Search results for: health monitoring

Authors: Franck Kem Acho

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The trends of diseases have been changed from the last few years, now the burden of non-communicable diseases is increasing day by day. In all the non-communicable diseases, Hypertension is one of the leading causes of premature death and morbidity worldwide. This disease is a silent killer, it mostly affects the people with no obvious symptoms. Not only the heart it also increases the risk of brain, kidney and other diseases, now a days it is a serious medical problem. Over a billion people near about 1 in 4 men and 1 in 5 women having hypertension. In this case study men and women of ages between 30-80 years with Hypertension were identified in community remote area with their Health status being checked and monitored for one week and Health Education was provided for the importance of regular Health checkup alongside the continuous taking of medications.

Keywords: hypertension, health status, health check up, health education

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Authors: Brighton Chamunorwa

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The current remote sensing of water quality procedures does not provide a step representing physical visualisation of the monitored dam. The application of the remote sensing of water quality techniques may benefit from use of mathematical morphology operators for shape identification. Given an input of dam outline, morphological operators such as the hit or miss transform identifies if the water body is present on input remotely sensed images. This study seeks to determine the accuracy of the hit or miss transform to identify dams monitored by the water resources authorities in South Africa on satellite images. To achieve this objective the study download a Landsat image acquired in winter and tested the capability of the hit or miss transform using shapefile boundaries of dams in the crocodile marico catchment. The results of the experiment show that it is possible to detect most dams on the Landsat image after the adjusting the erosion operator to detect pixel matching a percentage similarity of 80% and above. Successfully implementation of the current study contributes towards optimisation of mathematical morphology image operators. Additionally, the effort helps develop remote sensing of water quality monitoring with improved simulation of the conventional procedures.

Keywords: hit or miss transform, mathematical morphology, remote sensing, water quality monitoring

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Authors: Adam Gushgari

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The SARS-CoV-2 pandemic has catalyzed the rapid adoption of wastewater-based epidemiology (WBE) methodologies both domestically and internationally. To support the rapid scale-up of pandemic-response wastewater surveillance systems, multiple federal agencies (i.e. US CDC), non-government organizations (i.e. Water Environment Federation), and private charities (i.e. Bill and Melinda Gates Foundation) have funded over $220 million USD supporting development and expanding equitable access of surveillance methods. Funds were primarily distributed directly to municipalities under the CARES Act (90.6%), followed by academic projects (7.6%), and initiatives developed by private companies (1.8%). In addition to federal funding for wastewater monitoring primarily conducted at wastewater treatment plants, state/local governments and private companies have leveraged wastewater sampling to obtain health and lifestyle data on student, prison inmate, and employee populations. We explore the viable paths for expansion of the WBE m1ethodology across a variety of analytical methods; the development of WBE-specific samplers and real-time wastewater sensors; and their application to various governments and private sector industries. Considerable investment in, and public acceptance of WBE suggests the methodology will be applied to other future notifiable diseases and health risks. Early research suggests that WBE methods can be applied to a host of additional “biological insults” including communicable diseases and pathogens, such as influenza, Cryptosporidium, Giardia, mycotoxin exposure, hepatitis, dengue, West Nile, Zika, and yellow fever. Interest in chemical insults is also likely, providing community health and lifestyle data on narcotics consumption, use of pharmaceutical and personal care products (PPCP), PFAS and hazardous chemical exposure, and microplastic exposure. Successful application of WBE to monitor analytes correlated with carcinogen exposure, community stress prevalence, and dietary indicators has also been shown. Additionally, technology developments of in situ wastewater sensors, WBE-specific wastewater samplers, and integration of artificial intelligence will drastically change the landscape of WBE through the development of “smart sewer” networks. The rapid expansion of the WBE field is creating significant business opportunities for professionals across the scientific, engineering, and technology industries ultimately focused on community health improvement.

Keywords: wastewater surveillance, wastewater-based epidemiology, smart cities, public health, pandemic management, substance abuse

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Authors: Yasmine Ahmed Shemeis

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Determining the impact of productivity increases on wage levels is often difficult due to the unavailability of individual-level productivity data. Accordingly, we proxy for productivity using a self-perceived measure of health based on the postulated positive relationship between better health and productivity improvements. Using Egypt’s labour market data for the years 2012 and 2018 and utilizing a Maximum Likelihood Estimation method, we address two issues: the endogeneity of health in the estimation of wages and a sample selection bias. Our findings indicate the great value that better health has in enhancing wage levels in Egypt’s private sector. Also, we find that overlooking the endogeneity of health underestimates its effect on wages. Thus, the improvement of health states is likely to be beneficial in improving labour market outcomes in terms of wages as well as labour productivity in Egypt.

Keywords: labour, Productivity, Wages, Endogeneity, Sample Selection

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Authors: Vahid Bayrami Rad

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In this research, common methods and techniques have been investigated with a focus on intelligent fault finding and monitoring systems in the oil industry. In fact, remote and intelligent control methods are considered a necessity for implementing various operations in the oil industry, but benefiting from the knowledge extracted from countless data generated with the help of data mining algorithms. It is a avoid way to speed up the operational process for monitoring and troubleshooting in today's big oil companies. Therefore, by comparing data mining algorithms and checking the efficiency and structure and how these algorithms respond in different conditions, The proposed (Bayesian) algorithm using data clustering and their analysis and data evaluation using a colored Petri net has provided an applicable and dynamic model from the point of view of reliability and response time. Therefore, by using this method, it is possible to achieve a dynamic and consistent model of the remote control system and prevent the occurrence of leakage in oil pipelines and refineries and reduce costs and human and financial errors. Statistical data The data obtained from the evaluation process shows an increase in reliability, availability and high speed compared to other previous methods in this proposed method.

