bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Method for Analyzing Aero-Optical Effects with Transient Simulation
Authors: Yang Bo , Fan Zichen , Yu He , Hu Haidong , Yang Zhaohua
Published: March 2021

Aero-optical effects reduce the accuracy of optical sensors on high-speed aircraft. Current research usually focuses on light refraction caused by large-scale density structures in turbulence. A method for analyzing photon energy scattering caused by micro-scale structures is proposed in this paper, which can explain the macro image distortion caused by moving molecules in inhomogeneous airflow. Quantitative analysis of the propagation equation indicates that micro-scale structures may contribute more to the wavefront distortion than the widely considered large-scale structures. To analyze the micro mechanism of aero-optical effects, a transient simulator is designed based on the scaling model of transient distorted wavefronts and the artificial vortex structure. The simulation results demonstrate that correct aero-optical phenomena can be obtained from the micro mechanism of photon energy scattering. Examples of using the transient simulator to optimize the parameters of the star sensor on a hypersonic vehicle are provided. The proposed analysis method for micro-scale structures provides a new idea for studying the aero-optical effects.
Hepatocellular Carcinoma Automatic Diagnosis within CEUS and B-Mode Ultrasound Images Using Advanced Machine Learning Methods
Authors: Mitrea Delia , Badea Radu , Mitrea Paulina , Brad Stelian , Nedevschi Sergiu
Published: March 2021

Hepatocellular Carcinoma (HCC) is the most common malignant liver tumor, being present in 70% of liver cancer cases. It usually evolves on the top of the cirrhotic parenchyma. The most reliable method for HCC diagnosis is the needle biopsy, which is an invasive, dangerous method. In our research, specific techniques for non-invasive, computerized HCC diagnosis are developed, by exploiting the information from ultrasound images. In this work, the possibility of performing the automatic diagnosis of HCC within B-mode ultrasound and Contrast-Enhanced Ultrasound (CEUS) images, using advanced machine learning methods based on Convolutional Neural Networks (CNN), was assessed. The recognition performance was evaluated separately on B-mode ultrasound images and on CEUS images, respectively, as well as on combined B-mode ultrasound and CEUS images. For this purpose, we considered the possibility of combining the input images directly, performing feature level fusion, then providing the resulted data at the entrances of representative CNN classifiers. In addition, several multimodal combined classifiers were experimented, resulted by the fusion, at classifier, respectively, at the decision levels of two different branches based on the same CNN architecture, as well as on different CNN architectures. Various combination methods, and also the dimensionality reduction method of Kernel Principal Component Analysis (KPCA), were involved in this process. These results were compared with those obtained on the same dataset, when employing advanced texture analysis techniques in conjunction with conventional classification methods and also with equivalent state-of-the-art approaches. An accuracy above 97% was achieved when our new methodology was applied.
A Novel Method for the Complex Tube System Reconstruction and Measurement
Authors: Guo Xiang , Su Xin , Yuan Yingtao , Suo Tao , Liu Yan
Published: March 2021

Pipe structures are at the base of the entire industry. In the industry structure, heat and vibration are transmitted in each pipe. The minimum distance between each pipe is significant to the security. The assembly error and the deformation of the pipeline positions after multiple runs are significant problems. The reconstruction of the multi-pipe system is a critical technical difficulty in the complex tube system. In this paper, a new method for the multi-pipes structure inspection is presented. Images of the tube system are acquired from several positions. The photogrammetry technology calculates positions, and the necessary coordination of the structure is reconstructed. A convolution neural network is utilized to detect edges of tube-features. The new algorithm for tube identification and reconstruction is presented to extract the tube feature in the image and reconstruct the 3D parameters of all tubes in a multi-pipes structure. The accuracy of the algorithm is verified by simulation experiments. An actual engine of the aircraft is measured to verify the proposed method.
SSVM: An Ultra-Low-Power Strain Sensing and Visualization Module for Long-Term Structural Health Monitoring
Authors: Khan Suleman , Won Jongbin , Shin Junsik , Park Junyoung , Park Jong-Woong , Kim Seung-Eock , Jang Yun , Kim Dong Joo
Published: March 2021

