A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment.

Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients' health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients' health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients' health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients' sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.

Monitoring Value-at-Risk and Expected Shortfall Forecasts

This paper introduces formal monitoring procedures as a risk-management tool. Continuously monitoring risk forecasts allows practitioners to swiftly review and update their forecasting procedures as soon as forecasts turn inadequate. Similarly, regulators may take timely action in case reported risk forecasts become poor. Extant (one-shot) backtests require, however, that all data are available prior to testing and are not informative of when inadequacies might have occurred. To monitor value-at-risk and expected shortfall forecasts "online"-that is, as new observations become available-we construct sequential testing procedures. We derive the exact finite-sample distributions of the proposed procedures and discuss the suitability of asymptotic approximations. Simulations demonstrate good behavior of our exact procedures in finite samples. An empirical application to major stock indices during the COVID-19 pandemic illustrates the economic benefits of our monitoring approach.

Ecology in the age of climate change: Challenges facing the effective implementation of nature-based solutions in terrestrial ecosystems

At this stage of the Great Acceleration of the Anthropocene, humanity is experiencing the global issues of worsening climate change impacts, devastating damage from more frequent and severe natural disasters and the COVID-19 pandemic, all of which are attributable to ecosystem degradation and biodiversity loss. The global community recognises that these issues pose severe societal and economic risks. “Nature-based solutions” have been posited as a means to address these threats. Nature-based solutions utilise natural terrestrial ecosystem functions to provide environmental, social and economic benefits at low cost. The growing social demand for nature-based solutions constitutes an opportunity for the field of ecology to expand beyond the conventional focus on biodiversity and conservation and shift to presenting biodiversity and ecosystem functions as the basis of human well-being and social sustainability. We sought to identify a trajectory for ecological research that is aimed at contributing to the effective implementation of nature-based solutions. First, we summarise current social needs related to terrestrial ecosystem utilisation. Next, we provide an overview of existing literature and knowledge regarding biodiversity and terrestrial ecosystem function, which are critical to nature-based solutions. Finally, we