bluuebunny/crossref_metadata_2025 huggingface dataset:
Oil Spill Detection Using Machine Learning and Infrared Images
Authors: De Kerf Thomas , Gladines Jona , Sels Seppe , Vanlanduit Steve
Published: December 2020

The detection of oil spills in water is a frequently researched area, but most of the research has been based on very large patches of crude oil on offshore areas. We present a novel framework for detecting oil spills inside a port environment, while using unmanned areal vehicles (UAV) and a thermal infrared (IR) camera. This framework is split into a training part and an operational part. In the training part, we present a process for automatically annotating RGB images and matching them with the IR images in order to create a dataset. The infrared imaging camera is crucial to be able to detect oil spills during nighttime. This dataset is then used to train on a convolutional neural network (CNN). Seven different CNN segmentation architectures and eight different feature extractors are tested in order to find the best suited combination for this task. In the operational part, we propose a method to have a real-time, onboard UAV oil spill detection using the pre-trained network and a low power interference device. A controlled experiment in the port of Antwerp showed that we are able to achieve an accuracy of 89% while only using the IR camera.
Exploring the Adoption of Precision Agriculture for Irrigation in the Context of Agriculture 4.0: The Key Role of Internet of Things
Authors: Monteleone Sergio , Moraes Edmilson Alves de , Tondato de Faria Brenno , Aquino Junior Plinio Thomaz , Maia Rodrigo Filev , Neto André Torre , Toscano Attilio
Published: December 2020

In recent years, the concept of Agriculture 4.0 has emerged as an evolution of precision agriculture (PA) through the diffusion of the Internet of things (IoT). There is a perception that the PA adoption is occurring at a slower pace than expected. Little research has been carried out about Agriculture 4.0, as well as to farmer behavior and operations management. This work explores what drives the adoption of PA in the Agriculture 4.0 context, focusing on farmer behavior and operations management. As a result of a multimethod approach, the factors explaining the PA adoption in the Agriculture 4.0 context and a model of irrigation operations management are proposed. Six simulation scenarios are performed to study the relationships among the factors involved in irrigation planning. Empirical findings contribute to a better understanding of what Agriculture 4.0 is and to expand the possibilities of IoT in the PA domain. This work also contributes to the discussion on Agriculture 4.0, thanks to multidisciplinary research bringing together the different perspectives of PA, IoT and operations management. Moreover, this research highlights the key role of IoT, considering the farmer’s possible choice to adopt several IoT sensing technologies for data collection.
Reinforcement Learning-Based Joint User Pairing and Power Allocation in MIMO-NOMA Systems
Authors: Lee Jaehee , So Jaewoo
Published: December 2020

In this paper, we consider a multiple-input multiple-output (MIMO)—non-orthogonal multiple access (NOMA) system with reinforcement learning (RL). NOMA, which is a technique for increasing the spectrum efficiency, has been extensively studied in fifth-generation (5G) wireless communication systems. The application of MIMO to NOMA can result in an even higher spectral efficiency. Moreover, user pairing and power allocation problem are important techniques in NOMA. However, NOMA has a fundamental limitation of the high computational complexity due to rapidly changing radio channels. This limitation makes it difficult to utilize the characteristics of the channel and allocate radio resources efficiently. To reduce the computational complexity, we propose an RL-based joint user pairing and power allocation scheme. By applying Q-learning, we are able to perform user pairing and power allocation simultaneously, which reduces the computational complexity. The simulation results show that the proposed scheme achieves a sum rate similar to that achieved with the exhaustive search (ES).
Mode Analysis of Pt/LGS Surface Acoustic Wave Devices
Authors: Xu Hongsheng , Jin Hao , Dong Shurong , Song Xinyu , Chen Jinkai , Xuan Weipeng , Huang Shuyi , Shi Lin , Luo Jikui
Published: December 2020

