Real-time Information Research Articles

Leveraging Social Media Data for Forest Fires Sentiment Classification: A Data-Driven Method

Background: The rise in forest fires over the last two years, which is due to rise in dry weather conditions and human activities, have greatly impacted an area of 1.6 million hectares, leading to significant ecological, economic, and health issues, hence the need to improve disaster response strategies. Previous research determined the lack of coverage regarding public response during forest fires with conventional methods such as satellite images and sensor data. However, social media platforms provide real-time information generated by users, along with location information of disaster events. Sentiment analysis helps in understanding the public reactions and responses to natural disasters, thereby increasing awareness about forest fires. Objective: The purpose of this research is to assess the efficiency of Long Short-Term Memory (LSTM) method in classifying sentiment for social networks in regard to forest fires. This research aims to examine the effect of TF-IDF, unigram, and the FastText features on the effectiveness of the classification of sentiment. Methods: The precision, recall, and F1 score of 2, 3, and 4 determined in the LSTM models with commonly available sentiment analysis tools, such as the Vader Sentiment Analysis and SentiWordNet was used to evaluate the performance of the model. Results: With an improvement of roughly 10%, the four layers of the LSTM model generated the best performance for the evaluation of sentiments about forest fires. The LSTM model with FastText achieved F1, recall and precision scores of 0.649, 0.641, and 0.659, which exceeds lexicon-based method including SentiWordNet and Vader. Conclusion: The experimental results showed that the LSTM model outperformed lexicon-based methods when used to analyse the tweets related to forest fire. Additional research is required to integrate rule-based models and LSTM models to develop a more robust model for dynamic data. Keywords: Forest Fire, Disaster, Long Short-Term Memory, LSTM, Vader, SentiWordnet

Identification of a Non‐Commensurate Fractional‐Order Nonlinear System Based on the Separation Scheme

ABSTRACTThis article is aimed to study the parameter estimation problems of a non‐commensurate fractional‐order system with saturation and dead‐zone nonlinearity. In order to reduce the structural complexity of the system, the model separation scheme is used to decompose the fractional‐order nonlinear system into two subsystems, one includes the parameters of the linear part and the other includes the parameters of the nonlinear part. Then, we derive an auxiliary model separable gradient‐based iterative algorithm with the help of the model separation scheme. In addition, to improve the utilization of the real time information, an auxiliary model separable multi‐innovation gradient‐based iterative algorithm is presented based on the sliding measurement window. Finally, the feasibility of the presented algorithms is validated by numerical simulations.

Mesiri: Mephisto Early Supernovae Ia Rapid Identifier

The early time observations of Type Ia supernovae (SNe Ia) play a crucial role in investigating and resolving longstanding questions about progenitor stars and the explosion mechanisms of these events. Colors of supernovae (SNe) in the initial days after the explosion can help differentiate between different types of SNe. However, the use of true color information to identify SNe Ia at the early-time explosion is still in its infancy. The Multi-channel Photometric Survey Telescope (Mephisto) is a photometric survey telescope equipped with three CCD cameras, capable of simultaneously imaging the same patch of sky in three bands (u, g, i or v, r, z), yielding real-time colors of astronomical objects. In this paper, we introduce a new time-series classification tool named Mephisto Early Supernovae Ia Rapid Identifier (Mesiri), which, for the first time, utilizes real-time color information to distinguish early-time SNe Ia from core-collapse supernovae. Mesiri is based on the deep learning approach and can achieve an accuracy of 96.75% ± 0.79%, and AUC of 98.87% ± 0.53% in case of single epoch random observation before the peak brightness. These values reach towards perfectness if additional data points on several night observations are considered. The classification with real-time color significantly outperforms that with pseudo-color, especially at the early time, i.e., with only a few points of observations. The BiLSTM architecture shows the best performance compared to others that have been tested in this work.

