bluuebunny/crossref_metadata_2025 huggingface dataset:
Gradient Boosting Machine and Object-Based CNN for Land Cover Classification
Authors: Bui Quang-Thanh , Chou Tien-Yin , Hoang Thanh-Van , Fang Yao-Min , Mu Ching-Yun , Huang Pi-Hui , Pham Vu-Dong , Nguyen Quoc-Huy , Anh Do Thi Ngoc , Pham Van-Manh , Meadows Michael E.
Published: July 2021

In regular convolutional neural networks (CNN), fully-connected layers act as classifiers to estimate the probabilities for each instance in classification tasks. The accuracy of CNNs can be improved by replacing fully connected layers with gradient boosting algorithms. In this regard, this study investigates three robust classifiers, namely XGBoost, LightGBM, and Catboost, in combination with a CNN for a land cover study in Hanoi, Vietnam. The experiments were implemented using SPOT7 imagery through (1) image segmentation and extraction of features, including spectral information and spatial metrics, (2) normalization of attribute values and generation of graphs, and (3) using graphs as the input dataset to the investigated models for classifying six land cover classes, namely House, Bare land, Vegetation, Water, Impervious Surface, and Shadow. The results show that CNN-based XGBoost (Overall accuracy = 0.8905), LightGBM (0.8956), and CatBoost (0.8956) outperform the other methods used for comparison. It can be seen that the combination of object-based image analysis and CNN-based gradient boosting algorithms significantly improves classification accuracies and can be considered as alternative methods for land cover analysis.
Compact Measurement of the Optical Power in High-Power LED Using a Light-Absorbent Thermal Sensor
Authors: Kim You-Young , Joo Jae-Young , Kim Jong-Min , Lee Sun-Kyu
Published: July 2021

LED (Light-Emitting Diode) presents advantages such as luminescence, reliability, durability compared with conventional lighting. It has been widely applied for life, healthcare, smart farm, industry, and lighting from indoor to the automotive headlamp. However, the LED is vulnerable to thermal damage originated from the high junction temperature, especially in high power applications. Hence, it requires precise qualification on the optical power and the junction temperature from the pilot line to secure reliability. In this study, the photo-thermal sensor is proposed by employing a sheet-type thermocouple composed of photo-absorbent metal film and thermocouple. This sensor aims low-cost qualification in pilot line for high-power luminous devices and optical monitoring of costly luminaire such as automobile LED headlamp. The sensor is designed to detect the increased temperature response of LED hot spots from the transferred thermal power and absorbed optical power. The temperature response of each sheet-type thermocouple is utilized as a signal output of the absorbed optical power and hot spot temperature based on the introduced sensor equation. The proposed thermal sensor is evaluated by comparing the experiment with the measured reference value from the integrating sphere and the attached thermocouple at a junction. The experiment result reveals 3% of the maximum error for the optical power of 645 mW.
A Lightweight Prototype of a Magnetometric System for Unmanned Aerial Vehicles
Authors: Pisciotta Antonino , Vitale Giovanni , Scudero Salvatore , Martorana Raffaele , Capizzi Patrizia , D'Alessandro Antonino
Published: July 2021

Detection of the Earth’s magnetic field anomalies is the basis of many types of studies in the field of earth sciences and archaeology. These surveys require different ways to carry out the measures but they have in common that they can be very tiring or expensive. There are now several lightweight commercially available magnetic sensors that allow light-UAVs to be equipped to perform airborne measurements for a wide range of scenarios. In this work, the realization and functioning of an airborne magnetometer prototype were presented and discussed. Tests and measures for the validation of the experimental setup for some applications were reported. The flight sessions, appropriately programmed for different types of measurements, made it possible to evaluate the performance of this detection methodology, highlighting the advantages and drawbacks or limitations and future developments. From the results obtained it was possible to verify that the measurement system is capable of carrying out local and potentially archaeological magnetometric measurements with the necessary precautions.
Jitter Detection and Image Restoration Based on Generative Adversarial Networks in Satellite Images
Authors: Wang Zilin , Zhang Zhaoxiang , Dong Limin , Xu Guodong
Published: July 2021

High-resolution satellite images (HRSIs) obtained from onboard satellite linear array cameras suffer from geometric disturbance in the presence of attitude jitter. Therefore, detection and compensation of satellite attitude jitter are crucial to reduce the geopositioning error and to improve the geometric accuracy of HRSIs. In this work, a generative adversarial network (GAN) architecture is proposed to automatically learn and correct the deformed scene features from a single remote sensing image. In the proposed GAN, a convolutional neural network (CNN) is designed to discriminate the inputs, and another CNN is used to generate so-called fake inputs. To explore the usefulness and effectiveness of a GAN for jitter detection, the proposed GANs are trained on part of the PatternNet dataset and tested on three popular remote sensing datasets, along with a deformed Yaogan-26 satellite image. Several experiments show that the proposed model provides competitive results. The proposed GAN reveals the enormous potential of GAN-based methods for the analysis of attitude jitter from remote sensing images.
A Four-Channel Low-Noise Readout IC for Air Flow Measurement Using Hot Wire Anemometer in 0.18 μm CMOS Technology
Authors: Nam Kyeongsik , Kim Hyungseup , Kwon Yongsu , Choi Gyuri , Kim Taeyup , Kim Chulhong , Cho Dongil , Lee Junghoon , Ko Hyoungho
Published: July 2021

Air flow measurements provide significant information required for understanding the characteristics of insect movement. This study proposes a four-channel low-noise readout integrated circuit (IC) in order to measure air flow (air velocity), which can be beneficial to insect biomimetic robot systems that have been studied recently. Instrumentation amplifiers (IAs) with low-noise characteristics in readout ICs are essential because the air flow of an insect’s movement, which is electrically converted using a microelectromechanical systems (MEMS) sensor, generally produces a small signal. The fundamental architecture employed in the readout IC is a three op amp IA, and it accomplishes low-noise characteristics by chopping. Moreover, the readout IC has a four-channel input structure and implements an automatic offset calibration loop (AOCL) for input offset correction. The AOCL based on the binary search logic adjusts the output offset by controlling the input voltage bias generated by the R-2R digital-to-analog converter (DAC). The electrically converted air flow signal is amplified using a three op amp IA, which is passed through a low-pass filter (LPF) for ripple rejection that is generated by chopping, and converted to a digital code by a 12-bit successive approximation register (SAR) analog-to-digital converter (ADC). Furthermore, the readout IC contains a low-dropout (LDO) regulator that enables the supply voltage to drive digital circuits, and a serial peripheral interface (SPI) for digital communication. The readout IC is designed with a 0.18 μm CMOS process and the current consumption is 1.886 mA at 3.3 V supply voltage. The IC has an active area of 6.78 mm2 and input-referred noise (IRN) characteristics of 95.4 nV/√Hz at 1 Hz.
Vibration Property of a Cryogenic Optical Resonator within a Pulse-Tube Cryostat
Authors: Ye Yanxia , He Leilei , Sun Yunlong , Zhang Fenglei , Wang Zhiyuan , Lu Zehuang , Zhang Jie
Published: July 2021

Cryogenic ultrastable laser cavities push laser stability to new levels due to their lower thermal noise limitation. Vibrational noise is one of the major obstacles to achieve a thermal-noise-limited cryogenic ultrastable laser system. Here, we carefully analyze the vibrational noise contribution to the laser frequency. We measure the vibrational noise from the top of the pulse-tube cryocooler down to the experiment space. Major differences emerge between room and cryogenic temperature operation. We cooled a homemade 6 cm sapphire optical resonator down to 3.4 K. Locking a 1064 nm laser to the resonator, we measure a frequency stability of 1.3×10−15. The vibration sensitivities change at different excitation frequencies. The vibrational noise analysis of the laser system paves the way for in situ accurate evaluation of vibrational noise for cryogenic systems. This may help in cryostat design and cryogenic precision measurements.
Efficient Source Camera Identification with Diversity-Enhanced Patch Selection and Deep Residual Prediction
Authors: Liu Yunxia , Zou Zeyu , Yang Yang , Law Ngai-Fong Bonnie , Bharath Anil Anthony
Published: July 2021

Source camera identification has long been a hot topic in the field of image forensics. Besides conventional feature engineering algorithms developed based on studying the traces left upon shooting, several deep-learning-based methods have also emerged recently. However, identification performance is susceptible to image content and is far from satisfactory for small image patches in real demanding applications. In this paper, an efficient patch-level source camera identification method is proposed based on a convolutional neural network. First, in order to obtain improved robustness with reduced training cost, representative patches are selected according to multiple criteria for enhanced diversity in training data. Second, a fine-grained multiscale deep residual prediction module is proposed to reduce the impact of scene content. Finally, a modified VGG network is proposed for source camera identification at brand, model, and instance levels. A more critical patch-level evaluation protocol is also proposed for fair performance comparison. Abundant experimental results show that the proposed method achieves better results as compared with the state-of-the-art algorithms.
Frequency Disentanglement Distillation Image Deblurring Network
Authors: Liu Yiming , Guo Jianping , Yang Sen , Liu Ting , Zhou Hualing , Liang Mengzi , Li Xi , Xu Dahong
Published: July 2021