Keywords: wireless sensors, petroleum equipment troubleshooting, Bayesian algorithm, colored Petri net, rapid miner, data mining-reliability

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Authors: Bolin Hua

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In city governance, various data are involved, including city component data, demographic data, housing data and all kinds of business data. These data reflects different aspects of people, events and activities. Data generated from various systems are different in form and data source are different because they may come from different sectors. In order to reflect one or several facets of an event or rule, data from multiple sources need fusion together. Data from different sources using different ways of collection raised several issues which need to be resolved. Problem of data fusion include data update and synchronization, data exchange and sharing, file parsing and entry, duplicate data and its comparison, resource catalogue construction. Governments adopt statistical analysis, time series analysis, extrapolation, monitoring analysis, value mining, scenario prediction in order to achieve pattern discovery, law verification, root cause analysis and public opinion monitoring. The result of Multi-source data fusion is to form a uniform central database, which includes people data, location data, object data, and institution data, business data and space data. We need to use meta data to be referred to and read when application needs to access, manipulate and display the data. A uniform meta data management ensures effectiveness and consistency of data in the process of data exchange, data modeling, data cleansing, data loading, data storing, data analysis, data search and data delivery.

Keywords: multi-source data fusion, urban comprehensive management, information fusion, government data

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Authors: Stefania Fortino, Petr Hradil, Timo Avikainen

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Timber bridges have an excellent environmental performance, are economical, relatively easy to build and can have a long service life. However, the durability of these bridges is the main problem because of their exposure to outdoor climate conditions. The moisture content accumulated in wood for long periods, in combination with certain temperatures, may cause conditions suitable for timber decay. In addition, moisture content variations affect the structural integrity, serviceability and loading capacity of timber bridges. Therefore, the monitoring of the moisture content in wood is important for the durability of the material but also for the whole superstructure. The measurements obtained by the usual sensor-based techniques provide hygro-thermal data only in specific locations of the wood components. In this context, the monitoring can be assisted by numerical modelling to get more information on the hygro-thermal response of the bridges. This work presents a hygro-thermal model based on a multi-phase moisture transport theory to predict the distribution of moisture content, relative humidity and temperature in wood. Below the fibre saturation point, the multi-phase theory simulates three phenomena in cellular wood during moisture transfer, i.e., the diffusion of water vapour in the pores, the sorption of bound water and the diffusion of bound water in the cell walls. In the multi-phase model, the two water phases are separated, and the coupling between them is defined through a sorption rate. Furthermore, an average between the temperature-dependent adsorption and desorption isotherms is used. In previous works by some of the authors, this approach was found very suitable to study the moisture transport in uncoated and coated stress-laminated timber decks. Compared to previous works, the hygro-thermal fluxes on the external surfaces include the influence of the absorbed solar radiation during the time and consequently, the temperatures on the surfaces exposed to the sun are higher. This affects the whole hygro-thermal response of the timber component. The multi-phase model, implemented in a user subroutine of Abaqus FEM code, provides the distribution of the moisture content, the temperature and the relative humidity in a volume of the timber deck. As a case study, the hygro-thermal data in wood are collected from the ongoing monitoring of the stress-laminated timber deck of Tapiola Bridge in Finland, based on integrated humidity-temperature sensors and the numerical results are found in good agreement with the measurements. The proposed model, used to assist the monitoring, can contribute to reducing the maintenance costs of bridges, as well as the cost of instrumentation, and increase safety.

Keywords: moisture content, multi-phase models, solar radiation, timber decks, FEM

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Authors: Sania Bashir

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A promising approach to healthcare delivery, telemedicine makes use of communication technology to reach out to remote regions of the world, allowing for beneficial interactions between diabetic patients and healthcare professionals as well as the provision of affordable and easily accessible medical care. The emergence of contemporary care models, fueled by the pervasiveness of mobile devices, provides better information, offers low cost with the best possible outcomes, and is known as digital health. It involves the integration of collected data using software and apps, as well as low-cost, high-quality outcomes. The goal of this study is to assess how well telemedicine works for diabetic patients and how it impacts their HbA1c levels. A questionnaire-based survey of 300 diabetics included 150 patients in each of the groups receiving usual care and via telemedicine. A descriptive and observational study that lasted from September 2021 to May 2022 was conducted. HbA1c has been gathered for both categories every three months. A remote monitoring tool has been used to assess the efficacy of telemedicine and continuing therapy instead of the customary three monthly meetings like in-person consultations. The patients were (42.3) 18.3 years old on average. 128 men were outnumbered by 172 women (57.3% of the total). 200 patients (66.6%) have type 2 diabetes, compared to over 100 (33.3%) candidates for type 1. Despite the average baseline BMI being within normal ranges at 23.4 kg/m², the mean baseline HbA1c (9.45 1.20) indicates that glycemic treatment is not well-controlled at the time of registration. While patients who use telemedicine experienced a mean percentage change of 10.5, those who visit the clinic experienced a mean percentage change of 3.9. Changes in HbA1c are dependent on several factors, including improvements in BMI (61%) after 9 months of research and compliance with healthy lifestyle recommendations for diet and activity. More compliance was achieved by the telemedicine group. It is an undeniable reality that patient-physician communication is crucial for enhancing health outcomes and avoiding long-term issues. Telemedicine has shown its value in the management of diabetes and holds promise as a novel technique for improved clinical-patient communication in the twenty-first century.

