bluuebunny/crossref_metadata_2025 huggingface dataset:
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
Authors: Ullah Amin , Anwar Syed Muhammad , Bilal Muhammad , Mehmood Raja Majid
Published: May 2020

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.
Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data
Authors: Hu Tianyu , Zhang YingYing , Su Yanjun , Zheng Yi , Lin Guanghui , Guo Qinghua
Published: May 2020

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.
Compression-Assisted Adaptive ECC and RAID Scattering for NAND Flash Storage Devices
Authors: Lim Seung-Ho , Park Ki-Woong
Published: May 2020

NAND flash memory-based storage devices are vulnerable to errors induced by NAND flash memory cells. Error-correction codes (ECCs) are integrated into the flash memory controller to correct errors in flash memory. However, since ECCs show inherent limits in checking the excessive increase in errors, a complementary method should be considered for the reliability of flash storage devices. In this paper, we propose a scheme based on lossless data compression that enhances the error recovery ability of flash storage devices, which applies to improve recovery capability both of inside and outside the page. Within a page, ECC encoding is realized on compressed data by the adaptive ECC module, which results in a reduced code rate. From the perspective of outside the page, the compressed data are not placed at the beginning of the page, but rather is placed at a specific location within the page, which makes it possible to skip certain pages during the recovery phase. As a result, the proposed scheme improves the uncorrectable bit error rate (UBER) of the legacy system.
Validation of a Broadband Tissue-Equivalent Liquid for SAR Measurement and Monitoring of Its Dielectric Properties for Use in a Sealed Phantom
Authors: Gregory Andrew P. , Quéléver Kristell , Allal Djamel , Jawad Ourouk
Published: May 2020

We report on the development of a method for measuring the permittivity and conductivity of fluids inside a sealed tank (or a pipe) by using an embedded coaxial probe. Permittivity and conductivity in the frequency range 600 MHz to 6 GHz are determined from measurements of a complex reflection coefficient by using a vector network analyser (VNA) that is connected to the embedded probe via a coaxial cable. Substitution methods for calibration of an inaccessible probe are studied in this paper. These require the VNA with attached cable to be calibrated prior to connecting the cable to the embedded coaxial probe. Measurement of permittivity and conductivity of fluids inside sealed tanks and pipes is needed for monitoring industrial processes, such as fermentation. The authors’ requirement, however, was to allow monitoring of a tissue-equivalent liquid that is contained inside a sealed tank. This tank is a component of a commercial system for rapid, multiple-band measurement of the specific absorption rate (SAR) of mobile phone handsets. Monitoring of permittivity and conductivity is needed to ensure compliance with international standards for SAR measurement. The paper also presents data for a new broadband (600 MHz to 6 GHz) tissue-equivalent liquid that is based on an oil-in-water emulsion. It is demonstrated that over an extended period of time, the liquid is stable, and an embedded coaxial probe enables its properties to be monitored with the required accuracy.
Project Gatekeeper: An Entrance Control System Embedded Radiation Detection Capability for Security Applications
Authors: Martin Peter G. , Verbelen Yannick , Sciama Bandel Elia , Andrews Mark , Scott Thomas B.
Published: May 2020

Threat assessments continue to conclude that terrorist groups and individuals as well as those wanting to cause harm to society have the ambition and increasing means to acquire unconventional weapons such as improvised nuclear explosive devices and radiological disposal devices. Such assessments are given credence by public statements of intent by such groups/persons, by reports of attempts to acquire radioactive material and by law enforcement actions which have interdicted, apprehended or prevented attempts to acquire such material. As a mechanism through which to identify radioactive materials being transported on an individual’s person, this work sought to develop a detection system that is of lower-cost, reduced form-factor and more covert than existing infrastructure, while maintaining adequate sensitivity and being retrofittable into an industry standard and widely utilised Gunnebo Speed Gate system. The system developed comprised an array of six off-set Geiger–Muller detectors positioned around the gate, alongside a single scintillator detector for spectroscopy, triggered by the systems inbuilt existing IR proximity sensor. This configuration served to not only reduce the cost for such a system but also allowed for source localisation and identification to be performed. Utilising the current setup, it was possible to detect a 1 µSv/h source carried into the Speed Gate in all test scenarios, alongside locating and spectrally analysing the material in a significant number.
Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles
Authors: Viktorović Miloš , Yang Dujuan , Vries Bauke de
Published: May 2020

