Traditional Management Systems Research Articles

Quicklib: A Rapid Library Management Solution

QuickLib is a new kind of library management system that puts special focus on remote access features while streamlining resource monitoring, user administration, and cataloging. User convenience and accessibility are limited by the frequent lack of effective remote access options in traditional library management systems. QuickLib fills this need by enabling users to access library resources from any location with an internet connection. In comparison to conventional systems, QuickLib's efficiency, usability, and performance are highlighted in this paper's presentation of its architecture, implementation, and assessment. Findings show that QuickLib is more efficient in terms of speed, responsiveness, and user satisfaction than other solutions, which makes it a great tool for libraries and educational establishments looking to update their library services. Keywords— Library Management System, Remote Access, Efficiency, User-Centric Design

Medicare: Doctor Appointment Website and Hosting Using Cloud Services

The "Doctor Appointment Website Hosting with Cloud Services" project aims to revolutionize the healthcare appointment scheduling process by leveraging modern web technologies and cloud infrastructure. The web application provides a user-friendly platform for patients and healthcare providers to efficiently manage appointments, access relevant information, and communicate seamlessly. By integrating AWS cloud services, the platform ensures scalability, reliability, and accessibility, accommodating growing user bases and maintaining high availability. Additionally, the project incorporates telemedicine capabilities, enabling virtual appointments and improving accessibility to healthcare services. Through the implementation of innovative features and robust system architecture, the project addresses key challenges in traditional appointment management systems and enhances the overall patient experience. Future scope includes the integration of advanced functionalities such as health monitoring, AI-powered chatbots, and predictive analytics, further advancing the platform's capabilities and fostering a patient-centric approach to healthcare delivery. Overall, the project represents a significant step towards modernizing healthcare appointment scheduling and optimizing clinical workflows in a cloud-based environment. Keywords: - Doctor Appointment, Website Hosting, Cloud Services, Appointment Management, JavaScript.

Integrating Water Management Practices with Modern Irrigation Technologies in Aalo, West Siang District, Arunachal Pradesh

This research addresses the imperative of harmonizing traditional water management practices with contemporary irrigation technologies to ensure sustainable water use in Aalo, West Siang District, Arunachal Pradesh. Given the pressing need to optimize water resources for agricultural pursuits while upholding environmental integrity and socio-economic progress, a multidisciplinary methodology is employed. Aalo faces multifaceted challenges such as water scarcity, erratic rainfall patterns, and escalating agricultural demands. While traditional water management methods have historically upheld agricultural livelihoods, the advent of modern irrigation technologies presents promising avenues for bolstering water efficiency, crop productivity, and sustainable development. Through a mixed-methods approach, including quantitative analysis, qualitative assessments, and participatory research methods, the study endeavors to comprehensively understand water management practices and irrigation technologies in Aalo. Surveys, interviews, and field observations will facilitate the assessment of traditional water management systems' efficacy in mitigating water scarcity and supporting agricultural productivity. Concurrently, the study will evaluate the adoption and impact of modern irrigation technologies on water use efficiency, crop yields, and socio-economic indicators in the region. Anticipated findings are poised to unveil synergies and prospects for integrating traditional wisdom with modern irrigation practices to fortify sustainability and resilience in water management. Consequently, the study will offer actionable recommendations targeted at policymakers, agricultural extension services, and local communities. These recommendations encompass preserving traditional water management systems, promoting the uptake of modern irrigation technologies, fostering knowledge exchange, capacity-building initiatives, and nurturing community engagement in water management endeavors. Through these initiatives, the research aims to foster a holistic approach towards sustainable water management practices in Aalo, poised at the nexus of traditional knowledge and contemporary innovation.