Applications of Big Data and Blockchain Technology in Food Testing and Their Exploration on Educational Reform

This study reviews the applications of big data (BD) and blockchain technology in modern food testing and explores their impact on educational reform. The first part highlights the critical role of BD in ensuring food safety across the supply chain, discussing various data collection methods, such as national and international food safety databases, while addressing the challenges related to data storage and real-time information retrieval. Additionally, blockchain technology has been explored for its ability to enhance transparency, traceability, and security in the food-testing process by creating immutable records of testing data, ensuring data integrity, and reducing the risk of tampering or fraud. The second part focuses on the influence of BD and blockchain on educational reform, particularly within food science curricula. BD enables data-driven curriculum design, supporting personalized learning and more effective educational outcomes, while blockchain ensures transparency in course management and credentials. This study advocates integrating these technologies into curriculum reform to enhance both the efficiency and quality of education.

PERANCANGAN SISTEM ENTERPRISE RESOURCE PLANNING (ERP) TERHADAP PENGADAAN BAHAN BAKU, PENJUALAN, DAN PRODUKSI PADA CV. JAYA LOGAM MENGGUNAKAN SOFTWARE ODOO

CV. Jaya Logam faced various operational challenges caused by ineffective sales and raw material inventory recording systems, leading to production delays, errors in information delivery, and difficulties in monitoring and decision-making. This research was conducted to address these issues through system needs analysis, design, and testing of an Enterprise Resource Planning (ERP) system using Odoo software. The methodological process involved a detailed analysis of system requirements, system design using Unified Modelling Language (UML) diagrams to visualize the system architecture, and system testing using the Black Box Testing method to ensure the reliability and effectiveness of the designed system. The testing results showed that the developed ERP system successfully met all 12 test class parameters, indicating that the system is valid and can be implemented effectively. The implementation of this ERP system at CV. Jaya Logam resulted in significant improvements in operational efficiency and data accuracy, better business process integration, and the provision of real-time information that supports faster and more accurate decision-making processes.

Designing Privacy-Protecting System with Visual Masking Based on Investigation of Privacy Concerns in Virtual Screen Sharing Environments

Virtual meeting tools, such as Zoom, can sometimes lead to inadvertently sharing private information through screen sharing features. We conducted fundamental investigations on what concerns and strategies users have for protecting their privacy. The results indicate that while most users take actions to protect their privacy before or during meetings, a significant number of users also reported experiences with inadvertent information sharing. We also found that users avoid sharing not only personal data, such as usernames, but also information that could reveal their interests and activities, such as browsing histories or personalized recommendations. Thus, we propose a system that automatically occludes areas that users do not want to share to facilitate the management of privacy during screen sharing. We conducted a data collection experiment to construct a deep learning model to detect the areas that users do not want to share with others. We implemented our system by using this model to protect information in real time during screen sharing in virtual meeting tools.

Blockchain-based fast handover authentication protocol for Internet of Vehicles in small industrial parks

Autonomous vehicles safeguard the security and efficiency of Internet of Vehicles systems in small industrial parks by authenticating and exchanging real-time information with transportation infrastructure. Deploying a multi-server framework reduces the risk of message blocking and privacy information leakage from centralized services. However, in traditional handover authentication protocols, there are still potential security risks such as high-overhead performance issues and single point of failure attacks. Therefore, it is considered challenging to realize efficient authentication while protecting the privacy of vehicles. In this paper, we propose a secure and efficient handover authentication protocol for autonomous vehicles in a small industrial park to address the challenges. The protocol is based on blockchain and Pedersen verifiable secret sharing scheme, which not only ensures lightweight real-time interactions between autonomous vehicles and edge servers in multi-server environments, but also strictly protects the security and privacy of both vehicles and edge servers. We prove the semantic security of the protocol under the Real-Or-Random model and perform a informal analysis of its security attributes to show that it can withstand a wide range of malicious attacks. Performance evaluation shows that the proposed protocol satisfies more security requirements and has better computational efficiency and communication cost than other related protocols.

