DFT analysis of Thymine adsorption on Ti doped graphene for biosensor applications

Density functional theory quantum chemical calculations have been performed to investigate the adsorption of thymine on pristine graphene (Gr) and Titanium doped graphene (GrTi) in order to explore the potential of doped graphene as adsorbent for biomolecule DNA nucleobase thymine. The various parameters including adsorption energy, mode of charge transfer, dipole moment, HOMO-LUMO gap and DOS confirms the Ti doped graphene can be good candidate as adsorbent for thymine in terms of biosensor applications.

Decay and renormalization of a longitudinal mode in a quasi-two-dimensional antiferromagnet

An ongoing challenge in the study of quantum materials, is to reveal and explain collective quantum effects in spin systems where interactions between different modes types are important. Here we approach this problem through a combined experimental and theoretical study of interacting transverse and longitudinal modes in an easy-plane quantum magnet near a continuous quantum phase transition. Our inelastic neutron scattering measurements of Ba2FeSi2O7 reveal the emergence, decay, and renormalization of a longitudinal mode throughout the Brillouin zone. The decay of the longitudinal mode is particularly pronounced at the zone center. To account for the many-body effects of the interacting low-energy modes in anisotropic magnets, we generalize the standard spin-wave theory. The measured mode decay and renormalization is reproduced by including all one-loop corrections. The theoretical framework developed here is broadly applicable to quantum magnets with more than one type of low energy mode.

Self-updated four-node finite element using deep learning

This paper introduces a new concept called self-updated finite element (SUFE). The finite element (FE) is activated through an iterative procedure to improve the solution accuracy without mesh refinement. A mode-based finite element formulation is devised for a four-node finite element and the assumed modal strain is employed for bending modes. A search procedure for optimal bending directions is implemented through deep learning for a given element deformation to minimize shear locking. The proposed element is called a self-updated four-node finite element, for which an iterative solution procedure is developed. The element passes the patch and zero-energy mode tests. As the number of iterations increases, the finite element solutions become more and more accurate, resulting in significantly accurate solutions with a few iterations. The SUFE concept is very effective, especially when the meshes are coarse and severely distorted. Its excellent performance is demonstrated through various numerical examples.

Decay and renormalization of a longitudinal mode in a quasi-two-dimensional antiferromagnet

An ongoing challenge in the study of quantum materials, is to reveal and explain collective quantum effects in spin systems where interactions between different modes types are important. Here we approach this problem through a combined experimental and theoretical study of interacting transverse and longitudinal modes in an easy-plane quantum magnet near a continuous quantum phase transition. Our inelastic neutron scattering measurements of Ba2FeSi2O7 reveal the emergence, decay, and renormalization of a longitudinal mode throughout the Brillouin zone. The decay of the longitudinal mode is particularly pronounced at the zone center. To explain these observations, we develop a generalized linear spin-wave theory, including all of the one-loop corrections, which reproduces the measured mode decay and renormalization. The theoretical approach developed here is broadly applicable to quantum magnets with more than one type of low energy mode.

Experimental Analysis of Solar Assisted Refrigerating Electric Vehicle

Most refrigerating systems are driven by an internal combustion engine that increased the conventional vehicle's oil consumption and tailpipe emissions. The solar-assisted refrigerating electric vehicle (SAREV) system powered by a hybrid energy mode has been designed. The hybrid energy (solar + grid) was stored in the battery bank to complete this vehicle's necessary functions. The PV panels are prominently incorporated into this vehicle rooftop to charge the battery bank. In this study, the integrated system was driven by a hybrid energy mode that reducing the wastage and deterioration during temporary storage and transportation in different areas. The performance of the integrated system was tested under different operating conditions. The effect of load variation on maximum speed and travelling distance of vehicle was analyzed. The battery bank charging and discharge performance were studied with and without solar energy. The refrigerator was consuming 116 Wh energy per day to maintain a -12 oC lower temperature on the no-load condition at the higher thermostat position. The refrigerator was run continuously for 4-6 days on battery bank energy and 7-10 days on the full load condition of hybrid energy. The vehicle was travelling at a maximum of 23 km/h speed on full load condition. The vehicle needed torque 14-16 N-m at the initial phase for each load condition. Torque demand was decreasing with the increasing speed of the vehicle. The full-charged battery bank's initial voltage was 51.04 V, and the cut-off voltage was 46.51 V. The vehicle was covering a distance of 62.4 km with the battery bank alone at full load condition. It was travelling 68.3 km distance with hybrid energy mode. The vehicle's integrated system was the best in maintaining battery performance, power contribution capability, and drive range enhancement.

A novel energy-motion model for continuous sEMG decoding: from muscle energy to motor pattern

<p></p><p><i>Background:</i> <a>At present, the gesture recognition using sEMG signals requires vast amounts of training data or limits to a few hand movements. This paper presents a novel dynamic energy model that can decode continuous hand actions with</a> force information, by training small amounts of sEMG data.</p> <p><i>Method:</i> As activating the forearm muscles, the corresponding fingers are moving or tend to move (namely exerting force). The moving fingers store kinetic energy, and the fingers with moving trends store potential energy. The kinetic and potential energy of fingers is dynamically allocated due to the adaptive-coupling mechanism of five-fingers in actual motion. At this certain moment, the sum of the two energies is constant. We regarded energy mode with the same direction of acceleration of each finger, but likely different movements, as the same one, and divided hand movements into ten energy modes. Independent component analysis and machine learning methods were used to model associations between sEMG signals and energy mode, to determine the hand action, including speed and force adaptively. This theory imitates the self-adapting mechanism in the actual task; thus, ten healthy subjects were recruited, and three experiments mimicking activities of daily living were designed to evaluate the interface: (1) decoding untrained configurations, (2) decoding the amount of single-finger energy, and (3) real-time control.</p> <p><i>Results:</i>(1) Participants completed the untrained hand movements (100 /100, p < 0.0001). (2) The test of pricking balloon with a needle tip was designed with significantly better than chance (779 /1000, p < 0.0001).(3) The test of punching a hole in the plasticine on the balloon was with over 95% success rate (97.67±5.04 %, p <0.01).</p> <p><i>Conclusion: </i>The model can achieve continuous hand actions with force information, by training small amounts of sEMG data, which reduces trained complexity.</p><p></p>