bluuebunny/crossref_metadata_2025 huggingface dataset:
The Quantile-Matching Approach to Improving Radar Quantitative Precipitation Estimation in South China
Authors: Song Linye , Chen Shangfeng , Li Yun , Qi Duo , Wu Jiankun , Chen Mingxuan , Cao Weihua
Published: December 2021

Weather radar provides regional rainfall information with a very high spatial and temporal resolution. Because the radar data suffer from errors from various sources, an accurate quantitative precipitation estimation (QPE) from a weather radar system is crucial for meteorological forecasts and hydrological applications. In the South China region, multiple weather radar networks are widely used, but the accuracy of radar QPE products remains to be analyzed and improved. Based on hourly radar QPE and rain gauge observation data, this study first analyzed the QPE error in South China and then applied the Quantile Matching (Q-matching) method to improve the radar QPE accuracy. The results show that the rainfall intensity of the radar QPE is generally larger than that determined from rain gauge observations but that it usually underestimates the intensity of the observed heavy rainfall. After the Q-matching method was applied to correct the QPE, the accuracy improved by a significant amount and was in good agreement with the rain gauge observations. Specifically, the Q-matching method was able to reduce the QPE error from 39–44%, demonstrating performance that is much better than that of the traditional climatological scaling method, which was shown to be able to reduce the QPE error from 3–15% in South China. Moreover, after the Q-matching correction, the QPE values were closer to the rainfall values that were observed from the automatic weather stations in terms of having a smaller mean absolute error and a higher correlation coefficient. Therefore, the Q-matching method can improve the QPE accuracy as well as estimate the surface precipitation better. This method provides a promising prospect for radar QPE in the study region.
Analysis of Wetland Landcover Change in Great Lakes Urban Areas Using Self-Organizing Maps
Authors: Penfound Elissa , Vaz Eric
Published: December 2021

Wetland loss and subsequent reduction of wetland ecosystem services in the Great Lakes region has been driven, in part, by changing landcover and increasing urbanization. With landcover change data, digital elevation models (DEM), and self-organizing maps (SOM), this study explores changing landcover and the flood mitigation attributes of wetland areas over a 15-year period in Toronto and Chicago. The results of this analysis show that (1) in the city of Toronto SOM clusters, the landcover change correlations with wetland volume and wetland area range between −0.1 to −0.5, indicating that a more intense landcover change tends to be correlated with small shallow wetlands, (2) in the city of Chicago SOM clusters, the landcover change correlations with wetland area range between −0.1 to −0.7, the landcover change correlations with wetland volume per area range between −0.1 to 0.8, and the landcover change correlations with elevation range between −0.2 to −0.6, indicating that more intense landcover change tends to be correlated with spatially small wetlands that have a relatively high water-storage capacity per area and are located at lower elevations. In both cities, the smallest SOM clusters represent wetland areas where increased landcover change is correlated with wetland areas that have high flood mitigation potential. This study aims to offer a new perspective on changing urban landscapes and urban wetland ecosystem services in Toronto and Chicago.
Machine Learning Classification of Endangered Tree Species in a Tropical Submontane Forest Using WorldView-2 Multispectral Satellite Imagery and Imbalanced Dataset
Authors: Jackson Colbert M. , Adam Elhadi
Published: December 2021

Accurate maps of the spatial distribution of tropical tree species provide valuable insights for ecologists and forest management. The discrimination of tree species for economic, ecological, and technical reasons is usually necessary for achieving promising results in tree species mapping. Most of the data used in tree species mapping normally have some degree of imbalance. This study aimed to assess the effects of imbalanced data in identifying and mapping trees species under threat in a selectively logged sub-montane heterogeneous tropical forest using random forest (RF) and support vector machine with radial basis function (RBF-SVM) kernel classifiers and WorldView-2 multispectral imagery. For comparison purposes, the original imbalanced dataset was standardized using three data sampling techniques: oversampling, undersampling, and combined oversampling and undersampling techniques in R. The combined oversampling and undersampling technique produced the best results: F1-scores of 68.56 ± 2.6% for RF and 64.64 ± 3.4% for SVM. The balanced dataset recorded improved classification accuracy compared to the original imbalanced dataset. This research observed that more separable classes recorded higher F1-scores. Among the species, Syzygium guineense and Zanthoxylum gilletii were the most accurately mapped whereas Newtonia buchananii was the least accurately mapped. The most important spectral bands with the ability to detect and distinguish between tree species as measured by random forest classifier, were the Red, Red Edge, Near Infrared 1, and Near Infrared 2.
Efficient Occluded Road Extraction from High-Resolution Remote Sensing Imagery
Authors: Feng Dejun , Shen Xingyu , Xie Yakun , Liu Yangge , Wang Jian
Published: December 2021

Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain analysis is proposed to reconstruct the road. An experiment is carried out on a benchmark dataset to prove the effectiveness of this method, and the result is compared with that of several famous deep learning models including FCN8s, SegNet, U-Net and D-Linknet. The comparison shows that this model increases the IOU value and the F1 score by 3.35–12.8% and 2.41–9.8%, respectively. Additionally, the result proves the proposed method is effective at extracting roads from occluded areas.
Medical Range Radiation Dosimeter Based on Polymer-Embedded Fiber Bragg Gratings
Authors: Lebel-Cormier Marie-Anne , Boilard Tommy , Bernier Martin , Beaulieu Luc
Published: December 2021

Fiber Bragg gratings (FBGs) are valuable dosimeters for doses up to 100 kilograys (kGy), but have hardly been used for the low-dose range of a few grays (Gy) required in medical radiation dosimetry. We report that embedding a doped silica fiber FBG in a polymer material allows a minimum detectable dose of 0.3 Gy for γ-radiation. Comparing the detector response for different doped silica fibers with various core doping, we obtain an independent response, in opposition to what is reported for high-dose range. We hypothesized that the sensor detection is based on the radio-induced thermal expansion of the surrounding polymer. Hence, we used a simple physical model based on the thermal and mechanical properties of the surrounding polymer and obtained good accordance between measured and calculated values for different compositions and thicknesses. We report that over the 4 embedding polymers tested, polyether ether ketone and polypropylene have respectively the lowest (0.056 pm/Gy) and largest sensitivity (0.087 pm/Gy). Such FBG-based dosimeters have the potential to be distributed along the fiber to allow multipoint detection while having a sub-millimeter size that could prove very useful for low-dose applications, in particular for radiotherapy dosimetry.
A Resonant Coupler for Subcutaneous Implant
Authors: Bing Sen , Chawang Khengdauliu , Chiao J.-C.
Published: December 2021

A resonator coupler for subcutaneous implants has been developed with a new impedance matching pattern added to the conventional loop antenna. The tuning element of a concentric metal pad contributes distributed capacitance and inductance to the planar inductive loop and improves resonance significantly. It provides a better qualify factor for resonant coupling and a much lower reflection coefficient for the implant electronics. Practical constraints are taken into account for designs including the requirement of operation within a regulated frequency band and the limited thickness for a monolithic implant. In this work, two designs targeting to operate in the two industrial, scientific, and medical (ISM) bands at 903 MHz and 2.45 GHz are considered. The tuning metal pad improves their resonances significantly, compared to the conventional loop designs. Since it is difficult to tune the implant antenna after implantation, the effects of tissue depth variations due to the individual’s surgery and the appropriate implant depths are investigated. Simulations conducted with the dielectric properties of human skin documented in the literature are compared to measurements done with hydrated ground pork as phantoms. Experiments and simulations are conducted to explain the discrepancies in frequency shifts due to the uses of pork phantoms. The design method is thus validated for uses on human skin. A noninvasive localization method to identify the implant under the skin has been examined and demonstrated by both simulations and measurements. It can efficiently locate the subcutaneous implant based on the high quality-factor resonance owing to the tuning elements in both implant and transmitter couplers. The planar resonant coupler for wireless power transfer shows good performance and promise in subcutaneous applications for implants.
Me-Doped Ti–Me Intermetallic Thin Films Used for Dry Biopotential Electrodes: A Comparative Case Study
Authors: Lopes Cláudia , Fiedler Patrique , Rodrigues Marco Sampaio , Borges Joel , Bertollo Maurizio , Alves Eduardo , Barradas Nuno Pessoa , Comani Silvia , Haueisen Jens , Vaz Filipe
Published: December 2021

In a new era for digital health, dry electrodes for biopotential measurement enable the monitoring of essential vital functions outside of specialized healthcare centers. In this paper, a new type of nanostructured titanium-based thin film is proposed, revealing improved biopotential sensing performance and overcoming several of the limitations of conventional gel-based electrodes such as reusability, durability, biocompatibility, and comfort. The thin films were deposited on stainless steel (SS) discs and polyurethane (PU) substrates to be used as dry electrodes, for non-invasive monitoring of body surface biopotentials. Four different Ti–Me (Me = Al, Cu, Ag, or Au) metallic binary systems were prepared by magnetron sputtering. The morphology of the resulting Ti–Me systems was found to be dependent on the chemical composition of the films, specifically on the type and amount of Me. The existence of crystalline intermetallic phases or glassy amorphous structures also revealed a strong influence on the morphological features developed by the different systems. The electrodes were tested in an in-vivo study on 20 volunteers during sports activity, allowing study of the application-specific characteristics of the dry electrodes, based on Ti–Me intermetallic thin films, and evaluation of the impact of the electrode–skin impedance on biopotential sensing. The electrode–skin impedance results support the reusability and the high degree of reliability of the Ti–Me dry electrodes. The Ti–Al films revealed the least performance as biopotential electrodes, while the Ti–Au system provided excellent results very close to the Ag/AgCl reference electrodes.
Waymarking in Social Robots: Environment Signaling Using Human–Robot Interaction
Authors: Corrales-Paredes Ana , Malfaz María , Egido-García Verónica , Salichs Miguel A.
Published: December 2021

