Search

This study aims to compare the analysis of phytochemicals, nutrition, cytotoxicity, and antioxidant activity of A. viridis and A. spinosus leaves because they are commonly used as wild leafy vegetables worldwide and valuable for traditional medicinal purposes. To achieve these goals, the standard protocol was followed in each experiment. Comparative analysis revealed that the A. spinosus leaf had more phytochemicals, such as alkaloids, flavonoids, terpenoids, carbohydrates, glycosides, cardiac glycosides, amino acids, xanthoproteins, phenols, saponins, steroids, and coumarins, than A. viridis. Aqueous solvents were more effective in identifying the presence of phytochemicals than methanol and ethanol. A. viridis leaves contain higher amounts of carbohydrates (49 ± 2 mg gluc./g), protein (53 ± 3 mg BSA/g), calcium (2.67 ± 0.03 mg/kg), amino acids (7 essential and 5 non-essential), total phenol (21 ± 1 mg GAE/g), and high antioxidant activity (IC50 = 109.0 ± 0.2 μg/cm3) than A. spinosus. On the other hand, A. spinosus leaves contain higher ash (17.1 ± 0.2 %), total flavonoid (16 ± 1 mg QE/g), and total antioxidant capacity (50 ± 1 mg AA/g) than A. viridis. The findings of the heavy metals analysis revealed that the concentrations of zinc, manganese, iron, and chromium were below the recommended limits of FAO/WHO, whereas copper (in both species) and cadmium (in A. viridis) were over the acceptable levels. The leaves of both species showed significant cytotoxic activity (LC50

Natural fiber-reinforced composites are becoming popular day by day because of their low cost and renewable nature. However, a major concern of these composites is water absorption. To minimize the water absorption in natural fiber-reinforced composites, wood varnish coating was used in this paper to investigate the effect of this coating on water absorption and mechanical properties of jute fiber-reinforced composites. Four types of composites were fabricated using the hand lay-up technique, among them on Type 1 no coating was used, Type 2 was surface coated, Type 3 was fiber coated and Type 4 was both fiber and composite surface coated. Water absorption, tensile, bending, short beam shear, and impact tests were performed on the composite specimens according to their respective ASTM standards to see the effect of coating. Both fiber and fracture surface morphology were observed using Scanning Electron Microscope. It was found that the water absorption was significantly reduced for Type 2 (50.31 %) and Type 4 (56.01 %) specimens. However, the tensile strength and bending strength were reduced by 74.17 %, and 72.71 % for Type 3 and 77.07 % and 84.24 % for Type 4 composites while for Type 2, they were slightly increased (7.26 % and 1.13 %). The ILSS of Type 3 and Type 4 were reduced by 69.34 % and 58.07 % respectively while it was reduced by only 4.49 % for Type 2 composite. On the other hand, the impact energy absorption was increased by 85.32 % for Type 4 composite. The findings of this study suggest that Type 2 composite (only composite surface coating) has the overall best performance and wood varnish coating has the potential to reduce the water absorption on natural fiber-reinforced composites.

The demand for concrete production has led to a significant annual requirement for raw materials, resulting in a substantial amount of waste concrete. In response, recycled aggregate concrete has emerged as a promising solution. However, it faces challenges due to the vulnerability of the hardened mortar attached to natural aggregates, leading to susceptibility to cracking and reduced strength. This study focuses on predicting the split tensile strength of fiber reinforced recycled aggregate concrete using five prediction models, including two deep neural network models DNN1 and DNN2, one optimizable Gaussian process regression (OGPR), and two genetic programming based GEP1 and GEP2 models. The models exhibited high accuracy in predicting spilt tensile strength with robust R2, RMSE, and MAE values. DNN2 has the highest R2 value of 0.94 and GEP1 has the lowest R2 value of 0.76. DNN2 model R2 was 3.3% and 13.5% higher than OGPR and GEP2. Similarly, DNN2 and GEP2 model performed 9.3% and 9.21% better than DNN1 and GEP1 respectively in terms of R2. DNN2 model performed 20.32% and 31.5% better than OGPR and GEP2 in terms of MAE. Similarly, GEP2 and DNN2 MAE were 13.1% and 31.5% better than GEP1 and DNN1. Sensitivity analysis using the relevance factor and permutation feature importance revealed that the most significant positive factors are cement, natural coarse aggregates, density of recycle aggregates, and superplasticizer while recycle aggregate concrete, max size, and water content of recycle aggregates and water content have the most negative effect on STS values. The proposed ML methods, especially DNN2 and OGPR can be effectively utilized in practical projects, saving time and cost for eco-friendly fiber reinforced recycled aggregate concrete mixes. However, it is required to study more input variables and utilize hybrid models to further enhance the accuracy and reliability of the models.