For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geospatial data efficiently is missing. Therefore, we proposed to develop a new Connected Traffic Data Ontology (CTDO) on the foundations of Sensor, Observation, Sample, and Actuator (SOSA) ontology, to provide a more suitable ontology for large volumes of time-sensitive data coming from multi-sensory platforms, like connected vehicles, as the first step in closing the existing research gap. Additionally, as this research aims to further extend the CTDO in the future, a possible way to map to the CTDO with ontologies that represent road infrastructure has been presented. Finally, new CTDO ontology was benchmarked against SOSA, and better memory performance and query execution speeds have been confirmed.
Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality
Authors: González Izard Santiago , Sánchez Torres Ramiro , Alonso Plaza Óscar , Juanes Méndez Juan Antonio , García-Peñalvo Francisco José
Published: May 2020

The visualization of medical images with advanced techniques, such as augmented reality and virtual reality, represent a breakthrough for medical professionals. In contrast to more traditional visualization tools lacking 3D capabilities, these systems use the three available dimensions. To visualize medical images in 3D, the anatomical areas of interest must be segmented. Currently, manual segmentation, which is the most commonly used technique, and semi-automatic approaches can be time consuming because a doctor is required, making segmentation for each individual case unfeasible. Using new technologies, such as computer vision and artificial intelligence for segmentation algorithms and augmented and virtual reality for visualization techniques implementation, we designed a complete platform to solve this problem and allow medical professionals to work more frequently with anatomical 3D models obtained from medical imaging. As a result, the Nextmed project, due to the different implemented software applications, permits the importation of digital imaging and communication on medicine (dicom) images on a secure cloud platform and the automatic segmentation of certain anatomical structures with new algorithms that improve upon the current research results. A 3D mesh of the segmented structure is then automatically generated that can be printed in 3D or visualized using both augmented and virtual reality, with the designed software systems. The Nextmed project is unique, as it covers the whole process from uploading dicom images to automatic segmentation, 3D reconstruction, 3D visualization, and manipulation using augmented and virtual reality. There are many researches about application of augmented and virtual reality for medical image 3D visualization; however, they are not automated platforms. Although some other anatomical structures can be studied, we focused on one case: a lung study. Analyzing the application of the platform to more than 1000 dicom images and studying the results with medical specialists, we concluded that the installation of this system in hospitals would provide a considerable improvement as a tool for medical image visualization.
Phase Synchrony Analysis of Rolling Bearing Vibrations and Its Application to Failure Identification
Authors: Zhang Qing , Jiang Tingting , Yan Joseph D.
Published: May 2020

As the failure-induced component (FIC) in the vibration signals of bearings transmits through housings and shafts, potential phase synchronization is excited among multichannel signals. As phase synchrony analysis (PSA) does not involve the chaotic behavior of signals, it is suitable for characterizing the operating state of bearings considering complicated vibration signals. Therefore, a novel PSA method was developed to identify and track the failure evolution of bearings. First, resonance demodulation and variational mode decomposition (VMD) were combined to extract the mono-component or band-limited FIC from signals. Then, the instantaneous phase of the FIC was analytically solved using Hilbert transformation. The generalized phase difference (GPD) was used to quantify the relationship between FICs extracted from different vibration signals. The entropy of the GPD was regarded as the indicator for quantifying failure evolution. The proposed method was applied to the vibration signals obtained from an accelerated failure experiment and a natural failure experiment. Results showed that phase synchronization in bearing failure evolution was detected and evaluated effectively. Despite the chaotic behavior of the signals, the phase synchronization indicator could identify bearing failure during the initial stage in a robust manner.
Simulation of Disturbance Recovery Based on MPC and Whole-Body Dynamics Control of Biped Walking
Authors: Shi Xuanyang , Gao Junyao , Lu Yizhou , Tian Dingkui , Liu Yi
Published: May 2020

Biped robots are similar to human beings and have broad application prospects in the fields of family service, disaster rescue and military affairs. However, simplified models and fixed center of mass (COM) used in previous research ignore the large-scale stability control ability implied by whole-body motion. The present paper proposed a two-level controller based on a simplified model and whole-body dynamics. In high level, a model predictive control (MPC) controller is implemented to improve zero moment point (ZMP) control performance. In low level, a quadratic programming optimization method is adopted to realize trajectory tracking and stabilization with friction and joint constraints. The simulation shows that a 12-degree-of-freedom force-controlled biped robot model, adopting the method proposed in this paper, can recover from a 40 Nm disturbance when walking at 1.44 km/h without adjusting the foot placement, and can walk on an unknown 4 cm high stairs and a rotating slope with a maximum inclination of 10°. The method is also adopted to realize fast walking up to 6 km/h.
Exploring the Effects of Various Polymeric Backbones on the Performance of a Hydroxyaromatic 1,2,3-Triazole Anion Sensor
Authors: Ugboya Aikohi , Monroe Khristal , Ofulue Unodinma , Yates Kayley , Ghosh Debanjana , Landge Shainaz M. , Quirino Rafael Lopes , Aiken Karelle S.
Published: May 2020

