bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring the Vegetation Dynamics in the Dongting Lake Wetland from 2000 to 2019 Using the BEAST Algorithm Based on Dense Landsat Time Series
Authors: Cai Yaotong , Liu Shutong , Lin Hui
Published: June 2020

The dynamic monitoring and analysis of wetland vegetation play important roles in revealing the change, restoration and reconstruction of the ecosystem environment. The increasing availability of high spatial-temporal resolution remote sensing data provides an unprecedented opportunity for wetland dynamic monitoring and change detection. Using the reconstructed dense monthly Landsat time series, this study focuses on the continuous monitoring of vegetation dynamics in Dongting Lake wetland, south China, in the last two decades (2000–2019) by using the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) method. Firstly, the flexible spatiotemporal data fusion (FSDAF) model is applied to blend Landsat and moderate-resolution imaging spectroradiometer (MODIS) images on the basis of the input image pair selection strategy named “cross-fusion” to generate the monthly time-series normalized difference vegetation index (NDVI) with the spatial resolution of 30 m. Then, the abrupt changes, trend, and seasonality of the vegetation in the study area as well as the uncertainties of change detection are estimated by the BEAST method. Results show that there is a close relationship between the ground true data and the estimated changepoints. A high overall accuracy (OA) of 87.37% and Kappa coefficient of 0.85 were achieved by the proposed framework. Additionally, the temporal validation got the interval intersection of 86.57% and the absolute difference of mean interval length of 6.8 days. All of the results demonstrate that the vegetation changes in the Dongting Lake wetland varied spatially and temporally in the last two decades, because of extreme weathers and anthropogenic factors. The presented approach can accurately identify the vegetation changes and time of disturbance in both the spatial and temporal domains, and also can retrieve the evolution process of wetland vegetation under the influence of climate changes and human activities. Therefore, it can be used to reveal potential causes of the degradation and recovery of wetland vegetation in subtropical areas.
Erratum: Towards the Quantification of 5f Delocalization. Applied Sciences 2020, 10, 2918
Authors: Applied Sciences Editorial Office
Published: June 2020

We wish to make the following correction to the published paper [...]
Weather Simulation of Extreme Precipitation Events Inducing Slope Instability Processes over Mountain Landscapes
Authors: Golzio Alessio , Bollati Irene Maria , Luciani Marco , Pelfini Manuela , Ferrarese Silvia
Published: June 2020

Mountain landscapes are characterised by a very variable environment under different points of view (topography, geology, meteorological conditions), and they are frequently affected by mass wasting processes. A debris flow that occurred along the Croso stream, located in the Italian Lepontine Alps in the Northern Ossola Valley, during summer 2019, was analysed from a geological/geomorphological and meteorological point of view. The debris flow was triggered by an intense precipitation event that heavily impacted a very restricted area over the course of three hours. A previous debris flow along the same stream occurred in Autumn 2000, but it was related to an intense and prolonged rainfall event. The slope was characterised in terms of sediment connectivity, and data were retrieved and elaborated from the Web-GIS (Web-Geographic Information System) database of the IFFI-Italian Landslide Inventory and historical archives of landslides. Both the events were analysed through the weather research and forecasting (WRF) model applying a very high horizontal grid spacing with the aim of catching the precipitation patterns and timings. The obtained results are compared with the observed precipitation at a selection of weather stations in the area. The simulation of WRF that measured the timing in total precipitation and in its minor steps could be considered reliable. Moreover, it reveals to be appropriate for detecting in advance the meteorological conditions potentially triggering mass-wasting processes affecting slopes featuring high connectivity conditions and lithotypes characterised by a high Landslide Susceptibility Index.
Development of a Deep Learning-Based Algorithm to Detect the Distal End of a Surgical Instrument
Authors: Sugimori Hiroyuki , Sugiyama Taku , Nakayama Naoki , Yamashita Akemi , Ogasawara Katsuhiko
Published: June 2020

This work aims to develop an algorithm to detect the distal end of a surgical instrument using object detection with deep learning. We employed nine video recordings of carotid endarterectomies for training and testing. We obtained regions of interest (ROI; 32 × 32 pixels), at the end of the surgical instrument on the video images, as supervised data. We applied data augmentation to these ROIs. We employed a You Only Look Once Version 2 (YOLOv2) -based convolutional neural network as the network model for training. The detectors were validated to evaluate average detection precision. The proposed algorithm used the central coordinates of the bounding boxes predicted by YOLOv2. Using the test data, we calculated the detection rate. The average precision (AP) for the ROIs, without data augmentation, was 0.4272 ± 0.108. The AP with data augmentation, of 0.7718 ± 0.0824, was significantly higher than that without data augmentation. The detection rates, including the calculated coordinates of the center points in the centers of 8 × 8 pixels and 16 × 16 pixels, were 0.6100 ± 0.1014 and 0.9653 ± 0.0177, respectively. We expect that the proposed algorithm will be efficient for the analysis of surgical records.
High-k Polymer Nanocomposite Materials for Technological Applications
Authors: Shimoga Ganesh , Kim Sang-Youn
Published: June 2020