Due to the blur information and content information entanglement in the blind deblurring task, it is very challenging to directly recover the sharp latent image from the blurred image. Considering that in the high-dimensional feature map, blur information mainly exists in the low-frequency region, and content information exists in the high-frequency region. In this paper, we propose a encoder–decoder model to realize disentanglement from the perspective of frequency, and we named it as frequency disentanglement distillation image deblurring network (FDDN). First, we modified the traditional distillation block by embedding the frequency split block (FSB) in the distillation block to separate the low-frequency and high-frequency region. Second, the modified distillation block, we named frequency distillation block (FDB), can recursively distill the low-frequency feature to disentangle the blurry information from the content information, so as to improve the restored image quality. Furthermore, to reduce the complexity of the network and ensure the high-dimension of the feature map, the frequency distillation block (FDB) is placed on the end of encoder to edit the feature map on the latent space. Quantitative and qualitative experimental evaluations indicate that the FDDN can remove the blur effect and improve the image quality of actual and simulated images.
Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data
Authors: Peppes Nikolaos , Alexakis Theodoros , Adamopoulou Evgenia , Demestichas Konstantinos
Published: July 2021

In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle’s condition as well as the driver’s behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution of Information and Communication Technologies (ICT) push the transportation domain towards a new more intelligent and efficient era. The reduction of CO2 emissions and the minimization of the environmental footprint is, undeniably, of utmost importance for the protection of the environment. In this light, it is widely acceptable that the driving behaviour is directly associated with the vehicle’s fuel consumption and gas emissions. Thus, given the fact that, nowadays, vehicles are equipped with sensors that can collect a variety of data, such as speed, acceleration, fuel consumption, direction, etc. is more feasible than ever to put forward solutions which aim not only to monitor but also improve the drivers’ behaviour from an environmental point of view. The approach presented in this paper describes a holistic integrated platform which combines well-known machine and deep learning algorithms together with open-source-based tools in order to gather, store, process, analyze and correlate different data flows originating from vehicles. Particularly, data streamed from different vehicles are processed and analyzed with the utilization of clustering techniques in order to classify the driver’s behaviour as eco-friendly or not, followed by a comparative analysis of supervised machine and deep learning algorithms in the given labelled dataset.
Spatially Resolved Experimental Modal Analysis on High-Speed Composite Rotors Using a Non-Contact, Non-Rotating Sensor
Authors: Lich Julian , Wollmann Tino , Filippatos Angelos , Gude Maik , Czarske Juergen , Kuschmierz Robert
Published: July 2021

Due to their lightweight properties, fiber-reinforced composites are well suited for large and fast rotating structures, such as fan blades in turbomachines. To investigate rotor safety and performance, in situ measurements of the structural dynamic behaviour must be performed during rotating conditions. An approach to measuring spatially resolved vibration responses of a rotating structure with a non-contact, non-rotating sensor is investigated here. The resulting spectra can be assigned to specific locations on the structure and have similar properties to the spectra measured with co-rotating sensors, such as strain gauges. The sampling frequency is increased by performing consecutive measurements with a constant excitation function and varying time delays. The method allows for a paradigm shift to unambiguous identification of natural frequencies and mode shapes with arbitrary rotor shapes and excitation functions without the need for co-rotating sensors. Deflection measurements on a glass fiber-reinforced polymer disk were performed with a diffraction grating-based sensor system at 40 measurement points with an uncertainty below 15 μrad and a commercial triangulation sensor at 200 measurement points at surface speeds up to 300 m/s. A rotation-induced increase of two natural frequencies was measured, and their mode shapes were derived at the corresponding rotational speeds. A strain gauge was used for validation.
Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy
Authors: Sarjaš Andrej , Pongrac Blaž , Gleich Dušan
Published: July 2021

This paper presents an automatic classification of plastic material’s inorganic pigment using terahertz spectroscopy and convolutional neural networks (CNN). The plastic materials were placed between the THz transmitter and receiver, and the acquired THz signals were classified using a supervised learning approach. A THz frequency band between 0.1–1.2 THz produced a one-dimensional (1D) vector that is almost impossible to classify directly using supervised learning. This paper proposes a novel pre-processing of 1D THz data that transforms 1D data into 2D data, which are processed efficiently using a convolutional neural network. The proposed pre-processing algorithm consists of four steps: peak detection, envelope extraction, and a down-sampling procedure. The last main step introduces the windowing with spectrum dilatation that reorders 1D data into 2D data that can be considered as an image. The spectrum dilation techniques ensure the classifier’s robustness by suppressing measurement bias, reducing the complexity of the THz dataset with negligible loss of accuracy, and speeding up the network classification. The experimental results showed that the proposed approach achieved high accuracy using a CNN classifier, and outperforms 1D classification of THz data using support vector machine, naive Bayes, and other popular classification algorithms.
Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review
Authors: Kostrzewski Mariusz , Melnik Rafał
Published: July 2021