identify outstanding ecological hurdles to the implementation of these strategies and propose a way forward based on our findings. We explain that any basic presentation of ecological processes requires addressing the impacts of climate change and the interrelatedness of biodiversity, climate and social systems. Enhanced ecological process models are critical for linking biodiversity and ecosystems with climate and social systems. It is crucial to establish a framework that embeds monitoring systems, data infrastructure and delivery systems within society to mobilise terrestrial ecosystem and biodiversity data and results. Furthermore, the implementation of nature-based solutions must include acknowledging trade-offs in objectives and transdisciplinary research with other fields and stakeholders with the shared goal of transformative change. Ecological research must demonstrate more clearly how terrestrial biodiversity and ecosystems are linked to human health and well-being, as well as how they are affected by production and consumption systems. In the age of climate change, the knowledge and tools of the ecologist form the foundation of nature-based solutions and provide an indispensable theoretical basis for this approach.Alternate :æŠ„éŒ²äººæ–°ä¸–ã®å¤§åŠ é€Ÿã¨ã‚‚å‘¼ã°ã‚Œã‚‹æ°—å€™å¤‰å‹•ã®æ™‚ä»£ã«ãŠã„ã¦ã€æ°—å€™å¤‰å‹•å½±éŸ¿ã®é¡•åœ¨åŒ–ã€è‡ªç„¶ç½å®³ã®æ¿€ç”šåŒ–ãƒ»é »ç™ºåŒ–ã€COVID-19の世界的流行などの地球規模の問題が増大している。国際社会では、ã"ã‚Œã‚‰ã®å•é¡Œã¯ç”Ÿæ…‹ç³»ã®åŠ£åŒ–ã‚„ç”Ÿç‰©å¤šæ§˜æ€§ã®æå¤±ãŒè¦å› ã§ã‚ã‚‹ã"と、そして社会経済にも多大な損害ã‚'与える大きなリスクであるã"とが共通の認識となりつつある。そのような状況ã‚'åæ˜ ã—ã€é™¸åŸŸç”Ÿæ…‹ç³»ã®å¤šé¢çš„ãªæ©Ÿèƒ½ã‚'活用するã"とで、低いコストでç'°å¢ƒãƒ»ç¤¾ä¼šãƒ»çµŒæ¸ˆã«ä¾¿ç›Šã‚'もたらし、社会が抱える複数の課題の解決に貢献する「自然ã‚'基盤とした解決策」という新しい概念に大きな期待が寄せられている。ã"の解決策への社会的なニーズの高まりは、生態学が長年取り組ã‚"できた生物多様性や生態系の保全に関する課題ã‚'超えて、生態学が生物多様性や生態系が豊かな人é–"社会ã‚'継続し発展させる知的基盤となるã"とや、生態学の社会的有用性ã‚'示す機会である。そã"で本稿では、気候変動時代における「自然ã‚'åŸºç›¤ã¨ã—ãŸè§£æ±ºç­–ã€ã®å®Ÿè·µã«å‘ã‘ãŸç”Ÿæ…‹å­¦ç ”ç©¶ã®æ–¹å‘ã¥ã‘ã‚'目的とし、陸域生態系の活用に対する社会的なニーズの現状ã‚'概観する。その上で、「自然ã‚'åŸºç›¤ã¨ã—ãŸè§£æ±ºç­–ã€ã®éµã¨ãªã‚‹é™¸åŸŸç”Ÿæ ‹ç³»ã®ç”Ÿç‰©å¤šæ§˜æ€§ã‚„ç”Ÿæ…‹ç³»æ©Ÿèƒ½ã«é–¢ã™ã‚‹çŸ¥è¦‹ã‚'整理して課題ã‚'抽出し、ã"れらã‚'è¸ã¾ãˆã¦ä»Šå¾Œã®ç”Ÿæ…‹å­¦ç ”ç©¶ã®æ–¹å‘æ€§ã‚'å…·ä½"的に示す。まず、現象の基礎的な理解という観点からは、生物多様性ã‚'含む陸域生態系と気候システムや社会システムとの相äº'関係性ã‚'含めた包括的な気候変動影響のメカニズムの解明と、予測・評価のためのプロセスモデルの高度化ã‚'進めるã"と、そして同時に、陸域生態系と生物多様性の変化ã‚'ç¤ºã™ãŸã‚ã®åŠ¹æžœçš„ãªãƒ¢ãƒ‹ã‚¿ãƒªãƒ³ã‚°ã¨æƒ…å ±åŸºç›¤ã®å¼·åŒ–ã‚'行い、データや分析結果ã‚'社会に還元するフレームワークã‚'構築するã"ã¨ãŒå„ªå…ˆäº‹é …ã§ã‚ã‚‹ã€‚ã‚ˆã‚Šå®Ÿè·µçš„ãªè¦³ç‚¹ã‹ã‚‰ã¯ã€ã€Œè‡ªç„¶ã‚'基盤とした解決策」の実装や社会変革などにおいて共通の目標ã‚'ã‚‚ã¤ä»–åˆ†é‡Žã¨ã®å­¦éš›ç ”ç©¶ã‚'積極的に行うã"とにより、実装における目的é–"のトレードオフã‚'示すã"と、健康・福祉の課題や生産・消費システムの中での陸域生態系や生物多様性への影響や役割ã‚'示すã"ã¨ãªã©ãŒå„ªå…ˆäº‹é …ã¨ãªã‚‹ã€‚æ°—å€™å¤‰å‹•ã«ä»£è¡¨ã•ã‚Œã‚‹ä¸ç¢ºå®Ÿæ€§ã®é«˜ã„ç'°å¢ƒä¸‹ã§ã€åŠ¹æžœçš„な「自然ã‚'åŸºç›¤ã¨ã—ãŸè§£æ±ºç­–ã€ã®å®Ÿæ–½ãŸã‚ã«ã¯ã€ãã®ç§‘å­¦çš„åŸºç›¤ã¨ãªã‚‹ç”Ÿæ…‹å­¦ã®çŸ¥è¦‹ã¨ãƒ„ãƒ¼ãƒ«ã¯ä¸å¯æ¬ ã§ã‚ã‚Šã€ã¾ãŸãã®å®Ÿè£…ã‚'通じた社会変革へのé"筋においても生態学の貢献が期待されている。

