bluuebunny/crossref_metadata_2025 huggingface dataset:
The Religion of Idumea and Its Relationship to Early Judaism
Authors: Levin Yigal
Published: September 2020

For several hundred years, from the late Iron Age to the end of the 2nd century BCE, the southern neighbor of Judea was “Idumea”, populated by descendants of Edomites, together with Qedarite and other Arabs and a mix of additional ethnicities. This paper examines the known data on the identity, especially religious identity, of these Idumeans, using a wide range of written sources and archaeological data. Within the Bible, “Edom” is presented as Israel’s twin and its harshest enemy, but there are hints that the Edomites worshipped the God of Israel. While the origins of the “Edomite deity” Qaus remain obscure, as does the process of their migration into southern Judah, the many inscriptions from the Persian period show that Qaus became the most widely worshipped deity in the area, even if other gods, including Yahweh, were also recognized. The Hellenistic period brought heightened Greek and Phoenician influence, but also the stabilization of “Idumea” as an administrative/ethnic unit. Some of the practices of this period, such as male circumcision, show an affinity to the Judaism of the time. This paper also discusses the outcome of the Hasmonean conquest of Idumea and the incorporation of its inhabitants into the Jewish nation.
A Quantitative Monitoring Method for Determining Maize Lodging in Different Growth Stages
Authors: Guan HaiXiang , Liu HuanJun , Meng XiangTian , Luo Chong , Bao YiLin , Ma YuYang , Yu ZiYang , Zhang XinLe
Published: September 2020

Many studies have achieved efficient and accurate methods for identifying crop lodging under homogeneous field surroundings. However, under complex field conditions, such as diverse fertilization methods, different crop growth stages, and various sowing periods, the accuracy of lodging identification must be improved. Therefore, a maize plot featuring different growth stages was selected in this study to explore an applicable and accurate lodging extraction method. Based on the Akaike information criterion (AIC), we propose an effective and rapid feature screening method (AIC method) and compare its performance using indexed methods (i.e., variation coefficient and relative difference). Seven feature sets extracted from unmanned aerial vehicle (UAV) images of lodging and nonlodging maize were established using a canopy height model (CHM) and the multispectral imagery acquired from the UAV. In addition to accuracy parameters (i.e., Kappa coefficient and overall accuracy), the difference index (DI) was applied to search for the optimal window size of texture features. After screening all feature sets by applying the AIC method, binary logistic regression classification (BLRC), maximum likelihood classification (MLC), and random forest classification (RFC) were utilized to discriminate among lodging and nonlodging maize based on the selected features. The results revealed that the optimal window sizes of the gray-level cooccurrence matrix (GLCM) and the gray-level difference histogram statistical (GLDM) texture information were 17 × 17 and 21 × 21, respectively. The AIC method incorporating GLCM texture yielded satisfactory results, obtaining an average accuracy of 82.84% and an average Kappa value of 0.66 and outperforming the index screening method (59.64%, 0.19). Furthermore, the canopy structure feature (CSF) was more beneficial than other features for identifying maize lodging areas at the plot scale. Based on the AIC method, we achieved a positive maize lodging recognition result using the CSFs and BLRC. This study provides a highly robust and novel method for monitoring maize lodging in complicated plot environments.
A Deep Learning Network to Retrieve Ocean Hydrographic Profiles from Combined Satellite and In Situ Measurements
Authors: Buongiorno Nardelli Bruno
Published: September 2020

An efficient combination of remotely-sensed data and in situ measurements is needed to obtain accurate 3D ocean state estimates, representing a fundamental step to describe ocean dynamics and its role in the Earth climate system and marine ecosystems. Observations can either be assimilated in ocean general circulation models or used to feed data-driven reconstructions and diagnostic models. Here we describe an innovative deep learning algorithm that projects sea surface satellite data at depth after training with sparse co-located in situ vertical profiles. The technique is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and is applied here to the measurements collected between 2010 and 2018 over the North Atlantic Ocean. The model provides hydrographic vertical profiles and associated uncertainties from corresponding remotely sensed surface estimates, outperforming similar reconstructions from simpler statistical algorithms and feed-forward networks.
Frontier Progress of Unmanned Aerial Vehicles Optical Wireless Technologies
Authors: Ding Jupeng , Mei Hongye , I Chih-Lin , Zhang Hui , Liu Wenwen
Published: September 2020