Highly conductive and sensitive alginate hydrogel strain sensors fabricated using near-field electrohydrodynamic direct-writing process

Hydrogel flexible sensors have attracted considerable attention because of their wearability, biocompatibility, and precision signal transmission capability. However, the hydrogel strain sensors fabricated by conventional printing or hand-injection methods have difficulty balancing their mechanical strength and sensing characteristics, limiting the application of hydrogel strain sensors. Herein, polyvinyl alcohol and polyacrylamide were loosely crosslinked with sodium alginate through chemical cross-linking. Subsequently, MXene nanosheets were introduced for doping, the crosslinked hydrogel conductive network was constructed, and the hydrogel strain sensors were fabricated using the electrohydrodynamic (EHD) printing method. The ions in the EHD-printed hydrogel undergo directional movement under an externally enhanced electric field, causing the formation of more uniform and dense porous conductive networks inside the hydrogel, and high electrical conductivity (0.49 S m−1) is obtained. These hydrogel strain sensors have excellent mechanical properties (tensile strength: 0.17 MPa at 787 % strain), high sensitivity (gauge factor: 1.54 at 0–100 % strain), and low detection limits (1 % strain). Furthermore, demonstrations of real-time Morse code tapping information transmission, handwriting recognition during writing, and human physiological behavior monitoring demonstrations using the fabricated sensors indicate that the EHD-printed hydrogel strain sensor method has significant potential for wearable devices and human-computer interaction applications.

Research on Anomaly Detection of Ship Behavior Based on Data Mining

With the increase in the number of ships and the intensifying maritime traffic, maritime accidents such as ship collisions and grounding occur frequently, posing serious threats to people's lives and property. Therefore, timely detection and correction of abnormal ship behaviors are of great significance for improving navigation safety. The Automatic Identification System (AIS) can provide real-time information on ships' positions, speeds, headings, and more, offering a rich data resource for data mining. Through the analysis of these data, it is possible to deeply explore the navigation patterns and characteristics of abnormal behaviors of ships. This paper applies data mining technology to the detection of abnormal ship behaviors, which not only improves the accuracy and efficiency of detection but also provides maritime regulatory authorities with richer decision-making support.

Smart composite structural insulated panels (CSIPs) with embedded piezoelectric sensors for damage localization

Coastal communities face increasing vulnerability to natural disasters, necessitating resilient construction solutions. This study presents the development of smart composite structural insulated panels (CSIPs) embedded with piezoelectric sensors for damage localization in hurricane and tornado-prone areas. Experimental studies, such as four-point bending and in-panel compression tests, were conducted to analyse the shock response and failure modes of these smart CSIPs. Material characterization informs the development of finite element models for simulation, enhancing structural design. By integrating structural health monitoring capabilities, these smart CSIPs enable continuous tracking of their response to environmental factors, improving longevity and durability. Shock identification and localization in smart CSIPs are successfully demonstrated through numerical simulations and experimental validation, showcasing the potential step towards self-resilient structures in coastal regions. This novel approach provides real-time information on structural integrity, paving the way for improved safety and predictive maintenance of buildings and infrastructure utilizing CSIPs.

Wearable Biosensor Smart Glasses Based on Augmented Reality and Eye Tracking.

With the rapid development of wearable biosensor technology, the combination of head-mounted displays and augmented reality (AR) technology has shown great potential for health monitoring and biomedical diagnosis applications. However, further optimizing its performance and improving data interaction accuracy remain crucial issues that must be addressed. In this study, we develop smart glasses based on augmented reality and eye tracking technology. Through real-time information interaction with the server, the smart glasses realize accurate scene perception and analysis of the user's intention and combine with mixed-reality display technology to provide dynamic and real-time intelligent interaction services. A multi-level hardware architecture and optimized data processing process are adopted during the research process to enhance the system's real-time accuracy. Meanwhile, combining the deep learning method with the geometric model significantly improves the system's ability to perceive user behavior and environmental information in complex environments. The experimental results show that when the distance between the subject and the display is 1 m, the eye tracking accuracy of the smart glasses can reach 1.0° with an error of no more than ±0.1°. This study demonstrates that the effective integration of AR and eye tracking technology dramatically improves the functional performance of smart glasses in multiple scenarios. Future research will further optimize smart glasses' algorithms and hardware performance, enhance their application potential in daily health monitoring and medical diagnosis, and provide more possibilities for the innovative development of wearable devices in medical and health management.