Polymeric chemosensors are vital sensing tools because of higher sensitivity compared to their monomeric counterparts and tunable mechanical properties. This study focuses on the incorporation of a hydroxyaromatic 1,2,3-triazole sensor, 2-(4-phenyl 1H-1,2,3-triazol-1-yl)phenol (PTP), into polymers. By itself, the triazole has a selective, fluorometric response to the fluoride, acetate, and dihydrogen phosphate anions, and is most responsive to fluoride. Current investigations probe the suitability of various polymeric backbones for the retention and enhancement of the triazole’s sensing capabilities. Backbones derived from acrylic acid, methyl methacrylate, divinylbenzene, and styrene were explored. UV-illumination, Nuclear Magnetic Resonance (NMR) titration, and ultraviolet-visible (UV-Vis) absorption and fluorescence spectroscopy studies are used to investigate the performance of newly synthesized polymers and the derivatives of PTP that serve as the polymers’ precursors. Among the polymers investigated, copolymers with styrene proved best; these systems retained the sensing capabilities and were amenable to tuning for sensitivity.
A Nonlinear Double Model for Multisensor-Integrated Navigation Using the Federated EKF Algorithm for Small UAVs
Authors: Yang Yue , Liu Xiaoxiong , Zhang Weiguo , Liu Xuhang , Guo Yicong
Published: May 2020

Aimed at improving upon the disadvantages of the single centralized Kalman filter for integrated navigation, including its fragile robustness and low solution accuracy, a nonlinear double model based on the improved decentralized federated extended Kalman filter (EKF) for integrated navigation is proposed. The multisensor error model is established and simplified in this paper according to the near-ground short distance navigation applications of small unmanned aerial vehicles (UAVs). In order to overcome the centralized Kalman filter that is used in the linear Gaussian system, the improved federated EKF is designed for multisensor-integrated navigation. Subsequently, because of the navigation requirements of UAVs, especially for the attitude solution accuracy, this paper presents a nonlinear double model that consists of the nonlinear attitude heading reference system (AHRS) model and nonlinear strapdown inertial navigation system (SINS)/GPS-integrated navigation model. Moreover, the common state parameters of the nonlinear double model are optimized by the federated filter to obtain a better attitude. The proposed algorithm is compared with multisensor complementary filtering (MSCF) and multisensor EKF (MSEKF) using collected flight sensors data. The simulation and experimental tests demonstrate that the proposed algorithm has a good robustness and state estimation solution accuracy.
Directional Response of Randomly Dispersed Carbon Nanotube Strain Sensors
Authors: Güemes Alfredo , Pozo Morales Angel Renato , Fernandez-Lopez Antonio , Sanchez-Romate Xoan Xose F. , Sanchez Maria , Ureña Alejandro
Published: May 2020

Tests on a double lap bonded joint, with transverse strips of randomly oriented carbon nanotubes (CNT) sprayed onto an epoxy adhesive film, showed a positive increment in electrical resistance under tensile load, even though the transverse strains were negative. Other experiments included in this work involved placing longitudinal and transversal CNT sensors in a tensile loaded aluminum plate, and, as reported by other authors, the results confirm that the resistance change is not only dependent on the strains oriented with the electrode line, while the other strain components also influence the response. This behavior is quite different to that of conventional strain gages which have a near zero sensitivity to strains not aligned to the sensor direction. The dependence of the electrical response on all the strain components makes it quite difficult, possibly unfeasible, to experimentally determine the individual strain components with this kind of sensors; however, the manufacturing of aligned CNT sensors could deal with this issue.
Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method
Authors: Sun Haotai , Zhu Xiaodong , Liu Yuanning , Liu Wentao
Published: May 2020

Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and the expectation of the public. This study proposed a method for constructing a hybrid dual frequency received signal strength indicator (HDRF-RSSI) fingerprint library, which is different from the traditional RSSI fingerprint library constructing method in indoor space using 2.4G radio frequency (RF) under the same Wi-Fi infrastructure condition. The proposed method combined 2.4G RF and 5G RF on the same access point (AP) device to construct a HDRF-RSSI fingerprint library, thereby doubling the fingerprint dimension of each reference point (RP). Experimental results show that the feature discriminability of HDRF-RSSI fingerprinting is 18.1% higher than 2.4G RF RSSI fingerprinting. Moreover, the hybrid radio frequency fingerprinting model, training loss function, and location evaluation algorithm based on the machine learning method were designed, so as to avoid limitation that transmission point (TP) and AP must be visible in the positioning method. In order to verify the effect of the proposed HDRF-RSSI fingerprint library construction method and the location evaluation algorithm, dual RF RSSI fingerprint data was collected to construct a fingerprint library in the experimental scene, which was trained using the proposed method. Several comparative experiments were designed to compare the positioning performance indicators such as precision and accuracy. Experimental results demonstrate that compared with the existing machine learning method based on Wi-Fi 2.4G RF RSSI fingerprint, the machine learning method combining Wi-Fi 5G RF RSSI vector and the original 2.4G RF RSSI vector can effectively improve the precision and accuracy of indoor positioning of the smart phone.
BIM in People2People and Things2People Interactive Process
Authors: Mataloto Bruno , Ferreira João , Resende Ricardo , Moura Rita , Luís Sílvia
Published: May 2020

