bluuebunny/crossref_metadata_2025 huggingface dataset:
Infrared Optical Observability of an Earth Entry Orbital Test Vehicle Using Ground-Based Remote Sensors
Authors: Niu Qinglin , Meng Xiaying , He Zhihong , Dong Shikui
Published: October 2019

Optical design parameters for a ground-based infrared sensor rely strongly on the target’s optical radiation properties. Infrared (IR) optical observability and imaging simulations of an Earth entry vehicle were evaluated using a comprehensive numerical model. Based on a ground-based IR detection system, this model considered many physical mechanisms including thermochemical nonequilibrium reacting flow, radiative properties, optical propagation, detection range, atmospheric transmittance, and imaging processes. An orbital test vehicle (OTV) was selected as the research object for analysis of its observability using a ground-based infrared system. IR radiance contours, maximum detecting range (MDR), and thermal infrared (TIR) pixel arrangement were modeled. The results show that the distribution of IR radiance is strongly dependent on the angle of observation and the spectral band. Several special phenomena, including a strong receiving region (SRR), a characteristic attitude, a blind zone, and an equivalent zone, are all found in the varying altitude MDR distributions of mid-wavelength infrared (MWIR) and long-wavelength infrared (LWIR) irradiances. In addition, the possible increase in detectivity can greatly improve the MDR at high altitudes, especially for the backward and forward views. The difference in the peak radiance of the LWIR images is within one order of magnitude, but the difference in that of the MWIR images varies greatly. Analyses and results indicate that this model can provide guidance in the design of remote ground-based detection systems.
JAXA Annual Forest Cover Maps for Vietnam during 2015–2018 Using ALOS-2/PALSAR-2 and Auxiliary Data
Authors: Truong Van Thinh , Hoang Thanh Tung , Cao Duong Phan , Hayashi Masato , Tadono Takeo , Nasahara Kenlo Nishida
Published: October 2019

Monitoring the temporal changes of forests is important for sustainable forest management. In this study, we investigated the potential of using multi-temporal synthetic aperture radar (SAR) images for mapping annual change in forest cover at a national scale. We assessed the robustness of using multi-temporal Phased Array L-band Synthetic Aperture Radar-2/Scanning Synthetic Aperture Radar (PALSAR-2/ScanSAR) mosaic images for forest mapping by comparison with single-temporal PALSAR-2 mosaic images for three test sites in North, Central, and Southern Vietnam. We then used a combination of multi-temporal PALSAR-2/ScanSAR images, multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) images, and Shuttle Radar Topography Mission (SRTM) images to map annual forest cover for mainland Vietnam during 2015–2018. Average overall accuracies of our forest/non-forest (FNF) maps (86.6% ± 3.1%) were greater than recent maps of Japan Aerospace Exploration Agency (JAXA, (77.5% ± 3.2%)) and European Space Agency (ESA, (85.4% ± 1.6%)). Our estimates of mainland Vietnam’s forest area were close to that of the Vietnamese government. A comparison of the spatial distribution of forest estimated from JAXA and ESA FNF maps showed that our FNF map in 2015 agreed relatively well with the ESA map, with 77% of pixels being consistent. This study demonstrates the merit of using multi-temporal PALSAR-2/ScanSAR images for annual forest mapping at a national scale.
Recent Advances in Palladium Nanoparticles-Based Hydrogen Sensors for Leak Detection
Authors: Ndaya Cynthia , Javahiraly Nicolas , Brioude Arnaud
Published: October 2019

Along with the development of hydrogen as a sustainable energy carrier, it is imperative to develop very rapid and sensitive hydrogen leaks sensors due to the highly explosive and flammable character of this gas. For this purpose, palladium-based materials are being widely investigated by research teams because of the high affinity between this metal and hydrogen. Furthermore, nanostructured palladium may provide improved sensing performances compared to the use of bulk palladium. This arises from a higher effective surface available for interaction of palladium with the hydrogen gas molecules. Several works taking advantage of palladium nanostructures properties for hydrogen sensing applications have been published. This paper reviews the recent advances reported in the literature in this scope. The electrical and optical detection techniques, most common ones, are investigated and less common techniques such as gasochromic and surface wave acoustic sensors are also addressed. Here, the sensor performances are mostly evaluated by considering their response time and limit of detection.
Machine Learning Based Prediction of Insufficient Herbage Allowance with Automated Feeding Behaviour and Activity Data
Authors: Shafiullah Abu Zar , Werner Jessica , Kennedy Emer , Leso Lorenzo , O’Brien Bernadette , Umstätter Christina
Published: October 2019

