A meshless method to the numerical solution of an inverse reaction–diffusion–convection problem

In this paper, the determination of the source term in a reaction–diffusion convection problem is investigated. First with suitable transformations, the problem is reduced, then a new meshless method based on the use of the heat polynomials as basis functions is proposed to solve the inverse problem. Due to the ill-posed inverse problem, the Tikhonov regularization method with a generalized cross-validation criterion is employed to obtain a numerical stable solution. Finally, some numerical examples are presented to show the accuracy and effectiveness of the algorithm.     

Effect of processing conditions on producing a reactive derivative from β‐cyclodextrin with itaconic acid

A reactive cyclodextrin was synthesised by reacting β‐cyclodextrin with itaconic acid to enable it to fix permanently onto cellulosic materials. Because synthesis is a complicated process that is greatly influenced by many factors, the response surface methodology was applied in this study to optimise production. To investigate the efficiency of the esterification reaction, the amount of carboxyl groups and the double bond content of the end product were measured and employed as the responses. The 3D response surface plots and the contour plots derived from the mathematical models were applied to evaluate the interactive effects of parameters affecting the reaction, such as itaconic acid and catalyst concentrations, material to liquor ratio, temperature and time of reaction. The amount of carboxyl groups and the double bond content of cyclodextrin itaconate (about 175 and 150 meq./100 g CDI, respectively) in the optimum conditions indicated that one to two itaconic acid molecules could react with cyclodextrin according to the esterification reaction. In addition, the presence of the new supplementary groups on cyclodextrin could effect on the aggregation behaviour of this new cyclodextrin derivative as demonstrated by dynamic light scattering and AFM.     

Energy and load balancing routing protocol for IoT

Due to the limitation of node energy resources, the management of energy consumption is one of the most important problems of the internet of things (IoT). Therefore, many studies have tried to optimize and manage energy consumption by focusing on different techniques. Although each of these studies has improved and optimized energy consumption, there are many important problems, including maintaining traffic balance and energy consumption of network nodes. Therefore, a new method is necessary to maintain the load and energy balancing of network nodes. Therefore, this paper introduces energy and load balancing routing protocol for IoT (ELBRP) based on the development of the RPL routing protocol and the efficiency of data distribution technique. The ELBRP performance has three steps. In the first step, along with the process of sending DODAG information object (DIO) messages, the status of network nodes is evaluated. In the second step, the DODAG communication graph is formed according to the ELBRP. In the third step, data transmission is done according to the distribution technique with the goal of balancing traffic and energy. The simulation results using cooja simulator showed the superiority of ELBRP in improving energy consumption and successful delivery ratio, reducing delay and increasing the network lifetime compared to the similar methods.     

Star-shaped polylactic acid-based triazine dendrimers: the catalyst type and time factors influence on polylactic acid molecular weight

Nowadays, extensive environmental problems due to the use of synthetic polymers have provoked efforts for their replacement with biopolymers as the most important research priorities. Polylactic acid (PLA) is one of the well-known biodegradable biopolymers. In this work, for the first time, PLA was modified with hydrophilic triazine-based dendrimers to obtain PLA with improved hydrophilic properties. In this regard, at first, dl-lactic acid (DLLA) was polymerized through the melt polycondensation to obtain poly-dl-lactic acid (PDLLA). The effects of reaction time and catalyst types on the molecular weight of the PDLLA were investigated. Also, the thermal behavior of PDLLAs with different molecular weights was evaluated using differential scanning calorimetry (DSC) technique. The obtained results from the DSC analysis showed that the PDLLA with higher molecular weight has a higher glass transition temperature (T_g) and melting point (T_m). In the following, various generations (G) of the triazine-based dendrimers were synthesized. To increase the hydrophilicity of the prepared PDLLA, chemical modification of PDLLA with the different generations of triazine-based dendrimer (G₁, G_1.5 and G₂) was performed. Due to the modification of PDLLA with dendrimers, the number of functional groups and hydrophilicity of PDLLA increased. Based on the obtained results, it is expected that the prepared systems could be a good and promising candidate for the production of biocompatible plastics with more hydrolytic degradation ability.

     

An improved pseudospectral meshless radial point interpolation (PSMRPI) method for 3D wave equation with variable coefficients

In this paper, a pseudospectral meshless radial point interpolation (PSMRPI) technique is applied to the three-dimensional wave equation with variable coefficients subject to given appropriate initial and Dirichlet boundary conditions. The present method is a kind of combination of meshless methods and spectral collocation techniques. The point interpolation method along with the radial basis functions is used to construct the shape functions as the basis functions in the frame of the spectral collocation methods. These basis functions will have Kronecker delta function property, as well as unitary possession. In the proposed method, operational matrices of higher order derivatives are constructed and then applied. The merit of this innovative method is that, it does not require any kind of integration locally or globally over sub-domains, as it is essential in meshless methods based on Galerkin weak forms, such as element-free Galerkin and meshless local Petrov–Galerkin methods. Therefore, computational cost of PSMRPI method is low. Further, it is proved that the procedure is stable with respect to the time variable over some conditions on the 3D wave model, and the convergence of the technique is revealed. These latest claims are also shown in the numerical examples, which demonstrate that PSMRPI provides excellent rate of convergence.

