Flow methods to estimate flow for ungauged catchments for the development of small hydroelectric power in Northern California watersheds

The demand for energy is increasing daily. Currently, non-renewable energy is used to meet much of the energy demand. Nonrenewable resources cause an increase in atmospheric carbon concentration and are a significant cause of climate change. Governments throughout the world are supporting the use of renewable sources by providing financial and legislative support to reduce the carbon footprint in energy generation. In addition, having a variety of renewable energy sources in the portfolio would also complement each other, thereby providing a continuous supply of electricity to the people. Redwood Coast Energy Authority is a local government Joint Powers Agency in Humboldt County, California. The agency supports an assessment of the potential for small-scale hydropower to facilitate the development of small hydroelectric projects in Northern California Watersheds for a more complete renewable energy portfolio. Evaluating the appropriateness of developing a preliminary guideline for a small run-of-the-river hydropower production requires consideration of not only the river hydrology and topography but many other related criteria that will impact the project. This thesis will not address all critical components needed to decide whether to move forward with hydroelectric development but will focus on generating appropriate flow data using techniques like the drainage area ratio method (RAM), lumped modeling (L), semi-lumped modeling (SL), and distributed modeling. The analysis showed reliable results for all the catchments considered under the case study with different potential generation outputs. Results also showed how each flow estimation method performed, and the results looked very similar to one another. In addition, the analysis also showed that the best time for power generation is between the months of November through March, and the sites have almost no power potential in the summer months, especially for the months of July through September. Overall, the results were very similar and showed similar trends where the models were unable to predict the very high flows that usually occur in December but were able to predict flows in the remaining time very well when compared with the observed data

IoT-Based Applications in Healthcare Devices

The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things (IoT) has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up-to-date summary of the potential healthcare applications of IoT- (HIoT-) based technologies. Herein, the advancement of the application of the HIoT has been reported from the perspective of enabling technologies, healthcare services, and applications in solving various healthcare issues. Moreover, potential challenges and issues in the HIoT system are also discussed. In sum, the current study provides a comprehensive source of information regarding the different fields of application of HIoT intending to help future researchers, who have the interest to work and make advancements in the field to gain insight into the topic

Mapping Concurrent Wasting and Stunting Among Children Under Five in India: A Multilevel Analysis

Objectives: The study aims to examine the coexisting forms, patterns, and predictors of concurrent wasting and stunting (WaSt) among children under five in India.Methods: We used data from the National Family Health Survey to understand the trend and association of WaSt among children under five-year-old in India. Univariate analysis and cross-tabulations were performed for WaSt cases. The association was determined using multilevel binary logistic regression and multilevel regression, and the results were provided as adjusted odds ratios (aOR) with 95% confidence intervals at the significance level of p < 0.05.Results: The prevalence of WaSt has decreased from 8.7% in 2005–06 to 5.2 percent in 2019–2020. The proportion of WaSt children grew rapidly from 6 to 18 months, peaked at 19 months (8%), then dropped after 24 months. The prevalence of concurrent wasting and stunting is higher among boys compared to girls. Compared to children of different birth orders, those in the higher birth order are 1.2 times more likely to be WaSt cases (aOR = 1.20, 95% CI = 1.09, 1.33). The education of the mother is strongly correlated with WaSt instances, and children of more educated mothers have a 47% lower chance of being WaSt cases (aOR = 0.63, 95% CI = 0.57, 0.71). Children from wealthy families are 52% less likely to be WaSt cases (aOR = 0.48, 95% CI = 0.43, 0.55).Conclusion: This study emphasizes the importance of concurrent wasting and stunting and its relationship with socioeconomic factors among children under five in India

Internet of Things and Robotics in Transforming Current-Day Healthcare Services

Technology has become an integral part of everyday lives. Recent years have witnessed advancement in technology with a wide range of applications in healthcare. However, the use of the Internet of Things (IoT) and robotics are yet to see substantial growth in terms of its acceptability in healthcare applications. The current study has discussed the role of the aforesaid technology in transforming healthcare services. The study also presented various functionalities of the ideal IoT-aided robotic systems and their importance in healthcare applications. Furthermore, the study focused on the application of the IoT and robotics in providing healthcare services such as rehabilitation, assistive surgery, elderly care, and prosthetics. Recent developments, current status, limitations, and challenges in the aforesaid area have been presented in detail. The study also discusses the role and applications of the aforementioned technology in managing the current pandemic of COVID-19. A comprehensive knowledge has been provided on the prospect of the functionality, application, challenges, and future scope of the IoT-aided robotic system in healthcare services. This will help the future researcher to make an inclusive idea on the use of the said technology in improving the healthcare services in the future

Binding of phenazinium dye safranin T to polyriboadenylic acid: spectroscopic and thermodynamic study.

