bluuebunny/crossref_metadata_2025 huggingface dataset:
Prioritizing Technology in Pharmacy Education: A Document Analysis of Strategic Plans
Authors: Dresser Jacob D. , Burmeister Paul , Arya Vibhuti , Wilby Kyle John
Published: December 2020

The COVID-19 pandemic has required many pharmacy programs to increase their utilization of technology or shift the course of delivery entirely online. Delivery in this setting has exposed areas in the use of technology where pharmacy programs need to improve (such as staff and student training). This study performed a document analysis of strategic plans to identify technology-related strategies and where gaps in planning currently exist. Accredited pharmacy programs in Canada and the USA were included for analysis. A total of 77 strategic plans were identified. Strategic plans were searched for the phrases: "tech", "online", "distance" and "e-learning" to identify technology-related statements. Statements relating to technology in education were coded for (1) the prioritized “action” and (2) the objective or “goal” of this strategy. Quantitative analysis of these codes revealed that the “action” was most frequently to introduce or improve technology (54.4%), and the “goal” most frequently related to enhancing teaching/course delivery/learning (34.2%). Strategic plans appeared to frequently focus on the technology itself, with little consideration for the human aspect of operating technology or readiness of programs to embrace technology. Moving forward, strategic priorities with respect to technology should be refocused towards system readiness and account for resources necessary for target user upskilling and acceptance.
Pharmacists’ Knowledge, Attitude and Practice Regarding the Dispensing of Antibiotics without Prescription in Tanzania: An Explorative Cross-Sectional Study
Authors: Poyongo Baraka P. , Sangeda Raphael Zozimus
Published: December 2020

Inappropriate use of antibiotics has been reported to contribute to the emergence and increase of antimicrobial resistance (AMR) in the world. The pharmacist has the responsibility to supervise the dispensing of antibiotics with prescriptions to ensure rational use. An online semi-structured questionnaire was shared with approximately 1100 licensed pharmacists in Tanzania. Study data were collected and managed using REDCap electronic data capture tools before use for analysis. Of the 226 (20.5%) received responses, 197 had given consent and provided complete surveys. Notably, 153 (77.7%) of the 197 pharmacists had excellent knowledge about the legal requirements for dispensing antibiotics and the AMR challenge. Of the 197 surveyed pharmacists, 143 (72.6%) admitted to dispensing antibiotics without a prescription in their daily practice. Notably, 84.1% (37/44) of pharmacists with masters or PhD education were more likely to dispense without a prescription compared to 69.3% (106/153) among bachelor holders (p-value = 0.04). The reasons for administering antibiotics without a prescription included the pharmacy business looking for more profit, patient failure to obtain a prescription and the lack of stringent inspection of pharmacies by the regulatory authorities. Penicillins, macrolides and fluoroquinolones were the classes of antibiotics most commonly dispensed without a prescription. Stringent inspections by the regulatory authorities should detect and reduce dispensing antibiotics without a prescription. The community should be educated on the importance of medication prescription from a qualified medical practitioner.
Wavelength-Dependent Features of Photoelectron Spectra from Nanotip Photoemission
Authors: Wu Xiao-Yuan , Liang Hao , Ciappina Marcelo , Peng Liang-You
Published: December 2020

If a metal nanotip is irradiated with the light of a wavelength much larger than the nanotip’s radius of curvature, optical near-fields become excited. These fields are responsible for distinct strong-field electron dynamics, due to both the field enhancement and spatial localization. By classical trajectory, Monte Carlo (CTMC) simulation, and the integration of the time-dependent Schrödinger equation (TDSE), we find that the photoelectron spectra for nanotip strong-field photoemission, irradiated by mid-infrared laser pulses, present distinctive wavelength-dependent features, especially in the mid- to high-electron energy regions, which are different from the well known ones. By extracting the electron trajectories from the CTMC simulation, we investigate these particular wavelength-dependent features. Our theoretical results contribute to understanding the photoemission and electron dynamics at nanostructures, and pave new pathways for designing high-energy nanometer-sized ultrafast electron sources.
Green Composites Based on Unsaturated Polyester Resin from Recycled Poly(Ethylene Terephthalate) with Wood Flour as Filler—Synthesis, Characterization and Aging Effect
Authors: Pączkowski Przemysław , Puszka Andrzej , Gawdzik Barbara
Published: December 2020

