunization: A case study from Kinshasa, DRC

BackgroundIn response to the challenge of maintaining COVID-19 vaccination coverage amidst the pandemic, VillageReach, in collaboration with the Ministry of Public Health Prevention and Hygiene in Kinshasa, DRC, integrated COVID-19 vaccination with routine immunization services at two primary healthcare facilities. This initiative, launched in July 2022, represented the first of its kind in the DRC, aiming to assess the effectiveness and scalability of a multimodal vaccination approach. MethodsThrough a rapid appraisal involving key informant interviews and analysis of pre- and post-integration service delivery data, this case study explores the operational dynamics and outcomes of integrating COVID-19 and routine immunizations. ResultsResults demonstrated that the integrated approach not only maintained COVID-19 vaccine coverage but also significantly enhanced routine immunization uptake, particularly among under-immunized and zero-dose children. Overall, the vaccination sites, outreach, and integrated health facilities administered 229,983 (33 %) of COVID-19 vaccines in Kinshasa, of which 53 % were referred by community health workers. Additionally, 998 under-immunized children received routine immunizations, of whom 126 were zero-dose children. Key success factors included sustained community health worker engagement, neighborhood-specific strategies, accessible vaccination points, and robust data management. The findings suggest that such integrative strategies can effectively bolster immunization coverage in urban poor communities, offering valuable insights for similar initiatives in the DRC and beyond. ConclusionThis study advocates for sustained investment in innovative immunization models to strengthen primary healthcare systems post-pandemic.

Enhancing quantitative imaging to study DNA damage response: A guide to automated liquid handling and imaging

Laboratory automation and quantitative high-content imaging are pivotal in advancing diverse scientific fields. These innovative techniques alleviate the burden of manual labour, facilitating large-scale experiments characterized by exceptional reproducibility. Nonetheless, the seamless integration of such systems continues to pose a constant challenge in many laboratories. Here, we present a meticulously designed workflow that automates the immunofluorescence staining process, coupled with quantitative high-content imaging to study DNA damage signalling as an example. This is achieved by using an automatic liquid handling system for sample preparation. Additionally, we also offer practical recommendations aimed at ensuring the reproducibility and scalability of experimental outcomes. We illustrate the high level of efficiency and reproducibility achieved through the implementation of the liquid handling system but also addresses the associated challenges. Furthermore, we extend the discussion into critical aspects such as microscope selection, optimal objective choices, and considerations for high-content image acquisition. Our study streamlines the image analysis process, offering valuable recommendations for efficient computing resources and the integration of cutting-edge deep learning techniques. Emphasizing the paramount importance of robust data management systems aligned with the FAIR data principles, we provide practical insights into suitable storage options and effective data visualization techniques. Together, our work serves as a comprehensive guide for life science laboratories seeking to elevate their high-content quantitative imaging capabilities through the seamless integration of advanced laboratory automation.

Mtoto Wetu Growth and Temperature Monitoring System for Tanzania Child Healthcare Clinics

In most government maternal and child health (MCH) clinics in sub-Saharan countries caregivers rely on manual methods for data acquisition, storage, and analysis, leading to challenges such as errors, inefficiencies, and limited accessibility. Recognising the transformative potential of digital technology, this design aims to innovate the child growth monitoring paradigm by introducing a comprehensive Digitized Child Growth Monitoring System. The "Mtoto Wetu Growth and Temperature Monitoring System" is a comprehensive framework designed to monitor and assess the growth and temperature patterns of infants and young children on a monthly basis. This system integrates various components, including anthropometric measurements, temperature monitoring, immunisation schedule, automated growth chart generation, and parental, caregiver, and health care professionals’ involvement, to provide a holistic approach to child health monitoring. Implemented with standardised protocols, training sessions, and robust data management systems, the monitoring system aims to facilitate early detection of growth abnormalities, and health concerns. By promoting early intervention, preventive care measures, parental empowerment, and continuity of care, the Mtoto Wetu system strives to optimise health outcomes, foster parental engagement, and promote health equity among children. The system was able to provide results with a small amount of error compared to the standard weighing machines, tap measure and digital thermometer

