On-chip Magnetoresistive Sensors for Detection and Localization of Paramagnetic Particles

This paper presents the work towards miniaturized magnetic biosensor array based on the detection of paramagnetic particles using the giant magnetoresistance (GMR) effect. GMR sensors have been studied for many years, but its application for on-chip integration and in complex configurations, as well as effective localization for Lab-On-Chip and Tissue Engineering applications is not yet explored. This work demonstrates the development of initial prototypes of 5 and 9 sensor GMR arrays of varying geometries and corresponding calibration and localization algorithms to detect and localize paramagnetic materials in 2D. The generation of a uniform magnetic field using a 16 magnet Halbach cylinder was also analyzed and optimized using FEA for different sensor configurations. Results show excellent localization for the fully calibrated 5 sensor arrays, with a mean (SD) error of 2.45 (1.61) mm for the ferrofluid as compared to 1.48 (1.14) mm for a strong ferromagnet for a 25×25mm2 array surface. The 9sensor array similarly showed good results for full calibration

Somewhat Homomorphic Encryption Technique with its Key Management Protocol

Cloud computing has been contemplated as the architecture of various Business organizations, providing easy access to vast data storage and applications services. Most of the cloud service providers encrypt the data only on the network , while some even store the data in encrypted format. This means anyone with access to the cloud servers (cloud service providers) can appropriate it. Even if the data is encrypted during storage, keys are often stored along with your data .Thus an end-to-end encryption scheme has been proposed as a promising solution to data storage on cloud ,in order to perform computations on the encrypted data and thereby store the key securely. Somewhat Homomorphic Encryption is a fully homomorphic encryption technique which is compact, semantically secure with significantly smaller public key and is capable of encrypting integer plaintexts rather than single bits, with comparatively lower expansion and computational complexities Keywords-Cloud computing, Cryptography, Homomorphic Key Management (HKM), Homomorphic encryption, Somewhat Homomorphic encryption(SHE)

DEVELOPMENT OF A REAL-TIME SMARTWATCH ALGORITHM FOR THE DETECTION OF GENERALIZED TONIC-CLONIC SEIZURES

Generalized Tonic Clonic Seizure (GTCS) detection has been an ongoing problem in the healthcare industry. Algorithms and devices for this problem do exist on the market, but they either have poor False Positive Rates, are expensive, or cannot be used as anything other than a seizure detector. There is currently a need to provide a portable seizure detection algorithm that can meets patient demands. In this thesis, we develop a two-stage end-to-end seizure detection algorithm that is implemented on an Apple Watch, and validated on Epilepsy Monitoring Unit (EMU) patients. 124 features are extracted from the collected dataset, after which 9 are empirically selected. We have provided mutual information based feature selection methods that cannot yet be implemented on the watch due to computational restrictions. In stage one we compare common anomaly detection methods of One Class SVM, SVDD, Isolation Forest and Extended Isolation Forest over a thorough cross-validation to determine which is ideal to use as an anomaly detector. Isolation Forest (Sensitivity: 0.9, FPR: 3.4/day, Latency: 69s) was chosen despite the good sensitivity and latency of SVDD (Sensitivity: 1.0, FPR: 17.28/day, Latency: 8.9s) due to better implementation characteristics. During in-vivo testing, we record a sensitivity of 100% over 24 recorded tonic seizures with FPR: 1.29/day. To further limit false positive detections, a second stage is incorporated to separate between true and false positives using deep learning methods. We compare a Deep-LSTM, CNN-LSTM and TCN network. CNN-LSTM (Sensitivity: 0.93, FPR: 0.047/day) was finally used on the watch due to its tractable implementation, though TCN (Sensitivity: 1.0, FPR: 0/day) performed significantly better during cross-validation. During in-vivo testing, the 2-stage algorithm showed sensitivity: 100%, FPR: 0.05/day over 2004 tracked hours and 12 seizures. The mean latency was 62 seconds, which is on the threshold of clinical acceptability for this task

Early Colonoscopy in Hospitalized Patients With Acute Lower Gastrointestinal Bleeding: A Nationwide Analysis.