Keywords: diabetes, digital health, mobile app, telemedicine

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Authors: C. J. Rossouw, T. I. van Niekerk

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The global manufacturing industry is utilizing the internet and cloud-based services to further explore the anatomy and optimize manufacturing processes in support of the movement into the Fourth Industrial Revolution (4IR). The 4IR from a third world and African perspective is hindered by the fact that many manufacturing systems that were developed in the third industrial revolution are not inherently equipped to utilize the internet and services of the 4IR, hindering the progression of third world manufacturing industries into the 4IR. This research focuses on the development of a non-invasive and cost-effective cyber-physical IoT system that will exploit a machine’s vibration to expose semantic characteristics in the manufacturing process and utilize these results through a real-time cloud-based machine condition monitoring system with the intention to optimize the system. A microcontroller-based IoT sensor was designed to acquire a machine’s mechanical vibration data, process it in real-time, and transmit it to a cloud-based platform via Wi-Fi and the internet. Time-frequency Fourier analysis was applied to the vibration data to form an image representation of the machine’s behaviour. This data was used to train a Convolutional Neural Network (CNN) to learn semantic characteristics in the machine’s behaviour and relate them to a state of operation. The same data was also used to train a Convolutional Autoencoder (CAE) to detect anomalies in the data. Real-time edge-based artificial intelligence was achieved by deploying the CNN and CAE on the sensor to analyse the vibration. A cloud platform was deployed to visualize the vibration data and the results of the CNN and CAE in real-time. The cyber-physical IoT system was deployed on a semi-automated metal granulation machine with a set of trained machine learning models. Using a single sensor, the system was able to accurately visualize three states of the machine’s operation in real-time. The system was also able to detect a variance in the material being granulated. The research demonstrates how non-IoT manufacturing systems can be equipped with edge-based artificial intelligence to establish a remote machine condition monitoring system.

Keywords: IoT, cyber-physical systems, artificial intelligence, manufacturing, vibration analytics, continuous machine condition monitoring

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Authors: Mariana Fernandes

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Instituto Politécnico de Viana do Castelo (IPVC) has adopted a policy of inclusion and equity and the promotion of health and academic well-being, reinforcing measures already implemented in previous years, but also with the inclusion of new inclusion and equity policies that allow access, throughout all students, to Higher Education (ES). The Inclusive School project, the Plan for Equality, the IPVC's own Regulations for students with Special Educational Needs (SEN), and the support guaranteed by the Health and Wellbeing Office, Academic Services, and teaching staff are some of the examples of the varied strategies that IPVC undertakes to guarantee effective conditions so that students with disabilities can enter ES and experience a positive academic experience. This study's main objective is to reflect and disseminate the inclusion practices that IPVC practices with regard to Students with SEN. To this, a consultation and documentary analysis of internal documentation was carried out, consultation of the IPVC Quality Management System (QMS) process and, also, using the report referring to the ENEE questionnaire implemented in the year 2023, this report which presents the opinion of IPVC students with SEN, whether with support throughout the ENEE application submission process, with response deadlines, with the Individual Support Plan, as well as with physical and technological accessibility and communication. The results obtained show IPVC's effective commitment to this topic, in addition to the entire circuit created to guarantee equitable access for these students from the moment they join IPVC, a circuit that involves various human resources and( s) its sensitivity to this topic, it also promoted, through the Health and Wellbeing Office, the restructuring of the IPVC ENEE Regulation itself based on the needs and challenges felt in monitoring these students, the innovation of the services themselves of health and consequent awareness of all surrounding resources and services (from the Management, to the teaching staff and academic services). Currently, there is already an Individual Pedagogical Support Plan (PIAP), frequent meetings with the Reception Group, Psychology consultations – both clinically and educationally – and a growing concern in listening to the student community to improve the process. Based on these results, it is concluded that IPVC is an institution sensitive to promoting a positive, equitable, and, above all, inclusive higher education path.

Keywords: special educational needs, inclusion, equity, equality

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Authors: Vedant Janapaty

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Estuaries are wetlands where fresh water from streams mixes with salt water from the sea. Also known as “kidneys of our planet”- they are extremely productive environments that filter pollutants, absorb floods from sea level rise, and shelter a unique ecosystem. However, eutrophication and loss of native species are ailing our wetlands. There is a lack of uniform data collection and sparse research on correlations between satellite data and in situ measurements. Remote sensing (RS) has shown great promise in environmental monitoring. This project attempts to use satellite data and correlate metrics with in situ observations collected at five estuaries. Images for satellite data were processed to calculate 7 bands (SIs) using Python. Average SI values were calculated per month for 23 years. Publicly available data from 6 sites at ELK was used to obtain 10 parameters (OPs). Average OP values were calculated per month for 23 years. Linear correlations between the 7 SIs and 10 OPs were made and found to be inadequate (correlation = 1 to 64%). Fourier transform analysis on 7 SIs was performed. Dominant frequencies and amplitudes were extracted for 7 SIs, and a machine learning(ML) model was trained, validated, and tested for 10 OPs. Better correlations were observed between SIs and OPs, with certain time delays (0, 3, 4, 6 month delay), and ML was again performed. The OPs saw improved R² values in the range of 0.2 to 0.93. This approach can be used to get periodic analyses of overall wetland health with satellite indices. It proves that remote sensing can be used to develop correlations with critical parameters that measure eutrophication in situ data and can be used by practitioners to easily monitor wetland health.

