bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent Biomedical Approaches for Chitosan Based Materials as Drug Delivery Nanocarriers
Authors: Iacob Andreea Teodora , Lupascu Florentina Geanina , Apotrosoaei Maria , Vasincu Ioana Mirela , Tauser Roxana Georgiana , Lupascu Dan , Giusca Simona Eliza , Caruntu Irina-Draga , Profire Lenuta
Published: April 2021

In recent decades, drug delivery systems (DDSs) based on nanotechnology have been attracting substantial interest in the pharmaceutical field, especially those developed based on natural polymers such as chitosan, cellulose, starch, collagen, gelatin, alginate and elastin. Nanomaterials based on chitosan (CS) or chitosan derivatives are broadly investigated as promising nanocarriers due to their biodegradability, good biocompatibility, non-toxicity, low immunogenicity, great versatility and beneficial biological effects. CS, either alone or as composites, are suitable substrates in the fabrication of different types of products like hydrogels, membranes, beads, porous foams, nanoparticles, in-situ gel, microparticles, sponges and nanofibers/scaffolds. Currently, the CS based nanocarriers are intensely studied as controlled and targeted drug release systems for different drugs (anti-inflammatory, antibiotic, anticancer etc.) as well as for proteins/peptides, growth factors, vaccines, small DNA (DNAs) and short interfering RNA (siRNA). This review targets the latest biomedical approaches for CS based nanocarriers such as nanoparticles (NPs) nanofibers (NFs), nanogels (NGs) and chitosan coated liposomes (LPs) and their potential applications for medical and pharmaceutical fields. The advantages and challenges of reviewed CS based nanocarriers for different routes of administration (oral, transmucosal, pulmonary and transdermal) with reference to classical formulations are also emphasized.
The Loss of Self-Dignity and Anger among Polish Young Adults: The Moderating Role of Religiosity
Authors: Rodzeń Wojciech , Kulik Małgorzata Maria , Malinowska Agnieszka , Kroplewski Zdzisław , Szcześniak Małgorzata
Published: April 2021

Does the way we think or feel about ourselves have an impact on our anger-based reactions? Is the direction and strength of this relationship direct, or affected by other factors as well? Given that there is a lack of research on the loss of self-dignity and anger, the first aim of the present study consisted in examining whether or not there is a connection between both variables, with particular emphasis on early adulthood. The second purpose was to explore the moderating role of religiosity on the relationship between loss of self-dignity and anger. Methods: Data were gathered from 462 participants aged 18 to 35. The main methods applied were the Questionnaire of Sense of Self-Dignity, Buss–Perry Aggression Questionnaire, and Religious Meaning System Questionnaire. The results show a statistically significant positive correlation between loss of self-dignity and anger, a negative correlation between religiosity and anger, and no significant association between the loss of self-dignity and religiosity. However, all other dimensions of the sense of self-dignity correlated positively with religiosity. Our findings also confirm that the level of anger resulting from the loss of self-dignity is significantly lower as the level of religiosity increases. Such outcomes seem to support the conception that religiosity may act as a protective factor between the risk (loss of self-dignity) and the outcome factor (anger).
Contribution of Snow-Melt Water to the Streamflow over the Three-River Headwater Region, China
Authors: Li Sisi , Liu Mingliang , Adam Jennifer C. , Pi Huawei , Su Fengge , Li Dongyue , Liu Zhaofei , Yao Zhijun
Published: April 2021

