bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental and Numerical Analysis for the Mechanical Characterization of PETG Polymers Manufactured with FDM Technology under Pure Uniaxial Compression Stress States for Architectural Applications
Authors: Mercado-Colmenero Jorge Manuel , La Rubia M. Dolores , Mata-Garcia Elena , Rodriguez-Santiago Moises , Martin-Doñate Cristina
Published: September 2020

This paper presents the numerical and experimental analysis performed on the polymeric material Polyethylene Terephthalate Glycol (PETG) manufactured with Fused Deposition Modeling Technology (FDM) technology, aiming at obtaining its mechanical characterization under uniaxial compression loads. Firstly, with the objective of evaluating the printing direction that poses a greater mechanical strength, eighteen test specimens were manufactured and analyzed according to the requirements of the ISO-604 standards. After that, a second experimental test analyzed the mechanical behavior of an innovative structural design manufactured in Z and X–Y directions under uniaxial compression loads according to the requirements of the Spanish CTE standard. The experimental results point to a mechanical linear behavior of PETG in X, Y and Z manufacturing directions up to strain levels close to the yield strength point. SEM micrographs show different structural failures linked to the specimen manufacturing directions. Test specimens manufactured along X present a brittle fracture caused by a delamination process. On the contrary, test specimens manufactured along X and Y directions show permanent plastic deformations, great flexibility and less strength under compression loads. Two numerical analyses were performed on the structural part using Young’s compression modulus obtained from the experimental tests and the load specifications required for the Spanish CTE standards. The comparison between numerical and experimental results presents a percentage of relative error of 2.80% (Z-axis), 3.98% (X-axis) and 3.46% (Y-axis), which allows characterizing PETG plastic material manufactured with FDM as an isotropic material in the numerical simulation software without modifying the material modeling equations in the data software. The research presented here is of great help to researchers working with polymers and FDM technology for companies that might need to numerically simulate new designs with the PETG polymer and FDM technology.
Image Spectral Resolution Enhancement for Mapping Native Plant Species in a Typical Area of the Three-River Headwaters Region, China
Authors: Wang Benlin , An Ru , Jiang Tong , Xing Fei , Ju Feng
Published: September 2020

Large-scale multispectral remote sensing data are often unavailable for some practical applications. Spectral resolution enhancement for large-scale multispectral remote sensing images by incorporating small-scale hyperspectral remote sensing images is an alternative way to generate remote sensing images with both large spatial range and high spectral resolution. This paper proposes an improved spectral resolution enhancement method (ISREM) using spectral matrix and weighting the spectral angle of the transformation matrix. ISREM is tested in a typical area of the Three-River Headwaters region (TRHR) to produce a synthetic hyperspectral image (HSI). Two existing spectral resolution enhancement methods, the color resolution improvement software package (CRISP) and spectral resolution enhancement method (SREM), are adopted to compare with ISREM. To further test the practicality of the synthetic HSIs generated by the ISREM, CRISP and SREM, they are used to estimate the coverage of native plant species (NPS) using support vector machines (SVM) and random forest (RF) regressions. The experimental results are as follows. (1) For the Pearson correlation coefficient between the synthetic HSI and original image, ISREM yielded the largest value of 0.9582, followed by CRISP and SREM with values of 0.9480 and 0.9514. For spectral similarity, the HSI generated by the ISREM was the closest to the original reference HSI in the spectral curve. It also showed the best cumulative performance with the use of the three quality evaluation indexes. (2) The identification accuracies of native plant species were 93.51%, 90.91%, 89.61% and 89.61% using generated HSIs and original multispectral image (MSI) within a threshold of 20%, respectively. Compared with original MSI, the synthetic HSI showed better ability to identify NPS in the study area, which further illustrated the effectiveness of the ISREM. (3) The ISREM can reduce the strict requirement of pure pixels and maintain the quality of synthetic HSI by spectral angle weighting. Hence, the proposed ISREM outperforms the existing CRISP and SREM methods in image spectral resolution enhancement of multispectral remote sensing images.
A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals
Authors: Yang Dalin , Nguyen Trung-Hau , Chung Wan-Young
Published: September 2020

The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study—a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time—might also offer a reference for the other BCI controlled applications.
Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning
Authors: Dai Dejian , Jiang Tao , Lu Wei , Shen Xuan , Xiu Rui , Zhang Jingwei
Published: September 2020

Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy, and explain its mechanism. A variety of weak classifiers classify eggs based on the spectra after preprocessing and feature wavelength extraction to obtain three classifiers with the highest accuracy. The three classifiers are used as metamodels of stacking ensemble learning to improve the highest accuracy from 96.25% to 100%. Moreover, the highest accuracy of scattering, reflection, transmission, and mixed hyperspectral of eggs are 100.00%, 88.75%, 95.00%, and 96.25%, respectively, indicating that the scattering hyperspectral for egg freshness detection is better than that of the others. In addition, the accuracy is inversely proportional to the angle of incidence, i.e., the smaller the incident angle, the camera collects a larger proportion of scattering light, which contains more biochemical parameters of an egg than that of reflection and transmission. These results are very important for improving the accuracy of non-destructive testing and for selecting the incident angle of a light source, and they have potential applications for online non-destructive testing.
Random-Access Accelerator (RAA): A Framework to Speed Up the Random-Access Procedure in 5G New Radio for IoT mMTC by Enabling Device-To-Device Communications
Authors: Rodriguez Medel Abel , C. Brito Jose Marcos
Published: September 2020

Mobile networks have a great challenge by serving the expected billions of Internet of Things (IoT) devices in the upcoming years. Due to the limited simultaneous access in the mobile networks, the devices should compete between each other for resource allocation during a Random-Access procedure. This contention provokes a non-depreciable delay during the device’s registration because of the great number of collisions experienced. To overcome such a problem, a framework called Random-Access Accelerator (RAA) is proposed in this work, in order to speed up network access in massive Machine Type Communication (mMTC). RAA exploits Device-To-Device (D2D) communications, where devices with already assigned resources act like relays for the rest of devices trying to gain access in the network. The simulation results show an acceleration in the registration procedure of 99%, and a freed space of the allocated spectrum until 74% in comparison with the conventional Random-Access procedure. Besides, it preserves the same device’s energy consumption compared with legacy networks by using a custom version of Bluetooth as a wireless technology for D2D communications. The proposed framework can be taken into account for the standardization of mMTC in Fifth-Generation-New Radio (5G NR).
A Recurrent Deep Network for Estimating the Pose of Real Indoor Images from Synthetic Image Sequences
Authors: Acharya Debaditya , Singha Roy Sesa , Khoshelham Kourosh , Winter Stephan
Published: September 2020

Recently, deep convolutional neural networks (CNN) have become popular for indoor visual localisation, where the networks learn to regress the camera pose from images directly. However, these approaches perform a 3D image-based reconstruction of the indoor spaces beforehand to determine camera poses, which is a challenge for large indoor spaces. Synthetic images derived from 3D indoor models have been used to eliminate the requirement of 3D reconstruction. A limitation of the approach is the low accuracy that occurs as a result of estimating the pose of each image frame independently. In this article, a visual localisation approach is proposed that exploits the spatio-temporal information from synthetic image sequences to improve localisation accuracy. A deep Bayesian recurrent CNN is fine-tuned using synthetic image sequences obtained from a building information model (BIM) to regress the pose of real image sequences. The results of the experiments indicate that the proposed approach estimates a smoother trajectory with smaller inter-frame error as compared to existing methods. The achievable accuracy with the proposed approach is 1.6 m, which is an improvement of approximately thirty per cent compared to the existing approaches. A Keras implementation can be found in our Github repository.
A Magnetoelectric Automotive Crankshaft Position Sensor
Authors: Petrov Roman , Leontiev Viktor , Sokolov Oleg , Bichurin Mirza , Bozhkov Slavcho , Milenov Ivan , Bozhkov Penko
Published: September 2020

The paper is devoted to the possibility of using magnetoelectric materials for the production of a crankshaft position sensor for automobiles. The composite structure, consisting of a PZT or LiNbO3 piezoelectric with a size of 20 mm × 5 mm × 0.5 mm, and plates of the magnetostrictive material Metglas of the appropriate size were used as a sensitive element. The layered structure was made from a bidomain lithium niobate monocrystal with a Y + 128° cut and amorphous metal of Metglas. Various combinations of composite structures are also investigated; for example, asymmetric structures using a layer of copper and aluminum. The output characteristics of these structures are compared in the resonant and non-resonant modes. It is shown that the value of the magnetoelectric resonant voltage coefficient was 784 V/(cm·Oe), and the low-frequency non-resonant magnetoelectric coefficient for the magnetoelectric element was about 3 V/(cm·Oe). The principle of operation of the position sensor and the possibility of integration into automotive systems, using the CAN bus, are examined in detail. To obtain reliable experimental results, a special stand was assembled on the basis of the SKAD-1 installation. The studies showed good results and a high prospect for the use of magnetoelectric sensors as position sensors and, in particular, of a vehicle’s crankshaft position sensor.
Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning
Authors: Diyan Muhammad , Khan Murad , Nathali Silva Bhagya , Han Kijun
Published: September 2020

