Analysis of magnetic resistive flow of generalized brinkman type nanofluid containing carbon nanotubes with ramped heating

In recent times, scientists and engineers have been most attracted to electrically conducted nanofluids due to their numerous applications in various fields of science and engineering. For example, they are used in cancer treatment (hyperthermia), magnetic resonance imaging (MRI), drug-delivery, and magnetic refrigeration (MR). Bearing in mind the significance and importance of electrically conducted nanofluids, this article aims to study an electrically conducted water-based nanofluid containing carbon nanotubes (CNTs). CNTs are of two types, single-wall carbon nanotubes (SWCNTs) and multiple-wall carbon nanotubes (MWCNTs). The CNTs (SWCNTs and MWCNTs) have been dispersed in regular water as base fluid to form water-CNTs nanofluid. The Brinkman Type nanofluid model is developed in terms of time-fractional domain. The ramped heating and sinusoidal oscillations conditions have been taken at the boundary. The model has been solved for exact analytical solutions via the fractional Laplace transform method. The exact solutions have been graphically studied to explore the physics of various pertinent flow parameters on velocity and temperature fields. The empirical results reveal that the temperature and velocity fields decreased with increasing values of fractional parameters due to variation in thermal and momentum boundary layers. It is also indicated that the isothermal velocity and temperature are higher than ramped velocity and temperature

Epigenetic changes in histone acetylation underpin resistance to the topoisomerase I inhibitor irinotecan.

The topoisomerase I (TOP1) inhibitor irinotecan triggers cell death by trapping TOP1 on DNA, generating cytotoxic protein-linked DNA breaks (PDBs). Despite its wide application in a variety of solid tumors, the mechanisms of cancer cell resistance to irinotecan remains poorly understood. Here, we generated colorectal cancer (CRC) cell models for irinotecan resistance and report that resistance is neither due to downregulation of the main cellular target of irinotecan TOP1 nor upregulation of the key TOP1 PDB repair factor TDP1. Instead, the faster repair of PDBs underlies resistance, which is associated with perturbed histone H4K16 acetylation. Subsequent treatment of irinotecan-resistant, but not parental, CRC cells with histone deacetylase (HDAC) inhibitors can effectively overcome resistance. Immunohistochemical analyses of CRC tissues further corroborate the importance of histone H4K16 acetylation in CRC. Finally, the resistant clones exhibit cross-resistance with oxaliplatin but not with ionising radiation or 5-fluoruracil, suggesting that the latter two could be employed following loss of irinotecan response. These findings identify perturbed chromatin acetylation in irinotecan resistance and establish HDAC inhibitors as potential therapeutic means to overcome resistance

Prediction of Gut Wall Integrity Loss in Viral Gastroenteritis by Non-Invasive Marker

BACKGROUND: Intestinal fatty acid binding proteins (I-FABPs) are mainly expressed in the intestinal villi, which are the initial site of destruction in viral gastroenteritis.AIM: This study was designed to assess serum I-FABPs as a predictor of gut wall integrity loss in viral gastroenteritis.PATIENTS AND METHODS: This case-control cross-sectional study was conducted on 93 cases of acute viral gastroenteritis. Twenty-eight healthy children matching in age were recruited as control group. Serum I-FABPs were measured using ELISA technique. Viral detection and typing were done by PCR for adenovirus, and by Reverse transcriptase PCR for rotavirus, astrovirus and norovirus.RESULTS: Serum I-FABPs level was significantly higher in the cases compared to the controls and was also higher in the 46 rotavirus gastroenteritis cases compared to other viral gastroenteritis cases. Serum I- FABPs level was significantly higher in severely dehydrated cases as compared to mildly dehydrated ones (P=0.037).CONCLUSION: Serum I-FABPs could be used as an early and sensitive predictor marker of gut wall integrity loss in children with viral gastroenteritis and its level can indicate case severity

Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network

Advanced human activities, including modern agricultural practices, are responsible for alteration of natural concentration of nitrogen compounds in rivers. Future prediction of nitrogen compound concentrations (especially nitrate-nitrogen and ammonia-nitrogen) are important for countries where household water is obtained from rivers after treatment. Increased concentrations of nitrogen compounds result in the suspension of household water supplies. Artificial Neural Networks (ANNs) have already been deployed for the prediction of nitrogen compounds in various countries. But standalone ANN have several limitations. However, the limitations of ANNs can be resolved using hybrid models. This study proposes a new ACO-ENN hybrid model developed by integrating Ant Colony Optimization (ACO) with an Elman Neural Network (ENN). The developed ACO-ENN hybrid model was used to improve the prediction results of nitrate-nitrogen and ammonia-nitrogen prediction models. The results of new hybrid models were compared with multilayer ANN models and standalone ENN models. There was a significant improvement in the mean square errors (MSE) (0.196→0.049→0.012, i.e. ANN→ENN→Hybrid), mean absolute errors (MAE) (0.271→0.094→0.069) and Nash–Sutcliffe efficiencies (NSE) (0.7255→0.9321→0.984). The hybrid model had outstanding performance compared with the ANN and ENN models. Hence, the prediction accuracy of nitrate-nitrogen and ammonia-nitrogen has been improved using new ACO-ENN hybrid model

