Point-of-use water treatment for arsenic removal through iron oxide coated sand : application for the Terai region of Nepal

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Includes bibliographical references (leaves 155-159).Arsenic contaminated groundwater is prevalent in a number of countries around the world, most notably West Bengal, Bangladesh and now the Terai region of Nepal. Wide public awareness of the contamination was not until the 1990s, from years to several decades after tubewells were installed to extract groundwater for drinking water. Now, millions of people have arsenic poisoning which causes serious health effects such as arsenicosis, skin and liver cancer, circulatory disorder and hyperpigmentation. For the past three years, the MIT Nepal Water Project has been investigating arsenic contaminated tubewells in Nepal, and has begun to evaluate point-of-use arsenic removal technologies. These technologies must meet certain evaluation criteria: Effective removal of effective removal of total arsenic (As (III) + As (V)), minimally, below the Interim Nepali Standard of 50 [mu]/L; possibility of local manufacture with locally available materials; affordable to the Nepali citizens affected by arsenic contamination; socially acceptable in terms of maintenance, operation and water demand. The 2001-2002 MIT Nepal Project investigated three new technologies which might meet these criteria. Iron oxide coated sand is one of these technologies. Iron oxides are known to adsorb arsenic. Previous studies of arsenic and metal adsorption onto iron oxide coated sand prompted this investigation. Based on the methods utilized in these prior studies, the author produced seven different iron oxide coated sands, varying concentration of ferric nitrate used, coating mixture, and drying temperature. The arsenic removal capability of these sands was tested in Parasi, Nepal, Pepperell, Massachusetts and Salem, New Hampshire. Percent total arsenic removal varied from 11-99%. Considering the evaluation criteria such as arsenic removal performance, cost, availability of materials, and local production, iron oxide coated sand technology successfully meets most or all of these requirements. However, in this study, social acceptability has not been determined. Detailed testing and evaluation of the iron oxide properties, as well as sufficient resources allocated to production of the media, is crucial before iron oxide coated sand technology could be implemented for point-of-use arsenic removal in Nepal or other developing countries. The author also produced a digitized map representing the extent of arsenic contamination in the Terai using paper maps as a base.by Barika R. Poole.M.Eng

A review of automated sleep stage scoring based on physiological signals for the new millennia

Background and Objective: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal. Methods: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. Results: Our review shows that all of these signals contain information for sleep stage scoring. Conclusions: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost

A smart sleep apnea detection service

Over the last decades, sleep apnea has become one of the most prevalent healthcare problems. Diagnosis and treatment monitoring are key elements when it comes to addressing this public health crisis. A problem for diagnosis and treatment monitoring is a chronic lack of specialized lab facilities which results in long waiting times or the absence of such services. This can delay appropriate treatment which might prolong living with sleep apnea and thereby leading to health issues due to poor sleep. We address this problem with a smart sleep apnea detection service based on Heart Rate Variably (HRV) analysis. The service incorporates Internet of Medical Things (IoMT), mobile technology (MT), and advanced Artificial Intelligence (AI). The measured signals are relayed by a smart phone into a cloud server via IoMT protocols. Once the data is stored in the cloud server, a deep learning (DL) algorithm is used to detect sleep apnea events. Detecting these events can trigger a warning message which is sent to care givers. The smart sleep apnea detection service is beneficial for patients who find it difficult to access specialized lab facilities for diagnosis or treatment monitoring. Furthermore, the system prolongs the observation period, which can improve the diagnosis accuracy. The resource requirements for the proposed service are lower when compared to clinical facilities, this might lead to significant cost savings for healthcare providers

IoTSim-Stream: Modeling stream graph application in cloud simulation

In the era of big data, the high velocity of data imposes the demand for processing such data in real-time to gain real-time insights. Various real-time big data platforms/services (i.e. Apache Storm, Amazon Kinesis) allow to develop real-time big data applications to process continuous data to get incremental results. Composing those applications to form a workflow that is designed to accomplish certain goal is the becoming more important nowadays. However, given the current need of composing those applications into data pipelines forming stream workflow applications (aka stream graph applications) to support decision making, a simulation toolkit is required to simulate the behaviour of this graph application in Cloud computing environment. Therefore, in this paper, we propose an IoT Simulator for Stream processing on the big data (named IoTSim-Stream) that offers an environment to model complex stream graph applications in Multicloud environment, where the large-scale simulation-based studies can be conducted to evaluate and analyse these applications. The experimental results show that IoTSim-Stream is effective in modelling and simulating different structures of complex stream graph applications with excellent performance and scalability