Condition monitoring of rail transport systems has become a phenomenon of global interest over the past half a century. The approaches to condition monitoring of various rail transport systems—especially in the context of rail vehicle subsystem and track subsystem monitoring—have been evolving, and have become equally significant and challenging. The evolution of the approaches applied to rail systems’ condition monitoring has followed manual maintenance, through methods connected to the application of sensors, up to the currently discussed methods and techniques focused on the mutual use of automation, data processing, and exchange. The aim of this paper is to provide an essential overview of the academic research on the condition monitoring of rail transport systems. This paper reviews existing literature in order to present an up-to-date, content-based analysis based on a coupled methodology consisting of bibliometric performance analysis and systematic literature review. This combination of literature review approaches allows the authors to focus on the identification of the most influential contributors to the advances in research in the analyzed area of interest, and the most influential and prominent researchers, journals, and papers. These findings have led the authors to specify research trends related to the analyzed area, and additionally identify future research agendas in the investigation from engineering perspectives.
The Fragile Axes of Life: A Capability Approach Perspective towards Graduates’ Education–Job Mismatches and Subjective Well-Being
Authors: Ilieva-Trichkova Petya , Boyadjieva Pepka
Published: July 2021

Using the capability approach as a theoretical framework, this article aims to: (1) explore how subjective individual well-being differs among higher education graduates and especially to what extent it is associated with graduates’ vertical education–job mismatches; (2) reveal the embeddedness of the link between graduates’ vertical education–job mismatches and subjective well-being in different socio-economic contexts; and (3) outline some policy implications of the analysis undertaken. It argues that vertical education–job mismatch among graduates has an important influence on experiences of the benefits that come from higher education. By analysing micro-level data from the European Social Survey, carried out in 2012 and macro-level data for 24 European countries via descriptive statistics and multilevel regression, the study shows that education–job mismatch is associated with capability deprivation, as graduates who are vertically mismatched have less interest in what they are doing, feel less autonomous and competent, and are less confident that they are leading a meaningful life or being treated with respect by others in comparison to those graduates who are employed in jobs which correspond to their level of education. The article also provides evidence that the association between graduates’ education–job mismatches and individual subjective well-being is embedded in different socio-economic contexts.
Social Security Enrollment as an Indicator of State Fragility and Legitimacy: A Field Experiment in Maghreb Countries
Authors: Merouani Walid , El Moudden Claire , Hammouda Nacer Eddine
Published: July 2021

State legitimacy and effectiveness can be observed in the state’s approach to delivering welfare to citizens, thus mitigating social grievances and avoiding conflicts. Social security systems in the Maghreb countries are relatively similar in their architecture and aim to provide social insurance to all the workers in the labor market. However, they suffer from the same main problem: a low rate of enrollment of workers. Many workers (employees and self-employed) work informally without any social security coverage. The issue of whether informal jobs are chosen voluntarily by workers or as a strategy of last resort is controversial. Many authors recognize that the informal sector is heterogeneous and assume that it is made up of (1) workers who voluntarily choose it, and (2) others who are pushed into it because of entry barriers to the formal sector. The former assumption tells us much about state legitimacy/attractiveness, and the latter is used to inform state effectiveness in delivering welfare. Using the Sahwa survey and discrete choice models, this article confirms the heterogeneity of the informal labor market in three Maghreb countries: Algeria, Morocco, and Tunisia. Furthermore, this article highlights the profiles of workers who voluntarily choose informality, an aspect that is missing from previous studies. Finally, this article proposes policy recommendations in order to extend social security to informal workers and to include them in the formal labor market.
Knowledge Mapping of Machine Learning Approaches Applied in Agricultural Management—A Scientometric Review with CiteSpace
Authors: Zhang Jingyi , Liu Jiaxin , Chen Yaqi , Feng Xiaochun , Sun Zilai
Published: July 2021

With the continuous development of the Internet of Things, artificial intelligence, big data technology, and intelligent agriculture have become hot topics in agricultural science and technology research. Machine learning is one of the core topics in artificial intelligence, and its application has penetrated every aspect of human social life. In modern agricultural intelligent management and decision making, machine learning plays an important role in crop classification, crop disease and insect pest prediction, agricultural product price prediction, and other aspects of management and decision-making processes in agriculture. To detect and recognize the latest research developing features in a quantitative and visual way, and based on machine learning methods in agricultural management, the authors of this paper used CiteSpace bibliometric methods to analyze relevant studies on the development process and hot spots. High-value references, productive authors, country and institution distributions, journal visualizations, research topics, and emerging trends were reviewed and analyzed. According to the keyword visualization and high-value references, machine learning approaches focus on sustainable agriculture, water resources, remote sensing, and machine learning methods. The research mainly focuses on six topics: learning technology, land environment, reference evapotranspiration, decision support systems for river geography, soil management, and winter wheat, while learning technology has been the most popular in recent years.
Sharing Leftover Food with Strangers via Social Media: A Value Perspective Based on Beliefs-Values-Behavior Framework
Authors: Hsieh Ying-Jiun , Yang Lin Shu-Min , Huang Lan-Ying
Published: July 2021