The paper investigates the synthesis of green composites and their properties before and after the laboratory accelerated aging tests. Materials were made of unsaturated polyester resins (UPRs) based on recycled poly(ethylene terephthalate) (PET) and wood flour (WF). The effect of dibenzylideneacetone (dba) addition on mechanical and thermomechanical properties was also determined. Green composites were obtained using environment friendly polymeric cobalt as an accelerator. Before and after exposition to the xenon lamp radiation, the UPRs physically modified by WF were characterized only by a greater flexural modulus compared with the analogous composites based on the pure resin. Addition of dba caused the increase of flexural modulus, flexural strength, strain at break and mechanical loss factor compared to the non-modified material. After aging only the last mentioned parameter took on lower values compared to the pure resin analogues.
Physico-Chemical and Antiadhesive Properties of Poly(Lactic Acid)/Grapevine Cane Extract Films against Food Pathogenic Microorganisms
Authors: Díaz-Galindo Edaena Pamela , Nesic Aleksandra , Cabrera-Barjas Gustavo , Dublan-García Octavio , Ventura-Aguilar Rosa Isela , Vázquez-Armenta Francisco Javier , Aguilar-Montes de Oca Saúl , Mardones Claudia , Ayala-Zavala Jesús Fernando
Published: December 2020

The aim of this study was evaluation of the physico-chemical properties and adhesion of microorganisms on poly(lactic acid) (PLA)-based films loaded with grapevine cane extract (5–15 wt%). The films were processed in a compression molding machine and characterized by mechanical, thermal, water vapor barrier and microbiological tests. The best physical-chemical properties for PLA film containing 10 wt% of extract were obtained. The addition of 10 wt% of extract into PLA films led to decrease of tensile strength for 52% and increase in elongation at break for 30%. The water vapor barrier of this film formulation was enhanced for 55%. All films showed thermal stability up to 300 °C. The low release of the active compounds from films negatively influenced their antimicrobial and antifungal activity. Botrytis cinerea growth inhibition onto PLA containing extracts (PLA-E) films was in the range between 15 and 35%. On the other side, PLA/extract films exhibited the antiadhesive properties against Pseudomonas aeruginosa, Pectobacterium carotovorum, Saccharomyces pastorianus, and Listeria monocytogenes, which could imply their potential to be used as sustainable food packaging materials for preventing microbial contamination of food.
Dielectric Response Spectroscopy as Means to Investigate Interfacial Effects for Ultra-Thin Film Polymer-Based High NA EUV Lithography
Authors: Severi Joren , De Simone Danilo , De Gendt Stefan
Published: December 2020

Extreme ultra-violet lithography (EUVL) is the leading-edge technology to produce advanced nanoelectronics. The further development of EUVL is heavily based on implementing the so-called high numerical aperture (NA) EUVL, which will enable even smaller pitches up to 8 nm half pitch (HP). In anticipation of this high NA technology, it is crucial to assess the readiness of the current resist materials for the high NA regime to comply with the demanding requirements of resolution, line-edge roughness, and sensitivity (RLS). The achievable tighter pitches require lower film thicknesses for both resist and underlying transfer layers. A concern that is tied to the thinning down is the potential change in resist properties and behavior due to the interaction with the underlayer. To increase the fundamental understanding of ultra-thin films for high NA EUVL, a method to investigate the interplay of reduced film thickness and different patterning-relevant underlayers is developed by looking at the glass transition temperature (Tg) of polymer-based resists. To minimize the ambiguity of the results due to resist additives (i.e., photoacid generator (PAG) and quencher), it was opted to move forward with polymer-only samples, the main component of the resist, at this stage of the investigation. By using dielectric response spectroscopy, the results obtained show that changing the protection group of the polymer, as well as altering the polymer film thickness impacts the dynamics of the polymer mobility, which can be assessed through the Tg of the system. Unexpectedly, changing the underlayer did not result in a clear change in the polymer mobility at the tested film thicknesses.
In Situ Deposition of Green Silver Nanoparticles on Urinary Catheters under Photo-Irradiation for Antibacterial Properties
Authors: Chutrakulwong Fueangfahkan , Thamaphat Kheamrutai , Tantipaibulvut Sukon , Limsuwan Pichet
Published: December 2020

