Estimation of Desired Motion Intention and compliance control for upper limb assist exoskeleton

International Journal of Control, Automation and Systems - In this paper, we have addressed two issues for upper limb assist exoskeleton. 1) Estimation of Desired Motion Intention (DMI); 2) Robust...     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Physico-chemical analysis of tannery solid waste and structural characterization of its isolated humic acids after composting

In Marrakech, solid by-products from tanneries are highly polluting, generating large amounts of nitrogenous and organic matter. In the present study composting is tested as a cost-effective method for waste management to overcome many of the environmental hazards and produce a stable, rich material for soil fertilization. Two composting trials were conducted after neutralization by ammonia or lime. The aim of the neutralization was to avoid the antimicrobial effects of the acidity in the tannery waste, thus ensuring correct composting. Different techniques such as elemental analysis and ¹³C NMR spectroscopy were applied to analyse humic acids isolated from raw and composted materials, and to monitor the process of tannery waste composting, and the stability and maturity of the final product according to the means of neutralization. Comparison of data showed similar behaviour in both trials, but the composting process appeared to be more complete following neutralization with lime. The C, H and N content decreased, while the O increased. The FTIR and ¹³C NMR spectra show the decrease of aliphatic compounds demonstrated by the reduction of absorbance around 2922 cm⁻¹ and of the resonance in the C-alkyl area around 0–55 ppm. The humic acids newly formed during composting were richer in the O–N alkyl and oxidized aromatic structures that increased almost twofold on composting after neutralization with lime. The first principal component axis PC1 (54%) separated C-aliphatic, C-carboxylic and other less stable and less polycondensed compounds such as polyphenols from the more polycondensed O–N alkyl and oxidized C-aromatic compounds.     

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous stages and grades associated with each type of gastric cancer. This paper proposed an optimized AI-based approach to diagnose and assess the risk factor of gastric cancer based on its type, stage, and grade in the endoscopic images for smart healthcare applications. The proposed method is categorized into five phases such as image pre-processing, Four-Dimensional (4D) image conversion, image segmentation, K-Nearest Neighbour (K-NN) classification, and multi-grading and staging of image intensities. Moreover, the performance of the proposed method has experimented on two different datasets consisting of color and black and white endoscopic images. The simulation results verified that the proposed approach is capable of perceiving gastric cancer with 88.09% sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.     

SiO/sub 2//AlGaN/InGaN/GaN MOSDHFETs

The characteristics of a novel nitride based field-effect transistor combining SiO/sub 2/ gate isolation and an AlGaN/InGaN/GaN double heterostructure design (MOSDHFET) are reported. The double heterostructure design with InGaN channel layer significantly improves confinement of the two-dimensional (2-D) electron gas and compensates strain modulation in AlGaN barrier resulting from the gate voltage modulations. These decrease the total trapped charge and hence the current collapse. The combination of the SiO/sub 2/ gate isolation and improved carrier confinement/strain management results in current collapse free MOSDHFET devices with gate leakage currents about four orders of magnitude lower than those of conventional Schottky gate HFETs.     

Effects of Al2O3 particle reinforcement on the lubricated sliding wear behavior of ZA-27 alloy composites

The present investigation deals with the effect of Al₂O₃ particle reinforcement on the lubricated sliding behavior of ZA-27 alloy. The composites with 3, 5, and 10 wt% of Al₂O₃ particles were produced by the compocasting procedure. Tribological properties of alloy and composites were studied, using block-on-disk tribometer at different specific loads and sliding speeds. The test results revealed that composite specimens exhibited significantly lower wear rate, but higher coefficient of friction than the matrix alloy specimens in all the combinations of applied loads and sliding speeds. The improved antiwear characteristics of the composites were influenced by positive effects of higher frictional heating on compatibility of the composite phases and suppressing micro-cracking tendency. Due to that, effects of reinforcing hard particles were manifested through the reduced wear rate of composites, especially in conditions of higher load, lower sliding speeds and higher Al₂O₃ particle content. In present wear tests, the significant forming of mechanically mixed layers was not noticed, what is confirmed by the SEM microphotographs.     

High-quality single-walled carbon nanotubes synthesis by hot filament CVD on Ru nanoparticle catalyst

We investigated the single-walled carbon nanotubes (SWCNTs) growth on Ru nanoparticle catalyst via hot filament assisted chemical vapor deposition (HFCVD) with two independent W filaments for the carbon precursor (methane) and the hydrogen dissociation respectively. The Ru nanoparticles were obtained following a two-step strategy. At first the growth substrate is functionalized by silanisation, then a self assembly of a ruthenium porphyrin complex monolayer on pyridine-functionalized metal oxide substrates. We have studied the impact of the filaments power and we optimized the SWCNTs growth temperature. The as grown SWCNTs were characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM) and Raman spectroscopy. It was found that the quality, density and the diameter of SWCNTs depends on the filament and growth temperature. Results of this study can be used to improve the understanding of the growth of SWCNTs by HFCVD.     