Integrating ChatGPT with Salesforce for Real-Time Market Insights on Accounts

This paper explores the integration of ChatGPT, an AI-driven natural language processing model, with Salesforce to enhance sales teams' access to real-time market information about specific accounts. By integrating ChatGPT with Salesforce through API calls, users can fetch the latest market trends, news articles, stock prices, financial data, legal filings, and acquisitions related to specific accounts directly on the Account detail page. This integration not only improves decision-making and customer engagement but also offers a scalable, automated solution that saves time and enhances data accuracy. The paper includes coding examples, architecture diagrams, and use cases to demonstrate the implementation, and it discusses key performance indicators (KPIs) for assessing its effectiveness in real-world applications. Keywords: Salesforce, ChatGPT, AI Integration, Real-Time Data, Account Insights, API, Market Trends, CRM, Natural Language Processing, Automation

The Nexus between Land Use/Cover changes and Land Surface Temperature: Remote sensing based Two-Decadal Analysis

The use of Remote Sensing (RS) data is crucial for promptly detecting and monitoring changes in both short and long term, providing real time information on Land Use/Cover (LULC), Land Surface Temperature (LST), and Enhanced Vegetation Index (EVI), adapting spatio-temporal variations. The primary focus of this study is to assess the effect of LULC changes on LST in Tashk-Bakhtegan and Maharloo (TBM) lakes basin, Iran, within 2001, 2011, and 2021, using MODIS data. Specifically, five main LULC classes involving: water body, rangeland, cropland, urban area, and bareland were identified. Beside accuracy and transition of LULC maps using User Accuracy (UA), Producer Accuracy (PA), and Kappa Coefficient (KC), the analysis included changes in LULC, Enhanced Vegetation Index (EVI), and LST, as well as the relationship among them when vegetation cover was at its peak. Moreover, a one-way analysis of variance (ANOVA) test was performed to group these variables using Duncan's test. The results showed that the accuracy of LULC maps were more than 84% for all the years. Furthermore, the conversion of croplands to rangelands showed the most significant changes, with a total of 1311.38 km2 during 2001–2021. Average EVI remained almost stable across the total area, whereas average LST generally increased by 0.65 °C. Barelands consistently exhibited the highest temperatures in all the years, followed by urban areas. While no significant changes were observed in the EVI averages, significant changes were observed in the LST across all LULC classes in different years. The results also indicated a consistent negative correlation between LST and EVI, stronger in croplands than rangelands, with Spearman's correlation coefficient of −0.714, −0.674, and −0.623 over the total area in 2001, 2011, and 2021, respectively. The findings are crucial for land planners to comprehend the effects of LULC changes on LST to adopt appropriate strategies in the TBM lakes basin.

Noninvasive optical monitoring of cerebral hemodynamics in a preclinical model of neonatal intraventricular hemorrhage.

Intraventricular hemorrhage (IVH) is a common complication in premature infants and is associated with white matter injury and long-term neurodevelopmental disabilities. Standard diagnostic tools such as cranial ultrasound and MRI are widely used in both preclinical drug development and clinical practice to detect IVH. However, these methods only provide endpoint assessments of blood accumulation and lack real-time information about dynamic changes in ventricular blood flow. This limitation could potentially result in missed opportunities to advance drug candidates that may have protective effects against IVH. In this pilot study, we aimed to develop a noninvasive optical approach using diffuse correlation spectroscopy (DCS) to monitor real-time hemodynamic changes associated with hemorrhagic and sub-hemorrhagic events in a preclinical rabbit model of IVH. DCS measurements were conducted during the experimental induction of IVH, and results were compared with ultrasound and histological analysis to validate findings. Significant changes in hemodynamics were detected in all animals subjected to IVH-inducing procedures, including those that did not show clear positive results on ultrasound. The study revealed progressively elevated coefficients of variation in blood flow, particularly driven by increased oscillations within the 0.05-0.1 Hz frequency band. These hemodynamic changes were more pronounced in animals that developed IVH, as confirmed by ultrasound. Our findings suggest that real-time optical monitoring with DCS can provide critical insights into pathological blood flow changes, offering a more sensitive and informative tool for evaluating potential therapeutics in the context of IVH.

