bluuebunny/crossref_metadata_2025 huggingface dataset:
Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies
Authors: Manssor Samah A. F. , Sun Shaoyuan , Elhassan Mohammed A. M.
Published: June 2021

Human recognition technology is a task that determines the people existing in images with the purpose of identifying them. However, automatic human recognition at night is still a challenge because of its need to align requirements with a high accuracy rate and speed. This article aims to design a novel approach that applies integrated face and gait analyses to enhance the performance of real-time human recognition in TIR images at night under various walking conditions. Therefore, a new network is proposed to improve the YOLOv3 model by fusing face and gait classifiers to identify individuals automatically. This network optimizes the TIR images, provides more accurate features (face, gait, and body segment) of the person, and possesses it through the PDM-Net to detect the person class; then, PRM-Net classifies the images for human recognition. The proposed methodology uses accurate features to form the face and gait signatures by applying the YOLO-face algorithm and YOLO algorithm. This approach was pre-trained on three night (DHU Night, FLIR, and KAIST) databases to simulate realistic conditions during the surveillance-protecting areas. The experimental results determined that the proposed method is superior to other results-related methods in the same night databases in accuracy and detection time.
Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data
Authors: Kang Yupeng , Meng Qingyan , Liu Miao , Zou Youfeng , Wang Xuemiao
Published: June 2021

A red edge band is a sensitive spectral band of crops, which helps to improve the accuracy of crop classification. In view of the characteristics of GF-6 WFV data with multiple red edge bands, this paper took Hengshui City, Hebei Province, China, as the study area to carry out red edge feature analysis and crop classification, and analyzed the influence of different red edge features on crop classification. On the basis of GF-6 WFV red edge band spectral analysis, different red edge feature extraction and red edge indices feature importance evaluation, 12 classification schemes were designed based on GF-6 WFV of four bands (only including red, green, blue and near-infrared bands), stepwise discriminant analysis (SDA) and random forest (RF) method were used for feature selection and importance evaluation, and RF classification algorithm was used for crop classification. The results show the following: (1) The red edge 750 band of GF-6 WFV data contains more information content than the red edge 710 band. Compared with the red edge 750 band, the red edge 710 band is more conducive to improving the separability between different crops, which can improve the classification accuracy; (2) According to the classification results of different red edge indices, compared with the SDA method, the RF method is more accurate in the feature importance evaluation; (3) Red edge spectral features, red edge texture features and red edge indices can improve the accuracy of crop classification in different degrees, and the red edge features based on red edge 710 band can improve the accuracy of crop classification more effectively. This study improves the accuracy of remote sensing classification of crops, and can provide reference for the application of GF-6 WFV data and its red edge bands in agricultural remote sensing.
Challenges in Bridge Health Monitoring: A Review
Authors: Rizzo Piervincenzo , Enshaeian Alireza
Published: June 2021

Bridge health monitoring is increasingly relevant for the maintenance of existing structures or new structures with innovative concepts that require validation of design predictions. In the United States there are more than 600,000 highway bridges. Nearly half of them (46.4%) are rated as fair while about 1 out of 13 (7.6%) is rated in poor condition. As such, the United States is one of those countries in which bridge health monitoring systems are installed in order to complement conventional periodic nondestructive inspections. This paper reviews the challenges associated with bridge health monitoring related to the detection of specific bridge characteristics that may be indicators of anomalous behavior. The methods used to detect loss of stiffness, time-dependent and temperature-dependent deformations, fatigue, corrosion, and scour are discussed. Owing to the extent of the existing scientific literature, this review focuses on systems installed in U.S. bridges over the last 20 years. These are all major factors that contribute to long-term degradation of bridges. Issues related to wireless sensor drifts are discussed as well. The scope of the paper is to help newcomers, practitioners, and researchers at navigating the many methodologies that have been proposed and developed in order to identify damage using data collected from sensors installed in real structures.
Design and Optimization of Sensitivity Enhancement Package for MEMS-Based Thermal Acoustic Particle Velocity Sensor
Authors: Chang Wenhan , Yang Lingmeng , Zhu Zhezheng , Yang Zhenchuan , Hao Yilong , Gao Chengchen
Published: June 2021

In this paper, small-sized acoustic horns, the sensitivity enhancement package for the MEMS-based thermal acoustic particle velocity sensor, have been designed and optimized. Four kinds of acoustic horns, including tube horn, double cone horn, double paradox horn, and exponential horn, were analyzed through numerical calculation. Considering both the amplification factor and effective length of amplification zone, a small-sized double cone horn with middle tube is designed and further optimized. A three-wire thermal acoustic particle velocity sensor was fabricated and packaged in the 3D printed double cone tube (DCT) horn. Experiment results show that an amplification factor of 6.63 at 600 Hz and 6.93 at 1 kHz was achieved. A good 8-shape directivity pattern was also obtained for the optimized DCT horn with the lateral inhibition ratio of 50.3 dB. No additional noise was introduced, demonstrating the DCT horn’s potential in improving the sensitivity of acoustic particle velocity sensors.
Evaluation of ECOSTRESS Thermal Data over South Florida Estuaries
Authors: Shi Jing , Hu Chuanmin
Published: June 2021

