A new nanostructured material based on fluorophosphate incorporated into a zinc–aluminium layered double hydroxide by ion exchange

Layered double hydroxides (LDHs), also called anionic clays, consist of cationic brucite-like layers and exchangeable interlayer anions. These hydrotalcite-like compounds, with Zn and Al in the layers and chloride in the interlayer space, were prepared following the coprecipitation method at constant pH. The effect of pH, aging time and anion concentration on the intercalation of fluorophosphate \((\hbox {PO}_{3}\hbox {F}^{2-}\), FP) in the [Zn–Al] LDH was investigated. The best crystalline material, with high exchange extent, was obtained by carrying out the exchange at 25\({^{\circ }}\hbox {C}\) in a 0.03 M FP solution at pH 7 with at least 42 h of aging time. A mechanism for the FP intercalation was confirmed by X-ray diffraction, infrared spectroscopy and thermogravimetry (TG) analyses (TG and DTG curves).     

Development of novel amisulpride-loaded solid self-nanoemulsifying tablets: preparation and pharmacokinetic evaluation in rabbits

Objective: The current investigation is focused on the formulation and in vivo evaluation of optimized solid self-nanoemulsifying drug delivery systems (S-SNEDDS) of amisulpride (AMS) for improving its oral dissolution and bioavailability.

Methods: Liquid SNEDDS (L-SNEDDS) composed of Capryol? 90 (oil), Cremophor^® RH40 (surfactant), and Transcutol^® HP (co-surfactant) were transformed to solid systems via physical adsorption onto magnesium aluminometasilicate (Neusilin US2). Micromeretic studies and solid-state characterization of formulated S-SNEDDS were carried out, followed by tableting, tablet evaluation, and pharmacokinetic studies in rabbits.

Results: Micromeretic properties and solid-state characterization proved satisfactory flow properties with AMS present in a completely amorphous state. Formulated self-nanoemulsifying tablets revealed significant improvement in AMS dissolution compared with either directly compressed or commercial AMS tablets. In vivo pharmacokinetic study in rabbits emphasized significant improvements in t_max, AUC_(0–12), and AUC_(0–∞) at p?<?.05 with 1.26-folds improvement in relative bioavailability from the optimized self-nanoemulsifying tablets compared with the commercial product.

Conclusions: S-SNEDDS can be a very useful approach for providing patient acceptable dosage forms with improved oral dissolution and biovailability.     

Enhanced the structure and optical properties for ZnO/PVP nanofibers fabricated via electrospinning technique

Zinc oxide/polyvinylpyrrolidone (ZnO/PVP) nanocomposite fibers with enhanced structural, morphological and optical properties were purposefully tailored using electrospinning technique. Meanwhile, ZnO nanoparticles (NPs),with particle size of ~50 nm, were synthesized using a co-precipitation method. The nanocomposite fibers were prepared by an electrospun solution of PVP containing ZnO NPs of 2, 4, 6 and 8 wt%. Evidently, the morphological, thermal and optical properties of the ZnO/PVP nanocomposite fibers were enhanced by dispersing ZnO NPs into PVP fibers. Typically, controlling the ZnO NPs content and their dispersibility (0–8 wt%) into PVP fibers result in improved the thermal stability (an increase of onset decomposition temperature by ~120 °C above pure PVP fibers) as well as the UV–Vis protection (reduction in UV transmission by 70%) and the photoluminescence properties (a sharp UV emission around 380 nm) Overall, based on the enhanced properties, the PVP/ZnO nanocomposite fibers can be considered a promise material in optoelectronic sensors and UV photoconductor.     

Lubricant Oils as a Certified Reference Material for Cleveland Open Cup Flash Point Testers

The flash point is an important indicator for the flammability of the liquid materials and also in the development of safe practices for handling and storage of these materials. The production of certified reference materials is essential to guarantee the performance of the flash point measurement apparatus so that it can be trusted and acceptable for its intended use. In this work lubricant oils of high molecular weight hydrocarbons (HM1, and HM2) were tested as certified reference materials in accordance with ISO guide 34 and 35 by using the high flash point temperature detector Cleveland-open cup as per ASTM D-92. The selected oils were tested for uncertified properties like pour point, viscosity, cloud point, density, and total acid number. The thermal analysis techniques DSC, and TGA were used to ensure the thermal stability of the lubricant oils and its ability to be used as high temperature flash point reference material. The certified value of the flash point temperature was assigned upon evaluation of the data acquired in an inter-laboratory comparison involving expert laboratories using the same measurement method. The certified values of the candidate reference materials with expanded uncertainty (coverage factor K = 2, approximate 95% confidence level) calculated using the results of the characterization, calibration (organizer lab), homogeneity, and stability assessment were 232 ± 9 °C for HM1, and 242 ± 10 °C for HM2.     

Nature or Science: what Google Trends says

Scientometrics - Nature and Science are two major multidisciplinary journals, well-known among the general public and highly-cited by scholarly communities. This article presents Google Trends, a...     