Platinum (Pt) gratings on langasite (LGS) substrates are a widely used structures in high temperature surface acoustic wave (SAW) devices. Multiple modes can be excited in Pt/LGS SAW devices owing to the heavy weight of the Pt electrode and leaky waves in the LGS substrate. In this work, we report on a detailed mode analysis of Pt/LGS SAW devices, where three commonly used LGS cuts are considered. A three-dimensional (3D) finite element method (FEM) numerical model was developed, and the simulation and experiment results were compared. The experiment and simulation results showed that there are two modes excited in the Pt/LGS SAW devices with Euler angle (0°, 138.5°, 27°) and (0°, 138.5°, 117°), which are Rayleigh-type SAW and SH-type leaky wave, respectively. Only the Rayleigh-type mode was observed in the Pt/LGS SAW devices with Euler angle (0°, 138.5°, 72°). It was found that the acoustic velocities are dependent on the wavelength, which is attributed to the change of wave penetration depth in interdigital transducers (IDTs) and the velocity dispersion can be modulated by the thickness of the Pt electrode. We also demonstrated that addition of an Al2O3 passivation layer has no effect on the wave modes, but can increase the resonant frequencies. This paper provides a better understanding of the acoustic modes of Pt/LGS SAW devices, as well as useful guidance for device design. It is believed that the Rayleigh-type SAW and SH-type leaky waves are potentially useful for dual-mode sensing applications in harsh environments, to achieve multi-parameter monitoring or temperature-compensation on a single chip.
A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques
Authors: Zekri Dorsaf , Delot Thierry , Thilliez Marie , Lecomte Sylvain , Desertot Mikael
Published: December 2020

A sensor-rich environment can be exploited for elder healthcare applications. In this work, our objective was to conduct a continuous and long-term analysis of elderly’s behavior for detecting changes. We indeed did not study snapshots of the behavior but, rather, analyzed the overall behavior evolution over long periods of time in order to detect anomalies. Therefore, we proposed a learning method and formalize a normal behavior pattern for elderly people related to her/his Activities of Daily Living (ADL). We also defined a temporal similarity score between activities that allows detecting behavior changes over time. During the periods of time when behavior changes occurred, we then focused on each activity to identify anomalies. Finally, when a behavior change occurred, it was also necessary to help caregivers and/or family members understand the possible pathology detected in order for them to react accordingly. Therefore, the framework presented in this article includes a fuzzy logic-based decision support system that provides information about the suspected disease and its severity.
Electrical Resistivity Measurements of Reinforced Concrete Slabs with Delamination Defects
Authors: Robles Kevin Paolo V. , Kim Dong-Won , Yee Jurng-Jae , Lee Jin-Wook , Kee Seong-Hoon
Published: December 2020

The main objectives of this research are to evaluate the effects of delamination defects on the measurement of electrical resistivity of reinforced concrete slabs through analytical and experimental studies in the laboratory, and to propose a practical guide for electrical resistivity measurements on concrete with delamination defects. First, a 3D finite element model was developed to simulate the variation of electric potential field in concrete over delamination defects with various depths and lateral sizes. Second, for experimental studies, two reinforced concrete slab specimens (1500 mm (width) by 1500 mm (length) by 300 mm (thickness)) with artificial delamination defects of various dimensions and depths were fabricated. Third, the electrical resistivity of concrete over delamination defects in the numerical simulation models and the two concrete slab specimens were evaluated by using a 4-point Wenner probe in accordance with AASHTO (American Association of State Highway and Transportation Office) T-358. It was demonstrated from analytical and experimental studies in this study that shallow (50 mm depth) and deep (250 mm depth) delamination defects resulted in higher and lower electrical resistivity (ER) values, respectively, as compared to measurements performed on solid concrete locations. Furthermore, the increase in size of shallow defects resulted in an increase in concrete resistivity, whereas the increase in sizes of deep delamination defects yielded opposite results. In addition, measurements done directly above the steel reinforcements significantly lowered ER values. Lastly, it was observed from experimental studies that the effect of delamination defects on the values of electrical resistivity decreases as the saturation level of concrete increases.
Gyro-Sensor-Based Vibration Control for Dynamic Humanoid-Robot Walking on Inclined Surfaces
Authors: Dutta Sunandan , Miura-Mattausch Mitiko , Ochi Yoshihiro , Yorino Naoto , Mattausch Hans Jürgen
Published: December 2020

An efficient motor-control system for stable walking of the lightweight humanoid robot KONDO KHR-3HV on inclined surfaces is investigated. The motor-control system is based on the angular velocity of the pitch motion of the robot, which is detected by a gyro sensor attached to the robot torso and referred to as the angular-pitch velocity. The robot gait is analyzed for different downslopes with and without the motor-feedback control. A novel method of frequency-domain analysis of the angular-pitch velocity is proposed for explaining the reasons behind the instabilities of dynamic humanoid-robot walking on inclined surfaces. The results show, that a nonlinear nature of the motor torque, due to a force induced by the slope, gives rise to harmonics of the fundamental walking frequency of 1.73 Hz. These harmonics are the origin of the unstable robot walking. Additionally, the feedback-gain parameters KA and KH affect the amplitudes of the harmonics, which give rise to vibrations at a higher surface inclination. Increased surface friction allows a reduction of the feedback gain, which reduces this specific contribution to the harmonics and thus stabilizes the robot. To improve the walking stability on inclined surfaces, it is found that the damped natural frequency of the motor-control system must be kept lower than the fundamental walking frequency.
Self-Biased Bidomain LiNbO3/Ni/Metglas Magnetoelectric Current Sensor
Authors: Bichurin Mirza I. , Petrov Roman V. , Leontiev Viktor S. , Sokolov Oleg V. , Turutin Andrei V. , Kuts Victor V. , Kubasov Ilya V. , Kislyuk Alexander M. , Temirov Alexander A. , Malinkovich Mikhail D. , Parkhomenko Yuriy N.
Published: December 2020