Operational coarse-resolution satellite thermal sensors designed for global oceans are often insufficient for evaluating surface temperature of small water bodies. Here, the quality of the thermal data, collected by the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), over several South Florida estuaries, Chesapeake Bay, and Lake Okeechobee is evaluated using both in situ and Moderate Resolution Imaging Spectroradiometer (MODIS) Sea Surface Temperature (SST) data. Overall, for SST between ~6 and ~32 °C, ECOSTRESS LST (Land Surface Temperature, used as a surrogate for SST in this study) appears to be slightly underestimated, with the underestimation being more severe at night (−1.13 °C) than during the day (−0.64 °C), in spring and summer (−1.25 ± 1.39 °C) than in autumn and winter (−0.57 ± 0.98 °C), and after May 2019 when two of the five bands failed. The root-mean-square uncertainties of ECOSTRESS SST are generally within 1–2 °C. Spatial analysis further suggests that ECOSTRESS SST covers waters closer to shore and reveals more spatial features than MODIS, with comparable image noise. From these observations, after proper georeferencing and empirical correction of the negative bias, ECOSTRESS SST may be used to evaluate the thermal environments of small water bodies, thus filling gaps in the coarse-resolution satellite data.
Skeleton Driven Action Recognition Using an Image-Based Spatial-Temporal Representation and Convolution Neural Network
Authors: Silva Vinícius , Soares Filomena , Leão Celina P. , Esteves João Sena , Vercelli Gianni
Published: June 2021

Individuals with Autism Spectrum Disorder (ASD) typically present difficulties in engaging and interacting with their peers. Thus, researchers have been developing different technological solutions as support tools for children with ASD. Social robots, one example of these technological solutions, are often unaware of their game partners, preventing the automatic adaptation of their behavior to the user. Information that can be used to enrich this interaction and, consequently, adapt the system behavior is the recognition of different actions of the user by using RGB cameras or/and depth sensors. The present work proposes a method to automatically detect in real-time typical and stereotypical actions of children with ASD by using the Intel RealSense and the Nuitrack SDK to detect and extract the user joint coordinates. The pipeline starts by mapping the temporal and spatial joints dynamics onto a color image-based representation. Usually, the position of the joints in the final image is clustered into groups. In order to verify if the sequence of the joints in the final image representation can influence the model’s performance, two main experiments were conducted where in the first, the order of the grouped joints in the sequence was changed, and in the second, the joints were randomly ordered. In each experiment, statistical methods were used in the analysis. Based on the experiments conducted, it was found statistically significant differences concerning the joints sequence in the image, indicating that the order of the joints might impact the model’s performance. The final model, a Convolutional Neural Network (CNN), trained on the different actions (typical and stereotypical), was used to classify the different patterns of behavior, achieving a mean accuracy of 92.4% ± 0.0% on the test data. The entire pipeline ran on average at 31 FPS.
Sensitivity Analysis of Single- and Bimetallic Surface Plasmon Resonance Biosensors
Authors: Mrozek Piotr , Gorodkiewicz Ewa , Falkowski Paweł , Hościło Bogusław
Published: June 2021

Comparative analysis of the sensitivity of two surface plasmon resonance (SPR) biosensors was conducted on a single-metallic Au sensor and bimetallic Ag–Au sensor, using a cathepsin S sensor as an example. Numerically modeled resonance curves of Au and Ag–Au layers, with parameters verified by the results of experimental reflectance measurement of real-life systems, were used for the analysis of these sensors. Mutual relationships were determined between ∂Y/∂n components of sensitivity of the Y signal in the SPR measurement to change the refractive index n of the near-surface sensing layer and ∂n/∂c sensitivity of refractive index n to change the analyte’s concentration, c, for both types of sensors. Obtained results were related to experimentally determined calibration curves of both sensors. A characteristic feature arising from the comparison of calibration curves is the similar level of Au and Ag–Au biosensors’ sensitivity in the linear range, where the signal of the AgAu sensor is at a level several times greater. It was shown that the influence of sensing surface morphology on the ∂n/∂c sensitivity component had to be incorporated to explain the features of calibration curves of sensors. The shape of the sensory surface relief was proposed to increase the sensor sensitivity at low analyte concentrations.
Confidence Score: The Forgotten Dimension of Object Detection Performance Evaluation
Authors: Wenkel Simon , Alhazmi Khaled , Liiv Tanel , Alrshoud Saud , Simon Martin
Published: June 2021