Understanding the properties of small molecules or monomers is decidedly important. The efforts of synthetic chemists and material engineers must be appreciated because of their knowledge of how utilize the properties of synthetic fragments in constructing long-chain macromolecules. Scientists active in this area of macromolecular science have shared their knowledge of catalysts, monomers and a variety of designed nanoparticles in synthetic techniques that create all sorts of nanocomposite polymer stuffs. Such materials are now an integral part of the contemporary world. Polymer nanocomposites with high dielectric constant (high-k) properties are widely applicable in the technological sectors including gate dielectrics, actuators, infrared detectors, tunable capacitors, electro optic devices, organic field-effect transistors (OFETs), and sensors. In this short colloquy, we provided an overview of a few remarkable high-k polymer nanocomposites of material science interest from recent decades.
Retinal Drug Delivery: Rethinking Outcomes for the Efficient Replication of Retinal Behavior
Authors: Souto Eliana B. , Sanchez-Lopez Elena , Campos Joana R. , da Ana Raquel , Espina Marta , Garcia Maria L. , Severino Patrícia , Batain Fernando , F. R. Alves Thais , Crescencio Kessi M. M. , Souto Selma B. , V. Chaud Marco
Published: June 2020

The retina is a highly organized structure that is considered to be "an approachable part of the brain." It is attracting the interest of development scientists, as it provides a model neurovascular system. Over the last few years, we have been witnessing significant development in the knowledge of the mechanisms that induce the shape of the retinal vascular system, as well as knowledge of disease processes that lead to retina degeneration. Knowledge and understanding of how our vision works are crucial to creating a hardware-adaptive computational model that can replicate retinal behavior. The neuronal system is nonlinear and very intricate. It is thus instrumental to have a clear view of the neurophysiological and neuroanatomic processes and to take into account the underlying principles that govern the process of hardware transformation to produce an appropriate model that can be mapped to a physical device. The mechanistic and integrated computational models have enormous potential toward helping to understand disease mechanisms and to explain the associations identified in large model-free data sets. The approach used is modulated and based on different models of drug administration, including the geometry of the eye. This work aimed to review the recently used mathematical models to map a directed retinal network.
Seismic Analysis of a Curved Bridge Considering Soil-Structure Interactions Based on a Separated Foundation Model
Authors: Zhang Lixin , Gu Yin
Published: June 2020

A separated foundation model was proposed in order to reduce the calculation scale of the numerical model for analyzing soil-bridge structure dynamics. The essence of the wave input analysis model considering soil-structure interaction was analyzed. Based on the large mass method, a one-dimensional time-domain algorithm of the free field was derived. This algorithm could simulate the specified ground motion input well. The displacement expansion solution of the free wave field was solved based on the propagation law of waves in a medium. By separating the soil foundations around the pile foundations of the bridge, the ground motion was transformed into an equivalent load applied on an artificial boundary. The separated foundation model could consider the incoherence effect and soil-structure interaction simultaneously; the number of model elements were reduced, and the computational efficiency was improved. In order to investigate the seismic response of a curved bridge considering soil-structure interaction under spatially varied earthquakes, a curved bridge with small radius was adopted in practical engineering. Spatially correlated multi-point ground motion time histories were generated, and the nonuniform ground motion field was simulated based on the wave input method on an artificial viscoelastic boundary. The effects of different apparent wave velocities, coherence values, and site conditions on the seismic response of the bridge were analyzed. The results showed that the spatial variation of seismic ground motion had a considerable effect on the bending moment and the torsion of the girder. The site effect had great influence on the bending moment of the pier bottom. When considering soil-structure interaction, the spatial variation of ground motion should be fully considered to avoid underestimating the structural response.
Hydrodynamic Simulation of the Semi-Submersible Wind Float by Investigating Mooring Systems in Irregular Waves
Authors: Lin Yu-Hsien , Yang Cheng-Hao
Published: June 2020

The present study aims to implement the software ANSYS AQWA to discuss the hydrodynamic analysis of the DeepCwind semi-submersible floating platform in waves based on the potential flow theory by considering the second-order wave exciting force. In this study, the linearized potential-flow hydrodynamic radiation and diffraction problems in the frequency domain were firstly solved by adopting the three-dimensional panel method. Subsequently, the hydrodynamic coefficients and wave loading data were transformed to time domain forms by the Cummins time domain equation as a system loading input. Furthermore, the quadratic transfer function (QTF) matrices with different frequencies and directions deduced based on the near field integration over the mean wetted hull surface were adopted for the calculation of slow-drift forces. In order to represent the damping in a real system for modeling potential flow without Morison’s elements, an additional quadratic damping matrix was added to capture the viscous drag. Eventually, both of the dynamic mooring model based on the lump-mass (LM) approach and the quasi-static mooring model based on the multi-segmented, quasi-static (MSQS) approach are introduced to discuss the mooring effect on the platform hydrodynamics. The effect of wave heading angles on the platform motion is considered as an influential parameter as well.
Robust Adaptive Control for Nonlinear Aircraft System with Uncertainties
Authors: Chen Jiao , Wang Jiangyun , Wang Weihong
Published: June 2020