A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart things in a smart home environment. Human activities comprise expected and unexpected behavior events; therefore, detecting these events consisting of mutual dependent activities poses a key challenge in the activities detection paradigm. Besides, the battery-powered sensor ubiquitously and extensively monitors activities, disputes, and sensor energy depletion. Therefore, to address these challenges, we propose an Energy and Event Aware-Sensor Duty Cycling scheme. The proposed model predicts the future expected event using the Bi-Directional Long-Short Term Memory model and allocates Predictive Sensors to the predicted event. To detect the unexpected events, the proposed model localizes a Monitor Sensor within a cluster of Hibernate Sensors using the Jaccard Similarity Index. Finally, we optimize the performance of our proposed scheme by employing the Q-Learning algorithm to track the missed or undetected events. The simulation is executed against the conventional Machine Learning algorithms for the sensor duty cycle, scheduling to reduce the sensor energy consumption and improve the activity detection accuracy. The experimental evaluation of our proposed scheme shows significant improvement in activity detection accuracy from 94.12% to 96.12%. Besides, the effective rotation of the Monitor Sensor significantly improves the energy consumption of each sensor with the entire network lifetime.
Catadioptric Optical System Design of 15-Magnitude Star Sensor with Large Entrance Pupil Diameter
Authors: Bai Yang , Li Jianlin , Zha Rongwei , Wang Ying , Lei Guangzhi
Published: September 2020

The optical system is one of the core components for star sensors, whose imaging quality directly influences the performance of star sensors for star detection, thereby determining the attitude control accuracy of spacecrafts. Here, we report a new type of optical system with a catadioptric structure and a large entrance pupil diameter for a 15-magnitude star sensor. It consists of an improved Cassegrain system (R-C system), an aperture correction spherical lens group and a field of view correction spherical lens group. By embedding the secondary mirror of the R-C system into the output surface of the negative spherical lens of the aperture correction spherical lens group, the blocking of incident light is eliminated from the secondary mirror holder. After the structure optimization, the catadioptric optical system (COS) had a spectral range of 450 nm–950 nm, an entrance pupil diameter of 250 mm, a half-diagonal field of view of 1.4° and a focal length of 390 mm. By using theoretical calculations and experimental measurements, it was verified that the COS, with the ability to correct astigmatism, lateral color and distortion, can fulfill the detection of 15-magnitude dark stars.
Estimating Residual Life Distributions of Complex Operational Systems Using a Remaining Maintenance Free Operating Period (RMFOP)-Based Methodology
Authors: Chen Qianyu , Nicholson Gemma , Ye Jiaqi , Zhao Yihong , Roberts Clive
Published: September 2020

Recent developments in the area of condition monitoring research have been targeted towards predicting machinery health condition for the purpose of preventative maintenance. Typically, published research uses data collected from rotating components (bearings, cutting tools, etc.) working in an idealized lab environment as the case study for prognosis algorithm validations. However, the operational implementation in industry is still very sporadic, mainly owing to the lack of proper data allowing sufficiently mature development of comprehensive methodologies. The prognosis methodology presented herein bridges the gap between academic research and industrial implementations by employing a novel time period for prognosis and implementing random coefficients regression models. The definition of the remaining maintenance-free operating period (RMFOP) is proposed first, which helps to transform the usefulness of the degradation data that is readily available from data short of failure. Degradation patterns are subsequently extracted from the original degradation data, before fitting into either of two regression models (linear or exponential). The system residual life distributions are then computed and updated by estimating the parameter statistics within the model. This RMFOP-based methodology is validated using real-world degradation data collected from multiple operational railway switch systems across Great Britain. The results indicate that both the linear model and the exponential model can produce residual life distributions with a sufficient prediction accuracy for this specific application. The exponential model gives better predictions, the accuracy of which also improves as more of system life percentage has elapsed. By using the RMFOP methodology, switch system health condition affected by an incipient overdriving fault is recognized and predicted.
Magnetic Imprinted Polymer-Based Quartz Crystal Microbalance Sensor for Sensitive Label-Free Detection of Methylene Blue in Groundwater
Authors: Hu Yufeng , Xing Hanwen , Li Gang , Wu Minghuo
Published: September 2020

Tiny changes in the mass of the sensor in a quartz crystal microbalance with dissipation monitoring (QCM-D) can be observed. However, the lack of specificity for target species has hindered the use of QCM-D. Here, molecularly imprinted polymers (MIPs) were used to modify a QCM-D sensor to provide specificity. The MIPs were formed in the presence of sodium dodecyl benzene sulfonate. Imprinted layers on Fe3O4 nanoparticles were formed using pyrrole as the functional monomer and cross-linker and methylene blue (MB) as a template. The MIPs produced were then attached to the surface of a QCM-D sensor. The MIPs-coated QCM-D sensor could recognize MB and gave a linear response in the concentration range 25 to 1.5 × 102 µg/L and a detection limit of 1.4 µg/L. The QCM-D sensor was selective for MB over structural analogs. The MIPs-coated QCM-D sensor was successfully used to detect MB in river water and seawater samples, and the recoveries were good. This is the first time MB has been detected using a QCM-D sensor. Mass is an intrinsic property of matter, so this method could easily be extended to other target species by using different MIPs.
Implementation of the UN Convention on the Rights of Persons with Disabilities: A Comparison of Four European Countries with Regards to Assistive Technologies
Authors: Bratan Tanja , Fischer Piret , Maia Maria , Aschmann Vera
Published: September 2020