When deploying a model for object detection, a confidence score threshold is chosen to filter out false positives and ensure that a predicted bounding box has a certain minimum score. To achieve state-of-the-art performance on benchmark datasets, most neural networks use a rather low threshold as a high number of false positives is not penalized by standard evaluation metrics. However, in scenarios of Artificial Intelligence (AI) applications that require high confidence scores (e.g., due to legal requirements or consequences of incorrect detections are severe) or a certain level of model robustness is required, it is unclear which base model to use since they were mainly optimized for benchmark scores. In this paper, we propose a method to find the optimum performance point of a model as a basis for fairer comparison and deeper insights into the trade-offs caused by selecting a confidence score threshold.
Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring
Authors: Hajari Nasim , Lastre-Dominguez Carlos , Ho Chester , Ibarra-Manzano Oscar , Cheng Irene
Published: June 2021

Pressure injury (PI) is a major problem for patients that are bound to a wheelchair or bed, such as seniors or people with spinal cord injuries. This condition can be life threatening in its later stages. It can be very costly to the healthcare system as well. Fortunately with proper monitoring and assessment, PI development can be prevented. The major factor that causes PI is prolonged interface pressure between the body and the support surface. A possible solution to reduce the chance of developing PI is changing the patient’s in-bed pose at appropriate times. Monitoring in-bed pressure can help healthcare providers to locate high-pressure areas, and remove or minimize pressure on those regions. The current clinical method of interface pressure monitoring is limited by periodic snapshot assessments, without longitudinal measurements and analysis. In this paper we propose a pressure signal analysis pipeline to automatically eliminate external artefacts from pressure data, estimate a person’s pose, and locate and track high-risk regions over time so that necessary attention can be provided.
Poor Motor Coordination Elicits Altered Lower Limb Biomechanics in Young Football (Soccer) Players: Implications for Injury Prevention through Wearable Sensors
Authors: Di Paolo Stefano , Zaffagnini Stefano , Pizza Nicola , Grassi Alberto , Bragonzoni Laura
Published: June 2021

Motor coordination and lower limb biomechanics are crucial aspects of anterior cruciate ligament (ACL) injury prevention strategies in football. These two aspects have never been assessed together in real scenarios in the young population. The present study aimed to investigate the influence of motor coordination on lower limb biomechanics in young footballers during an on-the-pitch training. Eighteen juvenile football players (10 y ± 2 m) were enrolled. Each player performed a training drill with sport-specific movements (vertical jump, agility ladders, change of direction) and the Harre circuit test (HCT) to evaluate players’ motor coordination. Wearable inertial sensors (MTw Awinda, Xsens) were used to assess lower limb joint angles and accelerations. Based on the results of the HCT, players were divided into poorly coordinated (PC) and well-coordinated (WC) on the basis of the literature benchmark. The PC group showed a stiffer hip biomechanics strategy (up to 40% lower flexion angle, ES = 2.0) and higher internal-external hip rotation and knee valgus (p < 0.05). Significant biomechanical limb asymmetries were found only in the PC group for the knee joint (31–39% difference between dominant and non-dominant limb, ES 1.6–2.3). Poor motor coordination elicited altered hip and knee biomechanics during sport-specific dynamic movements. The monitoring of motor coordination and on-field biomechanics might enhance the targeted trainings for ACL injury prevention.
Similar Negotiations over Childcare? A Comparative Study of Fathers’ Parental Leave Use in Finland and Sweden
Authors: Duvander Ann-Zofie , Mussino Eleonora , Tervola Jussi
Published: June 2021

Fathers’ leave use is promoted in many countries, but so far with different success. Major explanations of different usage revolve around economic bargaining between parents and economic constraints in the household. By using extensive register data from 1999–2009 in Finland and Sweden, this study asks whether fathers’ use of parental leave in the two countries is determined by the same socioeconomic characteristics on the individual and the household level once we control for sociodemographic factors. Striking similarities in what influences fathers’ use of leave in the two contexts are found, even though leave is used at very different levels and the policy design differs remarkably. Generally, fathers with a similar income level to the mother use leave the most, but in high-income households the mother’s higher income leads to the highest propensity of fathers’ leave take-up. The results indicate that equal bargaining positions are associated with fathers’ leave use but also that mothers’ stronger position often facilitates fathers’ leave. We conclude that the role of gendered bargaining positions should be studied in interaction with the level of resources in the household.
Quality of Work Life According to Walton’s Model: Case Study of the Higher Institute of Defense Studies of Mozambique
Authors: Sabonete Sérgio Abílio , Lopes Helga Santa Comba , Rosado David Pascoal , Reis João Carlos Gonçalves dos
Published: June 2021