Snowmelt water is essential to the water resources management over the Three-River Headwater Region (TRHR), where hydrological processes are influenced by snowmelt runoff and sensitive to climate change. The objectives of this study were to analyse the contribution of snowmelt water to the total streamflow (fQ,snow) in the TRHR by applying a snowmelt tracking algorithm and Variable Infiltration Capacity (VIC) model. The ratio of snowfall to precipitation, and the variation of the April 1 snow water equivalent (SWE) associated with fQ,snow, were identified to analyse the role of snowpack in the hydrological cycle. Prior to the simulation, the VIC model was validated based on the observed streamflow data to recognize its adequacy in the region. In order to improve the VIC model in snow hydrology simulation, Advanced Scanning Microwave Radiometer E (ASMR-E) SWE product data was used to compare with VIC output SWE to adjust the snow parameters. From 1971 to 2007, the averaged fQ,snow was 19.9% with a significant decreasing trend over entire TRHR (p < 0.05).The influence factor resulted in the rate of change in fQ,snow which were different for each sub-basin TRHR. The decreasing rate of fQ,snow was highest of 0.24%/year for S_Lantsang, which should be due to the increasing streamflow and the decreasing snowmelt water. For the S_Yangtze, the increasing streamflow contributed more than the stable change of snowmelt water to the decreasing fQ,snow with a rate of 0.1%/year. The April 1 SWE with the minimum value appearing after 2000 and the decreased ratio of snowfall to precipitation during the study period, suggested the snow solid water resource over the TRHR was shrinking. Our results imply that the role of snow in the snow-hydrological regime is weakening in the TRHR in terms of water supplement and runoff regulation due to the decreased fQ,snow and snowfall.
Earth Observation as a Facilitator of Climate Change Education in Schools: The Teachers’ Perspectives
Authors: Asimakopoulou Panagiota , Nastos Panagiotis , Vassilakis Emmanuel , Hatzaki Maria , Antonarakou Assimina
Published: April 2021

Climate change education (CCE) fosters the skills and behavioral patterns of students in regards to climate-related challenges and risks. Despite its importance, the integration of CCE in schools is challenging due to the interdisciplinary nature of climate science and the obstacles and demands of everyday school reality. Here, we examine the case of satellite Remote Sensing (RS) for Earth Observation (EO) as an innovative tool for facilitating CCE. We focus on Greece, a country that, despite being a hot spot for climate change, shows a low level of CCE integration in schools and awareness for EO-based educational resources. Based on interviews with in-service teachers, our research reveals the following: (a) there is a high interest in how satellites depict environmental phenomena; (b) EO is considered an efficient vehicle for promoting CCE in schools because it illustrates climate change impacts most effectively; (c) local natural disasters, such as intense forest fires and floods, are more familiar to students and, thus, preferable for teaching when compared to global issues, such as the greenhouse effect and sea level rise; and (d) educators are in favor of short, hands-on, EO-based activities (also known as “activity-shots”), as the most useful material format for integrating climate change topics in their everyday teaching practice.
Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies
Authors: Guillaume Annie S. , Leempoel Kevin , Rochat Estelle , Rogivue Aude , Kasser Michel , Gugerli Felix , Parisod Christian , Joost Stéphane
Published: April 2021

The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in the use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1 m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aim of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant, Arabis alpina, in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs, up to a spatial resolution of at least 1 m, rivalled the accuracy of LiDAR DEMs, largely owing to the customizability of PHOTO DEMs to the study sites compared to commercially available LiDAR DEMs. We obtained DEMs at spatial resolutions of 6.25 cm–8 m for PHOTO and 50 cm–32 m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32 m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50 cm in such studies.
City-Scale Mapping of Urban Façade Color Using Street-View Imagery
Authors: Zhong Teng , Ye Cheng , Wang Zian , Tang Guoan , Zhang Wei , Ye Yu
Published: April 2021

Precise urban façade color is the foundation of urban color planning. Nevertheless, existing research on urban colors usually relies on manual sampling due to technical limitations, which brings challenges for evaluating urban façade color with the co-existence of city-scale and fine-grained resolution. In this study, we propose a deep learning-based approach for mapping the urban façade color using street-view imagery. The dominant color of the urban façade (DCUF) is adopted as an indicator to describe the urban façade color. A case study in Shenzhen was conducted to measure the urban façade color using Baidu Street View (BSV) panoramas, with city-scale mapping of the urban façade color in both irregular geographical units and regular grids. Shenzhen’s urban façade color has a gray tone with low chroma. The results demonstrate that the proposed method has a high level of accuracy for the extraction of the urban façade color. In short, this study contributes to the development of urban color planning by efficiently analyzing the urban façade color with higher levels of validity across city-scale areas. Insights into the mapping of the urban façade color from the humanistic perspective could facilitate higher quality urban space planning and design.
Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing
Authors: Knapp Nikolai , Huth Andreas , Fischer Rico
Published: April 2021