Background: Performing colonoscopy within 24 h of presentation to the hospital is the accepted standard of care for patients with an acute lower gastrointestinal bleed (LGIB). Previous studies have failed to demonstrate the benefit of early colonoscopy (EC) on mortality. In this study, we wanted to see if there was a change in inpatient deaths (primary outcome), length of stay (LOS), and hospitalization charges (TOTCHG) (secondary outcomes) with EC compared to previous studies. Methods: Adults diagnosed with LGIB were identified using the International Classification of Disease 10th Revision codes from the National Inpatient Sample database for 2016 to 2019. EC was defined as the procedure performed within 24 h of hospitalization. Delayed colonoscopy (DC) was defined as a procedure performed after 24 h of presentation. The patient population was divided into EC and DC groups, and the effects of several covariates on outcomes were measured using binary logistic and multivariate regression analysis. Inverse probability treatment weighting (IPTW) was performed to adjust for confounding covariates. Results: There were 1,549,065 cases diagnosed with LGIB, of which 285,165 cases (18.4%) received a colonoscopy. A total of 107,045 (6.9%) patients received early colonoscopies. EC was associated with decreased inpatient deaths (0.9% in EC, and 1.4% in DC, P \u3c 0.001). However, upon IPTW, this difference was not present. EC was associated with a decreased LOS (median 3 days vs. 5 days, P \u3c 0.001) and TOTCHG (median $32,037 vs.$44,092, P \u3c 0.001). Weekend admissions (WA) were associated with fewer EC (31.6% in WA, and 39.5% in non-WA, P \u3c 0.001). WA did not affect inpatient deaths. Conclusions: EC was not associated with decreased inpatient deaths. There was no difference in endoscopic interventions in both EC and DC groups. The difference in inpatient deaths observed between the two groups was not evident upon adjusting the results for confounders. EC was associated with a decreased LOS, and TOTCHG in patients with LGIB

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

The current scope and stand of carbon capture storage and utilization ∼ A comprehensive review

Over here in this paper, we have tried to present detailed information to the best of our knowledge regarding carbon capture storage and its utilization. CCU as well as CCS may be imagined with corresponding parts inside a coordinated framework that could move towards zero discharges that may be required for environment adjustment in the near times. Carbon Capture Storage may be considered as the wide-range extraction of CO2 from various important resources, it is then allowed and made ready for long-term isolation from the atmosphere and, then followed by its appropriate use. Our objective for this paper was to incorporate many technologies in these fields from the research and developmental activities which were a part of many articles and papers till date to the commercial uses and the challenges which are faced in this field and future scopes in this area. The sole purpose of the paper is to present the information in this domain available via different sources in one single paper which would help researchers a lot during their future references or research. Still, there are many kinds of research going on in this field that could be enhanced and paced up by such informative papers where all the required information is stored in a single paper.For the past twenty years, the oil industry and several scientific institutions have given importance to the concept of carbon capture and storage (CCS). A feasible method for storing carbon must be economical, environmentally friendly, and sustainable over the long term. As a result, carbon capture, utilization, and storage (CCUS) has emerged from CCS. The development of CCUS technology goes beyond the narrow focus on storage as it expanded to use carbon dioxide in oil extraction, treat alkaline industrial waste, and conversion of CO2 into useful chemicals to make this greenhouse gas economically viable. Fossil fuels will continue to be a significant source of energy in the following decades despite worldwide commitments to limit CO2 emissions. By converting high-emission industries to low-emission ones, CCUS contributes to the development of the low-emission economy of the future. Therefore, to improve our understanding of the long-term implications of developing alternative options such as large-scale CO2 injection into geological formations, carbon mineralization, and conversion of CO2 to synthesis, state-of-the-art research on carbon storage and utilization is required, and further analysis to understand the risks of CCUS from a technical, legislative, and political perspective. For individuals up for the challenge, CCUS research offers a chance to tackle technological issues that result in innovations with substantial economic and political implications for the oil and petrol sector

An open repository of real-time COVID-19 indicators.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks