Real-time Alerts Research Articles

Climate adjusted projections of the distribution and frequency of poor air quality days for the contiguous United States

Unhealthy air quality conditions can strongly affect long-term human health and wellbeing, yet many air quality data products focus on near real-time alerts or short-term forecasts. Understanding the full state of air quality also requires examining the longer term frequency and intensity of poor air quality at ground level, and how it might change over time. We present a new modeling framework to compute climate-adjusted estimates of air quality hazards for the contiguous United States (CONUS) at 10 km horizontal resolution. The framework blends results from statistical, machine-learning, and climate-chemistry models—including a bias-adjusted version of the EPA Community Multiscale Air Quality Model (CMAQ) time series as described in (Wilson et al., 2022)—for ground-level ozone, anthropogenic fine particulate matter (PM2.5), and wildfire smoke PM2.5 into consistent estimates of days exceeding the “unhealthy for sensitive groups” (orange colored) classification on the EPA Air Quality Index for 2023 and 2053. We find that joint PM2.5 and ozone orange+ days range from 1 day to 41 days across CONUS, with a median value of 2 days, across all years. Considering all properties across CONUS, we find that 63.5% percent are exposed to at least one orange or greater day in 2023, growing to 72.1% in 2053. For a 7-day threshold, 3.8% and 5.7% of properties are exposed in 2023 and 2053, respectively. Our results also support the identification of which parts of the country are most likely to be impacted by additional climate-related air quality risks. With growing evidence that even low levels of air pollution are harmful, these results are an important step forward in empowering individuals to understand their air quality risks both now and into the future.

Detection of anomalous Nitrogen Dioxide concentration of Ankara: a Reconstruction-based approach

Air quality significantly impacts human health, particularly in urban areas, leading to global morbidity and mortality. Elevated air pollutant levels pose health risks, emphasizing the need for timely monitoring and detection. This study adopts an innovative approach to identify anomalies of daily NO2 concentration levels in a district of Ankara, Turkey. Leveraging both traditional statistical approaches and state-of-the-art techniques, the research aims to provide real-time alerts. Employing a multivariate strategy, the study generates new features based on historical and current data, and incorporates periodic variables, as well. Among the methods explored, Variational Autoencoder emerges as noteworthy, exhibiting superior performance with %98 recall, %82 precision and %0.12 false alarm rate. This approach not only demonstrates a high true positive rate, enhancing its efficacy in anomaly detection but also effectively mitigates false alarms, preventing alert fatigue. By using advanced methodologies with a focus on NO2 levels, the study contributes to proactive measures for public health, enabling prompt responses to potential air quality issues.

Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial.

Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent directly to front-line providers would reduce escalations. Single-center prospective pragmatic nonrandomized clustered clinical trial. Academic tertiary care medical center. Adult patients admitted to four medical-surgical units. Assignment to intervention or control arms was determined by initial unit admission. Real-time alerts stratified according to predicted likelihood of deterioration sent either to the primary team or directly to the rapid response team (RRT). Clinical care and interventions were at the providers' discretion. For the control units, alerts were generated but not sent, and standard RRT activation criteria were used. The primary outcome was the rate of escalation per 1000 patient bed days. Secondary outcomes included the frequency of orders for fluids, medications, and diagnostic tests, and combined in-hospital and 30-day mortality. Propensity score modeling with stabilized inverse probability of treatment weight (IPTW) was used to account for differences between groups. Data from 2740 patients enrolled between July 2019 and March 2020 were analyzed (1488 intervention, 1252 control). Average age was 66.3 years and 1428 participants (52%) were female. The rate of escalation was 12.3 vs. 11.3 per 1000 patient bed days (difference, 1.0; 95% CI, -2.8 to 4.7) and IPTW adjusted incidence rate ratio 1.43 (95% CI, 1.16-1.78; p < 0.001). Patients in the intervention group were more likely to receive cardiovascular medication orders (16.1% vs. 11.3%; 4.7%; 95% CI, 2.1-7.4%) and IPTW adjusted relative risk (RR) (1.74; 95% CI, 1.39-2.18; p < 0.001). Combined in-hospital and 30-day-mortality was lower in the intervention group (7% vs. 9.3%; -2.4%; 95% CI, -4.5% to -0.2%) and IPTW adjusted RR (0.76; 95% CI, 0.58-0.99; p = 0.045). Real-time machine learning alerts do not reduce the rate of escalation but may reduce mortality.

Effect of a smart temperature logger on correctly storing biological disease-modifying antirheumatic drugs at home: a pre-post study

ObjectivesBiological disease-modifying antirheumatic drugs (bDMARDs) require specific storage temperatures, but are frequently stored outside the recommended range of 2–8°C. As incorrect storage may affect therapy effectiveness and consequently lead to...