With the continuous maturity of unmanned aerial vehicles (UAV) in materials, communications, and other related technologies, the UAV industry has developed rapidly in recent years. In order to cope with the diversified emerging business forms, the explosive growth of the scale of data traffic, number of terminal connections, high reliability, low-latency, and high transmission rate provided by the fifth generation (5G) network will inject new vitality into the development of the UAVs industry. In this paper, optical wireless technology is introduced into the UAV platform, combining theory with practical applications. We explain many research advances and key technologies in the four aspects of “air, space, earth, and sea” to achieve a strong and broadband communication link. This discussion focuses on link modeling, parameter optimization, experimental testing, and the status quo of UAVs in different application scenarios with optical wireless link configurations. At the same time, based on the current situation of UAV optical wireless technology, the technical problems and the research direction in the future are also discussed.
Advancements in Microfabricated Gas Sensors and Microanalytical Tools for the Sensitive and Selective Detection of Odors
Authors: Ollé Enric Perarnau , Farré-Lladós Josep , Casals-Terré Jasmina
Published: September 2020

In recent years, advancements in micromachining techniques and nanomaterials have enabled the fabrication of highly sensitive devices for the detection of odorous species. Recent efforts done in the miniaturization of gas sensors have contributed to obtain increasingly compact and portable devices. Besides, the implementation of new nanomaterials in the active layer of these devices is helping to optimize their performance and increase their sensitivity close to humans’ olfactory system. Nonetheless, a common concern of general-purpose gas sensors is their lack of selectivity towards multiple analytes. In recent years, advancements in microfabrication techniques and microfluidics have contributed to create new microanalytical tools, which represent a very good alternative to conventional analytical devices and sensor-array systems for the selective detection of odors. Hence, this paper presents a general overview of the recent advancements in microfabricated gas sensors and microanalytical devices for the sensitive and selective detection of volatile organic compounds (VOCs). The working principle of these devices, design requirements, implementation techniques, and the key parameters to optimize their performance are evaluated in this paper. The authors of this work intend to show the potential of combining both solutions in the creation of highly compact, low-cost, and easy-to-deploy platforms for odor monitoring.
Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview
Authors: Aouadi Balkis , Zaukuu John-Lewis Zinia , Vitális Flora , Bodor Zsanett , Fehér Orsolya , Gillay Zoltan , Bazar George , Kovacs Zoltan
Published: September 2020

Amid today’s stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.
An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications
Authors: Trakadas Panagiotis , Simoens Pieter , Gkonis Panagiotis , Sarakis Lambros , Angelopoulos Angelos , Ramallo-González Alfonso P. , Skarmeta Antonio , Trochoutsos Christos , Calvο Daniel , Pariente Tomas , Chintamani Keshav , Fernandez Izaskun , Irigaray Aitor Arnaiz , Parreira Josiane Xavier , Petrali Pierluigi , Leligou Nelly , Karkazis Panagiotis
Published: September 2020

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.
Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices
Authors: Martinez-Alanis Marisol , Bojorges-Valdez Erik , Wessel Niels , Lerma Claudia
Published: September 2020

Most methods for sudden cardiac death (SCD) prediction require long-term (24 h) electrocardiogram recordings to measure heart rate variability (HRV) indices or premature ventricular complex indices (with the heartprint method). This work aimed to identify the best combinations of HRV and heartprint indices for predicting SCD based on short-term recordings (1000 heartbeats) through a support vector machine (SVM). Eleven HRV indices and five heartprint indices were measured in 135 pairs of recordings (one before an SCD episode and another without SCD as control). SVMs (defined with a radial basis function kernel with hyperparameter optimization) were trained with this dataset to identify the 13 best combinations of indices systematically. Through 10-fold cross-validation, the best area under the curve (AUC) value as a function of γ (gamma) and cost was identified. The predictive value of the identified combinations had AUCs between 0.80 and 0.86 and accuracies between 80 and 86%. Further SVM performance tests on a different dataset of 68 recordings (33 before SCD and 35 as control) showed AUC = 0.68 and accuracy = 67% for the best combination. The developed SVM may be useful for preventing imminent SCD through early warning based on electrocardiogram (ECG) or heart rate monitoring.
Multilayered Network Model for Mobile Network Infrastructure Disruption
Authors: Hrabcak David , Dobos Lubomir , Papaj Jan , Ovsenik Lubos
Published: September 2020