The United Nations Convention on the Rights of Persons with Disabilities (CRPD) is an international treaty that aims to promote, protect and ensure the rights of persons with disabilities so that they can fully participate in society and enjoy the same freedoms and opportunities as others. It provides an important framework for the inclusion of persons with disabilities with the help of Assistive Technologies (AT). This paper assesses and compares the implementation of the CRPD with regards to the availability of AT in four countries (Germany, Hungary, Portugal and Sweden), which to some extent represent different European regions. The paper is based on a review of relevant academic literature, the DOTCOM database and regulatory documents as well as on five validation interviews with national experts. In the countries studied, anti-discriminatory and other legislation is included at the highest level of the legal framework and contains detailed rules on definitions, remedies and legal procedures. There are specific prohibitions in several fields, such as employment, housing, and healthcare. Nonetheless, there are still cases of non-compliance with the CRPD and of laws and regulations which discriminate against persons with a disability. Additionally, there are great variations between countries. As very positive examples of favourable regulatory frameworks for furthering inclusion with the help of ATs do exist, there may be benefit in raising awareness of such examples to support other countries in developing their own measures.
Embedded Information Problem-Solving Instruction to Foster Learning from Digital Sources: Longitudinal Effects on Task Performance
Authors: Pifarré Manoli , Argelagós Esther
Published: September 2020

This research paper is based on a longitudinal study to find out how long-term embedded whole-task instruction can help students to develop more efficient information problem-solving (IPS) skills that could lead to a better use of internet information for learning and solving digital tasks more effectively. To this end, we designed, implemented and evaluated a three-year instruction programme to promote students’ development of key IPS skills in real-life classroom settings. This research involved sixty-one secondary education students. Forty-two of them received the IPS instruction and their results were analysed longitudinally and subsequently compared to a control group which received the regular courses. The results showed that students who received the IPS instruction improved their performance significantly in tasks in which the use of IPS skills was needed and these students organised and presented the information found on the internet critically and gave personal arguments. The findings also revealed that during the three-year project, the scores of IPS task performance were statistically higher in the instructed students than those obtained in control group students. Our study then provides an insight into how secondary students develop IPS skills throughout long-term instructional support and shows a series of educational implications.
Cloud Data Scraping for the Assessment of Outflows from Dammed Rivers in the EU. A Case Study in South Eastern Europe
Authors: Skoulikaris Charalampos , Krestenitis Yannis
Published: September 2020

The availability of environmentally related data is a crucial issue in simulating natural processes. The proposed research demonstrates that although currently there is a plethora of data published on the internet, by national and international official sources, their retrieval is sometimes hard to be achieved; hence smart programming tools/technologies could facilitate the automatic harvesting of these data in personal databases for the qualitative and quantitative assessment of hydrosystems that encompass hydropower schemes. The implemented methodology relies on the use of custom-made web scrapping tools to access dams’ hourly outflows in combination with water demand data in order to investigate the water balance, as modified by human intervention, in areas located downstream of dams. For this purpose, derivatives of the Electricity Market Directive and Water Framework Directive of the European Union (EU) have been regarded as web-based databases. The outcomes of the methodological approach demonstrate that the modelled net water discharges downstream—i.e., the river discharges after subtracting the water demands—are assessed with high simulation accuracy at a daily time scale. The reliability of the output estimates is further supported by the combination of measured data from gauge telemetry with the data derived by the proposed methodology. This research allows new insights, such as dams’ outflows and water demand retrieval and exploitation, into the sustainable management of water resources and contributes to the quantification of rivers’ outflows to the coastal zone.
The Impact of Airport Proximity on Single-Family House Prices—Evidence from Poland
Authors: Bełej Mirosław , Cellmer Radosław , Głuszak Michał
Published: September 2020

Airports in Poland are obliged to observe the sustainable development principle and therefore to reduce their environmental impact by creating so-called limited use areas (LUA) related to aircraft-generated noise. The research authors analyzed airports’ impact on the prices of single-family homes located in the vicinity of airports. The LUA is therefore defined as the area designated to study the airport’s specific impact on the single-family housing market. This is a formal limit which determines the examination of price changes and the decision-making conditions of market participants. This methodical approach is justified because no excessive noise is expected outside the LUA. Therefore, two markets in the vicinity of airports were examined. One is in an LUA which is closer to the airport, and the other market is outside the LUA where external noise effects are not present. Thus, we consider that real estate located outside the LUA is not subject to a significant negative impact from the airport. The study covered the Gdańsk Lech Walesa Airport and the Warsaw Chopin Airport in Poland in adjacent areas with the research time horizon of 2013–2017. The study examined single-family house prices. We used a time series analysis, a classic multiple regression model, a spatial autoregressive model, and geographically weighted regression models in our research. Additionally, Geographical Information System (GIS) tools were used to visualize the results of our study. The research result was to demonstrate different impact levels of airports on the prices of single-family houses located in limited-use areas in Gdańsk and Warsaw. This research carries significant implications for the general public and airports’ economic decisions in resolving conflicts between the airport and residential property owners in airports’ vicinities.
Integrated Tomato Picking and Distribution Scheduling Based on Maturity
Authors: Zhu Anqi , Bian Bei , Jiang Yiping , Hu Jiaxiang
Published: September 2020

Agriproducts have the characteristics of short lifespan and quality decay due to the maturity factor. With the development of e-commerce, high timelines and quality have become a new pursuit for agriproduct online retailing. To satisfy the new demands of customers, reducing the time from receiving orders to distribution and improving agriproduct quality are significantly needed advancements. In this study, we focus on the joint optimization of the fulfillment of online tomato orders that integrates picking and distribution simultaneously within the context of the farm-to-door model. A tomato maturity model with a firmness indicator is proposed firstly. Then, we incorporate the tomato maturity model function into the integrated picking and distribution schedule and formulate a multiple-vehicle routing problem with time windows. Next, to solve the model, an improved genetic algorithm (the sweep-adaptive genetic algorithm, S-AGA) is addressed. Finally, we prove the validity of the proposed model and the superiority of S-AGA with different numerical experiments. The results show that significant improvements are obtained in the overall tomato supply chain efficiency and quality. For instance, tomato quality and customer satisfaction increased by 5% when considering the joint optimization, and the order processing speed increased over 90% compared with traditional GA. This study could provide scientific tomato picking and distribution scheduling to satisfy the multiple requirements of consumers and improve agricultural and logistics sustainability.
COVID-19, the Food System and the Circular Economy: Challenges and Opportunities
Authors: Giudice Fabio , Caferra Rocco , Morone Piergiuseppe
Published: September 2020

This paper analyzes the causes and effects of the COVID-19 crisis, with a specific focus on the food system. Food consumption and production has not only been impacted by the crisis, but it may have also contributed to causing the pandemic. After providing a brief introductory framework, the paper presents the results of a pilot study on the link between COVID-19 and the food system, as indicated by the social media activity of selected European Union (EU) Twitter accounts, measured using an original “theme popularity” metric. Thereafter, a systematic review of the literature is proposed to identify the causes of the rise in popularity of a sustainable food system theme, the potential consequences of the COVID-19 crisis for the food system (targeting the production, consumption and waste disposal phases) and possible solutions, focusing on the circular economy. Challenges and opportunities for policymakers in the short and long term are discussed. A holistic approach is advocated, as the global food system is intimately connected with society and requires deep cooperation among nation states and economic actors.
Service-Learning Projects in University Degrees Based on Sustainable Development Goals: Proposals and Results
Authors: Castro Paula M. , Ares-Pernas Ana , Dapena Adriana
Published: September 2020

We present several Service-Learning projects developed considering Sustainable Development Goals to provide the students skills for the support of a sustainable society. This project begins with collecting initial impressions from surveys and reflections to know both the students’ expectations and their degree of involvement in the development of a sustainable society. Next, the students design academic projects taking into account the needs of the specific collective to which the Service-Learning activity is oriented. When this design has finished, such projects are developed with the users and the activity is evaluated. During 2019–2020, these projects were done with four entities oriented to people either with Asperger’s syndrome, Down syndrome, Alzheimer’s disease or mental health illnesses. A total of 35 and 10 students, respectively, from the Bachelor’s Degree of Engineering in Industrial Design and Product Development and from the Master’s Degree in Professorship of High School have participated in this experience. We analyze the results from the perspective of the different agents involved, considering both qualitative and quantitative metrics. The results show that both users and staff are satisfied with this collaboration between the university and their entities. Moreover, this experience clearly has contributed to a better personal and professional student’s development.
How Celebrities’ Green Messages on Twitter Influence Public Attitudes and Behavioral Intentions to Mitigate Climate Change
Authors: Park Sejung
Published: September 2020

This research adds to the growing body of literature on the role of celebrities as emergent spokespersons in climate advocacy using Twitter. This study investigates the effects of framing of celebrities’ messages (emotional framing and framing of celebrity involvement) on public attitudes and behaviors to address climate change. A sequential mediation process is examined with structural equation modeling. In addition, this study assesses the role of parasocial relationship (PSR) with celebrities as predictors and moderators of the impact of framing of celebrity involvement. The results indicate that fear appeals were more effective than hope appeals in driving participation in activism, but emotional framing did not affect any other variables. Framing of celebrity involvement appeals using first-person pronouns led to more positive attitudes, but had no effect on behaviors. In addition, PSR was a strong positive predictor of attitudes and behaviors.
Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations
Authors: Lou Zhilan , Jie Wanchen , Zhang Shuzhu
Published: September 2020

The order assignment in the food delivery industry is of high complexity due to the uneven distribution of order requirements and the large-scale optimization of workforce resources. The delivery performance of employees varies in different conditions, which further exacerbates the difficulty of order assignment optimization. In this research, a non-linear multi-objective optimization model is proposed with human factor considerations in terms of both deteriorating effect and learning effect, in order to acquire the optimal solutions in practice. The objectives comprised the minimization of the operational cost in multiple periods and the workload balancing among multiple employees. The proposed model is further transformed to a standardized mixed-integer linear model by the exploitation of linearization procedures and normalization operations. Numerical experiments show that the proposed model can be easily solved using commercial optimization softwares. The results indicate that the variance of employee performance can affect the entire delivery performance, and significant improvement of workload balancing can be achieved at the price of slight increase of the operational cost. The proposed model can facilitate the decision-making process of order assignment and workforce scheduling in the food delivery industry. Moreover, it can provide managerial insights for other labor-intensive service-oriented industries.
Dual Attention-Guided Multiscale Dynamic Aggregate Graph Convolutional Networks for Skeleton-Based Human Action Recognition
Authors: Hu Zeyuan , Lee Eung-Joo
Published: September 2020

Traditional convolution neural networks have achieved great success in human action recognition. However, it is challenging to establish effective associations between different human bone nodes to capture detailed information. In this paper, we propose a dual attention-guided multiscale dynamic aggregate graph convolution neural network (DAG-GCN) for skeleton-based human action recognition. Our goal is to explore the best correlation and determine high-level semantic features. First, a multiscale dynamic aggregate GCN module is used to capture important semantic information and to establish dependence relationships for different bone nodes. Second, the higher level semantic feature is further refined, and the semantic relevance is emphasized through a dual attention guidance module. In addition, we exploit the relationship of joints hierarchically and the spatial temporal correlations through two modules. Experiments with the DAG-GCN method result in good performance on the NTU-60-RGB+D and NTU-120-RGB+D datasets. The accuracy is 95.76% and 90.01%, respectively, for the cross (X)-View and X-Subon the NTU60dataset.
Inhibitory Effects of a Reengineered Anthrax Toxin on Canine and Human Osteosarcoma Cells
Authors: Mackowiak da Fonseca Jonathan , Mackowiak da Fonseca Ivone Izabel , Nagamine Marcia Kazumi , Massoco Cristina de Oliveira , Nishiya Adriana Tomoko , Ward Jerrold Michael , Liu Shihui , Leppla Stephen Howard , Bugge Thomas Henrik , Dagli Maria Lucia Zaidan
Published: September 2020

Canine and human osteosarcomas (OSA) share similarities. Novel therapies are necessary for these tumours. The Bacillus anthracis toxin was reengineered to target and kill cells with high expressions of matrix metalloproteinases (MMPs) and urokinase plasminogen activator (uPA). Since canine OSA express MMPs and uPA, we assessed whether the reengineered toxin could show efficacy against these tumours. Two OSA cell lines (canine D17 and human MG63) and a non-neoplastic canine osteoblastic cell line (COBS) were used. Cells were treated with different concentrations of the reengineered anthrax toxin and cell viability was quantified using MTT assay. The cell cycle, apoptosis, and necrosis were analysed by flow cytometry. The wound-healing assay was performed to quantify the migration capacity of treated cells. D17 and MG63 cells had significantly decreased viability after 24 h of treatment. Cell cycle analysis revealed that OSA cells underwent apoptosis when treated with the toxin, whereas COBS cells arrested in the G1 phase. The wound-healing assay showed that D17 and MG63 cells had a significantly reduced migration capacity after treatment. These results point for the first time towards the in vitro inhibitory effects of the reengineered anthrax toxin on OSA cells; this reengineered toxin could be further tested as a new therapy for OSA.
Point-Of-Care or Point-Of-Need Diagnostic Tests: Time to Change Outbreak Investigation and Pathogen Detection
Authors: Hansen Sören , Abd El Wahed Ahmed
Published: September 2020