Abstract The Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6) forecasts a sea level rise (SLR) of up to 2 m by 2100, which poses significant risks to regional geomorphology. As a country with a rapidly developing economy and substantial population, Bangladesh confronts unique challenges due to its extensive floodplains and 720 km-long Bay of Bengal coastline. This study uses nighttime light data to investigate the demographic repercussions and potential disruptions to economic clusters arising from land inundation attributable to SLR in the Bay of Bengal. By using geographical information system (GIS)-based bathtub modeling, this research scrutinizes potential risk zones under three selected shared socioeconomic pathway (SSP) scenarios. The analysis anticipates that between 0.8 and 2.8 thousand km2 of land may be inundated according to the present elevation profile, affecting 0.5–2.8 million people in Bangladesh by 2150. Moreover, artificial neural network (ANN)-based cellular automata modeling is used to determine economic clusters at risk from SLR impacts. These findings emphasize the urgency for land planners to incorporate modeling and sea inundation projections to tackle the inherent uncertainty in SLR estimations and devise effective coastal flooding mitigation strategies. This study provides valuable insights for policy development and long-term planning in coastal regions, especially for areas with a limited availability of relevant data.

Zinc (Zn) is an essential micronutrient for plant growth and development, and its deficiency in soil can be a significant problem for broccoli (Brassica oleracea L.) production. While the genetic efforts to breed Zn-efficient broccoli are complex, harnessing microbiomes is an emerging way to induce mineral efficiency in crops. In this study, broccoli, a non-host mycorrhizal species, was cultivated with or without zinc deficiency in the presence or absence of arbuscular mycorrhizal fungi (AMF), confirmed by the colonization efficiency in roots. Zn deficiency caused a significant decrease in morphological parameters and photosynthetic attributes being consistent with the decreased Zn levels in root and shoot relative to Zn-sufficient plants. However, the broccoli plants inoculated with AMF showed a substantial improvement in morphological and photosynthetic parameters in Zn-deficient conditions due to the significant increase in tissue Zn levels. In addition, AMF-inoculated plants under Zn deficiency showed a significant decrease in cell death (%), electrolyte leakage, and H2O2 levels compared to Zn-deficient plants, which further suggests the improvements in cellular status due to the AMF colonization. The real-time PCR experiments showed a significant induction in the expression of BoZIP1 and BoNRAMP1 genes in the roots of Zn-deficient broccoli inoculated with AMF, suggesting that AMF may be associated with the induction of these genes responsible for Zn-uptake and transport. We further observed the induction of POD (peroxidase), APX (ascorbate peroxidase) and SOD (superoxide dismutase), and S-metabolites (cysteine and glutathione) predominantly in the roots of Zn-starved broccoli inoculated with AMF which may confer tolerance to Zn-deficiency induced oxidative damage. This is the first report on the role of AMF in mitigating Zn-deficiency in broccoli which may promote the microbiome-aided improvement in plant health suffering from Zn-deficiency in broccoli and other vegetable crops.

Plastic asphalt mixtures (PAMs) have garnered attention recently, but their field application has been limited due to a lack of understanding of asphalt mix behavior following modification. A modelling tool that can calculate the plastic influence on the characteristics of asphalt mixtures is required to close this gap. Hence, this study offers a performance analysis of various machine learning (ML) models in predicting the performance of PAMs through its various properties. These models include three methods, decision tree (DT) as an individual technique, adaboost regressor (AR), and bagging regressor (BR), as ensemble techniques for prediction of fundamental properties of PAMs i.e. air voids (Va), marshall flow (MF), marshall stability (MS), tensile strength ratio (TSR), and indirect tensile strength (ITS). A series of experimental works and their results on the PAMs properties were collected through literature, to develop ML models and compare their accuracy. The comparative findings demonstrated that the BR model had the largest coefficient of determination (R2) and the lowest statistical errors, making it the model with the best predictive ability. According to the study's findings, ensemble approaches can be effectively utilized to forecast different properties of PAMs. Comparing ensemble models to the single model that served as their base learner, ensemble models were able to increase prediction accuracy. Furthermore, cross validation was performed to check the accuracy of developed models. SHAP analysis was conducted to examine the effects of input parameters on the fundamental properties of PAMs.

