Licensing Public Social Workers in Selected States

In Minnesota a coalition of seven professional associations (i.e., National Association of Social Workers, Minnesota Conference on Social Work Education, Minnesota School Social Workers\u27 Association, Minnesota Nursing Home Social Workers Association, Minnesota Society for Clinical Social Work, Minnesota Society for Social Work Administrators in Health Care, and the Minnesota Home Care Social Workers Association) are attempting to persuade state legislators to amend a current statute that exempts public social workers from social work licensure. Unlike Minnesota, a number of other states have licensing requirements for social workers employed by public human service agencies. A review of the literature shows that there are strong arguments against licensing in public human service agencies. The goal of this study was to conduct telephone interviews with key informants in states requiring licensure of public social workers to assess its benefits or problems. Thirty minute telephone interviews were conducted with the National Association of Social Workers (NASW) and state regulatory board employees from 10 states. Results indicate that declassification occurs more often in states where there is low involvement between NASW, social work educators, and public human service agencies. Participants indicated that public agencies have difficulty recruiting social workers of color and social workers in general to work in rural areas and, therefore hire applicants with human service related degree

When You Meet My Parents

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Multidimensional analysis using sensor arrays with deep learning for high-precision and high-accuracy diagnosis

In the upcoming years, artificial intelligence (AI) is going to transform the practice of medicine in most of its specialties. Deep learning can help achieve better and earlier problem detection, while reducing errors on diagnosis. By feeding a deep neural network (DNN) with the data from a low-cost and low-accuracy sensor array, we demonstrate that it becomes possible to significantly improve the measurements' precision and accuracy. The data collection is done with an array composed of 32 temperature sensors, including 16 analog and 16 digital sensors. All sensors have accuracies between 0.5-2.0$^\circ$C. 800 vectors are extracted, covering a range from to 30 to 45$^\circ$C. In order to improve the temperature readings, we use machine learning to perform a linear regression analysis through a DNN. In an attempt to minimize the model's complexity in order to eventually run inferences locally, the network with the best results involves only three layers using the hyperbolic tangent activation function and the Adam Stochastic Gradient Descent (SGD) optimizer. The model is trained with a randomly-selected dataset using 640 vectors (80% of the data) and tested with 160 vectors (20%). Using the mean squared error as a loss function between the data and the model's prediction, we achieve a loss of only 1.47x10$^{-4}$ on the training set and 1.22x10$^{-4}$ on the test set. As such, we believe this appealing approach offers a new pathway towards significantly better datasets using readily-available ultra low-cost sensors.Comment: Corrected typ

Heart rate measurement using the built-in triaxial accelerometer from a commercial digital writing device

Wearable devices are on the rise. Smart watches and phones, fitness trackers or smart textiles now provide unprecedented access to our own personal data. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor the heart's and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heartrate when they are attached to the chest. They can also help filter out some noise in ECG signal from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen), with a standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is done with eight volunteers, writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685x10$^{-3}$ comparing the DigiPen's computed ${\Delta}$t (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates from the pen accurately correlate with the reference ECG signals

Sustainable Advanced Manufacturing of Printed Electronics: An Environmental Consideration

Printing technologies have become a novel and disruptive innovation method of manufacturing electronic components to produce a diverse range of devices including photovoltaic cells, solar panels, energy harvesters, batteries, light sources, and sensors on really thin, lightweight, and flexible substrates. In traditional electronic manufacturing, a functional layer must be deposited, typically through a chemical vapor or physical vapor process for a copper layer for circuitry production. These subtractive techniques involve multiple production steps and use toxic etching chemicals to remove unwanted photoresist layers and metals. In printing, the same functional material can be selectively deposited only where it is needed on the substrate via plates or print heads. The process is additive and significantly reduces not only the number of manufacturing steps, but also the need for energy, time, consumables, as well as the waste. Thereby, printing has been in the focus for many applications as a green, efficient, energy-saving, environmentally friendly manufacturing method. This chapter presents a general vision on green energy resources and then details printed electronics that consolidates green energy and environment relative to traditional manufacturing system