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.
The Utilization of Artificial Neural Network Equalizer in Optical Camera Communications
Authors: Younus Othman Isam , Hassan Navid Bani , Ghassemlooy Zabih , Zvanovec Stanislav , Alves Luis Nero , Le-Minh Hoa
Published: April 2021

In this paper, we propose and validate an artificial neural network-based equalizer for the constant power 4-level pulse amplitude modulation in an optical camera communications system. We introduce new terminology to measure the quality of the communications link in terms of the number of row pixels per symbol Npps, which allows a fair comparison considering the progress made in the development of the current image sensors in terms of the frame rates and the resolutions of each frame. Using the proposed equalizer, we experimentally demonstrate a non-flickering system using a single light-emitting diode (LED) with Npps of 20 and 30 pixels/symbol for the unequalized and equalized systems, respectively. Potential transmission rates of up to 18.6 and 24.4 kbps are achieved with and without the equalization, respectively. The quality of the received signal is assessed using the eye-diagram opening and its linearity and the bit error rate performance. An acceptable bit error rate (below the forward error correction limit) and an improvement of ~66% in the eye linearity are achieved using a single LED and a typical commercial camera with equalization.
Predicting Seagoing Ship Energy Efficiency from the Operational Data
Authors: Vorkapić Aleksandar , Radonja Radoslav , Martinčić-Ipšić Sanda
Published: April 2021

This paper presents the application of machine learning (ML) methods in setting up a model with the aim of predicting the energy efficiency of seagoing ships in the case of a vessel for the transport of liquefied petroleum gas (LPG). The ML algorithm is learned from shipboard automation system measurement data, noon logbook reports, and related meteorological and oceanographic data. The model is tested with generalized linear model (GLM) regression, multilayer preceptor (MLP), support vector machine (SVM), and random forest (RF). Upon verification of modeling framework and analyzing the results to improve the prediction accuracy, the best numeric prediction algorithm is selected based on standard evaluation metrics for regression, i.e., primarily root mean square error (RMSE) and relative absolute error (RAE). Experimental results show that, by taking an adequate combination and processing of relevant measurement data, RF exhibits the lowest RMSE of 17.2632 and RAE 2.304%. Furthermore, this paper elaborates the selection of measurement data, the analysis of input parameters, and their significance in building the prediction model and selection of suitable output variables by the ship’s energy efficiency management plan (SEEMP). In addition, discretization was introduced to allow the end user to interpret the prediction results, placing them in the context of the actual ship operations. The results presented in this research can assist in setting up a decision support system whenever energy consumption savings in a marine transport are at stake.
Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices
Authors: Kazaz Billur , Poddar Subhadipto , Arabi Saeed , Perez Michael A. , Sharma Anuj , Whitman J. Blake
Published: April 2021

Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices.
Motionless Polarizing Structured Illumination Microscopy
Authors: Park Hyo Mi , Joo Ki-Nam
Published: April 2021

In this investigation, we propose a motionless polarizing structured illumination microscopy as an axially sectioning and reflective-type device to measure the 3D surface profiles of specimens. Based on the spatial phase-shifting technique to obtain the visibility of the illumination pattern. Instead of using a grid, a Wollaston prism is used to generate the light pattern by the stable interference of two beams. As the polarization states of two beams are orthogonal with each other, a polarization pixelated CMOS camera can simultaneously obtain four phase-shifted patterns with the beams after passing through a quarter wave plate based on the spatial phase-shifting technique with polarization. In addition, a focus tunable lens is used to eliminate a mechanical moving part for the axial scanning of the specimen. In the experimental result, a step height sample and a concave mirror were measured with 0.05 µm and 0.2 mm repeatabilities of step height and the radius of curvature, respectively.
Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
Authors: Poudel Sabitri , Moh Sangman
Published: April 2021