Urinary tract infections, especially catheter-associated urinary tract infections (CAUTIs), are the most common type of nosocomial infections. Patients with chronic indwelling urinary catheters have a higher risk of infection due to biofilm formation on the urinary catheter surface. Therefore, in this work, a novel, cost-effective antimicrobial urinary catheter was developed using green technology. Silver nanoparticles (AgNPs) synthesized from Mon Thong durian rind waste were used as an antimicrobial agent for the prevention of infection. Flavonoids, phenolic compounds, and glucose extracted from durian rind were used as a reducing agent to reduce the Ag+ dissolved in AgNO3 solution to form non-aggregated AgNPs under light irradiation. The AgNPs were simultaneously synthesized and coated on the inner and outer surfaces of silicone indwelling urinary catheters using the dip coating method. The results showed that the antimicrobial urinary catheter fabricated using a 0.3 mM AgNO3 concentration and 48 h coating time gave the highest antibacterial activity. The as-prepared spherical AgNPs with an average diameter of 9.1 ± 0.4 nm formed on catheter surfaces in a monolayer approximately 1.3 µm thick corresponding to a 0.712 mg/cm2 silver content. The AgNP layer was found to damage and almost completely inhibit the growth of Escherichia coli cells with antibacterial activity by 91%, equivalent to the commercial, high-price antimicrobial urinary catheter. The cumulative amount of silver released from the coated catheter through artificial urine over 10 days was about 0.040 µg/mL, which is less than the silver content that causes tissue and organ toxicity at 44 µg/mL. Thus, we concluded that the developed antimicrobial urinary catheter was useful in reducing the risk of infectious complications in patients with indwelling catheters.
CFD Modeling and Experimental Validation of an Alkaline Water Electrolysis Cell for Hydrogen Production
Authors: Rodríguez Jesús , Amores Ernesto
Published: December 2020

Although alkaline water electrolysis (AWE) is the most widespread technology for hydrogen production by electrolysis, its electrochemical and fluid dynamic optimization has rarely been addressed simultaneously using Computational Fluid Dynamics (CFD) simulation. In this regard, a two-dimensional (2D) CFD model of an AWE cell has been developed using COMSOL® software and then experimentally validated. The model involves transport equations for both liquid and gas phases as well as equations for the electric current conservation. This multiphysics approach allows the model to simultaneously analyze the fluid dynamic and electrochemical phenomena involved in an electrolysis cell. The electrical response was evaluated in terms of polarization curve (voltage vs. current density) at different operating conditions: temperature, electrolyte conductivity, and electrode-diaphragm distance. For all cases, the model fits very well with the experimental data with an error of less than 1% for the polarization curves. Moreover, the model successfully simulates the changes on gas profiles along the cell, according to current density, electrolyte flow rate, and electrode-diaphragm distance. The combination of electrochemical and fluid dynamics studies provides comprehensive information and makes the model a promising tool for electrolysis cell design.
Potential Impact of Biodegradable Surfactants on Foam-Based Microalgal Cultures
Authors: Vázquez María , Castilla-Alcántara José Carlos , Garbayo Inés , Vílchez Carlos , Cuaresma María
Published: December 2020

Microalgae cultivation in liquid foams is a promising concept which requires the use of a surfactant as a foam stabilizing agent. The biodegradable character of a surfactant is a key aspect regarding its applicability in a liquid foam-bed photobioreactor (LF-PBR), since it might influence microalgal growth and the stability of the foam-based cultivation. In this work, the effects of the biodegradable surfactants bovine serum albumin (BSA), Saponin and Tween 20 on the whole microbial community of microalgal cultures (i.e., microalgal and bacterial populations) were studied. The three surfactants enhanced bacterial and microalgal growth in non-axenic microalgal cultures, but they differed in their efficiency to sustain bacterial growth. In this sense, Saponin was proven to enhance the growth of S. obliquus-associated bacteria in microalgae-free cultures, and to sustain it even when other nutrients were lacking, suggesting that Saponin can be used as an energy and nutrients source by these bacteria. The degradation and consumption of Saponin by S. obliquus-associated bacteria was also confirmed by the foaming capacity decrease in Saponin-added bacterial cultures. The biodegradable character of BSA, Saponin and Tween 20 reduces their suitability to be used in a LF-PBR since they would not be able to maintain stable foaming.
Hallowed Haunts: The National African American Museum as Sacred Space
Authors: Newton Richard
Published: December 2020

This paper uses Stephen Best’s None Like Us and Charles H. Long’s Significations: Signs, Symbols, and Images in the Interpretation of Religion to redescribe the notion of sacred space in light of the national African American museum. After highlighting religion and the museum’s mutual Romantic origins, it underscores the invisible institution of slave religion as a modern counterpoint that is harrowingly evocative of the indeterminacy of human meaning-making. The national African American museum, represented by offerings from the Smithsonian Institution and the Equal Justice Initiative, operates as a social technology for working through the tensions of history. “Hallowed Haunts” examines its function as a matrix of haunting, where a variety of multi-sensory experiences lead visitors into a participatory reckoning with the legacy of slavery, one through which they determine how to face the challenges and potential opportunities that await them. As such, the national African American museum exemplifies Long’s thesis of sacred space as human centers, a metonym for the places humans visit for orientation.
Collapsed Building Detection Using 3D Point Clouds and Deep Learning
Authors: Xiu Haoyi , Shinohara Takayuki , Matsuoka Masashi , Inoguchi Munenari , Kawabe Ken , Horie Kei
Published: December 2020