In the recent years, the progress of international trade and travel has led to an increased risk of emerging infections. Around 75 percent of the pathogens causing these infections are of animal origin. Point-of-care tests (POCT) and point-of-need tests (PONT) have been established in order to directly provide accurate and rapid diagnostics at field level, the patient bed-side or at the site of outbreaks. These assays can help physicians and decision makers to take the right action without delay. Typically, POCT and PONT rely on genomic identification of pathogens or track their immunological fingerprint. Recently, protocols for metagenomic diagnostics in the field have been developed. In this review, we give an overview of the latest developments in portable diagnostic methods. In addition, four mobile platforms for the implementation of these techniques at point-of-care and point-of-need are described. These approaches can provide reliable diagnostics and surveillance, especially in low resource settings as well as at the level of one health.
Incorporating Uncertainty of the System Behavior in Flood Risk Assessment—Sava River Case Study
Authors: Kekez Toni , Knezić Snježana , Andričević Roko
Published: September 2020

This paper proposes a framework for evaluation of the sources of uncertainty that can disrupt the flood emergency response process. During the flood response, flood emergency managers usually choose between several decision options under limited available lead-time, but they are often compelled with different sources of uncertainty. These sources can significantly affect the quality of decisions related to adequate response and rapid recovery of the affected system. The proposed framework considers efficient identification, integration, and quantification of system uncertainties related to the flood risk. Uncertainty analysis is performed from a decision-maker’s perspective and focused on the time period near and during the flood event. The major scope of proposed framework is to recognize and characterize sources of uncertainty which can potentially appear within the behavior of the observed system. Using a Bayesian network approach, a model is developed capable for quantification of different sources uncertainty in respect to their particular type. The proposed approach is validated on the Sava River case study, in the area of the city of Slavonski Brod, following the destructive 2014 flood event. The results indicate that, despite improvements of structural measures, the weir failure can still cause flooding of the approximately 1 km2 of otherwise safe area, resulting in the increased flood risk.
Silicon Fractionation of Soluble Silicon in Volcanic Ash Soils That May Affect Groundwater Silicon Content on Jeju Island, Korea
Authors: Park Won-Pyo , Hyun Hae-Nam , Koo Bon-Jun
Published: September 2020

Silicon (Si) is found in various fractions of soil, depending on the pedogenic processes of the environment. Dissolved Si (DSi) is adsorbed in soil particles or leaches through the soil profile into the groundwater. The objective of this study is to quantify, using the sequential extraction method, the different Si fractions in volcanic ash soils on Jeju Island that may affect groundwater Si content, and to compare them with those in forest soils on mainland Korea. Most of the Si in these soils was bound in unavailable forms as primary and secondary silicates. The second largest proportion of Si in the non-Andisols of Jeju Island and Korean mainland soils was accumulated as amorphous Si, while in the Andisols of Jeju Island, the second most significant Si fraction was in pedogenic oxides and hydroxides. The products of these soil formations were short-range-order minerals such as allophane (4–40%). The adsorbed Si concentration tended to increase at lower depths in Andisols (100–1400 mg kg−1) and was approximately five times higher than that in non-Andisols. The results indicate that Si is more soluble in the Andisols of high precipitation regions and that Andisols on Jeju Island potentially affect groundwater Si concentration.
Effect of Inclination Angles on the Local Scour around a Submerged Cylinder
Authors: Wang Shaohua , Yang Shiyu , He Zhiguo , Li Li , Xia Yuezhang
Published: September 2020

In ocean engineering and coastal environmental studies, local scour around a submerged structure is a typical issue, which is affected by the inclination of the structure. To investigate the effect of inclination directions and angles on flow structure and the bed morphology, a three-dimensional numerical model of a submerged inclined cylinder was established. In this model, the hydrodynamics are solved from the RANS (Reynolds-averaged Navier–Stokes) equations closed with the RNG k-ε turbulence model, while the bed morphology evolution is captured by the sediment transport model. In the case of vertical-cylinder scour, the simulation results agree well with existing laboratory experiments. In the cases of inclined-cylinder scour, the results show that the inclination direction not only changes the intensity and the location of the downflow but also modulates the pattern of the horseshoe vortex in front of the cylinder, thus influencing the local scour depth and the morphology of the bed. Compared with the case of vertical cylinder, the scour around an upstream-inclined cylinder is deeper, mainly due to the enhancement of downflow in front of the cylinder. The scour around a downstream-inclined cylinder is shallower and broader due to the weakened downflow and accelerated incoming flow. The maximum scour depth decreases with the inclination angle in the downstream-inclination case. In the upstream-inclination case, the maximum scour depth does not vary monotonously with the inclination angle, which results from a competitive effect of the horseshoe vortex and downflow in the front of the cylinder.
Terahertz Imaging and Spectroscopy in Cancer Diagnostics: A Technical Review
Authors: Peng Yan , Shi Chenjun , Wu Xu , Zhu Yiming , Zhuang Songlin
Published: September 2020