Model reference adaptive control (MRAC) schemes are known as an effective method to deal with system uncertainties. High adaptive gains are usually needed in order to achieve fast adaptation. However, this leads to high-frequency oscillation in the control signal and may even make the system unstable. A robust adaptive control architecture was designed in this paper for nonlinear aircraft dynamics facing the challenges of input uncertainty, matched uncertainty, and unmatched uncertainty. By introducing a robust compensator to the MRAC framework, the high-frequency components in the control response were eliminated. The proposed control method was applied to the longitudinal-direction motion control of a nonlinear aircraft system. Flight simulation results demonstrated that the proposed robust adaptive method was able to achieve fast adaptation without high-frequency oscillations, and guaranteed transient performance.
A Linear Programming Method for Finding a Minimal Set of Axial Lines Representing an Entire Geometry of Building and Urban Layout
Authors: Jung Sung Kwon , Kim Youngchul
Published: June 2020

This paper devises an algorithm for finding the minimal set of axial lines that can represent a geometry of building and urban layout in two dimensions. Although axial lines are useful to analyze spatial configuration in the Space Syntax, existing methods for selecting axial lines seldom address the optimality of their solutions. The proposed algorithm uses linear programming to obtain a minimal set of axial lines. To minimize the number of axial lines that represent the entire geometry of building and urban layout, a linear programming problem is established in which a set of axial lines represents the entire geometry. The axial lines must have at least one intersection with every extension line of the wall edges to the sides of the reflex angles. If a solution to this linear programming problem exists, it will be guaranteed to be an optimum. However, some solutions of this general linear programming problem may include isolated lines, which are undesirable for an axial line analysis. To avoid isolated axial lines, this paper states a new formulation by adding a group of constraints to the original formulation. By examining the modified linear programming problem in various two-dimensional building maps and spatial layouts, this paper demonstrates that the proposed algorithm can guarantee a minimum set of axial lines to represent a two-dimensional geometry. This modified linear programming problem prevents isolated axial lines in the process of axial line reduction.
Assessing iMET-XQ Performance and Optimal Placement on a Small Off-the-Shelf, Rotary-Wing UAV, as a Function of Atmospheric Conditions
Authors: Kimball Sytske K. , Montalvo Carlos J. , Mulekar Madhuri S.
Published: June 2020

The accuracy and precision of iMET-XQ (InterMET Inc., Grand Rapids, MI, USA) temperature measurements in ten different locations on an off-the shelf rotary-wing unmanned aerial vehicle (rw-UAV) were assessed, as a function of atmospheric conditions. The rw-UAV hovered near an instrumented South Alabama Mesonet tower. The mean ± standard deviation of all the temperature differences between the tower and the ten iMET-XQ sensors for all experiments are −0.23 °C ±0.24 °C. Both the UAV and the environment influence the accuracy and precision of the iMET-XQ temperature measurements. Heat generated by the electronic components within the UAV body has a significant influence on the iMET-XQ temperature measurements, regardless of solar radiation conditions, and is highly dependent on wind direction. Electronic components within the UAV body heat up and can cause sensors downwind from the UAV body to record temperatures that are too warm, even if the sensors are aspirated by propeller wash. iMET-XQ sensors placed on rotor arms not near UAV body heat sources, and properly aspirated by propeller wash, perform well. Measurements from iMET-XQ sensors suspended below the UAV are also accurate. When using an off-the-shelf UAV for atmospheric temperature sensing, the electronic components inside the body of the UAV must be properly located. It is recommended that multiple sensors are placed on the UAV. Sensor redundancy will mitigate data loss in case of malfunction during flight and the identification of poorly performing sensors.
A Novel Method for Carbonate Quantification in Atmospheric Particulate Matter
Authors: Napolitano Denise C. , Hartnett Hilairy E. , Herckes Pierre
Published: June 2020

Inorganic carbonate can be an important component of atmospheric particulate matter in arid environments where mineral dust components contribute significantly to air particulate matter. Carbonate carbon (CC) is only rarely quantified in atmospheric studies and methods to quantify carbonate in atmospheric samples are rare. In this manuscript, we present a novel protocol for quantifying carbonate carbon in atmospheric particulate matter samples, through the acidification of aerosol filters at ambient pressure and temperature and subsequent measurement of carbon dioxide (CO2) released upon acidification. This method is applicable to a variety of filter media used in air pollution studies, such as Teflon, cellulose, or glass fiber filters. The method allows the customization of the filter area used for analysis (up to 24 cm2) so that sufficient CO2 can be detected when released and to assure that the sample aliquot is representative of the whole filter. The resulting detection limits can be as low as 0.12 µg/cm2. The analysis of a known amount of sodium bicarbonate applied to a filter resulted in a relative error within 15% of the known mass of bicarbonate when measured 20 min after acidification. A particulate matter sample with aerodynamic diameter larger than 2.5 µm (PM>2.5) collected via cascade impaction on a high-volume aerosol sampler yielded good precision, with a CC concentration of 4.4 ± 0.3 µgC/cm2 for six replicates. The precision, accuracy, and reproducibility of this method of CC measurement make it a good alternative to existing quantification methods.
Model Hyetographs of Short-Term Rainfall for Wrocław in the Perspective of 2050
Authors: Wartalska Katarzyna , Kotowski Andrzej
Published: June 2020

