Traditional Management Systems Research Articles

AI-Enhanced Prediction of Pavement Crack Propagation: A Study Using Traffic Load, Environmental and Material Data

This study develops an AI-based predictive model for forecasting pavement crack propagation by integrating traffic load, environmental conditions, and material property data. Traditional pavement management systems often struggle to accurately predict crack growth due to the complex interactions between these influencing factors. By leveraging data from various sources, including sensor-based traffic metrics, meteorological data, and material composition tests, this study identifies significant variables contributing to crack initiation and progression. The proposed model utilizes a blend of machine learning algorithms, including Random Forest and neural networks, with a cross-validation approach to ensure robustness. Results indicate that the model achieves high prediction accuracy, with an RMSE of 1.2 mm/year and an R-squared value close to 0.93. The findings support the use of AI-enhanced models as reliable tools for road infrastructure planning and maintenance, promising reductions in maintenance costs and improved pavement durability.

Using a Random Controlled Trial to Explore the Impact of AI-Enabled Learning

Much is being made of AI’s role in learning. However, there have been few studies that evaluate AI’s ability to make learning more efficient or more effective and specifically the learner’s willingness to embrace and use AI-enabled learning tools over conventional learning methodologies. Working with a manufacturing company, we used a randomized control trial approach (matching pairs) to present two groups of employees with a course on design thinking, one presented via a traditional learning management system (LMS Group) and other through an AI enabled tool (AI Group), with the content being identical in order to compare completion rates, time to completion, learning outcomes (subject matter knowledge retained) and the learner’s perceptions of value and experience. We also were able to compare the AI enabled version of the course to the same version with the addition of a face-to-face instructional component. While we found that those participants in the AI Group completed the course in a shorter period of time, had better learning outcomes and expressed higher perceptions of course value, the AI Group significantly underperformed the LMS Group in terms of course commencement and completion rates. Our finding suggest that the adoption of AI enabled learning tools may follow similar patterns of adoption rates of new technologies -- and require educating learners regarding the use, benefits, value and limitations of new AI enabled tools before widespread acceptance and usage.

Using Time-Series Databases for Energy Data Infrastructures

The management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently involves time sensitive operations, including rapid data ingestion, real-time querying, filling in gaps from missing or delayed data, and updating large volumes of timestamped and loosely structured data, all of which demand high processing power. Traditional relational database management systems (RDBMSs) often struggle with these operations, whereas time series databases (TSDBs) appear to be a more efficient solution, providing enhanced scalability, reliability, real-time data availability and superior performance. This paper examines the advantages of TSDBs over RDBMS for energy data management, demonstrating that TSDBs can either replace or complement RDBMSs. We present quantitative improvements in digestion, integration, architecture, and performance, demonstrating that operations such as importing and querying time-series energy data, along with the overall system’s efficiency, can be significantly improved, achieving up to 100 times faster operations compared to relational databases, all without requiring extensive modifications to the existing information system’s architecture.

NoSQL Technologies in Big Data Ecosystems: a Comprehensive Review of Architectural Paradigms and Performance Metrics

The exponential growth of data in volume, velocity, and variety has challenged traditional relational database management systems, leading to the emergence and widespread adoption of NoSQL and other innovative database technologies. This article provides a comprehensive review of NoSQL databases and their role in modern Big Data infrastructure stacks. Through a systematic analysis of current literature and industry case studies, we explore the architectural paradigms, performance metrics, and use cases of various NoSQL database types, including document-based, key-value, column-family, and graph databases. Our findings indicate that NoSQL solutions offer significant advantages in scalability, flexibility, and real-time processing capabilities, particularly for unstructured and semi-structured data. However, challenges persist in areas such as data consistency, security, and interoperability with existing systems. We also examine emerging trends, including NewSQL, time-series databases, and the integration of artificial intelligence in database management. This article contributes to the understanding of how organizations can leverage NoSQL technologies to optimize their Big Data infrastructure, highlighting both the opportunities and considerations for implementation. Our conclusions underscore the transformative impact of NoSQL on data management practices and provide directions for future research in this rapidly evolving field.