The quality of life at work has been one of the most important aspects to be considered by a given organization to achieve pre-defined objectives. Thus, this research aims to analyze the level of satisfaction with the quality of work life (QWL) of the employees of the Higher Institute of Defense Studies “Lieutenant-General Armando Emílio Guebuza” (ISEDEF) according to Walton’s model. A conceptual framework was built through a bibliographic and literature review. The instrument used for data collection was a questionnaire applied to 97 military and civilian personnel in August 2019. The results show that the general level of satisfaction with QWL is moderate, requiring an improvement in living conditions so that military education and training continues, as well as the development of models that enable the realization of benefits according to the social and economic level in the country.
Empirical Study on the Factors Affecting User Switching Behavior of Online Learning Platform Based on Push-Pull-Mooring Theory
Authors: Xu Heng , Wang Jingru , Tai Zhaodan , Lin Hao-Chiangkoong
Published: June 2021

Online learning is gaining popularity, but users can easily find alternatives and switch between learning platforms. Reducing users switching behavior is a critical condition for the sustainable development of an online learning platform; therefore, it is necessary to investigate the influence factors of users switching behavior between different platforms to retain users and enhance the competitiveness of enterprises. Push-Pull-Mooring (PPM) theory is adopted to construct a structural equation model of customer switching behavior on online learning platforms and to explore the mechanism of user switching behavior between learning platforms. The model is tested with data collected from 313 online learning users. The results show that information overload and dissatisfaction, as push factors, significantly affect user switching behavior. Functional value and network externality as pull factors positively affect user switching behavior, switching cost, and affective commitment as mooring factors negatively correlate with switching behavior. Further, this study also revealed that there are obvious different influencing factors for different online learning platforms. Overall, this study provides some practical strategies for the online learning platform and can help them to gain a competitive advantage.
Numerical Study of the Strength and Characteristics of Sandstone Samples with Combined Double Hole and Double Fissure Defects
Authors: Ma Junbiao , Jiang Ning , Wang Xujun , Jia Xiaodong , Yao Dehao
Published: June 2021

To explore the failure mechanism of rock with holes and fissures, uniaxial compression tests of sandstone samples with combined double hole and double fissure defects were carried out using Particle Flow Code 2D (PFC2D) numerical simulation software. The failure behaviour and mechanical properties of the sandstone samples with combined double hole and double fissure defects at different angles were analysed, and the evolution results of the stress field and crack propagation were studied. The results show that with a decrease in fissure angle, the crack initiation stress, damage stress, elastic modulus and peak stress of the defective rock decrease, while the peak strain increases, and the brittleness of the rock is weakened. Rocks with combined double hole and double fissure defects at different angles lead to different failure modes, crack initiation positions and crack development directions. After uniaxial compression, both compressive stress and tensile stress concentration areas are produced in the defective rock, but the compressive stress concentration is of primary importance. The concentration area is mainly distributed around the holes and fissures and the defect connecting line, and the stress concentration area decreases with the decreasing fissure angle. This study can correctly predict the mechanical properties of rock with combined double hole and double fissure defects at different angles and provide a reference for actual rock engineering.
Effect of Flipped Teaching on Cognitive Load Level with Mobile Devices: The Case of a Graphic Design Course
Authors: Chen Yi-Chieh , Fan Kuo-Kuang , Fang Kwo-Ting
Published: June 2021

Due to the emergence of computer education, AI education, the Internet of Things, big data, and technological wisdom, it is easy for students to be distracted when engaged in traditional education. Flipped teaching is a teaching strategy frequently used in colleges and universities. The focus of this research was conducted by a comparative analysis of the cognitive load between the experimental group and the control group through a quasi-experimental design for research with different learning methods and different classes. More specifically, flipped teaching was carried out with an experimental group, and traditional teaching a control group; they were observed at the same time, and 213 private university students participated in the experiment. The research proposes a practice of mixed teaching, carried out in a group communication behavior system, and enhancing the spirit of group interaction and learning through mobile devices. The core value of the research lies in (1) online learning, (2) group interaction, and (3) the learning load of the conceptual model. In addition, focus group interviews were used to provide feedback on participants’ cognition and emotions. The results indicate that there were differences in cognitive load between the two classes.
A Typological Classification for Assessing Farm Sustainability in the Italian Bovine Dairy Sector
Authors: Masi Margherita , Vecchio Yari , Pauselli Gregorio , Di Pasquale Jorgelina , Adinolfi Felice
Published: June 2021

Italy is among the most important countries in Europe for milk production. The new European policies encourage a transition towards sustainability and are leading European dairy farms to follow new trajectories to increase their economic efficiency, reduce their environmental impact, and ensure social sustainability. Few studies have attempted to classify dairy farms by analyzing the relationships between the structural profiles of farms and the social, environmental, and economic dimensions of sustainability. This work intends to pursue this aim through an exploratory analysis in the Italian production context. The cluster analysis technique made it possible to identify three types of dairy farms, which were characterized on the basis of indicators that represented the three dimensions of sustainability (environmental, social, and economic sustainability) and the emerging structural relationships based on the structural characteristics of the dairy farms. The classification made it possible to describe the state of the art of the Italian dairy sector in terms of sustainability and to understand how different types of farms can respond to the new European trajectories.
Fabricators and End-Users of Single-Pot Biomass Stoves: Conceptualizing, Hypothesis and Performance Metrics for Developing Energy Sustainability Framework
Authors: Obeng George Yaw , Mensah Ebenezer , Opoku Richard
Published: June 2021

