A large anisotropic damage theory based on an incremental complementary energy equivalence model

This paper presents a large anisotropic damage theory of continuum damage mechanics. It is developed via a new hypothesis of incremental complementary elastic energy equivalence. This hypothesis is more versatile and accurate if compared to the original hypothesis of total complementary energy equivalence. To model the large damage, we assumed that it occurs as a series of incremental small damage. An expression for the damage effect tensor M(D) for large damage is derived. It is shown that when the damage is small, that is, D _i1, the proposed large damage theory reduces to the small damage model of Chow and Wang [1]. To demonstrate this large damage theory, it is applied to model the following cases: (a) uniaxial tension, (b) pure torsion and (c) elastic perfectly-plastic material behavior. In all three cases, the results clearly show that when the damage is small, Chow and Wang's model is recovered. However, for large damage, there are significant differences in predictions. Since this large damage theory is formulated on the basis of the incremental complementary energy, it is applicable to a wider range of problems.     

Chemical-bath deposition of band-gap-tailored CdxPb1−xS films

An attempt was made to modify the band gap of CdS ( 2.4 eV) by preparing a mixed lattice with a low-band-gap material, PbS (0.3 eV), giving a new set of materials, Cd _x Pb_1–x S. Band gaps as low as 1.9 eV were achieved with increasing x. The preparation of Cd _x Pb_1–x S was carried out by chemical-bath deposition. Structural characterization studies using X-ray diffraction (XRD), energy dispersion analysis by X-rays (EDAX), and optical microscopy were performed. The optical-absorption studies used to find the band gap are also described.     

An anthropometric survey of Indian schoolchildren aged 3-5 years

Kindergartens of age group 3-5 years, a distinct and small subgroup of Indian schoolchildren, need special emphasis on human factors. The paper presents the results of an anthropometric survey conducted on schoolchildren. These data, previously lacking in India, can be of benefit for the designers of kindergartens. A set of 81 different body dimensions was taken from a sample of 198 children of age group 3-4 years and 4.1-5 years. Simple anthropometric techniques were used to collect the data. Statistical analyses of the data have been presented in a tabular form for designers as well as for non-technical users. It is emphasized that such data have to be developed at the national level, including every ethnic group, or on a regional basis. Designers, manufacturers and importers of goods should have access to these data.     

Fast Ion Transport in Li-Rich Alloy Anode for High-Energy-Density All Solid-State Lithium Metal Batteries

All-solid-state Li batteries (ASSLBs) with solid-polymer electrolytes are considered promising battery systems to achieve improved safety and high energy density. However, Li dendrite formation at the Li anode under high charging current density/capacity has limited their development. To tackle the issue, Li-metal alloying has been proposed as an alternative strategy to suppress Li dendrite growth in ASSLBs. One drawback of alloying is the relatively lower operating cell voltages, which will inevitably lower energy density compared to cells with pure Li anode. Herein, a Li-rich Li₁₃In₃ alloy electrode (LiRLIA) is proposed, where the Li₁₃In₃ alloy scaffold guides Li nucleation and hinders Li dendrite formation. Meanwhile, the free Li can recover Li's potential and facilitate fast charge transfer kinetics to realize high-energy-density ASSLBs. Benefitting from the stronger adsorption energy and lower diffusion energy barrier of Li on a Li₁₃In₃ substrate, Li prefers to deposit in the 3D Li₁₃In₃ scaffold selectively. Therefore, the Li–Li symmetric cell constructed with LiRLIA can operate at a high current density/capacity of 5 mA cm⁻²/5 mAh cm⁻² for almost 1000 h.     

Throughput analysis in censoring-based cooperative cognitive radio network with energy harvesting

An energy harvesting (EH) and cooperative cognitive radio (CR) network (CRN) is studied in this paper where CR users transmit data through a primary user (PU) channel if the channel remains idle, else an optimal number CRs helps in transmission of PU. To achieve the optimum number of CRs (ONCR) involved in cooperation, a novel scheme based on a combination of channel censoring and total error is proposed. The performance of the proposed scheme is investigated under RF harvesting scenario. The EH is dependent on sensing decision and a CR source harvests energy from PU's RF signal. The harvested energy (HE) is split into two parts: One part is used by the CR network (CRN) for its own transmission, and the other part is used for supporting PU. The effect of the energy allocation factor on total throughput is also investigated. New expressions for optimal number of CRs and throughput are developed. The effect of network parameters such as sensing time, censoring threshold, and energy allocation parameter (EAP) on throughput is investigated. Impact of distance between nodes is also studied.     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

Large‐Stroke Electrochemical Carbon Nanotube/Graphene Hybrid Yarn Muscles

Artificial muscles are reported in which reduced graphene oxide (rGO) is trapped in the helical corridors of a carbon nanotube (CNT) yarn. When electrochemically driven in aqueous electrolytes, these coiled CNT/rGO yarn muscles can contract by 8.1%, which is over six times that of the previous results for CNT yarn muscles driven in an inorganic electrolyte (1.3%). They can contract to provide a final stress of over 14 MPa, which is about 40 times that of natural muscles. The hybrid yarn muscle shows a unique catch state, in which 95% of the contraction is retained for 1000 s following charging and subsequent disconnection from the power supply. Hence, they are unlike thermal muscles and natural muscles, which need to consume energy to maintain contraction. Additionally, these muscles can be reversibly cycled while lifting heavy loads.