Terahertz (THz) waves are electromagnetic waves with frequency in the range from 0.1 to 10 THz. THz waves have great potential in the biomedical field, especially in cancer diagnosis, because they exhibit low ionization energy and can be used to discern most biomolecules based on their spectral fingerprints. In this paper, we review the recent progress in two applications of THz waves in cancer diagnosis: imaging and spectroscopy. THz imaging is expected to help researchers and doctors attain a direct intuitive understanding of a cancerous area. THz spectroscopy is an efficient tool for component analysis of tissue samples to identify cancer biomarkers. Additionally, the advantages and disadvantages of the developed technologies for cancer diagnosis are discussed. Furthermore, auxiliary techniques that have been used to enhance the spectral signal-to-noise ratio (SNR) are also reviewed.
Antioxidant and Some Quality Characteristics of Cheeses Manufactured Using Soy Drink
Authors: ER KEMAL Mehtap , TEMİZ Hasan
Published: September 2020

Plant-based milk substitutes have increasingly consumed around the world owing to its a good deal of human health positive effects. Cow's milk allergy, lactose-intolerance, calorie anxiety, and the prevalence of hypercholesterolemia, vegan diets play an essential role in preferring consumers towards these products. Products with plant-based substitutes, nutritionally deficient but rich in bioactive ingredients, can be great options for improving health. We investigated the changes in some chemical properties of cheeses producing from cow's milk containing 0%, 15%, and 25% soy drink in this study. FRAP values of cheeses containing 0%, 15%, and 25% soy drink were measured as 2390,76±44,37, 3367,69±32,63, 3993,84±13,05 µmol Trolox/g cheese, respectively. Due to an increased concentration of soy drink substitutes, their antioxidant activities were increased, but the dry matters of cheeses gradually were decreased. Cheese, which contains 25% soy drink, was found to be approximately two times the antioxidant activity of cheese without soy drink. This work is a preliminary study for soy drink substitution for cheese manufacture.
ON THE STABILITY OF PLANE MOVEMENT OF MOBILE ROBOTS
Authors: Briskin E. S. , Artemyev K. S. , Vershinina I. P. , Maloletov A. V.
Published: September 2020

The problem of stability of the plane motion of mobile robots, including those with walking propulsion devices, is considered. Two modes of propulsion devices are compared: "pushing" and "pulling". The solution of two model problems on the plane motion of a solid body caused by kinematic and force action is presented.
APPROACH TO RECONFIGURATION OF A MODULAR ROBOT SYSTEM WITH A SUBOPTIMAL SEARCH POLYNOMIAL ALGORITHM
Authors: Kovalev A. D.
Published: September 2020

An approach to connecting modular robots is presented, which includes the algorithm of suboptimal reconfiguration search GreedyCM. This component allows to find nearly optimal solutions in timeframes, polynomially dependent on the robotic system size.
ON THE STABILITY OF MOBILE ROBOTS MOVEMENT WITH CABLE PROPULSION DEVICES
Authors: Platonov V. N.
Published: September 2020

The movement of the platform of the underwater mobile robot with an excessive number of propulsion devices, clinging to the reservoir’s bottom, is considered. The task of determining the laws of the transition process, assessing its stability and quality indicators of control when establishing the specified relations between the efforts in the cables is studied.
RESEARCH OF INDICATORS OF PROFILE PASSABILITY OF A WALKING MACHINE WITH CYCLE MOVERS
Authors: Sharonov N. G. , Kirillov A. P. , Maloletov A. V.
Published: September 2020

The method of overcoming an obstacle of the "moat" type by a walking machine with cycle movers is considered. Quantitative characteristics of the profile passability in indicator are described. The results of experimental research of the proposed method of increasing the profile passability of a walking machine with cycle movers are presented.
Esporte de Lazer
Authors: Stucchi Sérgio
Published: September 2020

O esporte é um dos elementos do universo das atividades físicas do homem e encontra maior possibilidade de ser mantido e utilizado de forma mais coerente e equilibrada, por mais tempo na vida de todos, se orientado para isso. A parceria entre a família, a escola e as organizações de seu oferecimento, pode promover longevidade e sustentabilidade das atividades esportivas com planejamento que desvende com mais detalhes as necessidades individuais e coletivas, para que estejam presentes no tempo livre de cada fase do ciclo vital. Numa concepção pós-moderna de lazer é possível criar relações educativas interdisciplinares, com mais motivações para a aprendizagem dos movimentos nas fases iniciais do crescimento, demonstrando limites no uso do corpo adulto pela consciência dos envolvimentos nos espaços e conteúdos do lazer, possibilitando estender as capacidades físicas, emocionais e sociais com qualidade de vida útil por mais tempo.