Noncontact liquid–solid nanogenerators as self-powered droplet sensors

Liquid–solid triboelectric nanogenerators (L–S TENGs) can generate corresponding electrical signal responses through the contact separation of droplets and dielectrics and have a wide range of applications in energy harvesting and self-powered sensing. However, the contact between the droplet and the electret will cause the contact L–S TENG's performance degradation or even failure. Here we report a noncontact triboelectric nanogenerator (NCLS-TENG) that can effectively sense droplet stimuli without contact with droplets and convert them into electrical energy or corresponding electrical signals. Since there is no contact between the droplet and the dielectric, it can continuously and stably generate a signal output. To verify the feasibility of NCLS-TENG, we demonstrate the modified murphy's dropper as a smart infusion monitoring system. The smart infusion monitoring system can effectively identify information such as the type, concentration, and frequency of droplets. NCLS-TENG show great potential in smart medical, smart wearable and other fields.

Meeting public health objectives and supporting the resumption of tourist activity through COVID-19: a triangular perspective

Non-pharmaceutical interventions (NPIs) implemented during the COVID-19 pandemic (and previous health crises) have included measures to restrict interaction between people and minimize non-essential mobility. Therefore, tourism travel is one of the main areas affected by the restrictions. Even when the majority of the population is vaccinated, some risk of infection will remain, and governments are obliged to consider NPI measures that balance the health risk of outbreaks against the economic and social benefits of resuming tourist activity. This study analyzes the effect of each of four categories of NPIs (Social Distancing;Public Healthcare-System Improvements;Tourist Controls;and Capacity and Opening-Hours Regulation) on three major objectives (the resumption of tourism activity;tourist travel intention;and the minimization of public health risk), taking a triangular perspective (destination managers, domestic tourists, and public healthcare managers, respectively). While it is difficult to fulfil public healthcare objectives while simultaneously responding to the economic interests of tourism-industry stakeholders, the study finds that, under vaccinated-population conditions, tourist controls (e.g. COVID Certificate) alongside improvements to the public healthcare system (e.g. adequate resourcing and an efficient epidemiological monitoring system) could constitute a viable combination of measures.

Machine Learning based COVID-19 Patients Monitoring System using CNN

Ever since an anonymous disease broke out in late 2019, the whole world seems to have own ceased functioning. COVID-19 patients are proliferating at an exponential rate, straining healthcare systems around the world. Traditional techniques of screening every patient with a respiratory disease is unfeasible due to the restricted number of testing kits available. We presented a method for recognizing COVID-19 infected patients utilizing data collected from chest X-ray scans to overcome this challenge. This attempt will benefit both patients and doctors significantly. It becomes even more critical in nations where the number of people affected far outnumbers the number of laboratory kits available to test the disease. When current systems are confused whether to retain the patient on the ward with other patients or isolate them in COVID-19 zones, this could be useful in an inpatient setting. Apart from that, it would aid in the identification of patients with a high risk of COVID-19 and a false negative RT-PCR who would require a repeat. Most of the COVID-19 detection methods use traditional image classification models. This has the issue of low detection accuracy and incorrect COVID-19 detection. This method starts with a chest x-ray enhancement procedure like this: Rotation, translation, random conversion. The survey's accuracy has considerably increased as a result of this. For the COVID-19 infection, our model has 97.5 percent accuracy and 100 percent sensitivity (recall). In addition, we used a visualization technique that distinguishes our model from the others by displaying contaminated areas in X-ray pictures. © 2022 IEEE.