Structural health monitoring (SHM) is crucial for quantitative behavioral analysis of structural members such as fatigue, buckling, and crack propagation identification. However, formerly developed approaches cannot be implemented effectively for long-term infrastructure monitoring, owing to power inefficiency and data management challenges. This study presents the development of a high-fidelity and ultra-low-power strain sensing and visualization module (SSVM), along with an effective data management technique. Deployment of 24-bit resolution analog to a digital converter and precise half-bridge circuit for strain sensing are two significant factors for efficient strain measurement and power management circuit incorporating a low-power microcontroller unit (MCU), and electronic-paper display (EPD) enabled long-term operation. A prototype for SSVM was developed that performs strain sensing and encodes the strain response in a QR code for visualization on the EPD. For efficient power management, SSVM only activated when the trigger-signal was generated and stayed in power-saving mode consuming 18 mA and 337.9 μA, respectively. The trigger-signal was designed to be generated either periodically by a timer or intentionally by a push-button. A smartphone application and cloud database were developed for efficient data acquisition and management. A lab-scale experiment was carried out to validate the proposed system with a reference strain sensing system. A cantilever beam was deflected by increasing load at its free end, and the resultant strain response of SSVM was compared with the reference. The proposed system was successfully validated to use for long-term static strain measurement.
Imbalanced Loss-Integrated Deep-Learning-Based Ultrasound Image Analysis for Diagnosis of Rotator-Cuff Tear
Authors: Lee Kyungsu , Kim Jun Young , Lee Moon Hwan , Choi Chang-Hyuk , Hwang Jae Youn
Published: March 2021

A rotator cuff tear (RCT) is an injury in adults that causes difficulty in moving, weakness, and pain. Only limited diagnostic tools such as magnetic resonance imaging (MRI) and ultrasound Imaging (UI) systems can be utilized for an RCT diagnosis. Although UI offers comparable performance at a lower cost to other diagnostic instruments such as MRI, speckle noise can occur the degradation of the image resolution. Conventional vision-based algorithms exhibit inferior performance for the segmentation of diseased regions in UI. In order to achieve a better segmentation for diseased regions in UI, deep-learning-based diagnostic algorithms have been developed. However, it has not yet reached an acceptable level of performance for application in orthopedic surgeries. In this study, we developed a novel end-to-end fully convolutional neural network, denoted as Segmentation Model Adopting a pRe-trained Classification Architecture (SMART-CA), with a novel integrated on positive loss function (IPLF) to accurately diagnose the locations of RCT during an orthopedic examination using UI. Using the pre-trained network, SMART-CA can extract remarkably distinct features that cannot be extracted with a normal encoder. Therefore, it can improve the accuracy of segmentation. In addition, unlike other conventional loss functions, which are not suited for the optimization of deep learning models with an imbalanced dataset such as the RCT dataset, IPLF can efficiently optimize the SMART-CA. Experimental results have shown that SMART-CA offers an improved precision, recall, and dice coefficient of 0.604% (+38.4%), 0.942% (+14.0%) and 0.736% (+38.6%) respectively. The RCT segmentation from a normal ultrasound image offers the improved precision, recall, and dice coefficient of 0.337% (+22.5%), 0.860% (+15.8%) and 0.484% (+28.5%), respectively, in the RCT segmentation from an ultrasound image with severe speckle noise. The experimental results demonstrated the IPLF outperforms other conventional loss functions, and the proposed SMART-CA optimized with the IPLF showed better performance than other state-of-the-art networks for the RCT segmentation with high robustness to speckle noise.
Experimental Method for the Measurements and Numerical Investigations of Force Generated on the Rotating Cylinder under Water Flow
Authors: Abramowicz-Gerigk Teresa , Burciu Zbigniew , Jachowski Jacek , Kreft Oskar , Majewski Dawid , Stachurska Barbara , Sulisz Wojciech , Szmytkiewicz Piotr
Published: March 2021

The paper presents the experimental test setup and measurement method of hydrodynamic force generated on the rotating cylinder (rotor) under uniform flow including the free surface effect. The experimental test setup was a unique construction installed in the flume tank equipped with advanced flow generating and measuring systems. The test setup consisted of a bearing mounted platform with rotor drive and sensors measuring the hydrodynamic force. The low length to diameter ratio cylinders were selected as models of bow rotor rudders of a shallow draft river barge. The rotor dynamics was tested for the rotational speeds up to 550 rpm and water current velocity up to 0.85 m/s. The low aspect ratio of the cylinder and free surface effect had significant impacts on the phenomena influencing the generated hydrodynamic force. The effects of the rotor length to diameter ratio, rotational velocity to flow velocity ratio, and the Reynolds number on the lift force were analyzed. The validation of the computational model against experimental results is presented. The results show a similar trend of results for the simulation and experiment.
Induced Magnetic Field-Based Indoor Positioning System for Underwater Environments
Authors: Bian Sizhen , Hevesi Peter , Christensen Leif , Lukowicz Paul
Published: March 2021