In this paper, the novel study of the multilayered network model for the disrupted infrastructure of the 5G mobile network is introduced. The aim of this study is to present the new way of incorporating different types of networks, such as Wireless Sensor Networks (WSN), Mobile Ad-Hoc Networks (MANET), and DRONET Networks into one fully functional multilayered network. The proposed multilayered network model also presents the resilient way to deal with infrastructure disruption due to different reasons, such as disaster scenarios or malicious actions. In the near future, new network technologies of 5G networks and the phenomenon known as the Internet of Things (IoT) will empower the functionality of different types of networks and interconnects them into one complex network. The proposed concept is oriented on resilient, smart city applications such as public safety and health and it is able to provide critical communication when fixed network infrastructure is destroyed by deploying smart sensors and unmanned aerial vehicles. The provided simulations shows that the proposed multilayered network concept is able to perform better than traditional WSN network in term of delivery time, average number of hops and data rate speed, when disruption scenario occurs.
Deep Reinforcement Learning for Indoor Mobile Robot Path Planning
Authors: Gao Junli , Ye Weijie , Guo Jing , Li Zhongjuan
Published: September 2020

This paper proposes a novel incremental training mode to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate the related graphic search algorithms and Reinforcement Learning (RL) algorithms in a lightweight 2D environment. Then, we design the algorithm based on DRL, including observation states, reward function, network structure as well as parameters optimization, in a 2D environment to circumvent the time-consuming works for a 3D environment. We transfer the designed algorithm to a simple 3D environment for retraining to obtain the converged network parameters, including the weights and biases of deep neural network (DNN), etc. Using these parameters as initial values, we continue to train the model in a complex 3D environment. To improve the generalization of the model in different scenes, we propose to combine the DRL algorithm Twin Delayed Deep Deterministic policy gradients (TD3) with the traditional global path planning algorithm Probabilistic Roadmap (PRM) as a novel path planner (PRM+TD3). Experimental results show that the incremental training mode can notably improve the development efficiency. Moreover, the PRM+TD3 path planner can effectively improve the generalization of the model.
A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms
Authors: Sabra Adham , Fung Wai-Keung
Published: September 2020

This article proposes a holistic localisation framework for underwater robotic swarms to dynamically fuse multiple position estimates of an autonomous underwater vehicle while using fuzzy decision support system. A number of underwater localisation methods have been proposed in the literature for wireless sensor networks. The proposed navigation framework harnesses the established localisation methods in order to provide navigation aids in the absence of acoustic exteroceptive sensors navigation aid (i.e., ultra-short base line) and it can be extended to accommodate newly developed localisation methods by expanding the fuzzy rule base. Simplicity, flexibility, and scalability are the main three advantages that are inherent in the proposed localisation framework when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. A physics-based simulation platform that considers environment’s hydrodynamics, industrial grade inertial measurement unit, and underwater acoustic communications characteristics is implemented in order to validate the proposed localisation framework on a swarm size of 150 autonomous underwater vehicles. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation by 16.53% and 35.17%, respectively, when compared to the Extended Kalman Filter based localisation with round-robin scheduling.
Colorimetric Paper-Based Device for Hazardous Compounds Detection in Air and Water: A Proof of Concept
Authors: De Matteis Valeria , Cascione Mariafrancesca , Fella Gabriele , Mazzotta Laura , Rinaldi Rosaria
Published: September 2020

In the last decades, the increase in global industrialization and the consequent technological progress have damaged the quality of the environment. As a consequence, the high levels of hazardous compounds such as metals and gases released in the atmosphere and water, have raised several concerns about the health of living organisms. Today, many analytical techniques are available with the aim to detect pollutant chemical species. However, a lot of them are not affordable due to the expensive instrumentations, time-consuming processes and high reagents volumes. Last but not least, their use is exclusive to trained operators. Contrarily, colorimetric sensing devices, including paper-based devices, are easy to use, providing results in a short time, without particular specializations to interpret the results. In addition, the colorimetric response is suitable for fast detection, especially in resource-limited environments or underdeveloped countries. Among different chemical species, transition and heavy metals such as iron Fe(II) and copper Cu(II) as well as volatile compounds, such as ammonia (NH3) and acetaldehyde (C2H4O) are widespread mainly in industrialized geographical areas. In this work, we developed a colorimetric paper-based analytical device (PAD) to detect different contaminants, including Fe2+ and Cu2+ ions in water, and NH3 and C2H4O in air at low concentrations. This study is a “proof of concept” of a new paper sensor in which the intensity of the colorimetric response is proportional to the concentration of a detected pollutant species. The sensor model could be further implemented in other technologies, such as drones, individual protection devices or wearable apparatus to monitor the exposure to toxic species in both indoor and outdoor environments.
Water-Soluble Chemical Vapor Detection Enabled by Doctor-Blade-Coated Macroporous Photonic Crystals
Authors: Wu Min-Fang , Tsai Hui-Ping , Hsieh Chia-Hua , Lu Yi-Cheng , Pan Liang-Cheng , Yang Hongta
Published: September 2020

Water-soluble chemicals, involving a wide range of toxic chemicals in aqueous solutions, remain essential in both daily living or industrial uses. However, most toxicants are evaporated with water through their use and thus cause deleterious effects on the domestic environment and health in humans. Unfortunately, most current low-dose chemical vapor detection technologies are restricted by the use of sophisticated instruments and unable to promptly detect the quantity of diverse toxicants in a single analysis. To address these issues, this study reports the development of simple and fast chemical vapor detection using doctor-blade-coated macroporous poly(2-hydroxyethyl methacrylate)/poly(ethoxylated trimethylolpropane triacrylate) photonic crystals, in which the poly(2-hydroxyethyl methacrylate) has strong affinity to insecticide vapor owing to a favorable Gibbs free energy change for their mixing. The condensation of water-soluble chemical vapor therefore results in a significant reflection peak shift and an obvious color change. The visual colorimetric readout can be further improved by increasing the lattice spacing of the macroporous photonic crystals. Furthermore, the dependence of the reflection peak position on vapor pressure under actual conditions and the reproducibility of vapor detecting are also evaluated in this study.
Efficiency of Methods for Determining the Relevance of Criteria in Sustainable Transport Problems: A Comparative Case Study
Authors: Shekhovtsov Andrii , Kozlov Volodymyr , Nosov Viktor , Sałabun Wojciech
Published: September 2020

Problems related to sustainable urban transport have gained in importance with the rapid growth of urban agglomerations. There is, therefore, a need to support decision-making processes in this area, a trend that is visible in the literature. Many methods have already been presented as a useful decision-making tool in this field. However, it is still a significant challenge to properly determine the relevance of the criteria because it is one of the most critical points of many presented techniques to solve decision problems. In this work, we propose two new approaches to determining the relevance of particular decision criteria effectively in sustainable transport problems. For this purpose, we examine a study case for the evaluation of electric bikes evaluated against eight criteria, which have been taken from earlier work. We calculate the relevance of each criterion using four different approaches and then evaluate their effectiveness using a reference ranking and popular multi-criteria decision analysis methods. The results are compared with each other by using similarity coefficients. Finally, we summarize the results obtained and set out further methods of development.
Is Environmental Awareness a Good Predictor of an Individual’s Altruism Level?
Authors: Aruga Kentaka
Published: September 2020

This study investigated the validity of using a Likert-type environmental awareness index to predict an individual’s altruism level by comparing models identifying how respondents’ demographical characteristics (age, income, education, gender, etc.), degree of political and social awareness, and attitudes toward donation influence the environmental awareness index and the self-report altruism scale (SRAS). Using survey data from a sample of Japanese respondents, the study revealed a bidirectional relationship between environmental awareness and SRAS indices. Furthermore, through examining the effects of respondents’ demographical characteristics and other characteristics on the environmental awareness and SRAS indices, the study identifies that the directions of the influences of these covariates on the indices were all similar when their estimated coefficients were statistically significant. Hence, the study reveals that the environmental awareness index does capture the characteristics of the SRAS index.
Design for Additive Manufacturing: A Systematic Review
Authors: Alfaify Abdullah , Saleh Mustafa , Abdullah Fawaz M. , Al-Ahmari Abdulrahman M.
Published: September 2020

The last few decades have seen rapid growth in additive manufacturing (AM) technologies. AM has implemented a novel method of production in design, manufacture, and delivery to end-users. Accordingly, AM technologies have given great flexibility in design for building complex components, highly customized products, effective waste minimization, high material variety, and sustainable products. This review paper addresses the evolution of engineering design to take advantage of the opportunities provided by AM and its applications. It discusses issues related to the design of cellular and support structures, build orientation, part consolidation and assembly, materials, part complexity, and product sustainability.
Distribution Models of Timber Species for Forest Conservation and Restoration in the Andean-Amazonian Landscape, North of Peru
Authors: Cotrina Sánchez Dany A. , Barboza Castillo Elgar , Rojas Briceño Nilton B. , Oliva Manuel , Torres Guzman Cristóbal , Amasifuen Guerra Carlos A. , Bandopadhyay Subhajit
Published: September 2020