"AGRIVIGIL: ENHANCED THEFT DETERRENCE FOR IRRIGATION MACHINERY"

The rampant theft of water pumps in rural India poses a severe challenge to farmers, significantly undermining their irrigation capabilities and, by extension, crop health and productivity—particularly in regions susceptible to unpredictable rainfall and drought. This theft not only inflicts immediate equipment loss but also propels farmers, who typically operate within narrow financial margins, into further economic hardship due to the inability to afford replacements. The consequent delays in planting and irrigation lead to reduced crop yields and heightened financial strain. The vulnerability of these essential tools is amplified by the remote locations of farms and a pronounced lack of security measures, rendering water pumps an easy target for thieves. This dire situation underscores the urgent need for a comprehensive water pump theft deterrence system, which is essential not only for protecting valuable agricultural equipment but also for safeguarding the livelihoods of farmers. By leveraging advanced technology to provide real-time alerts and deter potential thefts, such a system can serve as a critical defence mechanism, enabling farmers to concentrate on their vital work without the looming threat of loss. The implementation of effective theft deterrence measures, supported by community, law enforcement, and policy maker collaboration, is crucial in ensuring the economic well-being and sustainability of rural Indian agriculture.

Smart Public Toilet Management and Monitoring System using IOT

This paper addresses the pervasive issue of inadequate public sanitation in various regions of India and slums, marked by rampant urination and poorly maintained public toilets. Despite significant governmental expenditure and efforts to maintain cleanliness, the absence of a centralized monitoring mechanism renders these endeavors ineffective. To overcome this challenge, the paper proposes an Internet of Things (IOT)-based toilet monitoring system utilizing a web server and a mobile cleaner application. The system aims to simplify monitoring processes for toilet cleaners and administrators by tracking multiple cleaning metrics and providing real-time alerts based on user input. The motivation behind this project stems from the alarming state of hygiene in government schools, where students or teachers often bear the responsibility of cleaning due to labor shortages and insufficient funds. The relevance of the proposed system lies in its potential to revolutionize the maintenance of school and public toilets, addressing the unhygienic conditions prevalent in India. The novelty of the system includes features like automatic water flush, UV sanitization, timely floor cleaning, automatic sanitary pad vending, water monitoring, smell detection, and automatic ventilation, all facilitated through IOT technology. By presenting a cost-effective and efficient solution, this research contributes to the improvement of public sanitation in various settings, including homes, schools, colleges, hospitals, businesses, and industries, thereby promoting urban sanitation in the contemporary environment

Enhancing Traffic Management in Smart Cities: A Cyber-Physical Approach

The challenges of managing traffic in rapidly urbanizing cities with increasing populations. It highlights the need for advanced technologies and frameworks in smart cities to develop intelligent traffic management systems. The proposed approach is a cyber-physical framework that integrates physical infrastructure with digital infrastructure for real-time data collection, analysis, and decision-making. By using sensors, IoT devices, and data analytics, the system captures and processes traffic-related information to predict traffic flow, optimize signal timings, and recommend alternate routes. The system also communicates with connected vehicles to provide real-time updates and alerts, improving driver safety and reducing congestion. The cyber-physical approach allows seamless integration with other smart city systems for efficient coordination and resource allocation. Implementing this system offers benefits such as reduced travel times, improved air quality, enhanced road safety, and increased overall efficiency in urban transportation. It can adapt and evolve by learning from new data inputs and incorporating emerging technologies, ensuring sustainability and scalability. Overall, this paper presents a comprehensive framework for enhancing traffic management in smart cities, transforming urban transportation, and improving residents' quality of life while making cities more sustainable and resilient.

Driving Behaviour Estimation System Considering the Effect of Road Geometry by Means of Deep NN and Hotelling Transform

