Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive effects of sowing date and nitrogen fertilizer on water and nitrogen use efficiency in millet cultivars under drought stress

Abstract This study was conducted to investigate the effects of two irrigation regimes (55 and 85% of soil available water depletion) and two nitrogen levels (0 and 112.5 kg ha−1) on yield as well as water and nitrogen use efficiencies of two millet cultivars (namely, Bastan and Pishahang) planted at two sowing dates during 2015 and 2016. Growth parameters and nitrogen use of the millet cultivars were found affected by drought, nitrogen level, and sowing date as well as their interactions. Nitrogen application was found to improve not only nitrogen uptake, chlorophyll content, and irrigation water use efficiency (IWUE) but also grain and biological yields; more positive effects were, however, observed under the control irrigation regime. Nevertheless, nitrogen uptake (NUpE), utilization (NUtE), and use (NUE) efficiencies decreased as a result of nitrogen application. The increases in grain yield (16 versus 7%) and IWUEg (17 versus 6%) due to nitrogen application were more pronounced in the drought-tolerant Bastan cultivar than in the drought-sensitive Pishahang. However, nitrogen application decreased NUpE in both cultivars. Although water stress increased soil N residual with all the sowing dates, the increase was greater with the early ones. The results of the experiment suggest that the application of higher nitrogen levels is not the proper strategy for compensating for the reduced yield under drought conditions. Rather, late sowing dates, due to the lower drought effects associated, might be the proper alternative for achieving higher yields as well as improved IWUE and NUE in areas plagued with water shortage.
Simulation-driven machine learning for robotics and automation

Abstract Mass personalization—a megatrend in industrial manufacturing and production—requires fast adaptations of robotics and automation solutions to continually decreasing lot sizes. In this paper, the challenges of applying robot-based automation in a highly individualized production are highlighted. To face these challenges, a framework is proposed that combines latest machine learning (ML) techniques, like deep learning, with high-end physics simulation environments. ML is used for programming and parameterizing machines for a given production task with minimal human intervention. If the simulation environment realistically captures physical properties like forces or elasticity of the real world, it provides a high-quality data source for ML. In doing so, new tasks are mastered in simulation faster than in real-time, while at the same time existing tasks are executed. The functionality of the simulation-driven ML framework is demonstrated on an industrial use case.
Motion Artifact Removal in Ambulatory ECG Signal for Heart Rate Variability Analysis

This work presents an efficient method for motion artifact removal from ambulatory electrocardiogram (ECG) signal for heart rate variability (HRV) in wearable/portable healthcare devices. HRV is the fluctuation in the time interval between the adjacent heartbeats. Motion artifacts affect HRV analysis by creating some outliers. A two-phase method using stationary wavelet transform with level thresholding (SWT-LT) is used to remove motion artifact from the ECG signal. Multi-channel system prototype is used for ambulatory ECG signal recording which is developed using commercial integrated circuit components. Motion artifact affected ECG signals are recorded by emulating daily activity movements. Recorded ECG database (60 signals) and Motion Artifact Contaminated ECG Database (27 signals) are used for validation of the proposed SWT-LT method. Implemented results show that the proposed SWT-LT method removes various in-band motion artifacts efficiently with an average correlation coefficient of 0.9337 and an average normalized mean square error of 0.012 which are better than the other reported methods. The proposed method has shown improvement in features of HRV analysis by removing outliers due to motion artifact from the ECG signal which is verified using MATLAB app HRVTool 1.03 developed by Marcus Vollmer.
Photon mapping in image-based visual comfort assessments with BSDF models of high resolution

