Hybrid optimization model for conjunctive use of surface and groundwater resources in water deficit irrigation system.

The increasing demand for food production with limited available water resources poses a threat to agricultural activities. Conventional optimization algorithms increase the processing stage and perform in the space allocated from user. Therefore, the proposed work was used to design better performance results. The conjunctive allocation of water resources maximizes the net benefit of farmers. In this study, the novel hybrid optimization model developed is the first of its kind and was designed to resolve the sharing of water resource conflict among different reaches based on a genetic algorithm (GA), bacterial foraging optimization (BFO) and ant colony optimization (ACO) to maximize the net benefit of the water deficit in Sathanur reservoir command. The GA-based optimization model considered crop-related physical and economic parameters to derive optimal cropping patterns for three different conjunctive use policies and further allocation of surface and groundwater for different crops are enhanced with the BFO. The allocation of surface and groundwater for the head, middle and tail reaches obtained from the BFO is considered as an input to the ACO as a guiding mechanism to attain an optimal cropping pattern. Comparing the average productivity values, policy 3 (3.665 Rs/m3) has better values relating to policy 1 (3.662 Rs/m3) and policy 2 (3.440 Rs/m3). Thus, the developed novel hybrid optimization model (GA-BFO-ACO) is very promising for enhancing farmer's net income and can be replicated in other irrigated regions to overcome chronic water problems. The productivity value of policy 3 was 6.54% greater than that of policy 2, whereas that of policy 1 was 6.45% greater. Overall, the comparison shows that the better performance analysis of various optimization was done successfully.

FEM Analysis of Sezawa Mode SAW Sensor for VOC Based on CMOS Compatible AlN/SiO2/Si Multilayer Structure.

A Finite Element Method (FEM) simulation study is conducted, aiming to scrutinize the sensitivity of Sezawa wave mode in a multilayer AlN/SiO2/Si Surface Acoustic Wave (SAW) sensor to low concentrations of Volatile Organic Compounds (VOCs), that is, trichloromethane, trichloroethylene, carbon tetrachloride and tetrachloroethene. A Complimentary Metal-Oxide Semiconductor (CMOS) compatible AlN/SiO2/Si based multilayer SAW resonator structure is taken into account for this purpose. In this study, first, the influence of AlN and SiO2 layers’ thicknesses over phase velocities and electromechanical coupling coefficients (k²) of two SAW modes (i.e., Rayleigh and Sezawa) is analyzed and the optimal thicknesses of AlN and SiO2 layers are opted for best propagation characteristics. Next, the study is further extended to analyze the mass loading effect on resonance frequencies of SAW modes by coating a thin Polyisobutylene (PIB) polymer film over the AlN surface. Finally, the sensitivity of the two SAW modes is examined for VOCs. This study concluded that the sensitivity of Sezawa wave mode for 1 ppm of selected volatile organic gases is twice that of the Rayleigh wave mode.

Analytical Method for the Optimization of the Open-Hole and Filled-Hole Laminates at the Preliminary Design Stage.

In recent years, there has been an increasing interest in open-hole and filled-hole laminate failure analysis. The open and filled-hole laminate failure analysis is used in several important areas, especially in designing mechanically fastened composite joints. Various analytical, empirical, and numerical methods are available for the design of mechanically fastened composite joints. The large number of material and geometrical design variables at the preliminary design stage makes the empirical and numerical methods effortful, expensive, and time-consuming. Therefore, analytical methods are recommended over numerical and empirical methods at the preliminary design stage merely because of their simplification in calculations, making them computationally efficient. Taking this into consideration, current research presents an improvement to the analysis capabilities of the previously introduced analytical method, i.e., the coupled approach of Classical laminate theory (CLT) and Lekhnitskii solutions. These improvements include the development of failure envelops for the open-hole and filled-hole laminates, estimation of optimized filling material for attaining maximum load-bearing capacity of filled-hole laminates, and optimization of stacking sequence for maximum load-bearing capacity of open-hole and filled-hole laminates. From the failure envelop results, it was found that failure envelopes of filled-hole laminates are bigger than open-hole laminates. Furthermore, it was found that the stiffness of the filling material should be equal to the stiffness of the laminate to achieve maximum bearing strength of the filled-hole laminate. It was also demonstrated that the coupled approach of CLT and Lekhnitskii solutions may provide carpet plots that can be utilized to optimize the stacking sequence for open-hole and filled-hole laminates.

A quadruplet 3-D laser scribed graphene/MoS2, functionalised N2-doped graphene quantum dots and lignin-based Ag-nanoparticles for biosensing.

Troponin I is a protein released into the human blood circulation and a commonly used biomarker due to its sensitivity and specificity in diagnosing myocardial injury. When heart injury occurs, elevated troponin Troponin I levels are released into the bloodstream. The biomarker is a strong and reliable indicator of myocardial injury in a person, with immediate treatment required. For electrochemical sensing of Troponin I, a quadruplet 3D laser-scribed graphene/molybdenum disulphide functionalised N2-doped graphene quantum dots hybrid with lignin-based Ag-nanoparticles (3D LSG/MoS2/N-GQDs/L-Ag NPs) was fabricated using a hydrothermal process as an enhanced quadruplet substrate. Hybrid MoS2 nanoflower (H3 NF) and nanosphere (H3 NS) were formed independently by varying MoS2 precursors and were grown on 3D LSG uniformly without severe stacking and restacking issues, and characterized by morphological, physical, and structural analyses with the N-GQDs and Ag NPs evenly distributed on 3D LSG/MoS2 surface by covalent bonding. The selective capture of and specific interaction with Troponin I by the biotinylated aptamer probe on the bio-electrode, resulted in an increment in the charge transfer resistance. The limit of detection, based on impedance spectroscopy, is 100 aM for both H3 NF and H3 NS hybrids, with the H3 NF hybrid biosensor having better analytical performance in terms of linearity, selectivity, repeatability, and stability.

Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings.

Conventional pipeline corrosion assessment methods produce conservative failure pressure predictions for pipes under the influence of both internal pressure and longitudinal compressive stress. Numerical approaches, on the other hand, are computationally expensive. This work provides an assessment method (empirical) for the failure pressure prediction of a high toughness corroded pipe subjected to combined loading, which is currently unavailable in the industry. Additionally, a correlation between the corrosion defect geometry, as well as longitudinal compressive stress and the failure pressure of a pipe based on the developed method, is established. An artificial neural network (ANN) trained with failure pressure from FEA of an API 5L X80 pipe for varied defect spacings, depths, defect lengths, and longitudinal compressive loads were used to develop the equation. With a coefficient of determination (R2) of 0.99, the proposed model was proven to be capable of producing accurate predictions when tested against arbitrary finite element models. The effects of defect spacing, length, and depth, and longitudinal compressive stress on the failure pressure of a corroded pipe with circumferentially interacting defects, were then investigated using the suggested model in a parametric analysis.

New DRASTIC framework for groundwater vulnerability assessment: bivariate and multi-criteria decision-making approach coupled with metaheuristic algorithm.

Unplanned anthropogenic activities and erratic climate events pose serious threats to groundwater contamination. Therefore, the vulnerability assessment model becomes an essential tool for proper planning and protection of this precious resource. DRASTIC is an extensively adopted groundwater vulnerability assessment model that suffers from several shortcomings in its assessment due to the subjectivity of its rates and weights. In this paper, a new framework was developed to address the subjectivity of DRASTIC model using a bivariate, multi-criteria decision-making approach coupled with a metaheuristic algorithm. Shannon entropy (SE) and stepwise weight assessment ratio analysis (SWARA) methods were coupled with biogeography-based optimization (BBO) to modify rates and weights. The performance of developed models was assessed using area under the receiver operating characteristic (AU-ROC) curve and weighted F1 score. The Shannon-MH model yields better results with an AUC value of 0.8249, whereas other models resulted in an AUC value of 0.8186, 0.7714, 0.7672, and 0.7378 for SWARA-MH, SWARA, SE, and original DRASTIC models, respectively. It is also evident from weighted F1 score that Shannon-MH model produced maximum accuracy with a value of 0.452 followed by 0.437, 0.419, 0.370, and 0.234 for SWARA-MH, SWARA, SE, and original DRASTIC models, respectively. The results indicated that Shannon model coupled with metaheuristic algorithm outperforms other developed models in groundwater vulnerability assessment.

Off-Axis and On-Axis Performance of Novel Acrylic Thermoplastic (Elium®) 3D Fibre-Reinforced Composites under Flexure Load.

The flexure response of novel thermoplastic (Elium®) 3D fibre-reinforced composites (FRC) was evaluated and compared with a conventional thermoset (Epolam®)-based 3D-FRC. Ten different types of sample 3D-FRC were prepared by varying fibre orientations, i.e., 0°, 30°, 45°, 60° and 90°, and resin system, i.e., thermoplastic and thermoset. The bending characteristics and failure mechanisms were determined by conducting a three-point bend test. Results elucidate that the on-axis specimens show linear response and brittle failure; in contrast, the off-axis specimens depicted highly nonlinear response and ductile failure. The thermoplastic on-axis specimen exhibited almost similar flexure strength; in comparison, the off-axis specimens show ~17% lower flexure strength compared to thermoset 3D-FRC. Thermoplastic 3D-FRC shows ~40% higher energy absorption, ~23% lower flexure modulus and ~27% higher flexure strains as compared to its thermoset counterpart.

A Comprehensive Review on Biopolymer Mediated Nanomaterial Composites and Their Applications in Electrochemical Sensors.

Biopolymers are an attractive green alternative to conventional polymers, owing to their excellent biocompatibility and biodegradability. However, their amorphous and nonconductive nature limits their potential as active biosensor material/substrate. To enhance their bio-analytical performance, biopolymers are combined with conductive materials to improve their physical and chemical characteristics. We review the main advances in the field of electrochemical biosensors, specifically the structure, approach, and application of biopolymers, as well as their conjugation with conductive nanoparticles, polymers and metal oxides in green-based noninvasive analytical biosensors. In addition, we reviewed signal measurement, substrate bio-functionality, biochemical reaction, sensitivity, and limit of detection (LOD) of different biopolymers on various transducers. To date, pectin biopolymer, when conjugated with either gold nanoparticles, polypyrrole, reduced graphene oxide, or multiwall carbon nanotubes forming nanocomposites on glass carbon electrode transducer, tends to give the best LOD, highest sensitivity and can detect multiple analytes/targets. This review will spur new possibilities for the use of biosensors for medical diagnostic tests.

A Review of Finite Element Analysis and Artificial Neural Networks as Failure Pressure Prediction Tools for Corroded Pipelines.

This paper discusses the capabilities of artificial neural networks (ANNs) when integrated with the finite element method (FEM) and utilized as prediction tools to predict the failure pressure of corroded pipelines. The use of conventional residual strength assessment methods has proven to produce predictions that are conservative, and this, in turn, costs companies by leading to premature maintenance and replacement. ANNs and FEM have proven to be strong failure pressure prediction tools, and they are being utilized to replace the time-consuming methods and conventional codes. FEM is widely used to evaluate the structural integrity of corroded pipelines, and the integration of ANNs into this process greatly reduces the time taken to obtain accurate results.