bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of Self-Healing EPDM/Ionomer Thermoplastic Vulcanizates by Ionic Cross-Links for Automotive Application
Authors: Jin Woo Seok , Sahu Pranabesh , Park Sung Min , Jeon Jun Ha , Kim Nam Il , Lee Jae Hyeon , Oh Jeong Seok
Published: March 2022

The development of smart elastomeric materials with inherent self-repairing abilities after mechanical damage has important technological and scientific implications, particularly in regard to the durability and life cycle of rubber products. The interest in self-healing materials for automotive applications is rapidly growing along with the increasing importance of vehicle scratch quality and quantity. The creation of a reversible network by noncovalent ionic cross-linking in elastomer/rubber blends is an effective approach to generate the self-healing phenomenon, with reprocessing and recycling properties. In this work, thermoplastic vulcanizates (TPVs) were prepared using ethylene–propylene–diene (EPDM) polymers and high-acid-containing thermoplastic ionomers. Along with the general EPDM, maleic anhydride grafted EPDM (EPDM-g-MAH) was also used for the preparation of the TPVs. The strategy was based on a simple ionic crosslinking reaction between the carboxyl groups present in the ionomer and zinc oxide (ZnO), where the formation of reversible Zn2+ salt bondings exhibits the self-healing behavior. The heterogeneous blending of EPDM and ionomers was also used to investigate the thermal and mechanical properties of the TPVs. The experimental findings were further supported by the surface morphology of the fracture surfaces viewed using microscopy. The self-healing behavior of the TPVs has been identified by scratch resistance testing, where the EPDM-g-MAH TPVs showed excellent healing efficiency of the scratch surface. Therefore, this work provides an efficient approach to fabricate new ionically cross-linked thermoplastic vulcanizates with excellent mechanical and self-repairing properties for the skins of automotive interior door trims and instrument panel applications.
Scientific Stylisation or the ‘Democracy Dilemma’ of Graphical Abstracts
Authors: Sancho Guinda Carmen
Published: March 2022

The need for more democratic models of interaction between scientists and non-expert audiences, the current commodification of research and the advancements of digital affordances have recently given rise to new online genres for science dissemination, such as the graphical abstract, increasingly demanded by high-impact journals despite its uncertain function. In this paper, I examine the problems posed by this hybrid genre as to the implementation of dialogical and more democratic models of science dissemination; namely, inferential confusion of concepts and narrative sequences, trivialisation and overall interpretive complexity, all of them caused or affected by visual stylisation. After scrutinising over 1000 graphical abstract samples from science blogs, research networks and random finds published in specialised high-rank international journals, I provide a taxonomy of stylisation and make the case for the explicit visual literacy training of students and scholars, as well as for a higher level of specification in the guidelines for potential authors of scientific journals.
The Self-Supervised Spectral–Spatial Vision Transformer Network for Accurate Prediction of Wheat Nitrogen Status from UAV Imagery
Authors: Zhang Xin , Han Liangxiu , Sobeih Tam , Lappin Lewis , Lee Mark A. , Howard Andew , Kisdi Aron
Published: March 2022

Nitrogen (N) fertilizer is routinely applied by farmers to increase crop yields. At present, farmers often over-apply N fertilizer in some locations or at certain times because they do not have high-resolution crop N status data. N-use efficiency can be low, with the remaining N lost to the environment, resulting in higher production costs and environmental pollution. Accurate and timely estimation of N status in crops is crucial to improving cropping systems’ economic and environmental sustainability. Destructive approaches based on plant tissue analysis are time consuming and impractical over large fields. Recent advances in remote sensing and deep learning have shown promise in addressing the aforementioned challenges in a non-destructive way. In this work, we propose a novel deep learning framework: a self-supervised spectral–spatial attention-based vision transformer (SSVT). The proposed SSVT introduces a Spectral Attention Block (SAB) and a Spatial Interaction Block (SIB), which allows for simultaneous learning of both spatial and spectral features from UAV digital aerial imagery, for accurate N status prediction in wheat fields. Moreover, the proposed framework introduces local-to-global self-supervised learning to help train the model from unlabelled data. The proposed SSVT has been compared with five state-of-the-art models including: ResNet, RegNet, EfficientNet, EfficientNetV2, and the original vision transformer on both testing and independent datasets. The proposed approach achieved high accuracy (0.96) with good generalizability and reproducibility for wheat N status estimation.
Three-Dimensional Surface Displacements of the 8 January 2022 Mw6.7 Menyuan Earthquake, China from Sentinel-1 and ALOS-2 SAR Observations
Authors: Liu Jihong , Hu Jun , Li Zhiwei , Ma Zhangfeng , Shi Jianwen , Xu Wenbin , Sun Qian
Published: March 2022