Design of a Traditional Village Cultural Heritage Preservation and Management System Based on Multi-Agent Networks

Preserving cultural heritage is essential for maintaining cultural diversity and promoting sustainable development. This study investigates the application of multi-agent networks in the protection and management of traditional village cultural heritage, capitalizing on their inherent distributed, collaborative, and adaptable characteristics. By integrating advanced technologies such as big data, data mining, and machine learning, the research meticulously develops a comprehensive system architecture and functional modules aimed at revitalizing traditional village spaces. The findings demonstrate significant enhancements in protection efficiency, while simultaneously fostering cultural inheritance and innovation within village communities. Through these innovative approaches, this study provides valuable insights into the preservation of traditional village cultural heritage and contributes to the broader objectives of cultural diversity conservation.

Use of Blockchain Technology for Management of Guest Room Keys

Purpose of the study: The hospitality industry is undergoing a transformation with the advent of blockchain technology, especially in hotel keys management. This chapter explores the use of blockchain to manage hotel guest room keys, focusing on its applications, benefits, and future ramifications. Methodology: The chapter begins by clarifying the basic concepts of blockchain, outlining its functionality as a decentralized distributed ledger system and then explores the challenges of traditional core system design and introduces blockchain-based key management solutions. This solution uses cryptographic encryption and decentralized access control mechanisms, integrating smart contracts to automate processes. Main Findings: Blockchain-based key management systems generate and distribute secure digital keys to guest smartphones, protecting it from unauthorized access and tampering blockchain’s immutable recordkeeping for a tamper-proof audit trail, enhancing security and accountability. Hotel networks allow guests to use digital keys in multiple ways, and connecting IoT devices allows for remote control and personalized experiences. Applications of the study: Blockchain technology provides a robust alternative to traditional hotel key management systems, improving security, transparency, and operational efficiency. Future trends, including artificial intelligence and machine learning, promise new innovations. A strategic and collaborative approach is recommended to maximize the benefits of blockchain in the hospitality industry. Novelty/Originality of the study: This chapter provides valuable insights for practitioners and researchers aiming to harness the transformative power of blockchain.

Sustainable Water Management Through Green Infrastructure

This research explores the implementation of sustainable water management practices through green infrastructure (GI) solutions. As urbanization intensifies, traditional water management systems face challenges such as flooding, water pollution, and reduced biodiversity. Green infrastructure, which integrates natural processes into urban planning, offers innovative strategies for stormwater management, water quality enhancement, and habitat restoration. This study examines various GI techniques, including rain gardens, permeable pavements, and green roofs, assessing their effectiveness in mitigating urban water challenges. Case studies from multiple cities demonstrate the benefits of GI in promoting resilience against climate change while enhancing community well-being. The findings underscore the potential of green infrastructure as a viable alternative to conventional water management methods, advocating for its broader adoption in urban environments. Key Words: Sustainable water management, Green infrastructure, Stormwater Management, Urbanization, Climate resilience, Water quality enhancement

Energy scheduling strategy for energy hubs using reinforcement learning approach

The rapid integration of Renewable Energy Sources (RES) into energy systems is critical for achieving a fossil fuel-free future. However, it also introduces significant challenges, such as increased system complexity and uncertainties in energy output. To overcome these challenges, this paper presents a model-free Deep Reinforcement Learning (DRL) method to energy hub scheduling using state-of-the-art algorithms, specifically Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO). Breaking away from traditional energy management systems that typically use discrete action spaces, this approach employs a multi-dimensional, continuous action space, providing a more accurate framework for decision-making. Furthermore, the current study adopts a comprehensive perspective by considering not only energy supply and operational costs, but also the often-overlooked impact of emitted emissions in the objective function. The results yield a Pareto space encompassing various criteria, offering a diverse scheduling framework that balances economic feasibility with environmental sustainability. By presenting a range of optimal solutions, this study contributes to the development of more resilient and sustainable energy systems.

Multi-Section Open Course Design: Design and Implications for Faculty, Sessional Instructors, and Learners

As open course designs are being implemented in regular post-secondary credit courses (beyond massive open online courses), new issues and processes need to be considered that are fundamentally different from courses offered within the traditional learning management system. In this paper, we will review our approach to multi-section open course design and share our experiences working through emergent issues, such as content placement, intellectual property, attribution, etc. We will provide insights and recommendations for institutional leadership and administration, faculty area advisors, technical support staff, and sessional instructors.

