bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatic Stockpile Extraction and Measurement Using 3D Point Cloud and Multi-Scale Directional Curvature
Authors: Yang Xingyu , Huang Yuchun , Zhang Qiulan
Published: March 2020

With the rapid increase of power supply demand, a large amount of stockpiles of coal have been formed during the process of coal excavation and transportation between the mines, ports, power plants and etc. Quantitative parameters, especially the volume of stockpile are important for the planning of coal production and consumption. Although laser scanning can collect the dense 3D coordinates of the stockpile surface for its quantification, stockpiles of coal have irregular shape, size, height, and base conditions, and may be overlapped with each other, which makes it hard to measure the different stockpiles automatically and accurately. This paper proposes an algorithm to extract and measure the stockpiles from the 3D point cloud data using the multi-scale directional curvature. Firstly, the second-order directional curvature analysis along multiple directions and at multiple scales is proposed to extract the distinctive ridge of crest in the point cloud of stockpiles. Then, starting with the crest points, a competitive growing strategy is proposed to accurately locate the points of slope in the stockpile. Finally, the stockpile’s volume is calculated by reconstructing the complete points of crest and slope with many meshes and triangular prisms through the subsurface fitting and surface reconstruction. Experiments on both the synthetic and real point cloud of stockpiles demonstrate that the proposed algorithm can extract the stockpiles with the average accuracy over 93.5% and measure the volume of stockpiles with the average precision over 93.7%. It is promising for automatically measuring the stockpiles like sand, soybean, etc., and facilitating the scientific storage and transportation management, as well as the maintenance of safety inventory during operation.
Effect of X-Ray Tube Configuration on Measurement of Key Soil Fertility Attributes with XRF
Authors: Tavares Tiago Rodrigues , Molin José Paulo , Nunes Lidiane Cristina , Alves Elton Eduardo Novais , Melquiades Fábio L. , de Carvalho Hudson Wallace Pereira , Mouazen Abdul Mounem
Published: March 2020

The successful use of energy-dispersive X-ray fluorescence (ED-XRF) sensors for soil analysis requires the selection of an optimal procedure of data acquisition and a simple modelling approach. This work aimed at assessing the performance of a portable XRF (XRF) sensor set up with two different X-ray tube configurations (combinations of voltage and current) to predict nine key soil fertility attributes: (clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable nutrients (P, K, Ca, and Mg). An XRF, operated at a voltage of 15 kV (and current of 23 μA) and 35 kV (and current of 7 μA), was used for analyzing 102 soil samples collected from two agricultural fields in Brazil. Two different XRF data analysis scenarios were used to build the predictive models: (i) 10 emission lines of 15 keV spectra (EL-15), and (ii) 12 emission lines of 35 keV spectra (EL-35). Multiple linear regressions (MLR) were used for model calibration, and the models’ prediction performance was evaluated using different figures of merit. The results show that although X-ray tube configuration affected the intensity of the emission lines of the different elements detected, it did not influence the prediction accuracy of the studied key fertility attributes, suggesting that both X-ray tube configurations tested can be used for future analyses. Satisfactory predictions with residual prediction deviation (RPD) ≥ 1.54 and coefficient of determination (R2) ≥ 0.61 were obtained for eight out of the ten studied soil fertility attributes (clay, OM, CEC, V, and extractable K, Ca, and Mg). In addition, simple MLR models with a limited number of emission lines was effective for practical soil analysis of the key soil fertility attributes (except pH and extractable P) using XRF. The simple and transparent methodology suggested also enables future researches that seek to optimize the XRF scanning time in order to speed up the XRF analysis in soil samples.
Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering
Authors: Siomos Nikolaos , Fountoulakis Ilias , Natsis Athanasios , Drosoglou Theano , Bais Alkiviadis
Published: March 2020

In this study, we present an aerosol classification technique based on measurements of a double monochromator Brewer spectrophotometer during the period 1998–2017 in Thessaloniki, Greece. A machine learning clustering procedure was applied based on the Mahalanobis distance metric. The classification process utilizes the UV Single Scattering Albedo (SSA) at 340 nm and the Extinction Angstrom Exponent (EAE) at 320–360 nm that are obtained from the spectrophotometer. The analysis is supported by measurements from a CIMEL sunphotometer that were deployed in order to establish the training dataset of Brewer measurements. By applying the Mahalanobis distance algorithm to the Brewer timeseries, we automatically assigned measurements in one of the following clusters: Fine Non Absorbing Mixtures (FNA): 64.7%, Black Carbon Mixtures (BC): 17.4%, Dust Mixtures (DUST): 8.1%, and Mixed: 9.8%. We examined the clustering potential of the algorithm by reclassifying the training dataset and comparing it with the original one and also by using manually classified cases. The typing score of the Mahalanobis algorithm is high for all predominant clusters FNA: 77.0%, BC: 63.9%, and DUST: 80.3% when compared with the training dataset. We obtained high scores as well FNA: 100.0%, BC: 66.7%, and DUST: 83.3% when comparing it with the manually classified dataset. The flags obtained here were applied in the timeseries of the Aerosol Optical Depth (AOD) at 340 nm of the Brewer and the CIMEL in order to compare between the two and also stress the future impact of the proposed clustering technique in climatological studies of the station.
Radar-Based Detection of Respiration Rate with Adaptive Harmonic Quefrency Selection
Authors: Lee JeeEun , Yoo Sun K.
Published: March 2020

Continuous respiration monitoring is important for predicting a potential disease. Due to respiration measurements using contact sensors, it is difficult to achieve continuous measurement because the sensors are inconvenient to attach. In this study, a radar sensor was used for non-contact respiration measurements. The radar sensor had a high precision and could even be used in the dark. It could also be used continuously regardless of time and place. The radar sensor relied on the periodicity of respiration to detect the respiration rate. A respiration adaptive interval was set and the respiration rate was detected through harmonic quefrency selection. As a result, it was confirmed that there was no difference between the respiratory rate measured using a respiration belt and the respiratory rate detected using a radar sensor. Furthermore, case studies on changes in the radar position and about measurement for long periods confirmed that the radar sensor could detect respiration rate continuously regardless of the position and measurement duration.
Automated Indoor Image Localization to Support a Post-Event Building Assessment
Authors: Liu Xiaoyu , Dyke Shirley J. , Yeum Chul Min , Bilionis Ilias , Lenjani Ali , Choi Jongseong
Published: March 2020

Image data remains an important tool for post-event building assessment and documentation. After each natural hazard event, significant efforts are made by teams of engineers to visit the affected regions and collect useful image data. In general, a global positioning system (GPS) can provide useful spatial information for localizing image data. However, it is challenging to collect such information when images are captured in places where GPS signals are weak or interrupted, such as the indoor spaces of buildings. The inability to document the images’ locations hinders the analysis, organization, and documentation of these images as they lack sufficient spatial context. In this work, we develop a methodology to localize images and link them to locations on a structural drawing. A stream of images can readily be gathered along the path taken through a building using a compact camera. These images may be used to compute a relative location of each image in a 3D point cloud model, which is reconstructed using a visual odometry algorithm. The images may also be used to create local 3D textured models for building-components-of-interest using a structure-from-motion algorithm. A parallel set of images that are collected for building assessment is linked to the image stream using time information. By projecting the point cloud model to the structural drawing, the images can be overlaid onto the drawing, providing clear context information necessary to make use of those images. Additionally, components- or damage-of-interest captured in these images can be reconstructed in 3D, enabling detailed assessments having sufficient geospatial context. The technique is demonstrated by emulating post-event building assessment and data collection in a real building.
Surface Plasmon Resonance-Based Sensing Utilizing Spatial Phase Modulation in an Imaging Interferometer
Authors: Kaňok Roman , Ciprian Dalibor , Hlubina Petr
Published: March 2020

Spatial phase modulation in an imaging interferometer is utilized in surface plasmon resonance (SPR) based sensing of liquid analytes. In the interferometer, a collimated light beam from a laser diode irradiating at 637.1 nm is passing through a polarizer and is reflected from a plasmonic structure of SF10/Cr/Au attached to a prism in the Kretschmann configuration. The beam passes through a combination of a Wollaston prism, a polarizer and a lens, and forms an interference pattern on a CCD sensor of a color camera. Interference patterns obtained for different liquid analytes are acquired and transferred to the computer for data processing. The sensing concept is based on the detection of a refractive index change, which is transformed via the SPR phenomenon into an interference fringe phase shift. By calculating the phase shift for the plasmonic structure of SF10/Cr/Au of known parameters we demonstrate that this technique can detect different weight concentrations of ethanol diluted in water, or equivalently, different changes in the refractive index. The sensitivity to the refractive index and the detection limit obtained are −278 rad/refractive-index-unit (RIU) and 3.6 × 10 − 6 RIU, respectively. The technique is demonstrated in experiments with the same liquid analytes as in the theory. Applying an original approach in retrieving the fringe phase shift, we revealed good agreement between experiment and theory, and the measured sensitivity to the refractive index and the detection limit reached −226 rad/RIU and 4.4 × 10 − 6 RIU, respectively. These results suggest that the SPR interferometer with the detection of a fringe phase shift is particularly useful in applications that require measuring refractive index changes with high sensitivity.
An Ingenious Design of a High Performance-Low Complexity Image Compressor for Wireless Capsule Endoscopy
Authors: Intzes Ioannis , Meng Hongying , Cosmas John
Published: March 2020