The 8 January 2022 Mw6.7 Menyuan earthquake was generated in the transition zone between the western Lenglongling fault and the eastern Tuolaishan fault, both being part of the Qilian–Haiyuan fault system with an important role in the adjustment of the regional tectonic regime. In this study, four pairs of SAR (synthetic aperture radar) data from Sentinel-1 and ALOS-2 (Advanced Land Observation Satellite-2) satellites were used to derive the surface displacement observations along the satellite line-of-sight (LOS) and azimuth directions using the differential interferometric SAR (InSAR, DInSAR), pixel offset-tracking (POT), multiple aperture InSAR (MAI), and burst overlap InSAR (BOI) methods. An SM-VCE method (i.e., a method for measuring three-dimensional (3D) surface displacements with InSAR based on a strain model and variance component estimation) was employed to combine these derived SAR displacement observations to calculate the 3D co-seismic displacements. Results indicate that the 2022 Menyuan earthquake was dominated by left-lateral slip, and the maximum horizontal and vertical displacements were 1.9 m and 0.6 m, respectively. The relative horizontal surface displacement across the fault was as large as 2–3 m, and the fault-parallel displacement magnitude was larger on the southern side of the fault compared with the northern side. Furthermore, three co-seismic strain invariants were also investigated, revealing that the near-fault area suffered severe deformation, and two obviously expanding and compressed zones were identified. We provide displacements/strains derived in this study in the prevailing geotiff format, which will be useful for the broad community studying this earthquake; in addition, the SM-VCE code used in this study is open to the public so that readers can better understand the method.
On-Orbit Geometric Calibration and Accuracy Validation for Laser Footprint Cameras of GF-7 Satellite
Authors: Chen Jiyi , Zhang Bin , Tang Xinming , Li Guoyuan , Zhou Xiaoqing , Hu Liuru , Dou Xianhui
Published: March 2022

The Gaofen-7 (GF-7) satellite uses a two-beam laser altimetry system in which each beam is equipped with a laser footprint camera (LFC) to provide geometric processing of the laser footprint images that assist in optical image stereo mapping. Because of the violent vibrations during launch and the difference in the environment before and after entering orbit, the key parameters for geometric processing of the laser footprint images may change, which will cause large geolocation errors. Therefore, it is essential to carry out on-orbit calibration and validation for the laser footprint cameras. This study first constructs a rigorous geometric positioning model for the LFC of the GF-7 satellite and analyses various error sources that affect the geometric positioning accuracy of laser footprint images. Then, a comprehensive calibration method, which effectively eliminates the distortion of the LFC optical system, and the positioning error caused by the long-period jitter of the satellite platform, is proposed based on the multi-scene images combined with image simulation. The proposed method can effectively eliminate various errors that affect the geometric positioning accuracy of the GF-7 laser footprint image. The internal geometric positioning accuracy of the calibrated LFC is better than 0.7 pixels, and the absolute geometric positioning accuracy is within 6.0 m after using precise post-processing orbital and attitude data. Our study will contribute to the processing and application of laser altimetry data from the GF-7 satellite.
Correlation Analysis of Noise, Vibration, and Harshness in a Vehicle Using Driving Data Based on Big Data Analysis Technique
Authors: Song Daehun , Hong Seongeun , Seo Jaejoon , Lee Kyounghoon , Song Youngeun
Published: March 2022

A new development process for the noise, vibration, and harshness (NVH) of a vehicle is presented using data analysis and machine learning with long-term NVH driving data. The process includes exploratory data analysis (EDA), variable importance analysis, correlation analysis, sensitivity analysis, and development target selection. In this paper, to dramatically reduce the development period and cost related to vehicle NVH, we propose a technique that can accurately identify the precise connectivity and relationship between vehicle systems and NVH factors. This new technique uses whole big data and reflects the nonlinearity of dynamic characteristics, which was not considered in existing methods, and no data are discarded. Through the proposed method, it is possible to quickly find areas that need improvement through correlation analysis and variable importance analysis, understand how much room noise increases when the NVH level of the system changes through sensitivity analysis, and reduce vehicle development time by improving efficiency. The method could be used in the development process and the validation of other deep learning and machine learning models. It could be an essential step in applying artificial intelligence, big data, and data analysis in the vehicle and mobility industry as a future vehicle development process.
An Ultra-Low-Cost RCL-Meter
Authors: Inácio Pedro M. C. , Guerra Rui , Stallinga Peter
Published: March 2022

