Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. ASD cannot be fully cured, but early-stage diagnosis followed by therapies and rehabilitation helps an autistic person to live a quality life. Clinical diagnosis of ASD symptoms via questionnaire and screening tests such as Autism Spectrum Quotient-10 (AQ-10) and Quantitative Check-list for Autism in Toddlers (Q-chat) are expensive, inaccessible, and time-consuming processes. Machine learning (ML) techniques are beneficial to predict ASD easily at the initial stage of diagnosis. The main aim of this work is to classify ASD and typical developed (TD) class data using ML classifiers. In our work, we have used different ASD data sets of all age groups (toddlers, adults, children, and adolescents) to classify ASD and TD cases. We implemented One-Hot encoding to translate categorical data into numerical data during preprocessing. We then used kNN Imputer with MinMaxScaler feature transformation to handle missing values and data normalization. ASD and TD class data is classified using Support vector machine, k-nearest-neighbor (KNN), random forest (RF), and artificial neural network classifiers. RF gives the best performance in terms of the accuracy of 100% with different training and testing data split for all four types of data sets and has no over-fitting issue. We have also examined our results with already published work, including recent methods like Deep Neural Network (DNN) and Convolution Neural Network (CNN). Even using complex architectures like DNN and CNN, our proposed methods provide the best results with low-complexity models. In contrast, existing methods have shown accuracy upto 98% with log-loss upto 15%. Our proposed methodology demonstrates the improved generalization for real-time ASD detection during clinical trials.

Micro-hydroxyapatite reinforced Ti-based composite with tailored characteristics to minimize stress-shielding impact in bio-implant applications.

Biomaterials having higher strength and increased bioactivity are widely researched topics in the area of scaffold and implant fabrication. Metal-based biomaterials are favorably suitable for load-bearing implants due to their outstanding mechanical and structural properties. The issue with pure metallic material used for bio-implant is the mismatch between the mechanical properties of the human body parts and the implant. The mismatch in modulus and hardness values causes damage to muscles and other body parts due to the phenomena of 'stress-shielding'. As per the rule of mixture, combining a biocompatible ceramic with metals will not only lower the overall mechanical strength, but will also enhance the composite's bioactivity. In the present work, a Metal-Ceramic composite of Ti and µ-HAp is processed through high-energy mechanical alloying. The µ-HAp powders (in a weight fraction of 1%, 2%, and 3%) were alloyed with Pure Ti powder sintered using microwave hybrid heating (MHH). The homogeneously alloyed materials were inspected for chemical and elemental characteristics using XRD, SEM-EDX, and FTIR analyses. Nano-mechanical and micro-hardness properties were inspected for the fabricated Ti- µ-HAp composites and it shows a decreasing trend. Elastic modulus declined from 130.8 GPa to 50.11 GPa for 3 wt% µ-HAp compared to pure-Ti sample. The mechanical behaviour of developed composites confirms that it can minimize the stress-shielding impact due to comparatively lesser strength and hardness than pure metallic samples.

Green Synthesized Nanomaterials for Safe Technology in Sustainable Agriculture.

Nanotechnology is a new emerging cutting-edge technology in the 21st century and has applications in medical, cosmetics, electronics, energy, food, agriculture, and many sectors. Nanomaterials (NMs) are the main component of nanotechnology. NMs prepared by chemical routes are very hazardous and not safe for life. Therefore, attempts are being made to prepare NMs via different green routes. It is expected that nanotechnology using green synthesized NMs will be safe. At the same time, green synthesized nanomaterials will be cost-effective. In this chapter, the applications of green synthesized NMs in agriculture have been discussed in detail.

Architectural design and development of an upper-limb rehabilitation device: a modular synthesis approach.

