Graspar: a flexible, easily controllable robotic hand

With only one motor per finger and a simple, easy to maintain, mechanical structure, the Graspar robotic hand uses minimal computation to provide secure grasping of objects ranging from eggs and light bulbs to tennis rackets, coffee pots and stuffed toys. We first discuss currently developed hands, and show the motivation for our work. Next, we present the mechanical structure of our robotic hand and the antagonistic tendoning system. We then show how this structure moves and complies to the object surface. We discuss how we chose the pulley diameters for our tendoning system by maximizing the workspace of the fingers while mechanically insuring that the hand cannot collide with itself. Our control algorithm which uses simple mechanical switches is presented     

Synthesis of a New Robust Exponential Sliding Mode Differentiator with its Observer Applications

This paper proposes a new robust exponential sliding mode differentiator for estimating the (n ? 1) derivatives of a non‐linear function. Finite time convergence, robustness, and exactness are also ensured analytically with the proposed methodology. In addition, the results of the proposed sliding mode differentiator are extended to derive theorems for the novel state (SO) and extended state observers (ESO), which would estimate the system states as well as uncertainties recursively in finite time. Finally, three examples are implemented to validate the proposed methodologies and the obtained simulations are compared with the previously developed methods in literature.     

Simulation of salinity effects on past, present, and future soil organic carbon stocks

Soil organic carbon (SOC) models are used to predict changes in SOC stocks and carbon dioxide (CO(2)) emissions from soils, and have been successfully validated for non-saline soils. However, SOC models have not been developed to simulate SOC turnover in saline soils. Due to the large extent of salt-affected areas in the world, it is important to correctly predict SOC dynamics in salt-affected soils. To close this knowledge gap, we modified the Rothamsted Carbon Model (RothC) to simulate SOC turnover in salt-affected soils, using data from non-salt-affected and salt-affected soils in two agricultural regions in India (120 soils) and in Australia (160 soils). Recently we developed a decomposition rate modifier based on an incubation study of a subset of these soils. In the present study, we introduce a new method to estimate the past losses of SOC due to salinity and show how salinity affects future SOC stocks on a regional scale. Because salinity decreases decomposition rates, simulations using the decomposition rate modifier for salinity suggest an accumulation of SOC. However, if the plant inputs are also adjusted to reflect reduced plant growth under saline conditions, the simulations show a significant loss of soil carbon in the past due to salinization, with a higher average loss of SOC in Australian soils (55 t C ha(-1)) than in Indian soils (31 t C ha(-1)). There was a significant negative correlation (p < 0.05) between SOC loss and osmotic potential. Simulations of future SOC stocks with the decomposition rate modifier and the plant input modifier indicate a greater decrease in SOC in saline than in non-saline soils under future climate. The simulations of past losses of SOC due to salinity were repeated using either measured charcoal-C or the inert organic matter predicted by the Falloon et al. equation to determine how much deviation from the Falloon et al. equation affects the amount of plant inputs generated by the model for the soils used in this study. Both sets of results suggest that saline soils have lost carbon and will continue to lose carbon under future climate. This demonstrates the importance of both reduced decomposition and reduced plant input in simulations of future changes in SOC stocks in saline soils.     

Development of a new solid-state absorber material for dye-sensitized solar cell (DSSC)

In contrast to the conventional DSSC systems, where the dye molecules are used as light harvesting material, here a solid-state absorber was used as a sensitizer in conjunction with the dye. The materials like ZnO and Al₂O₃ : C, which will show optically stimulated luminescence (OSL) upon irradiation were used as extremely thin absorber layers. This novel architecture allows broader spectral absorption, an increase in photocurrent, and hence, an improved efficiency because of the mobility of the trapped electrons in the absorber material after irradiation, to the TiO₂ conduction band. Nanocrystalline mesoporous TiO₂ photoanodes were fabricated using these solid-state absorber materials and after irradiation, a few number of samples were co-sensitized with N719 dye. On comparing both the dye loaded photoanodes (ZnO/TiO₂ and Al₂O₃ : C/TiO₂), it can be concluded from the present studies that, the Al₂O₃ : C is superior to ZnO under photon irradiation. Al₂O₃ : C is more sensitive to photon irradiation than ZnO and hence there can be more trap centres produced in Al₂O₃ : C.     

Study of Size Distribution in Nanostructured Se<Subscript>58</Subscript>Ge<Subscript>39</Subscript>Pb<Subscript>3</Subscript> Glass Using Various Characterization Methods

The present paper aims at the study of size distribution of particles in nanostructured Se₅₈Ge₃₉Pb₃ glass using X-ray diffraction (XRD), Transmission electron microscopy (TEM) and UV–visible spectrophotometer. The thin film sample has been prepared using melt quenching technique and inert gas consolidation method. The particle size distribution obtained from XRD and UV–Vis spectrophotometer shows more uncertainty than the results obtained from TEM measurements. The absorption spectra recorded on UV–Vis spectrophotometer is employed to get band gap values corresponding to different size distribution in sample. Further, it is concluded that TEM is the best measurement technique for size distribution as it has less uncertainty in the obtained results.     

Prediction of in vivo drug performance using in vitro dissolution coupled with STELLA: a study with selected drug products

Prediction of the in vivo performance of the drug product from the in vitro studies is the major challenging job for the pharmaceutical industries. From the current regulatory perspective, biorelevant dissolution media should now be considered as quality control media in order to avoid the risk associated. Physiological based pharmacokinetic models (PBPK) coupled with biorelevant dissolution medium is widely used in simulation and prediction of the plasma drug concentration and in vivo drug performance. The present investigation deals with the evaluation of biorelevant dissolution media as well as in vivo drug performance by PBPK modelling using STELLA® simulation software. The PBPK model was developed using STELLA® using dissolution kinetics, solubility, standard gastrointestinal parameters and post-absorptive disposition parameters. The drug product selected for the present study includes Linezolid film-coated immediate-release tablets (Zyvox), Tacrolimus prolonged-release capsules (Advagraf), Valganciclovir tablets (Valcyte) and Mesalamine controlled-release capsules (Pentasa) each belonging to different biopharmaceutics classification system (BCS). The simulated plasma drug concentration was analyzed and pharmacokinetic parameters were calculated and compared with the reported values. The result from the present investigation indicates that STELLA® when coupled with biorelevant dissolution media can predict the in vivo performance of the drug product with prediction error less than 20% irrespective of the dosage form (immediate release versus modified release) and BCS Classification. Thus, STELLA® can be used for in vivo drug prediction which will be helpful in generic drug development.     

Residual U-Network for Breast Tumor Segmentation from Magnetic Resonance Images

Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. Two features substantially influence the classification accuracy of malignancy and benignity in automated cancer diagnostics. These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis. In this research, the authors have proposed a ResU-Net (Residual U-Network) model for breast tumor segmentation. The proposed methodology renders augmented, and precise identification of tumor regions and produces accurate breast tumor segmentation in contrast-enhanced MR images. Furthermore, the proposed framework also encompasses the residual network technique, which subsequently enhances the performance and displays the improved training process. Over and above, the performance of ResU-Net has experimentally been analyzed with conventional U-Net, FCN8, FCN32. Algorithm performance is evaluated in the form of dice coefficient and MIoU (Mean Intersection of Union), accuracy, loss, sensitivity, specificity, F1score. Experimental results show that ResU-Net achieved validation accuracy & dice coefficient value of 73.22% & 85.32% respectively on the Rider Breast MRI dataset and outperformed as compared to the other algorithms used in experimentation.