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The purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.

Vibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.

The obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.

The proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.

In this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

The purpose of this paper is to study the thermal and mechanical disturbances in a piezo-electric microstretch thermoelastic medium due to the presence of ultra-short laser pulse as input heat source.

The medium is subjected to normal force, tangential force and thermal source. The solution of the problems is developed in terms of normal modes. Mathematical expressions have been obtained for normal stress, tangential stress, microstress, dielectric displacement vector and temperature change.

The numerically computed results are shown graphically. The effect of time and laser radius on temperature distribution is also shown graphically and comparison to theoretical results has been discussed. A mathematical model has been developed for the system of equations and various stress quantities have been analyzed. Some computer programs have also been written for this study. Two particular cases are also derived from the present investigation.

The effect of laser heat source is studied in piezo-electric microstretch thermoelastic medium. It is observed from the figures that the laser heat source has significant role on the values of coupled tangential stress.

The purposes of this paper are (1) to explore the overall development of AI technologies and applications that have been demonstrated to be fundamentally important in the healthcare industry, and their related commercialized products and (2) to identify technologies with promise as the basis of useful applications and profitable products in the AI-healthcare domain.

This study adopts a technology-driven technology roadmap approach, combined with natural language processing (NLP)-based patents analysis, to identify promising and potentially profitable existing AI technologies and products in the domain of AI healthcare.

Robotics technology exhibits huge potential in surgical and diagnostics applications. Intuitive Surgical Inc., manufacturer of the Da Vinci robotic system and Ion robotic lung-biopsy system, dominates the robotics-assisted surgical and diagnostic fields. Diagnostics and medical imaging are particularly active fields for the application of AI, not only for analysis of CT and MRI scans, but also for image archiving and communications.

This study is a pioneering attempt to clarify the interrelationships of particular promising technologies for application and related products in the AI-healthcare domain. Its findings provide critical information about the patent activities of key incumbent actors, and thus offer important insights into recent and current technological and product developments in the emergent AI-healthcare sector.

The purpose of this study is to study the mechanical, tribological and electrical properties of the copper-graphene (Cu-Gn) composites fabricated by a novel rapid tooling technique consist of three-dimensional printing and ultrasonic-assisted pressureless sintering (UAPS).

Four different Cu-Gn compositions with 0.25, 0.5, 1 and 1.5 per cent of graphene were fabricated using an amalgamation of three-dimensional printing and UAPS. The polymer 3d printed parts were used to prepare mould cavity and later the UAPS process was used to sinter Cu-Gn powder to acquire free-form shape. The density, hardness, wear rate, coefficient of friction and electrical conductivity were evaluated for the different compositions of graphene and compared with the pure copper. Besides, the comparison was performed with the conventional method.

Cu-Gn composites revealed excellent wear properties due to higher hardness, and the lubrication provided by the graphene. The electrical conductivity of the fabricated Cu-Gn composites started increasing initially but decreased afterwards with increasing the content of graphene. The UAPS fabricated composites outperformed the conventional method manufactured samples with better properties such as density, hardness, wear rate, coefficient of friction and electrical conductivity due to homogeneous mixing of metal particles and graphene.

The fabrication of Cu-Gn composite freeform shapes was found to be difficult using conventional methods. The novel technique using a combination of polymer three-dimensional printing and UAPS as rapid tooling was introduced for the fabrication of freeform shapes of Cu-Gn composites and mechanical, tribological and electrical properties were studied. The method can be used to fabricate optimized complex Cu-Gn structures with improved wear and electrical applications.

This paper is concerned with the study of the propagation of Rayleigh waves in a homogeneous isotropic, generalized thermoelastic medium with mass diffusion and double porosity structure using the theoretical framework of three-phase-lag model of thermoelasticity.

Using Eringen’s nonlocal elasticity theory and normal mode analysis technique, this paper solves the problem. The medium is subjected to isothermal, thermally insulated stress-free, and chemical potential boundary conditions.

The frequency equation of Rayleigh waves for isothermal and thermally insulated surfaces is derived. Propagation speed, attenuation coefficient, penetration depth and specific loss of the Rayleigh waves are computed numerically. The impact of nonlocal, void and diffusion parameters on different physical characteristics of Rayleigh waves like propagation speed, attenuation coefficient, penetration depth and specific loss with respect to wave number for isothermal and thermally insulated surfaces is depicted graphically.

Some limiting and particular cases are also deduced from the present investigation and compared with the existing literature. During Rayleigh wave propagation, the path of the surface particle is found to be elliptical. This study can be extended to fields like earthquake engineering, geophysics and the degradation of old building materials.

