Application of Neural Network-Support Vector Technique to Forecast U.S. Unemployment Rate

This research utilized five economic factors; 1) Consumer Price Index, 2) Return on Treasury Securities, 3) Total Nonfarm payroll, 4) Jobless Claims Filed, and 5) Stand & Poor 500 index to predict US unemployment rate. Historical time series data was obtained from the Economic Research web site of the Federal Reserve Bank of St. Louis and other finance web site.;Multiple Linear Regression, Back Propagation Algorithm, and Support Vector Regression techniques were utilized to predict US unemployment rate. Based on Mean Squared Error and adjusted R2 values, the Support Vector Regression technique provided superior results for the given dataset. Future US unemployment rate was predicted with an average absolute error value of 0.815, 0.13 and 0.07 using MLR, ANN and SVR, respectively

Gallium Nitride Integrated Microsystems for Radio Frequency Applications.

The focus of this work is design, fabrication, and characterization of novel and advanced electro-acoustic devices and integrated micro/nano systems based on Gallium Nitride (GaN). Looking beyond silicon (Si), compound semiconductors, such as GaN have significantly improved the performance of the existing electronic devices, as well as enabled completely novel micro/nano systems. GaN is of particular interest in the “More than Moore” era because it combines the advantages of a wide-band gap semiconductor with strong piezoelectric properties. Popular in optoelectronics, high-power and high-frequency applications, the added piezoelectric feature, extends the research horizons of GaN to diverse scientific and multi-disciplinary fields. In this work, we have incorporated GaN micro-electro-mechanical systems (MEMS) and acoustic resonators to the GaN baseline process and used high electron mobility transistors (HEMTs) to actuate, sense and amplify the acoustic waves based on depletion, piezoelectric, thermal and piezo-resistive mechanisms and achieved resonance frequencies ranging from 100s of MHz up to 10 GHz with frequency×quality factor (f×Q) values as high as 1013. Such high-performance integrated systems can be utilized in radio frequency (RF) and microwave communication and extreme-environment applications.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135799/1/azadans_1.pd

Predictive models for charitable giving using machine learning techniques

Private giving represents more than three fourths of all U.S. charitable donations, about 2% of total Gross Domestic Product (GDP). Private giving is a significant factor in funding the nonprofit sector of the U.S. economy, which accounts for more than 10% of total GDP. Despite the abundance of data available through tax forms and other sources, it is unclear which factors influence private donation, and a reliable predictive mechanism remains elu- sive. This study aims to develop predictive models to accurately estimate future charitable giving based on a set of potentially influential factors. We have selected several factors, including unemployment rate, household income, poverty level, population, sex, age, ethnic- ity, education level, and number of vehicles per household. This study sheds light on the relationship between donation and these variables. We use Stepwise Regression to identify the most influential variables among the available variables, based on which predictive mod- els are developed. Multiple Linear Regression (MLR) and machine learning techniques, including Artificial Neural Networks (ANN) and Support Vector Regression (SVR) are used to develop the predictive models. The results suggest that population, education level, and the amount of charitable giving in the previous year are the most significant, independent variables. We propose three predictive models (MLR, ANN, and SVR) and validate them using 10-fold cross-validation method, then evaluate the performance using 9 different mea- suring criteria. All three models are capable of predicting the amount of future donations in a given region with good accuracy. Based on the evaluation criteria, using a test data set, ANN outperforms SVR and MLR in predicting the amount of charitable giving in the following year

Positive Result for SARS-CoV-2 RNA Test after a Long Time for the Patient with COVID-19 even after Discharge from the Hospital

Background: Ruthin's coronavirus disease 2019 (COVID-19) diagnosis is based on the positive result of real-time polymerase chain reaction (PCR) from the nasal and oropharyngeal swab. However, chest CT scans can play an important role in diagnosing patients with COVID-19. Cases Report: In this study, we reported a 44 years old female with a mild form of the COVID-19 who showed a positive result for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA for 44 days after symptom onset. The suspected case was detected using real-time PCR. After two weeks of hospitalization, the patient was discharged, but her molecular tests were performed twice after one month and 44 days, and they remained positive for SARS-CoV-2 RNA. Conclusion: In theory, if the patient becomes re-infected or the virus reacts, these individuals may serve as a transmission source. So far, the only way to screen for possible reinfection has been by using PCR on separate specimens

Effect of intensive neuromuscular electrical stimulation on chronic neck pain: A case report