Autonomous underwater vehicles (AUV) are seen as an emerging technology for maritime exploration but are still restricted by the availability of short range, accurate positioning methods necessary, e.g., when docking remote assets. Typical techniques used for high-accuracy positioning in indoor use case scenarios, such as systems using ultra-wide band radio signals (UWB), cannot be applied for underwater positioning because of the quick absorption of the positioning medium caused by the water. Acoustic and optic solutions for underwater positioning also face known problems, such as the multi-path effects, high propagation delay (acoustics), and environmental dependency. This paper presents an oscillating magnetic field-based indoor and underwater positioning system. Unlike those radio wave-based positioning modalities, the magnetic approach generates a bubble-formed magnetic field that will not be deformed by the environmental variation because of the very similar permeability of water and air. The proposed system achieves an underwater positioning mean accuracy of 13.3 cm in 2D and 19.0 cm in 3D with the multi-lateration positioning method and concludes the potential of the magnetic field-based positioning technique for underwater applications. A similar accuracy was also achieved for various indoor environments that were used to test the influence of cluttered environment and of cross environment. The low cost and power consumption system is scalable for extensive coverage area and could plug-and-play without pre-calibration.
MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers
Authors: Kang Jaeyong , Ullah Zahid , Gwak Jeonghwan
Published: March 2021

Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.
Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors
Authors: Ward Sayed A. , El-Faraskoury Adel , Badawi Mohamed , Ibrahim Shimaa A. , Mahmoud Karar , Lehtonen Matti , Darwish Mohamed M. F.
Published: March 2021

Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV–40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques.
Geographies of Doing Nothing–Internal Displacement and Practices of Post-Disaster Recovery in Urban Areas of the Kathmandu Valley, Nepal
Authors: Titz Alexandra
Published: March 2021

Disaster-related internal displacement is on the rise in many countries and is increasingly becoming an urban phenomenon. For many people, as in the case of the earthquake disaster 2015 in Nepal, protracted or multiple disaster displacements are a lived reality. While the drivers of displacement are relatively well understood, significant uncertainties remain regarding the factors that trigger prolonged or secondary displacement and impede ending of displacement or achieving durable solutions. The purpose of this article is to illustrate and theorise the discourse of reconstruction and return that shapes experiences, strategies, and policies in order to gain a better understanding of the obstacles to pursuing durable solutions that are still shaping the reality of life for urban internally displaced people (IDPs) in Kathmandu Valley. I use the concepts of ‘fields of practice’ and ‘disaster justice’ to provide insights into the theorisation of the links between social inequality, structural forms of governance, and the reconstruction process itself. Findings demonstrate that the application of these concepts has great potential to expand our understanding of ‘realities of life’ and practices of IDPs, and thus contribute to a more differentiated evidence base for the development and implementation of appropriate disaster risk reduction policies and practices.
Towards a Model for Integrating Informal and Formal Learning for Children in Refugee Camps: The Example of the Lesbos School for Peace
Authors: Huss Ephrat , Ben Asher Smadar , Walden Tsvia , Shahar Eitan
Published: March 2021

The aim of this paper is to describe a unique, bottom-up model for building a school based on humanistic intercultural values in a post-disaster/refugee area. We think that this model will be of use in similar contexts. This single-case study can teach us about the needs of refugee children, as well as provide strategies to reach these needs with limited resources in additional similar contexts. Additionally, this paper will outline a qualitative arts-based methodology to understand and to evaluate refugee children’s lived experience of in-detention camp schools. Our field site is an afternoon school for refugee children operated and maintained by volunteers and refugee teachers. The methodology is a participatory case study using arts-based research, interviews, and observation of a school built for refugee camp children in Lesbos. Participants in this study included the whole school, from children to teachers, to volunteers and managers. The research design was used to inform the school itself, and to outline the key components found to be meaningful in making the school a positive experience. These components could be emulated by similar educational projects and used to evaluate them on an ongoing basis.
Urban Vulnerability Assessment for Pandemic Surveillance—The COVID-19 Case in Bogotá, Colombia
Authors: Prieto Jeisson , Malagón Rafael , Gomez Jonatan , León Elizabeth
Published: March 2021