Abstract Data-driven models replicate the irregular Bidirectional Scattering Distribution Functions (BSDFs) of optically Complex Fenestration Systems in daylight simulation. RADIANCE employs the tensor tree to store the BSDF at high directional resolution. Its application in backward ray-tracing is however challenging, since the density of stochastic samples must match the model resolution. BSDF proxy and peak extraction address this problem, but are limited to cases when either the fenestration geometry, or the shape and direction of the transmission peak are known. Photon Mapping is proposed to efficiently sample arbitrary BSDFs from the known sun direction. The existing implementation in RADIANCE is extended to account for light sources and their reflections in the field of view, that are of particular importance for visual comfort assessments. The method achieves a high degree of accordance with ray-tracing, and reduces simulation times by ≈95% with data-driven models of high resolution. Abbreviations: BRT: Backward Ray-Tracing; BSDF: Bidirectional Scattering Distribution Function; CBDM: Climate-Based Daylight Modelling; CFS: Complex Fenestration System; DGI: Daylight Glare Index; DGP: Daylight Glare Probability; FPM: Five Phase Method; GPGPU: General Purpose Graphics Processing Unit; LCP: Laser Cut Panel; OoC: Out-of-Core; PM: Photon Mapping; RMSE: Root Mean Squared Error
A Wide-PCE-Dynamic-Range CMOS Cross-Coupled Differential-Drive Rectifier for Ambient RF Energy Harvesting

This Brief reports a cross-coupled differential-drive (CCDD) rectifier with a wide dynamic range (DR) in its power conversion efficiency (PCE). Specifically, it features a self-body-biasing technique to reduce the forward V<sub>th</sub>, along with the diode-connected MOSFETs to stem the reverse-leakage current while improving the forward current. The merits of our CCDD rectifier are exemplified via two input-capacitor-coupling configurations: <italic>shared-capacitor coupling (SCC)</italic> and <italic>individual-capacitor coupling (ICC)</italic>. Fabricated in 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS, the SCC-CCDD and ICC-CCDD measure a DR of 13 and 14.5 dB, respectively, for a PCE >40%. Alternatively, the SCC-CCDD exhibits a peak PCE of 83.7% with a sensitivity of −19.2 dBm at 1 V, whereas the ICC-CCDD shows a peak PCE of 80.3% with a sensitivity of −18.7 dBm.
Development of S-Nitroso-N-Acetylpenicillamine Impregnated Medical Grade Polyvinyl Chloride for Antimicrobial Medical Device Interfaces.

In the clinical setting, polyvinyl chloride (PVC) accounts for 25% of all polymers used in medical device applications. However, medical devices fabricated with plasticized PVC, such as endotracheal tubes, extracorporeal circuits (ECCs), or intravenous catheters, can lead to thrombosis and infection complications. Mortality associated with hospital associated infections (HAIs) exceed 100,000 deaths each year. One method to overcome these challenges is to develop bioactive polymers with nitric oxide (NO) release. Nitric oxide exhibits many physiological roles including antibacterial, antithrombic, and anti-inflammatory activity. In this study, plasticized Tygon PVC tubing was impregnated with a NO donor molecule, S-nitroso-N-acetylpenicillamine (SNAP), via a simple solvent-swelling-impregnation method, where polymer samples were submerged in a SNAP impregnation solvent (methanol, acetone, plasticizer), rinsed, and dried. An additional topcoat of a biocompatible CarboSil 2080A (CB) was applied to reduce SNAP leaching. The SNAP-PVC-CB was characterized for NO release using chemiluminescence, leaching with UV-vis spectroscopy, surface characterization with scanning electron microscopy, tensile strength analysis, stability during storage and sterilization, and antimicrobial properties in vitro. The SNAP-PVC-CB exhibited an NO flux of 4.29 ± 0.80 × 10-10 mol cm-2 min-1 over the initial 24 h under physiological conditions and continued to release physiological levels of NO for up to 14 d (incubated in PBS buffer at 37 °C). The addition of the CB topcoat reduced the total SNAP leaching by 60% during incubation. Mechanical properties and surface topography remained similar to the original PVC after SNAP impregnation and application of the CB topcoat. After ethylene oxide sterilization and 1 month of storage, the SNAP-PVC-CB demonstrated excellent SNAP stability (ca. 90% SNAP remaining). In a 24 h antibacterial assay, SNAP-PVC reduced viable bacteria colonization (ca. 1 log reduction) of S. aureus and E. coli compared to PVC controls. This simple method for SNAP impregnation of medical grade plasticized PVC holds great potential for improving the biocompatibility of postfabricated, plasticized PVC medical devices.
A new nonparametric double exponentially weighted moving average control chart