The Andean-Amazonian landscape has been universally recognized for its wide biodiversity, and is considered as global repository of ecosystem services. However, the severe loss of forest cover and rapid reduction of the timber species seriously threaten this ecosystem and biodiversity. In this study, we have modeled the distribution of the ten most exploited timber forest species in Amazonas (Peru) to identify priority areas for forest conservation and restoration. Statistical and cartographic protocols were applied with 4454 species records and 26 environmental variables using a Maximum Entropy model (MaxEnt). The result showed that the altitudinal variable was the main regulatory factor that significantly controls the distribution of the species. We found that nine species are distributed below 1000 m above sea level (a.s.l.), except Cedrela montana, which was distributed above 1500 m a.s.l., covering 40.68%. Eight of 10 species can coexist, and the species with the highest percentage of potential restoration area is Cedrela montana (14.57% from Amazonas). However, less than 1.33% of the Amazon has a potential distribution of some species and is protected under some category of conservation. Our study will contribute as a tool for the sustainable management of forests and will provide geographic information to complement forest restoration and conservation plans.
A Review of Clean Electricity Policies—From Countries to Utilities
Authors: Sun Kaiqi , Xiao Huangqing , Liu Shengyuan , You Shutang , Yang Fan , Dong Yuqing , Wang Weikang , Liu Yilu
Published: September 2020

Due to the heavy stress on environmental deterioration and the excessive consumption of fossil resources, the transition of global energy from fossil fuel energy to clean energy has significantly accelerated in recent years. The power industry and policymakers in almost all countries are focusing on clean energy development. Thanks to progressive clean energy policies, significant progress in clean energy integration and greenhouse gas reduction has been achieved around the world. However, due to the differences in economic structures, clean energy distributions, and development models, clean energy policy scope, focus, and coverage vary between different countries, states, and utilities. This paper aims at providing a policy review for readers to easily obtain clean energy policy information on various clean energies in the U.S. and some other countries. Firstly, this paper reviews and compares some countries’ clean energy policies on electricity. Then, taking the U.S. as an example, this paper introduces the clean energy policies of some representative states and utilities in the U.S in perspectives of renewable energies, electric vehicles, and energy storage.
Learning Analytics Using Social Network Analysis and Bayesian Network Analysis in Sustainable Computer-Based Formative Assessment System
Authors: Choi Younyoung , Cho Young Il
Published: September 2020

The sustainable computer-based evaluation system (SCE) is a scenario-based formative evaluation system, in which students are assigned a task during a course. The tasks include the diversity conditions in real-world scenarios. The goals of this system are learning to think as a professional in a certain discipline. While the substantive, psychological, instructional, and task developmental aspects of the assessment have been investigated, few analytic methods have been proposed that allow us to provide feedback to learners in a formative way. The purpose of this paper is to introduce a framework of a learning analytic method including (1) an assessment design through evidence-centered design (ECD), (2) a data mining method using social network analysis, and (3) an analytic method using a Bayesian network. This analytic framework can analyze the learners’ performances based on a computational psychometric framework. The tasks were designed to measure 21st century learning skills. The 250 samples of data collected from the system were analyzed. The results from the social network analysis provide the learning path during a course. In addition, the 21st century learning skills of each learner were inferred from the Bayesian network over multiple time points. Therefore, the learning analytics proposed in this study can offer the student learning progression as well as effective feedback for learning.
Neighbourhood Environment and Cognitive Vulnerability—A Survey Investigation of Variations Across the Lifespan and Urbanity Levels
Authors: Cassarino Marica , Bantry-White Eleanor , Setti Annalisa
Published: September 2020

Background: Understanding the impact of local communities on wellbeing is a paramount Sustainable Development Goal. In order to inform people-centred planning interventions, it is important to understand what demographic groups need supportive communities the most and where. This study explored associations between perceived neighbourhood characteristics and cognitive vulnerability, and the moderating role of age and urbanity of the place of residence. Methods: A convenience sample of 224 Irish adults completed a survey assessing cognitive vulnerability and perceptions of neighbourhood qualities, together with sociodemographic and residential information. Correlational analyses explored bivariate associations as well as moderating effects. A subsample (n = 142) provided qualitative accounts of their preferences of neighbourhood characteristics. Results: Regression analyses showed that controlling for sociodemographic factors, higher self-reported neighbourhood pleasantness was associated with lower cognitive vulnerability, particularly in older adults who lived in the most rural and urban areas (p = 0.006). Qualitative accounts suggested urban–rural variations in perceived accessibility and perceptual stressors, and age-related variations in social preferences. Conclusions: Our findings indicate a complex association between neighbourhood characteristics and cognitive wellbeing, highlighting the potential benefits of neighbourhood pleasantness for cognition particularly for older people in very rural or very urbanised places. Implications for research and environmental interventions are discussed.
Impact of COVID-19 on Urban Mobility during Post-Epidemic Period in Megacities: From the Perspectives of Taxi Travel and Social Vitality
Authors: Nian Guangyue , Peng Bozhezi , Sun Daniel (Jian) , Ma Wenjun , Peng Bo , Huang Tianyuan
Published: September 2020