In this work, an intelligent hybrid model is proposed to identify hazardous or inattentive driving manoeuvres on roads, with the final goal being to increase and ensure travellers’ safety and comfort. The estimation is based on the effects that road geometry may have on vehicle accelerations, displacements and dynamics. The outputs of the intelligent systems proposed are how the type of driving can be characterized as normal, careless or distracted. The intelligent system consists of an LSTM (Long Short-Term Memory) neural network in a first step that distinguishes between normal and abnormal driving behaviour and then a second module that classifies abnormal forms of driving as aggressive or inattentive, with the latter implemented with another LSTM, a CNN (convolutional neural network) or the Hotelling transform. They are applied to some of the characteristics of vehicle dynamics to estimate the driving behaviour. Smartphone inertial sensors such as GPS, accelerometers and gyroscopes are used to measure these vehicle characteristics and to identify driving events in manoeuvres. Specifically, the critical acceleration due to the influence of the road geometry can be measured with inertial sensors, and then, this road acceleration with the lateral acceleration allows us to estimate the driver’s perceived acceleration. This perceived acceleration affects the driving style and, consequently, the estimation of the appropriate speed to travel on that road. There is use of both a traditional two-lane and a motorway route located in the Madrid region of Spain. Driving behaviour is determined by considering how changes in road geometry may affect one’s driving style and, consequently, the estimation of the proper speed. The results obtained with some of the proposed configurations of the intelligent hybrid system reach an accuracy of 97.21% in detecting dangerous driving or driving with a certain risk. This could allow generating real-time alerts for potentially dangerous or inattentive manoeuvres, leading to safer and more appropriate driving.

Real-Time Monitoring System with Digital Umbrella.

This research project focuses on enhancing the safety and convenience of street sellers and outdoor campers by developing a real-time monitoring system incorporating GSM, GPRS, GPS, and MCU nodes. The system provides live location tracking and an emergency button for immediate assistance. Designed for vegetable sellers, roadside vendors, and tourists, the project integrates security features and amenities like FM radio, mobile charging, lighting, and fan systems into an automated panel powered by solar energy. The solar umbrella captures and stores solar energy during the day to power LED lights and other devices at night, promoting sustainability. This innovative solution combines practicality with advanced technology, ensuring continuous energy supply and real-time emergency alerts, thus significantly improving user safety and convenience.

Android App for People with IDD to Report Abuse

Abuse is a significant concern within the Intellectual and Developmental Disabilities (IDD) community, often accompanied by communication challenges and vulnerability. Our project, the "Speak Up IDD" Android application, is designed to empower and safeguard individuals with IDD by offering a platform to educating them, reporting abuse and seek assistance. This innovative app addresses the urgent issue of abuse within the IDD community. "Speak Up IDD" focuses on providing individuals with IDD a means to express their concerns, ensuring their safety and well-being. The app features a user-friendly interface that caters to varying communication abilities. It offers diverse reporting options, including text-to-speech capabilities, symbol-based communication boards, and voice recognition, ensuring accessibility for users with diverse communication needs. The central function of the app revolves around abuse reporting. Users can confidentially report instances of abuse, whether physical, emotional, or neglect, through a secure and private channel. The app guides users through the reporting process, providing prompts and assistance while safeguarding their anonymity and protection. To further support users, "Speak Up IDD" includes real-time alerts and notifications that can be sent to designated contacts, such as caregivers, family members, or support organizations, ensuring swift responses to reported incidents. The "Speak Up IDD" app is more than just a reporting tool; it is a lifeline for those in the IDD community who are at risk of abuse. Its mission is to create a safer environment, raise awareness, and ensure that no individual with IDD suffers in silence. By providing them with the means to speak up, we aim to empower individuals with IDD, protect their rights, and foster a more supportive and secure community for all

IoT Water Quality Monitoring and Control System in Moving Bed Biofilm Reactor to Reduce Total Ammonia Nitrogen.

Traditional aquaculture systems appear challenged by the high levels of total ammoniacal nitrogen (TAN) produced, which can harm aquatic life. As demand for global fish production continues to increase, farmers should adopt recirculating aquaculture systems (RAS) equipped with biofilters to improve the water quality of the culture. The biofilter plays a crucial role in ammonia removal. Therefore, a biofilter such as a moving bed biofilm reactor (MBBR) biofilter is usually used in the RAS to reduce ammonia. However, the disadvantage of biofilter operation is that it requires an automatic system with a water quality monitoring and control system to ensure optimal performance. Therefore, this study focuses on developing an Internet of Things (IoT) system to monitor and control water quality to achieve optimal biofilm performance in laboratory-scale MBBR. From 35 days into the experiment, water quality was maintained by an aerator's on/off control to provide oxygen levels suitable for the aquatic environment while monitoring the pH, temperature, and total dissolved solids (TDS). When the amount of dissolved oxygen (DO) in the MBBR was optimal, the highest TAN removal efficiency was 50%, with the biofilm thickness reaching 119.88 μm. The forthcoming applications of the IoT water quality monitoring and control system in MBBR enable farmers to set up a system in RAS that can perform real-time measurements, alerts, and adjustments of critical water quality parameters such as TAN levels.