Travellers use the term waymarking to define the action of posting signs, or waymarks, along a route. These marks are intended to be points of reference during navigation for the environment. In this research, we will define waymarking as the skill of a robot to signal the environment or generate information to facilitate localization and navigation, both for its own use and for other robots as well. We present an automated environment signaling system using human–robot interaction and radio frequency identification (RFID) technology. The goal is for the robot, through human–robot interaction, to obtain information from the environment and use this information to carry out the signaling or waymarking process. HRI will play a key role in the signaling process since this type of communication makes it possible to exchange more specific and enriching information. The robot uses common phrases such as “Where am I?” and “Where can I go?”, just as we humans do when we ask other people for information about the environment. It is also possible to guide the robot and “show” it the environment to carry out the task of writing the signs. The robot will use the information received to create, update, or improve the navigation data in the RFID signals. In this paper, the signaling process will be described, how the robot acquires the information for signals, writing and updating process and finally, the implementation and integration in a real social robot in a real indoor environment.
DB-YOLO: A Duplicate Bilateral YOLO Network for Multi-Scale Ship Detection in SAR Images
Authors: Zhu Haozhen , Xie Yao , Huang Huihui , Jing Chen , Rong Yingjiao , Wang Changyuan
Published: December 2021

With the wide application of convolutional neural networks (CNNs), a variety of ship detection methods based on CNNs in synthetic aperture radar (SAR) images were proposed, but there are still two main challenges: (1) Ship detection requires high real-time performance, and a certain detection speed should be ensured while improving accuracy; (2) The diversity of ships in SAR images requires more powerful multi-scale detectors. To address these issues, a SAR ship detector called Duplicate Bilateral YOLO (DB-YOLO) is proposed in this paper, which is composed of a Feature Extraction Network (FEN), Duplicate Bilateral Feature Pyramid Network (DB-FPN) and Detection Network (DN). Firstly, a single-stage network is used to meet the need of real-time detection, and the cross stage partial (CSP) block is used to reduce the redundant parameters. Secondly, DB-FPN is designed to enhance the fusion of semantic and spatial information. In view of the ships in SAR image are mainly distributed with small-scale targets, the distribution of parameters and computation values between FEN and DB-FPN in different feature layers is redistributed to solve the multi-scale detection. Finally, the bounding boxes and confidence scores are given through the detection head of YOLO. In order to evaluate the effectiveness and robustness of DB-YOLO, comparative experiments with the other six state-of-the-art methods (Faster R-CNN, Cascade R-CNN, Libra R-CNN, FCOS, CenterNet and YOLOv5s) on two SAR ship datasets, i.e., SSDD and HRSID, are performed. The experimental results show that the AP50 of DB-YOLO reaches 97.8% on SSDD and 94.4% on HRSID, respectively. DB-YOLO meets the requirement of real-time detection (48.1 FPS) and is superior to other methods in the experiments.
Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization
Authors: Yu Dongyeon , Lee Honggyu , Kim Taehoon , Hwang Sung-Ho
Published: December 2021

It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories in complex traffic situations. However, predicting the future behavior of vehicles is a challenging issue because traffic vehicles each have different drivers with different driving tendencies and intentions and they interact with each other. This paper presents a Long Short-Term Memory (LSTM) encoder–decoder model that utilizes an attention mechanism that focuses on certain information to predict vehicles’ trajectories. The proposed model was trained using the Highway Drone (HighD) dataset, which is a high-precision, large-scale traffic dataset. We also compared this model to previous studies. Our model effectively predicted future trajectories by using an attention mechanism to manage the importance of the driving flow of the target and adjacent vehicles and the target vehicle’s dynamics in each driving situation. Furthermore, this study presents a method of linearizing the road geometry such that the trajectory prediction model can be used in a variety of road environments. We verified that the road geometry linearization mechanism can improve the trajectory prediction model’s performance on various road environments in a virtual test-driving simulator constructed based on actual road data.
Design and Analysis of In-Pipe Hydro-Turbine for an Optimized Nearly Zero Energy Building
Authors: Aziz Muhammad Shahbaz , Khan Muhammad Adil , Jamil Harun , Jamil Faisal , Chursin Alexander , Kim Do-Hyeun
Published: December 2021