Collapsed buildings should be detected with the highest priority during earthquake emergency response, due to the associated fatality rates. Although deep learning-based damage detection using vertical aerial images can achieve high performance, as depth information cannot be obtained, it is difficult to detect collapsed buildings when their roofs are not heavily damaged. Airborne LiDAR can efficiently obtain the 3D geometries of buildings (in the form of point clouds) and thus has greater potential to detect various collapsed buildings. However, there have been few previous studies on deep learning-based damage detection using point cloud data, due to a lack of large-scale datasets. Therefore, in this paper, we aim to develop a dataset tailored to point cloud-based building damage detection, in order to investigate the potential of point cloud data in collapsed building detection. Two types of building data are created: building roof and building patch, which contains the building and its surroundings. Comprehensive experiments are conducted under various data availability scenarios (pre–post-building patch, post-building roof, and post-building patch) with varying reference data. The pre–post scenario tries to detect damage using pre-event and post-event data, whereas post-building patch and roof only use post-event data. Damage detection is implemented using both basic and modern 3D point cloud-based deep learning algorithms. To adapt a single-input network, which can only accept one building’s data for a prediction, to the pre–post (double-input) scenario, a general extension framework is proposed. Moreover, a simple visual explanation method is proposed, in order to conduct sensitivity analyses for validating the reliability of model decisions under the post-only scenario. Finally, the generalization ability of the proposed approach is tested using buildings with different architectural styles acquired by a distinct sensor. The results show that point cloud-based methods can achieve high accuracy and are robust under training data reduction. The sensitivity analysis reveals that the trained models are able to locate roof deformations precisely, but have difficulty recognizing global damage, such as that relating to the roof inclination. Additionally, it is revealed that the model decisions are overly dependent on debris-like objects when surroundings information is available, which leads to misclassifications. By training on the developed dataset, the model can achieve moderate accuracy on another dataset with different architectural styles without additional training.
Multi-Year Winter Variations in Suspended Sediment Flux through the Bohai Strait
Authors: Liu Xingmin , Qiao Lulu , Zhong Yi , Xue Wenjing , Liu Peng
Published: December 2020

The Bohai Strait is the only channel that allows material exchanges between the Bohai Sea and the Yellow Sea. It is also the only channel for the transportation of materials from the Yellow River to the Yellow Sea and the East China Sea. The supply of suspended sediment from the Bohai Sea plays a decisive role in the evolution of the mud area in the northern Yellow Sea and even the muddy area in the southern Yellow Sea. Previous studies have demonstrated that sediment exchange through the Bohai Strait occurs mainly in winter, but due to the lack of long-term observational data, changes in the sediment flux over multiple years have not been studied. In this paper, based on L1B data from the MODIS (Moderate Resolution Imaging Spectroradiometer) -Aqua satellite, an interannual time series of the suspended sediment concentration (SSC) at each depth layers in the Bohai Strait in winter was established through 16 cruises that benefited from the complete vertical mixing water in the strait in winter. The numerical model FVCOM, (Finite-Volume Community Ocean Model) which is forced by the hourly averaged wind field, reflected the effect of winter gales. With the model simulated winter current from 2002 to the present and the SSC at each layer, multi-year winter suspended sediment flux data were obtained for the Bohai Strait. This study found that in the winter, the suspended sediment output from the Bohai Sea to the Yellow Sea through the southern part of the Bohai Strait, while the suspended sediment input from the Yellow Sea to the Bohai Sea is through the northern part. In terms of long-term changes, the net flux ranged between 1.22 to 2.70 million tons in winter and showed a weak downward trend. The output flux and input flux both showed an upward trend, but the increase rate of the input flux was 51,100 tons/year, which was higher than the increase of the output flux rate (46,100 tons/year). These changes were mainly controlled by the increasing strength of east component of winter wind. And the weak decrease in net flux is controlled by the difference of output and input flux.
An Analysis of the Reliability of a Bus Safety Structure on Carrying Out the Numerical and Experimental Tests
Authors: Pravilonis Tautvydas , Eidukynas Valdas , Sokolovskij Edgar
Published: December 2020