In unmanned aerial vehicle (UAV)-aided wireless sensor networks (UWSNs), a UAV is employed as a mobile sink to gather data from sensor nodes. Incorporating UAV helps prolong the network lifetime and avoid the energy-hole problem faced by sensor networks. In emergency applications, timely data collection from sensor nodes and transferal of the data to the base station (BS) is a prime requisite. The timely and safe path of UAV is one of the fundamental premises for effective UWSN operations. It is essential and challenging to identify a suitable path in an environment comprising various obstacles and to ensure that the path can efficiently reach the target point. This paper proposes a hybrid path planning (HPP) algorithm for efficient data collection by assuring the shortest collision-free path for UAV in emergency environments. In the proposed HPP scheme, the probabilistic roadmap (PRM) algorithm is used to design the shortest trajectory map and the optimized artificial bee colony (ABC) algorithm to improve different path constraints in a three-dimensional environment. Our simulation results show that the proposed HPP outperforms the PRM and conventional ABC schemes significantly in terms of flight time, energy consumption, convergence time, and flight path.
SSD-EMB: An Improved SSD Using Enhanced Feature Map Block for Object Detection
Authors: Choi Hong-Tae , Lee Ho-Jun , Kang Hoon , Yu Sungwook , Park Ho-Hyun
Published: April 2021

The development of deep learning has achieved great success in object detection, but small object detection is still a difficult and challenging task in computer vision. To address the problem, we propose an improved single-shot multibox detector (SSD) using enhanced feature map blocks (SSD-EMB). The enhanced feature map block (EMB) consists of attention stream and feature map concatenation stream. The attention stream allows the proposed model to focus on the object regions rather than background owing to channel averaging and the effectiveness of the normalization. The feature map concatenation stream provides additional semantic information to the model without degrading the detection speed. By combining the output of these two streams, the enhanced feature map, which improves the detection of a small object, is generated. Experimental results show that the proposed model has high accuracy in small object detection. The proposed model not only achieves good detection accuracy, but also has a good detection speed. The SSD-EMB achieved a mean average precision (mAP) of 80.4% on the PASCAL VOC 2007 dataset at 30 frames per second on an RTX 2080Ti graphics processing unit, an mAP of 79.9% on the VOC 2012 dataset, and an mAP of 26.6% on the MS COCO dataset.
CricShotClassify: An Approach to Classifying Batting Shots from Cricket Videos Using a Convolutional Neural Network and Gated Recurrent Unit
Authors: Sen Anik , Deb Kaushik , Dhar Pranab Kumar , Koshiba Takeshi
Published: April 2021

Recognizing the sport of cricket on the basis of different batting shots can be a significant part of context-based advertisement to users watching cricket, generating sensor-based commentary systems and coaching assistants. Due to the similarity between different batting shots, manual feature extraction from video frames is tedious. This paper proposes a hybrid deep-neural-network architecture for classifying 10 different cricket batting shots from offline videos. We composed a novel dataset, CricShot10, comprising uneven lengths of batting shots and unpredictable illumination conditions. Impelled by the enormous success of deep-learning models, we utilized a convolutional neural network (CNN) for automatic feature extraction, and a gated recurrent unit (GRU) to deal with long temporal dependency. Initially, conventional CNN and dilated CNN-based architectures were developed. Following that, different transfer-learning models were investigated—namely, VGG16, InceptionV3, Xception, and DenseNet169—which freeze all the layers. Experiment results demonstrated that the VGG16–GRU model outperformed the other models by attaining 86% accuracy. We further explored VGG16 and two models were developed, one by freezing all but the final 4 VGG16 layers, and another by freezing all but the final 8 VGG16 layers. On our CricShot10 dataset, these two models were 93% accurate. These results verify the effectiveness of our proposed architecture compared with other methods in terms of accuracy.
Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
Authors: Zulkifley Muhammad Aidiel , Behjati Mehran , Nordin Rosdiadee , Zakaria Mohamad Shanudin
Published: April 2021

