Preparation of a novel potassium ion imprinted polymeric nanoparticles based on dicyclohexyl 18C6 for selective determination of K+ ion in different water samples

This work reports the first application of the ion imprinting technology for determination of potassium ion by precipitation polymerization method. Ion imprinted polymeric (IIP) nanoparticles were prepared by using dicyclohexyl 18C6 (DC18C6) as a K⁺ ion selective crown ether, in the acetonitrile–dimethylsulfoxide (3:1; v/v) mixture as porogen. The imprint potassium ion was removed from the polymeric matrix using 0.5 M HNO₃. The scanning electron microscopy (SEM) micrographs showed colloidal nanoparticles of 60–90 nm in diameter and slightly irregular in shape. The obtained ion-imprinted particles for K⁺ showed selective recognition with rapid adsorption and desorption processes. It was found that imprinting results in increased affinity of the material toward K⁺ ion over other competitor metal ions with the same charge and/or close ionic radius. The synthesized IIP nanobeads were shown to be promising for solid-phase extraction coupled with flame photometry for determination of trace K⁺ ion in different water samples.     

Deformation and Mechanical Characteristics of Compacted Binary Mixtures of Plastic (Microcrystalline Cellulose), Elastic (Sodium Starch Glycolate), and Brittle (Lactose Monohydrate) Pharmaceutical Excipients

This work studies the tensile strength, coherence, elastic, and plastic energy of single and bi-component compacted tablets consisting of (i) microcrystalline cellulose (MCC) PH 102 as a plastic material, (ii) (SSG) as an elastic material, and (iii) alpha lactose monohydrate as a brittle material by direct compression. Compacted tablets were studied with various mass ratios formed at an ultimate compaction stress of 150 MPa. The loading and unloading stages of the compaction process for the single and binary tablets were evaluated based on the energies derived from the force-displacement data obtained. The resulting tablet quality was measured in terms of the tensile strength. Material that exhibit predominantly plastic deformation (MCC) shows a dominant property over elastically deforming sodium starch glycolate (SSG) and brittle (lactose) materials during the loading and unloading stages of the compaction process. In conclusion, the tensile strength of the formed tablets depends directly on the plastic energy and indirectly on the elastic energy and is negatively affected by the presence of a brittle material.     

Improved Constructions for Non-adaptive Threshold Group Testing

The basic goal in combinatorial group testing is to identify a set of up to d defective items within a large population of size n?d using a pooling strategy. Namely, the items can be grouped together in pools, and a single measurement would reveal whether there are one or more defectives in the pool. The threshold model is a generalization of this idea where a measurement returns positive if the number of defectives in the pool reaches a fixed threshold u>0, negative if this number is no more than a fixed lower threshold ?<u, and may behave arbitrarily otherwise. We study non-adaptive threshold group testing (in a possibly noisy setting) and show that, for this problem, O(d ^g+2(logd)log(n/d)) measurements (where g:=u???1 and u is any fixed constant) suffice to identify the defectives, and also present almost matching lower bounds. This significantly improves the previously known (non-constructive) upper bound O(d ^u+1log(n/d)). Moreover, we obtain a framework for explicit construction of measurement schemes using lossless condensers. The number of measurements resulting from this scheme is ideally bounded by O(d ^g+3(logd)logn). Using state-of-the-art constructions of lossless condensers, however, we obtain explicit testing schemes with O(d ^g+3(logd)quasipoly(logn)) and O(d ^g+3+β poly(logn)) measurements, for arbitrary constant β>0.     

Enhancing spatial and temporal utilities in differentially private moving objects database release

International Journal of Information Security - The pervasive use of mobile technologies and GPS-equipped vehicles has resulted in a large number of moving objects databases. Privacy protection is...     

GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles

Piles are widely applied to substructures of various infrastructural buildings. Soil has a complex nature; thus, a variety of empirical models have been proposed for the prediction of the bearing capacity of piles. The aim of this study is to propose a novel artificial intelligent approach to predict vertical load capacity of driven piles in cohesionless soils using support vector regression (SVR) optimized by genetic algorithm (GA). To the best of our knowledge, no research has been developed the GA-SVR model to predict vertical load capacity of driven piles in different timescales as of yet, and the novelty of this study is to develop a new hybrid intelligent approach in this field. To investigate the efficacy of GA-SVR model, two other models, i.e., SVR and linear regression models, are also used for a comparative study. According to the obtained results, GA-SVR model clearly outperformed the SVR and linear regression models by achieving less root mean square error (RMSE) and higher coefficient of determination (R²). In other words, GA-SVR with RMSE of 0.017 and R² of 0.980 has higher performance than SVR with RMSE of 0.035 and R² of 0.912, and linear regression model with RMSE of 0.079 and R² of 0.625.

     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.