There are many practical situations where the underlying distribution of the quality characteristic either deviates from normality or it is unknown. In such cases, practitioners often make use of the nonparametric control charts. In this paper, a new nonparametric double exponentially weighted moving average control chart on the basis of the signed‐rank statistic is proposed for monitoring the process location. Monte Carlo simulations are carried out to obtain the run length characteristics of the proposed chart. The performance comparison of the proposed chart with the existing parametric and nonparametric control charts is made by using various performance metrics of the run length distribution. The comparison showed the superiority of the suggested chart over its existing parametric and nonparametric counterparts. An illustrative example for the practical implementation of the proposed chart is also provided by using the industrial data set.
HEARTS - The Bath Spa experience

Higher Education the Arts and Schools (HEARTS) is a project established in 2004 to address a perceived deficit in training primary teachers to teach arts subjects. Based on surveys of Higher Education Institutions (HEIs) in England (Rogers 1998, 2003) offering primary initial teacher education (ITE) – revealing that the time allocated to the arts during one year programmes varied between four and 12 hours – the Esmee Fairbairn Trust and Gulbenkian Foundation offered funding for a series of pilots to demonstrate and stimulate innovation in this area of the primary training curriculum. Administered by the National Foundation for Educational Research (NFER), three HEIs were funded in 2004-5, a further three in 2005-6 and a final cohort of six in 20078, including Bath Spa University.
Experimental Research on Combustion Characteristics and NOx Emission of Three Kinds of Solid Fuels Preheated by a Self-Preheating Burner

As a novel technology, coal preheating technology has been proved to be effective on NOx reduction by previous experiments. However, the role of some components of the fuel is still not clear in the preheating process and combustion process. To clarify the effects of volatile and carbon on NOx formation and reduction, three fuels with different volatile contents were used, and the experiments were launched in a bench-scale coal preheating combustion test apparatus. Results showed that the self-preheating burner was widely adaptable to the fuels and the fuels could be stably preheated to be above 860 °C. The coal gas mainly containing the combustible components of CO, H2, and CH4 was generated in the self-preheating burner, accompanying with the high-temperature coal char. Due to the high contents of carbon and volatiles, more combustible components in coal gas could be yielded by the bituminous coal after being preheated. Most of the volatile matters and carbon were released into the coal gas, and the vol.
Realization and Highly Precise Measurement of 50kW HEECS Chopper with 99.5% Efficiency

This paper studies extremely high-precision efficiency measurement by building a back-to-back (BTB) system with a high-power DC-DC converter in order to realize a 100 kW high efficiency energy conversion system (HEECS) chopper with 99.5% efficiency. The newly proposed water-cooled 2-phase HEECS BTB chopper system achieves a reduction in input power and enables highly precise measurement of a high-power DC-DC converter with an extremely high efficiency of 99.5%.
Weight-Constrained Neural Networks in Forecasting Tourist Volumes: A Case Study

Tourism forecasting is a significant tool/attribute in tourist industry in order to provide for careful planning and management of tourism resources. Although accurate tourist volume prediction is a very challenging task, reliable and precise predictions offer the opportunity of gaining major profits. Thus, the development and implementation of more sophisticated and advanced machine learning algorithms can be beneficial for the tourism forecasting industry. In this work, we explore the prediction performance of Weight Constrained Neural Networks (WCNNs) for forecasting tourist arrivals in Greece. WCNNs constitute a new machine learning prediction model that is characterized by the application of box-constraints on the weights of the network. Our experimental results indicate that WCNNs outperform classical neural networks and the state-of-the-art regression models: support vector regression, k-nearest neighbor regression, radial basis function neural network, M5 decision tree and Gaussian processes.
Facile Electrodeposition of Poly(3,4-ethylenedioxythiophene) on Poly(vinyl alcohol) Nanofibers as the Positive Electrode for High-Performance Asymmetric Supercapacitor

Poly(vinyl alcohol)/poly(3,4-ethylenedioxythiophene) (PVA/PEDOT) nanofibers were synthesized as a positive electrode for high-performance asymmetric supercapacitor (ASC). PVA/PEDOT nanofibers were prepared through electrospinning and electrodeposition meanwhile reduced graphene oxide (rGO) was obtained by electrochemical reduction. The PVA/PEDOT nanofibers demonstrated cauliflower-like morphology showing that PEDOT was uniformly coated on the smooth cross-linking structure of PVA nanofibers. In addition, the ASC showed a remarkable energy output efficiency by delivering specific energy of 21.45 Wh·kg−1 at a specific power of 335.50 W·kg−1 with good cyclability performance (83% capacitance retained) after 5000 CV cycles. The outstanding supercapacitive performance is contributed from the synergistic effects of both PVA/PEDOT//rGO, which gives promising materials for designing high-performance supercapacitor applications.
Discrete Power Semiconductor Losses Versus Junction Temperature Estimation Based on Thermal Impedance Curves