Conventional and license-free radio-controlled drone activities are limited to a line-of-sight (LoS) operational range. One of the alternatives to operate the drones beyond the visual line-of-sight (BVLoS) range is replacing the drone wireless communications system from the conventional industrial, scientific, and medical (ISM) radio band to a licensed cellular-connected system. The Long Term Evolution (LTE) technology that has been established for the terrestrial area allows command-and-control and payload communications between drone and ground station in real-time. However, with increasing height above the ground, the radio environment changes, and utilizing terrestrial cellular networks for drone communications may face new challenges. In this regard, this paper aims to develop an LTE-based control system prototype for low altitude small drones and investigate the feasibility and performance of drone cellular connectivity at different altitudes with measuring parameters such as latency, handover, and signal strength. The measurement results have shown that by increasing flight height from ground to 170 m the received signal power and the signal quality levels were reduced by 20 dBm and 10 dB respectively, the downlink data rate decreased to 70%, and latency increased up to 94 ms. It is concluded that although the existing LTE network can provide a minimum requirement for drone cellular connectivity, further improvements are still needed to enhance aerial coverage, eliminate interference, and reduce network latency.
Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks
Authors: Frazão José , Palma Susana I. C. J. , Costa Henrique M. A. , Alves Cláudia , Roque Ana C. A. , Silveira Margarida
Published: April 2021

Liquid crystal (LC)-based materials are promising platforms to develop rapid, miniaturised and low-cost gas sensor devices. In hybrid gel films containing LC droplets, characteristic optical texture variations are observed due to orientational transitions of LC molecules in the presence of distinct volatile organic compounds (VOC). Here, we investigate the use of deep convolutional neural networks (CNN) as pattern recognition systems to analyse optical textures dynamics in LC droplets exposed to a set of different VOCs. LC droplets responses to VOCs were video recorded under polarised optical microscopy (POM). CNNs were then used to extract features from the responses and, in separate tasks, to recognise and quantify the vapours exposed to the films. The impact of droplet diameter on the results was also analysed. With our classification models, we show that a single individual droplet can recognise 11 VOCs with small structural and functional differences (F1-score above 93%). The optical texture variation pattern of a droplet also reflects VOC concentration changes, as suggested by applying a regression model to acetone at 0.9–4.0% (v/v) (mean absolute errors below 0.25% (v/v)). The CNN-based methodology is thus a promising approach for VOC sensing using responses from individual LC-droplets.
Validation of Scolioscan Air-Portable Radiation-Free Three-Dimensional Ultrasound Imaging Assessment System for Scoliosis
Authors: Lai Kelly Ka-Lee , Lee Timothy Tin-Yan , Lee Michael Ka-Shing , Hui Joseph Chi-Ho , Zheng Yong-Ping
Published: April 2021

To diagnose scoliosis, the standing radiograph with Cobb’s method is the gold standard for clinical practice. Recently, three-dimensional (3D) ultrasound imaging, which is radiation-free and inexpensive, has been demonstrated to be reliable for the assessment of scoliosis and validated by several groups. A portable 3D ultrasound system for scoliosis assessment is very much demanded, as it can further extend its potential applications for scoliosis screening, diagnosis, monitoring, treatment outcome measurement, and progress prediction. The aim of this study was to investigate the reliability of a newly developed portable 3D ultrasound imaging system, Scolioscan Air, for scoliosis assessment using coronal images it generated. The system was comprised of a handheld probe and tablet PC linking with a USB cable, and the probe further included a palm-sized ultrasound module together with a low-profile optical spatial sensor. A plastic phantom with three different angle structures built-in was used to evaluate the accuracy of measurement by positioning in 10 different orientations. Then, 19 volunteers with scoliosis (13F and 6M; Age: 13.6 ± 3.2 years) with different severity of scoliosis were assessed. Each subject underwent scanning by a commercially available 3D ultrasound imaging system, Scolioscan, and the portable 3D ultrasound imaging system, with the same posture on the same date. The spinal process angles (SPA) were measured in the coronal images formed by both systems and compared with each other. The angle phantom measurement showed the measured angles well agreed with the designed values, 59.7 ± 2.9 vs. 60 degrees, 40.8 ± 1.9 vs. 40 degrees, and 20.9 ± 2.1 vs. 20 degrees. For the subject tests, results demonstrated that there was a very good agreement between the angles obtained by the two systems, with a strong correlation (R2 = 0.78) for the 29 curves measured. The absolute difference between the two data sets was 2.9 ± 1.8 degrees. In addition, there was a small mean difference of 1.2 degrees, and the differences were symmetrically distributed around the mean difference according to the Bland–Altman test. Scolioscan Air was sufficiently comparable to Scolioscan in scoliosis assessment, overcoming the space limitation of Scolioscan and thus providing wider applications. Further studies involving a larger number of subjects are worthwhile to demonstrate its potential clinical values for the management of scoliosis.
EFAR-MMLA: An Evaluation Framework to Assess and Report Generalizability of Machine Learning Models in MMLA
Authors: Chejara Pankaj , Prieto Luis P. , Ruiz-Calleja Adolfo , Rodríguez-Triana María Jesús , Shankar Shashi Kant , Kasepalu Reet
Published: April 2021