Understanding the dynamics of customer satisfaction and repurchase intentions in the era of platform service supply chain – a case study of Foodpanda in Pakistan

ABSTRACT This study investigates the evolving restaurant sector in Pakistan, highlighting the significant role of Platform Service Supply Chains (PSSC) facilitated by food ordering apps like Foodpanda. Understanding PSSC is essential for businesses to thrive in the digital age and effectively manage the dynamics of digital service delivery. Foodpanda exemplifies a complex multi-stakeholder ecosystem that involves intricate technology integration, logistics challenges, robust data management, and a continuous need for innovation in a competitive market. This research aims to identify the key factors driving customer satisfaction and repurchase intention in this dynamic environment, acknowledging the transformative impact on consumer behavior. It also examines how health consciousness and food safety concerns influence these relationships in Pakistan. The study employs an integrated model based on the Theory of Planned Behaviour (TPB) and the Unified Theory of Acceptance and Use of Technology (UTAUT), collecting data from a convenient sample of Foodpanda app users. Using structural equation modeling, the findings highlight critical determinants such as effort expectancy, perceived usefulness, information quality, perceived risk, social influence, and trust in shaping customer satisfaction. This satisfaction, in turn, positively affects repurchase intention. Importantly, the study reveals that health consciousness and food safety concerns negatively impact the positive relationship between customer satisfaction and repurchase intention. The paper contributes theoretically by exploring customer behavior intricacies within the PSSC framework and offers practical insights for researchers and professionals in the food app industry. However, it acknowledges limitations, such as the use of a convenient sample, and suggests avenues for further research in this growing field.

Practical design and implementation of IoT-based occupancy monitoring systems for office buildings: A case study

This study introduces a scalable, cloud-based approach to occupancy monitoring designed to optimize HVAC operations in office buildings. It addresses the challenges of developing and implementing a multi-parameter IoT-based occupancy monitoring system by integrating various off-the-shelf sensors—CO2, infrared (IR), motion (PIR), and door status detection—into a cohesive system. Leveraging wireless LoRaWAN and novel cloud technologies, the system ensures easy installation, efficient maintenance, and robust data management. CO2-based occupancy detection models were trained using data from a reference office and validated in another office environment. Among the various models evaluated, the four best-performing ones—Decision Trees, Random Forest, LightGBM, and K-Nearest Neighbors—were selected for integration into a multi-parameter detection system. To further enhance system performance and identify optimal sensor combinations and configurations for cost-effective and accurate occupancy detection, a data fusion methodology was employed. This methodology, validated with ground-truth data from a test bed, tested the monitoring system in different office settings, ranging from single to quadruple-occupant rooms. Integration of additional parameters into the developed data fusion approach significantly improved system performance, achieving a True Positive Rate (TPR) of 95% compared to 81% with a simple baseline data fusion method. This approach also reduced false detections during unoccupied periods, as tested in multiple rooms within the studied building, thereby enhancing the system's reliability for integration into occupancy-aware HVAC control strategies.

PatCen: A blockchain-based patient-centric mechanism for the granular access control of infectious disease-related test records.

The recent global outbreaks of infectious diseases such as COVID-19, yellow fever, and Ebola have highlighted the critical need for robust health data management systems that can rapidly adapt to and mitigate public health emergencies. In contrast to traditional systems, this study introduces an innovative blockchain-based Electronic Health Record (EHR) access control mechanism that effectively safeguards patient data integrity and privacy. The proposed approach uniquely integrates granular data access control mechanism within a blockchain framework, ensuring that patient data is only accessible to explicitly authorized users and thereby enhancing patient consent and privacy. This system addresses key challenges in healthcare data management, including preventing unauthorized access and overcoming the inefficiencies inherent in traditional access mechanisms. Since the latency is a sensitive factor in healthcare data management, the simulations of the proposed model reveal substantial improvements over existing benchmarks in terms of reduced computing overhead, increased throughput, minimized latency, and strengthened overall security. By demonstrating these advantages, the study contributes significantly to the evolution of health data management, offering a scalable, secure solution that prioritizes patient autonomy and privacy in an increasingly digital healthcare landscape.

