Real-time Updates Research Articles

Solar-Powered EV Battery Charging and Payment System

Abstract: This innovative project is focused on harnessing solar energy efficiently to power electric vehicle (EV) charging stations. It employs a single-axis solar panel tracking system to follow the sun's path and optimize energy capture. The collected solar energy is stored in a high-capacity battery, ensuring uninterrupted charging, even during periods of limited sunlight. Users have the convenience of managing their accounts through a user friendly mobile app, providing real-time cost updates and secure payment options. The EV charging station maximizes the use of stored solar energy for vehicle charging, reducing reliance on the conventional power grid. Additionally, the system incorporates detailed billing and usage history features to enhance transparency and efficiency, ultimately creating an eco-conscious transportation ecosystem that integrates renewable energy and sustainable EV charging.

Brew Master: Smart Coffee Making Machine

Abstract: This paper presents the design and implementation of a smart coffee making machine utilizing servo motors and IoT technology. The system enables precise control over coffee-making parameters such as grind size, water temperature, and brewing time, all managed remotely via a mobile application. Servo motors ensure accurate operation of the grinder and brewing mechanisms. The IoT connectivity allows users to customize and monitor the brewing process from any location, providing real-time updates and notifications. The proposed solution demonstrates improved convenience, efficiency, and user satisfaction, showcasing the potential of integrating IoT and servo motor technologies in smart kitchen appliances.

Equitable resource allocation in health emergencies: addressing racial disparities and ethical dilemmas

This paper explores resource allocation complexities during health emergencies, focusing on pervasive racial disparities, notably affecting black communities. It aims to investigate alternatives to the Most Lives Saved approach, particularly...

CitizenConnectBot(CCB): The interactive guide to government schemes

In the ever-evolving landscape of digital governance, the Generative CitizenConnectBot (CCB) emerges as a transformative tool, seamlessly bridging the gap between citizens and government schemes.The absence of real-time updates results in outdated information, undermining the effectiveness of the service. Moreover, the current translation feature limits the accessibility of scheme details for a diverse audience, creating barriers to understanding and participation. Additionally, the lack of a chatbot on schemes dissemination in the available websites complicates the process for citizens to obtain necessary information and determine eligibility, impeding their ability to benefit from available support.The CitizenConnectBot(CCB), integrated into a user-friendly website, acts as a comprehensive information hub, offering citizens tailored insights into various government initiatives.The bot's adaptability ensures that information is generated according to the unique requirements of each user.This paper employs natural language processing (NLP) techniques, including text classification with SVM, TF-IDF vectorization, regular expressions for pattern matching, and session management for maintaining conversation context.

One-Stop Flair Slot Booking Platform for All Needs

Pick Your Slot (PYS) is a versatile booking and scheduling web-based application that caters to a single platform for all your needs. PYS offers services ranging from sports such as football, cricket and badminton, personal grooming services like salons, fitness facilities such as gyms, entertainment options like a dance studio, rage room, and car wash services. The platform, built using React.js and React Native for web and mobile respectively, utilizes a SQL database backend supported by Java. It is an open-source application accessible to both customers and vendors. The app facilitates slot booking according to customers’ flexible time, PYS not only accommodates flexible scheduling but also utilizes geolocation API to detect customer locations. This feature enables the app to suggest nearby services tailored to the customer's requirements, leveraging real-time availability updates and offering on-demand services. Real-time availability in our app ensures instant slot booking, adapting to users' dynamic schedules for hassle-free planning. Users can quickly secure appointments, saving time and reducing frustration. Security is paramount, with authentication mechanisms for both vendors and customers. With its blend of intuitive design and advanced functionality, PYS offers a comprehensive solution for booking needs, making it a reliable choice for users seeking convenience and peace of mind. In a world where time is precious, our app offers swift slot bookings, recognizing the impatience of modern life. With just a few taps, users can secure their slots, bypassing unnecessary delays in the nearest location. Designed for speed and convenience, we ensure no one waits longer than necessary.