A pandemic devastates the lives of global citizens and causes significant economic, social, and political disruption. Evidence suggests that the likelihood of pandemics has increased over the past century because of increased global travel and integration, urbanization, and changes in land use with a profound affectation of society–nature metabolism. Further, evidence concerning the urban character of the pandemic has underlined the role of cities in disease transmission. An early assessment of the severity of infection and transmissibility can help quantify the pandemic potential and prioritize surveillance to control highly vulnerable urban areas in pandemics. In this paper, an Urban Vulnerability Assessment (UVA) methodology is proposed. UVA investigates various vulnerability factors related to pandemics to assess the vulnerability in urban areas. A vulnerability index is constructed by the aggregation of multiple vulnerability factors computed on each urban area (i.e., urban density, poverty index, informal labor, transmission routes). This methodology is useful in a-priori evaluation and development of policies and programs aimed at reducing disaster risk (DRR) at different scales (i.e., addressing urban vulnerability at national, regional, and provincial scales), under diverse scenarios of resources scarcity (i.e., short and long-term actions), and for different audiences (i.e., the general public, policy-makers, international organizations). The applicability of UVA is shown by the identification of high vulnerable areas based on publicly available data where surveillance should be prioritized in the COVID-19 pandemic in Bogotá, Colombia.
Examining the Process Behind the Decision of Sports Fans to Attend Sports Matches at Stadiums Amid the SARS-CoV-2 Pandemic: The Case of South Korea
Authors: Jeong Yunduk , Kim Suk-Kyu , Yu Jae-Gu
Published: March 2021

The spread of SARS-CoV-2 has taken a toll on the sports industry worldwide. One of the key challenges for team managers or marketers is to understand how it affects the decision-making process of sports fans. This study examined the process behind the decision of sports fans to attend sports matches at stadiums amid the pandemic using the theory of planned behavior (TPB), which includes the moderating roles of team identification. Responses were obtained from 269 undergraduate and graduate students who had attended a sports match in 2019. Structural equation modeling was performed to evaluate three factors: the attitude toward attending sports matches, subjective norm, and perceived behavioral control on the decision to attend. In addition, a hierarchical regression analysis was conducted to test the moderating effects of team identification. The findings displayed the positive influences on attendance intention from the perspective of (a) attitude, (b) subjective norm and (c) perceived behavioral control. On the other hand, team identification did not have a moderating effect on the relationships among attitude, subjective norm and perceived behavioral control regarding the intention to attend a sport event. These findings suggest that it is important to grasp the role of volitional and non-volitional processes to explain the how the decision to attend sports matches is made.
Design of Multi-Information Fusion Based Intelligent Electrical Fire Detection System for Green Buildings
Authors: Ren Xiaogeng , Li Chunwang , Ma Xiaojun , Chen Fuxiang , Wang Haoyu , Sharma Ashutosh , Gaba Gurjot Singh , Masud Mehedi
Published: March 2021

Building management systems are costly for small- to medium-sized buildings. A massive volume of data is collected on different building contexts by the Internet of Things (IoT), which is then further monitored. This intelligence is integrated into building management systems (BMSs) for energy consumption management in a cost-effective manner. Electric fire safety is paramount in buildings, especially in hospitals. Facility managers focus on fire protection strategies and identify where system upgrades are needed to maintain existing technologies. Furthermore, BMSs in hospitals should minimize patient disruption and be immune to nuisance alarms. This paper proposes an intelligent detection technology for electric fires based on multi-information fusion for green buildings. The system model was established by using fuzzy logic reasoning. The extracted multi-information fusion was used to detect the arc fault, which often causes electrical fires in the low-voltage distribution system of green buildings. The reliability of the established multi-information fusion model was verified by simulation. Using fuzzy logic reasoning and the membership function in fuzzy set theory to solve the uncertain relationship between faults and symptoms is a widely applied method. In order to realize the early prediction and precise diagnosis of faults, a fuzzy reasoning system was applied to analyze the arcs causing electrical fires in the lines. In order to accurately identify the fault arcs that easily cause electrical fires in low-voltage distribution systems for building management, this paper introduces in detail a fault identification method based on multi-information fusion, which can consolidate the complementary advantages of different types of judgment. The results demonstrate that the multi-information fusion method reduces the deficiency of a single criterion in fault arc detection and prevents electrical fires in green buildings more comprehensively and accurately. For the real-time dataset, the data results are presented, showing disagreements among the testing methods.
Influence of Material Selection and Product Design on Automotive Vehicle Recyclability
Authors: He Xiaohui , Su Dongmei , Cai Wenchao , Pehlken Alexandra , Zhang Guofang , Wang Aimin , Xiao Jinsheng
Published: March 2021