In this research work, we present an IoT solution to environment variables using a LoRa transmission technology to give real-time information to users in a Things2People process and achieve savings by promoting behavior changes in a People2People process. These data are stored and later processed to identify patterns and integrate with visualization tools, which allow us to develop an environmental perception while using the system. In this project, we implemented a different approach based on the development of a 3D visualization tool that presents the system collected data, warnings, and other users’ perception in an interactive 3D model of the building. This data representation introduces a new People2People interaction approach to achieve savings in shared spaces like public buildings by combining sensor data with the users’ individual and collective perception. This approach was validated at the ISCTE-IUL University Campus, where this 3D IoT data representation was presented in mobile devices, and from this, influenced user behavior toward meeting campus sustainability goals.
A Canopy Information Measurement Method for Modern Standardized Apple Orchards Based on UAV Multimodal Information
Authors: Sun Guoxiang , Wang Xiaochan , Yang Haihui , Zhang Xianjie
Published: May 2020

To make canopy information measurements in modern standardized apple orchards, a method for canopy information measurements based on unmanned aerial vehicle (UAV) multimodal information is proposed. Using a modern standardized apple orchard as the study object, a visual imaging system on a quadrotor UAV was used to collect canopy images in the apple orchard, and three-dimensional (3D) point-cloud models and vegetation index images of the orchard were generated with Pix4Dmapper software. A row and column detection method based on grayscale projection in orchard index images (RCGP) is proposed. Morphological information measurements of fruit tree canopies based on 3D point-cloud models are established, and a yield prediction model for fruit trees based on the UAV multimodal information is derived. The results are as follows: (1) When the ground sampling distance (GSD) was 2.13–6.69 cm/px, the accuracy of row detection in the orchard using the RCGP method was 100.00%. (2) With RCGP, the average accuracy of column detection based on grayscale images of the normalized green (NG) index was 98.71–100.00%. The hand-measured values of H, SXOY, and V of the fruit tree canopy were compared with those obtained with the UAV. The results showed that the coefficient of determination R2 was the most significant, which was 0.94, 0.94, and 0.91, respectively, and the relative average deviation (RADavg) was minimal, which was 1.72%, 4.33%, and 7.90%, respectively, when the GSD was 2.13 cm/px. Yield prediction was modeled by the back-propagation artificial neural network prediction model using the color and textural characteristic values of fruit tree vegetation indices and the morphological characteristic values of point-cloud models. The R2 value between the predicted yield values and the measured values was 0.83–0.88, and the RAD value was 8.05–9.76%. These results show that the UAV-based canopy information measurement method in apple orchards proposed in this study can be applied to the remote evaluation of canopy 3D morphological information and can yield information about modern standardized orchards, thereby improving the level of orchard informatization. This method is thus valuable for the production management of modern standardized orchards.
Can Building “Artificially Intelligent Cities” Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar’s Perspective
Authors: Yigitcanlar Tan , Butler Luke , Windle Emily , Desouza Kevin C. , Mehmood Rashid , Corchado Juan M.
Published: May 2020

In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the urban and social implications of AI are still an understudied area. In order to contribute to the ongoing efforts to address this research gap, this paper introduces the notion of an artificially intelligent city as the potential successor of the popular smart city brand—where the smartness of a city has come to be strongly associated with the use of viable technological solutions, including AI. The study explores whether building artificially intelligent cities can safeguard humanity from natural disasters, pandemics, and other catastrophes. All of the statements in this viewpoint are based on a thorough review of the current status of AI literature, research, developments, trends, and applications. This paper generates insights and identifies prospective research questions by charting the evolution of AI and the potential impacts of the systematic adoption of AI in cities and societies. The generated insights inform urban policymakers, managers, and planners on how to ensure the correct uptake of AI in our cities, and the identified critical questions offer scholars directions for prospective research and development.
A RPCA-Based ISAR Imaging Method for Micromotion Targets
Authors: Lu Liangyou , Chen Peng , Wu Lenan
Published: May 2020