Recent Urban Issues Related to Particulate Matter in Ploiesti City, Romania

The present work aimed to assess the ambient levels of air pollution with particulate matter for both mass concentrations and number of particles for various fractions in Ploiesti city during the lockdown period determined by the COVID-19 pandemic (March–June 2020). The PM10 continuously monitored data was retrieved from four air quality automatic stations that are connected to the Romanian National Network for Monitoring Air Quality and located in the city. Because no other information was available for other more dangerous fractions, we used monitoring campaigns employing the Lighthouse 3016 IAQ particle counter near the locations of monitoring stations assessing size-segregated mass fraction concentrations (PM0.5, PM1, PM2.5, PM5, PM10, and TPM) and particle number concentration (differential Δ) range between 0.3 and 10 microns during the specified timeline between 8.00 and 11.00 a.m., which were considered the morning rush hours interval. Interpolation maps estimating the spatial distribution of the mass concentrations of various PM fractions and particle number concentration were drawn using the IDW algorithm in ArcGIS 10.8.2. Regarding the particle count of 0.5 microns during the lockdown, the smallest number was recorded when the restriction of citizens' movement was declared (24 March 2020), which was 5.8-times lower (17,301.3 particles/cm3) compared to a common day outside the lockdown period (100,047.3 particles/cm3). Similar results were observed for other particle sizes. Regarding the spatial distribution of the mass concentrations, the smaller fractions were higher in the middle of the city and west (PM0.5, PM1, and PM2.5) while the PM10 was more concentrated in the west. These are strongly related to traffic patterns. The analysis is useful to establish the impact of PM and the assessment of urban exposure and better air quality planning. Long-term exposure to PM in conjunction with other dangerous air pollutants in urban aerosols of Ploiesti can lead to potential adverse effects on the population, especially for residents located in the most impacted areas.

Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning

The global spread of COVID-19 highlights the urgency of quickly finding drugs and vaccines and suggests that similar challenges will arise in the future. This underscores the need for ongoing efforts to overcome the obstacles involved in the development of potential treatments. Although some progress has been made in the use of Artificial Intelligence (AI) in drug discovery, virologists, pharmaceutical companies, and investors seek more long-term solutions and greater investment in emerging technologies. One potential solution to aid in the drug-development process is to combine the capabilities of the Internet of Medical Things (IoMT), edge computing (EC), and deep learning (DL). Some practical frameworks and techniques utilizing EC, IoMT, and DL have been proposed for the monitoring and tracking of infected individuals or high-risk areas. However, these technologies have not been widely utilized in drug clinical trials. Given the time-consuming nature of traditional drug- and vaccine-development methods, there is a need for a new AI-based platform that can revolutionize the industry. One approach involves utilizing smartphones equipped with medical sensors to collect and transmit real-time physiological and healthcare information on clinical-trial participants to the nearest edge nodes (EN). This allows the verification of a vast amount of medical data for a large number of individuals in a short time frame, without the restrictions of latency, bandwidth, or security constraints. The collected information can be monitored by physicians and researchers to assess a vaccine's performance.

Relationship between Lightning, Lightning Fire and Human Activities

Lightning is the main source of natural fire, and lightning fire and other types of forest fires together constitute the global forest fire system. It is generally believed that lightning fire, as a natural fire source, has nothing to do with human beings and is different from man-made fire sources, but in fact, human activities have inextricable links with the occurrence of lightning fire. Since 2019, due to the severe impact of COVID-19 lockdowns, non-essential activities and mobility have decreased, which has led to a significant decrease in pollutant concentrations and lightning. In this paper, we linked the lightning fire with modernization process of human beings, the expansion of habitation, the change of underlying surface, the development of prediction technology and firefighting technology, and the laws and regulations of the country, to explore the impact of human activities on the occurrences of lightning and the forest lightning fire. Lightning is the fire source of the three elements in lightning fire occurrence, the lightning that can cause lightning fire is mainly cloud-to-ground lightning. The human activities in recent decades have profoundly affected the content of aerosols in environment. Aerosols are the main factors affecting lightning, and the large amount of pollution aerosols emitted from urban areas, soot aerosols emitted from biomass combustion and urban heat island effect have all increased the probability of lightning occurrence. The average annual ground lightning density of different land cover types is obviously different, and the construction land has the highest average annual ground lightning density. Intense lightning in forest areas has a higher density and slope. Most of the forests are located in high altitude areas, which is consistent with previous studies showing high lightning frequency in high altitude areas. The lightning in forests is intenser, steeper and more destructive, so forest areas are prone to lightning strikes. Lightning has the characteristic of selective discharge, that is, it will discharge into some special areas, which are also known as lightning selection areas, such as the place groundwater is exposed to the ground, where different conductive soils are connected, and where there are underground metal mines, such as copper and iron mines, and underground lake and water reservoir areas. Lightning strikes are caused by changes in soil conductivity caused by human activities such as mining waste rock sites, reservoir construction on mountain tops, and power transmission lines in mountainous areas. At the same time, due to the abundant trees in the mountainous area, it is also important to avoid the resulting lightning fire. With the development of lightning monitoring technology, a lightning location monitoring system has been established in some areas of China. Especially in 2021, the National Forestry and Grassland Administration launched the "Enlisting and Leading" emergency science and technology project of forest lightning fire prevention and control, and the project team has constructed a lightning fire sensing system in the Daxing'anling region with three-dimensional lightning full-wave detection network as the main body, covering the forest area of the Daxing'anling forest region, which can accurately locate the location of cloud-to-ground lightning in real time, improve the monitoring and warning ability of lightning fires, and improve the efficiency of lightning fire discovery. National laws and regulations indirectly affect lightning fires by affecting forest cover and climate change. This paper is expected to provide reference for the occurrence, prevention and control of forest lightning fire in the future, and provide a basis for the formulation of corresponding policies.

