Highly Bendable and Rotational Textile Structure with Prestrained Conductive Sewing Pattern for Human Joint Monitoring

Human joints have respective ranges of motion and joint forces corresponding to each kind of joint; this necessitates considerations of the characteristics of human joints to fabricate wearable strain sensors conformable to the human body, and capable of precisely monitoring complex motions of the human body. In the present study, the “all textile‐based highly stretchable structure” that is capable of precisely sensing motions (folding and rotation) of the human joints (finger, wrist, elbow, spine, and knee) is fabricated by optimizing patterns (straight, blind, and zigzag) of conductive yarns employed as the conductive part of the strain sensor, and several textile substrates (braided elastic fabric, knit fabric, and woven fabric), having preferable elasticity and conformability employed for the fabrication of strain sensors suitable for human joints. In particular, the technology, enabling the prestraining of textile substrate, is exploited to fabricate a strain sensor that is capable of outputting selective signals corresponding to the folding motion of the spinal joint over a predetermined angle of motion, and the gait pattern of the wearer of the sensor, attached to his or her knee joint doing folding and rotational motions, is analyzed.     

The relationship between oil prices and US economy revisited

The authors revisit the relationship between US economic growth and crude oil prices considering Industrial Production Index and West Texas Intermediate crude oil spot prices as respective proxies for a period spanning over January 1986 to June 2017. To capture the asymmetric and time-varying relationship, the authors employ maximum overlap discrete wavelet transform (MODWT)-based quantile regression (QR) analysis. Interestingly, MODWT-based QR analysis provides evidence of supply-driven link between crude oil prices and economic growth in the short run. However, in the medium to long run a demand-driven link is dominant. In addition, the QR results without MODWT also advocate a demand-driven link. Overall, the result of this study adds a new dimension to the literature on the relationship between crude oil prices and economic growth by focusing upon the time-frequency varying business cycle fluctuations.     

An Octagonal Shaped Monopole Antenna for UWB Applications with Band Notch Characteristics

Wireless Personal Communications - This article presents a microstrip-fed octagonal shaped monopole antenna with dual band notched characteristics for UWB applications. Partial ground plane has...     

Rumour detection using deep learning and filter-wrapper feature selection in benchmark twitter dataset

Multimedia Tools and Applications - Microblogs have become a customary news media source in recent times. But as synthetic text or ‘readfakes’ scale up the online disinformation...     

Nyctanthes arbor‐tristis leaves extract as Antagonizer of immunotoxic effects of chemical pesticides (experimental study)

Increased incidence of immunosuppression due to diseases like AIDS, drug resistant tuberculosis, malaria or exposure to chemicals, calls for general immunopotentiators or anti immunosuppressents. In our previous work while screening the chemical and biopesticides for their immunopotentiating activity, aqueous leaves extract of bio‐pesticidal plant Nyctanthes arbor‐tristis [HL(aq)] was found to be a potent immuno‐modulator. The present study was conducted to evaluate the immunorestorative or anti immunosuppressive activity of the N. arbor‐tristis leaves aqueous extract in the malathion exposed immunosuppressed mice. The immunorestorative potential of HL(aq) was evaluated by studying various immunological parameters (humoral, cell mediated immune, numerical values of immunocytes and functions of phagocytes) in HL(aq) treated or untreated malathion‐exposed mice. The results revealed that the immunological parameters which were suppressed with malathion either reverted back to normal or showed a trend towards normalacy, when treated with HL(aq) extract.     

Ionization of high Rydberg states of calcium by electric fields

Abstract

Measurements of electric field ionization of high Rydberg states populated by two-photon excitation are reported. We use a novel technique, in which the extraneous ionization background due to other causes is suppressed, resulting in higher accuracy and sensitivity than otherwise achievable. The method is applied to a series in the calcium spectrum which exhibits an interesting anomaly around n = 63 in previous experiments by laser spectroscopy using thermionic diode detection. In the present experiments, collisions are highly improbable, and it is demonstrated that electric field ionization then occurs without any detectable anomaly around n = 63. The implications of this for the interpretation of the effect are discussed.     

