Adherence rates to ferric citrate as compared to active control in patients with end stage kidney disease on dialysis

Introduction: Oral phosphate binders are the main stay of treatment of hyperphosphatemia. Adherence rates to ferric citrate, a recently approved phosphate binder, are unknown. Methods: We conducted a post‐hoc analysis to evaluate whether adherence rates were different for ferric citrate vs. active control in 412 subjects with end stage kidney disease (ESKD) who were randomized to ferric citrate vs. active control (sevelamer carbonate and/or calcium acetate). Adherence was defined as percent of actual number of pills taken to total number of pills prescribed. Findings: There were no significant differences in baseline characteristics including gender, race/ethnicity, and age between the ferric citrate and active control groups. Baseline phosphorus, calcium, and parathyroid hormone levels were similar. Mean (SD) adherence was 81.4% (17.4) and 81.7% (15.9) in the ferric citrate and active control groups, respectively (P = 0.88). Adherence remained similar between both groups after adjusting for gender, race/ethnicity, age, cardiovascular disease (CVD), and diabetic nephropathy (mean [95% CI]: 81.4% [78.2, 84.6] and 81.5% [77.7, 85.2] for ferric citrate and active control, respectively). Gender, race/ethnicity, age, and diagnosis of diabetic nephropathy did not influence adherence to the prescribed phosphate binder. Subjects with CVD had lower adherence rates to phosphate binder; this was significant only in the active control group. Discussion: Adherence rates to the phosphate binder, ferric citrate, were similar to adherence rates to active control. Similar adherence rates to ferric citrate are notable since tolerance to active control was an entry criteria and the study was open label. Gender, race/ethnicity, nor age influenced adherence.     

Nanomembrane‐Based,Thermal‐Transport Biosensor for Living Cells

Knowledge of materials' thermal‐transport properties, conductivity and diffusivity, is crucial for several applications within areas of biology, material science and engineering. Specifically, a microsized, flexible, biologically integrated thermal transport sensor is beneficial to a plethora of applications, ranging across plants physiological ecology and thermal imaging and treatment of cancerous cells, to thermal dissipation in flexible semiconductors and thermoelectrics. Living cells pose extra challenges, due to their small volumes and irregular curvilinear shapes. Here a novel approach of simultaneously measuring thermal conductivity and diffusivity of different materials and its applicability to single cells is demonstrated. This technique is based on increasing phonon‐boundary‐scattering rate in nanomembranes, having extremely low flexural rigidities, to induce a considerable spectral dependence of the bandgap‐emission over excitation‐laser intensity. It is demonstrated that once in contact with organic or inorganic materials, the nanomembranes' emission spectrally shift based on the material's thermal diffusivity and conductivity. This NM‐based technique is further applied to differentiate between different types and subtypes of cancer cells, based on their thermal‐transport properties. It is anticipated that this novel technique to enable an efficient single‐cell thermal targeting, allow better modeling of cellular thermal distribution and enable novel diagnostic techniques based on variations of single‐cell thermal‐transport properties.     

Novel method to transform ZnO nanorods into interlaced tattered nanosheets by changing the target inclination angle

ZnO nanoparticles were synthesized by liquid-phase pulse laser ablation of a Zn foil target immersed in deionized water. Nanosecond Q-switched Nd:YAG laser pulses of 532 nm were applied to the Zn foil target at a perpendicular and inclined (θ = 45°) angles. X-ray diffraction analysis revealed that both cases feature a ZnO nanostructure with a hexagonal wurtzite structure and that the particle size increases with the inclined target angle. Field emission scanning electron microscopy results of a colloidal drop cast on a glass substrate showed the ZnO has a nanorod structure in the case of a perpendicular target angle and an interlaced tattered nanosheet structure in the case of an inclined target angle. Photoluminescence spectra showed emission peaks in the UV, violet, blue, and green spectral regions, which correspond to excitonic and various defects resulting in an enhancement of emissions at inclined target angle.     