An ultra-low-cost RCL meter, aimed at IoT applications, was developed, and was used to measure electrical components based on standard techniques without the need of additional electronics beyond the AVR® micro-controller hardware itself and high-level routines. The models and pseudo-routines required to measure admittance parameters are described, and a benchmark between the ATmega328P and ATmega32U4 AVR® micro-controllers was performed to validate the resistance and capacitance measurements. Both ATmega328P and ATmega32U4 micro-controllers could measure isolated resistances from 0.5 Ω to 80 MΩ and capacitances from 100 fF to 4.7 mF. Inductance measurements are estimated at between 0.2 mH to 1.5 H. The accuracy and range of the measurements of series and parallel RC networks are demonstrated. The relative accuracy (ar) and relative precision (pr) of the measurements were quantified. For the resistance measurements, typically ar, pr < 10% in the interval 100 Ω–100 MΩ. For the capacitance, measured in one of the modes (fast mode), ar < 20% and pr < 5% in the range 100 fF–10 nF, while for the other mode (transient mode), typically ar < 20% in the range 10 nF–10 mF and pr < 5% for 100 pF–10 mF. ar falls below 5% in some sub-ranges. The combination of the two capacitance modes allows for measurements in the range 100 fF–10 mF (11 orders of magnitude) with ar < 20%. Possible applications include the sensing of impedimetric sensor arrays targeted for wearable and in-body bioelectronics, smart agriculture, and smart cities, while complying with small form factor and low cost.
Improving Data Glove Accuracy and Usability Using a Neural Network When Measuring Finger Joint Range of Motion
Authors: Connolly James , Condell Joan , Curran Kevin , Gardiner Philip
Published: March 2022

Data gloves capable of measuring finger joint kinematics can provide objective range of motion information useful for clinical hand assessment and rehabilitation. Data glove sensors are strategically placed over specific finger joints to detect movement of the wearers’ hand. The construction of the sensors used in a data glove, the number of sensors used, and their positioning on each finger joint are influenced by the intended use case. Although most glove sensors provide reasonably stable linear output, this stability is influenced externally by the physical structure of the data glove sensors, as well as the wearer’s hand size relative to the data glove, and the elastic nature of materials used in its construction. Data gloves typically require a complex calibration method before use. Calibration may not be possible when wearers have disabled hands or limited joint flexibility, and so limits those who can use a data glove within a clinical context. This paper examines and describes a unique approach to calibration and angular calculation using a neural network that improves data glove repeatability and accuracy measurements without the requirement for data glove calibration. Results demonstrate an overall improvement in data glove measurements. This is particularly relevant when the data glove is used with those who have limited joint mobility and cannot physically complete data glove calibration.
Attention Module Magnetic Flux Leakage Linked Deep Residual Network for Pipeline In-Line Inspection
Authors: Liu Shucong , Wang Hongjun , Li Rui
Published: March 2022

Pipeline operational safety is the foundation of the pipeline industry. Inspection and evaluation of defects is an important means of ensuring the safe operation of pipelines. In-line inspection of Magnetic Flux Leakage (MFL) can be used to identify and analyze potential defects. For pipeline MFL identification with inspecting in long distance, there exists the issues of low identification efficiency, misjudgment and leakage judgment. To solve these problems, a pipeline MFL inspection signal identification method based on improved deep residual convolutional neural network and attention module is proposed. A improved deep residual network based on the VGG16 convolution neural network is constructed to automatically learn the features from the MFL image signals and perform the identification of pipeline features and defects. The attention modules are introduced to reduce the influence of noises and compound features on the identification results in the process of in-line inspection. The actual pipeline in-line inspection experimental results show that the proposed method can accurately classify the MFL in-line inspection image signals and effectively reduce the influence of noises on the feature identification results with an average classification accuracy of 97.7%. This method can effectively improve identification accuracy and efficiency of the pipeline MFL in-line inspection.
A Simple and Efficient Method for Finger Vein Recognition
Authors: Zhang Zhongxia , Wang Mingwen
Published: March 2022

Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishing ability, security, and non-invasive procedure. The main idea of traditional schemes is to directly extract features from finger vein images and then compare features to find the best match. However, the features extracted from images contain much redundant data, while the features extracted from patterns are greatly influenced by image segmentation methods. To tackle these problems, this paper proposes a new finger vein recognition algorithm by generating code. The proposed method does not require an image segmentation algorithm, is simple to calculate, and has a small amount of data. Firstly, the finger vein images were divided into blocks to calculate the mean value. Then, the centrosymmetric coding was performed using the matrix generated by blocking and averaging. The obtained codewords were concatenated as the feature codewords of the image. The similarity between vein codes is measured by the ratio of minimum Hamming distance to codeword length. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed method. The results indicate that our method outperforms the state-of-the-art methods and has competitive potential in performing the matching task.
An Evaluation of Power Side-Channel Resistance for RNS Secure Logic
Authors: Selvam Ravikumar , Tyagi Akhilesh
Published: March 2022

In this paper, residue number system (RNS) based logic is proposed as a protection against power side-channel attacks. Every input to RNS logic is encrypted as a share of the original input in the residue domain through modulus values. Most existing countermeasures enhance side-channel privacy by making the power trace statistically indistinguishable. The proposed RNS logic provides cryptographic privacy that also offers side-channel resistance. It also offers side-channel privacy by mapping different input bit values into similar bit encodings for the shares. This property is also captured as a symmetry measure in the paper. This side-channel resistance of the RNS secure logic is evaluated analytically and empirically. An analytical metric is developed to capture the conditional probability of the input bit state given the residue state visible to the adversary, but derived from hidden cryptographic secrets. The transition probability, normalized variance, and Kullback–Leibler (KL) divergence serve as side-channel metrics. The results show that our RNS secure logic provides better resistance against high-order side-channel attacks both in terms of power distribution uniformity and success rates of machine learning (ML)-based power side-channel attacks. We performed SPICE simulations on Montgomery modular multiplication and Arithmetic-style modular multiplication using the FreePDK 45 nm Technology library. The simulation results show that the side-channel security metrics using KL divergence are 0.0204 for Montgomery and 0.0020 for the Arithmetic-style implementation. This means that Arithmetic-style implementation has better side-channel resistance than the Montgomery implementation. In addition, we evaluated the security of the AES encryption with RNS secure logic on a Spartan-6 FPGA Board. Experimental results show that the protected AES circuit offers 79% higher resistance compared to the unprotected AES circuit.
COIN: Counterfactual Image Generation for Visual Question Answering Interpretation
Authors: Boukhers Zeyd , Hartmann Timo , Jürjens Jan
Published: March 2022

Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent and advanced. However, they are still error-prone when dealing with relatively complex questions. Therefore, it is important to understand the behaviour of the VQA models before adopting their results. In this paper, we introduce an interpretability approach for VQA models by generating counterfactual images. Specifically, the generated image is supposed to have the minimal possible change to the original image and leads the VQA model to give a different answer. In addition, our approach ensures that the generated image is realistic. Since quantitative metrics cannot be employed to evaluate the interpretability of the model, we carried out a user study to assess different aspects of our approach. In addition to interpreting the result of VQA models on single images, the obtained results and the discussion provides an extensive explanation of VQA models’ behaviour.
Simultaneous In Situ Imaging of pH and Surface Roughening during the Progress of Crevice Corrosion of Stainless Steel
Authors: Miyamoto Ko-ichiro , Hiramitsu Rinya , Werner Carl Frederik , Yoshinobu Tatsuo
Published: March 2022

Stainless steel plays an important role in industry due to its anti-corrosion characteristic. It is known, however, that local corrosion can damage stainless steel under certain conditions. In this study, we developed a novel measurement system to observe crevice corrosion, which is a local corrosion that occurs inside a narrow gap. In addition to pH imaging inside the crevice, another imaging technique using an infrared light was combined to simultaneously visualize surface roughening of the test piece. According to experimental results, the lowering of local pH propagated inside the crevice, and after that, the surface roughening started and expanded due to propagation of corrosion. The real-time measurement of the pH distribution and the surface roughness can be a powerful tool to investigate the crevice corrosion.
Mechanistic Insights into WO3 Sensing and Related Perspectives
Authors: Epifani Mauro
Published: March 2022