In this study, interviewer-based questionnaires of 67 variables were administered to local fabricators and end-users of single-pot biomass stoves (SPBS) in Ghana. Additionally, two randomly selected traditional and improved SPBS were lab-tested using standard performance metrics. From the study, the relationship between fabricators and end-users was conceptualized based on selected indicators and assumptions. The study results indicated that the primary design resources for fabrication were patterns and templates, and that major challenges to fabrication were lack of training in design principles, standards and safety, poor emission efficiency and financial sustainability. Whereas end-users of improved SPBS were less affected by heat and smoke, end-users of traditional SPBS were mostly affected. From hypothesis test, because the calculated χ2cal = 24.05, and is greater than the tabulated χ2crit = 3.841, it is concluded that there is a relationship between heat, smoke effect and gender, and that female end-users of traditional SPBS were particularly affected during cooking. The traditional SPBS emitted more CO2 and CO than improved SPBS. Comparatively, 38% more end-users of traditional SPBS observed charcoal ash residue in the cooking area than improved SPBS users. Four basic practices of managing ashes from SPBS are developed. Finally, a fabricator and end-user framework are developed for energy sustainability and quality improvement.
Experiential Value of Exhibition in the Cultural and Creative Park: Antecedents and Effects on CCP Experiential Value and Behavior Intentions
Authors: Ding Haimeng , Hung Kuang-Peng , Peng Norman , Chen Annie
Published: June 2021

The protection of industrial cultural heritage is related to sustainable urban development. Cultural and creative parks (CCPs) are a way for many cities to protect their industrial cultural heritage. In the context of CCPs, this study examines the relationships among the antecedents of exhibition experiential value, CCP experiential value, and behavioral intentions. Surveying 428 visitors in two well-known CCPs in Taipei, this study found that the four antecedents (attractiveness, existential authenticity, self-congruence, and exhibition–park image congruence) have a positive impact on exhibition experiential value. Exhibition experiential value has a positive impact on CCP experiential value, which in turn, affects behavioral intentions toward the CCP. In addition, this study finds that exhibition experiential value has a mediating effect between the four antecedents and CCP experiential value. Moreover, CCP experiential value has a mediating effect between exhibition experiential value and behavioral intentions. The findings of this study provide a direction for CCPs to achieve sustainable development through exhibitions that can attract more tourists.
Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains
Authors: Mageto Joash
Published: June 2021

Sustainable supply chain management has been an important research issue for the last two decades due to climate change. From a global perspective, the United Nations have introduced sustainable development goals, which point towards sustainability. Manufacturing supply chains are among those that produce harmful effluents into the environment in addition to social issues that impact societies and economies where they operate. New developments in information and communication technologies, especially big data analytics (BDA), can help create new insights that can detect parts and members of a supply chain whose activities are unsustainable and take corrective action. While many studies have addressed sustainable supply chain management (SSCM), studies on the effect of BDA on SSCM in the context of manufacturing supply chains are limited. This conceptual paper applies Toulmin’s argumentation model to review relevant literature and draw conclusions. The study identifies the elements of big data analytics as data processing, analytics, reporting, integration, security and economic. The aspects of sustainable SCM are transparency, sustainability culture, corporate goals and risk management. It is established that BDA enhances SSCM of manufacturing supply chains. Cyberattacks and information technology skills gap are some of the challenges impeding BDA implementation. The paper makes a conceptual and methodological contribution to supply chain management literature by linking big data analytics and SSCM in manufacturing supply chains by using the rarely used Toulmin’s argumentation model in management studies.
Emergy as a Tool to Evaluate Ecosystem Services: A Systematic Review of the Literature
Authors: Nadalini Ana Carolina V. , Kalid Ricardo de Araujo , Torres Ednildo Andrade
Published: June 2021

The objective of this paper is to present a review of current research on the valuation of ecosystem services, using emergy evaluation methodology (EME). A bibliometric analysis and a systematic review were carried out between 2000 and 2020, using all of Web of Science database subfields that collected 187 papers, selected through the keywords “emergy” and “ecosystem services”. In the second part of the research, we carried out a new search on Web of Science of the 187 initial articles produced, with the words “valuation” and “economic”, in order to analyze those directly related to the evaluation of ecosystem services. The results showed that the EME method is an effective tool to evaluate ecosystem services, since it relates economic and ecological aspects in the evaluations. The research also indicated that the use of isolated methods does not appear to be the most appropriate solution, and that emergy used in combination with other methodologies can be used to obtain more accurate and comprehensive results to evaluate natural resources.
Impact of COVID-19 on the Educational Process in Saudi Arabia: A Technology–Organization–Environment Framework
Authors: Alshaikh Khloud , Maasher Shaikhah , Bayazed Afnan , Saleem Farrukh , Badri Sahar , Fakieh Bahjat
Published: June 2021