Micro-Doppler generated by the micromotion of a target contaminates the inverse synthetic aperture radar (ISAR) image heavily. To acquire a clear ISAR image, removing the Micro-Doppler is an indispensable task. By exploiting the sparsity of the ISAR image and the low-rank of Micro-Doppler signal in the Range-Doppler (RD) domain, a novel Micro-Doppler removal method based on the robust principal component analysis (RPCA) framework is proposed. We formulate the model of sparse ISAR imaging for micromotion target in the framework of RPCA. Then, the imaging problem is decomposed into iterations between the sub-problem of sparse imaging and Micro-Doppler extraction. The alternative direction method of multipliers (ADMM) approach is utilized to seek for the solution of each sub-problem. Furthermore, to improve the computational efficiency and numerical robustness in the Micro-Doppler extraction, an SVD-free method is presented to further lessen the calculative burden. Experimental results with simulated data validate the effectiveness of the proposed method.
Hollow-Core Photonic Crystal Fiber Gas Sensing
Authors: Yu Ruowei , Chen Yuxing , Shui Lingling , Xiao Limin
Published: May 2020

Fiber gas sensing techniques have been applied for a wide range of industrial applications. In this paper, the basic fiber gas sensing principles and the development of different fibers have been introduced. In various specialty fibers, hollow-core photonic crystal fibers (HC-PCFs) can overcome the fundamental limits of solid fibers and have attracted intense interest recently. Here, we focus on the review of HC-PCF gas sensing, including the light-guiding mechanisms of HC-PCFs, various sensing configurations, microfabrication approaches, and recent research advances including the mid-infrared gas sensors via hollow core anti-resonant fibers. This review gives a detailed and deep understanding of HC-PCF gas sensors and will promote more practical applications of HC-PCFs in the near future.
Global Health Diplomacy Amid the COVID-19 Pandemic: A Strategic Opportunity for Improving Health, Peace, and Well-Being in the CARICOM Region—A Systematic Review
Authors: Chattu Vijay Kumar , Chami Georgina
Published: May 2020

Increased globalization has ushered in changes in diplomatic purposes and practices. As such, global health diplomacy (GHD) has become a growing field connecting the urgencies of global health and foreign affairs. More academics and policy-makers are thinking about how to structure and utilize diplomacy in pursuit of global health goals. This article aims to explore how the health, peace, and well-being of people in the region can be achieved through global health diplomacy. A systematic review of the literature was conducted on various terms such as “Global Health Diplomacy OR Foreign Policy”; “Disasters”, “Infectious disease epidemics” OR “Non-Communicable diseases” AND “Caribbean” by searching PubMed, Scopus, Embase, Web of Science databases, and Google Scholar search engines. A total of 33 articles that met the inclusion criteria were analyzed, and the critical role of GHD was highlighted. There is an increasing need to understand the complex global health challenges, coupled with the need to design appropriate solutions. Many regional initiatives addressing infectious and chronic diseases have been successful in multiple ways by strengthening unity and also by showing directions for other nations at a global level, e.g., the Port of Spain Summit declaration. GHD has a great scope to enhance preparedness, mitigation, peace, and development in the region. Amid the COVID-19 pandemic, the region needs to strengthen its efforts on equity issues, health promotion, and sustainable development to promote peace and well-being.
Reliability of a Repeated High-Intensity Effort Test for Elite Rugby Union Players
Authors: Vachon Adrien , Berryman Nicolas , Mujika Iñigo , Paquet Jean-Baptiste , Monnet Tony , Bosquet Laurent
Published: May 2020

This study aimed to adapt a repeated high-intensity effort (RHIE) test to the rugby union physical demands and assess both sprint time and tackle indices reliability. Following a familiarization session, sixteen elite rugby union players completed two RHIE tests consisting of 12 × 20 m sprint + tackle. Total sprint time and total g-force during tackling, average sprint time and average g-force as well as percentage decrementsprint time and percentage decrementtackle were considered for the analysis. Sprint time indices showed high to very high absolute and relative reliability (intraclass coefficient correlation (ICC) = 0.95, Standard Error Measurement (SEM) = 1.30%; ICC = 0.95, SEM = 1.44%; ICC = 0.73, SEM = 23.0%, for total sprint time, average sprint time and percentage decrementsprint time, respectively). Tackle indices showed moderate to high reliability (ICC = 0.54, SEM = 16.5%; ICC = 0.61, SEM = 15.6%; ICC = 0.71, SEM = 12.3%, for total g-force, average g-force and percentage decrementtackle, respectively). The RHIE test provides reliable measures of sprint time and tackle indices. Tackle indices should be used as a validation criterion of the test, whereas total time should be considered as the test final result.
Individual Sprint Force-Velocity Profile Adaptations to In-Season Assisted and Resisted Velocity-Based Training in Professional Rugby
Authors: Lahti Johan , Jiménez-Reyes Pedro , Cross Matt R. , Samozino Pierre , Chassaing Patrick , Simond-Cote Benjamin , Ahtiainen Juha P. , Morin Jean-Benoit
Published: May 2020