PURPOSE: Enormous assistance is required during rehabilitation activities, which might result in a variety of complications if performed manually. To solve this issue, several solutions in the form of assistive devices have been presented recently. Another issue highlighted is the lack of kinematic compatibility in low degrees-of-freedom (dof) systems. The proposed approach of developing a human-motion-oriented rehabilitation device deals with the problem through hybrid architectures. A novel modular synthesis approach is used for the purpose to induce generality in the design process. MATERIALS AND METHODS: Using a modular strategy, three planar hybrid configurations are generated for two-dof mechanisms for supporting flexion/extension motion. Three such architectures are optimally synthesised and kinematically analysed over the entire workspace. A Genetic Algorithm (GA) is used to synthesise the architecture parameters optimally. Moreover, the outcomes are evaluated against a set of seven poses and posture locations of the wrist to choose the most suitable configuration among the others. Subsequently, kinematic compatibility is analysed for the coupled system - formed by the selected architecture and the human arm - while wearing the proposed mechanism. RESULTS: According to the findings of optimal synthesis, workspace and singularity analysis, configuration-III is capable of achieving the optimal postures for all task space locations (TSLs). Further, the work modifies the design by attaching additional three revolute passive joints for correcting misalignment concerns using coupled mobility analysis. CONCLUSION: The modular strategy for hybrid architectures and the subsequent mobility analysis provides an algorithmic framework for synthesising a task-based rehabilitation device.IMPLICATIONS OF REHABILITATIONManual physiotherapy is reported as repeated task, expensive and time-consuming, and considered stressful for the therapist or assistants to provide one-on-one physiotherapy to each patient in the traditional method. Robotic rehabilitation is, therefore, a viable option.In the several reported works on robotic rehabilitation exoskeletons, misalignment of the exoskeleton and the human motion is considered an open challenge. Normally, it is being managed through large number of degrees of freedom, which is certainly expensive and complex in control. The proposed approach of developing a human-motion-oriented rehabilitation device deals with the problem through hybrid architectures and modular strategy to develop them.While focusing upon the emulation of natural human motion trajectory, the compatibility of orthotic joint and human joint motion needs attention. As biological joint possesses complex kinematic characteristics, closed-loops are used in the design.Overall, a complete framework of a cost effective low-dof rehabilitation device is proposed and detailed through coupled analysis.

Laser Micromachining in Fabrication of Reverse-µEDM Tools for Producing Arrayed Protrusions.

This paper focuses on the fabrication of high-quality novel products using a µEDM process variant called Reverse-µEDM. The tool plate required for the Reverse-µEDM is fabricated using Nd: YAG-based laser beam micromachining (LBµM) at the optimized process parameters. The Grey relation analysis technique is used for optimizing LBµM parameters for producing tool plates with arrayed micro-holes in elliptical and droplet profiles. Titanium sheets of 0.5 mm thickness were used for such micro-holes, which can be used as a Reverse-µEDM tool. The duty cycle (a combination of pulse width and frequency) and current percentage are considered as significant input process parameters for the LBµM affecting the quality of the micro-holes. A duty cycle of 1.25% and a current of 20% were found to be an optimal setting for the fabrication of burr-free shallow striation micro-holes with a minimal dimensional error. Thereafter, analogous protrusions of high dimensional accuracy and minimum deterioration were produced by Reverse-µEDM using the LBµM fabricated tool plates.

DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection.

Brain Computer Interface technology enables a pathway for analyzing EEG signals for seizure detection. EEG signal decomposition, features extraction and machine learning techniques are more familiar in seizure detection. However, selecting decomposition technique and concatenation of their features for seizure detection is still in the state-of-the-art phase. This work proposes DWT-EMD Feature level Fusion-based seizure detection approach over multi and single channel EEG signals and studied the usability of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) feature fusion with respect to individual DWT and EMD features over classifiers SVM, SVM with RBF kernel, decision tree and bagging classifier for seizure detection. All classifiers achieved an improved performance over DWT-EMD feature level fusion for two benchmark seizure detection EEG datasets. Detailed quantification results have been mentioned in the Results section.