An emergent effort to reduce wasted food is to share uneaten food with others via social media. However, the following question arises: Are those unknown people willing to take my leftovers? Few studies address the above question. Hence, developing a comprehensive model that explains the acceptance of leftover food from strangers (LFFS) via social media warrants research attention. Considering the dual roles LFFS recipients play—namely, both peer-to-peer (P2P) technology user and service consumer—the study embraces diverse motivational factors across research disciplines to explain LFFS acceptance. Drawing on technology and marketing research, the study develops a value-based model to investigate consumer acceptance of LFFS via social media. The study examines the effects of two prominent consumers’ beliefs (cognitive interaction and affective trust) on their context-specific value perception (conditional and epistemic values) toward LFFS, and the impact of these perceived values on the acceptance of the leftover food from strangers. The study employed a two-stage data collection approach and collected 663 usable questionnaire packets from two major metropolitan areas in Taiwan. Using a Structural Equation Model (IBM SPSS Amos) to analyze the data, the results indicate that trust and interactivity relate positively to consumer perceived value (both conditional and epistemic) of LFFS. Furthermore, both conditional and epistemic values mediate the relationships between the proposed consumer beliefs and LFFS acceptance. The research helps create a sustainable society as sharing uneaten food with other unknown social community members provides a connected, diverse, and sustainable life.
Navigating Online Language Teaching in Uncertain Times: Challenges and Strategies of EFL Educators in Creating a Sustainable Technology-Mediated Language Learning Environment
Authors: Choi Leejin , Chung Sunjoo
Published: July 2021

With the spread of COVID-19 worldwide, teaching and learning have occurred remotely and on digital platforms. An abrupt transition to online education, however, has posited unprecedented challenges for educators, who have been forced to adjust to remote learning with little to no time to prepare. Focusing on the case of an English language program in South Korea, this case study examines the challenges and strategies that were emerging in the crisis-prompted online language learning and teaching context. In particular, this case study focuses on investigating what types of strategies English as-a foreign language (EFL) instructors with little prior experience teaching online used to create a sustainable and authentic technology-mediated language learning environment, and how they motivated language learners to actively participate in sustainable language development and use. Findings provide educators and administrators who have little to no experience teaching online with practical suggestions and ideas to consider. They can use these concepts to adapt their lesson plans to online platforms and design and deliver high-quality lessons that ensure students feel connected to their learning process and have sustainable language learning experiences.
Does Retail Food Diversity in Urban Food Environments Influence Consumer Diets?
Authors: Chege Christine G. Kiria , Wanyama Rosina , Lundy Mark , Nguru Wilson , Jäger Matthias
Published: July 2021

The food environment influences consumer diets in significant yet underexplored ways. In this study, we assess the way in which the Nairobi urban food environment—availability, accessibility, affordability, desirability, convenience and marketing—influences the dietary choices and quality of poor urban consumers, by combining market-level diversity scores (MLDS) with household and individual data collected from resource-poor (slum) neighbourhoods in Nairobi, Kenya. We find that urban-poor settings are characterized by a variety of food retail venues, including informal markets such as kiosks, mom-and-pop shops and tabletop vendors, as well as modern retail outlets such as supermarkets. Most of these food outlets predominantly sell unhealthy, highly-processed and energy-dense foods rather than nutritious foods such as vegetables, fruits and animal products. Our analyses show that supermarkets have the highest MLDS, yet they do not significantly influence the diets of resource-poor households. However, a high MLDS among informal retail outlets has a positive association with diet quality; conversely, open-air markets have a negative association. The nutritional status of urban-poor consumers can be improved by promoting the diversification of healthy, nutritious foods across traditional retail outlets and improving accessibility of the outlets to consumers.
Definition of an Operative Methodology for the Management of Rockfalls along with the Road Network
Authors: Giordan Daniele , Cignetti Martina , Godone Danilo , Bertolo Davide , Paganone Marco
Published: July 2021

Rockfalls are widespread events in mountain areas worldwide. The management of this process can be done using different approaches. In this paper, we want to analyze the procedure that can be adopted to manage a rockfall event considering the safety of infrastructure and settlements. Focusing on an Alpine region highly affected by rockfalls like the Aosta Valley Region (north-western Italy), we implemented a dedicated procedure for the road network emergency management. This procedure can be activated immediately after a rockfall, and it aims to identify the effect of the collapse, define the danger zone, plan the recovery project and propose temporary solutions for correct residual risk management until the end of the remedial works. In natural hazards, the lack of codified methods can create critical conditions and increase the responsibility of the single operators, who have to effectively manage a critical situation in a limited amount of time without a well-defined procedure. For this reason, the proposed method aims to be a first example of how a correct codification can be used for more sustainable management of this widespread phenomenon.
The Role of Trust and Risk in Citizens’ E-Government Services Adoption: A Perspective of the Extended UTAUT Model
Authors: Li Wenjuan
Published: July 2021

