Real-time Updates Research Articles

Smart Parking Slot Management System Using IOT

The paper proposes an innovative approach to tackle the pressing issue of efficient parking space management amidst the backdrop of rapid urbanization and escalating vehicle ownership rates. Through the amalgamation of Internet of Things (IoT) technology and ultrasonic sensors, the Smart Parking Slot Management System (SPSMS) emerges as a promising solution. Within this system, IoT-enabled devices, particularly ultrasonic sensors, are strategically deployed within parking slots to constantly monitor their occupancy status in real-time. These sensors function by detecting the presence or absence of vehicles through precise measurements of the distance between the sensor and the ground surface. Subsequently, the collected data is seamlessly transmitted wirelessly to a centralized server or cloud-based platform via internet connectivity. Upon reception of sensor data, the central server undertakes robust processing and analysis to ascertain the availability of parking slots within the designated area. End-users can conveniently access this pertinent information through a dedicated mobile application or a web interface, facilitating effortless identification and reservation of vacant parking spaces. The system further enhances user experience by incorporating automated notifications, alerting users to nearby available parking slots, alongside real-time updates on parking availability and robust reservation management functionalities. Keywords: Internet of Things, Thing Speak, Arduino Uno, ultrasonic sensor, Node MCU.

Monument Detection Using YOLO

In this Paper we’re going to cover the details of our project, Monument detection. The idea of this project is basically to bring an innovative and more realistic approach for the tourism field. What is to be implemented in this project and how it works is as such. As we know that tourism is one of the fields in the economic world that never stops. We always have numerous tourists always visiting the number of monuments and places throughout the world, on adventures, on the the side note most people prefer to be on their own, exploring these varies places. This is where our project starts being useful. Our goal in this is to make an API/Application that is will let these types of tourists and most for all cause to be independent and resourceful while exploring the ends of the world, using the camera device allowed to be operated through this application, we tend to run an algorithm that will find out what monument or historic architecture that is displayed when the camera through the application is pointed at it. The application will also provide insights on what the image for instance the history behind it and all the event that caused it, and all the information that the tourist will require on his adventure. Keywords—Data Collection and Pre-processing, Machine Learning Models based, Prediction and Visualization, Real-time Update.

Optimizing Performance in Distributed Cloud Architectures: A Review of Optimization Techniques and Tools

This research paper presents a groundbreaking hybrid transactional/analytical processing (HTAP) architecture designed to revolutionize real-time point cloud data processing, particularly in autonomous driving environments. Integrating elements from both columnar and row-based tables within a spatial database, the proposed architecture offers unparalleled efficiency in managing and updating point cloud data in real-time. The architecture's distributed nature operates through a seamless synergy of Edge and Cloud components. The Edge segment operates within the Robot Operating System (ROS) environment of the vehicle, while the Cloud counterpart functions within the PostgreSQL environment of cloud services. The communication between these components is facilitated by Kafka, ensuring rapid and reliable data transmission. A pivotal aspect of the proposed system lies in its ability to autonomously detect changes in point cloud data over time. This is achieved through a sophisticated algorithm that analyzes dissimilarities in the data, triggering real-time updates in areas where high dissimilarity is detected. The system ensures the maintenance of the latest state of point cloud data, contributing significantly to the generation of safe and optimized routes for autonomous vehicles. In terms of optimization, the paper demonstrates how the HTAP architecture achieves real-time online analytical processing through query parallelization in a distributed database cluster. The system's efficacy is evaluated through simulations conducted in the CloudSim framework, showcasing its scalability, adaptability, and robustness in handling point cloud data processing for a single vehicle. While acknowledging the achievement of the proposed architecture, certain limitations are recognized. The study highlights the need for further investigation into the system's performance under simultaneous analysis and updates from multiple vehicles. Additionally, ensuring seamless scalability and robustness for uninterrupted operation and expansion during runtime is identified as an area requiring further development.

Live Supplier Catalog and Pricing for Omnichannel

This paper proposes a technical architecture for an Omni-channel Supplier Catalog that provides real-time price updates. This system leverages a combination of open-source technologies, including Apache Camel, Spark Connector, Kafka Topics, Cassandra database, and Kubernetes, deployed on a resilient and elastic cloud platform. This paper details the data pipeline design, outlining how each component contributes to efficient data ingestion, processing, and dissemination. Additionally, the paper highlights the benefits of using this architecture, including improved decision-making, enhanced operational efficiency, and a seamless omnichannel experience for customers.