Tungsten trioxide (WO3) is taking on an increasing level of importance as an active material for chemoresistive sensors. However, many different issues have to be considered when trying to understand the sensing properties of WO3 in order to rationally design sensing devices. In this review, several key points are critically summarized. After a quick review of the sensing results, showing the most timely trends, the complex system of crystallographic WO3 phase transitions is considered, with reference to the phases possibly involved in gas sensing. Appropriate attention is given to related investigations of first principles, since they have been shown to be a solid support for understanding the physical properties of crucially important systems. Then, the surface properties of WO3 are considered from both an experimental and first principles point of view, with reference to the paramount importance of oxygen vacancies. Finally, the few investigations of the sensing mechanisms of WO3 are discussed, showing a promising convergence between the proposed hypotheses and several experimental and theoretical studies presented in the previous sections.
Time-Slotted Spreading Factor Hopping for Mitigating Blind Spots in LoRa-Based Networks
Authors: Iglesias-Rivera Alejandro , Van Glabbeek Roald , Guerra Erik Ortiz , Braeken An , Steenhaut Kris , Cruz-Enriquez Hector
Published: March 2022

It has been demonstrated that LoRa-based wide area networks (WANs) can cover extended areas under harsh propagation conditions. Traditional LoRaWAN solutions based on single-hop access face important drawbacks related to the presence of blind spots. This paper aims to tackle blind spots and performance issues by using a relaying approach. Many researchers investigating multi-hop solutions consider a fixed spreading factor (SF). This simplifies synchronization and association processes, but does not take advantage of the orthogonality between the virtual channels (i.e., frequency, SF) that help to mitigate blind spots. This paper proposes a time-slotted spreading factor hopping (TSSFH) mechanism that combines virtual channels and time slots into a frame structure. Pseudo-random scheduling is used inside blind spots, which simplifies the end-devices’ communication process and network organization. The results show how collisions decrease inside blind spots when more communication opportunities become available as more relaying nodes can be listening in different cells (i.e., frequency, SF-offset, time-offset). This has a direct impact on the collision-free packet delivery ratio (PDR) metric, which improves when more listening windows are opened, at the expense of faster battery depletion.
Optimization of Cladding Diameter for Refractive Index Sensing in Tilted Fiber Bragg Gratings
Authors: Korganbayev Sanzhar , Sypabekova Marzhan , Amantayeva Aida , González-Vila Álvaro , Caucheteur Christophe , Saccomandi Paola , Tosi Daniele
Published: March 2022

This work presents an experimental investigation of the effect of chemical etching on the refractive index (RI) sensitivity of tilted fiber Bragg gratings (TFBGs). Hydrofluoric acid (HF) was used stepwise in order to reduce the optical fiber diameter from 125 µm to 13 µm. After each etching step, TFBGs were calibrated using two ranges of RI solutions: the first one with high RI variation (from 1.33679 RIU to 1.37078 RIU) and the second with low RI variation (from 1.34722 RIU to 1.34873 RIU). RI sensitivity was analyzed in terms of wavelength shift and intensity change of the grating resonances. The highest amplitude sensitivities obtained are 1008 dB/RIU for the high RI range and 8160 dB/RIU for the low RI range, corresponding to the unetched TFBG. The highest wavelength sensitivities are 38.8 nm/RIU for a fiber diameter of 100 µm for the high RI range, and 156 nm/RIU for a diameter of 40 µm for the small RI range. In addition, the effect of the etching process on the spectral intensity of the cladding modes, their wavelength separation and sensor linearity (R2) were studied as well. As a result, an optimization of the etching process is provided, so that the best trade-off between sensitivity, intensity level, and fiber thickness can be obtained.
Coat Colour Grading of the Scots Pine Seeds Collected from Faraway Provenances Reveals a Different Germination Effect
Authors: Bacherikov Ivan V. , Raupova Diana E. , Durova Anastasia S. , Bragin Vladislav D. , Petrishchev Evgeniy P. , Novikov Arthur I. , Danilov Dmitry A. , Zhigunov Anatoly V.
Published: March 2022