The prevention and control of COVID-19 in megacities is under large pressure because of tens of millions and high-density populations. The majority of epidemic prevention and control policies implemented focused on travel restrictions, which severely affected urban mobility during the epidemic. Considering the impacts of epidemic and associated control policies, this study analyzes the relationship between COVID-19, travel of residents, Point of Interest (POI), and social activities from the perspective of taxi travel. First, changes in the characteristics of taxi trips at different periods were analyzed. Next, the relationship between POIs and taxi travels was established by the Geographic Information System (GIS) method, and the spatial lag model (SLM) was introduced to explore the changes in taxi travel driving force. Then, a social activities recovery level evaluation model was proposed based on the taxi travel datasets to evaluate the recovery of social activities. The results demonstrated that the number of taxi trips dropped sharply, and the travel speed, travel time, and spatial distribution of taxi trips had been significantly influenced during the epidemic period. The spatial correlation between taxi trips was gradually weakened after the outbreak of the epidemic, and the consumption travel demand of people significantly decreased while the travel demand for community life increased dramatically. The evaluation score of social activity is increased from 8.12 to 74.43 during the post-epidemic period, which may take 3–6 months to be fully recovered as a normal period. Results and models proposed in this study may provide references for the optimization of epidemic control policies and recovery of public transport in megacities during the post-epidemic period.
A Comparison between Collaborative and Individual Writings in Promoting Motivation and Language Acquisition
Authors: Martín José Luis Ortega , Hameleers Imke B. , Trujillo-Torres Juan-Manuel , Moreno-Guerrero Antonio-José
Published: September 2020

The study refers to collaborative writing. The main objective of this study is to show the effects of collaborative writing in the acquisition of the English language upon students of non-compulsory secondary education. The applied study is grounded on a quantitative focus of correlational character and descriptive basis, while applying a quasi-experimental design with a control group (CG) and an experimental group (EG). The results show that there is a significant relation that favors the method of collaborative writing upon the dimensions of feedback, motivation, collaboration, satisfaction, and ratings. It can be concluded that the collaborative writing method is effective if compared to the individual learning method.
Asymmetries in the Disturbance Compensation Methods for the Stable and Unstable First Order Plants
Authors: Huba Mikulas , Bistak Pavol , Vrancic Damir , Zakova Katarina
Published: September 2020

This paper analyzes the first-order and first-order time-delayed systems control approaches, focusing mainly on unstable systems. First, it discusses asymmetries between the disturbance observer-based (DOB) control with decoupled tracking and the disturbance rejection responses, stressing applications to stable and unstable plants. The paper analyzes some DOB-based control solutions for unstable systems which do not use internal closed-loop stabilization. The novelty of the paper is thorough study accompanied with a comprehensive explanation of the differences between two distinct approaches: the transfer-function- and the closed-loop-based feedforward control approach from the point of view of control constraints. It is clearly illustrated that the main cause of instability of DOB-based approaches, applied to unstable systems, is given by their effort to impose on the system the unstable dynamics of the chosen nominal process model. It is also shown that the closed-loop stability of the DOB-based control, applied to the unstable systems, can be restored by using the supervising reference model control (RMC). The main novelty of the proposed approach is that its eliminates the mentioned stability problems while maintaining the full functionality of the chosen control structures. RMC has so far only been implemented for generating a setpoint feedforward signal. However, by generalization of this approach for disturbance rejection, the methodology of DOB design, based on nominal models, can be extended to the control of unstable systems. Without the use of disturbance reference models, the interactions of the master stabilizer with disturbance compensation cannot be eliminated. Without the internal stabilization, the stable transients can only be achieved by designing controllers based on stable models, instead of unstable ones. The existing modifications of DOB-based schemes for unstable plants, proposed in some references, are shown to lead to traditional Proportional-Integrative (PI) control, thus losing all the advantages over the PI controllers. In all the considered structures, the role of integrating models is also emphasized.
Detection of Cyclic Imine Toxins in Dietary Supplements of Green Lipped Mussels (Perna canaliculus) and in Shellfish Mytilus chilensis
Authors: Otero Paz , Vale Carmen , Boente-Juncal Andrea , Costas Celia , Louzao M. Carmen , Botana Luis M.
Published: September 2020