Sensor technologies that measure grazing and ruminating behaviour as well as physical activities of individual cows are intended to be included in precision pasture management. One of the advantages of sensor data is they can be analysed to support farmers in many decision-making processes. This article thus considers the performance of a set of RumiWatchSystem recorded variables in the prediction of insufficient herbage allowance for spring calving dairy cows. Several commonly used models in machine learning (ML) were applied to the binary classification problem, i.e., sufficient or insufficient herbage allowance, and the predictive performance was compared based on the classification evaluation metrics. Most of the ML models and generalised linear model (GLM) performed similarly in leave-out-one-animal (LOOA) approach to validation studies. However, cross validation (CV) studies, where a portion of features in the test and training data resulted from the same cows, revealed that support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) performed relatively better than other candidate models. In general, these ML models attained 88% AUC (area under receiver operating characteristic curve) and around 80% sensitivity, specificity, accuracy, precision and F-score. This study further identified that number of rumination chews per day and grazing bites per minute were the most important predictors and examined the marginal effects of the variables on model prediction towards a decision support system.
System of Sensors and Actuators for the Production of Water Used in the Manufacture of Medicines
Authors: da Silva Fabricio Roosevelt Melo , Fonsêca Diego Antonio de Moura , da Silva Werbet Luiz Almeida , Villarreal Elmer Rolando Llanos , Echaiz Espinoza German Alberto , Salazar Andrés Ortiz
Published: October 2019

This paper presents the development and implementation of a centralized industrial network for an automatic purified water production system used in the pharmaceutical industry. This implementation is part of a project to adapt an industrial plant to cope with advances in industrial technology to achieve the level of Industry 4.0. The adequacy of the instruments and the interconnection of the controllers made it possible to monitor the process steps by transforming a manual plant, with discontinuous production into an automated plant, improving the efficiency and quality of the produced water. The development of a supervisory system provides the operator with a panoramic view of the process, informing in real-time the behavior of the variables in the process steps, as well as storing data, event history and alarms. This system also prevented the collection of erroneous or manipulated data, making the process more transparent and reliable. Accordingly, we have been able to tailor this water treatment plant to operate within the minimum requirements required by the regulator.
Spectrum-Efficient Resource Allocation in Multi-Radio Multi-Hop Cognitive Radio Networks
Authors: Han Bin , Luo Ying , Zeng Min , Jiang Hong
Published: October 2019

The multi-hop cognitive radio network (CRN) has attracted much attention in industry and academia because of its seamless wireless coverage by forming multi-hop links and high spectrum utilization of cognitive radio (CR) technology. Using multi-slot statistical spectrum status information (SSI), this work investigates the average spectrum efficiency (SE) of a multi-radio multi-hop (MRMH) CRN where each hop is permitted to use different spectra and long-distance hops can reuse the same idle primary user (PU) spectrum. Faced with the modeled SE problem, which is a complex non-convex fractional mixed integer nonlinear programming (MINLP) problem, the optimal spectrum and power allocation for multi-hop links in multi-slot and multi-channel scenarios can be obtained with the proposed successive multi-step convex approximation scheme (SMCA). As shown through computational complexity and simulation analysis, SMCA can obtain an approximate lower bound of the optimal solution for the modeled SE problem with a lower computational cost. Furthermore, some potential relationships between network performance and spectrum idle rate can be easily discussed with SMCA, which can provide some sensible deployment strategies for the MRMH CRN in future multi-slot scenarios.
Accurate and Robust Monocular SLAM with Omnidirectional Cameras
Authors: Liu Shuoyuan , Guo Peng , Feng Lihui , Yang Aiying
Published: October 2019

Simultaneous localization and mapping (SLAM) are fundamental elements for many emerging technologies, such as autonomous driving and augmented reality. For this paper, to get more information, we developed an improved monocular visual SLAM system by using omnidirectional cameras. Our method extends the ORB-SLAM framework with the enhanced unified camera model as a projection function, which can be applied to catadioptric systems and wide-angle fisheye cameras with 195 degrees field-of-view. The proposed system can use the full area of the images even with strong distortion. For omnidirectional cameras, a map initialization method is proposed. We analytically derive the Jacobian matrices of the reprojection errors with respect to the camera pose and 3D position of points. The proposed SLAM has been extensively tested in real-world datasets. The results show positioning error is less than 0.1% in a small indoor environment and is less than 1.5% in a large environment. The results demonstrate that our method is real-time, and increases its accuracy and robustness over the normal systems based on the pinhole model.
An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition
Authors: Dissanayake Theekshana , Rajapaksha Yasitha , Ragel Roshan , Nawinne Isuru
Published: October 2019

Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher classification accuracy incorporating a limited number of bio-sensors. In the domain of machine learning, ensemble learning methods have been successfully applied to solve different types of real-world machine learning problems which require improved classification accuracies. Emphasising on that, this research suggests an ensemble learning approach for developing a machine learning model that can recognize four major human emotions namely: anger; sadness; joy; and pleasure incorporating electrocardiogram (ECG) signals. As feature extraction methods, this analysis combines four ECG signal based techniques, namely: heart rate variability; empirical mode decomposition; with-in beat analysis; and frequency spectrum analysis. The first three feature extraction methods are well-known ECG based feature extraction techniques mentioned in the literature, and the fourth technique is a novel method proposed in this study. The machine learning procedure of this investigation evaluates the performance of a set of well-known ensemble learners for emotion classification and further improves the classification results using feature selection as a prior step to ensemble model training. Compared to the best performing single biosensor based model in the literature, the developed ensemble learner has the accuracy gain of 10.77%. Furthermore, the developed model outperforms most of the multiple biosensor based emotion recognition models with a significantly higher classification accuracy gain.
P3HT Processing Study for In-Liquid EGOFET Biosensors: Effects of the Solvent and the Surface
Authors: Parmeggiani Matteo , Verna Alessio , Ballesio Alberto , Cocuzza Matteo , Piatti Erik , Fra Vittorio , Pirri Candido Fabrizio , Marasso Simone Luigi
Published: October 2019

In-liquid biosensing is the new frontier of health and environment monitoring. A growing number of analytes and biomarkers of interest correlated to different diseases have been found, and the miniaturized devices belonging to the class of biosensors represent an accurate and cost-effective solution to obtaining their recognition. In this study, we investigate the effect of the solvent and of the substrate modification on thin films of organic semiconductor Poly(3-hexylthiophene) (P3HT) in order to improve the stability and electrical properties of an Electrolyte Gated Organic Field Effect Transistor (EGOFET) biosensor. The studied surface is the relevant interface between the P3HT and the electrolyte acting as gate dielectric for in-liquid detection of an analyte. Atomic Force Microscopy (AFM) and X-ray Photoelectron Spectroscopy (XPS) characterizations were employed to study the effect of two solvents (toluene and 1,2-dichlorobenzene) and of a commercial adhesion promoter (Ti Prime) on the morphological structure and electronic properties of P3HT film. Combining the results from these surface characterizations with electrical measurements, we investigate the changes on the EGOFET performances and stability in deionized (DI) water with an Ag/AgCl gate electrode.
Enhancement of Sensitivity and Accuracy of Micro/Nano Water Droplets Detection Using Galvanic-Coupled Arrays
Authors: Shrestha Rekha Goswami , Ando Tatsuya , Sakamoto Yukihiro , Kawakita Jin
Published: October 2019

A moisture sensor has been reported that detects invisibly small water droplets and distinguishes their particle size with high accuracy and high speed. This sensor uses narrow lines of dissimilar metals as electrodes, arranged with gaps of 0.5 to 10 μm. The working principle for this sensor is that it measures the galvanic current generated when a water droplet forms a bridge-like structure between the electrodes. In addition, the surface of the sensor was controlled by using hydrophilic polymer, GL, and hydrophobic polymer, PMMA. The study of the relationship between the contact angle, projected area of water droplets and current response from the sensor with a modified surface showed that in the case of GL, the contact angle was small (wettability increased) and the average value and distribution of the projected water droplet area and the sensor’s response increased. This enhanced the sensor’s sensitivity. On the other hand, in the case of PMMA, the contact angle was large (wettability decreased), the area of the water droplet and its distribution became small and the accuracy of discriminating the water droplet’s diameter by the sensor enhanced. Therefore, by rendering sensor’s surface hydrophilic and hydrophobic, the sensitivity and accuracy of the sensor could be enhanced.
Odometer Velocity and Acceleration Estimation Based on Tracking Differentiator Filter for 3D-Reduced Inertial Sensor System
Authors: Zhang Qing , Guan Lianwu , Xu Dexin
Published: October 2019

Velocity information from the odometer is the key information in a reduced inertial sensor system (RISS), and is prone to noise corruption. In order to improve the navigation accuracy and reliability of a 3D RISS, a method based on a tracking differentiator (TD) filter was proposed to track odometer velocity and acceleration. With the TD filter, an input signal and its differential signal are estimated fast and accurately to avoid the noise amplification that is brought by the conventional differential method. The TD filter does not depend on an object model, and has less computational complexity. Moreover, the filter phase lag is decreased by the prediction process with the differential signal of the TD filter. In this study, the numerical simulation experiments indicate that the TD filter can achieve a better performance on random noises and outliers than traditional numerical differentiation. The effectiveness of the TD filter on a 3D RISS is demonstrated using a group of offline data that were obtained from an actual vehicle experiment. We conclude that the TD filter can not only quickly and correctly filter velocity and estimate acceleration from the odometer velocity for a 3D RISS, but can also improve the reliability of the 3D RISS.
Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological Data
Authors: Chowdhury Alok Kumar , Tjondronegoro Dian , Chandran Vinod , Zhang Jinglan , Trost Stewart G.
Published: October 2019