Advancing Smart Lithium-Ion Batteries: A Review on Multi-Physical Sensing Technologies for Lithium-Ion Batteries

Traditional battery management systems (BMS) encounter significant challenges, including low precision in predicting battery states and complexities in managing batteries, primarily due to the scarcity of collected signals. The advancement towards a “smart battery”, equipped with diverse sensor types, promises to mitigate these issues. This review highlights the latest developments in smart sensing technologies for batteries, encompassing electrical, thermal, mechanical, acoustic, and gas sensors. Specifically, we address how these different signals are perceived and how these varied signals could enhance our comprehension of battery aging, failure, and thermal runaway mechanisms, contributing to the creation of BMS that are safer and more reliable. Moreover, we analyze the limitations and challenges faced by different sensor applications and discuss the advantages and disadvantages of each sensing technology. Conclusively, we present a perspective on overcoming future hurdles in smart battery development, focusing on appropriate sensor design, optimized integration processes, efficient signal transmission, and advanced management systems.

Smart Wet and Dry Waste Management System Using IoT

Abstract: Waste management is one of the important aspects when it comes to avoiding diseases caused by unsegregated waste. Many people have lesser knowledge about what is wet and dry waste. The escalating global waste generation hasled to a pressing need for innovative waste management solutions that are efficient, sustainable, and technologically advanced. This abstract introduces a "Smart Dry and Wet Waste Management System" that leverages the Internet of Things (IoT) to revolutionize waste management practices. Traditional waste management systems often lack real-time monitoring and optimization capabilities, resultingin inefficiencies, environmental concerns, and increased operational costs. The proposed system aims to address these challenges by integrating IoT technologies, sensor networks, and data analytics into waste collection and disposal processes.The main objective is to automate the segregation system thereby removing the human intervention. The hardware components such as Arduino, sensors, LCD are used to build the system. The integration of IoT in waste management contributes to a cleaner environment, reduced operational costs, and increased overall efficiency. By preventing overflows, minimizing unnecessary collection trips, and promoting recycling practices, the system aligns with sustainable waste management goals. Moreover, the collected data can be analyzed over time to identify longterm trends, enabling continuous optimization of waste management strategies..

Reference architecture design for developing data management systems in smart farming

The traditional data management systems prove inadequate to handle the volume, velocity, and variety of the data within farm business processes. Smart farming technologies offer advanced data management systems as a practical solution to these challenges. However, data is complex and originates from many sources; hence many aspects of data must be considered during the data management design of smart farming systems. This study proposes a reference architecture for data management in smart farming, developed through domain analysis and architecture modeling approaches. The domain analysis provides insights into the common and variant features and modules of the smart farming system, resulting in a blueprint representing family features across various smart farming domains. The effectiveness of the proposed reference architecture has been evaluated through two case studies, demonstrating its efficacy in designing data management systems for smart farming. The study found that the percentage of reused modules in the case studies, compared to the provided reference architecture, was 82.6%. The outcomes of this research will pave the way for further exploration in smart farming, particularly addressing data management issues within smart farming systems.

The Mouth of the Jebel Bani Mountains: Traditional Water Management at the Edge of the Sahara

ABSTRACT Along the southern Morocco-Algeria border runs the Jebel Bani mountain range, a feature that marks the edge of the Anti-Atlas mountain region and the beginning of the Sahara Desert. The area receives little annual rainfall and has almost no surface water. Despite the dry environment, there is a string of agricultural oases that use ancient underground technology to access groundwater. The khettara system (qanat) has allowed for life in this arid landscape for centuries but is currently facing rapid abandonment, and the oases are subsequently struggling to survive. This project, a community-based archaeological survey of the khettara oases along the Jebel Bani, demonstrates how modern irrigation practices are undermining the resilience and sustainability of Saharan oases and emphasizes the importance of traditional water management practices. This project provides a new avenue for understanding the diffusion, use, and abandonment of the khettara oasis systems and other traditional water management systems worldwide.