The article is devoted to the theoretical and experimental study of a magnetoelectric (ME) current sensor based on a gradient structure. It is known that the use of gradient structures in magnetostrictive-piezoelectric composites makes it possible to create a self-biased structure by replacing an external magnetic field with an internal one, which significantly reduces the weight, power consumption and dimensions of the device. Current sensors based on a gradient bidomain structure LiNbO3 (LN)/Ni/Metglas with the following layer thicknesses: lithium niobate—500 μm, nickel—10 μm, Metglas—29 μm, operate on a linear section of the working characteristic and do not require the bias magnetic field. The main characteristics of a contactless ME current sensor: its current range measures up to 10 A, it has a sensitivity of 0.9 V/A, its current consumption is not more than 2.5 mA, and its linearity is maintained to an accuracy of 99.8%. Some additional advantages of a bidomain lithium niobate-based current sensor are the increased sensitivity of the device due to the use of the bending mode in the electromechanical resonance region and the absence of a lead component in the device.
Toward Cancer Diagnostics of the Tumor Suppressor p53 by Surface Enhanced Raman Spectroscopy
Authors: Bizzarri Anna Rita , Cannistraro Salvatore
Published: December 2020

The tumor suppressor p53 protein plays a crucial role in many biological processes. The presence of abnormal concentrations of wild-type p53, or some of its mutants, can be indicative of a pathological cancer state. p53 represents therefore a valuable biomarker for tumor screening approaches and development of suitable biosensors for its detection deserves a high interest in early diagnostics. Here, we revisit our experimental approaches, combining Surface Enhanced Raman Spectroscopy (SERS) and nanotechnological materials, for ultrasensitive detection of wild-type and mutated p53, in the perspective to develop biosensors to be used in clinical diagnostics. The Raman marker is provided by a small molecule (4-ATP) acting as a bridge between gold nanoparticles (NPs) and a protein biomolecule. The Azurin copper protein and specific antibodies of p53 were used as a capture element for p53 (wild-type and its mutants). The developed approaches allowed us to reach a detection level of p53 down to 10−17 M in both buffer and serum. The implementation of the method in a biosensor device, together with some possible developments are discussed.
Optimizing Sensor Deployment for Multi-Sensor-Based HAR System with Improved Glowworm Swarm Optimization Algorithm
Authors: Tian Yiming , Zhang Jie
Published: December 2020

Human activity recognition (HAR) technology that analyzes and fuses the data acquired from various homogeneous or heterogeneous sensor sources has motivated the development of enormous human-centered applications such as healthcare, fitness, ambient assisted living and rehabilitation. The concurrent use of multiple sensor sources for HAR is a good choice because the plethora of user information provided by the various sensor sources may be useful. However, a multi-sensor system with too many sensors will bring large power consumption and some sensor sources may bring little improvements to the performance. Therefore, the multi-sensor deployment research that can gain a tradeoff among computational complexity and performance is imperative. In this paper, we propose a multi-sensor-based HAR system whose sensor deployment can be optimized by selective ensemble approaches. With respect to optimization of the sensor deployment, an improved binary glowworm swarm optimization (IBGSO) algorithm is proposed and the sensor sources that have a significant effect on the performance of HAR are selected. Furthermore, the ensemble learning system based on optimized sensor deployment is constructed for HAR. Experimental results on two datasets show that the proposed IBGSO-based multi-sensor deployment approach can select a smaller number of sensor sources while achieving better performance than the ensemble of all sensors and other optimization-based selective ensemble approaches.
IRRISENS: An IoT Platform Based on Microservices Applied in Commercial-Scale Crops Working in a Multi-Cloud Environment
Authors: Filev Maia Rodrigo , Ballester Lurbe Carlos , Agrahari Baniya Arbind , Hornbuckle John
Published: December 2020