This study examined the feasibility of a non-laboratory approach that uses machine learning on multimodal sensor data to predict relative physical activity (PA) intensity. A total of 22 participants completed up to 7 PA sessions, where each session comprised 5 trials (sitting and standing, comfortable walk, brisk walk, jogging, running). Participants wore a wrist-strapped sensor that recorded heart-rate (HR), electrodermal activity (Eda) and skin temperature (Temp). After each trial, participants provided ratings of perceived exertion (RPE). Three classifiers, including random forest (RF), neural network (NN) and support vector machine (SVM), were applied independently on each feature set to predict relative PA intensity as low (RPE ≤ 11), moderate (RPE 12–14), or high (RPE ≥ 15). Then, both feature fusion and decision fusion of all combinations of sensor modalities were carried out to investigate the best combination. Among the single modality feature sets, HR provided the best performance. The combination of modalities using feature fusion provided a small improvement in performance. Decision fusion did not improve performance over HR features alone. A machine learning approach using features from HR provided acceptable predictions of relative PA intensity. Adding features from other sensing modalities did not significantly improve performance.
Delay-Aware Reverse Approach for Data Aggregation Scheduling in Wireless Sensor Networks
Authors: Nguyen Dung T. , Le Duc-Tai , Kim Moonseong , Choo Hyunseung
Published: October 2019

Many time-sensitive applications require data to be aggregated from wireless sensor networks with minimum latency. However, the minimum latency aggregation scheduling problem has not been optimally solved due to its NP-hardness. Most existing ideas rely on local information (e.g., node degree, number of children) to organize the schedule order, hence results in solutions that might be far from optimal. In this work, we propose RADAS: a delay-aware Reverse Approach for Data Aggregation Scheduling that determines the transmissions sequence of sensors in a reverse order. Specifically, RADAS iteratively finds the transmissions starting from the last time slot, in which the last sender delivers data to the sink, down to the first time slot, when the data aggregation begins. In each time slot, RADAS intends to maximize the number of concurrent transmissions, while giving higher priority to the sender with potentially higher aggregation delay. Scheduling such high-priority sender first would benefit the maximum selections in subsequent time slots and eventually shorten the schedule length. Simulation results show that our proposed algorithm dominates the existing state-of-the-art schemes, especially in large and dense networks, and offers up to 30% delay reduction.
Fixed-Time Observer Based Prescribed-Time Containment Control of Unmanned Underwater Vehicles with Faults and Uncertainties
Authors: Yang Tingting , Yu Shuanghe
Published: October 2019

The problem of prescribed-time containment control of unmanned underwater vehicles (UUVs) with faults and uncertainties is considered. Different from both regular finite-time control and fixed-time control, the proposed prescribed-time control strategy is built upon a novel coordinate transformation function and the block decomposition technique, resulting in the followers being able to move into the convex hull spanned by the leaders in prespecifiable convergence time. Moreover, intermediate variables and the control input terms are also shown to remain uniformly bounded at the prescribed-time. To reduce the magnitude of the bounds, a novel fixed-time observer for the fault is proposed. Two numerical examples are provided to verify the effectiveness of the proposed prescribed-time control strategy.
Extended Subaperture Imaging Method for Airborne Low Frequency Ultrawideband SAR Data
Authors: An Daoxiang , Wang Wu , Chen Leping
Published: October 2019

The subaperture processing is one of the essential strategies for low frequency ultrawideband synthetic aperture radar (LF UWB SAR) imaging, especially for the real-time LF UWB SAR imaging because it can improve the parallelization of the imaging algorithm. However, due to the longer synthetic aperture of LF UWB SAR, the traditional subaperture imaging encounters an azimuth ambiguities problem, which severely degrades the focused quality of the imaging results. In this paper, the reason for the presence of azimuth ambiguities in the LF UWB SAR subaperture imaging and its influence on image quality is first analyzed in theory. Then, an extended subaperture imaging method based on the extension of subaperture length before Range Cell Migration Correction (RCMC) was proposed. By lengthening the subaperture length, the azimuth ambiguities are effectively eliminated. Finally, the extended part of subaperture is wiped off before the azimuth compression (AC), and the LF UWB SAR image of high focused quality is obtained. The correctness of the theory analysis and the effectiveness of the proposed method have been validated through simulated and real LF UWB SAR data.
Classifying and Predicting Salinization Level in Arid Area Soil Using a Combination of Chua’s Circuit and Fractional Order Sprott Chaotic System
Authors: Tian Anhong , Fu Chengbiao , Su Xiao-Yi , Yau Her-Terng , Xiong Heigang
Published: October 2019