Keywords: estuary, remote sensing, machine learning, Fourier transform

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Authors: Pedro P. Drumond, Priscilla M. Moura, Marcia M. L. P. Coelho

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The purpose of On-site Stormwater Detention (OSD) system is to promote the detention of addition stormwater runoff caused by impervious areas, in order to maintain the peak flow the same as the pre-urbanization condition. In recent decades, these systems have been built in many cities around the world. However, its real efficiency continues to be unknown due to the lack of research, especially with regard to monitoring its real performance. Thus, this study aims to compare the water level monitoring data of an OSD built in Belo Horizonte/Brazil with the results of theoretical methods simulations, usually adopted in OSD design. There were made two theoretical simulations, one using the Rational Method and Modified Puls method and another using the Soil Conservation Service (SCS) method and Modified Puls method. The monitoring data were obtained with a water level sensor, installed inside the reservoir and connected to a data logger. The comparison of OSD performance was made for 48 rainfall events recorded from April/2015 to March/2017. The comparison of maximum water levels in the OSD showed that the results of the simulations with Rational/Puls and SCS/Puls methods were, on average 33% and 73%, respectively, lower than those monitored. The Rational/Puls results were significantly higher than the SCS/Puls results, only in the events with greater frequency. In the events with average recurrence interval of 5, 10 and 200 years, the maximum water heights were similar in both simulations. Also, the results showed that the duration of rainfall events was close to the duration of monitored hydrograph. The rising time and recession time of the hydrographs calculated with the Rational Method represented better the monitored hydrograph than SCS Method. The comparison indicates that the real discharge coefficient value could be higher than 0.61, adopted in Puls simulations. New researches evaluating OSD real performance should be developed. In order to verify the peak flow damping efficiency and the value of the discharge coefficient is necessary to monitor the inflow and outflow of an OSD, in addition to monitor the water level inside it.

Keywords: best management practices, on-site stormwater detention, source control, urban drainage

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Authors: Alexis Piedrahita, Ludi Valencia, Aura Gutierrez

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The migration crisis that is currently being experienced in the world is a continuous phenomenon that has had solutions in form but not in substance, violating the international humanitarian law of people who are in transit through countries foreign to their roots, especially women of age reproductive, this has caused different governments and organizations worldwide to meet around this problem to define concise actions to protect the rights of migrant women in the world. This research compiles the stories of migrant women who arrive in Colombia seeking better opportunities, such as accessibility to comprehensive and quality health services, including primary health care. This is the gateway to the offer of health promotion and disease prevention services.

Keywords: accessibility, primary health care, sexual and reproductive health, sustainable development goals, women migrant

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Authors: Muhammad Farhan Ul Moazzam, Tamkeen Urooj Paracha, Ghani Rahman, Byung Gul Lee, Nasir Farid, Adnan Arshad

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The novel coronavirus pandemic disease (COVID-19) affected the whole globe, though there is a lack of clinical studies and its epidemiological features. But as per the observation, it has been seen that most of the COVID-19 infected patients show mild to moderate symptoms, and they get better without any medical assistance due to a better immune system to generate antibodies against the novel coronavirus. In this study, the active cases, serious cases, recovered cases, deaths and total confirmed cases had been analyzed using the geospatial inverse distance weightage technique (IDW) within the time span of 2nd March to 3rd June 2020. As of 3rd June, the total number of COVID-19 cases in Italy were 231,238, total deaths 33,310, serious cases 350, recovered cases 158,951, and active cases were 39,177, which has been reported by the Ministry of Health, Italy. March 2nd-June 3rd, 2020 a sum of 231,238 cases has been reported in Italy out of which 38.68% cases reported in the Lombardia region with a death rate of 18%, which is high from its national mortality rate followed by Emilia-Romagna (14.89% deaths), Piemonte (12.68% deaths), and Vento (10% deaths). As per the total cases in the region, the highest number of recoveries has been observed in Umbria (92.52%), followed by Basilicata (87%), Valle d'Aosta (86.85%), and Trento (84.54%). The COVID-19 evolution in Italy has been particularly found in the major urban area, i.e., Rome, Milan, Naples, Bologna, and Florence. Geospatial technology played a vital role in this pandemic by tracking infected patient, active cases, and recovered cases. Geospatial techniques are very important in terms of monitoring and planning to control the pandemic spread in the country.