We tested the hypothesis that the degree of adaptation to highly focused sprint training at opposite ends of the sprint Force-Velocity (FV) spectrum would be associated with initial sprint FV profile in rugby athletes. Training-induced changes in sprint FV profiles were computed before and after an eight-week in-season resisted or assisted sprint training protocol, including a three-week taper. Professional male rugby players (age: 18.9 ± 1.0 years; body height: 1.9 ± 0.0 m; body mass: 88.3 ± 10.0 kg) were divided into two groups based on their initial sprint FV profiles: 1) Heavy sled training (RESISTED, N = 9, velocity loss 70–80%), and 2) assisted acceleration training (ASSISTED, N = 12, velocity increase 5–10%). A total of 16 athletes were able to finish all required measurements and sessions. According to the hypothesis, a significant correlation was found between initial sprint FV profile and relative change in sprint FV profile (RESISTED: r = −0.95, p < 0.01, ASSISTED: r = −0.79, p < 0.01). This study showed that initial FV properties influence the degree of mechanical response when training at different ends of the FV spectrum. Practitioners should consider utilizing the sprint FV profile to improve the individual effectiveness of resisted and assisted sprint training programs in high-level rugby athletes.
Pythagorean Fuzzy SWARA–VIKOR Framework for Performance Evaluation of Solar Panel Selection
Authors: Rani Pratibha , Mishra Arunodaya Raj , Mardani Abbas , Cavallaro Fausto , Štreimikienė Dalia , Khan Syed Abdul Rehman
Published: May 2020

The age of industrialization and modernization has increased energy demands globally. Solar energy has been recognized as an inexhaustible source of energy and has been applied for desalination and electricity generation. Among different non-conventional energy resources, Solar Energy (SE) is one of the main contributors to the global energy system. A photovoltaic system (PS) is applied to produce SE using photovoltaic cells. The selection of a solar panel includes many intricate factors involving both subjective and quantifiable parameters; therefore, it can be regarded as a complex Multi-Criteria Decision-Making (MCDM) problem. As the uncertainty commonly occurs in the selection of an ideal solar panel, the theory of Pythagorean fuzzy sets has been proven as one of the flexible and superior tools to deal with the uncertainty and ambiguity that arise in real-life applications. The aim of the study is to present an MCDM framework for solving the Solar Panel Selection (SPS) problem within the Pythagorean fuzzy (PF) environment. For this, first, a new integrated method is proposed based on the Stepwise Weight Assessment Ratio Analysis (SWARA) and VlseKriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) approaches in the Pythagorean fuzzy sets (PFSs) context. In the proposed approach, subjective weights of the evaluation criteria are calculated by the SWARA method, and the preference order of alternatives is decided by the VIKOR method in the PF context. The criteria weights evaluated by this approach involve the imprecision of experts’ opinions, which makes them more comprehensible. The computational procedure of the proposed methodology is established through a case study of the SPS problem under PF environment, which proves the applicability and efficiency of the proposed method. Furthermore, this study performs sensitivity analysis to reveal the stability of the developed framework. This analysis signifies that the solar panel option R4 constantly secures its highest ranking despite how the parameter values vary. In addition, a comparative study is discussed to analyze the validity of the obtained result. The results show that the proposed approach is more efficient and applicable with previously developed methods in the PFS environment.
Taking Another Look at Airline CSR: How Required CSR and Desired CSR Affect Customer Loyalty in the Airline Industry
Authors: Kim Yaeri , Lee Seojin Stacey , Roh Taewoo
Published: May 2020

This study aims to take another look at corporate social responsibility (CSR) dimensions in airline industries and identify the moderating effects of socio-economic characteristics on customer loyalty. We applied Carroll’s four CSR dimensions and reorganized economic and legal responsibilities under ‘required CSR’ and philanthropic and environmental responsibilities under ‘desired CSR’. The current study reveals that customer loyalty increased when customers perceived airlines to be practicing desired CSR, but not for required CSR. However, this effect varies depending on the expected moderator of socio-economic variables. Regarding required CSR, customers with higher levels of either income or formal education showed increased customer loyalty. However, for desired CSR, only customers with higher levels of education showed significantly increased customer loyalty. Thus, the research findings imply that in order to effectively deliver CSR strategies, airline CSR initiatives also need to take into consideration the socio-economic levels of targeted consumers.
The Influence of ISO 9001 & ISO 14001 on Sustainable Supply Chain Management in the Textile Industry
Authors: Zimon Dominik , Madzik Peter , Sroufe Robert
Published: May 2020