Natural fiber-based composites are essential for the environment in this era of depletion of petroleum resources and for new green environmental rules, as they have limited detrimental behavior or response against the environment. In this paper, firstly, the suitability of replacing glass fiber with natural bamboo fiber in a flax/glass/epoxy hybrid composite under a fixed bending load is investigated using Finite Element Analysis (FEA) software ABAQUS with proper model validation. Finally, five different lay-up sequences of flax/bamboo/epoxy hybrid composite laminate for fixed volume fractions of flax and bamboo strips are investigated numerically to develop design strategies for binary hybrid composite. It is found that the bending stiffness and equivalent modulus of elasticity for model FB-1 are increased by 37.53 % and 37.52 % respectively compared to the base model while the energy absorption for model FB-3 is highest which is 105.89 % higher than the base model. Analysis of various lay-up sequences showed that flexural behavior can be improved by fabricating binary hybrid composites using the concept of a sandwich structure. It is shown that all highly stiff laminas should be placed on the outer sides of the composite to achieve the maximum bending stiffness whereas they should be placed at the center to attain the maximum energy absorption.

Aluminum is widely used in industries such as automobile and aerospace due to its lightweight, but its pure form lacks sufficient strength and the properties of aluminum can be modified by adding alloying elements. This paper presents the effect of adding Magnesium (Mg), Copper (Cu), and Zinc (Zn) on the mechanical, microstructural, heat treatment properties, and corrosion behavior of aluminum-based alloys. The aluminum-based alloys were manufactured with varying percentages (1 %, 3 %, 5 %, 8 %, and 10 %) of the three alloying elements using sand casting. Tension, compression, Brinell hardness, and corrosion tests were conducted to examine the effect of these alloying elements on the alloys' properties. The tensile strength is improved significantly with the increase in alloying elements for all alloys but the compressive strength and hardness are improved for Al–Cu alloy only. After heat treatment, the hardness of Al–Mg alloy increased at 8 % and 10 % Mg content while the hardness of Al–Cu alloy decreased after heat treatment for all concentrations of Cu. The hardness of Al–Zn alloys remained unaffected by the heat treatment process. The corrosion rate is minimum for Al–Mg and it is maximum for Al–Cu alloys but at 10 % concentrations, the corrosion rate of Al–Mg alloy is higher than Al–Cu alloy. The microstructural observation before and after heat treatment revealed that the grain refinement occurred for Al–Mg and Al–Cu alloys but the microstructure remained unaffected for Al–Zn alloys. The outcomes of this study offer valuable insights into customizing the mechanical characteristics of aluminum-based alloys to suit particular applications.

Printed circuit board (PCB) is the most important part of any electronic device which is made of copper-clad laminate and glass fiber-reinforced composites. Since the ply orientation of fiber-reinforced composites and lamina thickness have a significant influence on the mechanical properties of the whole composite laminate it is necessary to investigate the effects of ply orientation and lamina thickness on the bending properties of PCB so that they can be manufactured to meet the service requirements. In this work, the bending properties of PCB were investigated for seven different ply orientations of glass fiber-reinforced composite laminas and for eight different thickness combinations (for a constant laminate thickness) of glass fiber-reinforced composite lamina and copper-clad laminate. A commercially available finite element analysis software (Abaqus) was used to simulate a three-point bending test of PCBs and the simulation results were validated using experimental results. It is found that the bending stiffness is maximum for the cross-ply laminate. The introduction of angle ply improves the maximum von-Mises stress of the PCB with an insignificant cost of bending stiffness (less than 5% reduction). It is found that the bending stiffness and the maximum von-Mises stress of the PCB can be regulated through the variation of thickness of the constituent lamina. The laminate stiffness can be increased by increasing the thickness of stiffer laminas or by placing the stiffer laminas towards the surface of the laminate. The outcome of this research would provide a comprehensive understanding of the bending characteristics of the multi-layered PCBs which can be directly utilized by the PCB manufacturers. However, the effects of ply orientation and lamina thickness variation on the behavior of the PCBs subject to temperature variation, impact loading, vibration, and other conditions that might affect the service life of the PCBs should be investigated.