Seafood represents a significant part of the human staple diet. In the recent years, the identification of emerging lipophilic marine toxins has increased, leading to the potential for consumers to be intoxicated by these toxins. In the present work, we investigate the presence of lipophilic marine toxins (both regulated and emerging) in commercial seafood products from non-European locations, including mussels Mytilus chilensis from Chile, clams Tawerea gayi and Metetrix lyrate from the Southeast Pacific and Vietnam, and food supplements based on mussels formulations of Perna canaliculus from New Zealand. All these products were purchased from European Union markets and they were analyzed by UPLC-MS/MS. Results showed the presence of the emerging pinnatoxin-G in mussels Mytilus chilensis at levels up to 5.2 µg/kg and azaspiracid-2 and pectenotoxin-2 in clams Tawera gayi up to 4.33 µg/kg and 10.88 µg/kg, respectively. This study confirms the presence of pinnatoxins in Chile, one of the major mussel producers worldwide. Chromatograms showed the presence of 13-desmethyl spirolide C in dietary supplements in the range of 33.2–97.9 µg/kg after an extraction with water and methanol from 0.39 g of the green lipped mussels powder. As far as we know, this constitutes the first time that an emerging cyclic imine toxin in dietary supplements is reported. Identifying new matrix, locations, and understanding emerging toxin distribution area are important for preventing the risks of spreading and contamination linked to these compounds.
The Protective Effect of Staphylococcus epidermidis Biofilm Matrix against Phage Predation
Authors: Melo Luís D. R. , Pinto Graça , Oliveira Fernando , Vilas-Boas Diana , Almeida Carina , Sillankorva Sanna , Cerca Nuno , Azeredo Joana
Published: September 2020

Staphylococcus epidermidis is a major causative agent of nosocomial infections, mainly associated with the use of indwelling devices, on which this bacterium forms structures known as biofilms. Due to biofilms’ high tolerance to antibiotics, virulent bacteriophages were previously tested as novel therapeutic agents. However, several staphylococcal bacteriophages were shown to be inefficient against biofilms. In this study, the previously characterized S. epidermidis-specific Sepunavirus phiIBB-SEP1 (SEP1), which has a broad spectrum and high activity against planktonic cells, was evaluated concerning its efficacy against S. epidermidis biofilms. The in vitro biofilm killing assays demonstrated a reduced activity of the phage. To understand the underlying factors impairing SEP1 inefficacy against biofilms, this phage was tested against distinct planktonic and biofilm-derived bacterial populations. Interestingly, SEP1 was able to lyse planktonic cells in different physiological states, suggesting that the inefficacy for biofilm control resulted from the biofilm 3D structure and the protective effect of the matrix. To assess the impact of the biofilm architecture on phage predation, SEP1 was tested in disrupted biofilms resulting in a 2 orders-of-magnitude reduction in the number of viable cells after 6 h of infection. The interaction between SEP1 and the biofilm matrix was further assessed by the addition of matrix to phage particles. Results showed that the matrix did not inactivate phages nor affected phage adsorption. Moreover, confocal laser scanning microscopy data demonstrated that phage infected cells were less predominant in the biofilm regions where the matrix was more abundant. Our results provide compelling evidence indicating that the biofilm matrix can work as a barrier, allowing the bacteria to be hindered from phage infection.
ALGORITHMS FOR ORGANIZING WIRELESS INFORMATION INTERACTION OF SENSOR SYSTEMS AND ROBOTIC DEVICES
Authors: Denisov A. V.
Published: September 2020

Radio module distribution algorithms for agricultural territory are presented to ensure information interaction of sensor systems and unmanned ground robots. Algorithms can reduce the number of repeaters, which reduces noise and simplifies the schedule of data reception and transmission.
ABOUT THE OPTIMAL MODES OF CONTROLLED TRANSFER OF WALKING PROPULSION DEVICES
Authors: Miroshkina M. V.
Published: September 2020

The problem of walking machine leg transfer is considered. Optimal laws of transfer are determined with regards to geometrical features of ground underwater. Complex optimality criterion is introduced as sum of indexes of quality of the movement multiplied each by weight coefficients.
“Morangos com Açúcar” na Telinha
Authors: Silva Cinthia Lopes da
Published: September 2020

Este trabalho tem como finalidade descrever e analisar uma experiência pedagógica realizada junto a estudantes portugueses de Educação Física. Essa experiência consiste na problematização de uma série televisiva, denominada “Morangos com açúcar”. Os programas televisivos são produções que tem como finalidade difundir significados e entreter os telespectadores. Os estudantes de Educação Física, ao realizarem uma leitura de qualidade da mídia, terão condições de ter uma intervenção social futura efetiva. Como procedimentos metodológicos, é feita uma “descrição densa” da troca de significados junto aos participantes da pesquisa, caracterizando um estudo eminentemente qualitativo. Como resultado, a experiência pedagógica viabilizou aos estudantes revisão de conceitos e reflexões que contribuem para o desenvolvimento pessoal desses sujeitos e para sua formação profissional.
Social Capital in the Context of Fitness for Purpose Do Engineering Students in Africa Possesses the Right Capital
Authors:   Institute of System Science Durban University of Technology., Fomunyam Kehdinga George
Published: September 2020

