bluuebunny/crossref_metadata_2025 huggingface dataset:
A Flexible Single Loop Setup for Water-Borne Transient Electromagnetic Sounding Applications
Authors: Aigner Lukas , Högenauer Philipp , Bücker Matthias , Flores Orozco Adrián
Published: October 2021

Water-borne transient electromagnetic (TEM) soundings provide the means necessary to investigate the geometry and electrical properties of rocks and sediments below continental water bodies, such as rivers and lakes. Most water-borne TEM systems deploy separated magnetic transmitter and receiver loop antennas—typically in a central or offset configuration. These systems mostly require separated floating devices with rigid structures for both loop antennas. Here, we present a flexible single-loop TEM system, the light-weight design of which simplifies field procedures. Our system also facilitates the use of different geometries of the loop antenna permitting to adjust the depth of investigation (DOI) and the minimum sounding depth in the field. We measure the turn-off ramp with an oscilloscope and use the DOI to assess the minimum and maximum exploration depth of our single-loop TEM system, respectively. A reduction of the loop-antenna size improves early-time TEM data due to a reduced length of the turn-off ramp, whereas an increase of the loop-antenna size enhances the signal strength at late times, which allows to investigate deeper structures below the lake bed. We illustrate the capabilities of our system with a case study carried out at Lake Langau in Austria. Our results show that our system is capable of reaching a DOI of up to 50 m (with a maximum radius of the circular loop of 11.9 m), while it also resolves the water layer down to a minimum thickness of 6.8 m (when the radius is reduced to 6.2 m).
Roll Angle Estimation of a Motorcycle through Inertial Measurements
Authors: Maceira Diego , Luaces Alberto , Lugrís Urbano , Naya Miguel Á. , Sanjurjo Emilio
Published: October 2021

Currently, the interest in creating autonomous driving vehicles and progressively more sophisticated active safety systems is growing enormously, being a prevailing importance factor for the end user when choosing between either one or another commercial vehicle model. While four-wheelers are ahead in the adoption of these systems, the development for two-wheelers is beginning to gain importance within the sector. This makes sense, since the vulnerability for the driver is much higher in these vehicles compared to traditional four-wheelers. The particular dynamics and stability that govern the behavior of single-track vehicles (STVs) make the task of designing active control systems, such as Anti-lock Braking System (ABS) systems or active or semi-active suspension systems, particularly challenging. The roll angle can achieve high values, which greatly affects the general behavior of the vehicle. Therefore, it is a magnitude of the utmost importance; however, its accurate measurement or estimation is far from trivial. This work is based on a previous paper, in which a roll angle estimator based on the Kalman filter was presented and tested on an instrumented bicycle. In this work, a further refinement of the method is proposed, and it is tested in more challenging situations using the multibody model of a motorcycle. Moreover, an extension of the method is also presented to improve the way noise is modeled within this Kalman filter.
Hydrothermal Synthesis of Nitrogen, Boron Co-Doped Graphene with Enhanced Electro-Catalytic Activity for Cymoxanil Detection
Authors: Varodi Codruța , Pogăcean Florina , Coros Maria , Magerusan Lidia , Stefan-van Staden Raluca-Ioana , Pruneanu Stela
Published: October 2021

A sample of nitrogen and boron co-doped graphene (NB-Gr) was obtained by the hydrothermal method using urea and boric acid as doping sources. According to XRD analysis, the NB-Gr sample was formed by five-layer graphene. In addition, the XPS analysis confirmed the nitrogen and boron co-doping of the graphene sample. After synthesis, the investigation of the electro-catalytic properties of the bare (GC) and graphene-modified electrode (NB-Gr/GC) towards cymoxanil detection (CYM) was performed. Significant differences between the two electrodes were noticed. In the first case (GC) the peak current modulus was small (1.12 × 10−5 A) and appeared in the region of negative potentials (−0.9 V). In contrast, when NB-Gr was present on top of the GC electrode it promoted the transfer of electrons, leading to a large peak current increase (1.65 × 10−5 A) and a positive shift of the peak potential (−0.75 V). The NB-Gr/GC electrode was also tested for its ability to detect cymoxanil from a commercial fungicide (CURZATE MANOX) by the standard addition method, giving a recovery of 99%.
On Measuring Implant Fixation Stability in ACL Reconstruction
Authors: Benca Emir , Zderic Ivan , Caspar Jan , Knegsel Kenneth van , Hirtler Lena , Gueorguiev Boyko , Widhalm Harald , Windhager Reinhard , Varga Peter
Published: October 2021