IoT Based Crop Monitoring System

The IoT-Based crop monitoring System is a cutting-edge solution designed to enhance agricultural productivity and efficiency by utilizing sensor technology and automation. The core objective is to automate the irrigation process based on real-time soil moisture data. The systememploysanESP8266 module to facilitate the seamless exchange of data between various sensors and Firebase, a real-time cloud database. This interconnected system comprises two primary fields, each equipped with soil and soil moisture sensors, temperature sensors, smoke detectors, UV light, buzzer, solenoid valves, and mini water pump. The ESP8266 module serves as the central hub for collecting data from the sensors and transmitting it to the Firebase database. The data includes crucial information such as soil moisture levels, temperature, smoke detection, and UV light intensity. Furthermore, the system integrates alarm functionality through the buzzer and precise irrigation control via the solenoid valve and mini water pump. The data stored in Firebase is accessible through a web application built using React.js and Node.js. This web interface provides users with a graphical representation of the gathered data, allowing them to monitor the agricultural conditions in real-time. The main key features are Data Visualization in which users can remotely monitor soil moisture levels and receive real-time updates on crop conditions through a web application. Resource Efficiency by automating irrigation, this model optimizes water usage, reducing waste and improving water conservation. This paper not only showcases the integration of IoT technology in agriculture but also serves as a practical and environmentally responsible solution for modern farming practices.

Developing a Cost-Effective Surgical Scheduling System Applying Lean Thinking and Toyota's Methods for Surgery-Related Big Data for Improved Data Use in Hospitals: User-Centered Design Approach.

Surgical scheduling is pivotal in managing daily surgical sequences, impacting patient experience and hospital resources significantly. With operating rooms costing approximately US $36 per minute, efficient scheduling is vital. However, global practices in surgical scheduling vary, largely due to challenges in predicting individual surgeon times for diverse patient conditions. Inspired by the Toyota Production System's efficiency in addressing similar logistical challenges, we applied its principles as detailed in the book "Lean Thinking" by Womack and Jones, which identifies processes that do not meet customer needs as wasteful. This insight is critical in health care, where waste can compromise patient safety and medical quality. This study aims to use lean thinking and Toyota methods to develop a more efficient surgical scheduling system that better aligns with user needs without additional financial burdens. We implemented the 5 principles of the Toyota system: specifying value, identifying the value stream, enabling flow, establishing pull, and pursuing perfection. Value was defined in terms of meeting the customer's needs, which in this context involved developing a responsive and efficient scheduling system. Our approach included 2 subsystems: one handling presurgery patient data and another for intraoperative and postoperative data. We identified inefficiencies in the presurgery data subsystem and responded by creating a comprehensive value stream map of the surgical process. We developed 2 Excel (Microsoft Corporation) macros using Visual Basic for Applications. The first calculated average surgery times from intra- or postoperative historic data, while the second estimated surgery durations and generated concise, visually engaging scheduling reports from presurgery data. We assessed the effectiveness of the new system by comparing task completion times and user satisfaction between the old and new systems. The implementation of the revised scheduling system significantly reduced the overall scheduling time from 301 seconds to 261 seconds (P=.02), with significant time reductions in the revised process from 99 seconds to 62 seconds (P<.001). Despite these improvements, approximately 21% of nurses preferred the older system for its familiarity. The new system protects patient data privacy and streamlines schedule dissemination through a secure LINE group (LY Corp), ensuring seamless flow. The design of the system allows for real-time updates and has been effectively monitoring surgical durations daily for over 3 years. The "pull" principle was demonstrated when an unplanned software issue prompted immediate, user-led troubleshooting, enhancing system reliability. Continuous improvement efforts are ongoing, except for the preoperative patient confirmation step, which requires further enhancement to ensure optimal patient safety. Lean principles and Toyota's methods, combined with computer programming, can revitalize surgical scheduling processes. They offer effective solutions for surgical scheduling challenges and enable the creation of a novel surgical scheduling system without incurring additional costs.