Effective Delivery Management in Geographically Dispersed Teams: Overcoming Challenges in Salesforce Projects

In the era of digital transformation, Salesforce has emerged as a leading platform for customer relationship management (CRM), enabling businesses to streamline their operations and enhance customer engagement. However, the management of Salesforce projects in geographically dispersed teams presents unique challenges that can impact delivery effectiveness. This paper explores the complexities associated with delivering Salesforce projects when team members are located in different regions and proposes strategies to overcome these challenges. Geographically dispersed teams often encounter issues related to communication, coordination, and cultural differences. Communication barriers, such as time zone differences and language variations, can lead to misunderstandings and delays in project execution. Additionally, the lack of face-to-face interactions can hinder effective collaboration and team cohesion. Cultural differences may also affect team dynamics, impacting productivity and decision-making processes. To address these challenges, the paper emphasizes the importance of implementing robust communication strategies. Establishing clear communication protocols, utilizing collaboration tools, and setting up regular virtual meetings are crucial for maintaining alignment among team members. Leveraging technology such as video conferencing, project management software, and instant messaging platforms can bridge the gap caused by geographical distances, facilitating real-time updates and seamless information sharing.

Lab Inventory Management using QR Code

Laboratory inventory management is vital for the efficient operation of scientific research institutions. Conventional inventory tracking methods often involve manual data entry, which can be error-prone and time-consuming, leading to operational inefficiencies and potential research disruptions. To address these challenges, this project introduces a Lab Inventory Management System (LIMS) that leverages QR code technology. The LIMS assigns unique QR codes to each laboratory item, encoding critical information such as item name, quantity, and expiration date. This technology enables mobile devices equipped with QR code scanners to facilitate real-time updates, eliminating manual data entry errors and ensuring the accuracy of inventory information. Furthermore, a centralized database supports data integrity and enables seamless integration with existing laboratory systems. The project's advantages include enhanced accuracy, significant time and cost savings, and streamlined workflow efficiency. However, the successful implementation of the LIMS necessitates careful planning and comprehensive user training to ensure a smooth transition to this innovative inventory management solution. Key Words: Automated, Scientific Research, Data Integrity, Data Visualization, Barcode Scanning

HOSTEL HUNT: FIND YOUR PERFECT STAY & MESS

The “Hostel Hunt: Find Your Perfect Stay & Mess” Android application is a user-centric mobile solution design to provide students and hostel administrators with easy access to the functionalities of the online hostel and mess management system. This mobile app will offer a comprehensive platform that not only helps users discover the ideal place to stay but also facilitates seamless access to meal services within the same interface, enhancing the overall experience of residential living. It will have user-friendly interface, real-time updates, and integrated booking and payment systems enhance the overall residential living experience. Whether seeking a hostel room or planning daily meals, users can rely on Hostel Hunt to streamline the entire process, making it an indispensable tool for students and anyone in search of quality accommodation and dining service. The mobile app will be design to be compatible with a wide range of Android devices and screen sizes, making it accessible to broad user base. It serves as a valuable addition to web-based system, providing mobility and flexibility for both students and administrators. Key Words: K-means, Content-based filtering, recommendation system, clustering, geolocation, visualization

Machine Learning Project in Python to Predict Stock Price

The Stock Price Prediction System is an innovative application of machine learning and data analysis techniques aimed at forecasting the future prices of publicly traded stocks. Stock markets are dynamic and influenced by a multitude of factors, making stock price prediction a complex and challenging task. This system leverages historical stock price data, along with relevant financial and macroeconomic indicators, to provide accurate and valuable predictions for investors, traders, and financial analysts. Index Terms Data Collection and Preprocessing, Machine Learning Models based, Prediction and Visualization, Real-time Update.

