Synthesis and characterization of a new magnetic composite MnFe2O4/clay based on a natural clay obtained from Turkestan deposit

The work is devoted to the development of a new method for the synthesis of magnetic composites based on manganese ferrite on a natural clay, coupling with their physico-chemical characterization. In the study, a natural clay of Kazakhstan obtained from the Turkestan deposit was used for the preparation of magnetic composites. The formation of materials with magnetic properties is an urgent task of our time, due to the needs of various applications of magnetically controlled materials for biomedical systems, electronic devices, catalytic and adsorption processes. The advantage of such materials is the ability to control them using a magnetic field for shaking, recovery, induction heating, among others. In this work, samples were prepared by co-precipitation of manganese and iron salts with 5 mol L-1 NaOH over the Turkestan clay (TC). Materials were characterized by various analyses, such as Fourier-Transform infrared spectroscopy (FTIR), X-ray diffractometric analysis (XRD), and elemental analysis. According to the results of physical and chemical studies of the XRD and thermal analysis, kaolinite is the main mineral in the composition of TC. Magnetic adsorbents MnFe2O4/clay with perfect magnetic separation characteristics were successfully obtained by chemical co-precipitation

A Neural N-Gram-Based Classifier for Chinese Clinical Named Entity Recognition

Clinical Named Entity Recognition (CNER) focuses on locating named entities in electronic medical records (EMRs) and the obtained results play an important role in the development of intelligent biomedical systems. In addition to the research in alphabetic languages, the study of non-alphabetic languages has attracted considerable attention as well. In this paper, a neural model is proposed to address the extraction of entities from EMRs written in Chinese. To avoid erroneous noise being caused by the Chinese word segmentation, we employ the character embeddings as the only feature without extra resources. In our model, concatenated n-gram character embeddings are used to represent the context semantics. The self-attention mechanism is then applied to model long-range dependencies of embeddings. The concatenation of the new representations obtained by the attention module is taken as the input to bidirectional long short-term memory (BiLSTM), followed by a conditional random field (CRF) layer to extract entities. The empirical study is conducted on the CCKS-2017 Shared Task 2 dataset to evaluate our method and the experimental results show that our model outperforms other approaches.

Numerical Simulation of Convective-Radiative Heat Transfer

Heat transfer including heat conduction, thermal convection, and thermal radiation is a major transport process that occurs in various engineering and natural systems such as heat exchangers, solar collectors, nuclear reactors, atmospheric boundary layers, electronical and biomedical systems, and others. [...]

Graphene-Based Nanocomposites: Synthesis, Mechanical Properties, and Characterizations

Graphene-based nanocomposites possess excellent mechanical, electrical, thermal, optical, and chemical properties. These materials have potential applications in high-performance transistors, biomedical systems, sensors, and solar cells. This paper presents a critical review of the recent developments in graphene-based nanocomposite research, exploring synthesis methods, characterizations, mechanical properties, and thermal properties. Emphasis is placed on characterization techniques and mechanical properties with detailed examples from recent literature. The importance of characterization techniques including Raman spectroscopy, X-ray diffraction (XRD), atomic force microscopy (AFM), scanning electron microscopy (SEM), and high-resolution transmission electron microscopy (HRTEM) for the characterization of graphene flakes and their composites were thoroughly discussed. Finally, the effect of graphene even at very low loadings on the mechanical properties of the composite matrix was extensively reviewed.

Novel Reconfigurable Hardware Systems for Tumor Growth Prediction

An emerging trend in biomedical systems research is the development of models that take full advantage of the increasing available computational power to manage and analyze new biological data as well as to model complex biological processes. Such biomedical models require significant computational resources, since they process and analyze large amounts of data, such as medical image sequences. We present a family of advanced computational models for the prediction of the spatio-temporal evolution of glioma and their novel implementation in state-of-the-art FPGA devices. Glioma is a rapidly evolving type of brain cancer, well known for its aggressive and diffusive behavior. The developed system simulates the glioma tumor growth in the brain tissue, which consists of different anatomic structures, by utilizing MRI slices. The presented models have been proved highly accurate in predicting the growth of the tumor, whereas the developed innovative hardware system, when implemented on a low-end, low-cost FPGA, is up to 85% faster than a high-end server consisting of 20 physical cores (and 40 virtual ones) and more than 28× more energy-efficient than it; the energy efficiency grows up to 50× and the speedup up to 14× if the presented designs are implemented in a high-end FPGA. Moreover, the proposed reconfigurable system, when implemented in a large FPGA, is significantly faster than a high-end GPU (i.e., from 80% and up to 250% faster), for the majority of the models, while it is also significantly better (i.e., from 80% to over 1,600%) in terms of power efficiency, for all the implemented models.

A Framework for In Silico Clinical Trials for Medical Devices Using Concepts From Model Verification, Validation, and Uncertainty Quantification (VVUQ)

The rising costs of clinical trials for medical devices in recent years has led to an increased interest in so-called in silico clinical trials, where simulation results are used to supplement or to replace those obtained from human patients. Here we present a framework for executing such a trial. This framework relies heavily on ideas already developed for model verification, validation, and uncertainty quantification. The framework uses results from an initial cohort of human patients as model validation data, recognizing that the best model credibility evidence usually comes from real patients. The validation exercise leads to an assessment of the model’s suitability based on pre-defined acceptance criteria. If the model meets these criteria, then no additional human patients are required and the study endpoints that can be addressed using the model are met using the simulation results. Conversely, if the model is found to be inadequate, it is abandoned, and the clinical study continues using only human patients in a second cohort. Compared to other frameworks described in the literature based on Bayesian methods, this approach follows a strict model build-validate-predict structure. It can handle epistemic uncertainties in the model inputs, which is a common trait of models of biomedical systems. Another idea discussed here is that the outputs of engineering models rarely coincide with measures that are the basis for clinical endpoints. This manuscript discusses how the link between the model and clinical measure can be established during the trial.