The examination of implementing ISO standards can provide new insight as to their quality and environmental management benefits. These insights can be more impactful in manufacturing sectors and especially textiles as this sector is known for its environmental degradation and questionable supply chain practices. This exploratory study investigates the extent to which organizations that implement either ISO 9001 or 14001 standards impact sustainable supply chain management (SSCM). We also investigate the extent to which these two standards improve SSCM for the textile industry within a block of European countries consisting of Poland, Slovakia, and the Czech Republic. In this empirical study, we use several measures to help identify the extent of impacts of examined standardized management systems on improvements in different processes of SSCM. Our contributions to the field include finding that quality management systems support environmental processes, relationships with stakeholders, processes within the supply chain, and SSCM. Other findings include implementing environmental management system standards impact the rationalization of other processes. Finally, we find more significant supply chain impacts with the implementation of both standards than by either standard alone. The study concludes with suggestions for extension of this study and opportunities for further research.
Citizen Science for Scientific Literacy and the Attainment of Sustainable Development Goals in Formal Education
Authors: Queiruga-Dios Miguel Ángel , López-Iñesta Emilia , Diez-Ojeda María , Sáiz-Manzanares María Consuelo , Vázquez Dorrío José Benito
Published: May 2020

Curricular integration in formal teaching of citizen science can bring to the classroom aspects of scientific literacy that encourage the involvement of citizens. In particular, these include non-epistemic aspects related to the sociology of science (which are often not transferred to the classroom). Furthermore, this practice raises awareness among students, and encourages them to become participants in the attainment of the United Nations’ Sustainable Development Goals (SDGs). This article describes a proposal for the integration of a citizen science project into the secondary education curriculum that can be reproduced in any educational center. Eighty-three secondary school pupils (14–15 years old) took part in this research at a city-center school in Northern Spain. A questionnaire based on validated studies was created and used to analyze the changes in attitudes of pupils towards science and technology and their improvement in scientific literacy in terms of scientific processes and scientific situations. The results indicate a significant improvement in the attitudes towards science and technology among the participating learners, as well as a better understanding of scientific processes and situations. Likewise, the results reflect how the implementation of the citizen science project contributes to the SDGs.
Zapote Seed (Pouteria mammosa L.) Valorization for Thermal Energy Generation in Tropical Climates
Authors: Perea-Moreno Miguel-Angel , Hernandez-Escobedo Quetzalcoatl , Rueda-Martinez Fernando , Perea-Moreno Alberto-Jesus
Published: May 2020

According to the Law for the Use of Renewable Energies and the Financing of Energy Transition, Mexico’s goal for 2024 is to generate 35% of its energy from non-fossil sources. Each year, up to 2630 tons of residual biomass from the zapote industry are dismissed without sustainable use. The main purposes of this study were to determine the elemental chemical analysis of the zapote seed and its energy parameters to further evaluate its suitability as a solid biofuel in boilers for the generation of thermal energy in a tropical climate. Additionally, energy, economic, and environmental assessments of the installation were carried out. The results obtained show that zapote seed has a higher heating value (18.342 MJ/kg), which makes it appealing for power generation. The Yucatan Peninsula is the main zapote-producing region, with an annual production of 11,084 tons. If the stone of this fruit were used as biofuel, 7860.87 MWh could be generated and a CO2 saving of 1996.66 tons could be obtained. Additionally, replacing a 200 kW liquefied petroleum gas (LPG) boiler with a biomass boiler using zapote seed as a biofuel would result in a reduction of 60,960.00 kg/year of CO2 emissions. Furthermore, an annual saving of $7819.79 would be obtained, which means a saving of 53.19% relative to the old LPG installation. These results pave the way toward the utilization of zapote seed as a solid biofuel and contribute to achieving Mexico’s energy goal for 2024 while promoting sustainability in universities.
Treated Wastewater and Fertigation Applied for Greenhouse Tomato Cultivation Grown in Municipal Solid Waste Compost and Soil Mixtures
Authors: Tzortzakis Nikolaos , Saridakis Christos , Chrysargyris Antonios
Published: May 2020