Social capital is important as it becomes an imperative as key indices for development and growth of students who opt for engineering education. Engineering educators have important role to play in motivating engineering students with untapped potentials to possess the right capital by creating productive teaching platforms. This paper explored the relationship of social capital on engineering education in addition to students possessing the right capital in their respective course of study. This paper argued that engineering educators should develop students’ social capital within the context of social networks and norms by promoting knowledge-based social capital and its productivity among engineering students. This paper was guided by Social Capital Theory, which lay emphases on the views that student learning should be centred on education invested on human capital and social capital. Specifically, we explore engineering students having the right capital in their study and social capital is a quality criterion that enhances students in possessing the right capital towards EE in Africa. Thus, to address the social capital gaps in engineering education, it suggested that engineering educational curriculum as well as staff development and capacity building should be designed in developing engineering student to possess the right capital in their field of study. A number of educational-oriented recommendations for social capital in engineering education investment were made.
Theoretical Framework for the Freight Movements Through a Multicommodity Port
Authors:   Professor, Civil Engineering, Sethu Institute of Technology (Autonomous) Kariapatti., jayaprakash Dr. P. Oliver , Gunasekaran A Dr. K. ,   Professor& Head, Transportation Engg. Division, College of Engg. Guindy, Anna University, Chennai.
Published: September 2020

Port based freight movement planning is a complicated task that could be carried out efficiently to handle the cargo, optimally utilize the infrastructure and plan the future infrastructure requirements. The nature of activities at the port is dynamic with uncertainties,since the operationsare time bound, scholastic and probabilistic.As the huge capital is involved in port infrastructure, the inter-relationship between port activities need to be understood and a system model enveloping the relationship among the variables is much needed for optimal utilization of existing facilities and to predict the future infrastructure requirements. The conventional four step model approach for modeling the person trips would not effectively reflect the commercial scheduling constraintsand requirements of freight trips. This research workattemptsto model the port operations, to assess the level of service of roads and gate operations as subsystems to understand the interdependencies between the variables and the impact on the port operations as a whole.
Rheological Behavior of Ordinary Concrete, SCC with and without Glass and Steel Fibers
Authors:   Part time Research Scholar and Associate Professor at Alva Institute of Engineering, Moodabidri., H C M Swamy , Raj G. Prince Arul ,   Dean and Professor Karunya Institute of Science and Technology, Coimbatore( Tamil Nadu) India.
Published: September 2020

Rheology indicates its flowability and deformation. These two parameters indicate directly workability. It measures the normal and shearing forces in fresh concrete state. In this article the flowability and its measurement are discussed for ordinary and SCC with Glass and steel fibers are demonstrated. The strength parameter for a particular concrete mix is demonstrated with sampling and acceptance criteria. The new draft code on design of concrete mix (IS-10262) verified by compliance with specifications. The different parameters like percentage of Glass and Steel fibers, different percentages of silica fume, and different dosages of superplasticizer are tested and reported. A comparative analysis for, with and without glass fibers on ordinary and SCC predicted.
Additive Manufacturing Technology in Orthodontic Devices Development
Authors:   School of Science and Technology, The Open University of Hong Kong., Tang W.F. , Mak S.L. ,   School of Science and Technology, The Open University of Hong Kong., C.H.Li  ,   School of Science and Technology, The Open University of Hong Kong.
Published: September 2020

Traditional wires and brackets has been widely used as orthodontic devices for long time. The metal wires and brackets help to correct the position of teeth as well as fix the cavity. However, metal brace wires have quite a lot limitations. Patients wearing metal brace have many food restrictions and feel not comfortable. Brushing and flossing are required to remove the food debris frequently. Hence, clear plastic aligners have popped up recently. Since the metal brace fabrication process has associated with prolonged process time as a result of a long workflow process starting from brace mold presentation to the prosthesis execution. The growing of additive manufacturing technology make it possible to develop complex structures and shapes of dental brace. By combining 3D oral scanning, it is possible to shorten the lead time of orthodontic treatment process. This review, therefore, investigates the use of Digital Light Processing (DLP) Additive Manufacturing Technology for plastic dental brace development as a remedy to the problems associated with the traditional methods. The study reveals that it is feasible to fabricate these plastic braces utilising the DLP technology. DLP technology is affordable and arguably able to produce dental models with high levels of assurance and accuracy.