Using K-Means Cluster Analysis and Decision Trees to Highlight Significant Factors Leading to Homelessness

Nutritional needs, resources, and barriers among unhoused adults cared for by a street medicine organization in Chicago, Illinois: a cross-sectional study

BACKGROUND

Those experiencing houselessness rely on obtaining food from community organizers and donations. Simultaneously, the houseless face disproportionally high rates of medical conditions that may be affected by diet including diabetes, hypertension, and hyperlipidemia. There is limited literature on the resources and barriers of the houseless community regarding optimal nutrition from an actionable perspective. Further, less data is available on how street medicine organizations may best impact the nutrition of the unhoused they serve. Elucidating this information will inform how organizational efforts may best support the nutrition of the houseless community.

METHODS

In partnership with the medical student-run organization, Chicago Street Medicine, at Northwestern University Feinberg School of Medicine, twenty adults experiencing houselessness in Chicago, Illinois participated in the cross-sectional study. A 10-item survey was verbally administered to characterize the participants' daily food intake, food sources, barriers, resources, and nutritional preferences and needs. All data was directly transcribed into REDCap. Descriptive statistics were generated.

RESULTS

Individuals consumed a median of 2 snacks and meals per day (IQR: 1-3). No participant consumed adequate servings of every food group, with only one participant meeting the dietary intake requirements for one food group. Participants most often received their food from donations (n = 15), purchasing themselves (n = 11), food pantries (n = 4), and shelters (n = 3). Eleven of nineteen participants endorsed dental concerns as a major barrier to consuming certain foods. Twelve participants had access to a can opener and twelve could heat their meals on a stove or microwave. Seven had access to kitchen facilities where they may prepare a meal. Approximately half of participants had been counseled by a physician to maintain a particular diet, with most related to reducing sugar intake.

CONCLUSION

Most houseless participants were unable to acquire a balanced diet and often relied on organizational efforts to eat. Organizations should consider the chronic health conditions, dentition needs, and physical resources and barriers to optimal nutrition when obtaining food to distribute to the unhoused.

Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction.

E-Learning Development Based on Internet of Things and Blockchain Technology during COVID-19 Pandemic

The suspension of institutions around the world in early 2020 due to the COVID-19 virus did not stop the learning process. E-learning concepts and digital technologies enable students to learn from a safe distance while continuing their educational pursuits. Currently, the Internet of Things (IoT) is one of the most rapidly increasing technologies in today’s digital world; and e-learning is one of the most powerful learning methods available. In today’s world, smart devices and new technologies assist teachers in concentrating on new models of student learning while avoiding time wastage. By examining the characteristics of the Internet of Things and the challenges that exist in the field of e-learning, the potential functions, benefits, and advancements of utilizing the Internet of Things in online education are identified and discussed. This article examines the existing and future condition of the Internet of Things world as it pertains to the topic of education and sophisticated capabilities available through the Internet of Things that enable the application of e-learning after an architecture has been designed. Students’ pulse rates, brain waves, and skin resistance are measured in real time by a collection of IoT sensors, including cameras, microphones, and wearable gadgets. By utilizing the proposed architecture, universities can change their distance learning tactics to maximize resources and boost efficiency without changing their overall academic activities. According to the study’s findings, e-learning has a favorable and statistically significant impact on students’ flexibility, learning experience, educational productivity, and overall quality of education.