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 Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.     

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.