PVPF Tool: An Automated Web Application for Real-Time Photovoltaic Power Forecasting

In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimized neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records' time with respect to the current year. The machine learning system was pre-trained and optimized based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC

Optimizing the lateral beamforming step for filtered-delay multiply and sum beamforming to improve active contour segmentation using ultrafast ultrasound imaging

As an alternative to delay-and-sum beamforming, a novel beamforming technique called filtered-delay multiply and sum (FDMAS) was introduced recently to improve ultrasound B-mode image quality. Although a considerable amount of work has been performed to evaluate FDMAS performance, no study has yet focused on the beamforming step size, , in the lateral direction. Accordingly, the performance of FDMAS was evaluated in this study by fine-tuning to find its optimal value and improve boundary definition when balloon snake active contour (BSAC) segmentation was applied to a B-mode image in ultrafast imaging. To demonstrate the effect of altering in the lateral direction on FDMAS, measurements were performed on point targets, a tissue-mimicking phantom and in vivo carotid artery, by using the ultrasound array research platform II equipped with one 128-element linear array transducer, which was excited by 2-cycle sinusoidal signals. With 9-angle compounding, results showed that the lateral resolution (LR) of the point target was improved by 67.9% and 81.2%, when measured at −6 dB and −20 dB respectively, when was reduced from to . Meanwhile the image contrast ratio (CR) measured on the CIRS phantom was improved by 10.38 dB at the same reduction and the same number of compounding angles. The enhanced FDMAS results with lower side lobes and less clutter noise in the anechoic regions provides a means to improve boundary definition on a B-mode image when BSAC segmentation is applied

Regional changes in reactive hyperemic blood flow during exercise training: time-course adaptations

BACKGROUND: Few studies have examined the time-course of localized exercise training on regional blood flow in humans. The study examined the influence of handgrip exercise training on forearm reactive hyperemic blood flow and vascular resistance in apparently healthy men. METHODS: Forearm blood flow and vascular resistance were evaluated, in 17 individuals [Age: 22.6 ± 3.5], in both arms, at rest and following 5 minutes of arterial occlusion, using strain gauge plethysmography, prior to training (V1) and every week thereafter (V2-5) for 4 weeks. Handgrip exercise was performed in the non-dominant arm 5 d/wk for 20 minutes at 60% of maximum voluntary contraction, while the dominant arm served as control. RESULTS: Resting HR, BP, and forearm blood flow and vascular resistance were not altered with training. The trained arm handgrip strength and circumference increased by 14.5% (p = 0.014) and 1.56% (p = 0.03), respectively. ANOVA tests revealed an arms by visit interaction for the trained arm for reactive hyperemic blood flow (p = 0.02) and vascular resistance (p = 0.009). Post-hoc comparison demonstrated increased reactive hyperemic blood flow (p = 0.0013), and decreased post-occlusion vascular resistance (p = 0.05), following the 1(st )week of training, with no significant changes in subsequent visits. CONCLUSION: The results indicate unilateral improvements in forearm reactive hyperemic blood flow and vascular resistance following 1 week of handgrip exercise training and leveled off for the rest of the study

Intent recognition in smart living through deep recurrent neural networks

Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and time- consuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects' intents, to avoid the time consumed in pre-processing and feature extraction. The hyper-parameters are selected by an Orthogonal Array experiment method for efficiency. Our model is applied to an open EEG dataset provided by PhysioNet and achieves the accuracy of 0.9553 on the intent recognition. The applicability of our proposed model is further demonstrated by two use cases of smart living (assisted living with robotics and home automation).Comment: 10 pages, 5 figures,5 tables, 21 conference

Burden of musculoskeletal disorders in the Eastern Mediterranean Region, 1990-2013: findings from the Global Burden of Disease Study 2013.

OBJECTIVES: We used findings from the Global Burden of Disease Study 2013 to report the burden of musculoskeletal disorders in the Eastern Mediterranean Region (EMR). METHODS: The burden of musculoskeletal disorders was calculated for the EMR's 22 countries between 1990 and 2013. A systematic analysis was performed on mortality and morbidity data to estimate prevalence, death, years of live lost, years lived with disability and disability-adjusted life years (DALYs). RESULTS: For musculoskeletal disorders, the crude DALYs rate per 100 000 increased from 1297.1 (95% uncertainty interval (UI) 924.3-1703.4) in 1990 to 1606.0 (95% UI 1141.2-2130.4) in 2013. During 1990-2013, the total DALYs of musculoskeletal disorders increased by 105.2% in the EMR compared with a 58.0% increase in the rest of the world. The burden of musculoskeletal disorders as a proportion of total DALYs increased from 2.4% (95% UI 1.7-3.0) in 1990 to 4.7% (95% UI 3.6-5.8) in 2013. The range of point prevalence (per 1000) among the EMR countries was 28.2-136.0 for low back pain, 27.3-49.7 for neck pain, 9.7-37.3 for osteoarthritis (OA), 0.6-2.2 for rheumatoid arthritis and 0.1-0.8 for gout. Low back pain and neck pain had the highest burden in EMR countries. CONCLUSIONS: This study shows a high burden of musculoskeletal disorders, with a faster increase in EMR compared with the rest of the world. The reasons for this faster increase need to be explored. Our findings call for incorporating prevention and control programmes that should include improving health data, addressing risk factors, providing evidence-based care and community programmes to increase awareness