Numerous methods and devices are available for implant fixation in anterior cruciate ligament (ACL) reconstruction. Biomechanical data indicate high variability in fixation stability across different devices. This study aims to provide a better insight into measuring the structural characteristics and mechanical behavior of ACL implant fixations. Fourteen human tibial specimens with reconstructed ACLs were subjected to progressively increasing dynamic loading until failure. The motions of the tibia, the proximal and distal graft ends, as well as the testing frame and actuator, were continuously recorded via a motion tracking system. Significantly higher displacements of the machine actuator (1.0 mm at graft slippage onset, and 12.2 mm at ultimate load) were measured compared to the displacements of the proximal (0.8 and 4.3 mm, respectively) and distal graft (0.1 and 3.4 mm, respectively) ends. The displacements measured at different sites showed significant correlations. The provided data suggest significant and systematic inaccuracies in the stiffness and slippage of the fixation when using machine displacement, as commonly reported in the literature. The assessment of the distal graft displacement excludes the artifactual graft elongation, and most accurately reflects the graft slippage onset indicating clinical failure. Considering the high displacement at the ultimate load, the ultimate load could be used as a standardized variable to compare different fixation methods. However, the ultimate load alone is not sufficient to qualitatively describe fixation stability.
Measurement of Acceleration Response Functions with Scalable Low-Cost Devices. An Application to the Experimental Modal Analysis
Authors: Magdaleno Alvaro , Villacorta Juan J. , del-Val Lara , Izquierdo Alberto , Lorenzana Antolin
Published: October 2021

One of the most popular options in the Structural Health Monitoring field is the tracking of the modal parameters, which are estimated through the frequency response functions of the structure, usually in the form of accelerances, which are computed as the ratio between the measured accelerations and the applied forces. This requires the use of devices capable of synchronously recording accelerations at several points of the structure at high sampling rates and the subsequent computational analysis using the recorded data. To this end, this work presents and validates a new scalable acquisition system based on multiple myRIO devices and digital MEMS (Micro-Electro-Mechanical System) accelerometers, intended for modal analysis of large structures. A simple form of this system was presented by the authors in a previous work, showing that a single board with some accelerometers connected to it got to obtain high quality measurements in both time and frequency domains. Now, a larger system composed by several slave boards connected and synchronized to a master one is presented. Delays lower than 100 ns are found between the synchronised channels of the proposed system. For validation purposes, a case study is presented where the devices are deployed on a timber platform to estimate its modal properties, which are compared with the ones provided by a commercial system, based on analog accelerometers, to show that similar results are obtained at a significantly lower cost.
Digital Twin of an Optical Measurement System
Authors: Vlaeyen Michiel , Haitjema Han , Dewulf Wim
Published: October 2021

Digital twins of measurement systems are used to estimate their measurement uncertainty. In the past, virtual coordinate measuring machines have been extensively researched. Research on digital twins of optical systems is still lacking due to the high number of error contributors. A method to describe a digital twin of an optical measurement system is presented in this article. The discussed optical system is a laser line scanner mounted on a coordinate measuring machine. Each component of the measurement system is mathematically described. The coordinate measuring machine focuses on the hardware errors and the laser line scanner determines the measurement error based on the scan depth, in-plane angle and out-of-plane angle. The digital twin assumes stable measurement conditions and uniform surface characteristics. Based on the Monte Carlo principle, virtual measurements can be used to determine the measurement uncertainty. This is demonstrated by validating the digital twin on a set of calibrated ring gauges. Two validation tests are performed: the first verifies the virtual uncertainty estimation by comparison with experimental data. The second validates the measured diameter of different ring gauges by comparing the estimated confidence interval with the calibrated diameter.
Recent Technologies, Security Countermeasure and Ongoing Challenges of Industrial Internet of Things (IIoT): A Survey
Authors: Tan Soo Fun , Samsudin Azman
Published: October 2021

The inherent complexities of Industrial Internet of Things (IIoT) architecture make its security and privacy issues becoming critically challenging. Numerous surveys have been published to review IoT security issues and challenges. The studies gave a general overview of IIoT security threats or a detailed analysis that explicitly focuses on specific technologies. However, recent studies fail to analyze the gap between security requirements of these technologies and their deployed countermeasure in the industry recently. Whether recent industry countermeasure is still adequate to address the security challenges of IIoT environment are questionable. This article presents a comprehensive survey of IIoT security and provides insight into today’s industry countermeasure, current research proposals and ongoing challenges. We classify IIoT technologies into the four-layer security architecture, examine the deployed countermeasure based on CIA+ security requirements, report the deficiencies of today’s countermeasure, and highlight the remaining open issues and challenges. As no single solution can fix the entire IIoT ecosystem, IIoT security architecture with a higher abstraction level using the bottom-up approach is needed. Moving towards a data-centric approach that assures data protection whenever and wherever it goes could potentially solve the challenges of industry deployment.
Mobile Sensing with Smart Wearables of the Physical Context of Distance Learning Students to Consider Its Effects on Learning
Authors: Ciordas-Hertel George-Petru , Rödling Sebastian , Schneider Jan , Di Mitri Daniele , Weidlich Joshua , Drachsler Hendrik
Published: October 2021

Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners’ physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners’ smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner.
Vascular Auscultation of Carotid Artery: Towards Biometric Identification and Verification of Individuals
Authors: Salvi Rutuja , Fuentealba Patricio , Henze Jasmin , Bisgin Pinar , Sühn Thomas , Spiller Moritz , Burmann Anja , Boese Axel , Illanes Alfredo , Friebe Michael
Published: October 2021

Background: Biometric sensing is a security method for protecting information and property. State-of-the-art biometric traits are behavioral and physiological in nature. However, they are vulnerable to tampering and forgery. Methods: The proposed approach uses blood flow sounds in the carotid artery as a source of biometric information. A handheld sensing device and an associated desktop application were built. Between 80 and 160 carotid recordings of 11 s in length were acquired from seven individuals each. Wavelet-based signal analysis was performed to assess the potential for biometric applications. Results: The acquired signals per individual proved to be consistent within one carotid sound recording and between multiple recordings spaced by several weeks. The averaged continuous wavelet transform spectra for all cardiac cycles of one recording showed specific spectral characteristics in the time-frequency domain, allowing for the discrimination of individuals, which could potentially serve as an individual fingerprint of the carotid sound. This is also supported by the quantitative analysis consisting of a small convolutional neural network, which was able to differentiate between different users with over 95% accuracy. Conclusion: The proposed approach and processing pipeline appeared promising for the discrimination of individuals. The biometrical recognition could clinically be used to obtain and highlight differences from a previously established personalized audio profile and subsequently could provide information on the source of the deviation as well as on its effects on the individual’s health. The limited number of individuals and recordings require a study in a larger population along with an investigation of the long-term spectral stability of carotid sounds to assess its potential as a biometric marker. Nevertheless, the approach opens the perspective for automatic feature extraction and classification.
Evaluating the Dynamics of Bluetooth Low Energy Based COVID-19 Risk Estimation for Educational Institutes
Authors: Aljohani Abdulah Jeza , Shuja Junaid , Alasmary Waleed , Alashaikh Abdulaziz
Published: October 2021

COVID-19 tracing applications have been launched in several countries to track and control the spread of viruses. Such applications utilize Bluetooth Low Energy (BLE) transmissions, which are short range and can be used to determine infected and susceptible persons near an infected person. The COVID-19 risk estimation depends on an epidemic model for the virus behavior and Machine Learning (ML) model to classify the risk based on time series distance of the nodes that may be infected. The BLE technology enabled smartphones continuously transmit beacons and the distance is inferred from the received signal strength indicators (RSSI). The educational activities have shifted to online teaching modes due to the contagious nature of COVID-19. The government policy makers decide on education mode (online, hybrid, or physical) with little technological insight on actual risk estimates. In this study, we analyze BLE technology to debate the COVID-19 risks in university block and indoor class environments. We utilize a sigmoid based epidemic model with varying thresholds of distance to label contact data with high risk or low risk based on features such as contact duration. Further, we train multiple ML classifiers to classify a person into high risk or low risk based on labeled data of RSSI and distance. We analyze the accuracy of the ML classifiers in terms of F-score, receiver operating characteristic (ROC) curve, and confusion matrix. Lastly, we debate future research directions and limitations of this study. We complement the study with open source code so that it can be validated and further investigated.
Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data
Authors: Yanan Pu , Jilong Yan , Heng Zhang
Published: October 2021

Compared with optical sensors, wearable inertial sensors have many advantages such as low cost, small size, more comprehensive application range, no space restrictions and occlusion, better protection of user privacy, and more suitable for sports applications. This article aims to solve irregular actions that table tennis enthusiasts do not know in actual situations. We use wearable inertial sensors to obtain human table tennis action data of professional table tennis players and non-professional table tennis players, and extract the features from them. Finally, we propose a new method based on multi-dimensional feature fusion convolutional neural network and fine-grained evaluation of human table tennis actions. Realize ping-pong action recognition and evaluation, and then achieve the purpose of auxiliary training. The experimental results prove that our proposed multi-dimensional feature fusion convolutional neural network has an average recognition rate that is 0.17 and 0.16 higher than that of CNN and Inception-CNN on the nine-axis non-professional test set, which proves that we can better distinguish different human table tennis actions and have a more robust generalization performance. Therefore, on this basis, we have better realized the enthusiast of table tennis the purpose of the action for auxiliary training.
Validation of Low-Cost Impedance Analyzer via Nitrate Detection
Authors: De Beer Dirk Johannes , Joubert Trudi-Heleen
Published: October 2021