Pakistan receives Direct Normal Irradiation (DNI) exceeding 2000 kWh/m²/annum on approximately 83% of its land, which is very suitable for photovoltaic production. This energy can be easily utilized in conjunction with other renewable energy resources to meet the energy demands and reduce the carbon footprint of the country. In this research, a hybrid renewable energy solution based on a nearly Zero Energy Building (nZEB) model is proposed for a university facility. The building in consideration has a continuous flow of water through its water delivery vertical pipelines. A horizontal-axis spherical helical turbine is designed in SolidWorks and is analyzed through a computational fluid dynamics (CFD) analysis in ANSYS Fluent 18.1 based on the K-epsilon turbulent model. Results obtained from ANSYS Fluent have shown that a 24 feet vertical channel with a water flow of 0.2309 m3/s and velocity of 12.66 m/s can run the designed hydroelectric turbine, delivering 168 W of mechanical power at 250 r.p.m. Based on the turbine, a hybrid renewable energy system (HRES) comprising photovoltaic and hydroelectric power is modelled and analyzed in HOMER Pro software. Among different architectures, it was found that architecture with hydroelectric and photovoltaic energy provided the best COE of $0.09418.
Efficient Chemical Surface Modification Protocol on SiO2 Transducers Applied to MMP9 Biosensing
Authors: Hernandez Ana L. , Pujari Sidharam P. , Laguna María F. , Santamaría Beatriz , Zuilhof Han , Holgado Miguel
Published: December 2021

The bioreceptor immobilization process (biofunctionalization) turns to be one of the bottlenecks when developing a competent and high sensitivity label-free biosensor. Classical approaches seem to be effective but not efficient. Although biosensing capacities are shown in many cases, the performance of the biosensor is truncated by the inefficacious biofunctionalization protocol and the lack of reproducibility. In this work, we describe a unique biofunctionalization protocol based on chemical surface modification through silane chemistry on SiO2 optical sensing transducers. Even though silane chemistry is commonly used for sensing applications, here we present a different mode of operation, applying an unusual silane compound used for this purpose (3-Ethoxydimethylsilyl)propylamine, APDMS, able to create ordered monolayers, and minimizing fouling events. To endorse this protocol as a feasible method for biofunctionalization, we performed multiple surface characterization techniques after all the process steps: Contact angle (CA), X-ray photoelectron spectroscopy (XPS), ellipsometry, and fluorescence microscopy. Finally, to evidence the outputs from the SiO2 surface characterization, we used those SiO2 surfaces as optical transducers for the label-free biosensing of matrix metalloproteinase 9 (MMP9). We found and demonstrated that the originally designed protocol is reproducible, stable, and suitable for SiO2-based optical sensing transducers.
Characterization of Supersonic Compressible Fluid Flow Using High-Speed Interferometry
Authors: Psota Pavel , Çubreli Gramoz , Hála Jindřich , Šimurda David , Šidlof Petr , Kredba Jan , Stašík Marek , Lédl Vít , Jiránek Michal , Luxa Martin , Lepicovsky Jan
Published: December 2021

This paper presents a very effective interference technique for the sensing and researching of compressible fluid flow in a wind tunnel facility. The developed technique is very sensitive and accurate, yet easy to use under conditions typical for aerodynamic labs, and will be used for the nonintrusive investigation of flutter in blade cascades. The interferometer employs a high-speed camera, fiber optics, and available “of-the-shelf” optics and optomechanics. The construction of the interferometer together with the fiber optics ensures the high compactness and portability of the system. Moreover, single-shot quantitative data processing based on introducing a spatial carrier frequency and Fourier analysis allows for almost real-time quantitative processing. As a validation case, the interferometric system was successfully applied in the research of supersonic compressible fluid discharge from a narrow channel in a wind tunnel. Density distributions were quantitatively analyzed with the spatial resolution of about 50 μm. The results of the measurement revealed important features of the flow pattern. Moreover, the measurement results were compared with Computational Fluid Dynamics (CFD) simulations with a good agreement.
Individual Tree Structural Parameter Extraction and Volume Table Creation Based on Near-Field LiDAR Data: A Case Study in a Subtropical Planted Forest
Authors: Gao Sha , Zhang Zhengnan , Cao Lin
Published: December 2021

Individual tree structural parameters are vital for precision silviculture in planted forests. This study used near-field LiDAR (light detection and ranging) data (i.e., unmanned aerial vehicle laser scanning (ULS) and ground backpack laser scanning (BLS)) to extract individual tree structural parameters and fit volume models in subtropical planted forests in southeastern China. To do this, firstly, the tree height was acquired from ULS data and the diameter at breast height (DBH) was acquired from BLS data by using individual tree segmentation algorithms. Secondly, point clouds of the complete forest canopy were obtained through the combination of ULS and BLS data. Finally, five tree taper models were fitted using the LiDAR-extracted structural parameters of each tree, and then the optimal taper model was selected. Moreover, standard volume models were used to calculate the stand volume; then, standing timber volume tables were created for dawn redwood and poplar. The extraction of individual tree structural parameters exhibited good performance. The volume model had a good performance in calculating the standing volume for dawn redwood and poplar. Our results demonstrate that near-field LiDAR has a strong capability of extracting tree structural parameters and creating volume tables for subtropical planted forests.
Application of Excimer Lamp in Quantitative Detection of SF6 Decomposition Component SO2
Authors: Chen Tunan , Li Kang , Ma Fengxiang , Qiu Xinjie , Qiu Zongjia , Liao Zhenghai , Zhang Guoqiang
Published: December 2021