Low-Cost Systematic Methodology for Rapidly Constructing a Physiological Monitoring Interface in ICU

During the COVID-19 pandemic, which emerged in 2020, many patients were treated in isolation wards because of the high infectivity and long incubation period of COVID-19. Therefore, monitoring systems have become critical to patient care and to safeguard medical professional safety. The user interface is very important to the surveillance system;therefore, we used web technology to develop a system that can create an interface based on user needs. When the surveillance scene needs to be changed, the surveillance location can be changed at any time, effectively reducing the costs and time required, so that patients can achieve timely and appropriate goals of treatment. ZigBee was employed to develop a monitoring system for intensive care units (ICUs). Unlike conventional GUIs, the proposed GUI enables the monitoring of various aspects of a patient, and the monitoring interface can be modified according to the user needs. A simulated ICU environment monitoring system was designed to test the effectiveness of the system. The simulated environment and monitoring nodes were set up at positions consistent with the actual clinical environments to measure the time required to switch between the monitoring scenes or targets on the GUI. A novel system that can construct ZigBee-simulated graphical monitoring interfaces on demand was proposed in this study. The locations of the ZigBee monitoring nodes in the user interface can be changed at any time. The time required to deploy the monitoring system developed in this study was 4 min on average, which is much shorter than the time required for conventional methods (131 min). The system can effectively overcome the limitations of the conventional design methods for monitoring interfaces. This system can be used to simultaneously monitor the basic physiological data of numerous patients, enabling nursing professionals to instantly determine patient status and provide appropriate treatments. The proposed monitoring system can be applied to remote medical care after official adoption.

Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years' worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models' performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

Medical Internet of Things and Data Analytics for Post-COVID Care: An Analysis

The healthcare services across the world have been badly affected by the pandemic since December 2019. People have suffered in terms of medical supplies and treatments because existing medical infrastructure has failed to accommodate huge number of COVID infected patients. Further, patients with existing morbidities have been the worst hit so far and need attention. Therefore, there is a need of post-COVID care for such patients which can be achieved by using technologies such as Internet of Things (IoT) and data analytics. This paper presents medical IoT-based data analysis for post-COVID care. This paper, further, presents post-COVID data analysis to get an insight into the various symptoms across the different perspectives. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula

In orbit since late 2017, the Tropospheric Monitoring Instrument (TROPOMI) is offering new outstanding opportunities for better understanding the emission and fate of nitrogen dioxide (NO2) pollution in the troposphere. In this study, we provide a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula during 2018–2021, considering the recently developed Product Algorithm Laboratory (PAL) product. We complement our analysis with estimates of NOx anthropogenic and natural soil emissions. Closely related to cloud cover, the data availability of TROPOMI observations ranges from 30 %–45 % during April and November to 70 %–80 % during summertime, with strong variations between northern and southern Spain. Strongest TrC-NO2 hotspots are located over Madrid and Barcelona, while TrC-NO2 enhancements are also observed along international maritime routes close the strait of Gibraltar, and to a lesser extent along specific major highways. TROPOMI TrC-NO2 appear reasonably well correlated with collocated surface NO2 mixing ratios, with correlations around 0.7–0.8 depending on the averaging time.We investigate the changes of weekly and monthly variability of TROPOMI TrC-NO2 depending on the urban cover fraction. Weekly profiles show a reduction of TrC-NO2 during the weekend ranging from -10 % to -40 % from least to most urbanized areas, in reasonable agreement with surface NO2. In the largest agglomerations like Madrid or Barcelona, this weekend effect peaks not in the city center but in specific suburban areas/cities, suggesting a larger relative contribution of commuting to total NOx anthropogenic emissions. The TROPOMI TrC-NO2 monthly variability also strongly varies with the level of urbanization, with monthly differences relative to annual mean ranging from -40 % in summer to +60 % in winter in the most urbanized areas, and from -10 % to +20 % in the least urbanized areas. When focusing on agricultural areas, TROPOMI observations depict an enhancement in June–July that could come from natural soil NO emissions. Some specific analysis of surface NO2 observations in Madrid show that the relatively sharp NO2 minimum used to occur in August (drop of road transport during holidays) has now evolved into a much broader minimum partly de-coupled from the observed local road traffic counting;this change started in 2018, thus before the COVID-19 outbreak. Over 2019–2021, a reasonable consistency of the inter-annual variability of NO2 is also found between both datasets.Our study illustrates the strong potential of TROPOMI TrC-NO2 observations for complementing the existing surface NO2 monitoring stations, especially in the poorly covered rural and maritime areas where NOx can play a key role, notably for the production of tropospheric O3.

Android Application for Human Respiratory System Diagnosis: A Systematic Review

This paper presents a systematic review of android app respiratory system on smartphone. For some diseases, doctors have succeeded in inventing the necessary treatments that lasts for a short period, but in several cases, the treatment can stay for a lifetime. The goal of this system is to detect if a patient has any respiratory disease(s) by specifying the symptoms the patient encounters, schedules an appointement in the hospital for patient through the system to the linked specialist doctors to avoid contact in the case of Covid-19 patient. This research will help raise patient's awareness of the high risk of late discovery of having respiratory diseases (like Lung Cancer. corona virus etc), and also to develop a model that will help detect this disease early through mobile application. The focus of this review is to encourage medical institutions to adopt the health android app that can help patients in self-managing behavioral activities such as physical activities, using symptoms to determine the stage(early or critical) of the disease and drug suggestions with research evaluation using the app, this could help patients monitor and manage their health conditions. © 2022 IEEE.

Spatio-temporal dynamics of aerosol distribution in an urban environment recorded in situ by means of a bike based monitoring system

Aerosol pollution in urban areas is highly variable due to numerous single emission sources such as automobiles, industrial and commercial activities as well as domestic heating, but also due to complex building structures redirecting air mass flows, producing leeward and windward turbulences and resuspension effects. In this publication, it is shown that one or even few aerosol monitoring sites are not able to reflect these complex patterns. In summer 2019, aerosol pollution was recorded in high spatial resolution during six night and daytime tours with a mobile sensor platform on a trailer pulled by a bicycle. Particle mass loadings showed a high variability with PM10 values ranging from 1.3 to 221 µg m-3 and PM2.5 values from 0.7 to 69.0 µg m-3. Geostatistics were used to calculate respective models of the spatial distributions of PM2.5 and PM10. The resulting maps depict the variability of aerosol concentrations within the urban space. These spatial distribution models delineate the distributions without cutting out the built-up structures. Elsewise, the overall spatial patterns do not become visible because of being sharply interrupted by those outcuts in the resulting maps. Thus, the spatial maps allow to identify most affected urban areas and are not restricted to the street space. Furthermore, this method provides an insight to potentially affected areas, and thus can be used to develop counter measures. It is evident that the spatial aerosol patterns cannot be directly derived from the main wind direction, but result far more from an interplay between main wind direction, built-up patterns and distribution of pollution sources. Not all pollution sources are directly obvious and more research has to be carried out to explain the micro-scale variations of spatial aerosol distribution patterns. In addition, since aerosol load in the atmosphere is a severe issue for health and well-being of city residents more attention has to be paid to these local inhomogeneities.