Impedance spectroscopy is a widely used electrochemical technique with a wide variety of applications. Many of these applications benefit from the additional accessibility provided by low-cost impedance devices. With this in mind, a low-cost impedance device was designed for a high performance-to-cost ratio. The performance of this analyzer was validated against a high-performance DropSens µStat-i 400s potentiostat by performing an application-based experiment. Nitrate detection provides a relevant experiment because of the importance of maintaining precise nitrate concentrations to mitigate the impact of nitrate fluctuations on the environment. Dissolved nitrate samples of different concentrations, in the range 3–1000 mg/L, were confirmed colorimetrically and measured with both instruments. A calibration curve of the real impedance matched a sigmoidal transfer, with a linear region for concentrations below 10 mg/L. The device under investigation exhibited an average magnitude error of 1.28% and an average phase error of 0.96∘ relative to the high-performance standard, which validates the performance of the low-cost device. A cost analysis is presented that highlights some of the complexities of cost comparisons.
A Pilot Trial to Evaluate the Accuracy of a Novel Non-Invasive Glucose Meter
Authors: Schwarz Yair , Konvalina Noa , Tirosh Amir
Published: October 2021

The non-invasive self-monitoring of blood glucose (SMBG) has been the subject of intense investigation over recent decades. We conducted a pilot study designed to examine a novel non-invasive glucometer, the HGR GWave, utilizing radiofrequency (RF) sensing. Blood glucose levels assessed by this HGR prototype were compared to measurements performed by a hexokinase core laboratory assay during an oral glucose tolerance test (oGTT) for 5 subjects with type 2 diabetes. The HGR glucose meter readings were also compared to two Abbot Freestyle® glucose meters, which were also used for calibration. The accuracy of the results was evaluated through the calculation of relative absolute difference (RAD), specified percentage differences between 43 reference glucose measurements, and using comparator measurements. The median RAD was −4.787. We detected 79.04%, 92.99% and 97.64% of HGR readings within ±10%, ±15% and ±20% of the reference glucose measurements. The HGR readings had a high correlation with reference lab glucose measurements with R2 = 0.924 (95% CI 0.929–0.979; p < 0.0001). When compared to the Freestyle® glucose meters 94.3% and 100% of the readings were within ±5% and ±10%, with R2 = 0.975 (0.975–0.994; p < 0.0001). The HGR prototype glucose meter was found to be accurate in detecting real-time blood glucose during an oGTT in this small pilot study. A study with a broader range of blood glucose levels is needed to further assess its accuracy and its suitability for clinical use.
Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning
Authors: Gupta Anmol , Siddhad Gourav , Pandey Vishal , Roy Partha Pratim , Kim Byung-Gyu
Published: October 2021

Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual’s workload using EEG is of paramount importance. Multiple cognitive, psychiatric and behavioral phenotypes have already been known to be linked with “functional connectivity”, i.e., correlations between different brain regions. In this work, we explored the possibility of using different model-free functional connectivity metrics along with deep learning in order to efficiently classify the cognitive workload of the participants. To this end, 64-channel EEG data of 19 participants were collected while they were doing the traditional n-back task. These data (after pre-processing) were used to extract the functional connectivity features, namely Phase Transfer Entropy (PTE), Mutual Information (MI) and Phase Locking Value (PLV). These three were chosen to do a comprehensive comparison of directed and non-directed model-free functional connectivity metrics (allows faster computations). Using these features, three deep learning classifiers, namely CNN, LSTM and Conv-LSTM were used for classifying the cognitive workload as low (1-back), medium (2-back) or high (3-back). With the high inter-subject variability in EEG and cognitive workload and recent research highlighting that EEG-based functional connectivity metrics are subject-specific, subject-specific classifiers were used. Results show the state-of-the-art multi-class classification accuracy with the combination of MI with CNN at 80.87%, followed by the combination of PLV with CNN (at 75.88%) and MI with LSTM (at 71.87%). The highest subject specific performance was achieved by the combinations of PLV with Conv-LSTM, and PLV with CNN with an accuracy of 97.92%, followed by the combination of MI with CNN (at 95.83%) and MI with Conv-LSTM (at 93.75%). The results highlight the efficacy of the combination of EEG-based model-free functional connectivity metrics and deep learning in order to classify cognitive workload. The work can further be extended to explore the possibility of classifying cognitive workload in real-time, dynamic and complex real-world scenarios.
Healthy Kidney Segmentation in the Dce-Mr Images Using a Convolutional Neural Network and Temporal Signal Characteristics
Authors: Klepaczko Artur , Eikefjord Eli , Lundervold Arvid
Published: October 2021