Grocery Shop Application

The grocery shop app is a user-friendly mobile application designed to streamline the grocery shopping experience. With a clean and intuitive interface, users can easily browse a wide selection of products, add items to their virtual cart, and conveniently place orders for home delivery or in-store pickup. The app features personalized recommendations based on past purchases, real-time inventory updates, and a robust search function for efficient product discovery. Users can create and manage shopping lists, take advantage of exclusive deals and discounts, and enjoy a seamless checkout process with various payment options. The app aims to enhance the overall convenience and efficiency of grocery shopping, providing users with a modern and hassle-free way to meet their daily food and household needs. KEYWORDS - Fresh,Convenient, Variety, Quality, Affordable, Fresh produce, Organic options,Dairy products, Household essentials

IoT-Enabled Automated Teller Machine (ATM) Theft Detection and Automatic Apprehension System

In this study, we present a cutting-edge IoT- based security system tailored for Automated Teller Machines (ATMs) with a primary focus on theft prevention. The core component of the system is an Arduino Uno microcontroller that orchestrates the seamless integration of various devices. A MEMS sensor strategically placed on the ATM door detects tilting, signaling potential unauthorized access. Upon detection, a servo motor is activated, releasing harmful gases as a deterrent to intruders. Simultaneously, a loud buzzer alerts nearby individuals, and an SMS notification is dispatched to predefined contacts to notify relevant authorities. To ensure the prompt containment of the situation. To augment the system's surveillance capabilities, an IP camera is incorporated to provide real-time streaming of the ATM's surroundings. This live feed enhances monitoring and allows for remote assessment of potential security threats. The integration of an LCD display serves as an intuitive user interface, offering real-time status updates and feedback on the security system's functionality. By combining these components, our proposed IoT-based ATM security system aims to deliver a comprehensive solution that not only deters potential theft but also actively responds to security breaches, ensuring the safety and integrity of ATM operations. Keywords: ATM, Theft Detection, Servo motor, DC Motor, Harmful gasses.

Abstract 2315: AI-enabled precision oncology era: Advanced and interactive interpretation of next-gneneration sequencing (NGS) reports

Abstract Background: Next-generation sequencing (NGS) has been widely adopted in clinical practice, but there are still unmet needs among physicians regarding the interpretation and application of NGS reports. These needs include selecting multi-targeted and combination therapies, keeping up with evolving treatment options and clinical trial results, and incorporating personalized medicine based on real clinical cases. Methods: To address these challenges, we propose using large language model (LLM) to analyze and interpret clinical questions, perform semantic searches in a high-quality database, and provide customized prompts and logical outputs. Our knowledge base includes diagnostic and treatment details for 600+ tumor subtypes, 620,000+ entries, 2,000+ drug details, and 440,000+ clinical trials. The database is updated automatically using a fact engine and medical literature sources. Personalized gene interpretation is incorporated using pretraining, cosine similarity, and context learning prompts. Results: Our tool, named "SmartMTB", enables personalized interpretation of genomic testing reports for precision oncology. Users input the latest clinical features using a selection-based or question-and-answer approach. The tool identifies the treatment stage and genomic variations, provides information on mutation frequencies, sensitivity and resistance of targeted and immunotherapies, and priority selection strategies for multi-gene mutations. It matches similar clinical cases and provides real-time updates on treatment options and clinical trial literature. It also facilitates patient matching for clinical trials and considers economic factors. Conclusion: We present a groundbreaking artificial intelligence (AI) tool in cancer care that revolutionizes precision oncology. This AI system, trained on extensive datasets, swiftly and accurately analyzes and interprets complex genomic and clinical data. It enables clinicians to make informed decisions about personalized treatment strategies, significantly improving patient outcomes in cancer therapy. Citation Format: Hui Chen, Zanmei Xu, Lijuan Chen, Mingmin Wang, Peng Zhang, Fei Pang, Kai Wang. AI-enabled precision oncology era: Advanced and interactive interpretation of next-gneneration sequencing (NGS) reports [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2315.

Online Auction App

The online auction app described represents a sophisticated yet accessible platform, leveraging the robust capabilities of Firebase Real time Database and Firebase Storage. Its user- centric design fosters seamless interaction, enabling effortless login and signup processes for users eager to explore a treasure trove of valuable and rare products. Through a meticulously organized interface, users can not only peruse listings but also contribute their own prized possessions to the auction ecosystem, creating a dynamic marketplace of coveted items. The product description pages offer comprehensive insights accompanied by vivid imagery, empowering users to make informed decisions with confidence. Real-time bidding capabilities ensure an exhilarating experience, with automatic increments through proxy bidding ensuring fairness and efficiency. Moreover, the incorporation of secure payment gateways instills trust and reliability, enhancing the overall transactional experience. With a commitment to user satisfaction evident in its ad-free and free-of-charge model, this app stands as a testament to the seamless convergence of cutting- edge technology and user-centric design principles in the realm of online auctions. Keywords: Online auction app, Firebase Real time Database, Firebase Storage, User-friendly, Valuable and rare products, Login and signup, Product description, Bidding, Real-time updates, Proxy bidding, Secure payment gateways, User satisfaction, No ads, Free-of-charge, Seamless customization, Detailed information, Images, Categorization, Tags, Trustworthy transactional experience