Ganoderma lucidum is known as lingzhi mushroom, which is said to have medicinal properties by the local residents. This research was focused to assess the antidepressant, anxiolytic, and sedative activities of the mentioned mushroom extracts by means of in vivo and in silico approaches. The antidepressant, anxiolytic, and sedative properties of the methanol extracts of G. lucidum (MEGL) were assessed using the forced swim test hole board, open field test, elevated plus maze, hole cross test, and thiopental sodium-induced sleeping time. The extracts revealed significant antidepressant, anxiolytic, and sedative activities in a dose-dependent manner. Rutin and quercetin were found to be the most effective enzyme inhibitors in the molecular docking study. According to the findings of in vivo and molecular docking study, it could be forecast that, the extract could have substantial antidepressant, anxiolytic, and sedative characteristics and deep molecular strategies on this extracts might create a target for the development of novel therapeutics. Further investigations are needed to appraise the molecular mechanisms implicated and isolate the bioactive components.

Cost overruns are a ubiquitous feature of construction projects, and realistic budgeting at the development stage plays a significant role in their control. However, the application of existing models to budgeting for power plant projects is restricted by the limited amount of project-specific cost data available. This study overcomes this by using a Classification and Regression Tree (CART) approach involving mixed methods of website visits, document study, and expert opinion to predict the amount of project cost (PC) and cost contingency (CC) needed to cover probable cost increases by the use of models containing readily available project attributes and national economic parameters at the project development stage. The modeling process is demonstrated and tested with a case study involving 58 Bangladeshi power plant projects – producing average absolute errors ranging from 0.7% to 1.7% and enabling project cost, inflation rate, and GDP to be identified as significant factors affecting PC and CC modeling. The approach can be applied to predict the PC during preliminary budgeting and selecting a project type and location aligned to the country’s economic status and policy-making strategies, thus facilitating further investment decisions.

A series of quinazolin-4(3H)-one derivatives were synthesised and evaluated for their cytotoxicity against human Caucasian breast adenocarcinoma (MCF-7) and human ovarian carcinoma (A2780) cell lines. Cytotoxicity of the most tested compounds was 2- to 30-fold more than the positive control lapatinib (IC50 of 2j = 3.79 ± 0.96; 3j = 0.20 ± 0.02; and lapatinib = 5.9 ± 0.74) against MCF7 cell lines except two compounds (IC50 of 2 b = 15.72 ± 0.07 and 2e = 14.88 ± 0.99). On the other hand, cytotoxicity was 4 − 87 folds (IC50 of 3a = 3.00 ± 1.20; 3 g = 0.14 ± 0.03) more the positive control lapatinib (IC50 = 12.11 ± 1.03) against A2780 cell lines except compound 2e (IC50 = 16.43 ± 1.80). Among the synthesised quinazolin-4(3H)-one derivatives, potent cytotoxic 2f-j and 3f-j were investigated for molecular mechanism of action. Inhibitory activities of the compounds were tested against multiple tyrosine protein kinases (CDK2, HER2, EGFR and VEGFR2) enzymes. As expected, all the quinazolin-4(3H)-one derivatives were showed comparable inhibitory activity against those kinases tested, especially, compound 2i and 3i showed potent inhibitory activity against CDK2, HER2, EGFR tyrosine kinases. Therefore, molecular docking analysis for quinazolin-4(3H)-one derivatives 2i and 3i were performed, and it was revealed that compounds 2i and 3i act as ATP non-competitive type-II inhibitor against CDK2 kinase enzymes and ATP competitive type-I inhibitor against EGFR kinase enzymes. However, in case of HER2, compounds 2i act as ATP non-competitive type-II inhibitor and 3i act as ATP competitive type-I inhibitor. Docking results of known inhibitors were compared with synthesised compounds and found synthesised 2i and 3i are superior than the known inhibitors in case of interactions. In addition, in silico drug likeness properties of quinazolin-4(3H)-one derivatives showed better predicted ADME values than lapatinib.