     

A comparison of the performance of some extreme learning machine empirical models for predicting daily horizontal diffuse solar radiation in a region of southern Iran

This study uses the empirical models of extreme learning machine (ELM) method to predict daily horizontal diffuse solar radiation (HDSR). As a possibility for modification, the recent hybrid ELM methods such as complex ELM (C-ELM), self-adaptive evolutionary ELM (SaE-ELM), and online sequential ELM (OS-ELM) have been developed for the prediction of the daily HDSR. The empirical model of ELM predicts the HDSR using clearness index as the sole predictor. For this aim, two types of correlations are evaluated: (1) the diffuse fraction-clearness index and (2) the diffuse coefficient-clearness index. The measured diffuse and global solar radiation data sets of southern Iranian cities (Yazd, Shiraz, Bandar Abbas, Bushehr, and Zahedan) are utilized to evaluate the models. The precision of the C-ELM, SaE-ELM, OS-ELM, and ELM models is evaluated for different regions on the basis of five statistical performance evaluation parameters. The results confirm that the performance of hybrid ELM is pretty accurate and trustworthy. Fully complex ELM exhibits the best performance among these hybrid methods, having values of 0.87 and 0.30 for R² and RMSE measures, respectively, in the testing phase. Therefore, it is able to be recognized as an appropriate tool for daily solar radiation forecasting issues.     

Ensemble-based clustering of large probabilistic graphs using neighborhood and distance metric learning

Graphs are commonly used to express the communication of various data. Faced with uncertain data, we have probabilistic graphs. As a fundamental problem of such graphs, clustering has many applications in analyzing uncertain data. In this paper, we propose a novel method based on ensemble clustering for large probabilistic graphs. To generate ensemble clusters, we develop a set of probable possible worlds of the initial probabilistic graph. Then, we present a probabilistic co-association matrix as a consensus function to integrate base clustering results. It relies on co-occurrences of node pairs based on the probability of the corresponding common cluster graphs. Also, we apply two improvements in the steps before and after of ensembles generation. In the before step, we append neighborhood information based on node features to the initial graph to achieve a more accurate estimation of the probability between the nodes. In the after step, we use supervised metric learning-based Mahalanobis distance to automatically learn a metric from ensemble clusters. It aims to gain crucial features of the base clustering results. We evaluate our work using five real-world datasets and three clustering evaluation metrics, namely the Dunn index, Davies–Bouldin index, and Silhouette coefficient. The results show the impressive performance of clustering large probabilistic graphs.

     

Effects of Glow-Discharge Parameters on Silicon Coating Thickness Deposited on 316 Stainless Steel Samples

Silicon as a light element can be used as the tokamak first wall coating in order to protect the plasma from impurities which come from the wall. In this paper, we deposit an amorphous-silicon layer by the use of the glow discharge technique on the wall surface of the test chamber made from SS316. To do so, we employ silane as a source of silicon ions. The siliconization process is conducted at two time durations of 90 and 150 min. SEM image of the coated surface shows that the structure of the coated layer deposited by the 150-min is more compact in comparison with that of 90-min. Effect of current density on coating properties is evaluated by placing four samples at different locations on the wall surface which shows that the optimal values of current density are in the range of 14–20 μA/cm² and the higher range of current density can damage the coating. The results confirm that by increasing the time of siliconization the coating thickness is increased successfully from 100 to 250 nm without appearance of any damage in coating.     

Polyunsaturated Fatty Acids and Modulation of Cholesterol Homeostasis in THP-1 Macrophage-Derived Foam Cells

Transformation of macrophages to foam cells is determined by the rates of cholesterol uptake and efflux. This study uses a real time RT-PCR technique to investigate the role of conjugated linoleic acid (CLA), α-linolenic acid (ALA) and eicosapentaenoic acid (EPA) in the regulation of the ATP-binding cassette A1 (ABCA1) and liver X receptor α (LXR) genes, which are involved in cholesterol homeostasis. Accordingly, these fatty acids significantly reduced the total, free and esterified cholesterols within the foam cells. While the expression of the ABCA1 and LXRα genes was increased in the presence of the pharmacological LXRα ligand, T0901317, their mRNA expression was not significantly affected by CLA, ALA and EPA. These results suggest that although polyunsaturated fatty acids have an effect on cholesterol homeostasis, they cannot change the expression of the ABCA1 and LXRα genes. Alternatively, several other genes and proteins may be involved.     

Identification of B-Cell Epitopes for Eliciting Neutralizing Antibodies against the SARS-CoV-2 Spike Protein through Bioinformatics and Monoclonal Antibody Targeting

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global public health crisis. Effective COVID-19 vaccines developed by Pfizer-BioNTech, Moderna, and Astra Zeneca have made significant impacts in controlling the COVID-19 burden, especially in reducing the transmission of SARS-CoV-2 and hospitalization incidences. In view of the emergence of new SARS-CoV-2 variants, vaccines developed against the Wuhan strain were less effective against the variants. Neutralizing antibodies produced by B cells are a critical component of adaptive immunity, particularly in neutralizing viruses by blocking virus attachment and entry into cells. Therefore, the identification of protective linear B-cell epitopes can guide epitope-based peptide designs. This study reviews the identification of SARS-CoV-2 B-cell epitopes within the spike, membrane and nucleocapsid proteins that can be incorporated as potent B-cell epitopes into peptide vaccine constructs. The bioinformatic approach offers a new in silico strategy for the mapping and identification of potential B-cell epitopes and, upon in vivo validation, would be useful for the rapid development of effective multi-epitope-based vaccines. Potent B-cell epitopes were identified from the analysis of three-dimensional structures of monoclonal antibodies in a complex with SARS-CoV-2 from literature mining. This review provides significant insights into the elicitation of potential neutralizing antibodies by potent B-cell epitopes, which could advance the development of multi-epitope peptide vaccines against SARS-CoV-2.