Glass design using machine learning property models with prediction uncertainties: Nuclear waste glass formulation

The United States Department of Energy is responsible for managing the legacy nuclear waste stored in underground tanks at the Hanford Site. The waste will be separately vitrified as low-activity waste and high-level waste fractions. Waste glass formulation algorithms have been traditionally developed using partial quadratic mixture property-composition models. Recently, machine learning (ML) techniques have been used to predict glass properties and discover new glass materials for nuclear waste vitrification, and these advancements can be utilized to improve waste glass composition design. In this proof-of-principle study, ML algorithms such as Gaussian process regression (GPR) were used to interpolate glass properties (e.g., viscosity, electrical conductivity, chemical durability). After selecting appropriate sets of GPR hyper-parameters for each property, an optimization program was developed to formulate glass compositions to maximize waste loading while simultaneously satisfying property constraints. The results of the ML-based waste loadings and glass compositions were compared to those obtained using the traditional methods. Comparing to the previous glass design framework, the ML-based optimization methods offer improved glass designs and a streamlined approach to generation of optimally designed data and near real-time updates.

Web-Based Information System for Boarding House Information

Web-Based Information System for Boarding House Information is a platform designed to facilitate efficient search and management of boarding houses. This research produces a system that allows boarding house owners to present complete information regarding facilities, prices and availability of boarding rooms through a user-friendly web interface. Prospective renters can quickly find boarding houses that suit their preferences, reducing time and effort in the housing search process. This system not only provides static information about boarding houses, but also provides real-time updates regarding room availability. With the implementation of this Web-Based Information System, it is hoped that it can increase the efficiency of boarding house management, increase the accessibility of information, and provide a better experience for boarding house seekers. This system is a leading solution in supporting the development of the housing industry, providing an easy-to-use platform to meet the needs of tenants and boarding house owners in this computerized period

Ultra Fast Circuit Breaker By IJISRT

Ensuring the longevity and safety of electronic devices is crucial in the field of electrical engineering. Since they run on extremely low voltage, electronic devices—which are widely used in both home and commercial settings—are vulnerable to a number of risks, including overloading, short circuiting, and surging. It is therefore essential to put in place strong preventative measures in order to guard against future harm and guarantee continuous functioning. The design and construction of an Ultra Fast Circuit Breaker (UFCB) intended specifically for home and industrial use are presented in this study. The UFCB is designed to reduce the hazards associated with electrical problems by offering quick and dependable protection against voltage surges, overcurrent situations, and short circuits. Careful attention to detail was required during the design and execution phases of the UFCB's development. The UFCB achieves unmatched levels of reactivity and efficiency in halting electrical currents when abnormal conditions are recognized by utilizing state-of-the-art electronic components and technologies. The UFCB's strong design to resist challenging working conditions, high sensitivity to fault conditions, and ultra-fast tripping capabilities are some of its key features. Moreover, the UFCB has advanced diagnostics and monitoring features that allow proactive defect identification and real-time status updates. The implementation of the UFCB presents numerous advantages for industries and consumers alike. Economically, it lessens the possibility of expensive equipment damage and downtime due to electrical malfunctions, which results in significant cost savings on repairs and replacements. Furthermore, by reducing the amount of time needed for troubleshooting and system restoration, the UFCB improves operational efficiency, which raises productivity and overall competitiveness.

Enhancing Emergency Department Patient Experience and Flow Through Real-Time Text-Messaging Updates

Surging patient volumes in emergency departments require innovative approaches to improve patients’ experiences, given the expectations they have formed by interacting with industries such as travel, retail, and dining, where they receive personalized text messaging or in-application updates on their order or the status of their flight. This technology has not been widely adopted in the health care industry, and it presents an opportunity to meet and exceed consumer expectations. At Inova Health System, a diverse team comprising physicians, nurses, patient engagement leaders, and informaticists developed a text-messaging application for delivering real-time updates to emergency department patients and their family members during their visit. This process occurs almost entirely in the background without burdening clinical and nursing staff with additional administrative tasks. The team demonstrated substantial improvement in key patient experience metrics and revealed the broader operational utilities of automated messaging to enhance departmental efficiency.

