Joint fatigue-based optimal posture prediction for maximizing endurance time in box carrying task

Multibody System Dynamics - In this study, the three-compartment controller fatigue model is integrated with an inverse dynamics optimization routine to predict the optimal posture, joint fatigue,...     

Design of agrivoltaic system to optimize land use for clean energy-food production: a socio-economic and environmental assessment

Clean Technologies and Environmental Policy - Accessing solar photovoltaic energy is a key point to develop sustainable energy and the economy of a developing country like India. The country has...     

Aloe vera incorporated biomimetic nanofibrous scaffold: a regenerative approach for skin tissue engineering

Aloe vera (AV) is one of the medicinal herbs with a well-established spectrum of wound healing, antimicrobial and anti-inflammatory property. AV-mediated therapeutics present significant tissue regenerative activity by modulating the inflammatory and proliferative phases of wound healing. The purpose of the present work was to combine the biological properties of AV and the advantages of electrospun meshes to prepare a potent transdermal biomaterial. The polycaprolactone (PCL) containing 5 and 10 wt % of lyophilized powder of AV was studied for electrospinning into nanoscale fiber mats and compared with PCL/Collagen blend for dermal substitutes. SEM revealed the average diameters of PCL, PCL-AV 5 %, PCL-AV 10 % and PCL/Collagen nanofiber scaffolds in the range of 519 ± 28, 264 ± 46, 215 ± 63 and 249 ± 52 nm, respectively. PCL-AV 10 % nanofiber scaffolds showed finer fiber morphology with improved hydrophilic properties and higher tensile strength of 6.28 MPa with a Young’s modulus of 16.11 MPa desirable for skin tissue engineering. The nanofibers were then used to investigate differences in biological responses in terms of proliferation and cell morphology of mice dermal fibroblasts. It was found that PCL-AV 10 % nanofibrous matrix favored cell proliferation compared to other scaffolds which almost increased linearly by (p ≤ 0.01) 17.79 % and (p ≤ 0.01) 21.28 % compared to PCL on sixth and ninth day. CMFDA dye expression, secretion of collagen and F-actin expression were significantly increased in PCL-AV 10 % scaffolds compared to other nanofibrous scaffolds. The obtained results proved that the PCL-AV 10 % nanofibrous scaffold is a potential biomaterial for skin tissue regeneration.     

Performance Analysis of Gate-Stack Dual-Material DG MOSFET Using Work-Function Modulation Technique for Lower Technology Nodes

Silicon - Short channel effects (SCEs) along with mobility degradation has a great impact on CMOS technology below 100 nm. These effects can be overcome by using gate and channel...     

In vivo bioactivity of herbal‐drug‐incorporated nanofibrous matrixes

Aloe‐vera‐incorporated polycaprolactone nanofibrous matrixes were prepared by an electrospinning method. These developed matrixes were evaluated for their water absorption capacity, water vapor permeability, and contact angle, and, in an in vivo animal model, wound‐healing ability. The incorporation of the herbal drug made the matrixes hydrophilic with improved water retention and permeability properties. The in vivo studies were carried out in a rat model and showed improved results with respect to healing. Thus, this study confirmed that the developed matrixes could be used for wound‐healing applications. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42178.     

A spiking half-cognitive model for classification

This paper describes a spiking neural network that learns classes. Following a classic Psychological task, the model learns some types of classes better than other types, so the net is a spiking cognitive model of classification. A simulated neural system, derived from an existing model, learns natural kinds, but is unable to form sufficient attractor states for all of the types of classes. An extension of the model, using a combination of singleton and triplets of input features, learns all of the types. The models make use of a principled mechanism for spontaneous firing, and a compensatory Hebbian learning rule. Combined, the mechanisms allow learning to spread to neurons not directly stimulated by the environment. The overall network learns the types of classes in a fashion broadly consistent with the Psychological data. However, the order of speed of learning the types is not entirely consistent with the Psychological data, but may be consistent with one of two Psychological systems a given person possesses. A Psychological test of this hypothesis is proposed.