The lockdown of universities and educational institutions during the COVID-19 pandemic has negatively impacted the educational process. Saudi Arabia became a forerunner during COVID-19 by taking initial precautions of curfews and total restrictions. However, these restrictions had a disruptive effect on various sectors, specifically the educational sector. The Ministry of Education strived to cope with the consequences of these changes swiftly by shifting to online education. This paper aims to study the impact of COVID-19 on the educational process through a comparative study of the responses collected from different cases, and the challenges that are faced throughout the educational process. The study conducted a cross-sectional, self-administered online questionnaire during the outbreak and distance learning, which was designed based on the Technology–Organization–Environment (TOE) framework of students. Most questions used a five-point Likert scale. The responses were randomly collected from 150 undergraduate and postgraduate students who were studying in Saudi Arabian universities, to study the overall performance of education institutions during COVID-19. The collected data were analyzed and compared to the results in the literature. The main factors impacted by this transformation are addressed. These factors are based on research and observations and aim to overcome the encountered limitations and to present their level of impact on distance education. The research framework can be useful for higher educational authorities aiming to overcome the issues highlighted and discussed in this study.
Sustainable Growth Variables by Industry Sectors and Their Influence on Changes in Business Models of SMEs in the Era of Digital Transformation
Authors: KIM Seok-Soo
Published: June 2021

SMEs around the world are constantly under threats to their survival and sustainable growth. This study evaluated the variables influencing the sustainable growth of SMEs in the technological evolution of digital transformation. I propose a business model innovation strategy for the sustainable growth of SMEs. Furthermore, I evaluated the variables influencing growth by industry sector and present strategies for innovation in the business model. Data were collected from 205 CEOs in six sector industries in Korea, and I investigated the effect of variables that affected the company’s sustainability when introducing digital transformation. A partial least-squares regression was used for the statistical analysis and was performed using a data group analysis. I found that the SME performance varied by industry and that technological competency had a mediating effect. The variables that most affected the SME performance were management, technology, and technical competence in marketing and innovation. This research went beyond the limitations of studying only the effects of each of the previous literature variables through causal studies linking these variables. The variables affecting the performance of the industries were found to be different. By comparing the variables that led to a difference in performance in the industrial sector, I developed a business model innovation strategy that can affect the sustainable performance of SMEs.
Spatial Modelling of Modal Shift Due to COVID-19
Authors: Šinko Simona , Prah Klemen , Kramberger Tomaž
Published: June 2021

The outbreak of COVID-19 caused many changes in people’s life. One of the most significant is the travel behaviour and transport mode choice. This study focus on the changes that the inhabitants of Vienna made in their travel choices because of the virus. The same research about spatial modelling the transport mode choice of commuters in Vienna was completed in 2019 and is a topic addressed in our previous work. Based on our developed methodology, this article indicates that public transport is not a dominant transport mode choice as it was before the virus outbreak. The main result of this paper is geographically defined areas of application of individual alternatives shown on the final map of modal shift in Vienna, which could provide theoretical support for policymakers and transportation planners. For the city of Vienna, we found that the area of the city where cars are now used has increased, which certainly has a negative impact on air quality and life in the city. The advantage of the methodology is that it can also be applied to other cities in the world.
Knowledge Management and Sustainable Balanced Scorecard: Practical Application to a Service SME
Authors: Ferber Pineyrua Diego G. , Redondo Alfonso , Pascual José A. , Gento Ángel M.
Published: June 2021

Many works highlight the importance and added value of Corporate Social Responsibility (CSR) in companies, as well as the role of Knowledge Management (KM) for their revitalization, but even more important is to ensure that all efforts and resources are aligned with the organization’s strategic objectives (Balanced Scorecard, BSC). The aim of this work is to integrate BSC, CSR and KM based on the results of two previous researches: one on BSC–CSR and the other on KM, both of a projective nature (description-action-contrast). The research carried out has made it possible to identify 45 KM indicators, 102 CSR indicators and their corresponding correlations with the strategic objectives and perspectives of the BSC. In addition, an Excel tool was designed and developed for SMEs that is customizable, intuitive and useful for decision-making, which allowed us to contrast the obtained results.
Risk-Averse Scheduling of Combined Heat and Power-Based Microgrids in Presence of Uncertain Distributed Energy Resources
Authors: Rabiee Abbas , Abdali Ali , Mohseni-Bonab Seyed Masoud , Hazrati Mohsen
Published: June 2021

