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Structures play a very important role in developing pressure sensors with good sensitivity and linearity, as they undergo deformation to the input pressure and function as the primary sensing element of the sensor. To achieve high sensitivity, thinner diaphragms are required; however, excessively thin diaphragms may induce large deflection and instability, leading to the unfavorable performances of a sensor in terms of linearity and repeatability. Thereby, importance is given to the development of innovative structures that offer good linearity and sensitivity. This paper aims to investigate the sensitivity of a bossed diaphragm coupled fixed guided beam three-dimensional (3D) structure for pressure sensor applications.

The proposed sensor comprises of mainly two sensing elements: the first being the 3D mechanical structure made of bulk silicon consisting of boss square diaphragm along with a fixed guided beam landing on to its center, forming the primary sensing element, and the diffused piezoresistors, which form the secondary sensing element, are embedded in the tensile and compression regions of the fixed guided beam. This micro mechanical 3 D structure is packaged for applying input pressure to the bottom of boss diaphragm. The sensor without pressure load has no deflection of the diaphragm; hence, no strain is observed on the fixed guided beam and also there is no change in the output voltage. When an input pressure P is applied through the pressure port, there is a deformation in the diaphragm causing a deflection, which displaces the mass and the fixed guided beam vertically, causing strain on the fixed guided beam, with tensile strain toward the guided end and compressive strain toward the fixed end of the close magnitudes. The geometrical dimensions of the structure, such as the diaphragm, boss and fixed guided beam, are optimized for linearity and maximum strain for an applied input pressure range of 0 to 10 bar. The structure is also analyzed analytically, numerically and experimentally, and the results are compared.

The structure offers equal magnitudes of tensile and compressive stresses on the surface of the fixed guided beam. It also offers good linearity and sensitivity. The analytical, simulation and experimental studies of this sensor are introduced and the results correlate with each other. Customized process steps are followed wherein two silicon-on-insulator (SOI) wafers are fusion bonded together, with SOI-1 wafer used to realize the diaphragm along with the boss and SOI-2 wafer to realize the fixed guided beam, leading to formation of a 3D structure. The geometrical dimensions of the structure, such as the diaphragm, boss and fixed guided beam, are optimized for linearity and maximum strain for an applied input pressure range of 0 to10 bar.

This paper presents a unique and compact 3D micro-mechanical structure pressure sensor with a rigid center square diaphragm (boss diaphragm) and a fixed guided beam landing at its center, with diffused piezoresistors embedded in the tensile and compression regions of the fixed guided beam. A total of six masks were involved to realize and fabricate the 3D structure and the sensor, which is presumed to be the first of its kind in the fabrication of MEMS-based piezoresistive pressure sensor.

Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA).

MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments.

ANN shows more accuracy than MLR with an R²-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R²-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing.

ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.

A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.

Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t-test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.

The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.

This paper aims to explore on stagnation point flow of Ag-CuO/water over a horizontal stretching/shrinking cylinder by adding the effect of chemical reaction, B together with the magnetic field, M.

A set of reduced ordinary differential equations from the governing equations of partial differential equations is obtained through similarities requirements. The resulting equations are solved using bvp4c in MATLAB2019a. The impact of various physical parameters such as curvature parameter, ϒ, chemical reaction rate, B, magnetic field, M and Schmidt numbers, Sc on shear stress, f′′0 local heat flux, -θ′(0) and mass transfer, -∅′(0) also for velocity, f′(η), temperature, θ(η) and concentration, ∅(η) profiles have been plotted and briefly discussed. In this work, some vital characteristics such as local skin friction, Cf, local Nusselt number, Nux and local Sherwood number, Shx are chosen for physical and numerical analysis.

The findings expose that the duality of solutions appears in a shrinking region ( ε < 0). The value of skin friction, heat transfer rate and mass transfer rate reduction for existing of M, but in contrary result obtain for larger ϒ, B and Sc. Furthermore, the hybrid nanofluid demonstrates better heat transfer compared to nanofluid.

The hybrid nanofluid has widened its applications such as in electronic cooling, manufacturing, automotive, heat exchanger, solar energy, heat pipes and biomedical, as their efficiency in the heat transfer field is better compared to nanofluid.