From 2008 to 2020, Chinese automobile production and sales have ranked first in the world. The huge production, sales, and ownership of automobiles will inevitably lead to a rapid increase of end-of-life vehicles in the future and a corresponding issue of resource recycling. Based on the analysis of a practical dismantling study and statistics declared by the supplier of 19.5% of components and parts with a weight greater than 0.5 kg from two typical vehicle models from 2011 to 2013, this paper focuses on nonmetallic components and parts, the connection of components and parts materials, and the product life cycle of each stage, to find rational technical solutions, and therefore maximize recyclability and recoverability and achieve sustainable development. On one hand, recycling at each stage for vehicles is considered in the design and development of products. As a result, it is found that the main methods, which are conducive to recycling, are increasing the use ratio of materials that are easy to recycle. In addition, general principles of material selection are summarized. On the other hand, vehicles’ dismantling is considered in the initial stage of product design and methods of structural design are summarized.
Sustainable City Lighting Impact and Evaluation Methodology of Lighting Quality from a User Perspective
Authors: Rozman Cafuta Melita
Published: March 2021

High quality artificial lighting of public places is one of the necessary functions of sustainable cities. It is much more than just providing sufficient light intensity with reduced energy consumption and low environmental impact. The solution to this challenge is not only technological, but also requires the consideration of the subjective approach, such as psychological, sociological, and aesthetic-functional. The article presents the SEC (Suitable for everyone, Environmentally-accepted, Cost-effective) methodology and model, which are placed in the context of sustainable city lighting. A holistic assessment approach of user environmental perception, priorities, and spatial responses is required to evaluate the impact of artificial night illumination. Lighting efficiency was expressed as the correspondence between environmental perception and spatial utilization during day and night. Ten sites with different urban background and illumination characteristics were selected. It was assumed that artificial night lighting influences the perception, priorities, and responsiveness of users. The validity of this argument was demonstrated at a relevant statistical level of p <0.05. The research results provided useful information about the current state, which is needed to prepare a sustainable lighting strategy, and could be compared between different or similar sites. Based on the obtained results, it can be concluded that the artificial lighting of urban open spaces is of high quality when the difference in the spatial perception and spatial utilization between day and night is as small as possible.
Differences in Occurrence of Unethical Business Practices in a Post-Transitional Country in the CEE Region: The Case of Slovakia
Authors: Lašáková Anna , Remišová Anna , Bajzíková Ľubica
Published: March 2021

This study aims to contribute to the understanding of unethical practices in business and asks whether certain types of organizations are considerably more exposed to unethical business practices than others are. Drawing from the tenets of institutional theory, the paper investigates the occurrence of unethical practices in different organizational “fields”, namely the industry sector (with focus on Finance and Construction), company membership in professional networks, company ownership (public/private), and company age. The method of stratified random sampling by proportional allocation is used to establish the sample (n = 1295), composed mostly of company owners and higher managers. Results show that, in general, the industry sector, membership in professional networks, and company age are associated with significant variance in the perceived incidence of unethical practices, whereas company ownership has no significant effect in this regard. More specifically, the construction sector is significantly more exposed to unethical practices than other sectors in the sample, while the finance sector is not. Companies with membership in professional networks report a significantly lower occurrence of unethical practices. Young companies are significantly more exposed than their more mature counterparts; however, here the effect of company size must be accounted for. The research was conducted in one of the former CEE block countries—Slovakia. Given their common communist past and comparable peripeties with the transition process, these findings might be useful for understanding business ethics issues in a wider context of the CEE region.
Open Innovation with Relational Capital, Technological Innovation Capital, and International Performance in SMEs
Authors: Ryu Dongwoo , Baek Kwang Ho , Yoon Junghyun
Published: March 2021