Dental Composites with Magnesium Doped Zinc Oxide Nanoparticles Prevent Secondary Caries in the Alloxan-Induced Diabetic Model

Antibacterial restorative materials against caries-causing bacteria are highly preferred among high-risk patients, such as the elderly, and patients with metabolic diseases such as diabetes. This study aimed to enhance the antibacterial potential of resin composite with Magnesium-doped Zinc oxide (Mg-doped ZnO) nanoparticles (NPs) and to look for their effectiveness in the alloxan-induced diabetic model. Hexagonal Mg-doped ZnO NPs (22.3 nm diameter) were synthesized by co-precipitation method and characterized through ultraviolet-visible (UV-Vis), Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) analysis. The Mg-doped ZnO NPs (1, 2.5 and 5% w/w) were then evaluated for antibacterial activity using a closed system in vitro biofilm model. Significant enhancement in the antibacterial properties was observed in composites with 1% Mg-doped ZnO compared to composites with bare ZnO reinforced NPs (Streptococcus mutans, p = 0.0005; Enterococcus faecalis, p = 0.0074, Saliva microcosm, p < 0.0001; Diabetic Saliva microcosm, p < 0.0001). At 1–2.5% Mg-doped ZnO NPs concentration, compressive strength and biocompatibility of composites were not affected. The pH buffering effect was also achieved at these concentrations, hence not allowing optimal conditions for the anaerobic bacteria to grow. Furthermore, composites with Mg-doped ZnO prevented secondary caries formation in the secondary caries model of alloxan-induced diabetes. Therefore, Mg-doped ZnO NPs are highly recommended as an antibacterial agent for resin composites to avoid biofilm and subsequent secondary caries formation in high-risk patients.     

Majority bargaining for resource division

We address the problem of how a set of agents can decide to share a resource, represented as a unit-sized pie. The pie can be generated by the entire set but also by some of its subsets. We investigate a finite horizon non-cooperative bargaining game, in which the players take it in turns to make proposals on how the resource should for this purpose be allocated, and the other players vote on whether or not to accept the allocation. Voting is modelled as a Bayesian weighted voting game with uncertainty about the players’ weights. The agenda, (i.e., the order in which the players are called to make offers), is defined exogenously. We focus on impatient players with heterogeneous discount factors. In the case of a conflict, (i.e., no agreement by the deadline), no player receives anything. We provide a Bayesian subgame perfect equilibrium for the bargaining game and conduct an ex-ante analysis of the resulting outcome. We show that the equilibrium is unique, computable in polynomial time, results in an instant Pareto optimal outcome, and, under certain conditions provides a foundation for the core and also the nucleolus of the Bayesian voting game. In addition, our analysis leads to insights on how an individual’s bargained share is influenced by his position on the agenda. Finally, we show that, if the conflict point of the bargaining game changes, then the problem of determining the non-cooperative equilibrium becomes NP-hard even under the perfect information assumption. Our research also reveals how this change in conflict point impacts on the above mentioned results.     

Nano finish grinding of brittle materials using electrolytic in-process dressing (ELID) technique

Recent developments in grinding have opened up new avenues for finishing of hard and brittle materials with nano-surface finish, high tolerance and accuracy. Grinding with superabrasive wheels is an excellent way to produce ultraprecision surface finish. However, superabrasive diamond grits need higher bonding strength while grinding, which metal-bonded grinding wheels can offer. Truing and dressing of the wheels are major problems and they tend to glaze because of wheel loading. When grinding with superabrasive wheels, wheel loading can be avoided by dressing periodically to obtain continuous grinding. Electrolytic inprocess dressing (ELID) is the most suitable process for dressing metal-bonded grinding wheels during the grinding process. Nano-surface finish can be achieved only when chip removal is done at the atomic level. Recent developments of ductile mode machining of hard and brittle materials show that plastically deformed chip removal minimizes the subsurface damage of the workpiece. When chip deformation takes place in the ductile regime, a defect-free nano-surface is possible and it completely eliminates the polishing process. ELID is one of the processes used for atomic level metal removal and nano-surface finish. However, no proper and detailed studies have been carried out to clarify the fundamental characteristics for making this process a robust one. Consequently, an attempt has been made in this study to understand the fundamental characteristics of ELID grinding and their influence on surface finish.