A residual a posteriori error estimate for partition of unity finite elements for three-dimensional transient heat diffusion problems using multiple global enrichment functions

In this article, a study of residual based a posteriori error estimation is presented for the partition of unity finite element method (PUFEM) for three-dimensional (3D) transient heat diffusion problems. The proposed error estimate is independent of the heuristically selected enrichment functions and provides a useful and reliable upper bound for the discretization errors of the PUFEM solutions. Numerical results show that the presented error estimate efficiently captures the effect of h-refinement and q-refinement on the performance of PUFEM solutions. It also efficiently reflects the effect of ill-conditioning of the stiffness matrix that is typically experienced in the partition of unity based finite element methods. For a problem with a known exact solution, the error estimate is shown to capture the same solution trends as obtained by the classical L₂ norm error. For problems with no known analytical solutions, the proposed estimate is shown to be used as a reliable and efficient tool to predict the numerical errors in the PUFEM solutions of 3D transient heat diffusion problems.     

Recent Progress on Microfine Design of Metal–Organic Frameworks: Structure Regulation and Gas Sorption and Separation

Metal–organic frameworks (MOFs) have emerged as an important and unique class of functional crystalline hybrid porous materials in the past two decades. Due to their modular structures and adjustable pore system, such distinctive materials have exhibited remarkable prospects in key applications pertaining to adsorption such as gas storage, gas and liquid separations, and trace impurity removal. Evidently, gaining a better understanding of the structure–property relationship offers great potential for the enhancement of a given associated MOF property either by structural adjustments via isoreticular chemistry or by the design and construction of new MOF structures via the practice of reticular chemistry. Correspondingly, the application of isoreticular chemistry paves the way for the microfine design and structure regulation of presented MOFs. Explicitly, the microfine tuning is mainly based on known MOF platforms, focusing on the modification and/or functionalization of a precise part of the MOF structure or pore system, thus providing an effective approach to produce richer pore systems with enhanced performances from a limited number of MOF platforms. Here, the latest progress in this field is highlighted by emphasizing the differences and connections between various methods. Finally, the challenges together with prospects are also discussed.     

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%–83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.     

Survey of Robotics in Education,Taxonomy, Applications,and Platforms during COVID-19

The coronavirus disease 2019 (COVID-19) is characterized as a disease caused by a novel coronavirus known as severe acute respiratory coronavirus syndrome 2 (SARS-CoV-2; formerly known as 2019-nCoV). In December 2019, COVID-19 began to appear in a few countries. By the beginning of 2020, it had spread to most countries across the world. This is when education challenges began to arise. The COVID-19 crisis led to the closure of thousands of schools and universities all over the world. Such a situation requires reliance on e-learning and robotics education for students to continue their studies to avoid the mingling between people and students. In relation to this alternative learning solution, the present study was conducted. A systematic literature review on educational robotics (ER) keywords between 2015–2020 was carried out for the purpose to review a total of 2253 articles from the selected sources; Scopus (452), Taylor & Francis (311), Science Direct (427), IEEE Xplore (221), and Web of Science (842). This review procedure was labelled as Taxonomy 1. After filtering Taxonomy 1, it was found that 98 scientific articles formed the so-called Taxonomy II that was categorized into six categories: (i) Robotics concepts, (ii) Device, (iii) Robotic applications, (iv) Manufacturing robots, (v) Robotics analysis, and (vi) Education/taxonomy. For this study, only 35 articles in this specific field were selected, of which were then assigned into three categories: (i) Application, (ii) Platform, and (iii) Educational. The results show that the application category carries 17.4%, platform 20%, and education 22.85%. This study serves as the application platform to help students, academics, and researchers.     

A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

Data fusion is one of the challenging issues, the healthcare sector is facing in the recent years. Proper diagnosis from digital imagery and treatment are deemed to be the right solution. Intracerebral Haemorrhage (ICH), a condition characterized by injury of blood vessels in brain tissues, is one of the important reasons for stroke. Images generated by X-rays and Computed Tomography (CT) are widely used for estimating the size and location of hemorrhages. Radiologists use manual planimetry, a time-consuming process for segmenting CT scan images. Deep Learning (DL) is the most preferred method to increase the efficiency of diagnosing ICH. In this paper, the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning (DL) model, abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification, also known as FFEDL-ICH. The proposed FFEDL-ICH model has four stages namely, preprocessing, image segmentation, feature extraction, and classification. The input image is first preprocessed using the Gaussian Filtering (GF) technique to remove noise. Secondly, the Density-based Fuzzy C-Means (DFCM) algorithm is used to segment the images. Furthermore, the Fusion-based Feature Extraction model is implemented with handcrafted feature (Local Binary Patterns) and deep features (Residual Network-152) to extract useful features. Finally, Deep Neural Network (DNN) is implemented as a classification technique to differentiate multiple classes of ICH. The researchers, in the current study, used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance. The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance. For future researches, the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.