Wireless Capsule Endoscopy is a state-of-the-art technology for medical diagnoses of gastrointestinal diseases. The amount of data produced by an endoscopic capsule camera is huge. These vast amounts of data are not practical to be saved internally due to power consumption and the available size. So, this data must be transmitted wirelessly outside the human body for further processing. The data should be compressed and transmitted efficiently in the domain of power consumption. In this paper, a new approach in the design and implementation of a low complexity, multiplier-less compression algorithm is proposed. Statistical analysis of capsule endoscopy images improved the performance of traditional lossless techniques, like Huffman coding and DPCM coding. Furthermore the Huffman implementation based on simple logic gates and without the use of memory tables increases more the speed and reduce the power consumption of the proposed system. Further analysis and comparison with existing state-of-the-art methods proved that the proposed method has better performance.
Digital Circuit for Seamless Resampling ADC Output Streams
Authors: D’Arco Mauro , Napoli Ettore , Zacharelos Efstratios
Published: March 2020

Fine resolution selection of the sample rate is not available in digital storage oscilloscopes (DSOs), so the user has to rely on offline processing to cope with such need. The paper first discusses digital signal processing based methods that allow changing the sampling rate by means of digital resampling approaches. Then, it proposes a digital circuit that, if included in the acquisition channel of a digital storage oscilloscope, between the internal analog-to-digital converter (ADC) and the acquisition memory, allows the user to select any sampling rate lower than the maximum one with fine resolution. The circuit relies both on the use of a short digital filter with dynamically generated coefficients and on a suitable memory management strategy. The output samples produced by the digital circuit are characterized by a sampling rate that can be incoherent with the clock frequency regulating the memory access. Both a field programmable gate array (FPGA) implementation and an application specific integrated circuit (ASIC) design of the proposed circuit are evaluated.
Fast Prototyping Microfluidics: Integrating Droplet Digital Lamp for Absolute Quantification of Cancer Biomarkers
Authors: Oliveira Beatriz , Veigas Bruno , Fernandes Alexandra R. , Águas Hugo , Martins Rodrigo , Fortunato Elvira , Baptista Pedro Viana
Published: March 2020

Microfluidic (MF) advancements have been leveraged toward the development of state-of-the-art platforms for molecular diagnostics, where isothermal amplification schemes allow for further simplification of DNA detection and quantification protocols. The MF integration with loop-mediated isothermal amplification (LAMP) is today the focus of a new generation of chip-based devices for molecular detection, aiming at fast and automated nucleic acid analysis. Here, we combined MF with droplet digital LAMP (ddLAMP) on an all-in-one device that allows for droplet generation, target amplification, and absolute quantification. This multilayer 3D chip was developed in less than 30 minutes by using a low-cost and extremely adaptable production process that exploits direct laser writing technology in “Shrinky-dinks” polystyrene sheets. ddLAMP and target quantification were performed directly on-chip, showing a high correlation between target concentration and positive droplet score. We validated this integrated chip via the amplification of targets ranging from five to 500,000 copies/reaction. Furthermore, on-chip amplification was performed in a 10 µL volume, attaining a limit of detection of five copies/µL under 60 min. This technology was applied to quantify a cancer biomarker, c-MYC, but it can be further extended to any other disease biomarker.
Stochastic Selection of Activation Layers for Convolutional Neural Networks
Authors: Nanni Loris , Lumini Alessandra , Ghidoni Stefano , Maguolo Gianluca
Published: March 2020

In recent years, the field of deep learning has achieved considerable success in pattern recognition, image segmentation, and many other classification fields. There are many studies and practical applications of deep learning on images, video, or text classification. Activation functions play a crucial role in discriminative capabilities of the deep neural networks and the design of new “static” or “dynamic” activation functions is an active area of research. The main difference between “static” and “dynamic” functions is that the first class of activations considers all the neurons and layers as identical, while the second class learns parameters of the activation function independently for each layer or even each neuron. Although the “dynamic” activation functions perform better in some applications, the increased number of trainable parameters requires more computational time and can lead to overfitting. In this work, we propose a mixture of “static” and “dynamic” activation functions, which are stochastically selected at each layer. Our idea for model design is based on a method for changing some layers along the lines of different functional blocks of the best performing CNN models, with the aim of designing new models to be used as stand-alone networks or as a component of an ensemble. We propose to replace each activation layer of a CNN (usually a ReLU layer) by a different activation function stochastically drawn from a set of activation functions: in this way, the resulting CNN has a different set of activation function layers.
Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches
Authors: Salamone Francesco , Bellazzi Alice , Belussi Lorenzo , Damato Gianfranco , Danza Ludovico , Dell’Aquila Federico , Ghellere Matteo , Megale Valentino , Meroni Italo , Vitaletti Walter
Published: March 2020