Disaster Guard for Disaster Management

The "Disaster Guard" project presents a comprehensive disaster management mobile app designed to empower individuals and communities in navigating the challenges of disaster preparedness, response, and recovery. With a focus on fostering resilience, the app integrates cutting-edge features, including real-time disaster alerts, location-based services, and a user-friendly emergency services directory. The inclusion of SOS and distress signaling, coupled with two-way communication channels, ensures swift assistance during critical moments. Leveraging data prediction and early warning systems, the platform enables proactive planning, enhancing the overall preparedness for potential disasters.

Coastal Data Information Program: advances in measuring and modeling wave activity, climate, and extremes

ABSTRACT The Coastal Data Information Program (CDIP) provides wave data to the public in near real-time, maintains an operational wave model for the California coast, and is engaged in ocean wave research on a global scale. CDIP’s array of moored wave buoy stations are instrumented with Datawell Waveriders. CDIP’s recent work to improve monitoring capabilities for directional surface wave spectra, surface current, and temperature is described. This includes data quality assurance and control, real-time alerts, telemetry and dissemination information technology infrastructure and methodology, data visualization tools, and mooring and instrumentation innovation. CDIP’s goal is to maximize data reliability, availability, accuracy, and precision. Validation of commonly used wave models – operational, hindcast, and forecast – with CDIP data is automated to provide assessments of relative skills at a variety of coastal locations and wave conditions. Wave heights measured by the buoys typically exceed modeled heights during the most energetic events, such as hurricanes, nor’easters, and bomb cyclones. In a practical validation example, the global wave model used to drive CDIP’s California coastal wave forecasts was recently changed, based on comparisons against buoy data. Forecast performance has improved for swell events, which are a challenge to model accurately in the Southern California Bight.

Bridging the Gap between Technology and Medicine through the Revolutionary Impact of the Healthcare Internet of Things on Remote Patient Monitoring

Healthcare Internet of Things (IoT) initiatives that aim to integrate technology and medicine are shaking the sector to its foundations. The revolutionary potential of the proposed strategy is shown here as we investigate the far-reaching consequences of the Healthcare IoT on remote patient monitoring. The beginning sets the stage by underlining the significance of bridging the gap between technology and medicine. Our multi-pronged approach comprises Internet of Things (IoT) remote monitoring, cloud-based analysis, artificial intelligence (AI) integrated diagnostics, real-time alerts, and predictive analytics. Our study's results demonstrate that the proposed approach is superior to the status quo. The area of remote patient monitoring has profited considerably from the employment of traditional approaches, such as the fusion of data from wearable sensors, analysis in the cloud, diagnostics that utilize artificial intelligence, real-time monitoring, predictive modeling, and smart alarm systems. The suggested strategy, however, performs very well across all of the most important measures of assessment. Comparatively, the accuracy rate of the conventional wearable sensor fusion approach was only 76%, whereas our suggested method reached 89%. Our strategy was also more accurate than the standard approach (88% vs. 73%). When compared to the recall rate of 68% produced by conventional methods, our suggested strategy significantly outperformed the competition. It's a great option for hospitals and clinics since it improves diagnostic precision and speed without breaking the bank.

Lessons From the Household Humidifier Disinfectant Tragedy (HHDT) With Focus on the Chemical Poisoning Surveillance System: Review and Recommendation.

Lessons learned from the Household Humidifier Disinfectant Tragedy (HHDT) in Korea, which poisoned thousands of citizens over a period of years, necessitated an examination of national poison prevention and surveillance systems. The objectives of this study are to identify essential changes needed in chemical poisoning prevention regulations and surveillance systems for effective poison control by comparing recent trends in international poison control center (PCC) operations, and to delineate the critical elements for establishing a state-of-the-art poison control surveillance system in Korea based on recent advances in PCCs with toxicovigilance. A comprehensive review of Korea's regulatory and surveillance systems for chemical health hazards, with a focus on household products under the HHDT, was conducted. A review of toxicovigilance systems in major countries shows that creating an effective national PCC requires key elements: a centralized database of toxic substances and poisoning cases, mandatory or voluntary reporting of poisoning cases, real-time alerts, collaboration among health organizations, and targeted follow-up of poisoned individuals. Significant deficiencies in Korea's legislation, toxicological data management, and poisoning surveillance systems, explained the inadequate response of the Korean government to the HHDT for nearly 17 years until the end of 2011. Based on a review of PCC toxicovigilance systems in major countries, a national framework with five core components is recommended for establishing a modern comprehensive Korea PCC system with toxicovigilance capacity. The core components include establishment of a centralized database of toxic substances information and clinical poisoning cases, implementation of mandatory or permissive reporting of poisoning cases, real-time alert mechanisms, collaborative systems among health-related organizations, and clinical follow-up of poisoned sub-groups. A rationale and framework for a state-of-the-art national Korean PCC with toxicovigilance is justified and offered. This proposed system could assist neighboring countries in establishing their own sophisticated, globally integrated PCC networks.