Keywords: COVID-19, public health, geospatial analysis, IDW, Italy

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Authors: Ardalan Sabamehr, Ashutosh Bagchi

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Recently, Structural Health Monitoring (SHM) based on the vibration of structures has attracted the attention of researchers in different fields such as: civil, aeronautical and mechanical engineering. Operational Modal Analysis (OMA) have been developed to identify modal properties of infrastructure such as bridge, building and so on. Frequency Domain Decomposition (FDD), Stochastic Subspace Identification (SSI) and Continuous Wavelet Transform (CWT) are the three most common methods in output only modal identification. FDD, SSI, and CWT operate based on the frequency domain, time domain, and time-frequency plane respectively. So, FDD and SSI are not able to display time and frequency at the same time. By the way, FDD and SSI have some difficulties in a noisy environment and finding the closed modes. CWT technique which is currently developed works on time-frequency plane and a reasonable performance in such condition. The other advantage of wavelet transform rather than other current techniques is that it can be applied for the non-stationary signal as well. The aim of this paper is to compare three most common modal identification techniques to find modal properties (such as natural frequency, mode shape, and damping ratio) of three story steel frame which was built in Concordia University Lab by use of ambient vibration. The frame has made of Galvanized steel with 60 cm length, 27 cm width and 133 cm height with no brace along the long span and short space. Three uniaxial wired accelerations (MicroStarin with 100mv/g accuracy) have been attached to the middle of each floor and gateway receives the data and send to the PC by use of Node Commander Software. The real-time monitoring has been performed for 20 seconds with 512 Hz sampling rate. The test is repeated for 5 times in each direction by hand shaking and impact hammer. CWT is able to detect instantaneous frequency by used of ridge detection method. In this paper, partial derivative ridge detection technique has been applied to the local maxima of time-frequency plane to detect the instantaneous frequency. The extracted result from all three methods have been compared, and it demonstrated that CWT has the better performance in term of its accuracy in noisy environment. The modal parameters such as natural frequency, damping ratio and mode shapes are identified from all three methods.

Keywords: ambient vibration, frequency domain decomposition, stochastic subspace identification, continuous wavelet transform

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Michael Leontiev, Danil Zhilikov, Dmitry Lobanov, Lenar Klimov, Vyacheslav Chertan, Daniel Bobrov, Vladislav Maslov, Vasilii Vologdin, Ksenia Balabaeva

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Pharmaceutical manufacturing is a complex process, where each stage requires a high level of safety and sterility. Personal Protective Equipment (PPE) is used for this purpose. Despite all the measures of control, the human factor (improper PPE wearing) causes numerous losses to human health and material property. This research proposes a solid computer vision system for ensuring safety in pharmaceutical laboratories. For this, we have tested a wide range of state-of-the-art object detection methods. Composing previously obtained results in this sphere with our own approach to this problem, we have reached a high accuracy ([email protected]) ranging from 0.77 up to 0.98 in detecting all the elements of a common set of PPE used in pharmaceutical laboratories. Our system is a step towards safe medicine production.

Keywords: sterility and safety in pharmaceutical development, personal protective equipment, computer vision, object detection, monitoring in pharmaceutical development, PPE

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Authors: James Skon, Irina Beshentseva, Michelle Polak

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Wifi solutions come in two major classes. Small Office/Home Office (SOHO) WiFi, characterized by inexpensive WiFi routers, with one or two service set identifiers (SSIDs), and a single shared passphrase. These access points provide no significant user management or monitoring, and no aggregation of monitoring and control for multiple routers. The other solution class is managed enterprise WiFi solutions, which involve expensive Access Points (APs), along with (also costly) local or cloud based management components. These solutions typically provide portal based login, per user virtual local area networks (VLANs), and sophisticated monitoring and control across a large group of APs. The cost for deploying and managing such managed enterprise solutions is typically about 10 fold that of inexpensive consumer APs. Low revenue organizations, such as schools, non-profits, non-government organizations (NGO's), small businesses, and even homes cannot easily afford quality enterprise WiFi solutions, though they may need to provide quality WiFi access to their population. Using available lower cost Wifi solutions can significantly reduce their ability to provide reliable, secure network access. This project explored and created a new approach for providing secured managed enterprise WiFi based on low cost hardware combined with both new and existing (but modified) open source software. The solution provides a cloud based management interface which allows organizations to aggregate the configuration and management of small, medium and large WiFi solutions. It utilizes a novel approach for user management, giving each user a unique passphrase. It provides unlimited SSID's across an unlimited number of WiFI zones, and the ability to place each user (and all their devices) on their own VLAN. With proper configuration it can even provide user local services. It also allows for users' usage and quality of service to be monitored, and for users to be added, enabled, and disabled at will. As inferred above, the ultimate goal is to free organizations with limited resources from the expense of a commercial enterprise WiFi, while providing them with most of the qualities of such a more expensive managed solution at a fraction of the cost.

Keywords: wifi, enterprise, cloud, managed

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Authors: Tanmay L. Joshi

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The Mental health allows children and young people to develop the resilience to cope with whatever life throws at them and grow into well-rounded, healthy adults. In urban area, many health professionals are working for the well being for younger population. There is so much of potential in rural area. However, the rural population is somehow neglected. Apart from lack of availability of basic needs like transport, electricity, telecommunication etc; the Psychological health is also overlooked in such area. There are no mental health professionals like Psychologists, counselors etc. So the researcher tries to throw some light on the mental health of Higher Secondary School going children in rural area. The current research tries to study the Mental Health (Confidence, Sociability and Neurotic Tendency) of Higher Secondary School going children. Researchers have used the tool Vyaktitva Shodhika (a personality inventory) by Dr. U. Khire (JPIP,Pune). The Sample size is 45 (N= 40, 24 boys/21 girls). The present study may provide a good support to inculcate emotional-management programs for higher secondary school going children in rural areas.