Personal Thermal Comfort models consider personal user feedback as a target value. The growing development of integrated “smart” devices following the concept of the Internet of Things and data-processing algorithms based on Machine Learning techniques allows developing promising frameworks to reach the best level of indoor thermal comfort closest to the real needs of users. The article investigates the potential of a new approach aiming at evaluating the effect of visual stimuli on personal thermal comfort perception through a comparison of 25 participants’ feedback exposed to a real scenario in a test cell and the same environment reproduced in Virtual Reality. The users’ biometric data and feedback about their thermal perception along with environmental parameters are collected in a dataset and managed with different Machine Learning techniques. The most suitable algorithm, among those selected, and the influential variables to predict the Personal Thermal Comfort Perception are identified. The Extra Trees classifier emerged as the most useful algorithm in this specific case. In real and virtual scenarios, the most important variables that allow predicting the target value are identified with an average accuracy higher than 0.99.
Biofeedback Systems for Gait Rehabilitation of Individuals with Lower-Limb Amputation: A Systematic Review
Authors: Escamilla-Nunez Rafael , Michelini Alexandria , Andrysek Jan
Published: March 2020

Individuals with lower-limb amputation often have gait deficits and diminished mobility function. Biofeedback systems have the potential to improve gait rehabilitation outcomes. Research on biofeedback has steadily increased in recent decades, representing the growing interest toward this topic. This systematic review highlights the methodological designs, main technical and clinical challenges, and evidence relating to the effectiveness of biofeedback systems for gait rehabilitation. This review provides insights for developing an effective, robust, and user-friendly wearable biofeedback system. The literature search was conducted on six databases and 31 full-text articles were included in this review. Most studies found biofeedback to be effective in improving gait. Biofeedback was most commonly concurrently provided and related to limb loading and symmetry ratios for stance or step time. Visual feedback was the most used modality, followed by auditory and haptic. Biofeedback must not be obtrusive and ideally provide a level of enjoyment to the user. Biofeedback appears to be most effective during the early stages of rehabilitation but presents some usability challenges when applied to the elderly. More research is needed on younger populations and higher amputation levels, understanding retention as well as the relationship between training intensity and performance.
Adaptive Real-Time Routing Protocol for (m,k)-Firm in Industrial Wireless Multimedia Sensor Networks
Authors: Kim Beom-Su , Kim Sangdae , Kim Kyong Hoon , Sung Tae-Eung , Shah Babar , Kim Ki-Il
Published: March 2020

Many applications are able to obtain enriched information by employing a wireless multimedia sensor network (WMSN) in industrial environments, which consists of nodes that are capable of processing multimedia data. However, as many aspects of WMSNs still need to be refined, this remains a potential research area. An efficient application needs the ability to capture and store the latest information about an object or event, which requires real-time multimedia data to be delivered to the sink timely. Motivated to achieve this goal, we developed a new adaptive QoS routing protocol based on the (m,k)-firm model. The proposed model processes captured information by employing a multimedia stream in the (m,k)-firm format. In addition, the model includes a new adaptive real-time protocol and traffic handling scheme to transmit event information by selecting the next hop according to the flow status as well as the requirement of the (m,k)-firm model. Different from the previous approach, two level adjustment in routing protocol and traffic management are able to increase the number of successful packets within the deadline as well as path setup schemes along the previous route is able to reduce the packet loss until a new path is established. Our simulation results demonstrate that the proposed schemes are able to improve the stream dynamic success ratio and network lifetime compared to previous work by meeting the requirement of the (m,k)-firm model regardless of the amount of traffic.
Degenerate Near-Planar 3D Reconstruction from Two Overlapped Images for Road Defects Detection
Authors: Hu Yazhe , Furukawa Tomonari
Published: March 2020

This paper presents a technique to reconstruct a three-dimensional (3D) road surface from two overlapped images for road defects detection using a downward-facing camera. Since some road defects, such as potholes, are characterized by 3D geometry, the proposed technique reconstructs road surfaces from the overlapped images prior to defect detection. The uniqueness of the proposed technique lies in the use of near-planar characteristics of road surfaces‘ in the 3D reconstruction process, which solves the degenerate road surface reconstruction problem. The reconstructed road surfaces thus result from the richer information. Therefore, the proposed technique detects road surface defects based on the accuracy-enhanced 3D reconstruction. Parametric studies were first performed in a simulated environment to analyze the 3D reconstruction error affected by different variables and show that the reconstruction errors caused by the camera’s image noise, orientation, and vertical movement are so small that they do not affect the road defects detection. Detailed accuracy analysis then shows that the mean and standard deviation of the errors are less than 0.6 mm and 1 mm through real road surface images. Finally, on-road tests demonstrate the effectiveness of the proposed technique in identifying road defects while having over 94% in precision, accuracy, and recall rate.
Wheel Alignment of a Suspension Module Unit Using a Laser Module
Authors: Kim Seong Han , Lee Kang In
Published: March 2020