Multimodal Learning Analytics (MMLA) researchers are progressively employing machine learning (ML) techniques to develop predictive models to improve learning and teaching practices. These predictive models are often evaluated for their generalizability using methods from the ML domain, which do not take into account MMLA’s educational nature. Furthermore, there is a lack of systematization in model evaluation in MMLA, which is also reflected in the heterogeneous reporting of the evaluation results. To overcome these issues, this paper proposes an evaluation framework to assess and report the generalizability of ML models in MMLA (EFAR-MMLA). To illustrate the usefulness of EFAR-MMLA, we present a case study with two datasets, each with audio and log data collected from a classroom during a collaborative learning session. In this case study, regression models are developed for collaboration quality and its sub-dimensions, and their generalizability is evaluated and reported. The framework helped us to systematically detect and report that the models achieved better performance when evaluated using hold-out or cross-validation but quickly degraded when evaluated across different student groups and learning contexts. The framework helps to open up a “wicked problem” in MMLA research that remains fuzzy (i.e., the generalizability of ML models), which is critical to both accumulating knowledge in the research community and demonstrating the practical relevance of these techniques.
A Hybrid Visual Tracking Algorithm Based on SOM Network and Correlation Filter
Authors: Zhang Yuanping , Huang Xiumei , Yang Ming
Published: April 2021

To meet the challenge of video target tracking, based on a self-organization mapping network (SOM) and correlation filter, a long-term visual tracking algorithm is proposed. Objects in different videos or images often have completely different appearance, therefore, the self-organization mapping neural network with the characteristics of signal processing mechanism of human brain neurons is used to perform adaptive and unsupervised features learning. A reliable method of robust target tracking is proposed, based on multiple adaptive correlation filters with a memory function of target appearance at the same time. Filters in our method have different updating strategies and can carry out long-term tracking cooperatively. The first is the displacement filter, a kernelized correlation filter that combines contextual characteristics to precisely locate and track targets. Secondly, the scale filters are used to predict the changing scale of a target. Finally, the memory filter is used to maintain the appearance of the target in long-term memory and judge whether the target has failed to track. If the tracking fails, the incremental learning detector is used to recover the target tracking in the way of sliding window. Several experiments show that our method can effectively solve the tracking problems such as severe occlusion, target loss and scale change, and is superior to the state-of-the-art methods in the aspects of efficiency, accuracy and robustness.
A New Cache Update Scheme Using Reinforcement Learning for Coded Video Streaming Systems
Authors: Kim Yu-Sin , Lee Jeong-Min , Ryu Jong-Yeol , Ban Tae-Won
Published: April 2021

As the demand for video streaming has been rapidly increasing recently, new technologies for improving the efficiency of video streaming have attracted much attention. In this paper, we thus investigate how to improve the efficiency of video streaming by using clients’ cache storage considering exclusive OR (XOR) coding-based video streaming where multiple different video contents can be simultaneously transmitted in one transmission as long as prerequisite conditions are satisfied, and the efficiency of video streaming can be thus significantly enhanced. We also propose a new cache update scheme using reinforcement learning. The proposed scheme uses a K-actor-critic (K-AC) network that can mitigate the disadvantage of actor-critic networks by yielding K candidate outputs and by selecting the final output with the highest value out of the K candidates. The K-AC exists in each client, and each client can train it by using only locally available information without any feedback or signaling so that the proposed cache update scheme is a completely decentralized scheme. The performance of the proposed cache update scheme was analyzed in terms of the average number of transmissions for XOR coding-based video streaming and was compared to that of conventional cache update schemes. Our numerical results show that the proposed cache update scheme can reduce the number of transmissions up to 24% when the number of videos is 100, the number of clients is 50, and the cache size is 5.
An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities
Authors: Wu Jiaen , Kuruvithadam Kiran , Schaer Alessandro , Stoneham Richie , Chatzipirpiridis George , Easthope Chris Awai , Barry Gill , Martin James , Pané Salvador , Nelson Bradley J. , Ergeneman Olgaç , Torun Hamdi
Published: April 2021