Accurate quantitative detection for trace gas has long been the center of failure diagnosis for gas-insulated equipment. An absorption spectroscopy-based detection system was developed for trace SF6 decomposition SO2 detection in this paper. In order to reduce interference from other decomposition, ultraviolet spectrum of SO2 was selected for detection. Firstly, an excimer lamp was developed in this paper as the excitation of the absorption spectroscopy compared with regular light sources with electrodes, such as electrodeless lamps that are more suitable for long-term monitoring. Then, based on the developed excimer lamp, a detection system for trace SO2 was established. Next, a proper absorption peak was selected by calculating spectral derivative for further analysis. Experimental results indicated that good linearity existed between the absorbance and concentration of SO2 at the chosen absorption peak. Moreover, the detection limit of the proposed detection system could reach the level of 10−7. The results of this paper could serve as a guide for the application of excimer lamp in online monitoring for SF6-insulated equipment.
An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals
Authors: Li Hongzu , Boulanger Pierre
Published: December 2021

Today’s wearable medical devices are becoming popular because of their price and ease of use. Most wearable medical devices allow users to continuously collect and check their health data, such as electrocardiograms (ECG). Therefore, many of these devices have been used to monitor patients with potential heart pathology as they perform their daily activities. However, one major challenge of collecting heart data using mobile ECG is baseline wander and motion artifacts created by the patient’s daily activities, resulting in false diagnoses. This paper proposes a new algorithm that automatically removes the baseline wander and suppresses most motion artifacts in mobile ECG recordings. This algorithm clearly shows a significant improvement compared to the conventional noise removal method. Two signal quality metrics are used to compare a reference ECG with its noisy version: correlation coefficients and mean squared error. For both metrics, the experimental results demonstrate that the noisy signal filtered by our algorithm is improved by a factor of ten.
Toward Modular and Flexible Open RAN Implementations in 6G Networks: Traffic Steering Use Case and O-RAN xApps
Authors: Dryjański Marcin , Kułacz Łukasz , Kliks Adrian
Published: December 2021

The development of cellular wireless systems has entered the phase when 5G networks are being deployed and the foundations of 6G solutions are being identified. However, in parallel to this, another technological breakthrough is observed, as the concept of open radio access networks is coming into play. Together with advancing network virtualization and programmability, this may reshape the way the functionalities and services related to radio access are designed, leading to modular and flexible implementations. This paper overviews the idea of open radio access networks and presents ongoing O-RAN Alliance standardization activities in this context. The whole analysis is supported by a study of the traffic steering use case implemented in a modular way, following the open networking approach.
Deep Learning Techniques in the Classification of ECG Signals Using R-Peak Detection Based on the PTB-XL Dataset
Authors: Śmigiel Sandra , Pałczyński Krzysztof , Ledziński Damian
Published: December 2021

Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the application of which in electrocardiographic signals is gaining importance. So far, limited studies or optimizations using DNN can be found using ECG databases. To explore and achieve effective ECG recognition, this paper presents a convolutional neural network to perform the encoding of a single QRS complex with the addition of entropy-based features. This study aims to determine what combination of signal information provides the best result for classification purposes. The analyzed information included the raw ECG signal, entropy-based features computed from raw ECG signals, extracted QRS complexes, and entropy-based features computed from extracted QRS complexes. The tests were based on the classification of 2, 5, and 20 classes of heart diseases. The research was carried out on the data contained in a PTB-XL database. An innovative method of extracting QRS complexes based on the aggregation of results from established algorithms for multi-lead signals using the k-mean method, at the same time, was presented. The obtained results prove that adding entropy-based features and extracted QRS complexes to the raw signal is beneficial. Raw signals with entropy-based features but without extracted QRS complexes performed much worse.
Sensor-Based Prediction of Mental Effort during Learning from Physiological Data: A Longitudinal Case Study
Authors: Agarwal Ankita , Graft Josephine , Schroeder Noah , Romine William
Published: December 2021