The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the spatial pattern analysis of crashes and recognition of the primary and contributing factors may assist in reducing the severity of road traffic crashes (R.T.C.s). For crash severity prediction, along with spatial patterns, various machine learning models are used, and the spatial relations of R.T.C.s with neighboring areas are evaluated. In this study, tree-based ensemble models (gradient boosting and random forest) and a logistic regression model are compared for the prediction of R.T.C. severity. Sample data of road crashes in Al-Ahsa, the eastern province of Saudi Arabia, were obtained from 2016 to 2018. Random forest (R.F.) identifies significant features strongly correlated with the severity of the R.T.C.s. The analysis findings showed that the cause of the crash and the type of collision are the most crucial elements affecting the severity of injuries in traffic crashes. Furthermore, the target-specific model interpretation results showed that distracted driving, speeding, and sudden lane changes significantly contributed to severe crashes. The random forest (R.F.) method surpassed other models in terms of injury severity, individual class accuracies, and collective prediction accuracy when using k-fold (k = 10) based on various performance metrics. In addition to taking into account the machine learning approach, this study also included spatial autocorrelation analysis based on G.I.S. for identifying crash hotspots, and Getis Ord Gi* statistics were devised to locate cluster zones with high- and low-severity crashes. The results demonstrated that the research area’s spatial dependence was very strong, and the spatial patterns were clustered with a distance threshold of 500 m. The analysis’s approaches, which included Getis Ord Gi*, the crash severity index, and the spatial autocorrelation of accident incidents according to Moran’s I, were found to be a successful way of locating and rating crash hotspots and crash severity. The techniques used in this study could be applied to large-scale crash data analysis while providing a useful tool for policymakers looking to improve roadway safety.

The conventional method for determining the Marshall Stability (MS) and Marshall Flow (MF) of asphalt pavements entails laborious, time-consuming, and expensive laboratory procedures. In order to develop new and advanced prediction models for MS and MF of asphalt pavements the current study applied three soft computing techniques: Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multi Expression Programming (MEP). A comprehensive database of 343 data points was established for both MS and MF. The nine most significant and straightforwardly determinable geotechnical factors were chosen as the predictor variables. The root squared error (RSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RRMSE), coefficient of determination (R2), and correlation coefficient (R), were all used to evaluate the performance of models. The sensitivity analysis (SA) revealed the rising order of input significance of MS and MF. The results of parametric analysis (PA) were also found to be consistent with previous research findings. The findings of the comparison showed that ANN, ANFIS, and MEP are all reliable and effective methods for the estimation of MS and MF. The mathematical expressions derived from MEP represent the novelty of MEP and are relatively reliable and simple. Roverall values for MS and MF were in the order of MEP > ANFIS > ANN with all values over the permissible range of 0.80 for both MS and MF. Therefore, all the techniques showed higher performance, possessed high prediction and generalization capabilities, and assessed the relative significance of input parameters in the prediction of MS and MF. In terms of training, testing, and validation data sets and their closeness to the ideal fit, i.e., the slope of 1:1, MEP models outperformed the other two models. The findings of this study will contribute to the choice of an appropriate artificial intelligence strategy to quickly and precisely estimate the Marshall Parameters. Hence, the findings of this research study would assist in safer, faster, and more sustainable predictions of MS and MF, from the standpoint of time and resources required to perform the Marshall tests.

The present study explores the structural pavement design techniques related to pavement distresses in terms of pavement rutting, cracking and International Roughness Index (IRI) based on the materials properties, roadbed characteristics, climate and traffic loads for highway network of Saudi Arabia (KSA). The study was focused on selected site conditions at four regions in KSA: Central (Riyadh); Eastern (Al-Ahsa); Western (Jeddah) and Northern (Arar). Mechanistic-Empirical Pavement Design Guide (MEPDG) software was used to calibrate and predict pavement design life according to the mentioned distresses for different regions in the KSA. This is the first time where the exact weather stations were selected to run analysis on the software determining realistic pavement distresses. In the study, the pavement structure design is different for low traffic (700 AADTT) and high traffic (2000, 6000, and 10,000 AADTT). The tests were run on the MEPDG software to analyze the distresses predicted by the software for an interval of 5, 10, 15, and 20 years. The results predicted by the software show that the preliminary example design satisfies all the target distresses for the mentioned design life, even for 20 years. The study provides a base pavement design for pavement designers that can be modified as per project requirements using the specific data for traffic, material properties, thickness, and distress limit to achieve target design life.

The finite element method (FEM) is an essential method for predicting the response of the spar platform considering all nonlinear variables. Although FEM is an extremely laborious and time-consuming process for predicting platform responses using hydrodynamic loads, artificial neural networks (ANNs) can predict the response quickly, as required for platform management to either linger or stop the production of oil and gas. The application of ANN approaches to estimate the wave height and period from the expected wind forces is investigated in this paper. The ANN model can also predict the nonlinear responses of the spar platform subjected to the structural parameter as well as the wave height and wave period. The backpropagation technique depletes feed-forward neural networks, allowing the network to be trained. Following the formation of the neural network, rapid reactions from a freshly anticipated wind force are obtained. The results are validated via a comparison with results from a conventional finite element analysis. The findings demonstrate that the artificial neural network (ANN) technique is effective and is able to significantly reduce the required time to make predictions when compared to the conventional FEM.