An optimization approach to estimate and calibrate column water vapour for hyperspectral airborne data

The article describes a novel approach to estimate and calibrate column water vapour (CWV), a key parameter for atmospheric correction of remote-sensing data. CWV is spatially and temporally variable, and image-based methods are used for its inference. This inference, however, is affected by methodological and numeric limitations, which likely propagate to reflectance estimates. In this article, a method is proposed to estimate CWV iteratively from target surface reflectances. The method is free from assumptions for at sensor radiance-based CWV estimation methods. We consider two cases: (a) CWV is incorrectly estimated in a processing chain and (b) CWV is not estimated in a processing chain. To solve (a) we use the incorrect estimations as initial values to the proposed method during calibration. In (b), CWV is estimated without initial information. Next, we combined the two scenarios, resulting in a generic method to calibrate and estimate CWV. We utilized the hyperspectral mapper (HyMap) and airborne prism experiment (APEX) instruments for the synthetic and real data experiments, respectively. Noise levels were added to the synthetic data to simulate real imaging conditions. The real data used in this research are cloud-free scenes acquired from the airborne campaigns. For performance assessment, we compared the proposed method with two state-of-the-art methods. Our method performed better as it minimizes the absolute error close to zero, only within 8–10 iterations. It thus suits existing operational chains where the number of iterations is considerable. Finally, the method is simple to implement and can be extended to address other atmospheric trace gases.     

DFuzzy: a deep learning-based fuzzy clustering model for large graphs

Graph clustering is successfully applied in various applications for finding similar patterns. Recently, deep learning- based autoencoder has been used efficiently for detecting disjoint clusters. However, in real-world graphs, vertices may belong to multiple clusters. Thus, it is obligatory to analyze the membership of vertices toward clusters. Furthermore, existing approaches are centralized and are inefficient in handling large graphs. In this paper, a deep learning-based model ‘DFuzzy’ is proposed for finding fuzzy clusters from large graphs in distributed environment. It performs clustering in three phases. In first phase, pre-training is performed by initializing the candidate cluster centers. Then, fine tuning is performed to learn the latent representations by mining the local information and capturing the structure using PageRank. Further, modularity is used to redefine clusters. In last phase, reconstruction error is minimized and final cluster centers are updated. Experiments are performed over real-life graph data, and the performance of DFuzzy is compared with four state-of-the-art clustering algorithms. Results show that DFuzzy scales up linearly to handle large graphs and produces better quality of clusters when compared to state-of-the-art clustering algorithms. It is also observed that deep structures can help in getting better graph representations and provide improved clustering performance.     

Development and analysis of a tool for speed up of EnergyPlus through parallelization

This paper describes how the EPparallel tool splits a single annual simulation into 12 simulations of one month each and runs them in parallel. The paper also describes the methodology to prepare input files, enable file sharing between nodes, collate results generated by the nodes, and ensure quality check on the simulations. The EPparallel tool uses Message Passing Interface library and runs on Linux. The tool has been tested on 16 commercial reference buildings over 16 US weather files. The results of these 256 runs which include the run times, computing time overheads, speed gains and accuracy of results are presented in this paper. The speed gain ranged from 2.9×to 7.8×and deviation (percentage of output values obtained in parallel simulation which were off by more than 1% as compared to output values obtained in annual simulation) ranged from 0% to 4%.     

Grain Size and Texture of Cu<Subscript>2</Subscript>ZnSnS<Subscript>4</Subscript> Thin Films Synthesized by Cosputtering Binary Sulfides and Annealing: Effects of Processing Conditions and Sodium

We investigate the synthesis of kesterite Cu₂ZnSnS₄ (CZTS) polycrystalline thin films using cosputtering from binary sulfide targets followed by annealing in sulfur vapor at 500°C to 650°C. The films are the kesterite CZTS phase as indicated by x-ray diffraction, Raman scattering, and optical absorption measurements. The films exhibit (112) fiber texture and preferred low-angle and Σ3 grain boundary populations which have been demonstrated to reduce recombination in Cu(In,Ga)Se₂ and CdTe films. The grain growth kinetics are investigated as functions of temperature and the addition of Na. Significantly, lateral grain sizes above 1 μm are demonstrated for samples grown on Na-free glass, demonstrating the feasibility for CZTS growth on substrates other than soda lime glass.