© Nova Science Publishers, Inc. Chronic neck pain is a relatively common problem that can interfere with daily activities, and it is often experienced following musculoskeletal injuries. To identify the impact of intensive neuromuscular electrical stimulation (INES) for reducing chronic neck pain in a 21-year-old female athlete, following a traumatic sports injury, which occurred two years earlier. A treatment package including three separate sessions of intensive neuromuscular electrical stimulation and exercise therapy were prescribed. Outcomes measurements were short form McGill pain questionnaire (SF-MPQ), visual analogue scale (VAS), and the neck disability index (NDI). Measurements were performed at baseline, following the intervention, and three months later. Following our intervention; VAS score decreased from 6/10 to 3/10, and 1/10 after three months; and NDI decreased from 54/100 to 18/100, and 10/100 after three months. A combination of INES and resistance training significantly reduced neck pain after three months in a female gymnast. Further research is required to determine the effectiveness of this combination of treatments in larger cohorts with more diffuse musculoskeletal conditions

Multimodal impact of acupuncture, exercise therapy, and concurrent functional electrical stimulation on osteoarthritis of the knee: a case report

Knee osteoarthritis (OA) causes functional limitation in weight-bearing activities including walking. To investigate the multimodal impact of acupuncture, exercise therapy, and concurrent functional electrical stimulation (FES) on knee osteoarthritis. We designed a multidisciplinary treatment package including acupuncture; home based exercise therapy, and concurrent functional electrical stimulation during treadmill walking. Outcomes measurements included the numerical rating scale (NRS), the Knee Injury and Osteoarthritis Outcome Score (KOOS), and the Tampa Scale of Kinesiophobia (TSK). Measurements were completed at baseline and following the treatment phase which consisted of six individual sessions. A 48-year-old male, office worker presented with a history of chronic right knee. During the previous year, he was diagnosed with knee osteoarthritis after clinical physical examination by a sports medicine physician. Following our novel training intervention, the patient reported a reduction in pain intensity from 8 to 2 on the NRS, improved in all KOOS subscale scores, and improved in the TSK scale (reduction from 15 to 11). In addition, the patient reported that he was able to return to work and undertake normal activities of daily living with reduced knee pain. This case report showed that our novel multimodal intervention including six sessions of acupuncture, exercise therapy, and treadmill walking with functional electrical stimulation (FES) had a positive impact on knee pain and function in a middle-aged male with knee osteoarthritis

Impact of Sumac on postprandial high-fat oxidative stress

Background and Objective: High-fat diet causes a sudden increase in blood lipids and oxidative stress after each meal, which can affect the trigger mechanisms of atherosclerosis and cause some acute changes in the function of vessels' endothelial cells. With respect to the antioxidant properties of Sumac (Rhus coriaria), the present research was conducted to determine the effect of taking Sumac along with food on some atherosclerosis risk factors resulting from high-fat diet in hypercholesterolemic rabbits. Methodology: In this experimental study, 24 New Zealand rabbits were randomly designated into three eight-member groups as follows: normal diet, high-cholesterol diet (1%), high-cholesterol diet and Sumac powder 2%. Oxidative stress factors and those influencing atherosclerosis or arterial function including glucose, total cholesterol (TC), triglyceride (TG), Apo lipoprotein B (Apo B), low-density lipoprotein (LDL-C), nitrate, nitrite, fibrinogen and factor VII, and also liver enzymes (ALT, AST) were measured and compared in each group. Results: High cholesterol diet significantly increased total cholesterol, fibrinogen, triglycerides, glucose, nitrate, LDL-C and the liver enzymes ALT and AST (p 0.05). Conclusions: This study demonstrates the protective effect of consuming Sumac with food on some risk factors of atherosclerosis and oxidative stress (glucose, LDL-C, total cholesterol and fibrinogen) and also liver enzymes induced by high fat food

The Effect of Dry Needling on Lower Limb Dysfunction in Poststroke Survivors

Background: Spasticity is one of the main complications in poststroke survivors leading to difficulties in walking and standing resulting in high levels of disability. Objective: The aim of the study was to investigate the effects of deep dry needling on lower limb dysfunction in poststroke spastic patients. Methods: A randomized clinical trial conducted in poststroke survivors who were assigned to one of 2 groups: Deep dry needling (intervention group) and sham dry needling (control group). The primary outcome measures were Modified Modified Ashworth Scale (MMAS) and functional tests (timed up and go test, 10-meter walk test). Secondary outcome measures were active ankle dorsiflexion range of motion (AROM), passive ankle dorsiflexion range of motion (PROM), single leg stance test, and Barthel index. All measurements were assessed at baseline (T0), immediately after the third session 1 week later (T1), and 1 month after the end of the intervention (T2). Results: We recruited 24 patients (71% male; mean age 57 ± 10 years; 26.4 ± 1.8 kg•m−2; time since event: 25.2 ± 12.5 months). There were significant improvements in MMAS, timed up and go test, 10-meter walk test, Barthel scale, and PROM (P . 05). Conclusions: Deep dry needling decreases muscle spasticity and improves lower limb function and gait speed in poststroke survivors