One of the most important problems while modeling stormwater drainage systems is the choice of rainfall scenario, which will take into account the real rainfall distribution over time. This problem is particularly significant due to the climate change observed in recent decades, manifested, among others, in the increase in the precipitation intensities or changes in their structure. Taking into account these forecasts is essential to safely design sewer systems and their proper operation. The work aims to verify the Euler type II standard rainfall used so far to model sewage systems in Poland and to develop the forecasted form of this pattern in the perspective of 2050. Precipitation data from measurement stations in Wrocław were used as research material. The prediction model of maximum rainfall amounts allowed to determine the forecasted increase in intensities of short-term rainfall (for the occurrence frequencies recommended by Standard EN 752:2017 for the dimensioning and modeling of sewage systems). On this basis, model hyetographs forecasted for 2050 were prepared for Wrocław. Their choice—as a future rainfall load in hydrodynamic modeling—will allow one to meet the requirements for the frequency of flooding occurrence from sewer systems and their safe operation over several decades.
A Comparative Study on Oxidation of Acidic Red 18 by Persulfate with Ferrous and Ferric Ions
Authors: Li Xin , Yuan Lijing , Zhao Liangfu
Published: June 2020

Ferrous and ferric salts were tested for the persulfate activation (PS/Fe2+ and PS/Fe3+) and the oxidation of Acid Red 18 (AR18). A complete removal was attained after 90 min in both PS/Fe2+ and PS/Fe3+ processes with the persulfate concentration of 6 mM. High concentrations of PS, Fe2+, and Fe3+ promoted the AR18 degradation in both processes and the optimized pH were 3 and 3.3 for PS/Fe2+ and PS/Fe3+ processes, respectively. The mechanism of PS activation by Fe3+ was also investigated. It was found that hydroxyl radical (HO•) and sulfate radical (SO4−•) were formed and acted as dominating radicals in both processes. It is also deduced that Fe recycle offers Fe2+ for PS activation in PS/Fe3+ process to produce HO• and SO4−•. The less radical side reactions lead to a higher contribution of HO• and SO4−• on AR18 degradation in PS/Fe3+ process.
Photocatalytic Degradation of Chlorpyrifos with Mn-WO3/SnS2 Heterostructure
Authors: Kgoetlana Charlie M. , Malinga Soraya P. , Dlamini Langelihle N.
Published: June 2020

Tungsten trioxide (WO3) is a photocatalyst that has gained interest amongst researchers because of its non-toxicity, narrow band gap and superior charge transport. Due to its fast charge recombination, modification is vital to counteract this limitation. In this paper, we report on the fabrication of Mn-doped WO3/SnS2 nanoparticles, which were synthesised with the aim of minimising the recombination rates of the photogenerated species. The nanomaterials were characterised using spectroscopic techniques (UV-Vis-diffuse reflectance spectroscopy (DRS), Raman, XRD, photoluminescence (PL) and electrochemical impedance spectroscopy (EIS)) together with microscopic techniques (FESEM-EDS and high resolution transmission electron microscopy selected area electron diffraction (HRTEM-SAED)) to confirm the successful formation of Mn-WO3/SnS2 nanoparticles. The Mn-doped WO3/SnS2 composite was a mixture of monoclinic and hexagonal phases, confirmed by XRD and Raman analysis. The Mn-WO3/SnS2 heterojunction showed enhanced optical properties compared to those of the un-doped WO3/SnS2 nanoparticles, which confirms the successful charge separation. The Brunauer–Emmett–Teller (BET) analysis indicated that the nanoparticles were mesoporous as they exhibited a Type IV isotherm. These nanomaterials appeared as a mixture of rectangular rods and sheet-like shapes with an increased surface area (77.14 m2/g) and pore volume (0.0641 cm3/g). The electrochemical measurements indicated a high current density (0.030 mA/cm2) and low charge transfer resistance (157.16 Ω) of the Mn-WO3/SnS2 heterojunction, which infers a high charge separation, also complemented by photoluminescence with low emission peak intensity. The Mott–Schottky (M-S) plot indicated a positive slope characteristic of an n–n heterojunction semiconductor, indicating that electrons are the major charge carriers. Thus, the efficiency of Mn-WO3/SnS2 heterojunction photocatalyst was monitored for the degradation of chlorpyrifos. The effects of pH (3–9), catalyst loading (0.1–2 g) and initial chlorpyrifos concentration (100 ppb–20 ppm) were studied. It was observed that the degradation was purely due to photocatalysis, as no loss of chlorpyrifos was observed within 30 min in the dark. Chlorpyrifos removal using Mn-WO3/SnS2 was performed at the optimum conditions of pH = 7, catalyst loading = 1 g and chlorpyrifos concentration = 1000 ppb in 90 min. The complete degradation of chlorpyrifos and its major degradation by-product 3,5,6-trichloropyridin-2-ol (TCP) was achieved. Kinetic studies deduced a second order reaction at 209 × 10−3 M−1s−1.
Conductive Coatings of Cotton Fabric Consisting of Carbonized Charcoal for E-Textile
Authors: Kasaw Esubalew , Haile Adane , Getnet Melkie
Published: June 2020