The importance of international markets is constantly emphasized for small and medium enterprises(SMEs). In previous studies, technological innovation capabilities were emphasized as a factor that enables SMEs to compete in the international market. To this end, SMEs need to cooperate with external partners to strengthen their technological innovation capabilities to thus improve their international performance. With the perspective view of open innovation, this research explores the effects of relational capital and technological innovation capability on international performance, with a particular focus on the moderating effect of alliance proactiveness. Building on previous literature regarding internationalization, technological innovation, and alliance proactiveness, research hypotheses were developed and tested using data collected from 175 SMEs. A hierarchical regression analysis was applied. The analysis showed that, first, relational capital had a significant effect on the technological innovation capability. Second, technological innovation capability has a significant influence on the international performance. Third, technological innovation capability mediated the relationship between relational capital and international performance. Finally, alliance proactiveness was found to moderate the relationship between technological innovation capability and international performance. The key research findings imply that relational capital and alliance proactiveness are the key factors of international performance, as they improved the development of the technological innovation capability.
A Diverse Organizational Culture and Its Impact on Innovative Work Behavior of Municipal Employees
Authors: Jankelová Nadežda , Skorková Zuzana , Joniaková Zuzana , Némethová Ildikó
Published: March 2021

Representatives of municipalities and their management are invited to develop new managerial skills in order to implement modern approaches to management. Diversity management means using the diverse potential of employees to meet the innovation and development goals of the offices. The aim of our research is to examine the context of beliefs, values, and rules of diverse organizational culture (DOC) and innovative work behavior (IWB) through the mediation effect of teamwork climate, transmitting and moderating this direct effect in the environment of Slovak municipalities. A mediation model was applied using the Sobel test for the mediation effect. A series of regression analyses were used to identify the proposed hypotheses. The mediation effect was correctly estimated. The direct effect of the beliefs, values, and rules of DOC has enhanced IWB. However, a certain part of them that leads through teamwork climate slightly dampens IWB.
Effect of Soil Characteristics on Arsenic Accumulation in Phytolith of Gramineae (Phragmites japonica) and Fern (Thelypteris palustris) Near the Gilgok Gold Mine
Authors: Min Hyun-Gi , Kim Min-Suk , Kim Jeong-Gyu
Published: March 2021

In South Korea, most metal mines were abandoned and caused contamination for more than 30 years. Even the soil is highly contaminated with trace elements, plants still grow in the area and can affect the contamination. Phytolith is amorphous silica in the plant body. Phytolith is resistant to decomposition, and the stabilization of carbon, nutrients, and toxic substances accumulated in the phytolith is being studied. In this study, the Gilgok gold mine, which is contaminated with arsenic was selected as the research site. We selected Phragmites japonica and Thelypteris palustris as targets for the analysis of arsenic accumulation in plants and phytolith. Plants accumulate more phytolith at the riverside. The higher water content of soil increased the Arsenic (As) concentration in the frond of the T. palustris. Soil available silicon (Si) did not affect phytolith accumulation but increased As accumulation in the plant and phytolith. The research result showed that P. japonica and T. palustris have the ability to accumulate As in phytolith and the accumulation can be changed with soil characteristics and plant species. This As accumulation in phytolith can affect plant tolerance in contaminated areas and change the As availability in the soil. The result of the research can be used as a database to build a sustainable environment.
A Survey of Technologies and Recent Developments for Sustainable Smart Cycling
Authors: Oliveira Franklin , Nery Dilan , Costa Daniel G. , Silva Ivanovitch , Lima Luciana
Published: March 2021

Among the problems resulted from the continuous urbanization process, inefficient urban mobility and high pollution levels have been complex challenges that have demanded a lot of public investments and research efforts. Recently, some alternative transportation means have been leveraged as sustainable options for such challenges, which has brought bicycles to a more relevant setting. Besides the sometimes obvious benefits of adopting bikes for transportation, technologies around the Internet of Things (IoT) paradigm have been advocated as important supportive tools to boost smart cycling initiatives. Actually, new technologies can be exploited to improve the efficiency of bike paths and parking spots, while reducing accidents and enhancing the cycling experience of the users. Therefore, in this highly vibrating scenario, this article facilitates the understating of current research trends and promising developments, surveying and classing recent works. Since there is a global interest for the promotion of cleaner and more sustainable solutions in large cities, this survey can be valuable when supporting new developments in this highly relevant research area.
Identifying the Potential for Partial Integration of Private and Public Transportation
Authors: Bojic Iva , Kondor Dániel , Tu Wei , Mai Ke , Santi Paolo , Ratti Carlo
Published: March 2021