Soil salinization is very complex and its evolution is affected by numerous interacting factors produce strong non-linear characteristics. This is the first time fractional order chaos theory has been applied to soil salinization-level classification to decrease uncertainty in salinization assessment, solve fuzzy problems, and analyze the spectrum chaotic features in soil with different levels of salinization. In this study, typical saline soil spectrum data from different human interference areas in Fukang City (Xinjiang) and salt index test data from an indoor chemical analysis laboratory are used as the base information source. First, we explored the correlation between the spectrum reflectance features of soil with different levels of salinization and chaotic dynamic error and chaotic attractor. We discovered that the chaotic status error in the 0.6 order has the greatest change. The 0.6 order chaotic attractors are used to establish the extension matter-element model. The determination equation is built according to the correspondence between section domain and classic domain range to salinization level. Finally, the salt content from the chemical analysis is substituted into the discriminant equation in the extension matter-element model. Analysis found that the accuracy of the discriminant equation is higher. For areas with no human interference, the extension classification can successfully identify nine out of 10 prediction data, which is a 90% identification accuracy rate. For areas with human interference, the extension classification can successfully identify 10 out of 10 prediction data, which is a success rate of 100%. The innovation in this study is the building of a smart classification model that uses a fractional order chaotic system to inversely calculate soil salinization level. This model can accurately classify salinization level and its predictive results can be used to rapidly calculate the temporal and spatial distribution of salinization in arid area/desert soil.
Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios
Authors: Uria-Rivas R. , Rodriguez-Sanchez M. C. , Santos O. C. , Vaquero J. , Boticario J. G.
Published: October 2019

Physiological sensors can be used to detect changes in the emotional state of users with affective computing. This has lately been applied in the educational domain, aimed to better support learners during the learning process. For this purpose, we have developed the AICARP (Ambient Intelligence Context-aware Affective Recommender Platform) infrastructure, which detects changes in the emotional state of the user and provides personalized multisensorial support to help manage the emotional state by taking advantage of ambient intelligence features. We have developed a third version of this infrastructure, AICARP.V3, which addresses several problems detected in the data acquisition stage of the second version, (i.e., intrusion of the pulse sensor, poor resolution and low signal to noise ratio in the galvanic skin response sensor and slow response time of the temperature sensor) and extends the capabilities to integrate new actuators. This improved incorporates a new acquisition platform (shield) called PhyAS (Physiological Acquisition Shield), which reduces the number of control units to only one, and supports both gathering physiological signals with better precision and delivering multisensory feedback with more flexibility, by means of new actuators that can be added/discarded on top of just that single shield. The improvements in the quality of the acquired signals allow better recognition of the emotional states. Thereof, AICARP.V3 gives a more accurate personalized emotional support to the user, based on a rule-based approach that triggers multisensorial feedback, if necessary. This represents progress in solving an open problem: develop systems that perform as effectively as a human expert in a complex task such as the recognition of emotional states.
Energy-Efficient UAV-Enabled MEC System: Bits Allocation Optimization and Trajectory Design
Authors: Li Linpei , Wen Xiangming , Lu Zhaoming , Pan Qi , Jing Wenpeng , Hu Zhiqun
Published: October 2019

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system is attracting a lot of attentions for the potential of low latency and low transmission energy consumption, due to the advantages of high mobility and easy deployment. It has been widely applied to provide communication and computing services, especially in Internet of Things (IoT). However, there are still some challenges in the UAV-enabled MEC system. Firstly, the endurance of the UAV is limited and further impacts the performance of the system. Secondly, mobile devices are battery-powered and the batteries of some devices are hard to change. Therefore, in this paper, a UAV-enabled MEC system in which the UAV is empowered to have computing capability and provides tasks offloading service is studied. The total energy consumption of the UAV-enabled system, which includes the energy consumption of the UAV and the energy consumption of the ground users, is minimized under the constraints of the UAV’s energy budget, the number of each task’s bits, the causality of the data and the velocity of the UAV. The bits allocation of uploading data, computing data, downloading data and the trajectory of the UAV are jointly optimized with the goal of minimizing the total energy consumption. Moreover, a two-stage alternating algorithm is proposed to solve the non-convex formulated problem. Finally, the simulation results show the superiority of the proposed scheme compared with other benchmark schemes. Finally, the performance of the proposed scheme is demonstrated under different settings.
Muscle Activation in Traditional and Experimental Barbell Bench Press Exercise: A Potential New Tool for Fitness Maintenance
Authors: Melani Andrea , Gobbi Giuliana , Galli Daniela , Carubbi Cecilia , Masselli Elena , Neri Luca Maria , Giovinco Gaspare , Cicchella Antonio , Galuppo Laura , Presta Valentina , Vaccarezza Mauro , Vitale Marco , Mirandola Prisco
Published: October 2019