In the paper, the reliability of a spatial tubular structure of a bus safety frame formed of different steel profiles is discussed. A methodology for the bus safety structure modeling is presented herein by applying numerical methods that enable us to simulate virtually a test for assessing bus rollover crashworthiness according to the United Nations Economic Commission for Europe (UNECE) Regulation No. 66, and also to assess and ensure the reliability and safety of the structure under operating conditions. The simulation has been performed by applying the mixed method of kinematical analysis and finite elements. In the course of the calculations, physical and geometrical non-linearity of materials was assessed. In addition, an experimental rollover test according to UNECE Regulation No. 66 was performed in this work, striving to verify the provided methodology for modeling by applying numerical methods. For the experiment, an identical safety structure and a rollover stand (identical to the one used in modeling) were used. The rollover test was shot by a Phantom v711 high-speed camera. In the paper, the results of kinematical and dynamic analysis from applying the finite element method and the ones of the experimental test, as well as their comparisons, are provided. It is assessed whether the developed safety structure model is reliable and suitable for use.
Multi-Sensor Fusion: A Simulation Approach to Pansharpening Aerial and Satellite Images
Authors: Siok Katarzyna , Ewiak Ireneusz , Jenerowicz Agnieszka
Published: December 2020

The growing demand for high-quality imaging data and the current technological limitations of imaging sensors require the development of techniques that combine data from different platforms in order to obtain comprehensive products for detailed studies of the environment. To meet the needs of modern remote sensing, the authors present an innovative methodology of combining multispectral aerial and satellite imagery. The methodology is based on the simulation of a new spectral band with a high spatial resolution which, when used in the pansharpening process, yields an enhanced image with a higher spectral quality compared to the original panchromatic band. This is important because spectral quality determines the further processing of the image, including segmentation and classification. The article presents a methodology of simulating new high-spatial-resolution images taking into account the spectral characteristics of the photographed types of land cover. The article focuses on natural objects such as forests, meadows, or bare soils. Aerial panchromatic and multispectral images acquired with a digital mapping camera (DMC) II 230 and satellite multispectral images acquired with the S2A sensor of the Sentinel-2 satellite were used in the study. Cloudless data with a minimal time shift were obtained. Spectral quality analysis of the generated enhanced images was performed using a method known as “consistency” or “Wald’s protocol first property”. The resulting spectral quality values clearly indicate less spectral distortion of the images enhanced by the new methodology compared to using a traditional approach to the pansharpening process.
Towards Continuous and Ambulatory Blood Pressure Monitoring: Methods for Efficient Data Acquisition for Pulse Transit Time Estimation
Authors: Ode Oludotun , Orlandic Lara , Inan Omer T.
Published: December 2020

We developed a prototype for measuring physiological data for pulse transit time (PTT) estimation that will be used for ambulatory blood pressure (BP) monitoring. The device is comprised of an embedded system with multimodal sensors that streams high-throughput data to a custom Android application. The primary focus of this paper is on the hardware–software codesign that we developed to address the challenges associated with reliably recording data over Bluetooth on a resource-constrained platform. In particular, we developed a lossless compression algorithm that is based on optimally selective Huffman coding and Huffman prefixed coding, which yields virtually identical compression ratios to the standard algorithm, but with a 67–99% reduction in the size of the compression tables. In addition, we developed a hybrid software–hardware flow control method to eliminate microcontroller (MCU) interrupt-latency related data loss when multi-byte packets are sent from the phone to the embedded system via a Bluetooth module at baud rates exceeding 115,200 bit/s. The empirical error rate obtained with the proposed method with the baud rate set to 460,800 bit/s was identically equal to 0%. Our robust and computationally efficient physiological data acquisition system will enable field experiments that will drive the development of novel algorithms for PTT-based continuous BP monitoring.
Railway Infrastructure Classification and Instability Identification Using Sentinel-1 SAR and Laser Scanning Data
Authors: Chang Ling , Sakpal Nikhil P. , Elberink Sander Oude , Wang Haoyu
Published: December 2020