This research particularly aims to investigate how trust and perceived risk influence citizens’ e-government adoption. The findings of the study reveal that citizens’ trust of the government (TOG) and trust of the internet (TOI) positively affect citizens’ e-government adoption (EGA); perceived risk (PR) is negatively associated with citizens’ EGA. Interestingly, this study also demonstrates the negative moderating effect of PR on the relationship between TOG and EGA, TOI and EGA. The results also indicate that performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitation conditions (FC) influence citizens’ EGA positively. Lastly, implications for practice and research are discussed.
Dynamic Connectedness and Portfolio Diversification during the Coronavirus Disease 2019 Pandemic: Evidence from the Cryptocurrency Market
Authors: Nasreen Samia , Tiwari Aviral Kumar , Yoon Seong-Min
Published: July 2021

This paper examines interlinkages and hedging opportunities between nine major cryptocurrencies from 30 September 2015 to 4 June 2020, a period which notably includes the COVID-19 outbreak lasting from early 2020 to the end of the sample period. Estimated time-varying correlation coefficients that are based on a TVP-VAR show a high degree of interconnectedness among cryptocurrencies throughout the sample period. Notably, the correlations reach their joint minimum during the COVID-19 pandemic indicating that cryptocurrencies acted as a hedge or safe haven during the stressful period of the COVID-19 pandemic. The cryptocurrency weights of the minimum connectedness portfolio were significantly reduced and their hedging effectiveness varied greatly during the pandemic, implying that investors’ preferences changed during the COVID-19 period.
Building on Strategic eLearning Initiatives of Hybrid Graduate Education a Case Study Approach: MHEI-ME Erasmus+ Project
Authors: Madleňák Radovan , D’Alessandro Stephen P. , Marengo Agostino , Pange Jenny , Neszmélyi György Iván
Published: July 2021

Online courses are gaining popularity because they provide extensive and varied course material, information, knowledge, and skills, whilst also creating an effective educational online community. This research adopts a case study approach to focus on the teaching method and the manner in which a strategic commitment to eLearning provides scope for the development and implementation of top quality educational online fully accredited programs. Entrepreneurship focuses on developing businesses that add value and create wealth and prosperity in our societies. Therefore, entrepreneurship is a key area of learning for graduate students seeking to set up and operate their own SME organizations. It can serve as a benchmark for the teaching of other graduate subjects that require a sound correlation for the correlation of concepts and theories to the challenging complexities of the real world. The program was developed on the basis of the implementation of a state-of-the-art eLearning platform that allowed for a combination of varied self-learning and collaborative learning elements and activities within a single platform. This enabled students to access the online content material efficiently and effectively. It allows for the development of a program based on the flipped classroom teaching methodology. The underlying concept of the flipped classroom methodology is that effective eLearning should comprise both synchronous and asynchronous learning activities. This combination of self-learning and collaborative learning calls for careful planning by the tutor to ensure that the learning objectives are clearly defined for each activity and that the relevant deliverables are monitored. The content material for each subject course module was designed, developed, produced, and presented by the different project partners in a holistic manner structured to motivate participants to learn. The results of our analysis have shown that students were able to learn, discuss their projects, and cooperate during an online course in an effective and participant-focused manner with their tutors. The feedback given highlights the importance of ongoing communications between students and the tutors who often need to act as mentors to retain student engagement.
Life Cycle Assessment on Construction and Demolition Waste: A Systematic Literature Review
Authors: Mesa Jaime A. , Fúquene-Retamoso Carlos , Maury-Ramírez Aníbal
Published: July 2021

Life Cycle Assessment (LCA) is considered an innovative tool to analyze environmental impacts to make decisions aimed at improving the environmental performance of building materials and construction processes throughout different life cycle stages, including design, construction, use, operation, and end-of-life (EOL). Therefore, during the last two decades, interest in applying this tool in the construction field has increased, and the number of articles and studies has risen exponentially. However, there is a lack of consolidated studies that provide insights into the implementation of LCA on construction and demolition waste (C&DW). To fill this research gap, this study presents a literature review analysis to consolidate the most relevant topics and issues in the research field of C&DW materials and how LCA has been implemented during the last two decades. A systematic literature search was performed following the PRISMA method: analysis of selected works is based on bibliometric and content-based approaches. As a result, the study characterized 150 selected works in terms of the evolution of articles per year, geographical distribution, most relevant research centers, and featured sources. In addition, this study highlights research gaps in terms of methodological and design tools to improve LCA analysis, indicators, and connection to new trending concepts, such as circular economy and industry 4.0.
Online Exercise Classes during the COVID-19 Related Lockdown in Germany: Use and Attitudes
Authors: Füzéki Eszter , Schröder Jan , Groneberg David A. , Banzer Winfried
Published: July 2021