IoT-Based Wearable and Smart Health Device Solutions for Capnography: Analysis and Perspectives

The potential of the Internet of Health Things (IoHT), also identified in the literature as the Internet of Medical Things (IoMT), is enormous, since it can generate expressive impacts on healthcare devices, such as the capnograph. When applied to mechanical ventilation, it provides essential healthcare to the patient and helps save lives. This survey elaborates on a deep review of related literature about the most robust and effective innovative healthcare solutions using modern technologies, such as the Internet of Things (IoT), cloud computing, Blynk, Bluetooth Low Energy, Robotics, and embedded systems. It emphasizes that IoT-based wearable and smart devices that work as integrated systems can be a faster response to other pandemic crises, respiratory diseases, and other problems that may occur in the future. It may also extend the performance of e-Health platforms used as monitoring systems. Therefore, this paper considers the state of the art to substantiate research about sensors, highlighting the relevance of new studies, strategies, approaches, and novelties in the field.

Detection of Social Isolation Based on Meal-Taking Activity and Mobility of Elderly People Living Alone

Objectives Background Social isolation is probably one of the most affected health outcomes in the elderly people, particularly those living alone, due to the COVID-19 pandemic. Therefore, we try to identify it by detecting changes in the elderly such as malnutrition and lack of mobility. Material and methods The system consists of two types of sensors installed at various locations in the user's home: Passive infrared (PIR) sensors and reed switch sensors. It was implemented for 15 days in the home of a 26-year-old student living alone, as a first step to later be deployed in the home of elderly people. Results Our study showed strong similarities between the activities detected by the algorithm and the real activity pattern of the interviewed individual. In addition, the system was able to identify two daily patterns (weekday and weekend) of the person as he is a student and is present in class during the week. Conclusion A system composed of low-cost, unobtrusive, non-intrusive and miniaturized sensors is able to detect meal-taking activity and mobility. These results are an intermediate step in assessing the potential risk of social isolation in older people living alone based on these ADLs.Copyright © 2023 AGBM

Smart Integrated Patient Monitoring System Based Internet of Things

Some diseases today have a rapid and dangerous rate of transmission. This causes doctors or medical personnel have a high risk of transmission. It caused the need for a system that can monitor the patient's condition in order to minimize the risk of contacting medical personnel. The research aims to design and build an integrated IoT-based patient monitoring system that provided information about the patient's temperature, infusion fluid level, and heart rate. This system is equipped with a database of patient conditions and can be accessed by web-based users. This system is integrated between hardware, software, and IoT system, which allows users to access data (based on their respective roles) from various places, because they can access it via the internet. The research stages are hardware and software design, design implementation, software embedded system development, IoT design, system integration, and web development that is integrated with IoT. The system has been running well and patient's information can be accessed by the user. This system is also equipped with indicators of normal and abnormal conditions, so that medical personnel can anticipate early if there are conditions that are dangerous for patients. Even though Covid cases have decreased, technology is still needed, especially to be used to monitor the condition of patients who require intensive monitoring. © 2022 IEEE.

Real-Time Health Monitoring System Using Predictive Analytics

Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of Potential Vulnerable Patients Using Oximeter

Agriculture, education and health systems have all progressed in the last decade. In times of pandemic crises like COVID-19, IoT and sensors play a critical role in the medical industry. Sensors and IoT-based health care gadgets have emerged as saviors for humanity in the face of resource shortage. Pulse oximeters are one such instrument that has been utilized widely during pandemics. Since a long time, pulse oximeters have been used to measure crucial body functions such as saturation of peripheral oxygen (SpO2) and pulse rate. They have been utilized to detect vital signs in patients in order to diagnose cardiac trouble early. However, oximeters have been widely utilized to detect SPO2 levels in persons during the current pandemic. People are being attacked by the COVID-19, which is silently destroying their lungs, causing pneumonia and lowering oxygen levels to dangerously low levels. We propose a strategy in this study for detecting possibly vulnerable individuals by classifying them using data obtained from pulse oximeters. We propose an approach by involving volunteers who will record their vitals and share it with administrators on a regular basis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.