Keywords: mental health, neurotic tendency, rural area, school going children

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Authors: Md Rafiqul Islam, Ashir Ahmed, Anwaar Ulhaq, Abu Raihan M. Kamal, Yuan Miao, Hua Wang

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Mental health of people is equally important as of their physical health. Mental health and well-being are influenced not only by individual attributes but also by the social circumstances in which people find themselves and the environment in which they live. Like physical health, there is a number of internal and external factors such as biological, social and occupational factors that could influence the mental health of people. People living in poverty, suffering from chronic health conditions, minority groups, and those who exposed to/or displaced by war or conflict are generally more likely to develop mental health conditions. However, to authors’ best knowledge, there is dearth of knowledge on the impact of workplace (especially the highly stressed IT/Tech workplace) on the mental health of its workers. This study attempts to examine the factors influencing the mental health of tech workers. A publicly available dataset containing more than 65,000 cells and 100 attributes is examined for this purpose. Number of machine learning techniques such as ‘Decision Tree’, ‘K nearest neighbor’ ‘Support Vector Machine’ and ‘Ensemble’, are then applied to the selected dataset to draw the findings. It is anticipated that the analysis reported in this study would contribute in presenting useful insights on the attributes contributing in the mental health of tech workers using relevant machine learning techniques.

Keywords: mental disorder, diagnosis, occupational stress, IT workplace

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Authors: Tabitha D. Hussein, Arigu M. Sunday

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This paper is an attempt to gauge the conception and misconception of mental health in the Gwafan community, Jos North Plateau State, Nigeria (West Africa), to ascertain which side of the scale the pendulum swings. Mental health education and awareness are necessary for healthy community development. However, in a society such as ours in Nigeria (Africa) that is not knowledge-based, it is easy for people to embrace hearsay, speculations, superstition, and the supernatural. The objectives of this paper are two folds: first, to examine the level of awareness of the people on mental health, or the lack of it; second, to ascertain their access to mental health services and good practices. The methodology adopted is qualitative research, and the instrument used is T-test related. The findings include, first, the fact that a larger population has misconceptions about Mental Health. Second, this misconception is a result of a lack of basic knowledge of Mental Health. Based on the aforementioned, the paper recommends that there is a need for more Mental Health Education among the populace and access to Mental Health services and practices (Preventative and Curative). The paper concludes that in a society that is not knowledgebase, every attempt should be made to shift people’s mindset from superstitious knowledge to evidence-based knowledge, which can empower them to make informed decisions not only on mental Health but on associative Health issues that can aid their wellbeing, livelihood and sustainable development as a whole.

Keywords: conceptions, misconceptions, mental health, knowledge, superstition, service delivery

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Authors: Asma Omri, Iheb Sifaoui, Sofiane Sayahi, Hichem Besbes

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Future vehicle systems demand advanced capabilities, notably in-cabin life detection and driver monitoring systems, with a particular emphasis on drowsiness detection. To meet these requirements, several techniques employ artificial intelligence methods based on real-time vital sign measurements. In parallel, Frequency-Modulated Continuous-Wave (FMCW) radar technology has garnered considerable attention in the domains of healthcare and biomedical engineering for non-invasive vital sign monitoring. FMCW radar offers a multitude of advantages, including its non-intrusive nature, continuous monitoring capacity, and its ability to penetrate through clothing. In this paper, we propose a system utilizing the AWR6843AOP radar from Texas Instruments (TI) to extract precise vital sign information. The radar allows us to estimate Ballistocardiogram (BCG) signals, which capture the mechanical movements of the body, particularly the ballistic forces generated by heartbeats and respiration. These signals are rich sources of information about the cardiac cycle, rendering them suitable for heart rate estimation. The process begins with real-time subject positioning, followed by clutter removal, computation of Doppler phase differences, and the use of various filtering methods to accurately capture subtle physiological movements. To address the challenges associated with FMCW radar-based vital sign monitoring, including motion artifacts due to subjects' movement or radar micro-vibrations, Long Short-Term Memory (LSTM) networks are implemented. LSTM's adaptability to different heart rate patterns and ability to handle real-time data make it suitable for continuous monitoring applications. Several crucial steps were taken, including feature extraction (involving amplitude, time intervals, and signal morphology), sequence modeling, heart rate estimation through the analysis of detected cardiac cycles and their temporal relationships, and performance evaluation using metrics such as Root Mean Square Error (RMSE) and correlation with reference heart rate measurements. For dataset construction and LSTM training, a comprehensive data collection system was established, integrating the AWR6843AOP radar, a Heart Rate Belt, and a smart watch for ground truth measurements. Rigorous synchronization of these devices ensured data accuracy. Twenty participants engaged in various scenarios, encompassing indoor and real-world conditions within a moving vehicle equipped with the radar system. Static and dynamic subject’s conditions were considered. The heart rate estimation through LSTM outperforms traditional signal processing techniques that rely on filtering, Fast Fourier Transform (FFT), and thresholding. It delivers an average accuracy of approximately 91% with an RMSE of 1.01 beat per minute (bpm). In conclusion, this paper underscores the promising potential of FMCW radar technology integrated with artificial intelligence algorithms in the context of automotive applications. This innovation not only enhances road safety but also paves the way for its integration into the automotive ecosystem to improve driver well-being and overall vehicular safety.