Development of smart textiles is an emerging discipline in the last two decades where a conductive element is integrated into a textile material by some means. The purpose of this research was to develop a conductive textile fabric by coating with charcoal as a conductive element. The charcoal was produced by carbonizing the eucalyptus wood at a temperature of 928 °C for 37 min producing 59.17% w/w of fixed carbon yield and conductivity of 463.34 Sm−1 (Siemens per meter) compared to immeasurable conductivity of the wood. This was followed by characterization of physical and chemical properties of charcoal. Thereafter, a cotton fabric was pad-coated with a dispersion based on the charcoal. The paper herein reports the results of preparing different recipes using different quantities of charcoal particles with other components of the coating mixture, which was tested to obtain the best coating in terms of electrical conductivity. The optimal concentration of the conductive particles of the charcoal was studied. Performance evaluation of the coated fabric was assessed for the durability of fabric towards different fastness agents. The effect of charcoal loading on thermal and sensorial comfort of the fabric in addition to the air and water permeability was studied and a significant change was observed. Finally, a proof of concept was developed to demonstrate if the resulting pieces of information during the process were viable. As observed, the pad-coated cotton fabric using charcoal showed increased electrical conductivity from 1.58 × 10−12 Scm−1 (Siemens per centimeter) for the controlled sample to 124.49 Scm−1 for the coated sample designating that the resulting fabric is in a conductor category.
Special Issue on Surface Treatment by Laser-Assisted Techniques
Authors: Comesaña Rafael
Published: June 2020

Laser radiation is a powerful tool for surface modification owing to its spatial and material absorbance selectivity [...]
Investigation of Stability of CO2 Microbubbles—Colloidal Gas Aphrons for Enhanced Oil Recovery Using Definitive Screening Design
Authors: Nguyen Hai Le Nam , Sugai Yuichi , Sasaki Kyuro
Published: June 2020

CO2 microbubbles have recently been used in enhanced oil recovery for blocking the high permeability zone in heterogeneous reservoirs. Microbubbles are colloidal gas aphrons stabilized by thick shells of polymer and surfactant. The stability of CO2 microbubbles plays an important role in improving the performance of enhanced oil recovery. In this study, a new class of design of experiment (DOE)—definitive screening design (DSD) was employed to investigate the effect of five quantitative parameters: xanthan gum polymer concentration, sodium dodecyl sulfate surfactant concentration, salinity, stirring time, and stirring rate. This is a three-level design that required only 11 experimental runs. The results suggest that DSD successfully evaluated how various parameters contribute to CO2 microbubble stability. The definitive screening design revealed a polynomial regression model has ability to estimate the main effect factor, two-factor interactions and pure-quadratic effect of factors with high determination coefficients for its smaller number of experiments compared to traditional design of experiment approach. The experimental results showed that the stability depend primarily on xanthan gum polymer concentration. It was also found that the stability of CO2 microbubbles increases at a higher sodium dodecyl sulfate surfactant concentration and stirring rate, but decreases with increasing salinity. In addition, several interactions are presented to be significant including the polymer–salinity interaction, surfactant–salinity interaction and stirring rate–salinity interaction.
Liquid Phase Separation in Ag-Co-Cr-Fe-Mn-Ni, Co Cr-Cu-Fe-Mn-Ni and Co-Cr-Cu-Fe-Mn-Ni-B High Entropy Alloys for Biomedical Application
Authors: Nagase Takeshi , Todai Mitsuharu , Nakano Takayoshi
Published: June 2020

The liquid phase separation (LPS) behavior in Co-Cr-based high-entropy alloys (HEAs) is an important target for the development of Co-Cr-based HEAs for metallic biomaterials (BioHEAs). The solidification microstructure in Ag-Co-Cr-Fe-Mn-Ni-Ag, Co-Cr-Cu-Fe-Mn-Ni-Cu, and Co-Cr-Cu-Fe-Mn-Ni-B HEAs, which were designed as the combination of the equiatomic CoCrFeMnNi with Ag, Cu, and the interstitial element of B, was investigated as the fundamental research of LPS in Co-Cr-based HEAs. Ingots of equiatomic AgCoCrFeMnNi, equiatomic CoCrCuFeMnNi, non-equiatomic CoCrCuxFeMnNi (x = 2, 3), and CoCrCuxFeMnNiB0.2 (x = 1, 2, 3) with a small amount of B were fabricated using the arc-melting process. A macroscopic phase-separated structure was observed in the ingots of the equiatomic AgCoCrFeMnNi and CoCrCuxFeMnNiB0.2 (x = 2, 3) HEAs. The addition of a small amount of B enhanced the LPS tendency in the Co-Cr-Fe-Mn-Ni-Cu HEAs. The LPS behavior was discussed through the heat of mixing and computer coupling of phase diagrams and thermochemistry (CALPHAD).
A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson’s Disease Using Wearable Based Gait Signals
Authors: Aich Satyabrata , Youn Jinyoung , Chakraborty Sabyasachi , Pradhan Pyari Mohan , Park Jin-han , Park Seongho , Park Jinse
Published: June 2020