In this paper, we explore the potential for a partial integration of private and public transportation as provided by taxi and bus services on the level of individual trips. While these modes are typically separated for economic reasons, in the future with shared Autonomous Vehicles (AVs) providing cheap and efficient transportation services, such distinctions will blur. Consequently, optimization based on real-time data will allow exploiting parallels in demand in a dynamic way, such as the proposed approach in the current work. New operational and pricing strategies will then need to be considered, along with regulatory tools to limit congestion, encourage sharing and provide services in a more sustainable way by utilizing a dynamic landscape of urban transportation. In the current work, we evaluate existing parallels between individual bus and taxi trips in two Asian cities and show how exploiting these synergies could form the basis of achieving some of the above goals in the future.
Satellite-Based Meteorological and Agricultural Drought Monitoring for Agricultural Sustainability in Sri Lanka
Authors: Alahacoon Niranga , Edirisinghe Mahesh , Ranagalage Manjula
Published: March 2021

For Sri Lanka, as an agricultural country, a methodical drought monitoring mechanism, including spatial and temporal variations, may significantly contribute to its agricultural sustainability. Investigating long-term meteorological and agricultural drought occurrences in Sri Lanka and assessing drought hazard at the district level are the main objectives of the study. Standardized Precipitation Index (SPI), Rainfall Anomaly Index (RAI), and Vegetation Health Index (VHI) were used as drought indicators to investigate the spatial and temporal distribution of agriculture and meteorological droughts. Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data from 1989 to 2019 was used to calculate SPI and RAI. MOD13A1 and MOD11A2 data from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 to 2019, were used to generate the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). Agricultural drought monitoring was done using VHI and generated using the spatial integration of VCI and TCI. Thus, various spatial data analysis techniques were extensively employed for vector and raster data integration and analysis. A methodology has been developed for the drought declaration of the country using the VHI-derived drought area percentage. Accordingly, for a particular year, if the country-wide annual extreme and severe drought area percentage based on VHI drought classes is ≥30%, it can be declared as a drought year. Moreover, administrative districts of Sri Lanka were classified into four hazard classes, No drought, Low drought, Moderate drought, and High drought, using the natural-beak classification scheme for both agricultural and meteorological droughts. The findings of this study can be used effectively by the relevant decision-makers for drought risk management (DRM), resilience, sustainable agriculture, and policymaking.
Deregulation in the Energy Sector and Its Economic Effects on the Power Sector: A Literature Review
Authors: Necoechea-Porras Pablo David , López Asunción , Salazar-Elena Juan Carlos
Published: March 2021

Energy reforms play an essential role in technological change as they aim to contribute to an open market: costs reduction, competitiveness, and technology development. This article seeks to assess the impact and effect of reforms on the energy sector. The article’s objective is to evaluate the process of deregulation policies and their micro and macroeconomic effects on the energy sector, and specifically on electricity, by analyzing literature related to electricity reforms. Further, the article intends to explore the impacts of deregulation on power pricing, power market, electricity accessibility, innovation, and competitiveness. Another objective of the article is to analyze the role played by various stakeholders in the deregulation policies, including the government, national entities like states, the private sector, and consumers. The article identified ways to improve the economic impacts of deregulation policies in the energy sector. After a systemic review of specialized articles regarding their theoretical approach, results showed a positive relationship between reform and innovation, competitiveness, opening-up of the market, technology, and price changes. Although deregulation measures aimed to reduce the consumers’ electricity cost, the changes in power prices were achievable only in the long-term and not in the short-term. Additionally, government regulators and stakeholders participated in implementing various measures to ensure that deregulation achieved its primary objective of reducing power prices. Such efforts include developing divestiture policies and implementing rate cuts.
Applicability of TiO2 Penetration Method to Reduce Particulate Matter Precursor for Hardened Concrete Road Structures
Authors: Lee Seung Woo , Ahn Hui Rak , Kim Kyoung Su , Kim Young Kyu
Published: March 2021

Recently, air pollution is increasing sharply in Korea, caused by fine particulate matter. Nitrogen oxides (NOx) are particulate matter precursors that significantly contribute to air pollution. They are transmitted into the atmosphere by a large amount, especially in high-volume traffic areas. This pollutant is particularly harmful. Therefore, there are increasing efforts focused on NOx removal from the air. As the photocatalytic reaction of titanium dioxide (TiO2) is the mechanism that eliminates NOx, the ultraviolet (UV) rays in sunlight and TiO2 in existing concrete structure need to be contacted for reaction process. Generally, TiO2 concrete is produced by mixing the concrete binder with TiO2. However, a significant amount of TiO2 in the concrete cannot be exposed to air pollutants or UV. Additionally, this technique may not be applicable to existing structures. Therefore, an alternative method that utilizes surface penetration agents is used to add TiO2 to the concrete surface of the structures. This proposed method may not only be economical but also applicable to various types of structures. The applicability of the TiO2 penetration application method to existing concrete road structures for reducing NOx is the purpose of this study. The penetration depth of TiO2 particles was measured using scanning electron microscopy (SEM) combined with energy dispersive analysis of X-rays (EDAX). Additionally, the NOx removal efficiency was evaluated using the NOx analyzing system. The results of this study showed that the TiO2 penetration method was advantageous in removing NOx effectively and securing economic feasibility.
Ultrasound-Assisted Vacuum Impregnation as a Strategy for the Management of Potato By-Products
Authors: Mierzwa Dominik , Szadzińska Justyna , Radziejewska-Kubzdela Elżbieta , Biegańska-Marecik Róża
Published: March 2021