Satellite radar interferometry (InSAR) techniques have been successfully applied for structural health monitoring of line-infrastructure such as railway. Limited by meter-level spatial resolution of Sentinel-1 satellite radar (SAR) imagery and meter-level geolocation precision, it is still challenging to (1) categorize radar scatterers (e.g., persistent scatterers (PS)) and associate radar scatterers with actual objects along railways, and (2) identify unstable railway segments using InSAR Line of Sight (LOS) deformation time series from a single viewing geometry. In response to this, (1) we assess and improve the 3-D geolocation quality of Sentinel-1 derived PS using a 2-step method for PS 3-D geolocation improvement aided by laser scanning data; after geolocation improvement, we step-wisely classify railway infrastructure into rails, embankments and surroundings; (2) we recognize unstable rail segments by utilizing the (localized) differential settlement of rails in the normal direction (near vertical) which is yielded from the LOS deformation decomposition. We tested and evaluated the methods using 170 Sentinel-1a/b ascending data acquired between January 2017 and December 2019, over the Betuwe freight train track, in the Netherlands. The results show that 98% PS were associated with real objects with a significance level of 25%, the PS settlement measurements were generally in line with the in-situ track survey Rail Infrastructure aLignment Acquisition (RILA) measurements, and the standard deviations of the PS settlement measurements varied slightly with an average value of 6.16 mm.
A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction
Authors: Zhao Chengying , Huang Xianzhen , Li Yuxiong , Yousaf Iqbal Muhammad
Published: December 2020

In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.
Temperature Compensation for Conductivity-Based Phase Fraction Measurements with Wire-Mesh Sensors in Gas-Liquid Flows of Dilute Aqueous Solutions
Authors: Wiedemann Philipp , Dias Felipe de Assis , Schleicher Eckhard , Hampel Uwe
Published: December 2020

Wire-mesh sensors are well-established scientific instruments for measuring the spatio-temporal phase distribution of two-phase flows based on different electrical conductivities of the phases. Presently, these instruments are also applied in industrial processes and need to cope with dynamic operating conditions increasingly. However, since the quantification of phase fractions is achieved by normalizing signals with respect to a separately recorded reference measurement, the results are sensitive to temperature differences in any application. Therefore, the present study aims at proposing a method to compensate temperature effects in the data processing procedure. Firstly, a general approach is theoretically derived from the underlying measurement principle and compensation procedures for the electrical conductivity from literature models. Additionally, a novel semi-empirical model is developed on the basis of electrochemical fundamentals. Experimental investigations are performed using a single-phase water loop with adjustable fluid temperature in order to verify the theoretical approach for wire-mesh sensor applications and to compare the different compensation models by means of real data. Finally, the preferred model is used to demonstrate the effect of temperature compensation with selected sets of experimental two-phase data from a previous study. The results are discussed in detail and show that temperature effects need to be handled carefully—not merely in industrial applications, but particularly in laboratory experiments.
Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records
Authors: Hsu Jia-Lien , Hsu Teng-Jie , Hsieh Chung-Ho , Singaravelan Anandakumar
Published: December 2020

The International Statistical Classification of Disease and Related Health Problems (ICD) is an international standard system for categorizing and reporting diseases, injuries, disorders, and health conditions. Most previously-proposed disease predicting systems need clinical information collected by the medical staff from the patients in hospitals. In this paper, we propose a deep learning algorithm to classify disease types and identify diagnostic codes by using only the subjective component of progress notes in medical records. In this study, we have a dataset, consisting of about one hundred and sixty-eight thousand medical records, from a medical center, collected during 2003 and 2017. First, we apply standard text processing procedures to parse the sentences and word embedding techniques for vector representations. Next, we build a convolution neural network model on the medical records to predict the ICD-9 code by using a subjective component of the progress note. The prediction performance is evaluated by ten-fold cross-validation and yields an accuracy of 0.409, recall of 0.409 and precision of 0.436. If we only consider the “chapter match” of ICD-9 code, our model achieves an accuracy of 0.580, recall of 0.580, and precision of 0.582. Since our diagnostic code prediction model is solely based on subjective components (mainly, patients’ self-report descriptions), the proposed approach could serve as a remote and self-diagnosis assistance tool, prior to seeking medical advice or going to the hospital. In addition, our work may be used as a primary evaluation tool for discomfort in the rural area where medical resources are restricted.
LiDAR-Based Bridge Displacement Estimation Using 3D Spatial Optimization
Authors: Cha Gichun , Sim Sung-Han , Park Seunghee , Oh Taekeun
Published: December 2020

As civil engineering structures become larger, non-contact inspection technology is required to measure the overall shape and size of structures and evaluate safety. Structures are easily exposed to the external environment and may not be able to perform their original functions depending on the continuous load for a long time. Therefore, in this study, we propose a method for estimating the vertical displacement of structures using light detection and ranging, which enables non-contact measurement. The point cloud acquired through laser scanning was rearranged into a three-dimensional space, and internal nodes were created by continuously dividing the space. The generated node has its own location information, and the vertical displacement value was calculated by searching for the node where the deformation occurred. The performance of the proposed displacement estimation technique was verified through static loading experiments, and the octree space partitioning method is expected to be applied and utilized in structural health monitoring.
Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts
Authors: Rossi Alessio , Pedreschi Dino , Clifton David A. , Morelli Davide
Published: December 2020