Fluctuations in motor symptoms are mostly observed in Parkinson’s disease (PD) patients. This characteristic is inevitable, and can affect the quality of life of the patients. However, it is difficult to collect precise data on the fluctuation characteristics using self-reported data from PD patients. Therefore, it is necessary to develop a suitable technology that can detect the medication state, also termed the “On”/“Off” state, automatically using wearable devices; at the same time, this could be used in the home environment. Recently, wearable devices, in combination with powerful machine learning techniques, have shown the potential to be effectively used in critical healthcare applications. In this study, an algorithm is proposed that can detect the medication state automatically using wearable gait signals. A combination of features that include statistical features and spatiotemporal gait features are used as inputs to four different classifiers such as random forest, support vector machine, K nearest neighbour, and Naïve Bayes. In total, 20 PD subjects with definite motor fluctuations have been evaluated by comparing the performance of the proposed algorithm in association with the four aforementioned classifiers. It was found that random forest outperformed the other classifiers with an accuracy of 96.72%, a recall of 97.35%, and a precision of 96.92%.
Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks
Authors: Vázquez-Romero Adrián , Gallardo-Antolín Ascensión
Published: June 2020

This paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio–Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built. Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier.
Exergoeconomic Analysis of Corn Drying in a Novel Industrial Drying System
Authors: Li Bin , Li Chengjie , Huang Junying , Li Changyou
Published: June 2020

The improvement of the design and operation of energy conversion systems is a theme of global concern. As an energy intensive operation, industrial agricultural product drying has also attracted significant attention in recent years. Taking a novel industrial corn drying system with drying capacity of 5.5 t/h as a study case, based on existing exergoeconomic and exergetic analysis methodology, the present work investigated the exergetic and economic performance of the drying system and identified its energy use deficiencies. The results showed that the average drying rate for corn drying in the system is 1.98 gwater/gdry matter h. The average exergy rate for dehydrating the moisture from the corn kernel is 345.22 kW and the exergy efficiency of the drying chamber ranges from 14.81% to 40.10%. The average cost of producing 1 GJ exergy for removing water from wet corn kernels is USD 25.971, while the average cost of removing 1 kg water is USD 0.159. These results might help to further understand the drying process from the exergoeconomic perspective and aid formulation of a scientific index for agricultural product industrial drying. Additionally, the results also indicated that, from an energy perspective, the combustion chamber should be firstly optimized, while the drying chamber should be given priority from the exergoeconomics perspective. The main results would be helpful for further optimizing the drying process from both energetic and economic perspectives and provide new thinking about agricultural product industrial drying from the perspective of exergoeconomics.
An Upper Bound on the Error Induced by Saddlepoint Approximations—Applications to Information Theory
Authors: Anade Dadja , Gorce Jean-Marie , Mary Philippe , Perlaza Samir M.
Published: June 2020

This paper introduces an upper bound on the absolute difference between: ( a ) the cumulative distribution function (CDF) of the sum of a finite number of independent and identically distributed random variables with finite absolute third moment; and ( b ) a saddlepoint approximation of such CDF. This upper bound, which is particularly precise in the regime of large deviations, is used to study the dependence testing (DT) bound and the meta converse (MC) bound on the decoding error probability (DEP) in point-to-point memoryless channels. Often, these bounds cannot be analytically calculated and thus lower and upper bounds become particularly useful. Within this context, the main results include, respectively, new upper and lower bounds on the DT and MC bounds. A numerical experimentation of these bounds is presented in the case of the binary symmetric channel, the additive white Gaussian noise channel, and the additive symmetric α -stable noise channel.
A New Belief Entropy in Dempster–Shafer Theory Based on Basic Probability Assignment and the Frame of Discernment
Authors: Li Jiapeng , Pan Qian
Published: June 2020

Dempster–Shafer theory has been widely used in many applications, especially in the measurement of information uncertainty. However, under the D-S theory, how to use the belief entropy to measure the uncertainty is still an open issue. In this paper, we list some significant properties. The main contribution of this paper is to propose a new entropy, for which some properties are discussed. Our new model has two components. The first is Nguyen entropy. The second component is the product of the cardinality of the frame of discernment (FOD) and Dubois entropy. In addition, under certain conditions, the new belief entropy can be transformed into Shannon entropy. Compared with the others, the new entropy considers the impact of FOD. Through some numerical examples and simulation, the proposed belief entropy is proven to be able to measure uncertainty accurately.
Relative Consistency of Sample Entropy Is Not Preserved in MIX Processes
Authors: Żurek Sebastian , Grabowski Waldemar , Wojtiuk Klaudia , Szewczak Dorota , Guzik Przemysław , Piskorski Jarosław
Published: June 2020