Research has shown the multitude of applications that Internet of Things (IoT), cloud computing, and forecast technologies present in every sector. In agriculture, one application is the monitoring of factors that influence crop development to assist in making crop management decisions. Research on the application of such technologies in agriculture has been mainly conducted at small experimental sites or under controlled conditions. This research has provided relevant insights and guidelines for the use of different types of sensors, application of a multitude of algorithms to forecast relevant parameters as well as architectural approaches of IoT platforms. However, research on the implementation of IoT platforms at the commercial scale is needed to identify platform requirements to properly function under such conditions. This article evaluates an IoT platform (IRRISENS) based on fully replicable microservices used to sense soil, crop, and atmosphere parameters, interact with third-party cloud services for scheduling irrigation and, potentially, control irrigation automatically. The proposed IoT platform was evaluated during one growing season at four commercial-scale farms on two broadacre irrigated crops with very different water management requirements (rice and cotton). Five main requirements for IoT platforms to be used in agriculture at commercial scale were identified from implementing IRRISENS as an irrigation support tool for rice and cotton production: scalability, flexibility, heterogeneity, robustness to failure, and security. The platform addressed all these requirements. The results showed that the microservice-based approach used is robust against both intermittent and critical failures in the field that could occur in any of the monitored sites. Further, processing or storage overload caused by datalogger malfunctioning or other reasons at one farm did not affect the platform’s performance. The platform was able to deal with different types of data heterogeneity. Since there are no shared microservices among farms, the IoT platform proposed here also provides data isolation, maintaining data confidentiality for each user, which is relevant in a commercial farm scenario.
Robust Visual Tracking Based on Adaptive Multi-Feature Fusion Using the Tracking Reliability Criterion
Authors: Zhou Lin , Wang Han , Jin Yong , Hu Zhentao , Wei Qian , Li Junwei , Li Jifang
Published: December 2020

Multi-resolution feature fusion DCF (Discriminative Correlation Filter) methods have significantly advanced the object tracking performance. However, careless choice and fusion of sample features make the algorithm susceptible to interference, leading to tracking failure. Some trackers embed the re-detection module to remedy tracking failures, yet distinguishing ability and stability of the sample features are scarcely considered when training the detector, resulting in low effectiveness detection. Firstly, this paper proposes a criterion of feature tracking reliability and conduct a novel feature adaptive fusion framework. The feature tracking reliability criterion is proposed to evaluate the robustness and distinguishing ability of the sample features. Secondly, a re-detection module is proposed to further avoid tracking failures and increase the accuracy of target re-detection. The re-detection module consists of multiple SVM detectors trained by different sample features. When the tracking fails, the SVM detector trained by the most reliable sample feature will be activated to recover the target and adjust the target position. Finally, comparison experiments on OTB2015 and UAV123 databases demonstrate the accuracy and robustness of the proposed method.
Smartphone-Based Portable Bioluminescence Imaging System Enabling Observation at Various Scales from Whole Mouse Body to Organelle
Authors: Hattori Mitsuru , Shirane Sumito , Matsuda Tomoki , Nagayama Kuniaki , Nagai Takeharu
Published: December 2020

Current smartphones equipped with high-sensitivity and high-resolution sensors in the camera can respond to the needs of low-light imaging, streaming acquisition, targets of various scales, etc. Therefore, a smartphone has great potential as an imaging device even in the scientific field and has already been introduced into biomolecular imaging using fluorescence tags. However, owing to the necessity of an excitation light source, fluorescence methods impair its mobility. Bioluminescence does not require illumination; therefore, imaging with a smartphone camera is compact and requires minimal devices, thus making it suitable for personal and portable imaging devices. Here, we report smartphone-based methods to observe biological targets in various scales using bioluminescence. In particular, we demonstrate, for the first time, that bioluminescence can be observed in an organelle in a single living cell using a smartphone camera by attaching a detachable objective lens. Through capturing color changes with the camera, changes in the amount of target molecules was detected using bioluminescent indicators. The combination of bioluminescence and a mobile phone makes possible a compact imaging system without an external light source and expands the potential of portable devices.
Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment
Authors: Shirali Mohsen , Bayo-Monton Jose-Luis , Fernandez-Llatas Carlos , Ghassemian Mona , Traver Salcedo Vicente
Published: December 2020

Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents’ health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident’s activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.
SwimOne. New Device for Determining Instantaneous Power and Propulsive Forces in Swimming
Authors: Hermosilla Francisco , Corral-Gómez Lis , González-Ravé José M. , Juárez Santos-García Daniel , Rodríguez-Rosa David , Juárez-Pérez Sergio , Castillo-Garcia Fernando J.
Published: December 2020

