Ironcore: A Full-Stack Web-Based Gym Management System Using Node.Js and Mongodb

Effective library management is essential for streamlining book circulation, maintaining accurate records, and enhancing user experience. This paper presents Book Vault, a comprehensive library management application designed to simplify the administrative and transactional operations of libraries. The system supports user and admin roles, allowing administrators to manage books, memberships, and transactions, while enabling users to view availability, borrow, and return books. Key features include a fine calculation system for late returns, automated report generation, and real-time status tracking of issued and overdue books. Developed using a RESTful architecture, Book Vault ensures seamless interaction between the frontend and backend, with a user-friendly interface built using basic web technologies. The application improves efficiency, reduces manual workload, and promotes organized library operations. Keywords: Library management system, Book Vault, REST API, User authentication, Book circulation, Fine management, Reporting system, Web application.

Purpose of the study: This study aimed to develop and evaluate a web-based patient service management information system to improve administrative efficiency at Su’adah Clinic in Palembang. Methodology: This research employed a research and development approach using the Waterfall system development model. Problem identification was conducted using the PIECES framework (Performance, Information, Economy, Control, Efficiency, and Service). Data were collected through observation, interviews, and documentation analysis at the clinic. The developed system integrates patient registration, administrative data management, payment processing, and automated reporting into a single web-based platform. Main Findings: The implementation of the developed system significantly improved administrative performance at the clinic. The average patient registration time decreased from approximately 10 minutes to 3 minutes, while average patient waiting time was reduced from 25 minutes to 10 minutes. The time required to retrieve patient records decreased from 5–7 minutes to less than 1 minute, and the preparation of monthly administrative reports was reduced from 2–3 hours to approximately 15 minutes. In addition, daily paper usage decreased from about 120 sheets to around 30 sheets after system implementation. User evaluation results indicated high acceptance of the system, with an overall satisfaction score of 4.5 out of 5. Novelty/Originality of this study: The novelty of this study lies in the development of an integrated web-based patient service management system specifically designed for small-scale healthcare clinics, combining online patient registration, administrative management, payment documentation, and automated reporting within a single accessible platform.

This proposal outlines the design and development of a Java-based software artifact implementing a custom API Gateway to manage security and access control, replacing the need for JWT token systems. The proposal encompasses the definition of functional requirements, software design and architecture, implementation, and system deployment, aiming to offer a secure and efficient solution for communication and control between clients and a REST API. The Design Science Research (DSR) methodology was applied, focusing on the design and implementation process of the software product, which enables secure integration between a client and a REST API. Security in web applications and REST API architectures is essential to protect system integrity, confidentiality, and availability in an increasingly threat-exposed digital environment. Additionally, the Scrum methodology facilitates agile and collaborative project management, ideal for iteratively and adaptively implementing security solutions. During the design and coding phases in Java, the custom API Gateway is defined to manage incoming requests and safely route traffic to REST APIs. The custom API Gateway incorporates features such as authentication, user role validation, and request logging. Simple endpoints are created on the REST API to ensure seamless communication with the gateway. This proposal provides a robust solution for managing security and access control in distributed applications. By using the API Gateway instead of JWT tokens, the system centralizes security and authentication efficiently, enhancing fault tolerance and maintaining stable performance.

Teaching assistants (TAs) play a crucial role in supporting practical learning processes. However, TA management often encounters challenges in class and teaching time allocation, TA attendance recapitulation, and end-of-semester evaluations. To address these issues, a system that facilitates the management of practical sessions involving TAs is necessary. This web application system is designed to streamline task management, class scheduling, material collection, attendance tracking, and TA evaluations at the end of each semester. Furthermore, supporting features such as an integrated dashboard and automated notifications can enhance the delivery of practical services to students. This research employs the Design Thinking methodology, an innovative, user-centered design approach, to develop solutions that meet user needs. Through the stages of empathize, define, ideate, prototype, and test, the resulting web application not only simplifies practical management but also improves the user experience for both lecturers and TAs. Based on a questionnaire administered to 20 teaching assistants, usability testing of the web application using the System Usability Scale (SUS) method yielded an average score of 77.25, which falls under the 'good' category. Thus, this web application is expected to serve as an innovative solution that fosters a more flexible and integrated teaching and learning environment.