Quantification of renal perfusion based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) requires determination of signal intensity time courses in the region of renal parenchyma. Thus, selection of voxels representing the kidney must be accomplished with special care and constitutes one of the major technical limitations which hampers wider usage of this technique as a standard clinical routine. Manual segmentation of renal compartments—even if performed by experts—is a common source of decreased repeatability and reproducibility. In this paper, we present a processing framework for the automatic kidney segmentation in DCE-MR images. The framework consists of two stages. Firstly, kidney masks are generated using a convolutional neural network. Then, mask voxels are classified to one of three regions—cortex, medulla, and pelvis–based on DCE-MRI signal intensity time courses. The proposed approach was evaluated on a cohort of 10 healthy volunteers who underwent the DCE-MRI examination. MRI scanning was repeated on two time events within a 10-day interval. For semantic segmentation task we employed a classic U-Net architecture, whereas experiments on voxel classification were performed using three alternative algorithms—support vector machines, logistic regression and extreme gradient boosting trees, among which SVM produced the most accurate results. Both segmentation and classification steps were accomplished by a series of models, each trained separately for a given subject using the data from other participants only. The mean achieved accuracy of the whole kidney segmentation was 94% in terms of IoU coefficient. Cortex, medulla and pelvis were segmented with IoU ranging from 90 to 93% depending on the tissue and body side. The results were also validated by comparing image-derived perfusion parameters with ground truth measurements of glomerular filtration rate (GFR). The repeatability of GFR calculation, as assessed by the coefficient of variation was determined at the level of 14.5 and 17.5% for the left and right kidney, respectively and it improved relative to manual segmentation. Reproduciblity, in turn, was evaluated by measuring agreement between image-derived and iohexol-based GFR values. The estimated absolute mean differences were equal to 9.4 and 12.9 mL/min/1.73 m2 for scanning sessions 1 and 2 and the proposed automated segmentation method. The result for session 2 was comparable with manual segmentation, whereas for session 1 reproducibility in the automatic pipeline was weaker.
Sensorless Estimation Based on Neural Networks Trained with the Dynamic Response Points
Authors: Rodríguez-Abreo Omar , Castillo Velásquez Francisco Antonio , Zavala de Paz Jonny Paul , Martínez Godoy José Luis , Garcia Guendulain Crescencio
Published: October 2021

In the present work, a neuronal dynamic response prediction system is shown to estimate the response of multiple systems remotely without sensors. For this, a set of Neural Networks and the response to the step of a stable system is used. Six basic characteristics of the dynamic response were extracted and used to calculate a Transfer Function equivalent to the dynamic model. A database with 1,500,000 data points was created to train the network system with the basic characteristics of the dynamic response and the Transfer Function that causes it. The contribution of this work lies in the use of Neural Network systems to estimate the behavior of any stable system, which has multiple advantages compared to typical linear regression techniques since, although the training process is offline, the estimation can perform in real time. The results show an average 2% MSE error for the set of networks. In addition, the system was tested with physical systems to observe the performance with practical examples, achieving a precise estimation of the output with an error of less than 1% for simulated systems and high performance in real signals with the typical noise associated due to the acquisition system.
Protection of Immigrant Children and Youth at Risk: Experiences and Strategies of Social Integration in Portugal
Authors: Casquilho-Martins Inês , Matela Thais
Published: October 2021

Over the years, social projects and programmes in Portugal have resulted in actions and outcomes to improve the integration and social inclusion of immigrant children and young people in socially vulnerable territories. This article aims to analyse the intervention experiences of teams with immigrant children and young people at risk. The developed study focused on a qualitative approach through the systematisation of measures to protect the rights of immigrant children and young people in Portugal. Semi-structured interviews were also carried out with professionals working in multidisciplinary teams intervening with immigrant children and young people. The results allow the identification of strategies and intervention methods with a positive impact on social integration supported by collaborative and participatory methodologies, but also highlight limitations such as cultural and linguistic barriers, and lack of children’s participation. Thus, it becomes fundamental to value the central role of children and young people in promoting and guaranteeing their rights.
The “Undeserving” Narrative in Child and Family Social Work and How It Is Perpetuated by “Progressive Neoliberalism”: Ideas for Social Work Education
Authors: Fenton Jane
Published: October 2021

“Progressive neoliberalism” is the current hegemonic approach to understanding social justice in Western liberal democracies. “Progressive neoliberalism” also resurrects the “deserving” vs. “undeserving” narrative that can lead to punitive and pathologising approaches to poor and unemployed people—the demographic comprising the majority of child and family social work service users. Indeed, research suggests that social workers’ attitudes towards families in poverty are strikingly congruent with “progressive neoliberalism.” This article suggests that generational changes and the particular form of group-based identity, postmodern social justice ideology often taught in social work education have unwittingly conspired to create this concerning picture. This article suggests that the resurrection of radical social work, with attention to economic inequality, is one way to counteract this trend.
From Tabletop to Screen: Playing Dungeons and Dragons during COVID-19
Authors: Scriven Paul
Published: October 2021