The propulsive forces and instantaneous power that are generated by a swimmer have a great influence on the swimming performance. This works presents a new device, called SwimOne, for measuring propulsive force and estimating the instantaneous power of the swimmer. In addition, the detailed prototype is able to exert a customizable opposition force to the swimmer for training purpose. The conceptual idea is presented by describing the differential equation of the swimmer and the protocol for a factible estimation of the instantaneous power. The variables that are to be measured and estimated are identified and, consequently, the sensor and actuator systems can be selected. The high-level and detailed designs of the prototype are presented together with the protocol that is carried out in order to validate the sensor and actuation systems. The device is able to monitor the variables of interest of the swimmer together with the propulsive force and instant power. Finally, some experiments are carried out providing the results of several participants swimming in crawl, backstroke, butterfly, and breaststroke styles in the presence of different opposition force. The preliminary results show that SwimOne is valid for measuring instantaneous force and power with different loads in swimming.
Consideration of Thermo-Vacuum Stability of a MEMS Gyroscope for Space Applications
Authors: Liu Jili , Fu Mingrui , Meng Chao , Li Jianpeng , Li Kai , Hu Jun , Chen Xiaojuan
Published: December 2020

Thermo-vacuum stability of the aerospace gyroscopes is one of the crucial issues in the harsh and remote environment of space. This paper reports a bias drift compensation algorithm for the MEMS (microelectromechanical systems) gyroscope with atmosphere package. This approach takes advantage of linear frequency–temperature dependence and linear amplitude–pressure dependence for self-compensation of the gyroscope bias drifts in real-time. The dependences were analyzed and evaluated by subjecting the gyroscope to a thermo-vacuum condition. The real-time self-compensation yielded a total bias error of 0.01°/s over a temperature range of 7–45 °C. A MEMS rate sensor was flown in space and the on-orbit data also verify the effectiveness of the approach.
The Influence of the Presence of Borax and NaCl on Water Absorption Pattern during Sturgeon Caviar (Acipenser transmontanus) Storage
Authors: Brambilla Massimo , Buccheri Marina , Grassi Maurizio , Stellari Annamaria , Pazzaglia Mario , Romano Elio , Cattaneo Tiziana M. P.
Published: December 2020

Sturgeon caviar quality relies not only on the perfect dosage of the ingredients but also on the long sturgeon breeding cycle (about 12–15 years) and the exact timing of the egg extraction. For the improvement and the promotion of Italian caviar, the development of an analytical system dedicated to fish products, and caviar, in particular, is fundamental. The use of near-infrared spectrometry (NIRS) technology is auspicious. The aquaphotomics approach proved to be an adequate analytical tool to highlight, in real-time, the differences in caviar quality stored with, or without, borax as a preservative. Seventy-five sturgeon caviar (Acipenser transmontanus) samples underwent spectral NIR characterization using a microNIR1700 in the 900–1700 nm range. Data processing was carried out according to the literature. Tenderometric and sensory analyses were also carried out in parallel. The results suggest that a process line under strict control and monitoring can result in high-quality caviar without any other preservative than salt. The challenge of producing caviar without any potentially-toxic preservatives could now be a reality. NIR spectroscopy and aquaphotomics can be, in the future, non-invasive methods to monitor the whole production chain.
IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition
Authors: Konak Orhan , Wegner Pit , Arnrich Bert
Published: December 2020

Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.
A Critical Evaluation of Privacy and Security Threats in Federated Learning
Authors: Asad Muhammad , Moustafa Ahmed , Yu Chao
Published: December 2020

With the advent of smart devices, smartphones, and smart everything, the Internet of Things (IoT) has emerged with an incredible impact on the industries and human life. The IoT consists of millions of clients that exchange massive amounts of critical data, which results in high privacy risks when processed by a centralized cloud server. Motivated by this privacy concern, a new machine learning paradigm has emerged, namely Federated Learning (FL). Specifically, FL allows for each client to train a learning model locally and performs global model aggregation at the centralized cloud server in order to avoid the direct data leakage from clients. However, despite this efficient distributed training technique, an individual’s private information can still be compromised. To this end, in this paper, we investigate the privacy and security threats that can harm the whole execution process of FL. Additionally, we provide practical solutions to overcome those attacks and protect the individual’s privacy. We also present experimental results in order to highlight the discussed issues and possible solutions. We expect that this work will open exciting perspectives for future research in FL.
Siamese Architecture-Based 3D DenseNet with Person-Specific Normalization Using Neutral Expression for Spontaneous and Posed Smile Classification
Authors: Lee Kunyoung , Lee Eui Chul
Published: December 2020