Lockdown measures during the COVID-19 pandemic have led to reductions in physical activity (PA) worldwide. Leading public health organizations have recommended the use of online exercise classes (OEC) to compensate the loss of regular exercise classes. As of now, no data are available on the uptake of OEC and on users’ attitudes. The aim of the current online survey was to assess the use of and attitudes towards OEC in Germany. Respondents indicated awareness and use of OEC, and levels of agreement with statements on OEC. Frequency of awareness and use of OEC according to PA status were calculated with contingency tables and the Χ2 test. Differences between users and non-users were tested with the Student’s t-test and the Mann–Whitney U test. Data on attitudes are presented as percentages, and Spearman correlations were calculated between attitudes and activity status, frequency of use, educational attainment, age and body mass index. A total of 979 datasets were analyzed. Of the respondents, 681 were aware of OEC and 180, 118 and 84 used them <1 per week, 1–2 per week and ≥3 per week, respectively. Significantly more active respondents were aware of and used OEC compared to less active respondents. All in all, regular OEC use was quite limited. OEC was differentially attractive to people according to PA status, frequency of use, BMI and age. Tailoring OEC to current non-users and adding motivational support might enhance the regular use of OEC.
The Capitalization Effect and Its Consequence of Agricultural Support Policies—Based on the Evidence of 800 Villages in China
Authors: Lin Wensheng , Chen Rongyuan
Published: July 2021

The theoretical and empirical results of the capitalization effect of agricultural support policies on land rental price remain inconclusive. Based on the survey of Chinese Household Income Projects in 2007 and 2008, this paper adopts the panel data of 800 villages in 11 provinces in China to empirically analyze the impact of agricultural support policies on village-level land rental price. It shows that both output price support and agricultural subsidies have a significant positive effect on land rental price in the village. For each 1 CNY/kg increase in output price support, the land rental price in the village will rise by about 322.44 CNY/mu, while with an additional 1 CNY/mu increase in agricultural subsidies, it will increase by CNY 0.45. The stronger the social relationships in the village, the less area of land transfer for a fee and the lower the land rental price. For villages with weak social relationships or renting land to outsiders, output price support and agricultural subsidies can significantly increase the land rental price. Output price support and agricultural subsidies not only have a significant positive effect on the area of land transfer in the village but also an indirect negative effect on it by raising the price of regional land rent.
Online Sellers’ Reuse Behaviour for Third-Party Logistics Services: An Innovative Model Development and E-Commerce
Authors: Ngah Abdul Hafaz , Anuar Marhana Mohamed , Rozar NorLinda Nohd , Ariza-Montes Antonio , Araya-Castillo Luis , Kim Jinkyung Jenny , Han Heesup
Published: July 2021

The increase in customer complaints on the reliability of 3PL services among online sellers has become prominent in the industry, as confirmed by 3PL service providers in Malaysia. The increase in customer complaints increases the tendency to switch to other 3PL service providers. As Asian markets lead the growth of e-commerce, covering approximately 50% of the global e-commerce market, whether the customer will continue to shop online or not is no longer the issue. Although having the proper logistics provider to support online sellers’ business is crucial, most studies have focused on the online shopper’s perspective and employ the service quality theory. Observably, the satisfaction and reuse intention of the 3PL from the online retailer’s perspective is largely neglected. This study identified the factors influencing the satisfaction and reuse intention of 3PL services among online sellers in Malaysia by employing and expanding the stimulus–organism–response (SOR) theory. A purposive sampling method was employed, and the data were gathered via an online survey among online sellers. Additionally, smart partial least squares (SmartPLS) was applied to test the hypotheses. The results indicated that reliability had a positive effect on satisfaction, and satisfaction had a positive relationship with the reuse intention of certain 3PL services. Moreover, satisfaction mediated the relationship between reliability and reuse intention, whereas price fairness strengthened the positive relationship between satisfaction and reuse intention. The findings enhance studies that employed the SOR theory, particularly relating to online sellers’ behaviour on 3PL services. The findings also benefit online sellers and 3PL service providers to create attractive marketing strategies for business sustainability.
Social Impact of Value-Based Banking: Best Practises and a Continuity Framework
Authors: Kocornik-Mina Adriana , Bastida-Vialcanet Ramon , Eguiguren Huerta Marcos
Published: July 2021