Media reports suggest that the tabletop roleplaying game Dungeons & Dragons saw its biggest year to date in 2020, with many such reports touting the interactive and social benefits for people facing COVID-19 lockdowns. This paper explores the reported challenges and benefits of playing D&D through teleconferencing hardware and software, and the experience of using virtual tabletops. A thematic analysis of a sample of Reddit threads discussing player experiences of transitioning D&D to remote play during COVID-19 social distancing was undertaken. The findings highlight a variety of player attitudes and preferences towards playing D&D remotely. The data suggest a mostly negative sentiment towards playing D&D online for groups that had transitioned from in-person to remote play. Loss of in-person socialisation was identified as an important contributor to a poor play experience, but groups would persevere with remote play to maintain social relationships, suggesting that, for many players, D&D serves an important social function beyond mere play. Some avenues for future research are identified.
Educational Intervention through a Board Game for the Teaching of Mathematics to Dyslexic Greek Students
Authors: Malliakas Efstratios , Jiménez-Fanjul Nοelia , Marín-Díaz Verónica
Published: October 2021

The main purpose of this study was to investigate the effectiveness of an intervention strategy in teaching mathematics based on a board game, using the curriculum of mathematics in Greece, called Adapted Analytical Programs (A.A.P.). This research was conducted on secondary students in Greek General Schools over a period of five weeks. One hundred and twenty-four 12-to-13-year-old dyslexic students participated in the study. A pre-test and a post-test with exercises in the chapter of fractions were used to assess the improvement in students’ performance. The assessment showed that this intervention strategy improved dyslexic students’ performance. Τhe results of the research indicate that integrating a board game adapted to mathematics into the secondary school curriculum could have positive effects on dyslexic students.
Humane Education’s Effect on Middle School Student Motivation and Standards-Based Reading Assessment
Authors: O’Connor Julie , Samuels William Ellery
Published: October 2021

Students educated in the juvenile justice system face acute challenges such as lack of motivation and negative attitudes toward school. Schools in the system are expected to provide rigorous, Common Core-standards-aligned instruction. Humane education—lessons that nurture kindness and empathy towards humans, animals, and the environment—has been shown to motivate students and encourage their pro-social sentiments. This randomized control trial (with constraints) study of 192 12- and 13-year-old students from New Jersey asked students to complete five standards-aligned reading passages with text-based questions. The experimental-group assessments contained humane education themes and the control-group assessments had non-animal related high interest topics. The passages were equated in reading level, word count, etc. Analyses of the results showed that not only did students who received humane education passages do better overall, but also did much better on questions addressing specific Common Core Reading for Information standards. This study can be a starting point for applying and researching the effectiveness of humane education on the juvenile justice population, specifically, because they are expected to learn standards-aligned curricula and are in particular need of academic motivation and pro-social encouragement.
Pre-Exercise Whole- or Partial-Body Cryotherapy Exposure to Improve Physical Performance: A Systematic Review
Authors: Partridge Emily M. , Cooke Julie , McKune Andrew J. , Pyne David B.
Published: October 2021

Whole- (WBC) and partial-body cryotherapy (PBC) are commonly used sports medicine modalities for the treatment of injury and exercise recovery. Physiological and perceptual effects have the potential to be utilised in a novel application that involves pre-exercise WBC and PBC exposure to improve physical performance. A systematic literature search of multiple databases was conducted in July 2021 to identify and evaluate the effects of pre-exercise exposure of WBC or PBC on physical performance measures, and any potential translational effects. The following inclusion criteria were applied: (1) use of WBC or PBC exposure pre-exercise, (2) use of WBC or PBC in healthy and/or athletic populations, (3) control group was used in the data collection, and (4) investigated physiological, psychosocial or direct physical performance impacts of pre-exercise cryotherapy exposure. A total of 759 titles were identified, with twelve relevant studies satisfying the inclusion criteria after full-text screening. The twelve studies were categorised into three key areas: performance testing (n = 6), oxidative stress response (n = 4) and lysosomal enzyme activity (n = 2). The potential for eliciting favourable physical and physiological responses from pre-exercise WBC or PBC is currently unclear with a paucity of good quality research available. Furthermore, a lack of standardisation of cryotherapy protocols is a current challenge.
Go Greener, Less Risk: Access to Nature Is Associated with Lower Risk Taking in Different Domains during the COVID-19 Lockdown
Authors: Panno Angelo , Theodorou Annalisa , Carbone Giuseppe Alessio , De Longis Evelina , Massullo Chiara , Cepale Gianluca , Carrus Giuseppe , Imperatori Claudio , Sanesi Giovanni
Published: October 2021