Trackers for activity and physical fitness have become ubiquitous. Although recent work has demonstrated significant relationships between mental effort and physiological data such as skin temperature, heart rate, and electrodermal activity, we have yet to demonstrate their efficacy for the forecasting of mental effort such that a useful mental effort tracker can be developed. Given prior difficulty in extracting relationships between mental effort and physiological responses that are repeatable across individuals, we make the case that fusing self-report measures with physiological data within an internet or smartphone application may provide an effective method for training a useful mental effort tracking system. In this case study, we utilized over 90 h of data from a single participant over the course of a college semester. By fusing the participant’s self-reported mental effort in different activities over the course of the semester with concurrent physiological data collected with the Empatica E4 wearable sensor, we explored questions around how much data were needed to train such a device, and which types of machine-learning algorithms worked best. We concluded that although baseline models such as logistic regression and Markov models provided useful explanatory information on how the student’s physiology changed with mental effort, deep-learning algorithms were able to generate accurate predictions using the first 28 h of data for training. A system that combines long short-term memory and convolutional neural networks is recommended in order to generate smooth predictions while also being able to capture transitions in mental effort when they occur in the individual using the device.
Gender Effect on the Relationship between Talent Identification Tests and Later World Triathlon Series Performance
Authors: Cuba-Dorado Alba , Vleck Veronica , Álvarez-Yates Tania , Garcia-Garcia Oscar
Published: December 2021

Background: We examined the explanatory power of the Spanish triathlon talent identification (TID) tests for later World Triathlon Series (WTS)-level racing performance as a function of gender. Methods: Youth TID (100 m and 1000 m swimming and 400 m and 1000 m running) test performance times for when they were 14–19 years old, and WTS performance data up to the end of 2017, were obtained for 29 female and 24 male “successful” Spanish triathletes. The relationships between the athletes’ test performances and their later best WTS ranking positions and performance times were modeled using multiple linear regression. Results: The swimming and running TID test data had greater explanatory power for best WTS ranking in the females and for best WTS position in the males (R2a = 0.34 and 0.37, respectively, p ≤ 0.009). The swimming TID times were better related to later race performance than were the running TID times. The predictive power of the TID tests for WTS performance was, however, low, irrespective of exercise mode and athlete gender. Conclusions: These results confirm that triathlon TID tests should not be based solely on swimming and running performance. Moreover, the predictive value of the individual tests within the Spanish TID battery is gender specific.
Generation Y’s Sustainable Purchasing Intention of Green Personal Care Products
Authors: Moslehpour Massoud , Chaiyapruk Panita , Faez Sahand , Wong Wing-Keung
Published: December 2021

Global warming and depletion of the environment are more than a hunch; they are irrefutable facts. While caring for the environment seemed like a luxurious hobby twenty years ago, it has become a crucial necessity these days. However, the sustainable demand for eco-friendly products seems to be less than it should be. In other words, the consumers seem to lack the intent to purchase green products. Therefore, studies on factors affecting sustainable green purchasing intent seem to be crucial now more than ever. This study attempts to shed some light on the matter using structural equation modeling. After receiving the answers of 357 respondents from Thai Generation Y participants, the estimations reveal that attitude towards green packaging and green marketing awareness have the strongest effects on purchasing intention. On the other hand, environmental concern seems to have the weakest effect on purchasing intention. The findings suggest that the producers must pay more attention to packaging and other novel forms of marketing to increase the customers’ intent for purchasing green products.
Love Thy Neighbour: Social Benefits and Port-City Relationships
Authors: Roberts Toby , Williams Ian , Preston John , Clarke Nick , Odum Melinda , O’Gorman Stefanie
Published: December 2021

As awareness of the negative externalities created by ports increases, and the perceived local benefits decrease, ports must find new ways to improve the local noneconomic benefits they provide if they are to obtain local support. This global survey collected data from 51 ports in 26 countries. The results highlight a recognition by port authorities that ports face increasing pressure from local residents to reduce their negative impacts and that they should seek to improve the public perception towards the port by increasing local benefits. At present, port information and social media (81%), port events (67%) and education (63%) are the most adopted options. There is a lack of evidence that these measures are effective in improving local perceptions. Maritime museums and public access show a positive association with increasing local awareness of the benefits a port provides, despite their lower levels of adoption (45%). Port centres are the least adopted option at present (29%) and can be expected to increase significantly, with a 43% increase anticipated between numbers of current and expected future centres. Education (14%), public access (13%) and maritime museums (4.5%) also show increases in levels of interest. Maritime museums and public access should be pursued as proven, effective options for improving local perceptions of ports, whilst port centres may provide a new focal point for port-related social and cultural activities.
Addressing Sustainability within Public Procurement of Food: A Systematic Literature Review
Authors: Molin Elvira , Martin Michael , Björklund Anna
Published: December 2021