Design and implementation of an IoT-based monitoring system for early detection of lumpy skin disease in cattle

Livestock farming serves a crucial role in global food security and sustainability, yet infections like Lumpy Skin Disease (LSD) present huge difficulties. LSD reduces the marketability and productivity of cattle, but it is not fatal. Current preventive measures that rely on routine veterinary testing can be time-consuming and miss early signs of infection, requiring a more effective management strategy first works This article presents an IoT-enabled intelligent system to prevent LSD in animals. The system uses a smart wearable sensor device to collect real-time health information including body temperature, heart rate and 3-axis motion data. Advanced analysis of data is considered to identify early diagnosis of potential LSD infection results based on deviations from normal parameters, this data prevents further infection within livestock by supporting immediate isolation. On a cloud-based online platform, and integrated into the mobile application provides a comprehensive experience and timely alerts to provide potential health issues. Furthermore, the IoT features of the system enable farmers and veterinarians to quickly guide and optimize disease control strategies, improving biosecurity protocols and overall livestock health management. This creative approach represents a significant improvement over traditional methods, providing a cost-effective, accurate and real-time solution for animal health monitoring and management.

Autonomous Vehicle Diagnostics and Support: A Framework for API-Driven Microservices

The rapid evolution of autonomous vehicles (AVs) demands robust and scalable diagnostic and support systems to ensure seamless operations, safety, and performance. This paper presents a framework for API-driven microservices tailored to address the unique diagnostic and support requirements of AVs. Traditional monolithic systems are insufficient for managing the complex, real-time data generated by AVs, necessitating a shift toward microservices architecture. By leveraging API-driven microservices, the proposed framework ensures modularity, scalability, and real-time communication between various vehicle components and external support systems. The framework is designed to support continuous vehicle monitoring, predictive maintenance, fault detection, and real-time decision-making, all while minimizing latency. APIs play a critical role in enabling interaction between different microservices, allowing for seamless data exchange across diverse platforms. This interoperability facilitates integration with third-party services, such as cloud-based diagnostics, real-time traffic information, and software updates. Additionally, the microservices architecture ensures fault isolation, meaning that failures in one service do not compromise the entire system. The framework also emphasizes the importance of cybersecurity and data privacy. Given the sensitive nature of AV data, API security protocols such as OAuth2 and TLS encryption are incorporated to safeguard communication between microservices and external systems. Furthermore, the design is scalable, allowing for the integration of future AV technologies and third-party service enhancements without disrupting existing functionalities. In conclusion, API-driven microservices provide an efficient, flexible, and secure solution for autonomous vehicle diagnostics and support. This framework has the potential to improve AV reliability, reduce downtime, and enhance safety by enabling real-time, data-driven decision-making and proactive maintenance. Future work may explore the integration of machine learning algorithms to further optimize diagnostics and predictive maintenance capabilities. Keywords: Autonomous Vehicles, Microservices, API-Driven Architecture, Diagnostics, Predictive Maintenance, Real-Time Data, Cybersecurity, Vehicle Support Systems, Modularity, Scalability.