Application of ultra–short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people’s daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase.
Deep Learning-Based Cell-Level and Beam-Level Mobility Management System
Authors: Klus Roman , Klus Lucie , Solomitckii Dmitrii , Talvitie Jukka , Valkama Mikko
Published: December 2020

The deployment with beamforming-capable base stations in 5G New Radio (NR) requires an efficient mobility management system to reliably operate with minimum effort and interruption. In this work, we propose two artificial neural network models to optimize the cell-level and beam-level mobility management. Both models consist of convolutional, as well as dense, layer blocks. Based on current and past received power measurements, as well as positioning information, they choose the optimum serving cell and serving beam, respectively. The obtained results show that the proposed cell-level mobility model is able to sustain a strong serving cell and reduce the number of handovers by up to 94.4% compared to the benchmark solution when the uncertainty (representing shadowing, interference, etc.) is introduced to the received signal strength measurements. The proposed beam-level mobility management model is able to proactively choose and sustain the strongest serving beam, even when high uncertainty is introduced to the measurements.
A Preliminary Study on the Differentiation of Linseed and Poppy Oil Using Principal Component Analysis Methods Applied to Fiber Optics Reflectance Spectroscopy and Diffuse Reflectance Imaging Spectroscopy
Authors: Amato Silvia Rita , Burnstock Aviva , Michelin Anne
Published: December 2020

This paper presents results from the examination of a set of experimental samples using fibre optic reflectance spectroscopy (FORS) and diffuse reflectance imaging spectroscopy in the short-wave infrared (SWIR) range, combined with statistical analysis of the data for the discrimination and mapping of poppy and linseed oil. The aim was to evaluate the efficacy of this non-invasive approach for the study of paint samples with a view to the application of the method for characterisation of the two drying oils in painted art. The approach allowed discrimination between the two drying oils based on FORS spectra and the hyperspectral cube, indicating the influence of the spectral region around 1700 nm on the statistical discrimination based on the anti-symmetric and symmetric first overtone stretching of methylenic CH2 groups. This method is being studied as a potential non-invasive method of organic analysis of oil types that have formerly been studied using gas chromatography-mass spectrometry (GC-MS), which requires micro-samples.
Gait Trajectory and Gait Phase Prediction Based on an LSTM Network
Authors: Su Binbin , Gutierrez-Farewik Elena M.
Published: December 2020

Lower body segment trajectory and gait phase prediction is crucial for the control of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, we propose an approach to predict segment trajectories up to 200 ms ahead (angular velocity of the thigh, shank and foot segments) and five gait phases (loading response, mid-stance, terminal stance, preswing and swing), based on collected data from inertial measurement units placed on the thighs, shanks, and feet. The approach we propose is a long-short term memory (LSTM)-based network, a modified version of recurrent neural networks, which can learn order dependence in sequence prediction problems. The algorithm proposed has a weighted discount loss function that places more weight in predicting the next three to five time frames but also contributes to an overall prediction performance for up to 10 time frames. The LSTM model was designed to learn lower limb segment trajectories using training samples and was tested for generalization across participants. All predicted trajectories were strongly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The proposed LSTM approach can also accurately predict the five gait phases, particularly swing phase with 95% accuracy in inter-subject implementation. The ability of the LSTM network to predict future gait trajectories and gait phases can be applied in designing exoskeleton controllers that can better compensate for system delays to smooth the transition between gait phases.
A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers
Authors: Guillén-Navarro Miguel A. , Martínez-España Raquel , Bueno-Crespo Andrés , Morales-García Juan , Ayuso Belén , Cecilia José M.
Published: December 2020