An empirical investigation into the enhancement of decision-making capabilities in corporate sustainability leadership through Internet of Things (IoT) integration

This research investigates the impact of Internet of Things (IoT) applications on decision-making capacity (DMC) development in Corporate Sustainable Leadership (CSL). The research focuses on how IoT enhances Environmental, Social, and Governance (ESG) practices within organizations, addressing key sustainability challenges and supporting Sustainable Development Goals (SDGs). Through quantitative and qualitative analyses, the study identifies three primary findings: (1) IoT applications directly enhance power source management in the environmental domain, improving Business Strategy and Long-term Growth (BSLG) and Measurement and Reporting (M&R) capabilities. This is achieved through real-time monitoring, smart building management, and predictive maintenance. In the social sphere, IoT strengthens supply chain transparency, bolstering Stakeholder Management (SM) through real-time tracking and blockchain-integrated systems for product authenticity verification. IoT comprehensively empowers performing risk management in the governance realm, improving Organizational Culture (OC) through early warning systems and predictive analytics for risk mitigation. (2) IoT integration facilitates Data-Driven Decision Making (DDDM), Real-Time Responsiveness (RTR), and Business Risk Management (BRM), key components of IoT-DMC. Furthermore, it supports the development of Effective Stakeholder Management (ESM), Resource Efficiency (RE), and Sustainable Competitive Outcomes (SCO) within IoT-CSL. (3) While highlighting the potential of IoT in advancing CSL, the research also acknowledges challenges such as initial investment costs, data privacy concerns, technological complexity, energy consumption of IoT devices, and electronic waste management. The study concludes that successful IoT implementation in CSL requires careful planning, robust data management, and a holistic approach considering both benefits and potential drawbacks.

Blockchain-Integrated Security Framework for Fog Networking of Connected Vehicles

In the rapidly evolving domain of connected vehicles, ensuring robust security and efficient data management is paramount. Traditional centralized architectures struggle with latency, scalability, and vulnerability to single points of failure. This research proposes a novel Blockchain-Integrated Security Framework (BISF) for fog networking in connected vehicles. The BISF leverages blockchain technology to enhance data integrity, transparency, and security while utilizing fog computing to reduce latency and improve real-time data processing. The proposed framework integrates smart contracts for automated trust management and distributed ledgers for immutable data records. Simulation results demonstrate the effectiveness of BISF in enhancing security, reducing latency, and improving overall network performance compared to traditional centralized approaches.

Advancements in Securing Cloud-Stored Data and Managing Sensitive Information: A Comprehensive Review of Ciphertext-Policy Attribute-Based Encryption (CP-ABE) Techniques

Abstract—The exploring comprehensive review of cutting-edge techniques for securing cloud-stored data and managing sensitive information in the context of smart cities through the application of Ciphertext-Policy Attribute-Based Encryption (CP-ABE). It highlights the innovative integration of blockchain technology with CP-ABE, which introduces a decentralized and tamper- resistant key management system, thereby enhancing the overall security framework in cloud environments where data sharing is prevalent. Introducing an online/offline multi-authority CP- ABE scheme, characterized by hidden policies, offers significant advancements in protecting user attributes and access structures, ensuring that sensitive information remains confidential even during encryption and decryption processes. This dual approach not only fortifies security but also optimizes the efficiency of data- sharing mechanisms. Furthermore, the paper delves into imple- menting hidden sensitive policies and keyword search techniques within smart city infrastructures, which are designed to facilitate secure and efficient data retrieval. These techniques ensure that while data remains accessible to authorized users, privacy is rigorously maintained. Collectively, these approaches represent significant strides in bolstering the security and confidentiality of data in both cloud-based and smart city applications, addressing the growing demand for robust and efficient data management solutions in increasingly interconnected environments. Index Terms—Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Blockchain, Key Management, Decentralized Secu- rity, Cloud-Stored Data, Yolo V7, Explainable AI (XAI), On- line/Offline Multi-Authority Scheme, Hidden policies, Privacy Preservation, Data Protection, Secure Data Management