Clinical studies have demonstrated that spontaneous and posed smiles have spatiotemporal differences in facial muscle movements, such as laterally asymmetric movements, which use different facial muscles. In this study, a model was developed in which video classification of the two types of smile was performed using a 3D convolutional neural network (CNN) applying a Siamese network, and using a neutral expression as reference input. The proposed model makes the following contributions. First, the developed model solves the problem caused by the differences in appearance between individuals, because it learns the spatiotemporal differences between the neutral expression of an individual and spontaneous and posed smiles. Second, using a neutral expression as an anchor improves the model accuracy, when compared to that of the conventional method using genuine and imposter pairs. Third, by using a neutral expression as an anchor image, it is possible to develop a fully automated classification system for spontaneous and posed smiles. In addition, visualizations were designed for the Siamese architecture-based 3D CNN to analyze the accuracy improvement, and to compare the proposed and conventional methods through feature analysis, using principal component analysis (PCA).
Experimental Study of the Guided Wave Directivity Patterns of Thin Removable Magnetostrictive Patches
Authors: Zitoun Akram , Dixon Steven , Edwards Graham , Hutchins David
Published: December 2020

The characteristics of removable magnetostrictive thin patches are investigated for the generation of guided waves in plates. The directivity patterns of SH, S0 and A0 modes have been measured in a thin metallic plate for different combinations of static and dynamic magnetic field directions. This used different coil geometries such as racetrack and spiral coils to generate the dynamic magnetic field, as well as separate biasing static magnetic fields from permanent magnets. This arrangement generated signals via both Lorentz and magnetostrictive forces, and the resultant emitted guided waves were studied for different dynamic and static magnetic field directions and magnitudes. It is demonstrated that different guided wave modes can be produced by controlling these parameters.
Big Data as a Tool to Monitor and Deter Environmental Offenders in the Global South: A Multiple Case Study
Authors: Kshetri Nir , Rojas Torres Diana Carolina , Besada Hany , Moros Ochoa Maria Andreina
Published: December 2020

While prior research has looked at big data’s role in strengthening the environmental justice movement, scholars rarely examine the contexts, mechanisms and processes associated with the use of big data in monitoring and deterring environmental offenders, especially in the Global South. As such, this research aims to substitute for this academic gap through the use of multiple case studies of environmental offenders’ engagement in illegal deforestation, as well as legal deforestation followed by fire. Specifically, we have chosen four cases from three economies in the Global South: Indonesia, Peru and Brazil. We demonstrate how the data utilized by environmental activists in these four cases qualify as true forms of big data, as they have searched and aggregated data from various sources and employed them to achieve their goals. The article shows how big data from various sources, mainly from satellite imagery, can help discern the true extent of environmental destruction caused by various offenders and present convincing evidence. The article also discusses how a rich satellite imagery archive is suitable for analyzing chronological events in order to establish a cause-effect chain. In all of the cases studied, such evidentiary provisions have been used by environmental activists to oblige policy makers to take necessary actions to counter environmental offenses.
Social Sustainability Strategy across the Supply Chain: A Conceptual Approach from the Organisational Perspective
Authors: Najjar Mohammad , Small Michael H. , M. Yasin Mahmoud
Published: December 2020

Much of the existing literature on the social aspects of sustainability in the supply chain has focused on dyadic buyer-supplier relationships. However, supply chains are much more extensive, featuring multi-tiered systems consisting of many interconnected sequential and parallel dyadic relationships; therefore, a more expansive and holistic approach to exploring the management and integration of social sustainability standards across the extended supply chain is desirable. This research attempts to help fill this void and considers the extent to which a series of sequential upstream and downstream supply chain partners, rather than only a focal organization’s immediate suppliers and buyers, influence the formulation process of the social aspects of a sustainability strategy and the deployment of associated practices across the extended supply chain. Findings in the literature indicate that, inter alia, sustainability efforts in the supply chain are likely to be guided by stakeholders’ sustainability desires/requirements, the geographical location of buyers and suppliers and the associated sustainability enforcement regulations and cultural norms, and the volume of trade between the buyer and supplier. This paper uses the results gleaned from a review of the literature to propose a conceptual framework for selection of sustainability strategy across the multi-tiered supply chain. Finally, we introduce a conceptual approach to the process of implementing and deploying the social aspects of sustainability strategies and practices across the supply chain using an integrated social-sustainability information management system (ISIMS).
Policies towards Migrants in the Yangtze River Delta Urban Region, China: Does Local Hukou Still Matter after the Hukou Reform?
Authors: Zhang Qian , Hoekstra Joris
Published: December 2020