Keywords: ballistocardiogram, FMCW Radar, vital sign monitoring, LSTM

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Authors: Rabbia Idrees, Ananda Maiti, Saurabh Garg, Muhammad Bilal Amin

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IoT devices are the basic building blocks of IoT network that generate enormous volume of real-time and high-speed data to help organizations and companies to take intelligent decisions. To integrate this enormous data from multisource and transfer it to the appropriate client is the fundamental of IoT development. The handling of this huge quantity of devices along with the huge volume of data is very challenging. The IoT devices are battery-powered and resource-constrained and to provide energy efficient communication, these IoT devices go sleep or online/wakeup periodically and a-periodically depending on the traffic loads to reduce energy consumption. Sometime these devices get disconnected due to device battery depletion. If the node is not available in the network, then the IoT network provides incomplete, missing, and inaccurate data. Moreover, many IoT applications, like vehicle tracking and patient tracking require the IoT devices to be mobile. Due to this mobility, If the distance of the device from the sink node become greater than required, the connection is lost. Due to this disconnection other devices join the network for replacing the broken-down and left devices. This make IoT devices dynamic in nature which brings uncertainty and unreliability in the IoT network and hence produce bad quality of data. Due to this dynamic nature of IoT devices we do not know the actual reason of abnormal data. If data are of poor-quality decisions are likely to be unsound. It is highly important to process data and estimate data quality before bringing it to use in IoT applications. In the past many researchers tried to estimate data quality and provided several Machine Learning (ML), stochastic and statistical methods to perform analysis on stored data in the data processing layer, without focusing the challenges and issues arises from the dynamic nature of IoT devices and how it is impacting data quality. A comprehensive review on determining the impact of dynamic nature of IoT devices on data quality is done in this research and presented a data quality model that can deal with this challenge and produce good quality of data. This research presents the data quality model for the sensors monitoring water quality. DBSCAN clustering and weather sensors are used in this research to make data quality model for the sensors monitoring water quality. An extensive study has been done in this research on finding the relationship between the data of weather sensors and sensors monitoring water quality of the lakes and beaches. The detailed theoretical analysis has been presented in this research mentioning correlation between independent data streams of the two sets of sensors. With the help of the analysis and DBSCAN, a data quality model is prepared. This model encompasses five dimensions of data quality: outliers’ detection and removal, completeness, patterns of missing values and checks the accuracy of the data with the help of cluster’s position. At the end, the statistical analysis has been done on the clusters formed as the result of DBSCAN, and consistency is evaluated through Coefficient of Variation (CoV).

Keywords: clustering, data quality, DBSCAN, and Internet of things (IoT)

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Authors: Rupert G. S. Legg

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The connection between poor health outcomes and living near contaminated land has long been understood. Less examined has been the impact of remediation on residents’ health. The cleaning process undoubtedly changes the local area in which it occurs, leading to the possibility that local housing and rental prices could increase resulting in the displacement of those least able to cope. Whether or not this potential displacement resulting from remediation has a considerable impact on health remains unknown. This review aims to determine how these health effects have been approached in the health geography literature. A systematic review of health geographies literature was conducted, searching for two-word clusters: ‘health’ and ‘remediation’ (100 articles); and ‘health’, ‘displacement’ and ‘gentrification’ (43 articles). 43 articles were selected for their relevance (7 from the first cluster, 20 from the second, and 16 from those cited within the reviewed articles). Several of the reviewed cases identified that potential displacement was a contributor to stress and worry in residents living near remediation projects. Likewise, the experience of displacement in other cases beyond remediation was linked with several mental health issues. However, no remediation cases followed-up on the ultimate effects of experiencing displacement on residents’ health. A reason identified for this was a tendency for reviewed studies to adopt a contextual or compositional approach, as opposed to a relational approach, which is more concerned with dimensions of mobility and temporality. Given that remediation and displacement both involve changing mobility and temporality, focussing solely on contextual or compositional factors is problematic. This review concludes by suggesting that more thorough, relational research is conducted into the extent to which potential displacement resulting from remediation affects health.

Keywords: contamination, displacement, health geography, remediation

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Authors: Deniz Sari, Selahattin İncecik, Nesimi Ozkurt

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Surface ozone, which named as one of the most critical pollutants in the 21th century, threats to human health, forest and vegetation. Specifically, in rural areas surface ozone cause significant influences on agricultural productions and trees. In this study, in order to understand to the surface ozone levels in rural areas we focus on the north-western side of Biga Peninsula which covers by the mountainous and forested area. Ozone concentrations were measured for the first time with passive sampling at 10 sites and two online monitoring stations in this rural area from 2013 and 2015. Using with the daytime hourly O3 measurements during light hours (08:00–20:00) exceeding the threshold of 40 ppb over the 3 months (May, June and July) for agricultural crops, and over the six months (April to September) for forest trees AOT40 (Accumulated hourly O3 concentrations Over a Threshold of 40 ppb) cumulative index was calculated. AOT40 is defined by EU Directive 2008/50/EC to evaluate whether ozone pollution is a risk for vegetation, and is calculated by using hourly ozone concentrations from monitoring systems. In the present study, we performed the trajectory analysis by The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to follow the long-range transport sources contributing to the high ozone levels in the region. The ozone episodes observed between 2013 and 2015 were analysed using the HYSPLIT model developed by the NOAA-ARL. In addition, the cluster analysis is used to identify homogeneous groups of air mass transport patterns can be conducted through air trajectory clustering by grouping similar trajectories in terms of air mass movement. Backward trajectories produced for 3 years by HYSPLIT model were assigned to different clusters according to their moving speed and direction using a k-means clustering algorithm. According to cluster analysis results, northerly flows to study area cause to high ozone levels in the region. The results present that the ozone values in the study area are above the critical levels for forest and vegetation based on EU Directive 2008/50/EC.