Web 2.0 phenomenon, REST APIs and growing mobile service consumption are leading the development of web applications to a new paradigm, named cross-device web application. Most organizations often possess legacy systems which should face an ongoing evolution process to enhance its accessibility and interoperability. In such scenario, a REST API plays a key role, defining the interaction layer between the legacy system and all its heterogeneous frontends. This work presents a model-driven approach to derive a REST API from a legacy web application within the frame defined by a modernization process. In this work we detail the API generation process and provide a sample implementation instrumenting one of the studied web development frameworks to evaluate the suitability of the approach.

The rapid growth of digital technologies has transformed traditional supply chain management systems, particularly in the food sector. Conventional food procurement processes often suffer from inefficiencies such as lack of transparency, delayed communication, and limited market access for farmers and suppliers. This research proposes a Digital Food Procurement Platform designed to streamline the procurement process by connecting farmers, suppliers, distributors, and buyers through an integrated digital system. The platform enables real-time product listing, online ordering, inventory management, and digital payment processing, thereby improving operational efficiency and transparency in food supply chains. The proposed system utilizes modern web technologies to create a scalable and user-friendly environment that supports efficient procurement operations. Experimental evaluation indicates that the platform significantly reduces manual intervention, improves procurement speed, and enhances traceability in the food distribution process. This research contributes to the development of sustainable and technology-driven food supply chain management systems. Keywords Digital Procurement, Food Supply Chain, Smart Agriculture, Web Application, Inventory Management, E-Commerce Platform.

Managing real estate properties efficiently presented challenges due to the complexity of tasks involved, including tenant communication, maintenance scheduling, and financial tracking. Traditional methods often relied on manual processes, leading to inefficiencies, errors, and delays. Motivated by the need for a streamlined solution, this paper introduced a web-based real estate property management system designed to address these challenges. By employing a descriptive research design and utilizing the mixed methodology approach, the study investigates the challenges faced by realtors in managing property information. It also evaluated the current information management systems of each realtor in handling property listing, sales, leases, and rentals. The research findings revealed that 45% of respondents encounter challenges in property information management, primarily related to inefficient handling of sales information and lease agreements. While 72.7% of respondents use real estate software, shortcomings such as a lack of detailed property information and communication issues underline the need for a comprehensive web-based management system meeting various real estate management needs, as preferred by 90.9% of respondents. The results of the study will be used to propose a web-based real estate property management system for the overall efficiency of real estate operations and revolutionize how real estate assets were managed and optimized in digital age

The Hostel Management System is a web-based application designed to automate hostel operations, improve efficiency, and ensure better data security. The system streamlines processes such as student registration, room allocation, attendance tracking, fee management, and visitor logs. This paper discusses the limitations of traditional manual hostel management systems and presents a comprehensive automated solution developed using PHP, MySQL, and HTML/CSS. Implementation results demonstrate significant improvements in operational efficiency, accuracy, and security. Keywords Hostel Management, Web Application, Automation, PHP, MySQL, Database Security.

With the advancement of technology, the Internet users are increasing exponentially. In this technology enhancement era, web-based complaint management system becomes more favored and therefore, it becomes more effective and efficient to manage the complaints received, especially for the service-oriented organizations or business operators that are profit-oriented. Up to date, little literature could be found with regards to community welfare and public health complaint systems developed for the non-profit-oriented organizations or companies, especially as an intervention to combat tobacco use at public or work places. This study aims to fill in the gap by designing and developing a web-based smoking complaint management system as an innovative initiative to support the Blue Ribbon Campaign initiated by the World Health Organization, to fight the tobacco epidemics within the context of Malaysia. A framework for this web-based smoking complaint management system is proposed. This study employs the iterative waterfall model that incorporates the adapted human-centered agile work flow. The interface design and the development of the system are described. Limitation and recommendations for future works are also presented.