Here, we report results from experiments designed to explore the association of the phenazinium dye safranin T (ST, 3,7-diamino-2,8-dimethyl-5-phenylphenazinium chloride) with single and double stranded form of polyriboadenylic acid (hereafter poly-A) using several spectroscopic techniques. We demonstrate that the dye binds to single stranded polyriboadenylic acid (hereafter ss poly-A) with high affinity while it does not interact at all with the double stranded (ds) form of the polynucleotide. Fluorescence and absorption spectral studies reveal the molecular aspects of binding of ST to single stranded form of the polynucleotide. This observation is also supported by the circular dichroism study. Thermodynamic data obtained from temperature dependence of binding constant reveals that association is driven by negative enthalpy change and opposed by negative entropy change. Ferrocyanide quenching studies have shown intercalative binding of ST to ss poly-A. Experiments on viscosity measurements confirm the binding mode of the dye to be intercalative. The effect of [Na⁺] ion concentration on the binding process suggests the role of electrostatic forces in the complexation. Present studies reveal the utility of the dye in probing nucleic acid structure

Automated Detection of Caffeinated Coffee-Induced Short-Term Effects on ECG Signals Using EMD, DWT, and WPD

The effect of coffee (caffeinated) on electro-cardiac activity is not yet sufficiently researched. In the current study, the occurrence of coffee-induced short-term changes in electrocardiogram (ECG) signals was examined. Further, a machine learning model that can efficiently detect coffee-induced alterations in cardiac activity is proposed. The ECG signals were decomposed using three different joint time–frequency decomposition methods: empirical mode decomposition, discrete wavelet transforms, and wavelet packet decomposition with varying decomposition parameters. Various statistical and entropy-based features were computed from the decomposed coefficients. The statistical significance of these features was computed using Wilcoxon’s signed-rank (WSR) test for significance testing. The results of the WSR tests infer a significant change in many of these parameters after the consumption of coffee (caffeinated). Further, the analysis of the frequency bands of the decomposed coefficients reveals that most of the significant change was localized in the lower frequency band (<22.5 Hz). Herein, the performance of nine machine learning models is compared and a gradient-boosted tree classifier is proposed as the best model. The results suggest that the gradient-boosted tree (GBT) model that was developed using a db2 mother wavelet at level 2 decomposition shows the highest mean classification accuracy of 78%. The outcome of the current study will open up new possibilities in detecting the effects of drugs, various food products, and alcohol on cardiac functionality

Development of a low-cost food color monitoring system

The colorimetric analysis of food has gained much popularity in the last few decades. The present study reports the development of a low-cost food color quality testing and process monitoring system. The proposed device consists of a hardware and a software (Color Magic) combination, which may allow the hardware to be used either for quality testing or process monitoring applications. In the quality-testing mode, the software captures the image of the food products through the imaging system. Subsequently, the software processes the acquired image and computes color parameters in RGB (red, green, blue), CIELAB, and HSI (hue, saturation, intensity) color spaces. The software also synthesizes and displays the perceived color information in the display panel. The datalog of the sample color information can be sent to the user. Further, a separate software (Color Process), which is installed in a central server, was developed to implement a wireless star network topology for multi-node process monitoring. The “Color Process” software allows users to acquire color information from multiple hardware. The software monitors the Hue from all the devices. It alerts the user via email if the Hue is beyond the range in any of the nodes. Finally, the device was tested for quality testing and process monitoring applications using colored placards and apple slices. The implementation of the wireless sensor network (WSN) in designing the multi-node process monitoring makes the proposed device a unique system. This further would allow the device to be used for process monitoring at multiple remote locations

Socioeconomic Inequalities in the Prevalence of Non-Communicable Diseases among Older Adults in India

Understanding socioeconomic inequalities in non-communicable disease prevalence and preventive care usage can help design effective action plans for health equality programs among India&rsquo;s aging population. Hypertension (HTN) and diabetes mellitus (DM) are frequently used as model non-communicable diseases for research and policy purposes as these two are the most prevalent NCDs in India and are the leading causes of mortality. For this investigation, data on 31,464 older persons (aged 60 years and above) who took part in the Longitudinal Ageing Survey of India (LASI: 2017&ndash;2018) were analyzed. The concentration index was used to assess socioeconomic inequality whereas relative inequalities indices were used to compare HTN, DM, and preventive care usage between the different groups of individuals based on socioeconomic status. The study reveals that wealthy older adults in India had a higher frequency of HTN and DM than the poor elderly. Significant differences in the usage of preventive care, such as blood pressure/blood glucose monitoring, were found among people with HTN or DM. Furthermore, economic position, education, type of work, and residential status were identified as important factors for monitoring inequalities in access to preventive care for HTN and DM. Disparities in non-communicable diseases can be both a cause and an effect of inequality across social strata in India