A deep dive into green infrastructure failures using fault tree analysis

Green Infrastructure has transformed traditional urban stormwater management systems by fostering a wide range of service functions. Despite their popularity, green infrastructure's performance can deteriorate over their lifecycle, leading to operational failures. The operation of green infrastructure has predominantly relied on reactive maintenance strategies. To anticipate malfunctions and enhance the performance of green infrastructure in the long run, failure data needs to be recorded so that deterioration processes and component vulnerabilities can be recognized, modelled and included in predictive maintenance schemes. This study investigates possible failures in representative GIs and provides insights into the most important events that should be prioritized in the data collection process. A method for qualitative Fault Tree Analysis using minimal cut sets are introduced, aiming to identify potential failures with the minimum number of events. To identify events of interest fault trees were constructed for bioswales, rain gardens and green roofs, for three groups of service function failures, namely runoff quantity control, runoff quality control and additional service functions. The resulting fault trees consisted of 45 intermediate and 54 basic events. The minimal cut set analysis identified recurring basic events that could affect operation among all three green infrastructure instances. These events are ‘trash accumulation’, ‘clogging due to sediment accumulation’, and ‘overly dense vegetation’. Among all the possible cut sets, events such as ‘plants not thriving’, ‘invasive plants taking over’, and ‘deterioration caused by external influences’ could potentially disrupt most of the service functions green infrastructure provides. Furthermore, the analysis of interactions between component failures shows vegetation and filter media layer failures have the highest influence over other components. The constructed fault trees and identified basic events could be potentially employed for additional research on data collection processes and calculating the failure rates of green infrastructure and as a result, contribute to a shift toward their proactive operation and maintenance.

Blockchain Based MediChain System Connecting Pharma, Medicals and Patients Together.

In today’s world almost all hospital uses hardcopy for patient data store and for booking appointment. All patient data is available on paper and user need to manage that all over he goes. Consider ’A’ patient got admitted in City Pune, all his data is stored on paper and what medicines he took. All the info about his health is stored there. After someday ‘A’ patient gone to another city far away without carrying any documents and there he got and health emergency and is not in condition of speaking and need urgently help. But due to lack of info available about patient doctor could not urgently take decisions. So, to reduce this risk our system is developed. The distribution of Health records becomes a time consuming and expensive process when we use the traditional client-server healthcare data management system where each hospital/clinic maintains its own database of patients’ medical records. A patient’s treatment is further delayed if the patient moves from one hospital to another hospital across different regions or countries. Moreover, most of the time a patient must repeat several laboratory and radiology tests. So, to address this the patient’s medical data from different hospitals are stored in a Blockchain based storage making it easily accessible by patients and the hospitals. And the pharma company will be able to store the information of medicines and medicals will be available to verify the medicines if they are from trustworthy companies. Keyword - - Dapp, Web3.Js, CSS.

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities.

Introduction: It is known that patients with immune-abnormal co-pregnancies are at a higher risk of adverse pregnancy outcomes. Traditional pregnancy risk management systems have poor prediction abilities for adverse pregnancy outcomes in such patients, with many limitations in clinical application. In this study, we will use machine learning to screen high-risk factors for miscarriage and develop a miscarriage risk prediction model for patients with immune-abnormal pregnancies. This model aims to provide an adjunctive tool for the clinical identification of patients at high risk of miscarriage and to allow for active intervention to reduce adverse pregnancy outcomes. Methods: Patients with immune-abnormal pregnancies attending Sichuan Provincial People's Hospital were collected through electronic medical records (EMR). The data were divided into a training set and a test set in an 8:2 ratio. Comparisons were made to evaluate the performance of traditional pregnancy risk assessment tools for clinical applications. This analysis involved assessing the cost-benefit of clinical treatment, evaluating the model's performance, and determining its economic value. Data sampling methods, feature screening, and machine learning algorithms were utilized to develop predictive models. These models were internally validated using 10-fold cross-validation for the training set and externally validated using bootstrapping for the test set. Model performance was assessed by the area under the characteristic curve (AUC). Based on the best parameters, a predictive model for miscarriage risk was developed, and the SHapley additive expansion (SHAP) method was used to assess the best model feature contribution. Results: A total of 565 patients were included in this study on machine learning-based models for predicting the risk of miscarriage in patients with immune-abnormal pregnancies. Twenty-eight risk warning models were developed, and the predictive model constructed using XGBoost demonstrated the best performance with an AUC of 0.9209. The SHAP analysis of the best model highlighted the total number of medications, as well as the use of aspirin and low molecular weight heparin, as significant influencing factors. The implementation of the pregnancy risk scoring rules resulted in accuracy, precision, and F1 scores of 0.3009, 0.1663, and 0.2852, respectively. The economic evaluation showed a saving of ¥7,485,865.7 due to the model. Conclusion: The predictive model developed in this study performed well in estimating the risk of miscarriage in patients with immune-abnormal pregnancies. The findings of the model interpretation identified the total number of medications and the use of other medications during pregnancy as key factors in the early warning model for miscarriage risk. This provides an important basis for early risk assessment and intervention in immune-abnormal pregnancies. The predictive model developed in this study demonstrated better risk prediction performance than the Pregnancy Risk Management System (PRMS) and also demonstrated economic value. Therefore, miscarriage risk prediction in patients with immune-abnormal pregnancies may be the most cost-effective management method.