In this paper, a robust scheduling model is proposed for combined heat and power (CHP)-based microgrids using information gap decision theory (IGDT). The microgrid under study consists of conventional power generation as well as boiler units, fuel cells, CHPs, wind turbines, solar PVs, heat storage units, and battery energy storage systems (BESS) as the set of distributed energy resources (DERs). Additionally, a demand response program (DRP) model is considered which has a successful performance in the microgrid hourly scheduling. One of the goals of CHP-based microgrid scheduling is to provide both thermal and electrical energy demands of the consumers. Additionally, the other objective is to benefit from the revenues obtained by selling the surplus electricity to the main grid during the high energy price intervals or purchasing it from the grid when the price of electricity is low at the electric market. Hence, in this paper, a robust scheduling approach is developed with the aim of maximizing the total profit of different energy suppliers in the entire scheduling horizon. The employed IGDT technique aims to handle the impact of uncertainties in the power output of wind and solar PV units on the overall profit.
A Data-Driven Feature Based Learning Application to Detect Freeway Segment Traffic Status Using Mobile Phone Data
Authors: Liu Qiang , Xie Jianguang , Ding Fan
Published: June 2021

With the finishing of the construction of the main body of a freeway network, adequately monitoring the traffic status of the network has become an urgent need for both travelers and transportation operators. Various methods are proposed to collect traffic information for this purpose. In this article, a data-driven feature-based learning application is implemented to detect segment traffic status using mobile phone data, building on the practical success of deep learning models in other fields. The traffic status estimation is achieved via the application of a three-level long, short-term memory model. Two phone features are extracted from the raw mobile phone data. A large-scale field experiment was conducted using actual data in Jiangsu, China collected over the “National Holiday Golden Week” of 2014. To evaluate the performance, both precision and recall scores are given along with the overall accuracy. The final results of the large-scale experiment indicate that the proposed application performed well and can be an emerging solution for traffic state monitoring when only limited roadside sensing devices are installed.
A Novel Receiving End Grid Planning Method with Mutually Exclusive Constraints in Alternating Current/Direct Current Lines
Authors: Luo Yi , Zhang Yin , Tang Muyi , Zhou Youbin , Wang Ying , Cai Defu , Liu Haiguang
Published: June 2021

The large-scale application of high-voltage direct current (HVDC) transmission technology introduces mutually exclusive constraints (MEC) into the power grid planning, which deepens the complexity of power grid planning. The MECs decrease the planning efficiency and effectiveness of the conventional method. This paper proposes a novel hybrid alternating current (AC)/direct current (DC) receiving end grid planning method with MECs in AC/DC lines. The constraint satisfaction problem (CSP) is utilized to model the MECs in candidate lines and then the detailed planning model, in which mutually exclusive candidate lines are described by mutually exclusive variable and constraint sets. Additionally, the proposed planning model takes the hybrid AC/DC power system stability into consideration by introducing the multi-infeed short circuit ratio (MISCR). After establishing the hybrid AC/DC receiving end grid planning model with MECs, the backtracking search algorithm (BSA) is used to solve the optimal planning. The effectiveness of the proposed hybrid AC/DC grid planning method with MECs is verified by case studies.
Building a Sustainable Society: Construction, Public Procurement Policy and ‘Best Practice’ in the European Union
Authors: Olsson David , Öjehag-Pettersson Andreas , Granberg Mikael
Published: June 2021

Sustainability and sustainable development are political and essentially contested social phenomena. Despite this ambiguity, they continue to hold a central position as apolitical concepts in much of social science and policy making. In Europe, public procurement is increasingly used as a tool to reach sustainability, a fact that actualizes an inherent tension between politically charged objectives on the one hand, and technological processes and market logics on the other. Therefore, in this article, we investigate this tension by studying policies relating to sustainable public procurement of the built environment in the EU. We argue that governing any policy domain entails the construction and representation of particular policy problems. Hence, we focus on how the ‘problems’ of sustainable public procurement are represented in EU policy guidance and best practice documents. Our analysis shows that these central policy documents are dominated by a problem representation where unsustainability is constructed as technical design flaws and market failure. This has the primary effect that it renders sustainable development as, primarily, a technical issue, and beyond politics. Therefore, we conclude that current policy reproduces ‘weak’ forms of sustainable development, where the practice is depoliticized and premised upon continued growth and innovation.
Constructing and Communicating the Visual Identity of a University. Case Study: Visual Identity of Transilvania University of Brasov
Authors: Coman Claudiu , Bularca Maria , Repanovici Angela
Published: June 2021

In order to be successful in today’s competitive environment, universities must have well-established identities. This study focused on the construction and communication of the visual identity of Transilvania University of Brasov. The purpose of this study was to assess if the change produced the effects expected by the initiators. Interviews with students and teachers were conducted. We found that the current visual identity does not maintain a connection with the old one, and our research supported the idea that identity is dynamic and subject to change. The change took place from top to bottom. The current identity is linked to the university’s geographical position in the country. The change produced the desired outcomes, but there were some issues in regards to the meaning of the logo and students and teachers having difficulties in understanding it, and thus the university should better communicate how it wants to be perceived through it.
Development of Ecological Strategies for the Recovery of the Main Nitrogen Agricultural Pollutants: A Review on Environmental Sustainability in Agroecosystems
Authors: Losacco Daniela , Ancona Valeria , De Paola Domenico , Tumolo Marina , Massarelli Carmine , Gatto Angela , Uricchio Vito Felice
Published: June 2021