Vehicle wheel alignment inspection is generally carried out using a computer vision-based system. Due to its inspection mechanism using four wheel centers, the computer vision-based system cannot be applied to the wheel alignment inspection of suspension module units. However, when a vehicle suspension module is being developed, there is no complete car ready for wheel alignment inspection even though it is a very important procedure for suspension property tests. This study proposes a novel and efficient way to inspect vehicle wheel alignment for suspension modules. Two laser modules and several mechanical jigs were employed for wheel alignment inspection, allowing the toe and camber angles of the suspension module to be measured. For accurate wheel alignment results, calibration of the laser modules was performed prior to the inspection. This calibration procedure adjusts the yaw and pitch angles of the laser module so that they can be orthogonal to the mounting jig. For the calibration, a novel method of using laser straightness was adopted and, consequently, 0.02 degrees of orthogonality was achieved. The wheel alignment inspection results were determined then verified using a vision system with two cameras. In order to use this vision system, two cameras were used and a new method of modifying the measurement mechanism was developed. According to the verification results, the proposed wheel alignment inspection provided very high measurement accuracy. The wheel alignment inspection mechanism proposed in this study can not only give very reliable results but also provide a cost-efficient method of inspecting the wheel alignment of suspension modules to automakers.
A Parallel Evolutionary Computing-Embodied Artificial Neural Network Applied to Non-Intrusive Load Monitoring for Demand-Side Management in a Smart Home: Towards Deep Learning
Authors: Lin Yu-Hsiu
Published: March 2020

Non-intrusive load monitoring (NILM) is a cost-effective approach that electrical appliances are identified from aggregated whole-field electrical signals, according to their extracted electrical characteristics, with no need to intrusively deploy smart power meters (power plugs) installed for individual monitored electrical appliances in a practical field of interest. This work addresses NILM by a parallel Genetic Algorithm (GA)-embodied Artificial Neural Network (ANN) for Demand-Side Management (DSM) in a smart home. An ANN’s performance in terms of classification accuracy depends on its training algorithm. Additionally, training an ANN/deep NN learning from massive training samples is extremely computationally intensive. Therefore, in this work, a parallel GA has been conducted and used to integrate meta-heuristics (evolutionary computing) with an ANN (neurocomputing) considering its evolution in a parallel execution relating to load disaggregation in a Home Energy Management System (HEMS) deployed in a real residential field. The parallel GA that involves iterations to excessively cost its execution time for evolving an ANN learning model from massive training samples to NILM in the HEMS and works in a divide-and-conquer manner that can exploit massively parallel computing for evolving an ANN and, thus, reduce execution time drastically. This work confirms the feasibility and effectiveness of the parallel GA-embodied ANN applied to NILM in the HEMS for DSM.
Arc Tracking Control in Insulation Systems for Aeronautic Applications: Challenges, Opportunities, and Research Needs
Authors: Riba Jordi-Roger , Gómez-Pau Álvaro , Moreno-Eguilaz Manuel , Bogarra Santiago
Published: March 2020

Next generation aircrafts will use more electrical power to reduce weight, fuel consumption, system complexity and greenhouse gas emissions. However, new failure modes and challenges arise related to the required voltage increase and consequent rise of electrical stress on wiring insulation materials, thus increasing the risk of electrical arc appearance. This work performs a critical and comprehensive review concerning arc tracking effects in wiring insulation systems, underlying mechanisms, role of materials and possible mitigation strategies, with a special focus on aircraft applications. To this end an evaluation of the scientific and technological state of the art is carried out from the analysis of theses, research articles, technical reports, international standards and white papers. This review paper also reports the limitations of existing insulation materials, standard test methods and mitigation approaches, while identifying the research needs to comply with the future demands of the aircraft industry.
From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems
Authors: Zalewski Pawel , Marchegiani Letizia , Elsts Atis , Piechocki Robert , Craddock Ian , Fafoutis Xenofon
Published: March 2020

Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system.
Non-Destructive Evaluation Device for Monitoring Fluid Viscosity
Authors: Abdulkareem Ahmed , Erturun Ugur , Mossi Karla
Published: March 2020