Background: The bench press exercise (BP) is commonly practiced in both recreational and professional training. The weight is lowered from a position where the elbows are at a 90° angle at the start and <90° at the end of eccentric phase, and then returned to the elbows extended position. In order to focus the exercise more on the triceps brachii (TB) rather than the pectoralis major (PM), the inter-handle distance (IHD) is decreased diminishing the involvement of the PM in favor of the TB. Purpose: To improve performance of the exercise by reducing force dissociation and transmitting 100% of the external load to the muscle tissue we propose a prototype of the barbell with a bar on which two sleeves are capable of sliding. The dynamic modifications of the IHD keep the elbow flexion angle constant at 90°. Results: Analysis of the inter-handle distance (IHD) signals of the upper body muscles showed a marked increase in muscle activity using the experimental barbell for the PM (19.5%) and for the biceps brachii (173%). Conclusions: The experimental barbell increased the muscle activity typical of the bench press exercise, obtaining the same training induction with a lower load and consequently preventing articular stress.
Ecological Footprint and Water Footprint of Taipei
Authors: Lee Yung-Jaan
Published: October 2019

Taiwan suffers from many natural disasters and is vulnerable to climate change. A continuous increase in its ecological footprint (EF) would pose numerous threats to the city. Taipei is Taiwan’s most densely populated city. Whether its citizens are consuming more resources because of their high income and high degree of urbanization, thereby burdening the environment, warrants study. In contrast to most top-down EF analyses, in this study, 445 residents were surveyed to calculate their carbon, built-up land and water footprints. Gender, occupation, age, education level and personal annual income do not influence water footprint or EF. Moreover, an individual’s water footprint is not correlated with his or her EF. The built-up land footprint that is obtained in this bottom-up study is similar to that in Taiwan’s top-down national footprint account. However, the personal carbon footprint found herein is smaller than that in the national footprint account, because this study asked respondents only about consumption related to everyday activities. Since Taipei residents have a high income and high daily consumption, the water footprint herein is larger than the top-down water footprint. This bottom-up EF analysis reflects residents’ daily consumption patterns and can be used in future urban decision-making.
A Low-Cost Interactive Writing Board for Primary Education using Distinct Augmented Reality Markers
Authors: Khan Dawar , Rehman Inam ur , Ullah Sehat , Ahmad Waheed , Cheng Zhanglin , Jabeen Gul , Kato Hirokazu
Published: October 2019

Educational institutions demand cost-effective and simple-to-use augmented reality systems. ARToolKit, an open-source computer tracking library for the creation of augmented reality applications that overlay virtual imagery on the real world, is such a system. It uses a simple camera and black-and-white markers printed on paper. However, due to inter-marker confusion, if the marker distinctions are not ensured, the markers are often miss-recognized. This paper presents an ARToolKit-based Interactive Writing Board (IWB) with a simple mechanism for designing confusion-free marker libraries. The board is used for teaching single characters of Arabic/Urdu to primary level students. It uses a simple ARToolKit marker for the recognition of each character. After marker recognition, the IWB displays the corresponding image, helping students with character understanding and pronunciation. Experimental results reveal that the system improves students’ motivation and learning skills.
Extension of TAM by Perceived Interactivity to Understand Usage Behaviors on ACG Social Media Sites
Authors: Lee Jui-Hsiang , Lee Chang-Franw
Published: October 2019

Social media sites, as participatory artistic online platforms, have become popular spaces for amateur artists to exchange the creative artefacts of animation, comics and games (ACG) with others. Online social media platforms for ACG amateur artists offer interactive features for communication, information exchange and distance learning to facilitate connections among amateur artists worldwide. However, not too much of the available research has regarded investigating the determinative factors of ACG users’ behavior on social media. By integrating aspects of perceived interactivity and users’ willingness to exchange information, this study formulated a concept model to investigate the prerequisite factors of users’ “continuance use intention” on ACG social media sites. The snowballing method was used to recruit 367 participants who had experience in creating ACG works and using more than two ACG social media sites from the Japanese sub-cultural communities at a university in Northern Taiwan. The results of this study provided empirical evidence supporting perceived interactivity as a prerequisite factor for extending the technology acceptance model (TAM), perceiving interactivity, and supporting indicators such as continuance use intentions and the willingness to exchange information on ACG social media sites.
What Does In-Work Poverty Mean for Women: Comparing the Gender Employment Segregation in Belgium and China
Authors: Liu Jinghong
Published: October 2019

This article presents an analysis of the female working poor in relation to gender employment segregation. It draws a cross-national profile of the female working poor in Belgium and China: two different nations with distinct stories of socio-economic development and cultural heritage, while both are characterized by high female employment participation. Analyses show that (1) women share a higher proportion among the total working poor population in both nations during recent years, whereas (2) in-work poverty has been a chronic condition, particularly among female workers in low-quality jobs. Thus, to some extent, labor market institutions may shape this gendered tendency of in-work poverty. In this article, women’s position in the public sphere in relation to employment segregation is discussed, and a contextual analysis identifies the causes of gender employment segregation. The results shed light on the crucial role of gender employment segregations related to in-work poverty and show that gender ideology and stereotypes do matter in explaining such employment differences. We argue that the promotion of female participation should be combined with explicit measures to reduce the disadvantageous position of women in the labor market.
Bibliometric Analysis of Fitness Equipment: How Scientific Focuses Affect Life-Cycle Approaches and Sustainable Ways of Development
Authors: Addolorato Silvio , Calabuig Ferran , Prado-Gascó Vicente , Gallardo Leonor , García-Unanue Jorge
Published: October 2019