Public procurement has been recognized as a tool to promote more sustainable production and consumption practices. As such, an increasing body of literature has become available in recent years focusing on the sustainable public procurement of food. This article reviews the literature on the sustainable public procurement of food with the aim to analyze how sustainability is framed. This is done by analyzing what aspects of sustainability are emphasized and what practices are identified as sustainable. A systematic literature review was conducted between the years 2000 and 2020, identifying 103 articles. Results from the literature review indicate that the focus has primarily been on studies to evaluate and explore policy and good practices for procuring sustainable foods. A dominant focus on specific foods types and origins, e.g., those locally sourced and organic foods, is highlighted by a large share of the literature to address all three sustainability pillars. We observed that most articles focus on all three pillars of sustainability (environmental, social, and economic), addressing different aspects and types of foods, although the focus varied depending upon geographic location. Despite many studies identifying opportunities and potential, few articles assess the sustainability or outcomes of procurement processes through quantitative or qualitative methods or how actors in the procurement process can improve procurement toward more sustainable foods. This indicates a need for further case studies and guidelines to measure the development, progress, and performance of public food procurement.
Framework of Prediction Model for Mid- to Long-Term Performance Changes of Urban Railway Facilities Based on Performance Evaluation Reports
Authors: Kim Jonghyeob , Han Jae-Goo , Kang Goune , Chin Kyung-Ho
Published: December 2021

To maintain railway facilities in an appropriate state, systematic management based on mid- and long-term maintenance plans through future performance prediction must be carried out. To this end, it is necessary to establish and utilize a model that can predict mid- to long-term performance changes of railway facilities by predicting performance changes of individual sub-facilities. However, predicting changes in the performance of all sub-facilities can be difficult as it requires large volumes of data, and railway facilities are a collection of numerous sub-facilities. Therefore, in this study, a framework for a model that can predict mid- to long-term performance changes of railway facilities through analysis of continuously accumulated performance evaluation results is proposed. The model is a system with a series of flows that can classify performance evaluation results by individual sub-facilities, predict performance changes by each sub-facility using statistical methods, and predict mid- to long-term performance changes of the facility. The developed framework was applied to 36,537 sub-facilities comprising 12 lines of two urban railways in South Korea to illustrate the model and verify its applicability and effectiveness. This study contributes in terms of its methodology in establishing a framework for predicting mid- to long-term performance changes, providing the basis for the development of an automated model able to continuously predict performance changes of individual sub-facilities. In practical terms, it is expected that railway facility managers who allow trade-off between reliability and usability can contribute to establishing the mid- to long-term maintenance plans by utilizing the model proposed in this study, instead of subjectively building them.
The New Era of Business Digitization through the Implementation of 5G Technology in Romania
Authors: Ionescu Constantin Aurelian , Fülöp Melinda Timea , Topor Dan Ioan , Căpușneanu Sorinel , Breaz Teodora Odett , Stănescu Sorina Geanina , Coman Mihaela Denisa
Published: December 2021

The main objective of the present research is to identify the advantages and benefits that the use and implementation of 5G technology has on the development and evolution of the Romanian business environment. The study is based on a theoretical documentation regarding existing information in the field and a descriptive analysis of the evolution of the technology in Romania and worldwide. The research method chosen is a survey based on an opinion poll (questionnaire) to find out the availability of economic entities regarding the implementation of 5G technologies, the foreseen expectations and those realized by the business environment regarding the effects of 5G technologies on the economic activities and the benefits that 5G networks offer them. The analysis of the results of the questionnaire, through the correlations and contingency tables determined, allowed the validation of the research hypotheses, and the results show availability and interest for the implementation of 5G technology (over 69% agree with the 5G implementation over a period of up to 5 years), conditioned by the costs, the high degree of cyber security, and the competitive advantages it can generate. Competition, low productivity, and even external pressures are the main decisive factors in the implementation of 5G. Thus, it can be considered useful to extend the research by identifying possible viable solutions or alternatives, customized for the implementation of 5G technologies for economic entities from different sectors, as well as the analysis of the implementation costs and the potential economic benefits.
Institutional Diversity of Transferring Land Development Rights in China—Cases from Zhejiang, Hubei, and Sichuan
Authors: Shi Chen , Zhang Zhou
Published: December 2021

With the continuous urbanization, China is facing a dilemma of achieving two conflicting targets in land governance, i.e., the continuous supply of urban construction land to support urbanization and the preservation of cultivated land for food security. Under China’s dual land system, the implementation of the “Linkage between Urban-land Taking and Rural-land Giving” (Linkage) policy is of great significance in promoting more inclusive urbanization by commodifying the land development right and connecting urban and rural land markets. In the specific land property right system and changing land governance of China, this policy appears to provide an opportunity for stakeholders other than the state to compete for the value from the transfer of development rights (TDR) and triggers the emergence of diversified approaches in organizing land projects in rural China. Based on the theoretical perspective of New Institutional Economics and empirical evidence from Zhejiang Province, Hubei Province, and Sichuan Province, this paper conducts a comparative institutional analysis for China’s TDR practice and argues that the diversified operational approaches in China’s practice have aligned various interests of the stakeholders through flexible participation methods and elaborate reallocation of land property rights, in order to fit various institutional environments and material conditions
Editorial for the Special Issue on Modelling and Simulation of Human-Environment Interactions
Authors: Giabbanelli Philippe J. , Ligmann-Zielinska Arika
Published: December 2021