There is an increasing need for non-destructive, low-cost devices for real-time fluid viscosity monitoring. Therefore, in this study, a method based on structural health monitoring is adapted for monitoring fluid properties. A device is built such that an inexpensive and disposable viscosity probe be possible. The design incorporates a sensor/actuator pair using a piezoelectric material layered with copper/brass and capable of monitoring viscosity changes in low volume liquids (e.g., vacutainer vial). Experiments performed with the new device show a definite pattern of wave propagation in viscous solutions. A numerical model is built to investigate the wave propagation in the fluid. For experimental measurements, the sensor part of the device detects the generated pressure wave in fluid (e.g., air, water, glycerin) by the actuator part. The phase shift between the actuator and the sensor signals is then recorded and plotted for different concentrations of glycerin and water at room temperature. The results of this study show a direct correlation between the phase shift and varying viscosity in the ultrasonic frequency range from 6 to 9 MHz. The numerical simulation, performed utilizing acoustic modal and harmonic response analysis, results also demonstrate the same trend as the experimental results: a phase shift increases with the viscosity of the fluid.
Comparison of Different Remote Sensing Methods for 3D Modeling of Small Rock Outcrops
Authors: Mikita Tomáš , Balková Marie , Bajer Aleš , Cibulka Miloš , Patočka Zdeněk
Published: March 2020

This paper reviews the use of modern 3D image-based and Light Detection and Ranging (LiDAR) methods of surface reconstruction techniques for high fidelity surveys of small rock outcrops to highlight their potential within structural geology and landscape protection. LiDAR and Structure from Motion (SfM) software provide useful opportunities for rock outcrops mapping and 3D model creation. The accuracy of these surface reconstructions is crucial for quantitative structural analysis. However, these technologies require either a costly data acquisition device (Terrestrial LiDAR) or specialized image processing software (SfM). Recent developments in augmented reality and smartphone technologies, such as increased processing capacity and higher resolution of cameras, may offer a simple and inexpensive alternative for 3D surface reconstruction. Therefore, the aim of the paper is to show the possibilities of using smartphone applications for model creation and to determine their accuracy for rock outcrop mapping.
Towards Safety Improvement of Measurement and Control Signals Transmission in Marine Environment
Authors: Masnicki Romuald , Mindykowski Janusz , Grala Przemyslaw
Published: March 2020

This paper is dedicated to some aspects of maritime on-board safety improvement. The contribution of this article is a proposal to extend wireless communication on the ship, supported by examples of solutions that have not previously been used on board. Analyzed aspects concern the measurement and control signal transmission in hostile marine environment. A problem to solve is formulated, how to reduce a negative impact of this environment on effectiveness and reliability of maritime on board communication. The proposed ways for solving a problem under consideration cover recommendations concerning some aspects of ship construction and implementation of wireless sensor network. The main topic faced by the paper is concentrated on case study-based ZigBee as well as WiFi networks implementation in the area of the acquisition of data from sensors and measuring transducers connected to the terminal network. The exemplary implementation of ZigBee network, elaborated in Gdynia Maritime University is related, firstly, to the configuration of a simple wireless measurement and control channel, and secondly, to wireless communication channel supported by autonomously working microprocessor measurement and control system. WiFi networks were also tested in the same area of application. Finally, some concluding remarks are formulated.
On Maximizing Energy and Spectral Efficiencies Using Small Cells in 5G and Beyond Networks
Authors: Saha Rony Kumer
Published: March 2020

Addressing high capacity at low power as a key design goal envisages achieving high spectral efficiency (SE) and energy efficiency (EE) for the next-generation mobile networks. Because most data are generated in indoor environments, an ultra-dense deployment of small cells (SCs), particularly within multistory buildings in urban areas, is revealed as an effective technique to improve SE and EE by numerous studies. In this paper, we present a framework exploiting the four most interconnected-domain, including, power, time, frequency, and space, in the perspectives of SE and EE. Unlike existing literature, the framework takes advantage of higher degrees of freedom to maximize SE and EE using in-building SCs for 5G and beyond mobile networks. We derive average capacity, SE, and EE metrics, along with defining the condition for optimality of SE and EE and developing an algorithm for the framework. An extensive system-level evaluation is performed to show the impact of each domain on SE and EE. It is shown that employing multiband enabled SC base stations (SBSs) to increase operating spectrum in frequency-domain, reusing spectrum to SBSs more than once per building in spatial-domain, switching on and off each in-building SBS based on traffic availability to reduce SBS power consumption in power-domain, and using eICIC to avoid co-channel interference due to sharing spectrum with SBSs in time-domain can achieve massive SE and EE. Finally, we show that the proposed framework can satisfy SE, EE, as well as user experience data rate requirements for 5G and beyond mobile networks.
Investigating the Diversity of Marine Bacteriophage in Contrasting Water Masses Associated with the East Australian Current (EAC) System
Authors: Focardi Amaranta , Ostrowski Martin , Goossen Kirianne , Brown Mark V. , Paulsen Ian
Published: March 2020

