Edge Computing Using Embedded Webserver with Mobile Device for Diagnosis and Prediction of Metastasis in Histopathological Images

Diagnosis of different breast cancer stages using histopathology whole slide images is the gold standard in grading the tissue metastasis. Traditional diagnosis involves labor intensive procedures and is prone to human errors. Computer aided diagnosis assists medical experts as a second opinion tool in early detection which prevents further proliferation. Computing facilities have emerged to an extent where algorithms can attain near human accuracy in prediction of diseases, offering better treatment to curb further proliferation. The work introduced in the paper provides an interface in mobile platform, which enables the user to input histopathology image and obtain the prediction results with its class probability through embedded web-server. The trained deep convolutional neural networks model is deployed into a microcomputer-based embedded system after hyper-parameter tuning, offering congruent performance. The implementation results show that the embedded platform with custom-trained CNN model is suitable for medical image classification, as it takes less execution time and mean prediction time. It is also noticed that customized CNN classifier model outperforms pre-trained models when used in embedded platforms for prediction and classification of histopathology images. This work also emphasizes the relevance of portable and flexible embedded device in real time clinical applications.

Development of Water Quality Control and Monitoring Systems for Hydroponic Plants Based on the Embedded Web

Hydroponics is a farming technology using water mixed with mineral nutrients which is continuously flowed to the roots of plants for 24 hours every day. The height of the water reserves, the water temperature, the nutrient content of the water and the pH of the water are some of the factors that need to be considered in planting crops using the hydroponic method. Regular observations and control functions of water quality are very important to ensure hydroponic plants can grow well. The function of controlling and observing water quality can be accomplished by developing a hardware system with an embedded Web equipped with a dosing-pump module to determine water quality based on the calculation of input data from the water quality sensor. With the embedded Web application, the control and observation functions can also be carried out remotely and periodically the hydroponic system can report the current state directly via the TCP/IP network. In this study, a second generation minimum system model will be developed which is the result of improvements from previous research. Water pH and water conductivity (EC) sensors can be activated simultaneously. The addition of a dosing-pump module is used to provide a liquid to adjust the pH value of water and plant nutrient fluids, while the addition of an embedded Web application to a Wi-Fi hardware module is to improve the ease of control and monitoring functions. Keywords—Embedded Web, Hydroponic water quality, Remote control and monitoring

Python Computational Web Apps for STEM Engineering Education

The article discusses STEM technologies and tools used in engineering education for scientific and technical calculations in Python, which allow to make classes visual and fun for students. An integrated environment that supports all the stages of solving computational scientific problems from their formulation, solution to their sharing is considered. Dash, Panel, Voilà and Streamlit technologies for publishing computational web applications for multivariate calculations are discussed, a comparison of these technologies for use in the educational process is made. Web applications allow computational experiments, but prevent changes to the source code, eliminating the appearance of errors. The integrated environment includes a generator of static sites for publishing smart tutorials with embedded web applications. This allows publishing student-developed applications along with learning materials with a minimum of effort. The integrated environment is convenient for both face-to-face and distant learning.

Barriers to web-based symptom management systems (web-SyMS).

6545 Background: Web-SyMS can reduce the burdens of cancer and its treatment. While patients frequently express willingness to use these systems, only a subset actively engages with them. Some patients may lack the tools and confidence needed to benefit from web-SyMS. We sought to characterize these barriers among community-based cancer patients receiving care across six diverse healthcare systems. Methods: We surveyed patients receiving chemotherapy at three healthcare systems (Baptist, TN; Maine Medical, ME; Dana-Farber, MA) and patients recovering from cancer-directed surgery at three healthcare systems (West Virginia University, WV; Dartmouth-Hitchcock, NH; Lifespan, RI). Surveys were conducted as part of a pre-implementation analysis of eSyM – an EHR-embedded web-SyMS that collects, tracks, and manages patient reported outcomes during cancer therapy. Results: Among 563 respondents, access to tech devices (i.e., tablet, computer, or smartphone) was high: 78% reported access to ≥2 devices and only 5% reported access to no devices. However, confidence using tech devices to accomplish online tasks varied: 45% very confident, 38% somewhat confident, 11% little-no confidence. Compared to medical oncology patients, surgery patients were more likely to report being very confident (57% vs. 31%). There were significant differences based on patients’ self-reported tech confidence (Chi-square P<.05 for all values in the table). Conclusions: Low self-reported tech confidence may identify patients who are at high risk for experiencing the burdens of cancer but may be less likely to benefit from web-SyMS. Addressing this barrier is critical to improving outcomes and addressing disparities. Clinical trial information: NCT03850912. [Table: see text]