Secured intelligent transportation with privacy retention through blockchain framework

The Secured Independent Intelligent Transport System (SIITS) is poised to revolutionize traditional transport management systems, leveraging autonomous vehicles (AVs) connected through an open-channel Internet to link Traffic Command Centers (TCCs), Road Side Units (RSUs), and AVs within the SIITS framework. However, this reliance on the Internet exposes users to various security risks, safety vulnerabilities, and other challenges that impede the progress of SIITS applications. In this method, ensuring robust security management and trustworthiness is paramount for the long-term adoption of this innovative trend. While previous efforts have focused on integrating security methods from multiple disciplines into a unified reference design, this article presents a reference architecture primarily centered around ITS safety. Additionally, the article introduces a proposed framework for enhancing ITS safety, addressing the confidence issues. To further address these challenges, the article offers categorization of goods, Big Data methods and services, and validates the utility of ITS business analytics for corporate applications through a groundbreaking multi-tier ITS security architecture.

Forensic Evidence Management Using Blockchain Technology

In the realm of forensic investigations, the management of evidentiary artifacts is a critical aspect that influences the integrity and admissibility of evidence in legal proceedings. The trustworthiness of evidence can be jeopardized by traditional evidence management systems’ frequent problems with data manipulation, unauthorized access, and lack of openness. Blockchain technology has surfaced as a viable approach to tackle these issues by offering a decentralized, transparent, and immutable foundation for data management in recent times. This paper explores the application of blockchain technology in forensic evidence management, highlighting its potential to enhance the security, integrity, and traceability of evidentiary artifacts throughout their lifecycle. By leveraging blockchain's inherent features such as cryptographic hashing, consensus mechanisms, and smart contracts, forensic practitioners can establish a tamper-proof chain of custody, ensure data integrity, and streamline the evidence management process. Moreover, blockchain- based evidence management systems offer benefits such as enhanced transparency, reduced reliance on centralized authorities, and improved collaboration among stakeholders. The paper discusses various use cases and implementations of blockchain technology in forensic evidence management, ranging from digital chain of custody records to decentralized forensic laboratories. Through a comprehensive analysis of the potential benefits and challenges, this paper aims to provide insights into the transformative impact of blockchain technology on forensic evidence management practices, paving the way for more secure, efficient, and trustworthy forensic investigations in the digital age.

Application of Blockchain for Secure Real Estate Management Systems

Real estate management systems are crucial for efficiently handling property transactions, ownership records, and regulatory compliance in the real estate industry. However, traditional real estate management systems face challenges related to security, transparency, and trustworthiness, leading to issues such as fraud, disputes, and inefficiencies. This paper explores the application of blockchain for secure real estate management systems, focusing on its potential benefits, implementation challenges, and use cases. Smart contracts have a superb advantage for avoiding fraud cases and instilling behaviour among the stakeholders of the system. We examine how blockchain can enhance security, streamline processes, reduce fraud, and improve trust in real estate transactions by providing a decentralized and tamper-proof platform for recording and verifying property ownership, contracts, and transactions. Additionally, we discuss the technical considerations, regulatory implications, and adoption barriers associated with implementing blockchain-based real estate management systems. This paper advocates a trustful mechanism for securing online transactions in the modern-day real estate marketplace.

Machine Learning-Driven Energy Harvesting and Storage System Design for IoT Applications in Smart Buildings.