The increasing functional complexity and high-density integration of integrated circuits (ICs) present formidable routing challenges for printed circuit boards. Specifically for components utilizing high-density Ball Grid Array and other Grid Pin Array (GPA) packages, achieving efficient and reliable ordered escape routing is critical. This routing must strictly satisfy non-crossing and channel capacity constraints, which often become the bottleneck determining design success. Traditional manual or heuristic methods are increasingly inadequate for meeting the complexity and optimization demands of modern high-density designs. To address this challenge, this study proposes an innovative, automated routing strategy that leverages the robust search and optimization capabilities of the genetic algorithm (GA). We formulate the ordered escape routing problem on BGA/GPA as a rigorous combinatorial optimization problem. Through the GA mechanism, the core research objective is to explore the vast solution space and achieve the minimization of the total routing cost. This research establishes an automated routing framework capable of providing cost-effective escape routing solutions for high-density BGA, while strictly satisfying the non-crossing and single-capacity constraints. The proposed methodology not only significantly enhances routing efficiency and success rates but also provides essential technical support for the design and manufacturing processes of complex electronic products. For instance, in the simulation case of Case 7 (a 25 × 26 array with 60 active pins), this method achieved a routing compression ratio of 0.8230, equivalent to a 17.70% reduction in the total routing length, fully demonstrating the superior performance of the proposed algorithm in optimizing the routing cost.

The Rhizotrons method is an important means of detecting dynamic growth and development phenotypes of plant roots. However, the segmentation of root images is a critical obstacle restricting further development of this method. At present, researchers mostly use direct manual drawings or software-assisted manual drawings to segment root systems for analysis. Root systems can be segmented from root images obtained by the Rhizotrons method, and then, root system lengths and diameters can be obtained with software. This type of image segmentation method is extremely inefficient and very prone to human error. Here, we investigate the effectiveness of an automated image segmentation method based on the DeepLabv3+ convolutional neural network (CNN) architecture to streamline such measurements. We have improved the upsampling portion of the DeepLabv3+ network and validated it using in situ images of cotton roots obtained with a micro root window root system monitoring system. Segmentation performance of the proposed method utilizing WinRHIZO Tron MF analysis was assessed using these images. After 80 epochs of training, the final verification set F1-score, recall, and precision were 0.9773, 0.9847, and 0.9702, respectively. The Spearman rank correlation between the manually obtained Rhizotrons manual segmentation root length and automated root length was 0.9667 (p < 10–8), with r2 = 0.9449. Based on the comparison of our segmentation results with those of traditional manual and U-net segmentation methods, this novel method can more accurately segment root systems in complex soil environments. Thus, using the improved DeepLabv3+ to segment root systems based on micro-root images is an effective method for accurately and quickly segmenting root systems in a homogeneous soil environment and has clear advantages over traditional manual segmentation.

Aims: Any electronic device that delivers a collection of data, whether it be text referring to full text databases, electronic journals, photographs, other multimedia goods, or quantitative, visualizations, or time-based, is referred to as an electronic resource. These could be transmitted over the internet, tape, CD-ROM, tablets, smartphones, smart watches or another medium, these are now the basis of e-learning. Online searching has made it possible to get patent information more quickly, affordably, and conveniently than the traditional manual or CD-ROM based searching method. The ability to create and distribute documents in electronic form is now made possible by a number of established procedures and standards. So, in order to address the current problems, librarians are utilizing new media, particularly electronic resources, in their collection expansion makes the documentation of users better. As we can see, utilizing online resources is important in the modern world for a multitude of purposes. Because of this, it's important to understand the preferences, motives and usage of various ‘e- resources used by students who use online learning’. The aim of the present research paper was to examine the impact of e-resources using its usage and reading preferences. In this study, reasons such as time saving, more information, and busy schedule at college are considered. Methodology: Primary data was gathered from 250 students from Mumbai and Navi Mumbai who are using e-resources through the pre-structured questionnaire. The responses collected were recorded using the SPSS software for data analysis. In order to examine the link between causes, preferences, and the use of ‘e-resources’, a theoretical construct was developed grounded on a few assumptions. Statistical techniques like the chi-square test were used and data analysis was done using SPSS version 20 to examine the proposed construct. When doing the data analysis, the demographic profile, objectives, and hypothesis were all taken into consideration. Result: The average for each component that is time saving, more informative, and busy schedule at college was computed and was determined as 0.004, 0.004, and 0.000, correspondingly, for time saving, more informative content, and busy college schedule. As all of these values for all of the preferences under consideration are less than 0.05, it is clear that there is a connection between the usage of electronic resources and their underlying reasons and preferences. Conclusion: Hence, there is a substantial correlation between the reasons for using electronic resources and the different reading preferences, as well as between the two. Only three reasons namely time saving, more informative, and busy schedule at college are considered during this study. Data collection is done from Mumbai and Navi Mumbai region only.