Precision agriculture is a growing sector that improves traditional agricultural processes through the use of new technologies. In southeast Spain, farmers are continuously fighting against harsh conditions caused by the effects of climate change. Among these problems, the great variability of temperatures (up to 20 °C in the same day) stands out. This causes the stone fruit trees to flower prematurely and the low winter temperatures freeze the flower causing the loss of the crop. Farmers use anti-freeze techniques to prevent crop loss and the most widely used techniques are those that use water irrigation as they are cheaper than other techniques. However, these techniques waste too much water and it is a scarce resource, especially in this area. In this article, we propose a novel intelligent Internet of Things (IoT) monitoring system to optimize the use of water in these anti-frost techniques while minimizing crop loss. The intelligent component of the IoT system is designed using an approach based on a multivariate Long Short-Term Memory (LSTM) model, designed to predict low temperatures. We compare the proposed approach of multivariate model with the univariate counterpart version to figure out which model obtains better accuracy to predict low temperatures. An accurate prediction of low temperatures would translate into significant water savings, as anti-frost techniques would not be activated without being necessary. Our experimental results show that the proposed multivariate LSTM approach improves the univariate counterpart version, obtaining an average quadratic error no greater than 0.65 °C and a coefficient of determination R2 greater than 0.97. The proposed system has been deployed and is currently operating in a real environment obtained satisfactory performance.
Modelling the Influence of Electromagnetic Field on the User of a Wearable IoT Device Used in a WSN for Monitoring and Reducing Hazards in the Work Environment
Authors: Zradziński Patryk , Karpowicz Jolanta , Gryz Krzysztof , Morzyński Leszek , Młyński Rafał , Swidziński Adam , Godziszewski Konrad , Ramos Victoria
Published: December 2020

The aim of this study was to evaluate the absorption in a user’s head of an electromagnetic field (EMF) emitted by the Wi-Fi and/or Bluetooth module of a wearable small Internet of Things (IoT) electronic device (emitting EMF of up to 100 mW), in order to test the hypothesis that EMF has an insignificant influence on humans, and to compare the levels of such EMF absorption in various scenarios when using this device. The modelled EMF source was a meandered inverted-F antenna (MIFA)-type antenna of the ESP32-WROOM-32 radio module used in wearable devices developed within the reported study. To quantify the EMF absorption, the specific energy absorption rate (SAR) values were calculated in a multi-layer ellipsoidal model of the human head (involving skin, fat, skull bones and brain layers). The obtained results show up to 10 times higher values of SAR from the MIFA located in the headband, in comparison to its location on the helmet. Only wearable IoT devices (similar in construction and way of use to the investigated device) emitting at below 3 mW equivalent isotropically radiated power (EIRP) from Wi-Fi/Bluetooth communications modules may be considered environmentally insignificant EMF sources.
Design of a Test for Detecting the Presence of Impulsive Noise
Authors: Oh Hyungkook , Seo Dongho , Nam Haewoon
Published: December 2020

This paper proposes a new test method of detecting the presence of impulsive noise based on a complementary cumulative density function (CCDF). Impulsive noise severely degrades performance of communication systems and the conventional Kolmogorov–Smirnov (K–S) test may not perform well, because the test does not consider the characteristics of impulsive noise. In order to detect the presence of impulsive noise reliably, the CCDF of measurement samples is analyzed and compared with the CCDF of additive white Gaussian noise to find the difference between those CCDFs. Due to the nature of heavy-tails in impulsive noise, only the maximum difference may not be sufficient for the accurate detection of impulsive noise. Therefore, the proposed method applies the test hypothesis using the weighted sum of all the differences between those CCDFs. Simulation results justify that the proposed test is more robust and provides lower miss detection probability than the K–S test in the presence of impulsive noise.
Linear and Decoupled Decoders for Dual-Polarized Antenna-Based MIMO Systems
Authors: Qureshi Sara Shakil , Ali Sajid , Hassan Syed Ali
Published: December 2020

Quaternion orthogonal designs (QODs) have been used to design STBCs that provide improved performance in terms of various design parameters. In this paper, we show that all QODs obtained from generic iterative construction techniques based on the Adams-Lax-Phillips approach have linear and decoupled decoders which significantly reduce the computational complexity at the receiver. Our result is based on the quaternionic description of communication channels among dual-polarized antennas. Another contribution of this work is the linear and decoupled decoder for quasi-orthogonal codes for non-square as well as square designs. The proposed solution promises diversity gains with the quaternionic channel model and the decoding solution is independent of the number of receive dual-polarized antennas. A brief comparison is presented at the end to demonstrate the effectiveness of quaternion designs in two dual-polarized antennas over available STBCs for four single-polarized antennas. Linear and decoupled decoding of two quasi-orthogonal designs is shown, which has failed to exit previously. In addition, a QOD for 2×1 dual-polarized antenna configuration using quaternionic channel model shows a 3 dB gain at 10−5 in comparison to the same code evaluated for 2×2 complex representation of the quaternionic channel. This gain is further enhanced when the received diversity for these the cases is matched i.e., 2×2. The code using the quaternionic channel model shows a further 13 dB improvement at 10−5 BER.