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.
Chest-Worn Inertial Sensors: A Survey of Applications and Methods
Authors: Rahmani Mohammad Hasan , Berkvens Rafael , Weyn Maarten
Published: April 2021

Inertial Measurement Units (IMUs) are frequently implemented in wearable devices. Thanks to advances in signal processing and machine learning, applications of IMUs are not limited to those explicitly addressing body movements such as Activity Recognition (AR). On the other hand, wearing IMUs on the chest offers a few advantages over other body positions. AR and posture analysis, cardiopulmonary parameters estimation, voice and swallowing activity detection and other measurements can be approached through chest-worn inertial sensors. This survey tries to introduce the applications that come with the chest-worn IMUs and summarizes the existing methods, current challenges and future directions associated with them. In this regard, this paper references a total number of 57 relevant studies from the last 10 years and categorizes them into seven application areas. We discuss the inertial sensors used as well as their placement on the body and their associated validation methods based on the application categories. Our investigations show meaningful correlations among the studies within the same application categories. Then, we investigate the data processing architectures of the studies from the hardware point of view, indicating a lack of effort on handling the main processing through on-body units. Finally, we propose combining the discussed applications in a single platform, finding robust ways for artifact cancellation, and planning optimized sensing/processing architectures for them, to be taken more seriously in future research.
Thermionic Electron Beam Current and Accelerating Voltage Controller for Gas Ion Sources
Authors: Sikora Jarosław , Kania Bartosz , Mroczka Janusz
Published: April 2021

Thermionic emission sources are key components of electron impact gas ion sources used in measuring instruments, such as mass spectrometers, ionization gauges, and apparatus for ionization cross-section measurements. The repeatability of the measurements taken with such instruments depends on the stability of the ion current, which is a function, among other things, of the electron beam current and electron accelerating voltage. In this paper, a laboratory thermionic electron beam current and accelerating voltage controller is presented, based on digital algorithm implementation. The average value of the percentage standard deviation of the emission current is 0.021%, and the maximum electron accelerating voltage change versus the emission current is smaller than 0.011% in the full operating range of the emission current. Its application as a trap current or emission current-regulated ion source power supply could be useful in many measuring instruments, such as in microelectromechanical system (MEMS) mass spectrometers as universal gas sensors, where a stable emission current and electron energy are needed.
A Smartcard-Based User-Controlled Single Sign-On for Privacy Preservation in 5G-IoT Telemedicine Systems
Authors: Lin Tzu-Wei , Hsu Chien-Lung , Le Tuan-Vinh , Lu Chung-Fu , Huang Bo-Yu
Published: April 2021

Healthcare is now an important part of daily life because of rising consciousness of health management. Medical professionals can know users’ health condition if they are able to access information immediately. Telemedicine systems, which provides long distance medical communication and services, is a multi-functional remote medical service that can help patients in bed in long-distance communication environments. As telemedicine systems work in public networks, privacy preservation issue of sensitive and private transmitted information is important. One of the means of proving a user’s identity are user-controlled single sign-on (UCSSO) authentication scheme, which can establish a secure communication channel using authenticated session keys between the users and servers of telemedicine systems, without threats of eavesdropping, impersonation, etc., and allow patients access to multiple telemedicine services with a pair of identity and password. In this paper, we proposed a smartcard-based user-controlled single sign-on (SC-UCSSO) for telemedicine systems that not only remains above merits but achieves privacy preservation and enhances security and performance compared to previous schemes that were proved with BAN logic and automated validation of internet security protocols and applications (AVISPA).
Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters
Authors: Wijesingha Jayan , Dayananda Supriya , Wachendorf Michael , Astor Thomas
Published: April 2021

Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.
Analytical Evaluation of Signal-to-Noise Ratios for Avalanche- and Single-Photon Avalanche Diodes
Authors: Buchner Andre , Hadrath Stefan , Burkard Roman , Kolb Florian M. , Ruskowski Jennifer , Ligges Manuel , Grabmaier Anton
Published: April 2021