The Impact of Intelligence Gathering, Risk Analysis, and Scenario Planning on Defense Policy Formulation

In the evolving landscape of global security, the integration of intelligence gathering, risk analysis, and scenario planning is paramount for effective defense policy formulation. This study aims to underscore the critical role of these elements in contemporary defense strategies. Employing qualitative research methods, particularly secondary data analysis, this research investigates the transformative impact of artificial intelligence (AI) and machine learning (ML) on threat assessments, the benefits and challenges of big data analytics in risk analysis, and the value of interdisciplinary perspectives in scenario planning. The findings reveal that AI and ML significantly enhance the accuracy and reliability of threat assessments by enabling real-time data processing and predictive analytics. However, challenges such as data privacy and algorithmic biases persist. Big data analytics offers substantial benefits in identifying and mitigating emerging threats but requires robust data management frameworks to address issues of data quality and integration. Additionally, scenario planning is highlighted as a strategic tool that enhances defense strategies by anticipating various future scenarios and enabling proactive measures. Furthermore, the integration of interdisciplinary perspectives in scenario planning fosters more robust and adaptable defense policies, ensuring a comprehensive approach to security challenges. In conclusion, the integration of advanced technologies and interdisciplinary methods in intelligence gathering, risk analysis, and scenario planning is crucial for developing resilient and adaptive defense policies.

The Emerging Challenges of Customer Relationship Management with Customer Data Platforms (CDP): Constraints and Perspectives

Customers today are increasingly demanding a personalized shopping experience, coupled with stringent data security measures and prompt responses. As the landscape of online privacy evolves, particularly with the anticipated sunset of third-party cookies by 2024, businesses face a critical imperative to revamp their customer data management strategies. The emergence of Customer Data Platforms (CDPs) stands out as a pivotal solution in this evolving ecosystem. This article undertakes a detailed critique of CDPs, starting with insights gathered from both users and industry experts, and subsequently examining consumer perceptions and behaviors in the digital realm. At the heart of contemporary consumer expectations lies the desire for tailor-made interactions that reflect their preferences and behaviors. This demand necessitates robust data management tools capable of aggregating, analyzing, and activating customer data across multiple touchpoints. CDPs have emerged as a central tool in this endeavor, offering businesses a unified platform to integrate disparate data sources and derive actionable insights in real-time. By leveraging comprehensive customer profiles built from first-party data, CDPs enable businesses to deliver personalized experiences that foster customer loyalty and drive revenue growth. Moreover, the impending demise of third-party cookies, compounded by stringent data protection regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act), underscores the urgency for businesses to adopt privacy-centric data practices. CDPs play a crucial role in this paradigm shift by empowering businesses to ethically collect and utilize customer data, thereby enhancing transparency and trust. However, challenges persist, including the complexity of integrating CDPs into existing infrastructures and ensuring compliance with evolving regulatory frameworks. From a consumer standpoint, attitudes towards data privacy are evolving, with an increasing emphasis on transparency and control over personal information. Consumers are more inclined to engage with brands that demonstrate a commitment to data security and ethical data usage practices. This shift necessitates a nuanced approach from businesses using CDPs, emphasizing not only personalization but also data protection and consent management. While CDPs offer substantial benefits in navigating the complexities of modern data management and meeting consumer expectations for personalized experiences, their successful implementation hinges on addressing technical, regulatory, and ethical considerations. By doing so, businesses can not only adapt to the evolving landscape of digital privacy but also foster enduring customer relationships grounded in trust and mutual value.

Privacy challenges and policy implications of in-home monitoring systems

In the evolving landscape of the Internet of Things (IoT) home monitoring systems, this paper addresses the pressing privacy challenges and policy implications, highlighting the dual threats of sophisticated cyber-attacks and cloud-based vulnerabil- ities. We advocate a harmonized approach integrating technical solutions with regulatory frames to counter these issues. Our research systematically evaluates existing security mechanisms and introduces an innovative framework that blends advanced encryption techniques, robust data management practices, and comprehensive policy solutions to enhance IoT security. Central to this framework is a collaborative engagement model that brings together the efforts of legislative bodies, service provision entities, and the end-user community, ensuring a dynamic user- centric security posture against emerging threats. This study con- tributes a scalable, user-centric security model that significantly advances the discourse on IoT privacy and security, addressing the unique demands of IoT environments.

Evaluating Predictive Financial Analysis Techniques, Impacts, and Challenges in Nigerian Banking Institutions

This study examined the techniques, impacts, and challenges of predictive financial analysis in Nigeria Banking Institutions. The Nigerian Banking Institutions is the life wire of the nation's economy. They play a vital and massive role in the nation's economic development and financial system. The role of these institutions remains a catalyst for a nation's growth and development. Despite the importance of predictive financial analysis in improving financial performance, accurately forecasting financial outcomes, mitigating the impact of financial crises, and making informed decisions, many Nigerian banks still need help to adopt and use this technique effectively. As a result, this study aims to examine the predictive financial analysis, evaluate the techniques, impact, and challenges, and recommend possible solutions that can help address the challenges faced by the banking sector. Methodology: The study adopted a descriptive survey research design. A simple random sampling technique was used to select 7 respondents representing five banks (Access Bank, Fidelity Bank, First Bank, Wema Bank, and Zenith Bank). A self-designed online questionnaire (SDOQ) was used for data collection. The data collected were analyzed using descriptive statistics (percentage). Results: Banks: 2 of the respondents, representing 28.6%, work at Access Bank; 1 of the respondents, representing 14.3%, works at Fidelity Bank; 2 of the respondents representing 28.6%, works at First Bank,1 respondent representing 14.3%, works at Wema Bank and the remaining 1 representing 14.3% works at Zenith bank. Role of Respondents: AHOP, Compliance Officer, Data Administrator, Data Analyst/Analytics, Relationship Manager and Team Lead. Analysis techniques: 3 banks representing 42.9% rely on the time series analysis technique, 2 banks representing 28.6% rely on the Regression analysis technique and 2 banks representing 28.6% rely on the machine learning algorithms technique. Satisfaction Level: 3 respondents representing 42.9% are very satisfied with current predictive analytics capabilities, while 4 respondents representing 57.1% are moderately satisfied with current predictive analytics capabilities. Discussion: The study's findings revealed that the primary challenges faced by banks in implementing predictive financial analysis techniques include data quality issues, regulatory constraints, lack of expertise, and integration with existing systems. Similarly, most banks rely on time series analysis to forecast future financial trends. Conclusion and Recommendations: Based on the findings in this study, it was concluded that the primary challenges faced by banks in implementing predictive financial analysis techniques include data quality issues, regulatory constraints, lack of expertise, integration with existing systems, and others. Similarly, most banks rely on the time series analysis technique to forecast future financial trends. The findings in this study also revealed that respondents perceive the impact of technological advancements to improve accuracy significantly. It was therefore recommended that a robust data management system practice be established to ensure the accuracy and consistency of data. Also, beyond time series analysis, banks should explore other predictive analytics techniques to enhance forecasting accuracy.