The importance of heavy oil in the world oil market has increased over the past twenty years as light oil reserves have declined steadily. The high viscosity of this kind of unconventional oil results in high energy consumption for its transportation, which significantly increases production costs. A cost-effective solution for the long-distance transport of viscous crudes could be water-lubricated flow technology. A water ring separates the viscous oil-core from the pipe wall in such a pipeline. The main challenge in using this kind of lubricated system is the need for a model that can provide reliable predictions of friction losses. An artificial neural network (ANN) was used in this study to model pressure losses based on 225 data sets from independent sources. The seven input variables used in the current ANN model are pipe diameter, average velocity, oil density, oil viscosity, water density, water viscosity, and water content. The ANN developed using the backpropagation technique with seven processing neurons or nodes in the hidden layer demonstrated to be the optimal architecture. A comparison of ANN with other artificial intelligence and parametric techniques shows the promising precision of the current model. After the model was validated, a sensitivity analysis determined the relative order of significance of the input parameters. Some of the input parameters had linear effects, while other parameters had polynomial effects of varying degrees on the friction losses.

Flexible pavement deterioration due to moisture intrusion and aging is the key concern worldwide for highway engineers. However, this damage has not been properly investigated in detail due to lack of appropriate experimental and modeling techniques. Such lacking hinders the design of long-lasting pavements, as the effects of environmental damages are unknown, especially for modified asphalt. Therefore, the current study aims at determining a better approach for modeling asphalt adhesion damage using Artificial Neural Networks (ANNs). The Atomic Force Microscopy (AFM) test was deployed to determine the adhesion and cohesion forces of asphalt samples with varying contents of polymer and Antistripping Agents (ASAs). Two types of ANN models, namely multilayer perceptions (MLPs) and radial basis function neural network (RBFNN), were used in this effort. Two popular modifications, namely ensemble learning and hierarchical modeling, were also engaged to achieve convenient and accurate damage models. The analysis found that RBFNN was better suited for hierarchical models than MLP. RBFNN is preferred for aged and moisture-damaged samples which have less variation in their datasets. Hierarchical models are convenient to apply as they can be applied to any type of asphalt sample. However, they produced a small reduction in accuracy (less than 10%) as compared to other models. The accuracy of the hierarchical model was found to be satisfactory. The ensemble learning approach showed slight improvement in accuracy for all models ranging between 1–3%, i.e., 6–8 nN. This study recommends the use of hierarchical models, developed with ensemble learning, for prediction of asphalt damage. The results of the study will be helpful for researchers and practitioners working on pavement materials for developing prediction models to prepare a better mix design of polymer modified asphalt.

Columns at the ground floor and parking garages that could be hit by a car pose a significant risk to the structural stability of the building superstructures. Generally, these columns are not built to sustain the lateral impact force generated by car–column collision. In this study, the performance of axially loaded retrofitted reinforced concrete (RC) columns against car impact is evaluated using finite element (FE) simulation. The FE model of the RC column with axial load was validated with experimental results. For the car-crushing simulations, two SUV car models with a mass of about 2250 kg, which had been experimentally validated, were used to simulate the car–column collision. The results of the FE analysis revealed that once the impact speed exceeds 30 km/h, the horizontal impact force has a significant effect on the column joint at the foundation. The impact force increases linearly as the impact velocity of the car increases up to 20 km/h. When car impact velocities are more than 20 km/h, the generated impact force increases in power to the car-crashing velocity. Both types of cars have almost the same effect on the generation of impact force and the lateral displacement of the column. It is found that the generated impact forces are higher than the recommended design values of Eurocode 1. To protect the column from car impact damage, two types of column-strengthening systems were investigated. One form of strengthening system involves retrofitting the lower half of the column with an aramid fiber-reinforced polymer (AFRP) warp, while the other involves putting a reinforced concrete jacket of up to 1.3 m in the height of the column. Based on the comparative study, design recommendations are suggested to protect the RC column from accidental car-crashing damage.