Relative consistency is a notion related to entropic parameters, most notably to Approximate Entropy and Sample Entropy. It is a central characteristic assumed for e.g., biomedical and economic time series, since it allows the comparison between different time series at a single value of the threshold parameter r. There is no formal proof for this property, yet it is generally accepted that it is true. Relative consistency in both Approximate Entropy and Sample entropy was first tested with the M I X process. In the seminal paper by Richman and Moorman, it was shown that Approximate Entropy lacked the property for cases in which Sample Entropy did not. In the present paper, we show that relative consistency is not preserved for M I X processes if enough noise is added, yet it is preserved for another process for which we define a sum of a sinusoidal and a stochastic element, no matter how much noise is present. The analysis presented in this paper is only possible because of the existence of the very fast NCM algorithm for calculating correlation sums and thus also Sample Entropy.
Collective Intelligence in Polish-Ukrainian Internet Projects. Debate Models and Research Methods
Authors: Olszowski Rafał , Chmielowski Marcin
Published: June 2020

In this study, we focus on models of civic debate suitable for use in Polish-Ukrainian internet projects, as well as methods of researching collective intelligence that can help to monitor particular aspects of such debates and consequently create social bridging capital between these groups. The dynamic socio-political situation of recent years, both in Ukraine and in Poland, has created new conditions. Anti-government protests and social turmoil related to the war in Crimea and Donbas, as well as a high level of migration in the region in a short period led to the creation of a multi-ethnic society. This brings opportunities for the development of a new type of social capital: A new participative model of social life based on internet projects, with a relatively low entry barrier, space for creativity, and the widespread use of ICT technologies, can provide the new ways of debating, civic engagement, and collective action. Our research, based on a multidisciplinary literature review, as well as a series of qualitative in-depth interviews (IDIs), proved that the selected collective intelligence (CI) research methods and debate models can help to develop internet communities that will contribute to building bridging capital between Poles and Ukrainians.
Flexural Strengthening of RC Continuous T-Beams Using CFRP
Authors: Al-Khafaji Ayssar , Salim Hani
Published: June 2020

In this paper, experimental investigations for strengthening reinforced concrete (RC) continuous beams were performed. Eighteen T-beams were cast, twelve of which were inverted T-beams where the flange portion of the T-beam was subjected to positive flexure to represent the support region of a continuous beam. Six of the T-beams were non-inverted where the web is subjected to positive flexure. Carbon fiber reinforced polymer (CFRP) sheets with different widths were considered, and different strengthening configurations with the same area of CFRP were investigated. The use of one-layer, multiple layers, or multiple strips of CFRP were evaluated to investigate the effect of these configurations on the ultimate capacity and ductility of the strengthened beams. From the experimental observation of the non-inverted beams, it was found that the ultimate load capacities of the CFRP-strengthened beams were enhanced by 4% to 90% compared to the control beam. Using multiple layers of CFRP sheets enhanced the stiffness of the beams by 4% to 46%, depending on the CFRP area and configurations. The debonding of CFRP before the ultimate failure provided additional ductility to the tested beams. For the strengthening of the inverted beams, it was found that the addition of CFRP strips did not increase the strength of the beams when the width of CFRP to beam width ratio was less than 0.25, but the ductility of the beam was enhanced slightly. The use of multiple strips was found to be a more effective way for the strengthening of the negative moment region than using multiple layers. This can also provide more desirable modes of failure than when applying CFRP in multiple layers. Ductility was found to be lower if multiple layers were used compared to other configurations. Moreover, it was observed that as the compressive strength of concrete increased the addition of the CFRP improved the beams ductility.
Mechanical Properties and Microstructure of Fibre-Reinforced Clay Blended with By-Product Cementitious Materials
Authors: Abbey Samuel J. , Eyo Eyo U. , Oti Jonathan , Amakye Samuel Y. , Ngambi Samson
Published: June 2020

Clayey soils endure adverse changes in strength and volume due to seasonal changes in moisture content and temperature. It has been well recognised that high cement content has been successfully employed in improving the mechanical properties of clayey soils for geotechnical infrastructural purposes. However, the environmental setbacks regarding the use of high cement content in soil reinforcement have necessitated the need for a greener soil reinforcement technique by incorporating industrial by-product materials and synthetic fibres with a reduced amount of cement content in soil-cement mixtures. Therefore, this study presents an experimental study to investigate the mechanical performance of polypropylene and glass fibre-reinforced cement-clay mixtures blended with ground granulated blast slag (GGBS), lime and micro silica for different mix compositions and curing conditions. The unconfined compressive strength, linear expansion and microstructural analysis of the reinforced soils have been studied. The results show that an increase in polypropylene and glass fibre contents caused an increase in unconfined compressive strength but brought on the reduction of linear expansion of the investigated clay from 7.92% to 0.2% at fibre content up to 0.8% for cement-clay mixture reinforced with 5% Portland cement (PC). The use of 0.4–0.8% polypropylene and glass fibre contents in reinforcing cement-clay mixture at 5% cement content causes an increase in unconfined compressive strength (UCS) values above the minimum UCS target value according to American Society for Testing and Materials (ASTM) 4609 after 7 and 14 days curing at 20 °C to 50 °C temperature. Therefore, this new clean production of fibre-reinforced cement-clay mixture blended with industrial by-product materials has great potential for a wide range of applications in subgrade reinforcement.
Coronavirus Outbreak and Stress in Iranians
Authors: Maarefvand Masoomeh , Hosseinzadeh Samaneh , Farmani Ozra , Safarabadi Farahani Atefeh , Khubchandani Jagdish
Published: June 2020