Design, development and optimization of blockchain-based secure data management for nanotechnology in healthcare

The integration of nanotechnology in healthcare has brought about revolutionary advancements, particularly in diagnostics and therapeutics. However, managing the vast and sensitive data generated by these applications poses significant challenges, particularly concerning security, privacy, and regulatory compliance. This study explores the design, development, and optimization of a blockchain-based secure data management system tailored for nanotechnology applications in the United States healthcare sector. The proposed system leverages blockchain's decentralized and immutable ledger technology to enhance data security and privacy, ensure regulatory compliance, and improve overall system performance. Our implementation showed substantial improvements in data security, with an 86.67% reduction in data breaches and an 85% decrease in unauthorized access cases. Additionally, compliance with major data privacy standards like HIPAA, GDPR, and CCPA increased by over 20%. Performance metrics demonstrated robust scalability, maintaining high transaction speeds and manageable latency across various load conditions. Cost analysis revealed significant reductions in data storage, security, and compliance costs, highlighting the economic benefits of adopting blockchain technology in healthcare. Moreover, the system exhibited lower energy consumption compared to traditional data management systems, addressing environmental concerns. Correlation and scatter plot analyses provided insights into the scalability and performance dynamics of the blockchain system. These findings underscore the transformative potential of blockchain technology in addressing the data management challenges of nanotechnology in healthcare. The study concludes with recommendations for adopting blockchain to enhance data security, regulatory compliance, and economic efficiency in the U.S. healthcare sector.

Integration of Local Wisdom and Modern Policies: The Role of Traditional Village Government In The Implementation of Sasi In Maluku

This study aims to analyze the role of traditional village governments in implementing sasi in Negeri Lima, Leihitu District, Maluku, particularly focusing on the integration of local wisdom with modern policies in managing natural resources. Sasi, a traditional resource management system, has long been practiced in Maluku but has evolved alongside modern government policies and contemporary challenges. This research employs a qualitative method, with data collected through observation and in-depth interviews. The findings reveal that traditional village governments play a crucial role as intermediaries between local communities and modern policies. In Negeri Lima, the implementation of sasi is managed more systematically due to strong collaboration between traditional institutions, such as the Kewang, and local government policies. This integration enables more adaptive natural resource management, including boosting the economic value of local commodities like nutmeg and coconut. The study concludes that the role of traditional village governments extends beyond merely preserving traditions; it also involves innovating in sustainable natural resource management. This research offers a fresh perspective on how local wisdom can be preserved through the synergy between tradition and modern governance.

Genetic characterization of cashmere goat (Capra hircus) populations in Mongolia.

Characterization studies of the phenotypic and genetic diversity of Mongolian goats are limited, despite several goat breeds being registered in the country. This study aimed to evaluate the phenotypic and genetic diversity of 14 cashmere goat populations in Mongolia, consisting largely of identified goat breeds. Body weight, cashmere quality, and coat color were the phenotypic traits considered in this study. A linear model was used to fit body weight and cashmere traits, and least squares means (LSMs) were estimated for the region and location classes. Genetic diversity and structure were assessed using a goat 50K SNP array. The studied populations exhibited greater phenotypic diversity at the regional level. A very small overall differentiation index (Fst: 0.017) was revealed by Wright's Fst and a very small overall inbreeding index (F ROH1 :0.019) was revealed based on runs of homozygosity. Genetic clustering of populations by principal components showed large variances for the two goat populations of the Russian admixture (Gobi Gurvan Saikhan and Uuliin Bor), and smaller but differentiated clusters for the remaining populations. Similar results were observed in the admixture analysis, which identified populations with the highest (Govi Gurvan Saikhan and Uuliin Bor) and lowest (Tsagaan Ovoo Khar) exotic admixtures. A genomewide association study (GWAS) of body weight and cashmere traits identified a few significant variants on chromosomes 2, 4, 5, 9, and 15, with the strongest variant for cashmere yield on chromosome 4. The GWAS on coat color yielded nine significant variants, with the strongest variants located on chromosomes 6, 13, and 18 and potential associations with KIT, ASIP, and MC1R genes. These signals were also found in other studies on coat color and patterns in goats. Mongolian cashmere goats showed relatively low genetic differentiation and low inbreeding levels, possibly caused by the traditional pastoral livestock management system and the practice of trading breeding bucks across provinces, along with a recent increase in the goat population. Further investigation of cashmere traits using larger samples and alternative methods may help identify the genes or genomic regions underlying cashmere quality in goats.