Moisture penetration causes many direct and indirect distresses in flexible asphalt pavement. Due to damage in asphalt concrete and binder by moisture are the prime concern of failure for flexible pavement worldwide. The causes and prediction are investigated in this study. The asphalt binder was modified with carbon nanotubes (CNT) with very small percentages. The modified binder was simulated with moisture damage with AASHTO T-283 methods. In this study, polymer and carbon nanotubes (CNT) have been added to liquid asphalt binder to examine whether the resulting modified binder has improved moisture damage resistance. Using laboratory tested data, an artificial intelligence modeling technique has been utilized to determine the moisture damage behavior of the modified binder. Multi-Layer Perceptron (MLP) provides the best prediction for wet and dry samples AFM readings with R2 values respectively 0.6407 and 0.8371.

Recently, we have reported a series of isatin hydrazone, two of them, namely, 3-((2,6-dichlorobenzylidene)hydrazono)indolin-2-one (1) and 3-((2-chloro-6-fluorobenzylidene)hydrazono)indolin-2-one (2) having potent cytotoxicity, showing cyclin-dependent kinases (CDK2) inhibitory activity and bearing recommended drug likeness properties. Since both compounds (1 and 2) showed inhibitory activity against CDK2, we assumed it would also have multiple receptor tyrosine kinases (RTKs) inhibitory activity. Considering those points, here, above-mentioned two isatin hydrazone 1 and 2 were synthesized using previously reported method for further investigation of their potency on RTKs (EGFR, VEGFR-2 and FLT-3) inhibitory activity. As expected, Compound 1 exhibited excellent inhibitory activity against epidermal growth factor receptor (EGFR, IC50 = 0.269 µM), vascular epidermal growth factor receptor 2 (VEGFR-2, IC50 = 0.232 µM) and FMS-like tyrosine kinase-3 (FLT-3, IC50 = 1.535 µM) tyrosine kinases. On the other hand, Compound 2 also exhibited excellent inhibitory activity against EGFR (IC50 = 0.369 µM), VEGFR-2 (IC50 = 0.266 µM) and FLT-3 (IC50 = 0.546 µM) tyrosine kinases. A molecular docking study with EGFR, VEGFR-2 and FLT-3 kinase suggested that both compounds act as type I ATP competitive inhibitors against EGFR and VEGFR-2, and type II ATP non-competitive inhibitors against FLT-3.

The traditional procedure of predicting the settling velocity of a spherical particle is inconvenient as it involves iterations, complex correlations, and an unpredictable degree of uncertainty. The limitations can be addressed efficiently with artificial intelligence-based machine-learning algorithms (MLAs). The limited number of isolated studies conducted to date were constricted to specific fluid rheology, a particular MLA, and insufficient data. In the current study, the generalized application of ML was comprehensively investigated for Newtonian and three varieties of non-Newtonian fluids such as Power-law, Bingham, and Herschel Bulkley. A diverse set of nine MLAs were trained and tested using a large dataset of 967 samples. The ranges of generalized particle Reynolds number (ReG) and drag coefficient (CD) for the dataset were 10−3 < ReG (-) < 104 and 10−1 < CD (-) < 105, respectively. The performances of the models were statistically evaluated using an evaluation metric of the coefficient-of-determination (R2), root-mean-square-error (RMSE), mean-squared-error (MSE), and mean-absolute-error (MAE). The support vector regression with polynomial kernel demonstrated the optimum performance with R2 = 0.92, RMSE = 0.066, MSE = 0.0044, and MAE = 0.044. Its generalization capability was validated using the ten-fold-cross-validation technique, leave-one-feature-out experiment, and leave-one-data-set-out validation. The outcome of the current investigation was a generalized approach to modeling the settling velocity.

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

Isatin derivatives potentially act on various biological targets. In this article, a series of novel isatin-hydrazones were synthesized in excellent yields. Their cytotoxicity was tested against human breast adenocarcinoma (MCF7) and human ovary adenocarcinoma (A2780) cell lines using MTT assay. Compounds 4j (IC50 = 1.51 ± 0.09 µM) and 4k (IC50 = 3.56 ± 0.31) showed excellent activity against MCF7, whereas compound 4e showed considerable cytotoxicity against both tested cell lines, MCF7 (IC50 = 5.46 ± 0.71 µM) and A2780 (IC50 = 18.96± 2.52 µM), respectively. Structure-activity relationships (SARs) revealed that, halogen substituents at 2,6-position of the C-ring of isatin-hydrazones are the most potent derivatives. In-silico absorption, distribution, metabolism and excretion (ADME) results demonstrated recommended drug likeness properties. Compounds 4j (IC50 = 0.245 µM) and 4k (IC50 = 0.300 µM) exhibited good inhibitory activity against the cell cycle regulator CDK2 protein kinase compared to imatinib (IC50 = 0.131 µM). A molecular docking study of 4j and 4k confirmed both compounds as type II ATP competitive inhibitors that made interactions with ATP binding pocket residues, as well as lacking interactions with active state DFG motif residues.