Loss estimation in power semiconductor components is an important research topic for a long time with various methods that have been proposed in the literature. Although these methods have been implemented and allow estimating the losses with satisfactory results, the junction temperature of the component is generally controlled poorly while it influences the losses. This article presents a dynamic calorimetric method used to estimate the losses in power semiconductor devices, taking into account their evolution as a function of the junction temperature. The proposed method is based on the temperature measurement in a metal block fixed under the component. Through a thermal model based on the notion of thermal impedance, the transient temperature measurement in the block allows determining the losses and the junction temperature of the device. In order to size the test bench and determine the robustness of this method with respect to measurement or implementation uncertainties (heat transfer coefficients and thermal conductivity of materials), thermal simulations were performed using the Flotherm software. Due to the presence of noise in the measurements, post signal processing is also necessary. By applying the chosen setting, a corresponding experiment was conducted to verify the proposed method. This experiment showed promising results for both loss and junction temperature determination.
Remote Thermodynamic Impact of the Kuroshio Current on a Developing Tropical Cyclone Over the Western North Pacific in Boreal Fall

To examine the remote linkage between tropical cyclone (TC) development and moisture transport from the Kuroshio Current toward the TC, we conducted sensitivity experiments that modified the surface latent heat flux (LHF) over the Kuroshio, applying a cloud‐resolving regional model and Lagrangian diagnostics to Typhoon Chaba as it approached Japan in October 2010. The synoptic environment was characterized by a combination of an anticyclone of continental origin around Japan and the northward‐migrating TC, bringing about low‐level northeasterlies over the Kuroshio through an enhanced meridional pressure gradient. When the LHF over the Kuroshio was artificially reduced or removed under this environmental flow, the intensity of the TC, which was fully away from the domain in which the LHF is modified, appreciably attenuated during its developing stage, implying the importance of moisture influx due to the environmental flow in TC development. Since dry air of midlatitude origin could receive little of the moisture supply from the Kuroshio in the sensitivity experiments, the values of equivalent potential temperature within the inflow layer tend to decrease, thus facilitating the weakening of convective updrafts and downdrafts around the eyewall of the TC. It is suggested that such changes act as an inhibitory factor for TC development. The Kuroshio has the potential to remotely influence the TC intensity over the western North Pacific if favorable synoptic conditions are satisfied.
Robust optimization of sustainable food grain transportation with uncertain supply and intentional disruptions

The proliferating need for sustainability intervention in food grain transportation planning is anchoring the attention of researchers in the interests of stakeholders and environment at large. Uncertainty associated with food grain supply further intensifies the problem steering the need for designing robust, cost-efficient and sustainable models. In line with this, this paper aims to develop a robust and sustainable intermodal transportation model to facilitate single type of commodity food grain shipments while considering procurement uncertainty, greenhouse gas (GHG) emissions, and intentional hub disruption. The problem is designed as a mixed integer non-linear robust optimization model on a hub and spoke network for evaluating near optimal shipment quantity, route selection and hub location decisions. The robust optimization approach considers minimization of total relative regret associated with total cost subject to several real time constraints. A version of Particle Swarm Optimization with Differential Evolution (PSODE) is proposed to tackle the resulting NP-hard problem. The model is tested with two other state-of the art meta-heuristics for small, medium, and large datasets subject to different procurement scenarios inspired from real time food grain operations in Indian context. Finally, the solution is evaluated with respect to total cost, model and solution robustness for all instances.
Context-Aware Compressed Sensing of Hyperspectral Image