Social Media and the Spiritual Journey: The Place of Digital Technology in Enriching the Experience

This qualitative study explores the utilization of social media among Pakistani pilgrims during spiritual journeys and investigates its impact on their pilgrimage experiences. Thirty Pakistani pilgrims who had embarked on spiritual journeys to various religious sites were interviewed using semi-structured interviews. Thematic analysis was employed to analyze the interview transcripts, revealing five main stages of social media usage: pre-trip preparation, real-time updates and guidance, community building and support, sharing experiences and insights, and post-trip reflection and engagement. At each stage, social media played diverse roles, including providing information and support, fostering connections with fellow pilgrims, sharing personal experiences, and facilitating spiritual dialogue. This explorative study underscores the significance of social media in enhancing the pilgrimage experience for Pakistani pilgrims, serving as a valuable tool for information dissemination, community building, spiritual support, and personal reflection throughout the pilgrimage journey. The findings contribute to a deeper understanding of how social media shapes pilgrims’ experiences and fosters their spiritual growth, emphasizing the need for further research to explore the nuanced dynamics of social media usage in the context of pilgrimage. Overall, this study sheds light on the unique role of social media in the spiritual journeys of Pakistani pilgrims and highlights its implications for pilgrimage practices and the broader discourse on religious tourism.

Optimal time interval of information update using by exit-choice system to improve pedestrian evacuation efficiency

In the case of emergency evacuation of large places, the rapid evacuation of pedestrians has always been a matter of great concern. However, research on pre-movement behavior and decision-making fatigue behavior of pedestrians in emergency evacuation situations is limited. In order to study how to improve evacuation efficiency under the introduction of exit-choice system, this paper uses the cellular automata model to simulate different exit arrangements and exit numbers at different time intervals of information update by the system. It is found that there is an optimal time interval of information update when three exits of a room have different widths, and the evacuation time significantly reduces compared to real-time update. The potential mechanism of the optimal time interval of information update is that the evacuees can be reasonably allocated to each exit, so that the passage capacity of each exit can be maximized, and the evacuation time is significantly reduced. Sensitivity analysis shows that the change of exit location has an important influence on the evacuation time. The different pre-movement times of pedestrians have a certain degree of influence on the evacuation time of real-time update. The different distance parameters and flow parameters had a significant impact on the evacuation time in the real-time update case.

Blood Group Detection Using Image Processing

During medical crises, access to an ample blood supply is crucial for saving lives. Shortages of required blood types in hospitals can result in significant delays in patient treatment. To tackle this urgent issue, we introduce BloodHub, an extensive web platform designed to streamline blood search, availability assessment, and compatibility determination using Support Vector Machines (SVM). BloodHub acts as a centralized hub where both donors and recipients can register and participate in the blood donation process. The platform offers a user-friendly interface for individuals in need of blood donations, enabling them to locate specific blood types nearby. Additionally, BloodHub provides real-time updates on blood unit availability across registered blood banks and donation centers, simplifying the procurement process for healthcare facilities and emergency responders. One of BloodHub's standout features is its SVM- powered blood group detection capability. By examining genetic markers in blood samples, SVM algorithms accurately identify donors' blood groups, ensuring compatibility with recipient needs. This functionality not only improves blood-matching efficiency but also reduces the risk of transfusion-related complications. Moreover, BloodHub implements robust security measures to protect user privacy and confidentiality.

Room impulse response reshaping-based expectation–maximization in an underdetermined reverberant environment

Source separation in an underdetermined reverberation environment is a very challenging issue. The classical method is based on the expectation–maximization algorithm. However, it is limited to high reverberation environments, resulting in bad or even invalid separation performance. To eliminate this restriction, a room impulse response reshaping-based expectation–maximization method is designed to solve the problem of source separation in an underdetermined reverberant environment. Firstly, a room impulse response reshaping technology is designed to eliminate the influence of audible echo on the reverberant environment, improving the quality of the received signals. Then, a new mathematical model of time-frequency mixing signals is established to reduce the approximation error of model transformation caused by high reverberation. Furthermore, an improved expectation–maximization method is proposed for real-time update learning rules of model parameters, and then the sources are separated using the estimators provided by the improved expectation–maximization method. Experimental results based on source separation of speech and music mixtures demonstrate that the proposed algorithm achieves better separation performance while maintaining much better robustness than popular expectation–maximization methods.