The findings on stagnation point flow of Ag-CuO/water over a horizontal stretching/shrinking cylinder with the effect of chemical reaction, B and magnetic field, M is new and the originality is preserved for the benefits of future researchers.

The purpose of this study is to investigate the Cattaneo-Christov double diffusion, multiple slips and Darcy-Forchheimer’s effects on entropy optimized and thermally radiative flow, thermal and mass transport of hybrid nanoliquids past stretched cylinder subject to viscous dissipation and Arrhenius activation energy.

The presented flow problem consists of the flow, heat and mass transportation of hybrid nanofluids. This model is featured with Casson fluid model and Darcy-Forchheimer model. Heat and mass transportations are represented with Cattaneo-Christov double diffusion and viscous dissipation models. Multiple slip (velocity, thermal and solutal) mechanisms are adopted. Arrhenius activation energy is considered. For graphical and numerical data, the bvp4c scheme in MATLAB computational tool along with the shooting method is used.

Amplifying curvature parameter upgrades the fluid velocity while that of porosity parameter and velocity slip parameter whittles down it. Growing mixed convection parameter, curvature parameter, Forchheimer number, thermally stratified parameter intensifies fluid temperature. The rise in curvature parameter and porosity parameter enhances the solutal field distribution. Surface viscous drag gets controlled with the rising of the Casson parameter which justifies the consideration of the Casson model. Entropy generation number and Bejan number upgrades due to growth in diffusion parameter while that enfeeble with a hike in temperature difference parameter.

To the best of the authors’ knowledge, this research study is yet to be available in the existing literature.

The relationship between farm size and greenhouse gas (GHG) emissions has not been clearly defined. This paper aims to assess and compare the impact of farm size on greenhouse gas (GHG) emissions derived from wheat and maize production in the North China Plain (NCP), one of the most important agricultural regions in China.

A field survey through face-to-face interviews was conducted to collect the primary data, and life cycle assessment method, a worldwide comparable framework, was then adopted to characterize the farm-size effect on greenhouse gas (GHG) wheat and maize production in NCP.

It was confirmed that GHG emissions from N fertilizer production and use were the primary contributor to total carbon footprint (CF). As farm size increased, maize yield increased but wheat yield barely changed, while area-scaled and yield-scaled CF declined for both crops. These results were supposed to relate to utilize the inputs more efficiently resulting from increased application of modern agriculture methods on larger operations. It was also found maize not only had higher grain yields, but possessed much smaller CFs. More notably, the reduction of CF with farm size seemed to be more sensitive for maize as compared to wheat. To further mitigate GHG emissions, farm size should better be larger for wheat than for maize.

This study provides useful information guide for Chinese agriculture in increasing crop production, raising farm income and relieving environmental burdens caused by the misuse of agricultural resources.

The purpose of this study is to develop jute-glass hybrid fibre reinforced polyester-based bio-composites using an indigenously developed pultrusion set-up and to present a detailed discussion on their mechanical characterization.

The work was carried out to observe the hybridization effect of natural and synthetic fibres in combination with hybrid fillers loading mainly on strength and other properties. The used hybrid fillers were a combination of 9 Wt.% of carbon black%, 6 Wt.% of eggshell ash powder and 6 Wt.% of coconut coir ash powder. A lab-based developed pultrusion set-up was used to develop these hybrid GJFRP composites of 1,500 mm length. The developed composites were tested for tensile strength, compressive strength and impact strength.

The maximum tensile, compressive and impact strength obtained are 88.37 MPa, 56.13 MPa and 731.91 J/m from 9 Wt.%, 9 Wt.% and 0 Wt.% of hybrid fillers loading, respectively. Breaking energy was found maximum as 7.31 J in hybrid glass-jute hybrid fibre reinforced plastic composites with no filler loading and it was observed that filler loading was decreasing the impact strength of developed hybrid composites. Shrinkage and its variations in the diameter of the finally developed cylindrical shape composites were observed after cooling and solidification. Scanning electron microscopy was used to observe the internal cracks, bonding of fibres and resin, voids, etc.

Development of hybrid filler based novel eco-friendly bio-composites and its experimental investigation on the impact strength, tensile strength and compressive strength has not been attempted yet.