One of the most important problems of the modern world is food wastage. The issue occurs at every stage of the food chain, requiring new sustainable production and processing technologies. The processing of production waste and making it a wholesome ingredient may be a good opportunity to promote more sustainable development. This study analyzes the process of enrichment of model by-product (irregular potatoes cubes) with a functional compound (ascorbic acid) through vacuum impregnation, with two experiments on variants of the process, standard (VI) and ultrasonic-assisted (UVI). The research covers complete processing, including the stage of preserving impregnated products by convective drying. The analysis includes the impregnation efficiency, drying kinetics, and energy consumption, and selected quality parameters of the material, namely color and water activity. Based on the results, ultrasound increased the impregnation efficiency, but the quantitative effect depends on the application period. Ultrasound had a positive effect on the kinetics and energy consumption of convective drying. Ultrasound did not reduce quality. The proposed technology may be useful during the processing of by-products.
Business Model Adaptation in Spanish Sports Clubs According to the Perceived Context: Impact on the Social Cause Performance
Authors: Escamilla-Fajardo Paloma , Alguacil Mario , García-Pascual Fernando
Published: March 2021

The COVID-19 pandemic has generated an uncertain and changing context that has greatly affected the development and sustainability of all sports organizations. In this hostile context, adaptation of the business model (BMA) can be a strategic alternative for sports clubs. A change in value proposition, change in target market, and change in value delivery are some of the aspects considered in this study in relation to the social performance variable. It is well-known that sports clubs have a marked social function, therefore analyzing their social cause performance is of great importance in modern society. However, there are still few studies that have analyzed BMA in relation to this type of variable in sports clubs. Therefore, the objectives of this study were to find out what perceptions of the environment can influence the BMA, and to investigate what influence the BMA has on the social performance of this type of entity. To this end, 145 Spanish sports clubs were analyzed during a period of limitations and restrictions arising from COVID-19. The results show that there are differences in BMA according to the perceived impact of the crisis and the perception of risk. In addition, BMA predicts the social performance of sports clubs, and this study provides new information for academics and professionals. Practical implications and management proposals were developed based on the results, and conclusions drawn.
Innovative Techniques for Landscape Recovery after Clay Mining under Mediterranean Conditions
Authors: Turrión Diana , Morcillo Luna , Alloza José Antonio , Vilagrosa Alberto
Published: March 2021

Open-pit mining results in profound modifications at different environmental scales that may persist for very long time periods, or even indefinitely. Considerable research efforts in mine reclamation strategies have been made, although reclamation failures are still common. In dry climates, such as in the Mediterranean Basin, successful actions may depend on features related to proper species selection and restoration techniques, which may substantially contribute to provide substrate stability and facilitate the regeneration of the main ecological processes. In this context, we developed the TECMINE case-study aimed to evaluate the feasibility and suitability of innovative restoration practices applied to clay-mine reclamation under Mediterranean conditions. The restoration strategy was designed at the landscape level with two main approaches: the recovery of natural geomorphology shapes and ecological restoration, including vegetation recovery and soil quality, based on proper reference ecosystems. After the geomorphological land remodeling, a combination of several innovative restoration techniques was implemented to reclaim plant communities and ecosystem functioning. These techniques involved: (i) accurate species selection according to microhabitat characteristics; (ii) high-quality plant production; (iii) surface remodeling to improve substrate stabilization; and (iv) implementing rainfall collection to enhance resources availability, soil fertility improvement and the amelioration of abiotic conditions for seedlings. Finally, we developed a monitoring program to assess the success of the implemented restoration techniques over time. The application of these innovative techniques has reported interesting results and represents a step forward in the improvement of mine restoration under Mediterranean climate.