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The purpose of this paper is to present an applied method to design the low-speed contact between a mass and surface of a beam using an analytical solution based on the first-order shear deformation beam theory. Also, a simulation of impact process is carried out by ABAQUS finite element (FE) code.

In theoretical formulation, first strains and stresses are obtained, then kinetic and potential energies are written, and using a combination of Ritz and Lagrange methods, a set of system of motion equations in the form of mass, stiffness and force matrices is obtained. Finally, the motion equations are solved using Runge–Kutta fourth order method.

The von Mises stress contours at the impact point and contact force from the ABAQUS simulation are illustrated and it is revealed that the theoretical solution is in good agreement with the FE code. The effect of changes in projectile speed, projectile diameter and projectile mass on the results is carefully examined with particular attention to evaluate histories of the impact force and beam recess. One of the important results is that changes in projectile speed have a greater effect on the results than changes in projectile diameter, and also changes in projectile mass have the least effect.

This paper presents a combination of methods of energy, Ritz and Lagrange and also FE code to simulate the problem of sandwich beams under low velocity impact.

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

The purpose of this work is to optimize the monitoring of vibrations on dynamometric test rigs for railway brakes. This is a quite demanding application considering the continuous increase of performances of high-speed trains that involve higher testing specifications for brake pads and disks.

In this work, authors propose a mixed approach in which relatively simple finite element models are used to support the optimization of a diagnostic system that is used to monitor vibration levels and rotor-dynamical behavior of the machine. The model is calibrated with experimental data recorded on the same rig that must be identified and monitored. The whole process is optimized to not interfere with normal operations of the rig, using common inertial sensor and tools and are available as standard instrumentation for this kind of applications. So at the end all the calibration activities can be performed normally without interrupting the activities of the rig introducing additional costs due to system unavailability.

Proposed approach was able to identify in a very simple and fast way the vibrational behavior of the investigated rig, also giving precious information concerning the anisotropic behavior of supports and their damping. All these data are quite difficult to be found in technical literature because they are quite sensitive to assembly tolerances and to many other factors. Dynamometric test rigs are an important application widely diffused for both road and rail vehicles. Also proposed procedure can be easily extended and generalized to a wide value of machine with horizontal rotors.

Most of the studies in literature are referred to electrical motors or turbomachines operating with relatively slow transients and constant inertial properties. For investigated machines both these conditions are not verified, making the proposed application quite unusual and original with respect to current application. At the same time, there is a wide variety of special machines that are usually marginally covered by standard testing methodologies to which the proposed approach can be successfully extended.

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The purpose is to explore the free vibration behaviour of elastic foundation-supported porous functionally graded nanoplates using the Rayleigh-Ritz approach. The goal of this study is to gain a better knowledge of the dynamic response of nanoscale structures made of functionally graded materials and porous features. The Rayleigh-Ritz approach is used in this study to generate realistic mathematical models that take elastic foundation support into account. This research can contribute to the design and optimization of advanced nanomaterials with potential applications in engineering and technology by providing insights into the influence of material composition, porosity and foundation support on the vibrational properties of nanoplates.

A systematic methodology is proposed to evaluate the free vibration characteristics of elastic foundation-supported porous functionally graded nanoplates using the Rayleigh-Ritz approach. The study began by developing the mathematical model, adding material properties and establishing governing equations using the Rayleigh-Ritz approach. Numerical approaches to solve the problem are used, using finite element methods. The results are compared to current solutions or experimental data to validate the process. The results are also analysed, keeping the influence of factors on vibration characteristics in mind. The findings are summarized and avenues for future research are suggested, ensuring a robust investigation within the constraints.

The Rayleigh-Ritz technique is used to investigate the free vibration properties of elastic foundation-supported porous functionally graded nanoplates. The findings show that differences in material composition, porosity and foundation support have a significant impact on the vibrational behaviour of nanoplates. The Rayleigh-Ritz approach is good at modelling and predicting these properties. Furthermore, the study emphasizes the possibility of customizing nanoplate qualities to optimize certain vibrational responses, providing useful insights for engineering applications. These findings expand understanding of dynamic behaviours in nanoscale structures, making it easier to build innovative materials with specific features for a wide range of industrial applications.

The novel aspect of this research is the incorporation of elastic foundation support, porous structures and functionally graded materials into the setting of nanoplate free vibrations, utilizing the Rayleigh-Ritz technique. Few research have looked into this complex combo. By tackling complicated interactions, the research pushes boundaries, providing a unique insight into the dynamic behaviour of nanoscale objects. This novel approach allows for a better understanding of the interconnected effects of material composition, porosity and foundation support on free vibrations, paving the way for the development of tailored nanomaterials with specific vibrational properties for advanced engineering and technology applications.

Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.

In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.

Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

Concrete-filled double skin tube (CFDST) columns are considered one of the most effective steel-concrete composite sections owing to the higher load carrying capacity as compared to its counterpart concrete-filled tube (CFT) columns. This paper aims to numerically investigate the performance of axially loaded, circular CFDST short columns, with the innovative strengthening technique of providing stiffeners in outer tubes. Circular steel hollow sections have been adopted for inner as well as outer tubes, while varying the length of rectangular steel stiffeners, fixed inside the outer tubes only, to check the effect of stiffeners in partially and full-length stiffened CFDST columns.

The behaviour of these CFDST columns is investigated numerically by using a verified finite element analysis (FEA) model from the ABAQUS. The behaviour of 20-unstiffened, 80-partially stiffened and 20-full-length stiffened CFDST columns is studied, while varying the strength of steel (f_yo = 250–750 MPa) and concrete (30–90 MPa).

The FEA results are verified by comparing them with the previous test results. FEA study has exhibited that, there is a 7%–25% and 39%–49% increase in peak-loads in partially stiffened and full-length stiffened CFDST columns, respectively, compared to unstiffened CFDST columns.

Enhanced strength has been observed in partially stiffened and full-length stiffened CFDST columns as compared to unstiffened CFDST columns. Also, a significant effect of strength of concrete has not been observed as compared to the strength of steel.

The purpose of this study is to observe the practice of integrated reporting (IR) and investigate the impact of board characteristics on IR in three South Asian economies: Bangladesh, India and Sri Lanka.

The study uses the content analysis approach to measure the integrated reporting index (IRI) based on a structured checklist. To examine the impact of board characteristics (board size, board independence and gender diversity) on IRI, a multivariate analysis using pooled ordinary least square with panel-corrected standard error (PCSE) model has been conducted.

The content analysis findings show that the disclosure practice of IR is highest in India, followed by Sri Lanka and Bangladesh. The regression result indicates that all the proxies of board characteristics have a positive and significant impact on IRI.

The study’s outcomes may not be generalised for every region due to the differences in institutional contexts.

The findings of this study will assist the policymakers in understanding the importance of effective boards in enhancing the IR practice in their respective countries where the adoption of IR is still a voluntary requirement.

To the best of the authors’ knowledge, this is the first study in the field of existing literature to conduct a comparative analysis of IR practice among three South Asian countries. It shows how an effective board improves IR practice using a broader institutional context by underpinning the agency theory and legitimacy theory.

This study investigates the impact of the fire decay phase on structural damage using the sectional analysis method. The primary objective of this work is to forecast the non-dimensional capacity parameters for the axial and flexural load-carrying capacity of reinforced concrete (RC) sections for heating and the subsequent post-heating phase (decay phase) of the fire.

The sectional analysis method is used to determine the moment and axial capacities. The findings of sectional analysis and heat transfer for the heating stage are initially validated, and the analysis subsequently proceeds to determine the load capacity during the fire’s heating and decay phases by appropriately incorporating non-dimensional sectional and material parameters. The numerical analysis includes four fire curves with different cooling rates and steel percentages.

The study’s findings indicate that the rate at which the cooling process occurs after undergoing heating substantially impacts the axial and flexural capacity. The maximum degradation in axial and flexural capacity occurred in the range of 15–20% for cooling rates of 3 °C/min and 5 °C/min as compared to the capacity obtained at 120 min of heating for all steel percentages. As the fire cooling rate reduced to 1 °C/min, the highest deterioration in axial and flexural capacity reached 48–50% and 42–46%, respectively, in the post-heating stage.

The established non-dimensional parameters for axial and flexural capacity are limited to the analysed section in the study owing to the thermal profile, however, this can be modified depending on the section geometry and fire scenario.

The study primarily focusses on the degradation of axial and flexural capacity at various time intervals during the entire fire exposure, including heating and cooling. The findings obtained showed that following the completion of the fire’s heating phase, the structural capacity continued to decrease over the subsequent post-heating period. It is recommended that structural members' fire resistance designs encompass both the heating and cooling phases of a fire. Since the capacity degradation varies with fire duration, the conventional method is inadequate to design the load capacity for appropriate fire safety. Therefore, it is essential to adopt a performance-based approach while designing structural elements' capacity for the desired fire resistance rating. The proposed technique of using non-dimensional parameters will effectively support predicting the load capacity for required fire resistance.

The fire-resistant requirements for reinforced concrete structures are generally established based on standard fire exposure conditions, which account for the fire growth phase. However, it is important to note that concrete structures can experience internal damage over time during the decay phase of fires, which can be quantitatively determined using the proposed non-dimensional parameter approach.