Deep learning-based framework for real-time upper limb motion intention classification using combined bio-signals.

This research study proposes a unique framework that takes input from a surface electromyogram (sEMG) and functional near-infrared spectroscopy (fNIRS) bio-signals. These signals are trained using convolutional neural networks (CNN). The framework entails a real-time neuro-machine interface to decode the human intention of upper limb motions. The bio-signals from the two modalities are recorded for eight movements simultaneously for prosthetic arm functions focusing on trans-humeral amputees. The fNIRS signals are acquired from the human motor cortex, while sEMG is recorded from the human bicep muscles. The selected classification and command generation features are the peak, minimum, and mean ΔHbO and ΔHbR values within a 2-s moving window. In the case of sEMG, wavelength, peak, and mean were extracted with a 150-ms moving window. It was found that this scheme generates eight motions with an enhanced average accuracy of 94.5%. The obtained results validate the adopted research methodology and potential for future real-time neural-machine interfaces to control prosthetic arms.

The impact of foreign direct investment on environment: evidence from newly industrialized countries.

This research aims to investigate the effect of foreign direct investment on carbon emissions through the panel ARDL method using annual data for the 1990-2016 period for the newly industrialized countries (NICs), including China, Malaysia, Mexico, Philippines, Thailand, Turkey, India, and Brazil. The stationarity of the series was obtained through LLC, IPS, and Fisher ADF panel unit root tests, the cointegration relationship with the panel ARDL-PMG approach, and the causality relationship with Dumitrescu and Hurlin (DH) tests. As a result of the long-term analysis, the foreign direct investment, energy consumption, and trade openness have a positive and significant impact on carbon emissions, whereas economic growth has a negative and significant impact on carbon emissions. The result shows that a percent increase in foreign direct investment increases carbon emissions by 0.03%. As a result of the short-term analysis, it was seen that the coefficient of the error correction term (ECT) was negative and statistically significant. According to DH panel causality test results, there exists a bidirectional causality relationship among energy consumption and carbon emissions, and a unidirectional causality relationship from economic growth and trade openness to carbon emissions and from carbon emissions to foreign direct investment. As policy implication, in industrialized countries especially China and India, there is a greater need to invest in green energy consumption at a larger scale to achieve future sustainable development goals.

Punching Shear Strength of FRP-Reinforced Concrete Slabs without Shear Reinforcements: A Reliability Assessment.

The recent failure of buildings because of punching shear has alerted researchers to assess the reliability of the punching shear design models. However, most of the current research studies focus on model uncertainty compared to experimentally measured strength, while very limited studies consider the variability of the basic variables included in the model and the experimental measurements. This paper discusses the reliability of FRP-reinforced concrete slabs' existing punching shear models. First, more than 180 specimens were gathered. Second, available design codes and simplified models were selected and used in the calculation. Third, several reliability methods were conducted; therefore, three methods were implemented, including the mean-value first-order second moment (MVFOSM) method, the first-order second moment (FOSM) method, and the second-order reliability method (SORM). A comparison between the three methods showed that the reliability index calculated using the FOSM is quite similar to that using SORM. However, FOSM is simpler than SORM. Finally, the reliability and sensitivity of the existing strength models were assessed. At the same design point, the reliability index varied significantly. For example, the most reliable was the JSCE, with a reliability index value of 4.78, while the Elgendy-a was the least reliable, with a reliability index of 1.03. The model accuracy is the most significant parameter compared to other parameters, where the sensitivity factor varied between 67% and 80%. On the other hand, the column dimension and flexure reinforcement are the least significant parameters compared to other parameters where the sensitivity factor was 0.4% and 0.3%, respectively.