Traditional manual or semi-mechanized pesticide spraying methods often suffer from issues such as redundant coverage and cumbersome operational steps, which fail to meet current pest and disease control requirements. Therefore, there is an urgent need to develop an efficient pest control technology system. This paper builds upon the Deep Q-Network algorithm by integrating the Bi-directional Long Short-Term Memory structure to propose the BL-DQN algorithm. Based on this, a path planning framework for pest and disease control using agricultural drones is designed. This framework comprises four modules: remote sensing image acquisition via the Google Earth platform, task area segmentation using a deep learning U-Net model, rasterized environmental map creation, and coverage path planning. The goal is to enhance the efficiency and safety of pesticide application by drones in complex agricultural environments. Through simulation experiments, the BL-DQN algorithm achieved a 41.68% improvement in coverage compared with the traditional DQN algorithm. The repeat coverage rate for BL-DQN was 5.56%, which is lower than the 9.78% achieved by the DQN algorithm and the 31.29% of the Depth-First Search (DFS) algorithm. Additionally, the number of steps required by BL-DQN was only 80.1% of that of the DFS algorithm. In terms of target point guidance, the BL-DQN algorithm also outperformed both DQN and DFS, demonstrating superior performance.

The growing reliance on social networks like Facebook, Instagram, Twitter, and LinkedIn has made them central to modern communication and business, but it has also introduced threats from fake profiles. These accounts are created to mislead users, spread spam, conduct scams, or manipulate public opinion, undermining trust, privacy, and safety. Traditional manual or rule-based detection methods are ineffective due to the sheer volume of accounts and the adaptability of attackers. This research presents a machine learning framework for detecting fake profiles across multiple platforms. Logistic Regression and Random Forest algorithms are trained on datasets containing both genuine and fake accounts. Classification uses features such as followers-to-following ratio, bio completeness, profile picture presence, posting frequency, and account age. Results show that Random Forest consistently outperforms Logistic Regression, achieving accuracy above 90% on all platforms. The study demonstrates the effectiveness of machine learning in improving the safety and trustworthiness of social networks and provides a foundation for future extensions using more advanced detection methods.

Cracks are a common type of road distress. However, the traditional manual and vehicle-borne methods of detecting road cracks are inefficient, with a high rate of missed inspections. The development of unmanned aerial vehicles (UAVs) and deep learning has led to their use in crack detection and classification becoming an increasingly popular topic. In this paper, an aerial drone is used to efficiently and safely collect road data. However, this also brings many challenges. For example, flying too high or too fast may produce poor quality images, with unclear cracks that may be ignored or misjudged as other features and increased environmental noise that may make it difficult to distinguish between cracks and other noise features. To address the above challenges, this paper proposes the CrackNet model and CrackClassification algorithm. The CrackNet network is an encoder–decoder architecture. Low- and high-level semantic information are combined through the skip feature fusion layers between the encoder and decoder to enhance the model’s expression and ability to recover image details. Additionally, the MHDC module at the bottom of the network can significantly increase the receptive field without reducing the feature map resolution. The MHSA module can simultaneously capture features from multiple subspaces. The average precision (AP) scores of the CrackNet network on three datasets, namely UAVRoadCrack, CRKWH100, and CrackLS315, were 0.665, 0.942, and 0.895, respectively. In addition, values of the other two evaluation metrics, ODS and OIS, were the highest among the compared methods. Meanwhile, the proposed CrackClassification algorithm in this paper achieves 85% classification accuracy for transverse and longitudinal cracks and 78% classification accuracy for block cracks and reticulated cracks. Overall, the CrackNet algorithm provides a new baseline model for crack detection in UAV remote sensing image scenes. The CrackClassification algorithm provides a new approach for batch classification of highway cracks. The detection and classification algorithm proposed in this paper were applied to 108 km of road sections.