Research Question: Although fitness equipment is recognised worldwide as a tool for physical activities and a sustainable result in optimizing human movements within a wide range of environments, the state of the art and scientific advances have not been analysed from a bibliometric point of view. Research Methods: Using descriptive bibliometric software, this study examined the scientific production, and the most prolific authors, articles, and institutions. Bibliometric maps were used to visualise the content of published articles and to determine the most prolific terms, co-citation, and co-authorship. Results and Findings: A total of 678 original papers were retrieved from 447 journals in Web of Science®. The Journal of Strength and Conditioning Research had the highest number of articles (25), while the American Journal of Preventive Medicine the highest number of global citations (656). Citation and co-authorship networks were defined. Implications: Research on fitness equipment is still in an early maturation stage with 30 years of scientific development in its timeline. Fitness equipment and its related fields of application could represent a real step into a more sustainable balance between the economic, environmental, and social spheres.
Influence of Network Embeddedness and Network Diversity on Green Innovation: The Mediation Effect of Green Social Capital
Authors: Chen Yu-Shan , Wang Cui , Chen Ying-Rong , Lo Wei-Yuan , Chen Kuan-Ling
Published: October 2019

Due to the current rise in concern about the challenge of global warming, stakeholders are now more conscious than in the past regarding environmental concerns. Stakeholders are more likely than ever before to support companies that are environmentally friendly. Simultaneously, companies seek to develop their social networks in such a way as to enhance their green innovation. This study investigates the influences of network embeddedness and network diversity on green innovation. In addition, this research discusses the mediation effects of green social capital. We adopt structural equation modeling for testing the research framework. A total of 330 valid questionnaires are collected. The results demonstrate that network embeddedness positively affects green social capital and green innovation performance. However, although network diversity positively affects green innovation performance, it does not significantly influence green social capital. Furthermore, the study indicates that green social capital partially mediates the relationship between network embeddedness and green innovation performance.
The Role of Sustainability Key Performance Indicators (KPIs) in Implementing Sustainable Strategies
Authors: Hristov Ivo , Chirico Antonio
Published: October 2019

The role of sustainability dimensions in the value creation process has attracted considerable interest in the scientific academic world in the last two decades. The 2030 Agenda, which fixed the sustainable goals (SDGs) to safeguard our planet, highlighted the fundamental role of sustainability issues. In this context, companies around the world need to integrate their strategies with environmental, social, and economic dimensions. However, sustainability aspects are often not linked to company strategies, and there has been growing difficulty in measuring sustainable development by adopting an appropriate set of key performance indicators (KPIs). Accordingly, the aim of this study is as follows: (1) to identify the suitable KPIs that affect company performance, based on the literature and management practices, and (2) to propose a new perspective on a way to integrate sustainability issues in company strategies. Based on a systematic procedure, we obtained 82 papers that focus on KPIs related to sustainability issues. Following a review of papers and a survey conducted with Italian managers, we developed a sustainability perspective by selecting the most appropriate KPI system for each of the dimensions discussed. The proposed model suggests that incorporating sustainability dimensions within corporate strategy would allow strategic alignment in order to gain competitive advantage and therefore create sustainability value.
Eco-Innovative Museums and Visitors’ Perceptions of Corporate Social Responsibility
Authors: Chung Namho , Tyan Inessa , Lee Seung Jae
Published: October 2019

This study assumes that green technologies at tourist destinations are a part of corporate social responsibility (CSR). It investigates how technology-based eco-innovation is related to sustainable development goals (SDGs) and how eco-innovative CSR performance affects tourists’ perceptions of destinations, emotions, and WOM (word of mouth) intentions in the context of sustainability and smart tourism. A dataset collected from the Handok Museum in South Korea was analyzed via a partial least squares (PLS) method, using structural equation modeling. This study examines the link between museum visitors’ awareness of CSR and green technology. The constructs (perceived quality, image, and reputation) are positively correlated with CSR; museum visitors’ emotions are in a parallel relationship with WOM intentions. The results confirm that the green technology implemented in the tourist destination is perceived as CSR by visitors. These technologies have a positive impact on environmental sustainability and contribute to a positive tourist experience. Thus, this paper encourages social responsibility practices at tourist destinations, as well as the development of green technology. This is the first empirical study that demonstrates how the profit-related concept of CSR can be applied to nonprofit organizations, Furthermore, for the first time, the managerial concept of CSR has been reviewed with technology-based eco-innovation in a museum setting.
A New Eco-Friendly Porous Asphalt Mixture Modified by Crumb Rubber and Basalt Fiber
Authors: Cheng Yongchun , Chai Chao , Zhang Yuwei , Chen Yu , Zhu Bing
Published: October 2019