Body Part Implant Dashboard for Real-Time Monitoring and Reporting

Our innovative platform is revolutionizing the field of medical implants, harnessing the power of 3D printing to deliver unparalleled levels of customization, accuracy, and accessibility. It functions as an all-in-one online ecosystem that simplifies the process of obtaining custom 3D-printed implants for patients and healthcare providers. At its core, the platform features a user-friendly interface, allowing users to explore a diverse selection of medical implants tailored to individual needs, including orthopedic devices and dental prosthetics. The ordering process is straightforward—users can select implant specifications, upload medical data, and place orders through a secure payment gateway for safe transactions. The platform’s integrated tracking system provides real-time updates, allowing users to monitor the progress of their orders from production to delivery. Healthcare professionals’ benefit from a dedicated portal where they can submit custom designs, track orders, and communicate with experts. The platform also offers comprehensive customer support and post-implantation care resources to ensure a seamless experience. By embracing 3D printing technology, our platform is transforming healthcare delivery, paving the way for personalized medicine, and reshaping the future of medical implants. Join us in this journey towards a more accessible and patient-focused healthcare system

Movie Ticket Booking Application

In this digital age, buying movie tickets the old-fashioned way has been replaced by an efficient online experience. The main characteristics and advantages of a cinema ticket booking system are described in this abstract. This system provides a feature-rich online platform that may be accessed by mobile and web applications. Customers may quickly peruse a huge library of films, check the showtimes, and instantly select the seats they want. A seamless and safe transaction process is ensured by the system's integration of secure payment gateways. Customers can book tickets whenever and wherever they choose thanks to the system's simplicity and flexibility. Customized recommendations, dynamic pricing, and seat selection are examples of advanced features that improve the user experience overall. Theatre employees can effectively manage ticket inventory, track sales analytics, and optimise seat allocation with the system's comprehensive management interface. Administrators are informed and empowered to make data-driven choices thanks to real-time updates and notifications. Additionally, the system places a high priority on inclusion and accessibility, with features like multilingual help and seating accommodations for those with special needs. In conclusion, this technology offers a smooth, user-centric experience for both patrons and theatre operators, signalling a paradigm shift in the movie ticket booking industry. With its cutting-edge capabilities and user-friendly interface, it raises the bar for watching movies on a digital device

Energy-aware dynamic rescheduling of flexible manufacturing system using edge-cloud collaborative decision-making method

ABSTRACT With the development of cloud computing technology, energy-aware scheduling based on cloud computing is an essential means to achieve energy saving and carbon reduction in manufacturing systems. Due to various production disturbances in flexible production, the scheduling based on cloud computing has data security problems and poor real-time performance. How to use real-time information to improve the security and responsiveness of cloud scheduling is a research gap. This paper establishes a cloud-edge collaborative dynamic flexible job-shop energy-aware rescheduling decision-making model. According to the characteristics of dynamic production in a flexible manufacturing system, create a learning model for cloud scheduling with Deep Q Network (DQN) and send the training model to the edge for scheduling decisions. Based on real-time production interference data, edge scheduling model real-time update scheduling scheme. To improve the robustness of the cloud scheduling model based on DQN, Dynamic scheduling data at the edge will be uploaded to update the cloud model. In addition, the effectiveness and practicability of the model are verified in an extrusion workshop. The experimental results show that this method can improve the comprehensive objective evaluation of minimum energy consumption and completion time by 3.6% −49.3% compared with the traditional scheduling rules.