The Future of Database Management in the Era of Big Data and Cloud Computing

The landscape of database management is undergoing rapid transformation due to the rise of Big Data and cloud computing. Traditional database management systems (DBMS) are evolving to address new demands for scalability, flexibility, and efficiency. This paper explores the future of database management in the context of these advancements, focusing on emerging trends, technological innovations, and the challenges associated with recent developments is examined, providing a comprehensive overview of how database management is adapting and what future directions may hold. The research methods adopts a mixed-methods approach to explore the future of database management in the context of big data and cloud computing. This includes both qualitative and quantitative analyses to provide a comprehensive understanding of current trends, challenges, and advancements.

Experimental study of a novel guided sequential immersion cooling system for battery thermal management

Immersion cooling exhibits superior cooling performance compared to traditional battery thermal management systems (BTMS). However, a significant challenge of immersion cooling is the spatial variation of temperature within both the coolant and lithium-ion batteries (LIBs). This research proposes a guided sequential immersion cooling (GSIC) BTMS to address this issue. Experimental studies were conducted to evaluate the heat dissipation performance of the GSIC structure under various conditions, including extreme loads. The results indicate that under the static flow immersion cooling (SFIC) scheme, cooling the tabs significantly influences the overall performance. Immersing the tabs can reduce the maximum battery temperature by 10.403 °C, although the spatial variation of temperature persists. Under forced flow immersion cooling (FFIC) conditions, increasing the coolant flow rate dissipates the heat generated by LIBs more effectively. Even at an extreme discharge rate of 5C, the maximum temperature remains below 45 °C. The average temperature reduction at the tabs is greater than the battery body, and with increased flow rates, the temperature difference between the two can be maintained within 1 °C under all conditions. As the flow rate keeps increasing, the average temperature at the tabs gets even lower than the battery body at low loads. This demonstrates that the GSIC BTMS can suppress the temperature rise at the tabs, which is a critical heat risk. Moreover, the temperature uniformity of the LIBs module is improved. As the flow rate increases, the temperature difference within the LIBs module decreases when the discharge rates is between 1C and 5C. Theoretical analysis confirms that increasing flow rates for low loads can suppress temperature rise, albeit with increased power consumption and reduced cooling efficiency. High flow rates are more suitable for high load conditions of the LIBs. These results validate the feasibility of the GSIC BTMS and provide new insights for the development of immersion cooling BTMS.

Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification

Workflows orchestrate a collection of computing tasks to form a complex workflow logic. Different from the traditional monolithic workflow management systems, modern workflow systems often manifest high throughput, concurrency and scalability. As service-based systems, execution time monitoring is an important part of maintaining the performance for those systems. We developed a trace profiling approach that leverages quantitative verification (also known as probabilistic model checking) to analyse complex time metrics for workflow traces. The strength of probabilistic model checking lies in the ability of expressing various temporal properties for a stochastic system model and performing automated quantitative verification. We employ semi-Makrov chains (SMCs) as the formal model and consider the first passage times (FPT) measures in the SMCs. Our approach maintains simple mergeable data summaries of the workflow executions and computes the moment parameters for FPT efficiently. We describe an application of our approach to AWS Step Functions, a notable workflow web service. An empirical evaluation shows that our approach is efficient for computer high-order FPT moments for sizeable workflows in practice. It can compute up to the fourth moment for a large workflow model with 10,000 states within 70 s.

Enhancing Cybersecurity with Blockchain-based Identity Management

In the digital age, the rapid increase in cyber threats has highlighted the need for more robust and secure identity management systems. Traditional centralized identity management systems are often vulnerable to data breaches, unauthorized access, and identity theft, posing significant risks to individuals and organizations. This paper explores the potential of blockchain technology to enhance cybersecurity through decentralized identity management. By leveraging the inherent properties of blockchain—such as immutability, transparency, and decentralization—blockchain-based identity management systems offer a more secure and efficient alternative to traditional methods. The paper provides an in-depth analysis of how blockchain can be utilized to store and manage digital identities securely, reducing the risk of identity fraud and data breaches. It examines various blockchain-based identity management frameworks, highlighting their advantages and potential challenges in implementation. Furthermore, the study discusses the integration of blockchain with other emerging technologies, such as cryptographic algorithms and smart contracts, to create a more resilient cybersecurity infrastructure. Through a review of current literature and case studies, the paper demonstrates the effectiveness of blockchain in enhancing data security and privacy while promoting user control over personal information. The findings suggest that adopting blockchain-based identity management could significantly mitigate cybersecurity threats and enhance trust in digital transactions. However, the paper also acknowledges the need for addressing scalability, regulatory compliance, and interoperability issues to fully realize the potential of blockchain in cybersecurity. This research contributes to the growing body of knowledge on innovative cybersecurity solutions and provides a foundation for future studies in the field.