Performance of systems for optical detection depends on the choice of the right detector for the right application. Designers of optical systems for ranging applications can choose from a variety of highly sensitive photodetectors, of which the two most prominent ones are linear mode avalanche photodiodes (LM-APDs or APDs) and Geiger-mode APDs or single-photon avalanche diodes (SPADs). Both achieve high responsivity and fast optical response, while maintaining low noise characteristics, which is crucial in low-light applications such as fluorescence lifetime measurements or high intensity measurements, for example, Light Detection and Ranging (LiDAR), in outdoor scenarios. The signal-to-noise ratio (SNR) of detectors is used as an analytical, scenario-dependent tool to simplify detector choice for optical system designers depending on technologically achievable photodiode parameters. In this article, analytical methods are used to obtain a universal SNR comparison of APDs and SPADs for the first time. Different signal and ambient light power levels are evaluated. The low noise characteristic of a typical SPAD leads to high SNR in scenarios with overall low signal power, but high background illumination can saturate the detector. LM-APDs achieve higher SNR in systems with higher signal and noise power but compromise signals with low power because of the noise characteristic of the diode and its readout electronics. Besides pure differentiation of signal levels without time information, ranging performance in LiDAR with time-dependent signals is discussed for a reference distance of 100 m. This evaluation should support LiDAR system designers in choosing a matching photodiode and allows for further discussion regarding future technological development and multi pixel detector designs in a common framework.
Protocol Optimization of Proteomic Analysis of Korean Ginseng (Panax ginseng Meyer)
Authors: Braccia Clarissa , Prinsi Bhakti , Colzani Mara , Altomare Alessandra A. , Espen Luca , Lee Yoon-Mi , Aldini Giancarlo , Yeum Kyung-Jin
Published: April 2021

The benefits of ginseng have been mainly attributed to its triterpenoids, called ginsenosides. Recent genome sequencing of the Panax ginseng has paved the way for in-depth proteomic studies of this medicinal plant. The current study was conducted to deepen the proteomic information on the root proteome of Korean ginseng. Proteomic workflow was optimized by testing two different strategies, characterized by the phenol extraction procedure, the presence or the absence of SDS-PAGE fractionation step, and nano-scale liquid chromatographic tandem mass spectrometry (nLC-MS/MS) analysis. The results highlighted an evident improvement of proteome extraction by the combination of phenol extraction with SDS-PAGE before the nLC-MS/MS analysis. In addition, a dramatic impact of the steaming process (the treatment to produce red ginseng from ginseng) on protein properties was observed. Overall, the analyses of Korean ginseng permitted the characterization of a total of 2412 proteins. A large number of identified proteins belonged to the functional categories of protein and carbon/energy metabolism (22.4% and 14.6%, respectively). The primary and secondary metabolisms are major metabolic pathways, which emerged from the proteomic analysis. In addition, a large number of proteins known to play an important role in response to (a)biotic stresses were also identified. The current proteomic study not only confirmed the previous transcriptomic and proteomic reports but also extended proteomic information, including the main metabolic pathways involved in Korean ginseng.
Review of the International Systems of Quantities and Units Usage
Authors: Glavič Peter
Published: April 2021

The International System of Quantities has to be used by scientific and engineering journals as well as by authors of their articles, conference papers, and corresponding books, especially textbooks. This paper describes the historical development and the state of the art of international communications in science, engineering, technology, production, and sustainable development. The International System of Quantities (ISQ) which systematically elaborated on the standards of the International Organization for Standardization and International Electrotechnical Commission on quantities and units (ISO/IEC 80000) still needs to be generally accepted and used. The list of standardized base and derived quantities with their symbols, and rules for terminology of other quantities are presented. In addition, names and symbols of base, derived, and “compound” units for these quantities are given. The most frequent mistakes and some recommendations about the use of quantities, units, prefixes, quantity value expressions, numbers, and symbols of chemical elements are shown, too. The standards shall be available in open access. The lack of standardized quantities regarding science, engineering, and economics is drawn to attention. Further development of the international systems of quantities and units could bring substantial synergies worldwide.