Binary and Ternary Polymer Solar Cells Based on a Wide Bandgap D-A Copolymer Donor and Two Nonfullerene Acceptors with Complementary Absorption Spectral.

A new wide-bandgap conjugated D-A polymer denoted as P106 with a medium acceptor dithieno [2,3-e;3'2'-g]isoindole-7,9 (8H) (DTID) unit and strong 2-dodecylbenzo[1,2-b:3,4-b':6,5-b"]trithiophene (3TB) donor units shows an optical bandgap of 2.04 and highest occupied molecular orbital energy level of -5.56 eV. P106 is used as the donor and two nonfullerene acceptors-medium bandgap DBTBT-IC and narrow band Y18-DMO-are used as acceptors for the construction of binary and ternary bulk heterojunction polymer solar cells. The optimized polymer solar cells based on P106 : DBTBT-IC and P106 : Y18-DMO exhibit power conversion efficiencies of 11.76 % and 14.07 %, respectively. The short-circuit current density (22.78 mA cm-2 ) for the P106 : Y18-DMO device is higher than that for P106 : DBTBT-IC (18.56 mA cm-2 ) one, which could be attributed to the more photon harvesting efficiency of the P106 : Y18-DMO active layer. In light of the high short-circuit current densities and fill factors for the Y18-DMO based device and the high value of open circuit voltage of the DBTBT-IC based device, ternary polymer solar cells are fabricated by using ternary active layer (P106 : DBTBT-IC : Y18-DMO) and achieve a power conversion efficiency of 16.49 % with low energy loss of 0.47 eV.

Resilient and agile engineering solutions to address societal challenges such as coronavirus pandemic.

The world is witnessing tumultuous times as major economic powers including the US, UK, Russia, India, and most of Europe continue to be in a state of lockdown. The worst-hit sectors due to this lockdown are sales, production (manufacturing), transport (aerospace and automotive) and tourism. Lockdowns became necessary as a preventive measure to avoid the spread of the contagious and infectious "Coronavirus Disease 2019" (COVID-19). This newly identified disease is caused by a new strain of the virus being referred to as Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS CoV-2; formerly called 2019-nCoV). We review the current medical and manufacturing response to COVID-19, including advances in instrumentation, sensing, use of lasers, fumigation chambers and development of novel tools such as lab-on-the-chip using combinatorial additive and subtractive manufacturing techniques and use of molecular modelling and molecular docking in drug and vaccine discovery. We also offer perspectives on future considerations on climate change, outsourced versus indigenous manufacturing, automation, and antimicrobial resistance. Overall, this paper attempts to identify key areas where manufacturing can be employed to address societal challenges such as COVID-19.

Incipient plasticity in 4H-SiC during quasistatic nanoindentation.

Silicon carbide (SiC) is an important orthopedic material due to its inert nature and superior mechanical and tribological properties. Some of the potential applications of silicon carbide include coating for stents to enhance hemocompatibility, coating for prosthetic-bearing surfaces and uncemented joint prosthetics. This study is the first to explore nanomechanical response of single crystal 4H-SiC through quasistatic nanoindentation. Displacement controlled quasistatic nanoindentation experiments were performed on a single crystal 4H-SiC specimen using a blunt Berkovich indenter (300nm tip radius) at extremely fine indentation depths of 5nm, 10nm, 12nm, 25nm, 30nm and 50nm. Load-displacement curve obtained from the indentation experiments showed yielding or incipient plasticity in 4H-SiC typically at a shear stress of about 21GPa (~an indentation depth of 33.8nm) through a pop-in event. An interesting observation was that the residual depth of indent showed three distinct patterns: (i) positive depth hysteresis above 33nm, (ii) no depth hysteresis at 12nm, and (iii) negative depth hysteresis below 12nm. This contrasting depth hysteresis phenomenon is hypothesized to originate due to the existence of compressive residual stresses (upto 143MPa) induced in the specimen by the polishing process prior to the nanoindentation.