Nexikon Society (Society Management System)

The “The Nexikon Society” is an offers an integrated platform aimed at facilitating effective communication, information sharing, and issue resolution within residential communities. This platform serves as a centralized hub where society members can access real-time updates, notices, and event announcements, fostering a stronger sense of community engagement and collaboration. Through the Nexikon website, residents have easy access to various community-related information, including updates on ongoing projects, upcoming events, and important notices. Additionally, the platform allows members to view profiles of their neighbour’s, facilitating networking and social interactions within the community. A significant feature of Nexikon is its complaint management system, which enables members to report issues or concerns pertaining to the society. This feature ensures that grievances are addressed promptly and promotes transparency and accountability among society administrators. Moreover, the website restricts database modification privileges to the administrator, ensuring the integrity and security of the data. The administrator is tasked with managing member information, overseeing platform operations, and performing essential tasks to ensure its smooth functioning. Keywords— Society Management System, Residential Communities, Communication, Information Dissemination, Issue Resolution, Administrator, Database Management.

Affiliate Automation Project

The emergence of chatbots has revolutionized customer engagement and sales strategies for e-commerce platforms like Amazon. This project focuses on developing a Telegram chatbot specifically tailored for Amazon affiliate marketing. The chatbot serves as an intermediary between users and Amazon's vast product catalog, providing a seamless experience for browsing and purchasing products while integrating affiliate links to monetize interactions. Key functionalities of the chatbot include natural language processing for understanding user queries, product recommendation based on user preferences and historical data, real-time updates on product availability and pricing, and secure payment processing. The integration with Amazon's Affiliate Program ensures that affiliate links are dynamically generated and embedded within product recommendations, enabling the chatbot operator to earn commissions for referral sales.

Rescue Relief Agency Management For Disaster Recovery

In the aftermath of a natural or man-made disaster, the swift and coordinated deployment of rescue agencies is critical to minimizing casualties and maximizing the effectiveness of relief efforts. However, the current landscape of disaster response is often hampered by the absence of a centralized platform for communication and collaboration among various rescue agencies. This lack of a unified system often leads to duplication of efforts, inefficient resource allocation, and delays in providing aid to those in dire need. To address these challenges and enhance the efficiency of disaster response, we propose the development of a comprehensive mobile application that would enable rescue agencies to seamlessly register their information, share real-time location updates, and coordinate their operations effectively. This proposed mobile application would serve as a central hub for rescue agencies to register their information, including their location, contact details, areas of expertise, and available resources. This information could be entered manually or automated using GPS or other location tracking technologies. Once the database is populated, the application would provide a user-friendly interface that allows users to visualize the locations of registered rescue agencies on a map. Additionally, users could filter the results based on specific criteria, such as the type of disaster, the resources available, or the time since the last reported activity. Beyond displaying the locations of rescue agencies, the application would also facilitate communication and collaboration among these organizations. Rescue agencies could send alerts or requests for assistance directly through the application, enabling them to coordinate their efforts and optimize resource utilization. Moreover, the application could facilitate the sharing of critical resources such as medical equipment, transportation, and communication infrastructure, ensuring that these resources are deployed where they are most needed. Overall, the proposed mobile application has the potential to revolutionize disaster response by providing a centralized platform for communication, collaboration, and resource management among rescue agencies. By enabling real-time coordination and efficient resource allocation, this application could significantly enhance the effectiveness of disaster relief efforts, saving lives and minimizing the impact of natural and man-made disasters..