The 2014 hukou reform introduced by the Chinese central government was a turning point in China’s policies towards migration. Different from the previous hukou policies, which were largely exclusionary, the reformed policy encouraged migrants to permanently settle in their destination cities and make use of the public services available there. However, the actual results and consequences of this policy seem to vary between cities. This is due to the fact that Chinese municipal governments still have their own discretionary power when it comes to defining the criteria for accessing a local hukou. This raises the question of what the real impact of the hukou policy reform has been. This paper attempts to answer this question. It starts with a hukou access policy analysis of 20 different cities in the Yangtze River delta urban region. This analysis shows that the strictness of the local hukou access policy is related to city specific factors such as economic strength, share of migrant population, and population size. In the second part of the paper, we examine the impact of local hukou access policies on the intentions of migrants. Based on two logistic regression models, we find that the stricter the local hukou access policy is, the more willing migrants are to convert their current hukou into a local hukou. Furthermore, we observed that the settlement intention of migrants has a V-shaped rather than a linear relation with the strictness of local hukou access policies. Cities with relatively loose and cities with relatively strict hukou access policies are more desired as permanent settlement location than cities with moderately strict hukou access policies.
Freight Transportation and Economic Growth for Zones: Sustainability and Development Strategy in China
Authors: Ma Yanli , Zhu Jieyu , Gu Gaofeng , Chen Ke
Published: December 2020

The causal relationship between zonal freight turnover and gross domestic product (GDP) are receiving increasing attention to coordinate the balanced development of freight transportation and zonal economic level effectively. However, studies on the causality direction between freight transportation and economic have so far been in debate. To understand the relationship of causality direction between transportation and economic development for different zones which can provide decision support for public policies, econometrics is used to analyze the relationship between them. This paper investigates the relationship between GDP and freight turnover for economic zones of stationary series and non-stationary sequences using the vector autoregressive (VAR) and the vector error correction models (VECM). The impulse response analysis and variance decomposition are conducted to verify the effectiveness of the models. The Granger causality test is exploited to discover the relationship between transportation and economic development in each economic zone. The data on freight transportation and GDP in China from 2003 to 2018 is used. Results show that the relationship between freight turnover and GDP in the Northeast economic zone is bidirectional. A unidirectional relationship exists between freight turnover and GDP in the Circum Bohai-Sea, the Pearl River, Middle Part, Southwest, and Northwest zone. And the Granger causality is not obvious in the Yangtze River economic zone. In addition, suggestions for the zonal development of transportation and economic systems are provided. This study can provide a basis to adopt relevant policies and measures of sustainable development between transportation and economic growth for different zones.
A Study on State of Charge and State of Health Estimation in Consideration of Lithium-Ion Battery Aging
Authors: Choi Woongchul
Published: December 2020

Due to rapid development of industries around the world, more and more consumption of fossil fuels was unavoidable, resulting in serious environmental problems. The many pollutant emissions—a major contributor to global warming and weather pattern change—have been at the center of concern. In order to solve this issue, research and development of electric vehicles and energy storage systems made great progress and successfully introduced products in the market. Nevertheless, accurate measurement of the state of charge (SOC) and state of health (SOH) of the Li-ion battery, the most popular electric energy storage device, has not yet been fully understood due to the nature of battery aging. In this study, ideas to estimate the capacity and ultimately SOC and SOH of Li-ion batteries are discussed. With these ideas, we expect not only to accommodate the issues with battery aging but also to implement an algorithm for an on-board battery management system. The key idea is to chase and monitor internal resistance continuously in a fast and reliable manner in real time. With further investigation of the key idea, we also fully expect to come up with a reliable SOC and SOH measurement scheme in the near future.
Investigation of the Use of Low Temperature Geothermal Organic Rankine Cycle Engine in an Autonomous Polygeneration Microgrid
Authors: Kyriakarakos George , Ntavou Erika , Manolakos Dimitris
Published: December 2020