Health in times of uncertainty in the eastern Mediterranean region, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

Background: The eastern Mediterranean region is comprised of 22 countries: Afghanistan, Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, the United Arab Emirates, and Yemen. Since our Global Burden of Disease Study 2010 (GBD 2010), the region has faced unrest as a result of revolutions, wars, and the so-called Arab uprisings. The objective of this study was to present the burden of diseases, injuries, and risk factors in the eastern Mediterranean region as of 2013. Methods: GBD 2013 includes an annual assessment covering 188 countries from 1990 to 2013. The study covers 306 diseases and injuries, 1233 sequelae, and 79 risk factors. Our GBD 2013 analyses included the addition of new data through updated systematic reviews and through the contribution of unpublished data sources from collaborators, an updated version of modelling software, and several improvements in our methods. In this systematic analysis, we use data from GBD 2013 to analyse the burden of disease and injuries in the eastern Mediterranean region specifically. Findings: The leading cause of death in the region in 2013 was ischaemic heart disease (90·3 deaths per 100 000 people), which increased by 17·2% since 1990. However, diarrhoeal diseases were the leading cause of death in Somalia (186·7 deaths per 100 000 people) in 2013, which decreased by 26·9% since 1990. The leading cause of disability-adjusted life-years (DALYs) was ischaemic heart disease for males and lower respiratory infection for females. High blood pressure was the leading risk factor for DALYs in 2013, with an increase of 83·3% since 1990. Risk factors for DALYs varied by country. In low-income countries, childhood wasting was the leading cause of DALYs in Afghanistan, Somalia, and Yemen, whereas unsafe sex was the leading cause in Djibouti. Non-communicable risk factors were the leading cause of DALYs in high-income and middle-income countries in the region. DALY risk factors varied by age, with child and maternal malnutrition affecting the younger age groups (aged 28 days to 4 years), whereas high bodyweight and systolic blood pressure affected older people (aged 60–80 years). The proportion of DALYs attributed to high body-mass index increased from 3·7% to 7·5% between 1990 and 2013. Burden of mental health problems and drug use increased. Most increases in DALYs, especially from non-communicable diseases, were due to population growth. The crises in Egypt, Yemen, Libya, and Syria have resulted in a reduction in life expectancy; life expectancy in Syria would have been 5 years higher than that recorded for females and 6 years higher for males had the crisis not occurred. Interpretation: Our study shows that the eastern Mediterranean region is going through a crucial health phase. The Arab uprisings and the wars that followed, coupled with ageing and population growth, will have a major impact on the region's health and resources. The region has historically seen improvements in life expectancy and other health indicators, even under stress. However, the current situation will cause deteriorating health conditions for many countries and for many years and will have an impact on the region and the rest of the world. Based on our findings, we call for increased investment in health in the region in addition to reducing the conflicts

DAMTRNN: A Delta attention-based multi-task RNN for intention recognition

Recognizing human intentions from electroencephalographic (EEG) signals is attracting extraordinary attention from the artificial intelligence community because of its promise in providing non-muscular forms of communication and control to those with disabilities. So far, studies have explored correlations between specific segments of an EEG signal and an associated intention. However, there are still challenges to be overcome on the road ahead. Among these, vector representations suffer from the enormous amounts of noise that characterize EEG signals. Identifying the correlations between signals from adjacent sensors on a headset is still difficult. Further, research not yet reached the point where learning models can accept decomposed EEG signals to capture the unique biological significance of the six established frequency bands. In pursuit of a more effective intention recognition method, we developed DAMTRNN, a delta attention-based multi-task recurrent neural network, for human intention recognition. The framework accepts divided EEG signals as inputs, and each frequency range is modeled separately but concurrently with a series of LSTMs. A delta attention network fuses the spatial and temporal interactions across different tasks into high-impact features, which captures correlations over longer time spans and further improves recognition accuracy. Comparative evaluations between DAMTRNN and 14 state-of-the-art methods and baselines show DAMTRNN with a record-setting performance of 98.87% accuracy