How do financial institutions enable social impact? We examined this question in the context of values-based financial institutions, which are amongst the most experienced institutions around the world in addressing the very real banking needs of enterprises and individuals within their communities. There is, indeed, an urgency today to have the banking industry consider its social impact in a holistic way. This paper expands our understanding of how these financial institutions, all members of the Global Alliance for Banking on Values, define, design, implement, monitor and scale-up social impact. We used a multiple-case studies design to investigate their approach and inductive analysis to derive a model. From a theoretical perspective, we found that a social impact virtuous circular model best reflects how values-based financial institutions approach and practise social impact. Each step of the circular model clearly shows the way in which these institutions address and achieve social impact. Our findings have important implications for academic research focussed on understanding how finance can generate social impact. The findings of this article can, especially, also have practical implications for all types of financial institutions willing to improve the way in which they address social challenges and, ultimately, increase their social impact. At a time when more resources are needed to meet the Sustainable Development Goals, this is urgent.
Development of Supercapacitor-Aided Hybrid Energy Storage System to Enhance Battery Life Cycle of Electric Vehicles
Authors: Hsieh Min-Fu , Chen Po-Hsun , Pai Fu-Sheng , Weng Rui-Yang
Published: July 2021

This paper presents a C-rate control method for a battery/supercapacitor (SC) hybrid energy storage system (HESS) to enhance the life cycle of the battery in electric vehicles (EVs). The proposed HESS provides satisfactory power for dynamic movements of EVs (e.g., acceleration or braking) while keeping the battery current within a secure level to prevent it from degradation. The configurations of conventional HESSs are often complex due to the two energy storages and their current/voltage sensing involved. Therefore, in this paper, a simple current-sensing scheme is utilized and the battery is directly treated as a controlled variable to help the battery output current remain stable for different load conditions. While the proposed circuit requires only one current feedback signal, neither the SC nor load current sensors are needed, and the circuit design is thus significantly simplified. Both simulation and experimental results validated the effectiveness of the proposed HESS operating in conjunction with the motor drive system. The proposed method aims at fully utilizing recycled energy and prolonging battery lifespan.
A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0
Authors: Liaqait Raja Awais , Hamid Shermeen , Warsi Salman Sagheer , Khalid Azfar
Published: July 2021

Scheduling plays a pivotal role in the competitiveness of a job shop facility. The traditional job shop scheduling problem (JSSP) is centralized or semi-distributed. With the advent of Industry 4.0, there has been a paradigm shift in the manufacturing industry from traditional scheduling to smart distributed scheduling (SDS). The implementation of Industry 4.0 results in increased flexibility, high product quality, short lead times, and customized production. Smart/intelligent manufacturing is an integral part of Industry 4.0. The intelligent manufacturing approach converts renewable and nonrenewable resources into intelligent objects capable of sensing, working, and acting in a smart environment to achieve effective scheduling. This paper aims to provide a comprehensive review of centralized and decentralized/distributed JSSP techniques in the context of the Industry 4.0 environment. Firstly, centralized JSSP models and problem-solving methods along with their advantages and limitations are discussed. Secondly, an overview of associated techniques used in the Industry 4.0 environment is presented. The third phase of this paper discusses the transition from traditional job shop scheduling to decentralized JSSP with the aid of the latest research trends in this domain. Finally, this paper highlights futuristic approaches in the JSSP research and application in light of the robustness of JSSP and the current pandemic situation.
Comparison Study of Metal Oxides (CeO2, CuO, SnO2, CdO, ZnO and TiO2) Decked Few Layered Graphene Nanocomposites for Dye-Sensitized Solar Cells
Authors: Bykkam Satish , Prasad D. , Maurya Muni , Sadasivuni Kishor , Cabibihan John-John
Published: July 2021

Recent research is focused on few layered graphene (FLG) with various metal oxides (MOs) as (MOs; CeO2, CuO, SnO2, CdO, ZnO, and TiO2) nanocomposite materials are alternatives to critically important in the fabrication of solar cell devices. In this work, FLG with different MOs nanocomposites were prepared by a novel eco-friendly viable ultrasonic assisted route (UAR). The prepared FLG/MO nanocomposites were performed with various characterization techniques. The crystal and phase compositional were carried out through using X-ray diffraction technique. Surface morphological studies by field emission scanning electron microscope (FE-SEM) and high-resolution transmission electron microscopy (HR-TEM). Spectroscopic methods were done by Raman and UV-Vis Diffuse reflectance spectra (UV-DRS). The prepared FLG/MO nanocomposites materials were used as a photoanode, in the fabrication of dye sensitized solar cells (DSSCs). Compared to TiO2 nanoparticles (NPs) and other FLG/MO nanocomposites, FLG/TiO2 nanocomposites exhibited superior photovoltaic properties. The obtained results indicate that FLG/TiO2 nanocomposites significantly improved the power conversion efficiency (PCE) of DSSCs. The photovoltaic analyses were performed in a solar simulator with an air mass (AM) of 1.5 G, power density of 100 m W/m2, and current density-voltage (J-V) was investigated using N719 dye.