Traditional hyperspectral imaging technique obtains numerous hyperspectral images (HSIs) with hundreds of spectral bands, leading to high cost in data acquisition, transmission, and storage. Compressed sensing (CS) theory provides a new imaging mechanism, which relies on the assumption that signals can be sparsely represented over a dictionary. By the CS imaging technique, original HSIs can be approximately reconstructed from only a few sampled measurements. In this article, a novel context-aware CS (CACS) method for HSIs is proposed by incorporating contextual prior to the dictionary learning and the sparse reconstruction. First, a patch-based online dictionary learning (ODL) algorithm is developed by introducing a joint sparse constraint. On the one hand, the online dictionary learning mechanism enables a more adaptive representation of HSIs with different scenes than using fixed-basis-based dictionaries, e.g., the discrete cosine transform (DCT) and the discrete wavelet transform dictionaries. On the other hand, the introduced joint sparse constraint promotes the learned dictionary to more sparsely and structurally represent spectral pixels. Then, with the well-learned dictionary, a weighted smoothing regularization is introduced to develop a new sparse reconstruction model. Considering the high spectral–spatial similarity of pixels in a neighborhood, the new sparse reconstruction model will encourage a locally smoothing reconstruction result. In this way, the spectral–spatial structures of the HSI can be well preserved, while possible artifacts can be effectively reduced. Experimental results demonstrate the superiority of the proposed method over some state-of-the-art hyperspectral compressive imaging methods.
Determining the role played by Aryl Hydrocarbon Receptor (AHR) in the colon carcinoma tumor model

by Poonam Yakkundi University of the Pacific 2018 Aryl hydrocarbon receptor (AHR), commonly known as an environmental sensor involved in the metabolism and elimination of xenobiotic substances, is also an important modulator in the development and functioning of the immune system. AHR expression is varied in the T cell subsets with the highest expression in T-helper 17 and T regulatory cells. Work from many researchers has suggested that AHR can act as a tumor promoter or a tumor suppressor depending on the tumor type. Our goal is to understand the role played by AHR in MC38 syngeneic colon carcinoma tumor model. In the absence of AHR, MC38 tumor progresses by an increase in tumor associated macrophages (TAMs), M2 macrophages and a decrease in CD8a positive cytotoxic lymphocytes. Analysis of the intratumoral cytokines reveals a pro-inflammatory phenotype. This has been assessed by pharmacologic blocking of the receptor using CH223191 and in AHR deficient (AHR-/-) mice. Therefore AHR acts as a tumor suppressor gene in colon carcinoma tumor model and silencing it may lead to colon cancer progression.
Stabilization of red melinjo peel (gnetum gnemon L.) ethyl acetate extract as antibacterial agent

Melinjo (GnetumgnemonL.) is a typical Indonesian plant that has many benefits such as antimicrobial agent. The aim of this study was to determine the antimicrobial activity of red melinjo peel extract. In this study, extraction was conducted by maceration using ethyl acetate as solvent for 24 hours at room temperature. 4-16% red melinjo peel extract (w/v) could inhibit the growth of Staphylococcus aureusATCC 6538, Listeria monocytogenesATCC 7644 and SalmonellaTyphi ATCC 14028. However 4-16% red melinjo peel extract could not inhibit the growth of Candida albicansATCC 10231. In stability test, the selected extract had a stable inhibition at pH 4-7, heat treatment 65oC-95oC for 30 minutes, salt 1%-5%, and sugar 10%-50%. The selected extract produced the biggest inhibition diameter at low pH (pH 4) and produced the smallest inhibition diameter at neutral pH (pH 7). Heat treatment 65oC for 30 minutes produced the biggest inhibition diameter among tested bacteria and decreased with increasing heating temperature. Addition of 1-5% NaCl and 10-50% sucrose worked synergistically with the selected extract in inhibit the growth of the tested bacteria.
ONE-POT SYNTHESES OF 2-(1H-BENZO[d]OXAZOLE-2-YL)-N-ARYLBENZAMIDES BY SELF-CATALYSIS

A novel, efficient, and high-yielding one-pot three-component method was developed for the synthesis of 2-(1 H -benzo[ d ]oxazole-2-yl)-N-arylbenzamides by combining phthaldichloride with anilines and o-aminophenol in water without any external catalyst. The environmentally friendly procedure, easy operation and mild reaction conditions enable the tolerance of a wide scope of functionalities as well as high reaction efficiency.