Virus- and bacteriophage-induced mortality can have a significant impact on marine productivity and alter the flux of nutrients in marine microbial food-webs. Viral mediated horizontal gene transfer can also influence host fitness and community composition. However, there are very few studies of marine viral diversity in the Southern Hemisphere, which hampers our ability to fully understand the complex interplay of biotic and abiotic factors that shape microbial communities. We carried out the first genetic study of bacteriophage communities within a dynamic western boundary current (WBC) system, the east Australian current (EAC). Virus DNA sequences were extracted from 63 assembled metagenomes and six metaviromes obtained from various depths at 24 different locations. More than 1700 bacteriophage genomic fragments (>9 kbps) were recovered from the assembled sequences. Bacteriophage diversity displayed distinct depth and regional patterns. There were clear differences in the bacteriophage populations associated with the EAC and Tasman Sea euphotic zones, at both the taxonomic and functional level. In contrast, bathypelagic phages were similar across the two oceanic regions. These data provide the first characterisation of viral diversity across a dynamic western boundary current, which is an emerging model for studying the response of microbial communities to climate change.
Numerical and Analytical Study of a Battery Powered Vehicle Moving in a Vacuum Tunnel
Authors: Machaj Krystian , Malecha Ziemowit , Wrzecioniarz Piotr
Published: March 2020

The present work focuses on an aerodynamic and heat transfer study of a battery powered vehicle moving in a vacuum tunnel. The conducted research was based on analytical analysis and numerical calculations. Four different vacuum levels in the tunnel were considered—100 Pa, 1 kPa, 10 kPa and 100 kPa—and two distinct velocities of the vehicle—125 and 166 m/s—to address subsonic and supersonic conditions. It allowed defining limitations related to vacuum transportation in terms of velocity of the vehicle and a blocking ratio of the tunnel. Power consumption and drag coefficient for the considered tunnel pressures were analyzed. The cooling analysis of the batteries by passing air was performed numerically and analytically in the function of flow conditions in the tunnel. It gave some insight into main problems related to cooling of the batteries under low pressure and possible directions to solve it. It was shown that the proposed analytical model compared satisfactorily with the numerical results.
Discontinuous Space Vector PWM Strategy for Three-Phase Three-Level Electric Vehicle Traction Inverter Fed Two-Phase Load
Authors: Zhang Guozheng , Wan Yuwei , Wang Zhixin , Gao Le , Zhou Zhanqing , Geng Qiang
Published: March 2020

Discontinuous pulse width modulation (DPWM) strategies are usually adopted to reduce the switching loss and output current ripple of three-phase three-level traction inverters under three-phase load conditions. However, if there is a short circuit in any arbitrary phase or the inverter is used to feed a two-phase load, the output performance of conventional DPWM strategies will be deteriorated. Here, four improved DPWM (IDPWM) strategies for three-phase three-level neutral-point-clamped (NPC) traction inverter fed two-phase load are proposed. Unlike three-phase load conditions, the phase angle and the amplitude of each basic voltage vector in the space vector diagram are modified under two-phase load conditions. Consequently, sectors are re-divided and duty cycles of basic vectors during synthesis are recalculated. Clamping intervals of each phase for the four type discontinuous PWM (DPWM) strategies are rearranged according to the modified space vector diagram; then, the proposed DPWM strategies can be obtained. Compared with the conventional DPWM strategies, the output current waveform quality of the proposed strategy is significantly improved. Meanwhile, the amplitude of the neutral-point voltage ripple is also reduced.
Learning or leaving? An international qualitative study of factors affecting the resilience of female family doctors
Authors: Shiner Alice , Watson Jessica , Doohan Noemi , Howe Amanda
Published: March 2020

​Background

Many countries have insufficient numbers of family doctors, and more females than males leave the workforce at a younger age or have difficulty sustaining careers. Understanding the differing attitudes, pressures, and perceptions between genders toward their medical occupation is important to minimise workforce attrition.

​Aim

To explore factors influencing the resilience of female family doctors during lifecycle transitions.

​Design & setting

International qualitative study with female family doctors from all world regions.

​Method

Twenty semi-structured online Skype interviews, followed by three focus groups to develop recommendations. Data were transcribed and analysed using applied framework analysis.

​Results

Interview participants described a complex interface between competing demands, expectations of their gender, and internalised expectations of themselves. Systemic barriers, such as lack of flexible working, excessive workload, and the cumulative impacts of unrealistic expectations impaired the ability to fully contribute in the workplace. At the individual level, resilience related to: the ability to make choices; previous experiences that had encouraged self-confidence; effective engagement to obtain support; and the ability to handle negative experiences. External support, such as strong personal networks, and an adaptive work setting and organisation or system maximised interviewees’ professional contributions.

