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This paper investigates In0.7Ga0.3As/Ga0.5As0.5Sb-based Heterojunction Tunnel Field Effect Transistor with ferroelectric dielectric as stack layer, namely Ferroelectric Dual Material Stacked Double Gate Heterojunction TFET (FDMSDG-HJTFET) for the first time for performance and reliability. Ferroelectric gate dielectric has been inculcated in the device design in addition to the doping of Ga0.5As0.5Sb in the source region and In0.7Ga0.3As in the drain region, respectively. The performance of the proposed device is quantified in terms of DC electrical parameters and short channel parameters. The reliability analysis of the proposed device has been carried out in terms of influence of variations of interface trap charges, gate-source overlap length and temperature on the device transfer characteristics. From the simulation results, it can be manifested that the proposed device presents superior DC electrical characteristic and reduced short channel effects as compared to the GaSb/Si Dual Material Stacked Double Gate Heterojunction TFET (GaSb/Si DMSDG-HJTFET), under the influence of group III–V materials and ferroelectric gate dielectric. In addition to this, the proposed device depicts elevated immunity to presence of interface trap charges at the interface, variations in gate-source overlap length and temperature variations as well. The implication of TiO2 as dielectric stack over SiO2 provides exceptional capacitive coupling at the interface and thus assists in the improvement of electrical characteristics of the device by enhancing the electric field. The outcome of the simulation results presents that Ga0.5As0.5Sb/In0.7Ga0.3As-based Ferroelectric Dual Material Stacked Double Gate Heterojunction TFET (FDMSDG-HJTFET) can prove to be a competitive alternative for high performance applications with improved reliability. The design and simulation of the proposed device structure has been executed using technology computer-aided design (TCAD) tool.
Distal radio-ulnar joint (DRUJ) instability is increasingly recognised and assessment can be subjective and difficult. Previous research has used cadaveric models or in-vivo with CT, with variable results. A test device was designed to establish normal values of in-vivo DRUJ dorso-palmar translation.
Twenty volunteers were recruited. Those with previous wrist/forearm injuries were excluded. The device held the elbow at 90° flexion and neutral forearm rotation, with the distal ulna secured. A dorso-palmar shear force was applied to the distal radius and displacement measured three times on each wrist alternately by the same operator.
The mean translation of the DRUJ is 5.5 mm. Same-sided mean measurements for two subjects taken days apart varied by 1 mm. The intra-class correlation coefficient was 0.93.
The device is reliable, reproducible and appears to be a simple valid test. Contralateral sides were comparable. It will primarily be a research device to guide clinical practice in DRUJ instability.
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Emergent BioSolutions and Temasek Life Sciences Ventures Establish JV to Develop Broad Spectrum Pandemic Flu Vaccine and Therapeutic.
INDIA – Lack of access to technology ‘hampers detection of substandard drugs’.
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Presently, the design process of the AI remote server can enable the user to evaluate whether an authorized user can gain emotional responses when they establish an emotional product interface in the approach of the interaction with the device. Therefore, it is necessary for user experience and the ability to address the user’s emotional expectations. This paper proposes an artificial intelligence-based user face recognition response system (AI-UFRRS) to monitor users’ emotions in real life continually and provide new insights into their emotional responses and transitions. The user face recognition response system design is analyzed based on device intelligence. Eventually, the response system is improved and strategy based on an intelligent device. The proposed AI-UFRRS utilizes reinforcement learning technologies to maintain emotional processing in substantial information relating to the user’s identity. This paper offers AI remote strategies to reduce identification information and maximize information on emotions formed by reinforcement learning. The results suggest that the system provided can perform a convolute transformation to maintain user recognition accuracy and reduce face identity recognition. Thus, the experimental results of AI-UFRRS show the improved accuracy ratio of 95.6%, the recognition rate of 93.4%, emotion ratio of 95.5%, high response system ratio of 96.3%, and to increase user identification ratio of 91.8% and reduced false acceptance rate of 19.2%, the false rejection rate of 19.5% compared to other methods.
There are many tremendous risks of the secure and reliable operation of the power distribution network due to the fault, resonance oscillation, operation and lightning overvoltage in the power grid. The present measurement methods about over-voltage are based on the voltage transformer installed in the substation or its improved equipment and need complex data if just for over-voltage warning. This paper researches to use mechanism of production about corona to offer a simple method which can measure over-voltage qualitatively as a supplement of the over-voltage measurements. Experimental results show that, the needle plate electrode structure can be used as a supplemental measure about over-voltage measurement and warning of distribution network which is simple and practicable. In addition, in the normal operating voltage range the needle plate electrode structure can detect voltage waveform as non-contact voltage transformer to some extent. Capacitance of the needle plate electrode structure is tiny, so, it can detect transient voltage of charged conductor effectively.