Wearable Electrochemical Sensor for Sweat-Based Potassium Ion and Glucose Detection in Exercise Health Monitoring.

The increasing prevalence of wearable devices has sparked a growing interest in real-time health monitoring and physiological parameter tracking. This study focuses on the development of a cost-effective sweat analysis device, utilizing microfluidic technology and selective electrochemical electrodes for non-invasive monitoring of glucose and potassium ions. The device, through real-time monitoring of glucose and potassium ion levels in sweat during physical activity, issues a warning signal when reaching experimentally set thresholds (K+ concentration at 7.5 mM, glucose concentrations at 60 µM and 120 µM). This alerts users to potential dehydration and hypoglycemic conditions. Through the integration of microfluidic devices and precise electrochemical analysis techniques, the device enables accurate and real-time monitoring of glucose and potassium ions in sweat. This advancement in wearable technology holds significant potential for personalized health management and preventive care, promoting overall well-being, and optimizing performance during physical activities.

Utility of the Python package Geoweaver_cwl for improving workflow reusability: an illustration with multidisciplinary use cases.

Computational workflows are widely used in data analysis, enabling automated tracking of steps and storage of provenance information, leading to innovation and decision-making in the scientific community. However, the growing popularity of workflows has raised concerns about reproducibility and reusability which can hinder collaboration between institutions and users. In order to address these concerns, it is important to standardize workflows or provide tools that offer a framework for describing workflows and enabling computational reusability. One such set of standards that has recently emerged is the Common Workflow Language (CWL), which offers a robust and flexible framework for data analysis tools and workflows. To promote portability, reproducibility, and interoperability of AI/ML workflows, we developed geoweaver_cwl, a Python package that automatically describes AI/ML workflows from a workflow management system (WfMS) named Geoweaver into CWL. In this paper, we test our Python package on multiple use cases from different domains. Our objective is to demonstrate and verify the utility of this package. We make all the code and dataset open online and briefly describe the experimental implementation of the package in this paper, confirming that geoweaver_cwl can lead to a well-versed AI process while disclosing opportunities for further extensions. The geoweaver_cwl package is publicly released online at https://pypi.org/project/geoweaver-cwl/0.0.1/ and exemplar results are accessible at: https://github.com/amrutakale08/geoweaver_cwl-usecases.

Community venue exposure risk estimator for the COVID-19 pandemic.

Complexities of virus genotypes and the stochastic contacts in human society create a big challenge for estimating the potential risks of exposure to a widely spreading virus such as COVID-19. To increase public awareness of exposure risks in daily activities, we propose a birthday-paradox-based probability model to implement in a web-based system, named COSRE (community social risk estimator) and make in-time community exposure risk estimation during the ongoing COVID-19 pandemic. We define exposure risk to mean the probability of people meeting potential cases in public places such as grocery stores, gyms, libraries, restaurants, coffee shops, offices, etc. Our model has three inputs: the real-time number of active and asymptomatic cases, the population in local communities, and the customer counts in the room. With COSRE, possible impacts of the pandemic can be explored through spatiotemporal analysis, e.g., a variable number of people may be projected into public places through time to assess changes of risk as the pandemic unfolds. The system has potential to advance understanding of the true exposure risks in various communities. It introduces an objective element to plan, prepare and respond during a pandemic. Spatial analysis tools are used to draw county-level exposure risks of the United States from April 1 to July 15, 2020. The correlation experiment with the new cases in the next two weeks shows that the risk estimation model offers promise in assisting people to be more precise about their personal safety and control of daily routine and social interaction. It can inform business and municipal COVID-19 policy to accelerate recovery.

County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model.

Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-season and in-season soybean yield prediction in CONUS at the county-level. The model was trained by crop growth variables and environment variables, which include weather data, MODIS Land Surface Temperature (LST) data, and MODIS Surface Reflectance (SR) data; historical soybean yield data were employed as labels. Based on the Google Earth Engine (GEE), all these training data were combined and transformed into histogram-based tensors for deep learning. The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or LSTM model in both end-of-season and in-season. The proposed method shows great potential in improving the accuracy of yield prediction for other crops like corn, wheat, and potatoes at fine scales in the future.

The Effects of a Macromolecular Charring Agent with Gas Phase and Condense Phase Synergistic Flame Retardant Capability on the Properties of PP/IFR Composites.

In order to improve the efficiency of intumescent flame retardants (IFRs), a novel macromolecular charring agent named poly(ethanediamine-1,3,5-triazine-p-4-amino-2,2,6,6-tetramethylpiperidine) (PETAT) with gas phase and condense phase synergistic flame-retardant capability was synthesized and subsequently dispersed into polypropylene (PP) in combination with ammonium polyphosphate (APP) via a melt blending method. The chemical structure of PETAT was investigated by Fourier transform infrared spectroscopy (FTIR), and ¹H nuclear magnetic resonance (NMR) spectroscopy. Thermal properties of the PETAT and IFR systems were tested by thermogravimetric-derivative thermogravimetric analysis (TGA-DTG) and thermogravimetry-Fourier transform infrared spectroscopy (TG-FTIR). The mechanical properties, thermal stability, flame-retardant properties, water resistance, and structures of char residue in flame-retardant composites were characterized using tensile and flexural strength property tests, TGA, limiting oxygen index (LOI) values before and after soaking, underwritten laboratory-94 (UL-94) vertical burning test, cone calorimetric test (CCT), scanning electron microscopy with energy dispersive X-ray spectrometry (SEM-EDXS), and FTIR. The results indicated that PETAT was successfully synthesized, and when the ratio of APP to PETAT was 2:1 with 25 wt % loading, the novel IFR system could reduce the deterioration of tensile strength and enhance the flexural strength of composites. Meanwhile, the flame-retardant composite was able to pass the UL-94 V-0 rating with an LOI value of 30.3%, and the peak of heat release rate (PHRR), total heat release (THR), and material fire hazard values were considerably decreased compared with others. In addition, composites also exhibited excellent water resistance properties compared with traditional IFR composites. SEM-EDXS and FTIR analyses of the char residues, as well as TG-FTIR analyses of IFR were used to investigate the flame-retardant mechanism of the APP/PETAT IFR system. The results indicated that the efficient flame retardancy of PP/IFR composites could be attributed to the synergism of the free radical-quenching and char layer-protecting mechanisms in the gas phase and condense phase, respectively.