Comparative Analysis of Reinforcement Learning Approaches for Multi-Objective Optimization in Residential Hybrid Energy Systems

The rapid expansion of renewable energy in buildings has been expedited by technological advancements and government policies. However, including highly permeable intermittent renewables and energy storage presents significant challenges for traditional home energy management systems (HEMSs). Deep reinforcement learning (DRL) is regarded as the most efficient approach for tackling these problems because of its robust nonlinear fitting capacity and capability to operate without a predefined model. This paper presents a DRL control method intended to lower energy expenses and elevate renewable energy usage by optimizing the actions of the battery and heat pump in HEMS. We propose four DRL algorithms and thoroughly assess their performance. In pursuit of this objective, we also devise a new reward function for multi-objective optimization and an interactive environment grounded in expert experience. The results demonstrate that the TD3 algorithm excels in cost savings and PV self-consumption. Compared to the baseline model, the TD3 model achieved a 13.79% reduction in operating costs and a 5.07% increase in PV self-consumption. Additionally, we explored the impact of the feed-in tariff (FiT) on TD3’s performance, revealing its resilience even when the FiT decreases. This comparison provides insights into algorithm selection for specific applications, promoting the development of DRL-driven energy management solutions.

A Performance Analysis of Hybrid and Columnar Cloud Databases for Efficient Schema Design in Distributed Data Warehouse as a Service

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions.

A digital twin-based traffic light management system using BIRCH algorithm

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.

A blockchain-based solution for transparent intellectual property rights management: smart contracts as enablers

PurposeThe purpose of this study is to address the limitations of traditional methods for managing intellectual property rights (IPRs) by proposing a blockchain-based solution. By leveraging blockchain technology and smart contracts, the aim is to create a comprehensive ecosystem that offers advantages such as reduced transaction costs, improved transparency, enhanced security and increased liquidity levels for IP assets.Design/methodology/approachThis paper proposes using blockchain technology to manage intellectual property rights (IPRs) through a smart contract-based ecosystem. It outlines the use of non-fungible tokens (NFTs) on the blockchain to represent IPRs, with smart contracts automating interactions and encoding rules for various processes such as applications, licensing, transfers and royalty distribution. Governance mechanisms, such as decentralized autonomous organizations (DAOs), are employed to allow stakeholders to propose and vote on contract changes, ensuring adaptability. This approach aims to streamline IPR workflows, reduce transaction costs, improve transparency and enhance security.FindingsThe findings of this study suggest that implementing a blockchain-based ecosystem for managing intellectual property rights (IPRs) can lead to various benefits. These include reduced transaction costs, improved transparency, enhanced security, increased liquidity levels for IP assets and streamlined automated processes. The use of non-fungible tokens (NFTs) on the blockchain allows for detailed management, valuation and trading of IPRs. Furthermore, simulation results demonstrate the robustness and efficiency of our proposed ecosystem, outperforming traditional IP management systems in terms of transaction speed and cost-effectiveness. These simulations highlight the practical viability of integrating blockchain technology into IP management workflows.Practical implicationsThe practical implications of adopting this blockchain-based ecosystem for managing intellectual property rights (IPRs) are significant. By streamlining processes, reducing transaction costs and improving transparency and security, organizations can expedite the protection and commercialization of their IP assets. Additionally, the increased liquidity levels and accessibility of IP assets to investors and financiers can spur innovation and economic growth.Originality/valueThis paper contributes to the field by proposing a novel approach to managing intellectual property rights (IPRs) using blockchain technology and smart contracts. By leveraging non-fungible tokens (NFTs) on the blockchain, the proposed ecosystem offers a more efficient and transparent way of managing IPRs, reducing reliance on costly and opaque traditional methods. The potential benefits include improved efficiency, transparency, security and collaboration in the management and commercialization of IPRs.