Nitrogen (N) is a fundamental nutrient for plant growth and for the performance of biological functions. In agroecosystems, nitrogen fertilization is aimed at providing a suitable N dose for crop growth, avoiding the impoverishment or the improper enrichment of nitrogen compounds in soil. The high application of nitrogen fertilizers is the main cause of the increase in nitrate leaching and loss of the quality of natural resources (groundwater and soil). In the last decades, new sustainable technological approaches have been developed and applied on laboratory and field scales to reduce the impacts of nitrogen pollution on the environmental matrices and to improve the sustainability of agricultural management. This review highlights the results of the implementation of sustainable remediation new strategies to reduce pollution from a main agricultural contaminant (nitrate) and describes the benefits obtained from the use of these solutions in agroecosystems.
Dietary Exposure to Mycotoxins through Alcoholic and Non-Alcoholic Beverages in Valencia, Spain
Authors: Carballo Dionisia , Fernández-Franzón Mónica , Ferrer Emilia , Pallarés Noelia , Berrada Houda
Published: June 2021

The present study investigated the presence of 30 mycotoxins in 110 beverage samples of beer, wine, cava, and cider purchased in Valencia (Spain). A validated method based on dispersive liquid–liquid microextraction and chromatographic methods coupled with tandem mass spectrometry was applied. The method showed satisfactory recoveries ranging from 61 to 116% for the different beverages studied. The detection and quantification limits ranged from 0.03 to 2.34 µg/L and 0.1 to 7.81 µg/L, respectively. The results showed that beer samples were the most contaminated, even with concentrations ranging from 0.24 to 54.76 µg/L. A significant presence of alternariol was found in wine, which reached concentrations up to 26.86 µg/L. Patulin and ochratoxin A were the most frequently detected mycotoxins in cava and cider samples, with incidences of 40% and 26%, respectively. Ochratoxin A exceeded the maximum level set by the EU in one wine sample. The results obtained were statistically validated. The combined exposure was assessed by the sum of mycotoxin concentrations contaminating the same samples to provide information on the extent of dietary exposure to mycotoxins. No significant health risk to consumers was associated with the mycotoxin levels detected in the beverages tested.
The Presence of Aflatoxin M1 in Milk and Milk Products in Bangladesh
Authors: Sumon Abu Hasan , Islam Farjana , Mohanto Nayan Chandra , Kathak Rahanuma Raihanu , Molla Noyan Hossain , Rana Sohel , Degen Gisela H. , Ali Nurshad
Published: June 2021

As milk provides both micro- and macronutrients, it is an important component in the diet. However, the presence of aflatoxin B1 (AFB1) in the feed of dairy cattle results in contamination of milk and dairy products with aflatoxin M1 (AFM1), a toxic metabolite of the carcinogenic mycotoxin. With the aim to determine AFM1 concentrations in milk and milk products consumed in Bangladesh, in total, 145 samples were collected in four divisional regions (Sylhet, Dhaka, Chittagong, and Rajshahi). The samples comprised these categories: raw milk (n = 105), pasteurized milk (n = 15), ultra-high temperature (UHT)-treated milk (n = 15), fermented milk products such as yogurt (n = 5), and milk powder (n = 5). AFM1 levels in these samples were determined through competitive enzyme-linked immunosorbent assay (ELISA). Overall, AFM1 was present in 78.6% of milk and milk products in the range of 5.0 to 198.7 ng/L. AFM1 was detected in 71.4% of raw milk (mean 41.1, range 5.0–198.7 ng/L), and in all pasteurized milk (mean 106, range 17.2–187.7 ng/L) and UHT milk (mean 73, range 12.2–146.9 ng/L) samples. Lower AFM1 levels were found in yogurt (mean 16.9, range 8.3–41.1 ng/L) and milk powder samples (mean 6.6, range 5.9–7.0 ng/L). About one-third of the raw, pasteurized, and UHT milk samples exceeded the EU regulatory limit (50 ng/L) for AFM1 in milk, while AFM1 levels in yogurt and milk powder samples were well below this limit. Regarding regions, lower AFM1 contamination was observed in Chittagong (mean 6.6, max 10.6 ng/L), compared to Sylhet (mean 53.7, max 198.7 ng/L), Dhaka (mean 37.8, max 97.2 ng/L), and Rajshahi (mean 34.8, max 131.4 ng/L). Yet, no significant difference was observed in AFM1 levels between summer and winter season. In conclusion, the observed frequency and levels of aflatoxin contamination raise concern and must encourage further monitoring of AFM1 in milk and milk products in Bangladesh.