Cloud-Enabled Real-Time Monitoring and Alert System for Primary Network Resource Scheduling and Large-Scale Users

Cloud-Enabled Real-Time Monitoring and Alert System for Primary Network Resource Scheduling and Large-Scale Users

Upgrades of the ESPERTA forecast tool for solar proton events

The Empirical model for Solar Proton Events Real Time Alert (ESPERTA) exploits three solar parameters (flare longitude, soft X-ray fluence, and radio fluence) to provide a timely prediction for the occurrence of solar proton events (SPEs, i.e., when the &gt;10MeV proton flux is ≥10 pfu) after the emission of a ≥M2 flare. In addition, it makes a prediction for the most dangerous SPEs for which the &gt;10 MeV proton flux is ≥100 pfu. In this paper, we study two different ways to upgrade the ESPERTA model and implement it in real time: 1) by using ground based observations from the LOFAR stations; 2) by applying a novel machine learning algorithm to flare-based parameters to provide early warnings of SPE occurrence together with a fine-tuned radiation storm level. As a last step, we perform a preliminary study using a neural network to forecast the proton flux 1-hour ahead to complement the ESPERTA tool. We evaluate the models over flare and SPE data covering the last two solar cycles and discuss their performance, limits, and advantages.

Leveraging Artificial Intelligence to Reduce Human-Animal Conflict in India

The case focuses on the use of artificial intelligence (AI) and computer vision (CV) technologies to solve a socially complex issue of human-animal conflict in the geographical regions surrounding the Tadoba Andhari Tiger Reserve (TATR) in Maharashtra state in central India. Due to an increasing tiger population, the villages surrounding the TATR often encountered tigers that strayed outside the reserve, sometimes leading to injuries or fatalities either to the humans or the wild animals involved. Valiance, a technology company, collaborated with the Forest department and other local stakeholders to create a computer vision based AI virtual wall system as a pilot project. The technology solution monitored the location 24hrs a day 7 days a week and had the capability of identifying tigers, leopards, and other predators, and sending out automated, real time alerts to help prevent human-animal conflict situations. Based on the outcome of the pilot, the solution could be refined and then a wider rollout could be planned.

Machine Learning based Precision Agriculture Model for Farm Irrigation to Optimize Water Usage

The food demand is ever increasing each year and to meet this demand precision agricultural approach using machine learning tools play an important role. Precision irrigation systems integrate cutting-edge technologies, such as sensors, controllers, data analytics and internet, to achieve sustainability in agriculture and maximize water use so as to improve crop production while minimizing water wastage and climate impact. The main purpose of this article is to find out the precise water requirements for a particular area of the land by using soil moisture sensors. These sensors provide real-time data that is transmitted to a central control unit, which utilizes data driven algorithms to analyze moisture levels in the soil and controls the water supply. Furthermore, the model developed offers remote monitoring and control capabilities, enabling farmers to access and manage the system from anywhere using mobile or web application. This feature allows farmers to remotely adjust irrigation schedules, receive real-time alerts and notifications, and track water consumption, promoting convenient and efficient management of water resources. Thus by using effective water management techniques such as precision irrigation, controlling the water quality, will accomplish optimizing water usage and intern optimizes the yield.

An IoT-based animal detection system using an interdisciplinary approach

Nowadays, educational institutions particularly colleges, engaged with students and staff, frequently confront various security challenges in their day-to-day activities. One prominent concern involves the threat of animal bites on the campus. In response to this issue, campus management has traditionally resorted to human patrols and physical barriers to deter animals. To address this multifaceted security challenge, the proposed method “An IoT-based Animal Detection System Using Interdisciplinary Approaches” introduces an innovative solution that leverages the power of IoT technology to enhance campus safety and security significantly. The system deploys a surveillance robot equipped with ultrasonic sensors and ESP32 cameras, employingthe machine learning technique R-CNN for Animal Detection. This proposed method uses an interdisciplinary approach to develop an animal detection system capable of identifying and classifying various species. This proposed method aims to revolutionize campus security by seamlessly integrating advanced technology, mitigating risks proactively, streamlining processes through automation, and presenting a cost-effective alternative to traditional security approaches. Beyond the traditional methods, the proposed system achieves an impressive accuracy rate of animal detection approximately 97.6% enabling real-time alerts through push notifications to security personnel upon detection.