Practical Application of Digital Twins for Transportation Asset Data Management: Case Example of a Safety Hardware Asset

Digital Twins have emerged as a transformative technology with immense potential for enhancing asset data management in various industries. The transportation industry relies heavily on properly functioning and maintaining safety hardware assets, such as crash barriers, guiderails, and road signs, among others, to ensure the safety of road users. However, traditional asset data management practices for ancillary assets often fall short of providing real-time, comprehensive insights into the condition and performance of these critical assets. Conversely, Digital Twins can analyze data for predictive and preventive maintenance using cutting-edge technologies. This will enable asset managers to proactively address potential safety concerns, plan maintenance schedules efficiently, and optimize resource allocation. This can be achieved by leveraging historical asset data and real-time condition updates and providing the Digital Twin with an information-rich analytical model. The key steps to creating an information-rich analytical model are identifying the asset data that should be collected, understanding data architecture throughout the asset lifecycle, and generating asset data governance and stewardship frameworks. As such, this paper investigates the current state of practice for ancillary asset data collection and management and investigates the generation of information-rich analytical models for guiderails, a critical roadway safety hardware. Moreover, this study demonstrates how Digital Twins can be integrated and affect the development of transportation asset management systems. The findings of this article emphasize the significance of adopting Digital Twins in transportation asset data management. The case example showcases a compelling roadmap for other transportation agencies to consider integrating digital twins into their asset management strategies.

Robust hierarchical games of linear discrete-time systems based on off-policy model-free reinforcement learning

An off-policy model-free reinforcement learning (RL) algorithm is proposed for a robust hierarchical game while considering incomplete information and input constraints. The robust hierarchical game exhibits characteristics of a Stackelberg–Nash (SN) game, where equilibrium points are designated as Stackelberg–Nash–Saddle equilibrium (SNE) points. An off-policy method is employed for the RL algorithm, addressing input constraints by using excitation input instead of real-time update polices as control inputs. Moreover, a model-free method is implemented for the off-policy RL algorithm, accounting for the challenge posed by incomplete information. The goal of this paper is to develop an off-policy model-free RL algorithm to obtain approximate SNE polices of the robust hierarchical game with incomplete information and input constraints. Furthermore, the convergence and effectiveness of the off-policy model-free RL algorithm are guaranteed by proving the equivalence of Bellman equation between nominal SNE policies and approximate SNE policies. Finally, a simulation is provided to verify the advantage of the developed algorithm.

Mapping the seamless hourly surface visibility in China: a real-time retrieval framework using a machine-learning-based stacked ensemble model

Surface visibility (SV), a key indicator of atmospheric transparency, is used widely in the fields of environmental monitoring, transportation, and aviation. However, the sparse distribution and limited number of SV monitoring sites make it difficult to fulfill the urgent need for spatiotemporally seamless fine-scale monitoring. Here, we developed the operational real-time SV retrieval (RT-SVR) framework for China that incorporates information from multiple data sources, including Chinese Land Data Assimilation System meteorological data, in situ observations, and other ancillary data. Seamless hourly SV data with 6.25-km spatial resolution are available in real time via the operational RT-SVR model, which was built using a two-layer stacked ensemble approach that combines multiple machine learning algorithms and a deep learning module. Sample-based cross-validation of the RT-SVR model on approximately 41.3 million data pairs revealed strong robustness and high accuracy, with a Pearson correlation coefficient (R) value of 0.95 and a root mean square error (RMSE) of 3.17 km. An additional hindcast-validation experiment, performed with continuous observations obtained over one year (approximately 20.8 million data pairs), demonstrated the powerful generalization capabilities of the RT-SVR model, albeit with slight degradation in performance (R = 0.85, RMSE = 5.28 km). The seamless hourly SV data with real-time update capability enable tracking of the generation, development, and dissipation of various low-SV events (e.g., fog, haze, and dust storms) in China. The developed framework might also prove useful for quantitative retrieval of aerosol-related parameters (e.g., PM2.5, PM10, and aerosol optical depth).

The Royal Treatment

The Royal Treatment