​Conclusion

On an international scale, female family doctors experience similar pressures from competing demands during lifecycle transitions; some of which relate to expectations of the female's ’role’ in society, particularly around the additional personal pressures of caring commitments. Such situations could be predicted, planned for, and mitigated with explicit support mechanisms and availability of workplace choices. Healthcare organisations and systems around the world should recognise this need and implement recommendations to help reduce workforce losses. These findings are likely to be of interest to all health professional staff of any gender.
LA IMPLEMENTACIÓN DEL TRIBUTO VERDE, COMO ELEMENTO QUE CONTRIBUYE AL FOMENTO DE LA CULTURA VERDE
Authors: Franco Génesis , Loyo Paola , Sánchez Arianny
Published: March 2020

El presente artículo, producto de un trabajo de investigación que tuvo como objetivo proponer la implementación del Tributo Verde como elemento que contribuye al fomento de la Cultura Verde. La investigación fue orientada por la teoría de cultura verde, compromiso, cuidado ambiental, responsabilidad y también junto a los hechos y cambios positivos que se han generado en otros países por la inclusión del Tributo Verde. Metodológicamente se apoya en una investigación no experimental basada en el análisis de los objetivos específicos, con el fin de indagar sobre los posibles beneficios que obtendría la incorporación del Tributo Verde, enmarcado bajo la modalidad de proyecto factible, con estudio documental y de campo, la información analizada es de primera mano, contando con la participación de los encargados de comercios, elegidos como los protagonistas clave para el logro y cumplimiento del objetivo propuesto, se hicieron visitas a los diferentes locales con el fin de poder recolectar información oportuna para llegar a una conclusión clara y veraz, basada en lo que realmente conocen los comerciantes sobre este modo de recaudación y la finalidad que se logra al incorporar el Tributo Verde.
STUDY ON SEARCHING FOR ONLINE DOCUMENTATION OF HUE UNVERSITY OF MEDECINE AND PHARMACY STUDENTS
Authors: Hoang Thi Bach Yen , Pham Thi Hai , Hoang Dinh Tuyen , Le Thi Huong , Vo Van Thang
Published: March 2020

Background: In undergraduate education, the exploitation of information and use of the Internet in teaching and learning is an indispensable requirement to serve requests renewal of teaching methods of teachers, help students actively in research , seek knowledge, and the means can be used to examine and evaluate the learning outcomes of students. Objectives: 1. Survey on the use of the internet for searching and learning of students; 2. Study the training, guidance for digital resource for students; 3. Understand the factors that affect online documentation of students. Methodology: A cross-sectional study was implemented on 346 students of general doctor from 1st to 6th year. Results: 96.2% of students using internet; 91,6% using internet at home. 79.2% of students owning mobile phones can access the internet. 15.9% of students had been guided, trained for searching online documentation. The limiting factors in finding online materials for students was that did not know how to search online documentation (49.7%), family condition (45.7%), the computer was not connected to the internet (43.4). Conclusion: Searching for online documentation is one of the most essential skills to help medical students get high results.

Key words: Internet, online documentation, medical students.
AMPLIFICATION AND OVEREXPRESSION OF HER2/NEU IN GASTRIC CANCER ASSESSED BY FLUORESCENCE IN SITU HYBRIDIZATION AND IMMUNOHISTOCHEMISTRY
Authors: Tran Van Huy , Ha Thi Minh Thi , Le Viet Nho , Dang Cong Thuan , Van Trung Nghia , Le Phan Tuong Quynh , Ta Van To , Nguyen Viet Nhan
Published: March 2020

Background: HER2/neu is a predictive biomarker for treatment of gastric cancer using trastuzumab in combination with chemotherapy. This study aimed to: (1) assess the amplification and the overexpression of HER2/neu using fluorescence in situ hybridization (FISH) and immunohistochemistry in gastric cancer; (2) survey the association between HER2/neu and clinicopathologic characteristics of gastric cancer. Patients and methods: one hundred and sixty gastric cancer patients were assessed HER2/neu overexpression by IHC using Ventana anti-HER-2/neu (4B5) kit and were assessed HER2/neu gene amplification by FISH using PathVysionTM HER-2 DNA Probe kit with biopsy specimens.
Results: HER2/neu protein expression rates of IHC 0, 1+, 2+ and 3+ were 70%, 10.6%, 10.6% and 8.8%, respectively. HER2/neu gene amplification was identified in gastric cancer from 21 out of 160 (13.1%) patients. The concordance between IHC and FISH was 90.0%. The HER2/neu-positive rate assessed by both techniques was 13.1%. There was a significant difference in HER2/neu-positivity between cardia gastric cancer and non-cardia gastric cancer (36.4% vs 11.4%, p = 0.040); between intestinal type and diffuse type (20.7% vs 5.9%, p = 0.010); between well, moderately differentiated and poorly differentiated gastric cancer (18.6% and 23.8% vs 5.8%, p = 0.047). Conclusion: We applied successfully FISH and IHC technique with biopsy samples in gastric cancer detecting HER2/neu positivity in order to select patients that benefit from trastuzumab in combination with chemotherapy. HER2/neu status associated with tumor location, Lauren classification and differentiated grading.

Keywords: gastric cancer, fluorescence in situ hybridization (FISH), immunohistochemistry (IHC), HER-2/neu