Optimised neural network model for river nitrogen prediction utilizing a new training approach

In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for ‘blue baby syndrome’ when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92

Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study

The impact of the suspended sediment load (SSL) on environmental health, agricultural operations, and water resources planning, is significant. The deposit of SSL restricts the streamflow region, affecting aquatic life migration and finally causing a river course shift. As a result, data on suspended sediments and their fluctuations are essential for a number of authorities especially for water resources decision makers. SSL prediction is often difficult due to a number of issues such as site-specific data, site-specific models, lack of several substantial components to use in prediction, and complexity its pattern. In the past two decades, many machine learning algorithms have shown huge potential for SSL river prediction. However, these models did not provide very reliable results, which led to the conclusion that the accuracy of SSL prediction should be improved. As a result, in order to solve past concerns, this research proposes a Long Short-Term Memory (LSTM) model for SSL prediction. The proposed model was applied for SSL prediction in Johor River located in Malaysia. The study allocated data for suspended sediment load and river flow for period 2010 to 2020. In the current research, four alternative models—Multi-Layer Perceptron (MLP) neural network, Support Vector Regression (SVR), Random Forest (RF), and Long Short-term Memory (LSTM) were investigated to predict the suspended sediment load. The proposed model attained a high correlation value between predicted and actual SSL (0.97), with a minimum RMSE (148.4 ton/day and a minimum MAE (33.43 ton/day).and can thus be generalized for application in similar rivers around the world

Torsional Crack Localization in Palm Oil Clinker Concrete Using Acoustic Emission Method

Palm oil clinker (POC) aggregates is a viable alternative to the naturally occurring sand and gravel in the manufacturing of concrete. The usage of POC aggregates assists in the reduction of solid waste and preserves the consumption of natural resources. Although researchers investigated the mechanical response of POC-containing concrete, limited research is available for its torsional behavior. In general, the torsional strength depends on the tensile strength of concrete. This research investigates the compressive, tensile, and torsional response of concrete with various ratios of POC-aggregates. Five batches of concrete were casted with POC-aggregate replacing granite at ratios of 0, 20, 40, 60, and 100%. The selection for the mixture proportions for the various batches was based on the design of experiments (DOE) methodology. The hard density, compressive strength, splitting tensile strength, and flexural strength of concrete with a 100% replacement of granite with POC-aggregates reduced by 8.80, 37.25, 30.94, and 14.31%, respectively. Furthermore, a reduction in initial and ultimate torque was observed. While cracks increased with the increase in POC-aggregates. Finally, the cracking of concrete subjected to torsional loads was monitored and characterized by acoustic emissions (AE). The results illustrate a sudden rise in AE activities during the initiation of cracks and as the ultimate cracks were developed. This was accompanied by a sudden drop in the torque/twist curve

Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning

In recent years, researchers have investigated the development of artificial neural networks (ANN) and finite element models (FEM) for predicting crack propagation in reinforced concrete (RC) members. However, most of the developed prediction models have been limited to focus on individual isolated RC members without considering the interaction of members in a structure subjected to hazard loads, due to earthquake and wind. This research develops models to predict the evolution of the cracks in the RC beam-column joint (BCJ) region. The RC beam-column joint is subjected to lateral cyclic loading. Four machine learning models are developed using Rapidminer to predict the crack width experienced by seven RC beam-column joints. The design parameters associated with RC beam-column joints and lateral cyclic loadings in terms of drift ratio are used as inputs. Several prediction models are developed, and the highest performing neural networks are selected, refined, and optimized using the various split data ratios, number of inputs, and performance indices. The error in predicting the experimental crack width is used as a performance index

Basic Science

Abstract BACKGROUND: Intestinal fatty acid binding proteins (I-FABPs) are mainly expressed in the intestinal villi, which are the initial site of destruction in viral gastroenteritis

Mechanical and Thermal Properties of Composite Precast Concrete Sandwich Panels : A Review

Precast concrete sandwich panels (PCSPs) are utilized for the external cladding of structures (i.e., residential, and commercial) due to their high thermal efficiency and adequate composite action that resist applied loads. PCSPs are composed of an insulating layer with high thermal resistance that is mechanically connected to the concrete. In the recent decades, PCSPs have been a viable alternative for the fast deployment of structures due to the low fabrication and maintenance cost. Furthermore, the construction of light and thin concrete wythes that can transfer and resist shear loads has been achieved with the utilization of high-performance cementitious composites. As a result, engineers prefer PCSPs for building construction. PCSP design and use have been examined to guarantee that a building is energy efficient, has structural integrity, is sustainable, is comfortable, and is safe. Hence, this paper reviews the expanding knowledge regarding the current development of the mechanical and thermal properties of the PCSPs components; subsequently, future potential research directions are suggested.</p