REAL TIME AIR QUALITY PREDICTION AND ANOMALY DETECTION

When it comes to addressing the growing concerns about environmental health and safety, real-time air quality forecast and anomaly detection are essential. This study describes a robust system that effectively tracks, analyses, and forecasts air quality parameters in real-time using a combination of cutting-edge sensors, sophisticated data analytics, and machine learning algorithms. Utilising a wide range of environmental sensors, the system continuously monitors pollutants such as carbon monoxide, nitrogen dioxide, sulphur dioxide, particulate matter (PM2.5 and PM10), and ozone. Modern data analytics techniques are then used to process and analyse the gathered data. A machine learning platform at the centre of the system makes use of time-series forecasting methods to forecast air quality conditions. Additionally, it uses anomaly detection algorithms to identify anomalous trends or abrupt spikes in pollutant concentrations, which may indicate operational issues or environmental dangers. Environmental agencies and public health experts can take timely action and make informed judgements thanks to these skills. In addition, the system has an easy-to-use interface that provides both specialists and non-experts with real-time updates and visual representations of air quality statistics. Using IoT technology increases the system's expandability to larger regions and guarantees effective data flow. By providing consistent and accurate air quality information, this technique not only supports urgent mitigation measures but also facilitates strategic environmental and public health planning. Predictive analytics and proactive surveillance are intended to improve environmental governance and public health. Keywords—LSTM, Linear Regression and Minmax scalar.

Dynamic modeling and analysis considering friction-wear coupling of gear system

Gear wear becomes one of the important reasons for the fault of gear systems by causing tooth surface geometric deviations, changing time-varying mesh stiffness (TVMS), and increasing unloaded static transmission error (USTE). However, existing wear prediction models ignore the effects of friction and tooth profile parameter updates, which inevitably leads to unclear research on dynamic analysis. Based on the Archard equation, this work proposes a real-time iterative update model to calculate the wear evolution process with equivalent curvature radius updates. Subsequently, an improved friction-wear coupling model is developed to calculate the wear depth, TVMS, and USTE, with an iterative solution process based on the updating of friction and wear coefficients, and the load sharing factor (LSF). The effectiveness of the improved model is verified by wear experiments and finite element analysis. Based on the tribo-dynamic model of the gear-rotor-bearing systems, the dynamic characteristics considering friction-wear coupling are studied. The results indicate that the effect of friction intensifies as the speed decreases, while increased wear amplifies vibrations at most speeds, exacerbates meshing shocks, and initially expands then contracts the chaotic speed range. This work provides strong theoretical support for predicting wear depth and understanding gear wear behavior.

Exploring the capacities of ChatGPT: A comprehensive evaluation of its accuracy and repeatability in addressing helicobacter pylori-related queries.

Educational initiatives on Helicobacter pylori (H. pylori) constitute a highly effective approach for preventing its infection and establishing standardized protocols for its eradication. ChatGPT, a large language model, is a potentially patient-friendly online tool capable of providing health-related knowledge. This study aims to assess the accuracy and repeatability of ChatGPT in responding to questions related to H. pylori. Twenty-one common questions about H. pylori were collected and categorized into four domains: basic knowledge, diagnosis, treatment, and prevention. ChatGPT was utilized to individually answer the aforementioned 21 questions. Its responses were independently assessed by two experts on H. pylori. Questions with divergent ratings were resolved by a third reviewer. Cohen's kappa coefficient was calculated to assess the consistency between the scores of the two reviewers. The responses of ChatGPT on H. pylori-related questions were generally satisfactory, with 61.9% marked as "completely correct" and 33.33% as "correct but inadequate." The repeatability of the responses of ChatGPT to H. pylori-related questions was 95.23%. Among the responses, those related to prevention (comprehensive: 75%) had the best response, followed by those on treatment (comprehensive: 66.7%), basic knowledge (comprehensive: 60%), and diagnosis (comprehensive: 50%). In the "treatment" domain, 16.6% of the ChatGPT responses were categorized as "mixed with correct or incorrect/outdated data." However, ChatGPT still lacks relevant knowledge regarding H. pylori resistance and the use of sensitive antibiotics. ChatGPT can provide correct answers to the majority of H. pylori-related queries. It exhibited good reproducibility and delivered responses that were easily comprehensible to patients. Further enhancement of real-time information updates and correction of inaccurate information will make ChatGPT an essential auxiliary tool for providing accurate H. pylori-related health information to patients.