Linear and nonlinear viscoelastic and viscoplastic analysis of asphalt binders with warm mix asphalt additives

Warm-Mix Asphalt (WMA) is a widely used product, which proved a contribution to the reduction in asphalt mixing and compaction temperatures. This reduction leads to lower fuel consumption and smoke emission in asphalt plants. Most of the characterisation of binders used in WMA has focused in the past on measuring linear viscoelastic properties and associated Superpave parameters. Several studies have shown that the average stresses and strains of the asphalt mixture remain mostly within the linear viscoelastic response. However, localised strains in the binder phase of the mixture could reach values high enough to induce nonlinear viscoelastic and viscoplastic deformations. Therefore, this study focuses on an experimental and analytical evaluation of linear, nonlinear viscoelastic and viscoplastic responses of selected binders modified for use in WMA. The first part of the paper analyses the linear viscoelastic material properties and their ability to evaluate permanent deformation resistance. Then, the non-recoverable creep compliance parameter obtained from the Multiple Stress Creep Recovery (MSCR) test is analysed to assess the nonlinear response and permanent deformation of asphalt binders. The paper utilises a nonlinear plasto-viscoelastic (NPVE) approach to assess and quantify the nonlinear plasto-viscoelastic response of binders by separating the recoverable and irrecoverable strains measured in the MSCR test. Two WMA additives were included in this study by mixing them with polymer-modified and unmodified asphalt binders. Analysis of results showed that the NPVE approach captured a higher percentage of recovery than the NLVE approach. However, binder’s performance evaluation and ranking did not change by adopting the NPVE approach. The nonlinear viscoelastic parameters provided insight on the behaviour of asphalt binders mixed with WMA additives during loading cycles. Sasobit showed higher influence than Advera on binders in resisting permanent deformation by increasing the recoverable strain during the unloading phase.     

Electrical and Mechanical Behavior of Aerosol Jet–Printed Gold on Alumina Substrate for High-Temperature Applications

There is a growing interest in the development of microelectronics that can perform reliably and robustly at temperatures above 300 °C. Such devices require stable thermal properties, low thermal drift, and thermal cycling resistance. Conventional hybrid circuit technology demonstrates high-temperature packages, but the high costs and lead time are significant drawbacks. In contrast, additive manufacturing processes, including aerosol jet printing (AJP), offer cost and time benefits, as well as 3D structures and embedded features. However, the properties and reliability of additive packaging materials at extreme temperatures are not well known. Herein, the reliability at temperatures up to 750 °C in terms of electrical performance and mechanical strength of aerosol jet printed gold thick films onto ceramic substrates are assessed. Thermal coefficient of resistance of printed gold films is measured. The electrical resistance stability and leakage current of printed gold structures are also characterized during over 100 h of aging at temperatures up to 750 °C. Finally, the mechanical adhesion strength of the printed gold films is evaluated after aging for 100 h at temperatures up to 750 °C. The adhesion of the printed gold to the ceramic substrates remains high after aging, very stable resistances and minimal leakage currents have been observed.     

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.     

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System (IFFA-DTLMS). The proposed IFFA-DTLMS model majorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs. To attain this, the presented IFFA-DTLMS model primarily applies densely connected networks (DenseNet121) model to generate a collection of feature vectors. In addition, the firefly algorithm (FFA) is applied for the hyper parameter optimization of DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) model is exploited for the classification and identification of COVID19. For ensuring the enhanced performance of the IFFA-DTLMS model, a wide-ranging experiments were performed and the results are reviewed under distinctive aspects. The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks is skyrocketing due to the rapid advancement of sensors and wireless communication technology. In the upcoming years, it is anticipated that mobile data traffic would rise even more. The development of a new cellular network paradigm is being driven by the Internet of Things, smart homes, and more sophisticated applications with greater data rates and latency requirements. Resources are being used up quickly due to the steady growth of smartphone devices and multimedia apps. Computation offloading to either several distant clouds or close mobile devices has consistently improved the performance of mobile devices. The computation latency can also be decreased by offloading computing duties to edge servers with a specific level of computing power. Device-to-device (D2D) collaboration can assist in processing small-scale activities that are time-sensitive in order to further reduce task delays. The task offloading performance is drastically reduced due to the variation of different performance capabilities of edge nodes. Therefore, this paper addressed this problem and proposed a new method for D2D communication. In this method, the time delay is reduced by enabling the edge nodes to exchange data samples. Simulation results show that the proposed algorithm has better performance than traditional algorithm.