College Management System using Python & Django

The College Management System (CMS) is an advanced web-based application designed to streamline administrative and academic processes within educational institutions. Developed using the Django framework for backend functionalities and HTML, CSS, and JavaScript for frontend presentation, the CMS offers a comprehensive solution for managing various tasks efficiently. Leveraging SQLite as the databadminisratiase management system ensures robust data management capabilities, scalability, and ease of maintenance. The CMS provides a wide array of features to automate routine tasks and enhance collaboration among students, faculty, and administrators. Student admission and enrollment management, course scheduling, curriculum planning, and faculty assignment are seamlessly handled by the system, reducing manual workload and minimizing errors. Integrated messaging systems and announcement boards facilitate communication, fostering a collaborative learning environment. The examination management module automates the entire examination process, including exam scheduling, creation of question papers, venue allocation, and result processing. Online exam registration and automated grading streamline the process, ensuring accuracy and transparency while reducing administrative burden. Attendance tracking is simplified with the CMS, allowing faculty to record and monitor student attendance easily. Integration with biometric systems or RFID technology enhances accuracy and eliminates manual attendance taking. Comprehensive attendance reports provide insights into student progress and compliance with attendance policies. Resource management is optimized through the CMS, which enables efficient allocation and utilization of classrooms, laboratories, library resources, and equipment. Administrators can manage inventory, track asset availability, and schedule resource bookings to prevent conflicts and ensure optimal utilization. Reporting and analytics features empower administrators with real-time insights into various aspects of college operations. Customizable dashboards provide information on enrollment statistics, academic performance, financial data, and more, enabling informed decision-making and continuous improvement. In conclusion, the College Management System developed with Django, HTML/CSS/JS, and SQLite revolutionizes educational institution management. By combining advanced technology with intuitive design, the CMS enhances efficiency, transparency, and collaboration, ultimately contributing to the success and growth of the institution.

LUNGBANK: a novel biorepository strategy tailored for comprehensive multiomics analysis and P-medicine applications in lung cancer.

LUNGBANK was established as part of Project LUNGMARK, pioneering a biorepository dedicated exclusively to lung cancer research. It employs cutting-edge technologies to streamline the handling of biospecimens, ensuring the acquisition of high-quality samples. This infrastructure is fortified with robust data management capabilities, enabling seamless integration of diverse datasets. LUNGBANK functions not merely as a repository but as a sophisticated platform crucial for advancing lung cancer research, poised to facilitate significant discoveries. LUNGBANK was meticulously designed to optimize every stage of biospecimen handling, from collection and storage to processing. Rigorous standard operating procedures and stringent quality control measures guarantee the integrity of collected biospecimens. Advanced data management protocols facilitate the efficient integration and analysis of various datasets, enhancing the depth and breadth of research possibilities in lung cancer. LUNGBANK has amassed a comprehensive collection of biospecimens essential for unraveling the intricate molecular mechanisms of lung cancer. The integration of state-of-the-art technologies ensures the acquisition of top-tier data, fostering breakthroughs in translational and histological research. Moreover, the establishment of patient-derived systems by LUNGBANK underscores its pivotal role in personalized medicine approaches. The establishment of LUNGBANK marks a significant milestone in addressing the critical challenges of lung cancer research. By providing researchers with high-quality biospecimens and advanced research tools, LUNGBANK not only supports Project LUNGMARK's objectives but also contributes extensively to the broader landscape of personalized medicine. It promises to enhance our understanding of lung cancer initiation, progression, and therapeutic interventions tailored to individual patient needs, thereby advancing the field towards more effective diagnostic and therapeutic strategies.