Specific risk attitude and risky behavior had an important boost during the coronavirus disease 2019 (COVID-19) pandemic. In this contribution, we hypothesize that access to nature during home confinement will decrease both the tendency to passive risk taking and alcohol intake. To do so, we interviewed through an online survey two samples of Italian residents during the strict lockdown due to the first wave of the COVID-19 pandemic. Specifically, in Study 1, participants were 1519 Italian residents coming from different Italian regions, whilst in Study 2, participants were 182 students at a university of southern Italy who were monitored for one week. In Study 1, the hierarchical regression analysis attested that access to nature during the lockdown mitigated the tendency to passive risk taking, over and beyond the effect of socio-demographic variables and the psychological construct of impulsiveness, an important personality correlate of risk taking. In Study 2, the hierarchical regression showed that access to green was associated with fewer glasses of alcohol drunk in a week of lockdown. This effect held over and above the effect of socio-demographic variables and the drinking behavior before the outbreak of the COVID-19 pandemic. In both studies, findings confirmed the beneficial effect of access to nature in specific risk-taking domains. Theoretical future directions, as well as practical implications for the management of the COVID-19 emergency by policymakers, are discussed.
From Lean 5S to 7S Methodology Implementing Corporate Social Responsibility Concept
Authors: Fernández Carrera Jon , Amor del Olmo Alfredo , Romero Cuadrado María , Espinosa Escudero María del Mar , Romero Cuadrado Luis
Published: October 2021

Introducing methodologies that promote innovation and continuous improvement in organizations is no longer optional; therefore, organizations are increasingly using methodologies based on Lean principles. Among them, the 6S tool stands out from the rest, commonly used to establish and maintain a high-quality environment, which it has capacity for due to its status as a kaizen process. Thus, this research seeks to evaluate the relationship between Corporate Social Responsibility and the Lean 6S tool and, in the end, create synergies between them in order to enhance the 6S tool’s capabilities. To achieve this, a literature review and analysis of Lean 6S and CSR were performed, and a survey was also proposed to further the understanding of the relationship. With the analyzed sample, it can be confirmed that a relationship exists between the level of implementation of Lean tools and the level of development of CSR policies; therefore, companies that have implemented Lean tools such as 6S are organizations concerned with sustainability, and the hypothesis that organizations that apply Lean also have a high implementation of CSR is validated. Future work should further develop this relationship so that sustainability is no longer considered as implicit in the application of Lean tools but rather as part of them. This research proposes to develop the 6S tool toward the 7S tool to facilitate the inclusion of a CSR policy in a procedural and simple way.
Mapping the Research Landscape for the Motorcycle Market Policies: Sustainability as a Trend—A Systematic Literature Review
Authors: Cadavid Lorena , Salazar-Serna Kathleen
Published: October 2021

The motorcycle market has experienced an upward trend. That growth brings along mobility, accidents, and environment-related issues; nevertheless, there is a scarcity of literature on evaluating the impact of motorcycle market policies. Consequently, it has been challenging for researchers and policymakers to develop evidence-based strategies to promote or control the growth of this market. This paper aims to review and analyze the scientific literature about motorcycle market policies, using tech-mining techniques and a cluster analysis of keywords, to provide insights about the most relevant world trends in this research area. For this purpose, the bibliographic information of publications in the field was retrieved from the Scopus database. As a result, three thematic clusters (sustainability, mobility, and electric motorcycles) were identified and explained. According to our findings, greenhouse gas emissions, sustainability, environmental impact, and developing countries are the hot research topics. The research leader countries on said topics are the United States, Germany, and the United Kingdom. This study can, therefore, be used as a reference to define a future research agenda in the area. Consequently, it permits researchers and policymakers to identify trending topics and gaps in knowledge, as a baseline to include motorcycles in sustainable and affordable transport systems design.
Implication of Buffer Zones Delineation Considering the Landscape Connectivity and Influencing Patch Structural Factors in Nature Reserves
Authors: Zhang Junhao , Wang Xinjun , Xie Yujing
Published: October 2021

Since habitat fragmentation results in species losses worldwide, considering the influence of buffer zones on the maintenance of connectivity provides a new perspective for buffer delimitation. In our study, the implications of buffer zones around nature reserves were studied at four sites in Fuzhou from the perspective of landscape connectivity based on a distance threshold of 1 km. We applied Graph-based connectivity indices at the landscape level and patch level to reveal the overall connectivity and patterns of change in patch importance for maintaining connectivity with various buffer zones. Based on the results of these analyses, we showed the relationship between structural factors and changes in patch importance by Spearman correlation analysis and redundancy analysis. The results indicate that in the sites with smaller habitat proportion (HP), the connectivity is relatively lower, and the changes in patch importance will be greater when the buffer zone increases. Different buffer zone sizes are suggested in four sites to maximize its benefits. Relatively small patches with high shape complexity and close proximity to patches outside the boundary contribute greatly to connectivity by acting as stepping stones.
An Optimization Model for Water Management under the Dual Constraints of Water Pollution and Water Scarcity in the Fenhe River Basin, North China
Authors: Meng Chong , Zhou Siyang , Li Wei
Published: October 2021