Low-fertility soil and the use of brackish water for irrigation act as obstacles and limit crop production. The utilization of municipal solid waste (MSW), compost (C), and treated wastewater (TWW) is receiving attention nowadays not only to overcome the above limitations but also as an efficient way for waste management and reuse of raw materials. In the present study, MSW compost in different ratios (5%, 10%, 20%, and 40%), fertigation and/or irrigation with TWW were studied in tomato plants (Solanum lycopersicum L.). The addition of compost increased organic content, pH, electrical conductivity (EC), and mineral content of the growing media, while fertigation and TWW supported the mineral status of the growing media, and this was reflected in the increase of N, K, and Na in tomato leaves. Plants grown in compost-based media with fertigation produced more leaves, compared to the control, while irrigation with TWW did not increase the number of leaves. Plant biomass increased with the application of ≥20% C, fertigation, and/or TWW applications. Plant yield increased in 40% C, while fertigation increased yield in case of lower (5%-10% C) compost ratios, but TWW application did not change the yield. The combination of high C ratios and fertigation and/or TWW decreased tomato fresh weight. Different levels of C did not affect leaf photosynthesis, stomatal conductance, internal CO2 concentration, and chlorophyll fluorescence, but, in general, the combination of compost with fertigation and/or TWW affected them negatively. Fruit total soluble solids, acidity, ascorbic acid, firmness, and total phenolics were increased with the high ratios of compost and/or fertigation and TWW applications, but marketability did not. Bacteria (total coliform and Escherichia coli) units increased in growing media subjected to TWW, but lower levels were counted on the fruit, mainly due to splashing or fruit contact with the soil. The results indicate that up to 40% C can be added into the substrate, as increased plant growth and maintained plant yield for greenhouse tomato cultivation is observed, while fertigation and TWW could be used in a controlled manner as alternative means for nutrient and irrigation in vegetables following safety aspects.
A Comprehensive Assessment of the Existing Accident and Hazard Prediction Models for the Highway-Rail Grade Crossings in the State of Florida
Authors: Pasha Junayed , Dulebenets Maxim A. , Abioye Olumide F. , Kavoosi Masoud , Moses Ren , Sobanjo John , Ozguven Eren E.
Published: May 2020

Accidents at highway-rail grade crossings can cause fatalities and injuries, as well as significant property damages. In order to prevent accidents, certain upgrades need to be made at highway-rail grade crossings. However, due to limited monetary resources, only the most hazardous highway-rail grade crossings should receive a priority for upgrading. Hence, accident/hazard prediction models are required to identify the most hazardous highway-rail grade crossings for safety improvement projects. This study selects and evaluates the accident and hazard prediction models found in the highway-rail grade crossing safety literature to rank the highway-rail grade crossings in the State of Florida. Three approaches are undertaken to evaluate the candidate accident and hazard prediction models, including the chi-square statistic, grouping of crossings based on the actual accident data, and Spearman rank correlation coefficient. The analysis was conducted for the 589 highway-rail grade crossings located in the State of Florida using the data available through the highway-rail grade crossing inventory database maintained by the Federal Railroad Administration. As a result of the performed analysis, a new hazard prediction model, named as the Florida Priority Index Formula, is recommended to rank/prioritize the highway-rail grade crossings in the State of Florida. The Florida Priority Index Formula provides a more accurate ranking of highway-rail grade crossings as compared to the alternative methods. The Florida Priority Index Formula assesses the potential hazard of a given highway-rail grade crossing based on the average daily traffic volume, average daily train volume, train speed, existing traffic control devices, accident history, and crossing upgrade records.
A Combined Analysis of Sociological and Farm Management Factors Affecting Household Livelihood Vulnerability to Climate Change in Rural Burundi
Authors: Nyairo Risper , Machimura Takashi , Matsui Takanori
Published: May 2020

This paper analyzed the livelihood vulnerability of households in two communes using socio-economic data, where one site is a climate analogue of the other under expected future climate change. The analysis was undertaken in order to understand local variability in the vulnerability of communities and how it can be addressed so as to foster progress towards rural adaptation planning. The study identified sources of household livelihood vulnerability by exploring human and social capitals, thus linking the human subsystem with existing biophysical vulnerability studies. Selected relevant variables were used in Factor Analysis on Mixed Data (FAMD), where the first eight dimensions of FAMD contributed most variability to the data. Clustering was done based on the eight dimensions, yielding five clusters with a mix of households from the two communes. Results showed that Cluster 3 was least vulnerable due to a greater proportion of households having adopted farming practices that enhance food and water availability. Households in the other clusters will need to make appropriate changes to reduce their vulnerability. Findings show that when analyzing rural vulnerability, rather than broadly looking at spatial climatic and farm management differences, social factors should also be investigated, as they can exert significant policy implications.