At the core of the Anthropocene lies human influence on the environment [...]
Corporate Social Responsibility and Pro-Environmental Behavior of the Individuals from the Perspective of Protection Motivation Theory
Authors: Jilani Ghulam , Yang Guangqin , Siddique Irfan
Published: December 2021

Several research studies have investigated the relationship between corporate social responsibility (CSR) and pro-environmental behavior of individuals in the industrial context (considering the employees as individuals) while ignoring the non-industrial context. Climate change and related issues associated with the interaction of humans with the environment are of great importance in today’s context, hence, it is equally important to test such a relationship in a non-industrial context. Therefore, the current study aims to investigate the relationship between CSR and pro-environmental behavior of students (SPEB) in the higher education sector of Pakistan. Given that university students are more inclined to act pro-environmentally, as they partake in different climate change activities and are part of the debate on the global environmental crisis, considering this sector to promote SPEB is of the utmost importance. The current study also proposes emotional affinity towards students (EATN) as a mediator between the proposed relationship. The current study data were collected from university students (n = 656) studying in different universities of Pakistan. The results were validated by employing the structural equation modeling technique (SEM). The results confirmed that there exists a positive association between CSR perception of a university and SPEB, and EATN partially mediates this relationship. The findings of the current study will be helpful to the higher education policymakers in Pakistan to mitigate the country’s environmental footprint by promoting the sustainable behavior of students as an outcome of CSR. Moreover, these findings will also be helpful to the academia for policymaking pertinent to educational governance.
Sustainable Development as Freedom: Trends and Opportunities for the Circular Economy in the Human Development Literature
Authors: Lima Pedro A. B. , Jesus Gessica M. K. , Ortiz Camila R. , Frascareli Fernanda C. O. , Souza Fernando B. , Mariano Enzo B.
Published: December 2021

This paper identifies, through a literature review, how 53 circular economy (CE) practices are related to the capability approach (CA) proposed by Sen. The main goal was to identify how a virtuous cycle between CE and CA can be developed. Five instrumental freedoms (IF) were analysed: economic facilities, social opportunities, protective security, political freedoms, and transparency guarantees. These relationships were analysed in three flows: CE practices positively impacting IF, CE practices negatively impacting IF, and the feedback influence of IF on CE practices. The results show that 32 of the 53 practices previously mentioned have not yet been studied from the CA context, which indicates that there are several research opportunities. From the practices considered, 72 articles were analysed in the final sample. The results suggest that several CE practices are aligned with the CA, considering that all five IF were identified as positive outcomes of CE practices. However, in some contexts, certain practices can have negative outcomes, which indicates that CE, at least in some cases, may not be considered as sustainable, as it decreases IF and, therefore, the social aspect of sustainability. The results also highlight that there is a feedback from IF to CE, in such a way that investing in the expansion of IF can facilitate the development of CE practices. Therefore, this study concludes that CE is indeed a way to fully operationalize sustainable development.
“License Plate Lottery”: Why Are People So Keen to Participate in It?
Authors: Zhu Junze , Guan Hongzhi , Hao Mingyang , Qin Zhengtao , Wang Ange
Published: December 2021

Vehicle purchase restriction policies greatly influence people’s behavior, especially their participation in the license plate lottery. This paper focuses on the socioeconomic characteristics and psychological factors of citizens participating in the license plate lottery, which can serve as a reference for policy makers aiming to guide rational participation in the lottery. A Multi-Index and Multi-Causal model were established based on social psychology, combined with the Theory of Planned Behavior (TPB). Perceived car necessity, perceived behavioral control, car ownership attitude and subjective norms were regarded as four latent psychological variables. Furthermore, the behaviors of license plate lottery participants in cities with purchase restriction policies were statistically analyzed from the perspectives of personal socioeconomic characteristics and psychological factors. The empirical research results reveal that the socioeconomic attributes of citizens have different degrees of influence on latent variables. Perceived car necessity is observed to have a significant direct impact on a citizen’s behavioral intention to participate in the lottery, which is also affected by perceived behavioral control. Car ownership attitude has the strongest impact on citizen behavior towards participating in the license plate lottery, followed by subjective norms, perceived behavioral control, and perceived car necessity. More specifically, the economic benefit associated with perceived behavioral control is identified as the critical factor in further promoting participation in the license plate lottery.