The advent of Internet of Things (IoT) technologies has revolutionized the concept of smart buildings, integrating diverse sensors and devices for enhanced automation and efficiency. Avinash B. Raut, Energy harvesting and storage technologies have emerged as promising solutions to address the power requirements of IoT devices in smart buildings. This abstract provides a comprehensive overview of machine learning-driven energy harvesting and storage system design tailored specifically for IoT applications in smart buildings. Traditional energy management systems often face challenges such as suboptimal energy utilization, limited scalability, and lack of adaptability to dynamic environmental conditions. In contrast, machine learning techniques offer adaptive, data-driven solutions to optimize energy harvesting, storage, and distribution in smart buildings. [1] This paper explores various machine learning algorithms, including supervised learning, reinforcement learning, and deep learning, and their application in optimizing energy harvesting from ambient sources such as solar, kinetic, and thermal energy. Moreover, machine learning enables predictive energy demand modeling by analyzing historical data and environmental factors, thus enhancing the efficiency of energy storage and distribution systems. Real-world case studies and experimental results are presented to demonstrate the effectiveness and potential of machine learning-driven energy management systems in improving energy efficiency, reliability, and autonomy in IoT-enabled smart buildings DOI: https://doi.org/10.52783/tjjpt.v45.i02.6318

RF based Automatic Traffic Route Clearance System for Emergency Vehicle

Traditional time-based traffic management systems are often inadequate for addressing the complexities of modern urban traffic, resulting in delays for emergency service vehicles such as ambulances and fire services. This project proposes a wireless communication-based approach to alleviate ambulance congestion in urban areas, presenting an innovative traffic clearance system that is easily deployable and cost-effective. Keywords— Arduino, ESP 32 Microcontroller, Solar panel ,GPS,RFID Sensor.

Blockchain Based Placement Management System And Job Recommendation Using Content-Based Filtering

The integration of blockchain technology with placement management systems and job recommendation mechanisms presents a robust solution for enhancing the efficiency, transparency, and security of the recruitment process. This paper proposes a Blockchain-based Placement Management System (BPMS) coupled with Content-Based Filtering (CBF) techniques to streamline the job matching process for both job seekers and recruiters. The BPMS leverages the decentralized nature of blockchain to ensure immutable records of job listings, candidate profiles, and transactional data, thereby reducing the risk of fraudulent activities and ensuring data integrity. Smart contracts are utilized to automate various aspects of the recruitment process, such as job application submissions, candidate evaluations, and offer acceptance, facilitating trustless interactions between parties.In parallel, the CBF approach is employed to provide personalized job recommendations to candidates based on the analysis of their skills, qualifications, and preferences. By analyzing the content of job descriptions and candidate profiles, the system generates tailored recommendations that match the expertise and interests of the candidates, improving the likelihood of successful placements.The proposed system offers several advantages over traditional placement management systems, including increased transparency, reduced operational costs, minimized bias in job recommendations, and enhanced security of sensitive data. Through the synergy of blockchain technology and content-based filtering techniques, the BPMS aims to revolutionize the recruitment industry by fostering trust, efficiency, and fairness in the job market.

RUNNING A WORKFLOW WITHOUT WORKFLOWS: A BASIC ALGORITHM FOR DYNAMICALLY CONSTRUCTING AND TRAVERSING AN IMPLIED DIRECTED ACYCLIC GRAPH IN A NON-DETERMINISTIC ENVIRONMENT

This paper introduces a novel algorithm for dynamically constructing and traversing Directed Acyclic Graphs (DAGs) in workflow systems, particularly targeting distributed computation and data processing domains. Traditional workflow management systems rely on explicitly defined, rigid DAGs, which can be cumbersome to maintain, especially in response to frequent changes or updates in the system. Our proposed algorithm circumvents the need for explicit DAG construction, instead opting for a dynamic approach that iteratively builds and executes the workflow based on available data and operations, through a combination of entities like Data Kinds, Operators, and Data Units, the algorithm implicitly forms a DAG, thereby simplifying the process of workflow management. We demonstrate the algorithm’s functionality and assess its performance through a series of tests in a simulated environment. The paper discusses the implications of this approach, especially focusing on cycle avoidance and computational complexity, and suggests future enhancements and potential applications.