Design and Development of a Prototype Water Level Control System for Early Flood Detection Based on The Internet of Things: a Case Study of Kambaniru Dam

Flooding can occur due to overflowing water during the rainy season, therefore early detection of the water level in dams as water runoff processing structures is needed. When heavy rain occurs it can result in flood disasters that are detrimental to communities, dam infrastructure and agricultural land. This research aims to monitor water levels online as initial information about impending flood disasters. Monitoring using Internet of Things (IoT) based technology is intended to obtain water level information in real time. In this device the HC-SR04 ultrasonic sensor is used as a water level reader, a waterflow sensor as a water discharge reader and a servo motor as a sluice gate controller. The results of this research are a prototype of a water level detection device that can provide water level information and can control sluice gates. automatically and can be controlled manually or remotely via the latest notifications in the Blynk application using the MQTT protocol. This research was carried out by taking information based on the results of direct observations and interviews with officers at Kambaniru Dam, Lambanapu Village, Kambera District. In this way, the prototype of this detection tool will be easy to use as initial information about the possibility of flooding.

Design and Build Automatic Fan Control Based Internet of Things

This research aims to create and develop an automatic fan control system based on the Internet of Things (IoT) that can regulate the room temperature well. The NodeMCU ESP8266 serve as a microcontroller for this system. The DHT22 sensor detects temperature, the L298N Driver Motor controls the speed of the 12V DC fan, and the 16x2 LCD displays temperature information in realtime. When the room temperature exceeds the threshold, the fan will automatically turn on and off when the temperature returns to normal. The Blynk app also allows users to monitor and control fans through smartphones. It allows users to manually control the fan and monitor it remotely. The results of the study show that this system works well and improves user comfort by managing the room temperature. The system also offers high flexibility with IoT integration, allowing for further development for wider applications in home automation and energy management.

AI-FEED: Prototyping an AI-Powered Platform for the Food Charity Ecosystem

This paper presents the development and functionalities of the AI-FEED web-based platform (ai-feed.ai), designed to address food and nutrition insecurity challenges within the food charity ecosystem. AI-FEED leverages advancements in artificial intelligence (AI) and blockchain technology to facilitate improved access to nutritious food and efficient resource allocation, aiming to reduce food waste and bolster community health. The initial phase involved comprehensive interviews with various stakeholders to gather insights into the ecosystem’s unique challenges and requirements. This informed the design of four distinct modules in the AI-FEED platform, each targeting the needs of one of four stakeholder groups (food charities, donors, clients, and community leaders). Prototyping and iterative feedback processes were integral to refining these modules. The food charity module assists charities in generating educational content and predicting client needs through AI-driven tools. Based on blockchain technology, the food donor module streamlines donation processes, enhances donor engagement, and provides donor recognition. The client module provides real-time information on food charity services and offers a centralized repository for nutritional information. The platform includes a comprehensive mapping and proposal system for community leaders to strategically address local food insecurity issues. AI-FEED’s integrated platform approach allows data sharing across modules, enhancing overall functionality and impact. The paper also discusses ethical considerations, potential biases in AI systems, and the transformation of AI-FEED from a research project to a sustainable entity. The AI-FEED platform exemplifies the potential of interdisciplinary collaboration and technological innovation in addressing societal challenges, particularly in improving food security and community health.

Clinician and Visitor Activity Patterns in an Intensive Care Unit Room: A Study to Examine How Ambient Monitoring Can Inform the Measurement of Delirium Severity and Escalation of Care.

The early detection of the acute deterioration of escalating illness severity is crucial for effective patient management and can significantly impact patient outcomes. Ambient sensing technology, such as computer vision, may provide real-time information that could impact early recognition and response. This study aimed to develop a computer vision model to quantify the number and type (clinician vs. visitor) of people in an intensive care unit (ICU) room, study the trajectory of their movement, and preliminarily explore its relationship with delirium as a marker of illness severity. To quantify the number of people present, we implemented a counting-by-detection supervised strategy using images from ICU rooms. This was accomplished through developing three methods: single-frame, multi-frame, and tracking-to-count. We then explored how the type of person and distribution in the room corresponded to the presence of delirium. Our designed pipeline was tested with a different set of detection models. We report model performance statistics and preliminary insights into the relationship between the number and type of persons in the ICU room and delirium. We evaluated our method and compared it with other approaches, including density estimation, counting by detection, regression methods, and their adaptability to ICU environments.