Smart IV Bag and Health Monitoring System for Hospitals and Remote Places Using IOT

The health monitoring system utilizing an ESP32 controller and Arduino Nano presents an innovative approach to enhance patient care through continuous real-time monitoring of vital health parameters. This system integrates various sensors, including ECG, DHT11, HX711, and an R305 fingerprint sensor, to collect comprehensive physiological data and ensure secure access. The ESP32 microcontroller processes this data, providing immediate local alerts via a buzzer and LED indicators, while the Arduino Nano manages the GSM module to send remote notifications to healthcare professionals. Furthermore, the system employs IoT capabilities to transmit data to cloud platforms, facilitating remote monitoring and analysis. By automating the monitoring process, this project reduces the workload on healthcare staff and ensures timely medical interventions, thereby improving patient outcomes. The system's ability to provide real-time data visualization, secure access control, and remote monitoring aligns with the growing need for connected and intelligent healthcare solutions. This comprehensive health monitoring system not only enhances patient safety and care efficiency but also supports the advancement of modern healthcare infrastructures through robust data management and communication technologies.

Role of Artificial Intelligence in Revenue Management and Pricing Strategies in Hotels

Purpose: The general objective of the study was to investigate the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Preliminary empirical review revealed that the integration of artificial intelligence (AI) into revenue management and pricing strategies significantly enhanced the financial performance and operational efficiency of hotels. AI's ability to process large datasets in real-time improved demand forecasting and dynamic pricing, leading to increased revenue per available room (RevPAR) and average daily rate (ADR). Additionally, AI facilitated personalized guest experiences, boosting customer satisfaction and loyalty. Despite these benefits, the study identified challenges such as high implementation costs, data privacy concerns, and the need for robust data infrastructure. Addressing these issues through strategic planning and continuous staff training was deemed essential for maximizing AI's potential in the hotel industry. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations theory, Technology Acceptance Model (TAM) and Resource Based View (RBV) may be used to anchor future studies on the role of AI in revenue management and pricing strategies in hotels. The study concluded that integrating AI into hotel revenue management and pricing strategies significantly enhances performance, contributing to both theoretical and practical advancements. It enriched the Diffusion of Innovations Theory by demonstrating factors influencing AI adoption in hospitality. Practically, it provided actionable insights for hotel managers on leveraging AI for improved key performance indicators and balancing dynamic pricing with customer satisfaction. Policy recommendations included establishing guidelines for AI implementation, enhancing data infrastructure, fostering a culture of innovation, and addressing skills gaps through training and development programs. The study emphasized the need for robust data management systems and regulatory support to facilitate AI adoption.

Advancing Road Safety through Cloud Based RSU Solutions for Smart Internet of Vehicles

This research proposes a system to improve road safety by detecting and alerting drivers about hazardous road conditions in real-time. Vehicles equipped with sensors, GPS, and communication devices can autonomously detect hazardous road conditions and transmit alerts to a central cloud system through IoT MQTT protocol. Upon receiving alerts, vehicles can dynamically adjust their routes to avoid hazardous areas, reducing the risk of accidents. By utilizing IoT technology, the system ensures the reliability and authenticity of shared information, enhancing trust and overall road safety awareness. The proposed system architecture leverages cloud-based Roadside Units (RSUs) to facilitate communication among smart vehicles, providing real-time road condition information and ensuring secure and efficient data exchange. The system aims to address the challenges of existing IoV frameworks, such as data protection, key management, storage, concurrency performance, and response speed, by implementing robust security measures and efficient data management strategies.

Optimizing construction project outcomes through BIM and MEP system integration

The integration of Building Information Modeling (BIM) with Mechanical, Electrical, and Plumbing (MEP) systems represents a transformative approach to improving efficiency, coordination, and cost-effectiveness in construction projects. This paper explores a comprehensive methodology for BIM and MEP integration, encompassing framework development, data management, and collaboration mechanisms. It highlights the significant benefits such as enhanced project coordination, improved efficiency, and cost reduction while addressing key challenges including technical barriers, cultural and organizational changes, and data security concerns. Solutions such as the development of interoperability standards, middleware technologies, robust data management protocols, and collaborative platforms are discussed. The study underscores the necessity of a multifaceted strategy involving training, change management, and leadership commitment to drive successful implementation. Through detailed analysis and discussion, this paper demonstrates how BIM and MEP integration can lead to more streamlined, cost-effective, and high-quality construction projects.