The purpose of this paper is to deal with the study of plane waves and fundamental solution in a modified couple stress generalized thermoelastic solid with three-phase-lag (TPL) model of thermoelasticity.

It is found that for two-dimensional model, there exists two longitudinal waves, namely, longitudinal wave (P-wave), thermal wave (T-wave), and a set of coupled transverse waves (SV1 and SV2 waves). In addition, the fundamental solution for the system of differential equations for steady oscillations in terms of elementary functions has been constructed. Some properties of fundamental solution are also established. Various particular cases of interest are also deduced from the present investigations and compared with the known results.

The phase velocity, attenuation coefficient, specific loss and penetration depth are computed numerically and presented graphically to see the effect of TPL model, dual-phase-lag (DPL) model and GN-III model in the presence of couple stress parameter.

The results are compared with couple stress TPL model, couple stress DPL model and GN-III model.

In the framework of GN-II theory, this paper aims to address the plane wave propagation in a two-dimensional homogeneous, transversely isotropic magneto-thermoelastic medium with rotation and voids.

To investigate the problem, the fundamental governing equations are modified in the purview of Green-Naghdi theory without energy dissipation. These equations are converted to non-dimensional form using dimensionless quantities and are further solved to obtain four quasi plane waves travelling with different phase speeds in the considered medium. Amplitude ratios and energy ratios have been provided in explicit form after implementing the proper boundary conditions.

Numerical calculations are carried out using MATLAB software. For graphical representation of the expressions for phase velocities, reflection coefficients and energy ratios, a particular material is chosen to demonstrate the effects of magnetic field, rotation and void parameter.

The reflection coefficients are strongly affected by rotation, void parameter and magnetic field, as evidenced by conceptual and numerical findings. For validation of this study, the outcomes have also been compared to earlier published studies. In addition, it has also been established that the energy conservation law is also justified during the reflection phenomena. In the current research, the authors have included rotation and magnetic field in a transversely isotropic thermoelastic medium having voids, which has not yet been addressed in the published research. The results of current problem are very useful in a number of fields, such as soil dynamics, geophysical processes, chemical engineering and petroleum sector.

The authors examine network features such as connectivity, centrality, adjacency matrices, closeness and betweenness measures through a variety of indicators. The results of the study indicate that over time there is a tendency for markets to integrate and segment due to various factors such as pandemics, financial crises, global trade relations and international investments.

This paper employs a visualized network technique to study the dynamics of integration and comovements in global equity markets of emerging economies. Daily closing prices of stock market indices of 24 countries from January 2013 to July 2020 are used to construct a minimum spanning tree network (MSTN) and graph network (GN).

The authors identify India and China as global power hubs and clusters among the emerging economies. India and Bangladesh serve as bridging countries connecting to various other clusters. Bosnia serves as a center in the European region owing to Bosnia's trade relations with neighboring countries. Although Brazil has witnessed the worst recession in the early years of the decade, Brazil has risen to be a central cluster among the Latin American countries. Finally, the authors find that African countries tend to form links with the rest of the world rather than with economies within the Africa continent.

This is the pioneering study that uses network models such as MSTN and GN supplemented with measures of centrality and connectivity to study financial market integration in emerging countries. Against this backdrop, this paper aims to work on a network visualization strategy to examine global stock market integration. The authors also try to use graphs and the spanning trees instead of the correlation models to understand the association between the markets, avoiding the downsides of the existing models. The authors' approach tries to visualize the network integration to examine the interconnectedness in the global stock market.

The purpose of this research contribution is to examine the thermoelastic interactions in a fiber-reinforced thermoelastic medium under the action of initial stress and gravity in which, the conventional Fourier’s law is modified, introducing a thermal relaxation parameter in the GN-III model of generalized thermoelasticity.

The work presented in this manuscript proposes a thermoelastic problem where the Moore–Gibson–Thompson equation is used to model the thermal law. The technique of normal mode is employed to derive the exact expressions of various physical fields under consideration.

Under the application of moving thermal load, normal displacement, temperature distribution and stress components are calculated numerically with the help of MATLAB software and then presented graphically to support the theoretical formulation.

Field variables are affected by initial stress, gravity, fiber reinforcement, velocity of thermal load and time duration, according to numerical simulations. Some particular cases of interest have also been inferred from the present problem.

The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm.

Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives.

Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data.

The proposed method requires iterative calculation and can only identify parameters offline.

The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems.

In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.