Low-enthalpy geothermal resources (<150 °C) can be used for electricity generation and are widespread around the world, occurring at shallow depths. At the same time, in many parts of the world, there are existing low-enthalpy geothermal wells that are used for a multitude of applications such as for buildings’ heating and agriculture-related applications. The dominant technology to convert low-grade heat (<150 °C) to electricity is the Organic Rankine Cycle (ORC). The autonomous polygeneration microgrid (APM) concept aims to holistically meet in a sustainable way the needs of an off-grid community in terms of electrical loads, space heating and cooling, potable water production through desalination, and the use of hydrogen as fuel for transportation, in the most cost-effective manner possible. Photovoltaics (PVs) and wind turbines have been investigated extensively, since PVs can be installed practically anywhere in the world and wind turbines in areas with sufficient wind potential. The aim of this paper is to investigate techno-economically the potential of utilizing low-enthalpy geothermal resources in small-scale APMs through an ORC engine to fully satisfy the needs of small settlements. In order to accomplish this task with confidence, a case study for the Greek island of Milos has been developed and a typical settlement has been considered. It is worth mentioning that experimental results from a realized low-power (<10 kWe) ORC engine manufactured to operate at temperatures up to 140 °C are used to add reliability in the calculations. In order to meet the needs of the people, four different APMs based on PVs, wind turbines, and geothermal ORC of different but appropriate configurations were designed and sized through optimization. The optimization process was based on particle swarm optimization (PSO). The comparative examination of the results shows that the use of a low-power, low-temperature ORC engine in an APM is technically feasible; more cost effective than the configurations based on PVs, wind turbines, or combination of both; and has increased environmental sustainability.
Barriers to Managing Environmental Sustainability in Public Assembly Venues
Authors: Ross Walker J. , Mercado Haylee Uecker
Published: December 2020

Many sport and entertainment venues are still not major adopters of environmental social responsibility (ESR) despite evidence to suggest their benefit. This qualitative study investigated the major barriers that still prevent ESR adoption in public assembly venues (PAVs) used by sport and entertainment organizations. Using resource-based view (RBV) as a framework for analysis, PAV managers of stadiums, arenas, and performing arts centers were interviewed to collect data. Findings suggest a lack of resources to devote to ESR which displays an overall lack of priority given to ESR adoption and knowledge of ESR by PAV managers. If ESR was considered to offer competitive advantage, it would be given more priority, PAV managers would emphasize more training, and it would therefore receive more resource attention.
Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea
Authors: Seo Younghoon , Lim Donghyun , Son Woongbee , Kwon Yeongmin , Kim Junghwa , Kim Hyungjoo
Published: December 2020

Mobility services facilitate various tasks related to transportation and passenger movements. Because of the Fourth Industrial Revolution, the importance of mobility services has been recognized by many countries. Thus, research is ongoing to provide more convenience to passengers and to obtain more efficient transportation systems. In the Republic of Korea, the officials of Gyeonggi Province are interested in providing an advanced mobility service to its residents; however, they still do not have any specific or detailed policies. This study aimed at deriving the key issues facing mobility services, especially in the case of Gyeonggi Province, by using a text mining technique and a clustering algorithm. First, a survey was taken by traffic and urban experts to collect reasonable plans for Gyeonggi-Province-type mobility service, and a morpheme analysis was then used for text mining. Second, the results reveal that the term frequency–inverse document frequency (TF-IDF) algorithm has better performance than frequency analysis. Third, the K-means application results in six clusters and six mobility service policy issues were determined by combining the words in each cluster. Finally, the methodology confirmed the validity and effectiveness of the proposed method by showing that the results reflect the current situation in the province.
The Host Community and Its Role in Sports Tourism—Exploring an Emerging Research Field
Authors: Herbold Valentin , Thees Hannes , Philipp Julian
Published: December 2020

The increase in individual sports activities is stimulating the rise of sport-related forms of vacationing. This is further intensified by tourists seeking authentic experiences in host–guest relations, physical health and well-being, and the consumption of local products and services. Communities, especially those in developing countries, do not yet fully recognize the potential of local sports as a resource and competitive advantage in their destination development. In the same way, tourism research is lacking analyses regarding the role of the community and its specific sports identity. Given this, the present paper aims to systematically review the promising interface between community development and sports tourism. The research questions are based on the potential of identity-based sports and necessary local engagement. These efforts are pursued through a Systematic Literature Review (SLR) with 49 articles. The results reveal the thematic clusters, research features, and prevalent challenges and benefits that the community has to deal with. The discussion proposes the utilization of local sports identity in systematic product development and highlights increasing local engagement. In conclusion, we call for future research on non-event-related sports tourism, in-depth case studies, and multi-disciplinary concepts to foster local sustainability.
Through Sport to Innovation: Sustainable Socio-Economic Development in European Countries
Authors: Nałęcz Hanna , Skrok Łukasz , Majcherek Dawid , Biernat Elżbieta
Published: December 2020

Using clustering and principal component analysis, we demonstrate that—at the national level in Europe—innovativeness correlates strongly to both social capital and participation in sport. In this aspect, countries such as the Scandinavian countries and the countries of Central and Eastern Europe differ visibly. Referring to prior empirical research, we claim that a causal relation between sports, through social capital, and innovativeness can be established. In the context of social capital accumulation, we further discuss the role of sports clubs, often perceived as a socially intensive form of participation in sport, but most likely diminishing in this respect lately.