Sustainable watershed development suffers from severe challenges, such as water pollution and water scarcity. Based on an analysis of water quality and water utilization in the Fenhe River Basin, an inexact two-stage stochastic programming model with downside-risk aversion was built for optimal water resource allocations for the four primary water use sectors (industry, domestic use, agriculture, and the environment) in the Fenhe River Basin. The model aims to maximize the comprehensive watershed benefits, including water benefits, water costs, water treatment costs, and downside risks. The constraints are water quality, available water resources, and sectoral demands in different hydrological scenarios. The results show that pollutant emissions decrease as risk-aversion levels increase and show the opposite trend in the midstream and downstream areas. The increase in water resource allocation for agriculture and reduction in ecological water indicate that agriculture suffered the greatest water shortage and risk. Improving water recycling and coordinating the transferred water resources increases the comprehensive benefits and reduces sectoral risks. The model effectively manages rational water allocations under dual constraints and provides support for coordinating socio-economic development and environmental protection in the river basin.
Valorization of Agro-Industrial Residues: Bioprocessing of Animal Fats to Reduce Their Acidity
Authors: Martins António A. , Andrade Soraia , Correia Daniela , Matos Elisabete , Caetano Nídia S. , Mata Teresa M.
Published: October 2021

Adding value to agro-industrial residues is becoming increasingly important, satisfying needs to promote resources’ use efficiency and a more sustainable and circular economy. This work performs a parametric and kinetic study of enzymatic esterification of lard and tallow with high acidity, obtained by the rendering of slaughter by-products, allowing their use as a feed ingredient and increasing their market value. After an initial analysis of potential enzyme candidates, a Candida antarctica lipase B was selected as a biocatalyst for converting free fatty acids (FFA) to esters, using excess ethanol as the reagent. Results show that the fat acidity can be reduced by at least 67% in up to 3 h of reaction time at 45 °C, using the mass ratios of 3.25 ethanol/FFA and 0.0060 enzyme/fat. Kinetic modelling shows an irreversible second-order rate law, function of FFA, and ethanol concentration better fitting the experimental results. Activation energy is 54.7 kJ/mol and pre-exponential factor is 4.6 × 106 L mol−1 min−1.
Exploring Negative Spillover Effects on Stakeholders: A Case Study on Social Media Talk about Crisis in the Food Industry Using Data Mining
Authors: Zimand-Sheiner Dorit , Levy Shalom , Eckhaus Eyal
Published: October 2021

Focusing on public-centered, social-mediated crisis communication, the current exploratory study drew on situational crisis communication theory to formulate a comprehensive view of consumer reactions to crisis. Data mining and automated content analysis techniques were utilized to analyze social media posts by the public during a crisis in the cereals industry. Two path analyses showed that: (a) crisis-related social media posts tended to skip over competitor brand products, followed by two major reaction paths—(1) a rational path based on guilt attribution that justifies implications for the company and (2) an emotional path associated with public distrust; and (b) public self-blame spilled over to other stakeholders such as the government and economic system. The results give voice to issues that concern the public during crises, both as individuals and as a community. They highlight the fact that sustainable crisis management should involve additional stakeholders. Conclusions and implications for society and practice are suggested.
Does China’s Low-Carbon Pilot Policy Promote Foreign Direct Investment? An Empirical Study Based on City-Level Panel Data of China
Authors: Zhao Chang , Wang Bing
Published: October 2021

As an important driving force of China’s economic growth, foreign direct investment (FDI) may be affected by China’s low-carbon pilot policy. Therefore, this paper regards the low-carbon pilot policy as a quasi-natural experiment, and uses the difference-in-difference (DID) model and the panel data of 189 cities in China from 2011 to 2018 to explore the actual impact and intermediary mechanism of low-carbon pilot policy on FDI. The study found that low-carbon pilot policy has a significant promotion effect on FDI, and industrial optimization and upgrading is an important way. At the same time, we construct the difference-in-difference-in-difference (DDD) model, and discuss the heterogeneity of policy effect caused by resource endowments and the individual characteristics of government officials in the process of policy implementation. The results indicate that resource-rich cities can enhance the promotion effect of low-carbon pilot policy on FDI. Similarly, when the mayor of the pilot city is a female, or obtains a master’s degree or a doctorate degree, or majored in non-economics, respectively, the policy effect will be more obvious. In addition, in order to verify the reliability of the research conclusions, this paper also uses a placebo test and data truncation to conduct a series of robustness tests.