A Novel Approach of Traffic Congestion and Anomaly Detection with Prediction Using Deep Learning

Traffic congestion and anomalies present significant challenges to urban mobility, impacting economic activity and quality of life. Traditional traffic management systems often fall short in accurately predicting and mitigating these issues due to their reliance on static models and limited data sources. This study introduces a novel deep learning framework designed to enhance traffic congestion and anomaly detection and provide accurate traffic flow predictions. Leveraging a comprehensive dataset encompassing various traffic patterns and conditions, we employ a convolutional neural network (CNN) model, renowned for its efficacy in handling spatial data, combined with long short-term memory (LSTM) networks to capture temporal dependencies. Our approach distinguishes itself by incorporating real-time data and employing advanced feature extraction techniques, enabling the dynamic adjustment of traffic management strategies. The methodology section outlines the data preprocessing steps, model architecture, training process, and evaluation metrics employed. Experimental results demonstrate the model's superior performance over existing methods in terms of accuracy, precision, recall, and computational efficiency. The discussion elaborates on the model's practical implications for smart city initiatives and its potential to revolutionize traffic management systems. This study not only addresses the gaps identified in the literature review but also opens avenues for future research in applying deep learning to urban traffic challenges.

Impact of inventory management on SME performance: a systematic review

PurposeEffective inventory management is crucial for SMEs due to limited resources and higher risks like cash flow, storage space, and stockouts. Hence, the aim is to explore how technology and know-how can be integrated with inventory practices and impact operational performance.Design/methodology/approachThe basis of the analysis was collecting papers from a wide range of databases, which included Scopus, Web of Science, and Google Scholar. In the first phase of the process, a search string with as many as nine related keywords was used to obtain 175 papers. It further filtered them based on their titles and abstracts to retain 95 papers that were included for thorough analysis.FindingsThe study introduced innovative methods of measuring inventory practices by exploring the impact of know-how. It is the first of its kind to identify and demonstrate how technical, technological, and behavioral know-how can influence inventory management practices and ultimately impact the performance of emerging SMEs. This study stands out for its comprehensive approach, which covers traditional and modern inventory management technologies in a single study.Research limitations/implicationsThe study provides valuable insights into the interplay between technical, technological, and behavioral know-how in inventory management practices and their effects on the performance of emerging SMEs in Industry 5.0 in the light of RBV theory.Originality/valueThe RBV theory and the Industry 5.0 paradigm are used in this study to explore how developing SMEs' inventory management practices influence their performance. This study investigates the effects of traditional and modern inventory management systems on business performance. Incorporating RBV theory with the Industry 5.0 framework investigates firm-specific resources and technological advances in the current industrial revolution. This unique technique advances the literature on inventory management and has industry implications.

Effects of management, habitat and landscape characteristics on biodiversity of orchard meadows in Central Europe: A brief review

Increasing agricultural intensification, combined with land transformation and fragmentation, poses significant threats to biodiversity. While extensively cultivated landscapes serve as vital refuges against biodiversity loss, they are modified by land abandonment and intensification. Orchard meadows in Central Europe represent traditional extensive land management systems, exhibiting high biodiversity. Comprising cultivated grasslands and scattered fruit trees, orchard meadows feature structures rich in different habitats supporting a diverse flora and fauna. However, their decreasing economic importance in recent decades has resulted in severe degradation or abandonment. Despite their importance for biodiversity conservation, there remains no comprehensive overview of orchard meadow biodiversity and management in Central Europe. This review aims to summarize existing knowledge on orchard meadows’ role in biodiversity conservation and the effects of management practices on habitat diversity and quality at both smaller (structure and microhabitats, local scale) and larger scales (surrounding landscape, regional scale). The first part focuses on orchard meadow biodiversity, including both plants and animals and their link to landscape-scale factors. Biodiversity in orchard meadows is predominantly affected by patch size, determining species richness and composition, and connectivity to neighbouring orchard meadows, influencing species migration and recolonization success. The second part evaluates management impacts, illustrating differences in the benefits of mowing versus grazing across taxonomic groups. An intermediate management intensity for orchard meadows determines their conservation value in terms of species composition, varying among different taxonomic groups. To prevent area loss and abandonment of orchard meadows, we advocate for political and public support, along with incentives for farmers to maintain their biodiversity.