Developing anticancer therapeutics with no/few side effects is a challenge for medicinal chemists. The absence of antibacterial activity of an anticancer drug removes its detrimental effect toward intestinal flora and therefore leads to reduced side effects. Here, a series of novel N-benzylisatin-aryl-hydrazones was designed, synthesized and evaluated for their antimicrobial and antiproliferative activities with SAR and ADME studies, aiming to develop anticancer drugs with no antimicrobial, yet high antiproliferative activities. The results were then compared with the effects of first-line treatments for lung cancer drug Gefitinib. Novel N-benzylisatin-aryl-hydrazones were synthesized from isatin and benzyl bromide in three steps with good to moderate yields. Antimicrobial activity was tested with six Gram-positive/negative bacterial strains, antifungal activity with a fungal strain and antiproliferative activity against ‘A549’ and ‘HeLa cell lines’, respectively. As expected, synthesized hydrazones reveled no effects on any of the strains of bacteria and fungi up to 100-µg/disc concentration. However, four compounds showed two-to-four fold antiproliferative activity over Gefitinib. For instance, IC50 of a compound (6c) shows concentration of 4.35 µM, whereas gefitinib shows 15.23 µM against ‘A549.’ ADME predicted studies reveled that our synthesized hydrazones exhibited higher ADME values than the Gefitinib. Therefore, our synthesized hydrazones can be an excellent scaffold for the development of anticancer therapeutics after considering further investigations.

[This corrects the article DOI: 10.1371/journal.pone.0158718.].

The aim of the present study was to dissect the genetic inheritance and interplay of root, shoot and heading attributes for a better understanding of these traits in crop production. For this, we utilized quantitative trait loci (QTL) and candidate gene analysis approach using a second filial (F2) population originated from a cross between spring cultivar Cheri and wild barley accession ICB181160. The F2 population comprising 182 plants was phenotyped for root dry weight (RDW), root volume (RV), root length (RL) and shoot dry weight (SDW), tiller number per plant (TIL) and days to heading (HEA). In parallel, this population was genotyped using polymerase chain reaction (PCR) based cleaved amplified polymorphic sequence (CAPS) markers distributed across the whole genome. Marker by trait analysis revealed 16 QTL for root and shoot traits localized on chromosomes 1H, 3H, 4H, 5H and 7H. The strongest and a common QTL effect for root, shoot and heading traits was identified on chromosome 7H at the putative region of Vrn-H3 gene. Later, we have established PCR based gene specific marker HvVrnH3 revealing polymorphism for early heading Vrn-H3 allele in Cheri and late heading allele vrn-H3 in ICB181160. Genotyping of these alleles revealed a clear co-segregation of early heading Vrn-H3 allele with lower root and shoot attributes, while late heading vrn-H3 allele with more TIL and higher root biomass suggesting a primary insight on the function of Vrn-H3 gene beyond flowering. Genetic interactions of vernalization genes Vrn-H3 with Vrn-H2 and Vrn-H1 also suggested the major role of Vrn-H3 alleles in determining root and shoot trait variations in barley. We believe, these data provide an opportunity for further research to test a precise significance of early heading on yield components and root associated sustainability in crops like barley and wheat.

In this study, moisture damage in plastomeric polymer modified asphalt binder is investigated using Atomic Force Microscopy (AFM) with chemically functionalized AFM tips. Four different percentages of plastomeric polymers and two antistripping agents such as Kling Beta and Lime are used to modify a base asphalt binder. Chemical functional groups such as -COOH, -CH3, -NH3, and –OH, that are commonly present in plastomeric polymer modified asphalt system, are used to functionalize the AFM tips. The force distance mode of AFM is used to measure the adhesion forces between a modified asphalt sample surface and the functionalized AFM tips. This enables the measurement of adhesion within an asphalt binder system. It is shown that the adhesion force values in dry sample changed substantially from that in wet conditioned samples. It is evident from this study that plastomeric modification does not help reduce moisture damage in asphalt. The percentage change in adhesion forces due to moisture is about 20 nN for the lime modified samples, and about 50 nN for the Kling Beta modified samples. This indicates that lime is more effective than Kling Beta for reducing moisture damage in plastomeric polymer modified asphalt.