Keywords: AOT40, Biga Peninsula, HYSPLIT, surface ozone

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Authors: Kasman, Darmawansyah, Alimin Maidin, Amran Razak

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Background: In decentralization, advocacy is needed to increase the health budget in Parepare District. One of the advocacy methods recommended by the World Bank is the economic loss approach. Methods: This research is observational in the field of health economics that contributes directly to the magnitude of the economic loss of the community and the government and provides advocacy to the executive and legislative to see the harm it causes. Results: The research results show the amount of direct cost, which consists of household expenditure for transport Rp.295,865,500. Indirect Cost of YLD of Rp.14.688.000, and YLL of Rp.28.986.336.00, so the amount of DALY is Rp.43.674.336.000. The total economic loss of Rp.43.970.201.500. These huge economic losses can be prevented by increasing the allocation of health budgets for promotive and preventive efforts and expanding the coverage of health insurance for the community. Conclusion: There is a need to advocate the executive and legislative about the importance of guarantee on public health financing by conducting studies in terms of economic losses so that all strategic alliances believe that health is an investment.

Keywords: advocacy, economic lost, health insurance, economic losses

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Authors: Kathryn Watson

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In 2020 North Carolina passed legislation mandating all educators be trained in identifying, referring, and supporting students showing signs of mental health issues, drug use, suicidal ideation, and sex trafficking. This study collected survey responses from 226 educators in North Carolina to better understand their perspectives on the legislation and their self-efficacy in supporting student mental health needs. Key findings of the study reveal that the mandated trainings increased educator awareness of student mental health, and higher awareness was linked to higher self-efficacy in supporting student mental health needs. Additionally, the results showed that educators who identify as Black had lower levels of self-efficacy in supporting student mental health. Additionally, rural educators were least likely to support the legislation in comparison to their urban and suburban counterparts. These findings can help inform policymakers in evaluating the policy and district decision-makers in selecting and implementing school-based mental health training.

Keywords: school-based mental health, education policy, student health, North Carolina, K-12 education

Procedia PDF Downloads 53

Authors: A. Kulprasutidilok

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In order to address the social factors of elderly health care, researcher and community members have turned to more inclusive and participatory approaches to research and interventions. One such approach, community-based participatory research (CBPR) in public health, has received increased attention as the academic and public health communities struggle to address the persistent problems of disparities in the use of health care and health outcomes for several over the past decade. As Thailand becomes an ageing society, health services and proper care systems specifically for the elderly group need to be prepared and well established. The purpose of this assignment was to study the health problems and was to explore the process of community participation in elderly health care. Participants in this study were member of elderly group of Paisanee Ramintra 65 community in Bangkok, Thailand. The results indicated two important components of community participation process in elderly health care: 1) a process to develop community participation in elderly health care, and 2) outcomes resulting from such process. The development of community participation consisted of four processes. As for the outcomes of the community participation development process, they consisted of elderly in the community got jointly and formulated a group, which strengthened the project because of collaborative supervision among themselves. Moreover, inactive health care services have changed to being energetic and focus on health promotion rather than medical achievement and elderly association of community can perform health care activities for chronically illness through the achievement of this development; consequently, they increasingly gained access to physical, cognitive, and social activity.

Keywords: community-based participatory research, elderly, heath care, Thailand.

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Authors: Won-Seok Kim, Hyung-Ik Shin

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Self-rated health (SRH) is a subjective assessment of individual health and has been identified as a strong predictor for mortality and morbidity. However few studies have been directed to the factors associated with SRH in persons with disabilities (PWD). We used data of 7th Korean national survey for 5307 PWD in 2008. Multiple logistic regression analysis was performed to find out independent risk factors for poor SRH in PWD. As a result, indicators of physical condition (poor instrumental ADL), socioeconomic disadvantages (poor education, economically inactive, low self-rated social class, medicaid in health insurance, presence of unmet need for hospital use) and social participation and networks (no use of internet service) were selected as independent risk factors for poor SRH in final model. Findings in the present study would be helpful in making a program to promote the health and narrow the gap of health status between the PWD.

Keywords: disabilities, risk factors, self-rated health, socioeconomic disadvantages, social networks

Procedia PDF Downloads 390

Authors: Kawpong Polyorat, Nathamon Buaprommee

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Food safety crises including mad cow disease and bird flu have raised consumers’ concern in meat safety. In response, the meat industry has adopted traceability systems to standardize quality and safety of their meat production. Traceability, however, is still rarely positioned as a marketing tool to persuade consumers who are meat endusers. Therefore, the present study attempts to understand consumer behaviors in the context of meat with traceability system by conducting a study in Thailand where research in this area is scant. The study results, based on structural equation modeling with AMOS, reveal that, while health motivation has a significant, positive impact on traceability trust, health consciousness does not directly affect traceability. Health consciousness, nevertheless, have a positive influence on health motivation. Finally, traceability trust has a positive impact on purchase intention of meat with traceability. Research implications and future study directions conclude the study report.

Keywords: consumer behavior, health consciousness, health motivation, traceability, trust

Procedia PDF Downloads 321