In this paper, the performance of environmentally friendly porous asphalt mixture was optimized by the response surface method. Taking the asphalt-aggregate ratio, crumb-rubber content, and basalt fiber content as the independent variables, the air void, Marshall stability, flow value, Marshall quotient, and Cantabro particle loss are the response values. The best model was determined by fitting the experimental data. After the influence of the independent variables on the response values was clarified, the models were used to optimize the dosage of the asphalt, crumb rubber, and basalt fiber through comprehensive analysis. The results showed that the application of the response surface method can complete the establishment of the models and the optimization of the performance of the porous asphalt mixture with sufficient accuracy. The optimum dosage of the asphalt to aggregate ratio, crumb rubber, and basalt fiber is 4.51%, 11.21%, and 0.42%, respectively. The high-temperature stability, low-temperature crack resistance, water stability, and Cantabro particle loss resistance of the optimized porous asphalt mixture were effectively improved, which provides a reference for the construction of eco-friendly pavement.
Sustainability and Resource Equilibrium Evaluation of a Tourism Traffic Network Based on a Tourism Traffic Matching Curve
Authors: Wen Huiying , Zeng Yuchen , Tang Zuogan
Published: October 2019

The accompanying increased traffic due to the rapid development of tourism at present calls for the better sustainability and equilibrium of tourism and traffic resources in the future. In recent decades, considerable attention has been paid to highway network planning, while studies focusing on sustainable tourism traffic networks, especially evaluation methods of limited tourism and traffic resources, have proven less successful. Through conceptual transfer, we proposed the tourism traffic matching curve, which not only simplifies the method of sustainability and equilibrium evaluation, but also ensures that the results are more direct and effective. In the upper layer, we analyzed the main factors of tourism traffic network development, which contributed to a comprehensive benefit-oriented multi-objective model of tourism traffic network in MATLAB. In this model, the long-neglected tourism economy radiation effect was considered, and a genetic algorithm was introduced in the solution. In the lower layer, the characteristics of the tourism traffic matching curve and the two crucial equilibrium indexes, the Tourism Traffic Uniformity Coefficient ( T L C U ) and Curvature Coefficient ( T L C C ), were provided. With instances from Guangdong Province, the proposed system was proven to be more efficient and concise than other systems, which identified the need for future research on establishing effective criteria and a theoretical analysis method for dynamic adjustment.
Development of a Project-Based Online Course for BIM Learning
Authors: Tsai Meng-Han , Chen Kuan-Lin , Chang Yu-Lien
Published: October 2019

The purpose of this paper was to present a method for developing a project-based online course for building information modeling (BIM) learning. As the development of BIM has gradually increased its influence on the architecture, engineering, and construction (AEC) industries, countries around the world have begun to focus on BIM education and import it into university courses. However, there have been many studies pointing out several problems with the BIM education curriculum. For example, BIM education should not be limited to modeling work, and students do not exactly know the entire BIM workflow. Therefore, this research proposed a project-based online course development method for solving BIM educational issues. The proposed method contains a course development process and guidelines for designing the course. As an example implementation, a five-week Autodesk Revit learning curriculum was developed. Such a curriculum was then utilized in a 32 student class for testing its effectiveness. The results showed that students could understand the BIM process more effectively through the designed project-based course. Additionally, with online video tutorials, students are free to allocate their study time based on their personal needs and adjust their learning progress for better understanding of the course contents.
A Breakdown-Free Block COCG Method for Complex Symmetric Linear Systems with Multiple Right-Hand Sides
Authors: Zhong Hong-Xiu , Gu Xian-Ming , Zhang Shao-Liang
Published: October 2019

The block conjugate orthogonal conjugate gradient method (BCOCG) is recognized as a common method to solve complex symmetric linear systems with multiple right-hand sides. However, breakdown always occurs if the right-hand sides are rank deficient. In this paper, based on the orthogonality conditions, we present a breakdown-free BCOCG algorithm with new parameter matrices to handle rank deficiency. To improve the spectral properties of coefficient matrix A, a precondition version of the breakdown-free BCOCG is proposed in detail. We also give the relative algorithms for the block conjugate A-orthogonal conjugate residual method. Numerical results illustrate that when breakdown occurs, the breakdown-free algorithms yield faster convergence than the non-breakdown-free algorithms.
Some Chaos Notions on Dendrites
Authors: Fadel Asmaa , Dzul-Kifli Syahida Che
Published: October 2019

Transitivity is a key element in a chaotic dynamical system. In this paper, we present some relations between transitivity, stronger and alternative notions of it on compact and dendrite spaces. The relation between Auslander and Yorke chaos and Devaney chaos on dendrites is also discussed. Moreover, we prove that Devaney chaos implies strong dense periodicity on dendrites while the converse is not true.