Iran has faced one of the worst COVID-19 outbreaks in the world, and no studies to date have examined COVID-19-related stress in the general Iranian population. In this first population-based study, a web-based survey was conducted during the peak of the outbreak to assess stress and its correlates in the Iranian population. A 54-item, valid, and reliable questionnaire, including items on demographic characteristics and past medical history, stress levels, awareness about signs and symptoms of COVID-19, knowledge about at-risk groups and prevention methods, knowledge about transmission methods, trust in sources of information, and availability of facemasks and sanitizers, was deployed via social and mass media networks. A total of 3787 Iranians participated in the study where the majority of the participants were females (67.4%), employed (56.1%), from developed provinces (81.6%), without chronic diseases (66.6%), and with ≥13 years of formal education (87.9%). The mean age of study participants was 34.9 years (range = 12–73), and the average stress score was 3.33 (SD = ±1.02). Stress score was significantly higher for females, those who were 30–39 years old, housewives, those with chronic diseases, individuals who were aware that there is no vaccine to prevent COVID-19, those who could not get facemasks or sanitizers, and individuals with higher knowledge about at-risk groups (p < 0.05). There was a significant correlation of stress scores with knowledge about prevention methods for COVID-19 (r = 0.21, p = 0.01) and trust in sources of information about COVID-19 (r = −0.18, p = 0.01). All of the predictors, except knowledge of two important at-risk groups and education, had a significant effect on stress scores based on a multivariate regression model. The COVID-19 outbreak could increase stress among all population groups, with certain groups at higher risk. In the high-risk groups and based on experience with previous pandemics, interventions are needed to prevent long-term psychological effects. Professional support and family-centered programs should be a part of pandemic mitigation-related policymaking and public health practices.
Ethical Conflicts Experienced by Nurses in Geriatric Hospitals in South Korea: “If You Can’t Stand the Heat, Get Out of the Kitchen”
Authors: Kim Moonok , Oh Younjae , Kong Byunghye
Published: June 2020

Ethical conflicts among nurses can undermine nurses’ psychological comfort and compromise the quality of patient care. In the last decade, several empirical studies on the phenomena related to ethical conflicts, such as ethical dilemmas, issues, problems, difficulties, or challenges, have been reported; however, they have not always deeply explored the meaning of ethical conflicts experienced by nurses in geriatric care. This study aims to understand the lived experiences of ethical conflict of nurses in geriatric hospitals in South Korea. A phenomenological study was conducted. In-depth, face-to-face interviews were performed with nine registered nurses who cared for elderly patients in geriatric hospitals in South Korea between August 2015 and January 2016. Three main themes emerged from the analysis: (1) confusing values for good nursing, (2) distress resulting from not taking required action despite knowing about a problem, and (3) avoiding ethical conflicts as a last resort. It was found that for geriatric nurses to cope with ethical conflicts successfully, clear ethical guidance, continuing ethics education to improve ethical knowledge and moral behaviors, and a supportive system or program to resolve ethical conflicts involving nurses should be established.
Decision-Making Processes in Surrogates of Cancer Patients in a Taiwan Intensive Care Unit
Authors: Sun Wan-Na , Hsu Hsin-Tien , Ko Nai-Ying , Huang Yu-Tung
Published: June 2020

Background: Few studies in Asian countries have explored the emotional entanglements and conflicts that surrogates often experience during the medical decision-making process. This study was to explore decision-making processes in surrogates of cancer patients in a Taiwan intensive care unit (ICU). This qualitative study surveyed a purposive sample of surrogates (n = 8; average age, 48 years) of cancer patients in the ICU of a medical center in Taiwan. A phenomenological methodology was used, and a purposive sample of surrogates of cancer patients were recruited and interviewed during the first three days of the ICU stay. Results: Based on the interview results, four themes were generalized through text progression: (1) Use love to resist: internal angst. This theme was related to the reflexive self -blame, the feelings of inner conflict, and the reluctance to make healthcare decisions, which surrogates experienced when they perceived suffering by the patient. (2) Allow an angel to spread love among us: memories and emotional entanglements. Memories of the patient caused the surrogate to experience emotional entanglements ranging from happiness to sadness and from cheerfulness to anger. (3) Dilemmas of love: anxiety about ICU visitor restrictions. The confined space and restricted visiting hours of the ICU limited the ability of surrogates to provide emotional support and to share their emotions with the patient. (4) Suffocating love: entanglement in decision-making. Emotional entanglements among family members with different opinions on medical care and their struggles to influence decision-making often prevented surrogates from thinking logically. Conclusions: Expression of emotions by ICU surrogates is often restrained and implicit, particularly in Asian populations. These results can help health professionals understand the psychological shock and inner conflict experienced by surrogates and provide a useful reference for improving their communications with surrogates.