The physiological quality of pine seeds is characterized by laboratory and field germination. The present paper is intended for technologists of seed plants and specialists of forest nurseries. It offers a solution to improve the seeding characteristics of small seeds by their pre-sowing preparation. The success of reforestation activities directly depends on the quality of the seeds. The influence of seed sorting by seed size and seed coat colour has been theoretically substantiated and repeatedly tested in practice. However, the response of seeds in germination can vary depending on the year and place of seed collection. Scots pine (Pinus sylvestris L.) seeds were germinated under controlled conditions. Seedlings were obtained from seeds pre-sorted by seed coat colour into white, brown, and black groups, and further divided by size. The results of sorting by the colour of seed coat indicate a different effect of this pre-sowing treatment on the sowing qualities of seeds. Brown seed coat colour showed the highest percentage degree in the seedlots of all provenances. The seeds from the southern provenance with the brown seed coat colour shown the maximum germination. That said, the study raises new questions, indicating more comprehensive research in the future. Does the pattern of germination parameter distribution remain constant for seeds of other harvest years but of the same provenance? Does the variability of the germination factor the result of internal factors of the container location in the greenhouse? Is the genetic diversity of seedlings disturbed by sorting by size?
Smart Analysis of Learners Performance Using Learning Analytics for Improving Academic Progression: A Case Study Model
Authors: Krishnan Reshmy , Nair Sarachandran , Saamuel Baby Sam , Justin Sheeba , Iwendi Celestine , Biamba Cresantus , Ibeke Ebuka
Published: March 2022

In the current COVID-19 pandemic era, Learning Management Systems (LMS) are commonly used in e-learning for various learning activities in Higher Education. Learning Analytics (LA) is an emerging area of LMS, which plays a vital role in tracking and storing learners’ activities in the online environment in Higher Education. LA treats the collections of students’ digital footprints and evaluates this data to improve teaching and learning quality. LA measures the analysis and reports learners’ data and their activities to predict decisions on every tier of the education system. This promising area, which both teachers and students can use during this pandemic outbreak, converges LA, Artificial Intelligence, and Human-Centered Design in data visualization techniques, semantic and educational data mining techniques, feature data extraction, etc. Different learning activities of learners for each course are analyzed with the help of LA plug-ins. The progression of learners can be monitored and predicted with the help of this intelligent analysis, which aids in improving the academic progress of each learner in a secured manner. The Object-Oriented Programming course and Data Communication Network are used to implement our case studies and to collect the analysis reports. Two plug-ins, local and log store plug-ins, are added to the sample course, and reports are observed. This research collected and monitored the data of the activities each students are involved in. This analysis provides the distribution of access to contents from which the number of active students and students’ activities can be inferred. This analysis provides insight into how many assignment submissions and quiz submissions were on time. The hits distribution is also provided in the analytical chart. Our findings show that teaching methods can be improved based on these inferences as it reflects the students’ learning preferences, especially during this COVID-19 era. Furthermore, each student’s academic progression can be marked and planned in the department.
The Effect of Off-Farm Employment on Agricultural Production Efficiency: Micro Evidence in China
Authors: Chang Ming , Liu Jing , Shi Hongxu , Guo Tianfeng
Published: March 2022

With ensuring food security becoming a priority for the Chinese government, the prevalence of off-farm employment (OE) may cast a shadow on agricultural productivity. Based on the data of the China Family Panel Studies in 2018, the Tobit model and threshold effect model have been applied to investigate the impact of off-farm employment on agricultural productivity efficiency (APE), measured by data envelopment analysis (DEA). The result has shown that: (1) OE contributes to a low level of APE. (2) Both self-employed off-farm employment (SOE) and wage-based off-farm employment (WOE) result in lower APE levels when endogenous issues are addressed. However, SOE had a greater negative impact on APE than WOE. (3) There exists a threshold for OE associated with a lower level of APE, indicating that the negative effect of OE on APE disappears when the degree of OE is high enough, SOE has a lower threshold than WOE. The study findings have implications for improving agricultural production efficiency in the context of large-scale off-farm employment of Chinese farmers.
Design and Validation of a Novel Tool to Assess Citizens’ Netiquette and Information and Data Literacy Using Interactive Simulations
Authors: Bartolomé Juan , Garaizar Pablo
Published: March 2022

Until recently, most of the digital literacy frameworks have been based on assessment frameworks used by commercial entities. The release of the DigComp framework has allowed the development of tailored implementations for the evaluation of digital competence. However, the majority of these digital literacy frameworks are based on self-assessments, measuring only low-order cognitive skills. This paper reports on a study to develop and validate an assessment instrument, including interactive simulations to assess citizens’ digital competence. These formats are particularly important for the evaluation of complex cognitive constructs such as digital competence. Additionally, we selected two different approaches for designing the tests based on their scope, at the competence or competence area level. Their overall and dimensional validity and reliability were analysed. We summarise the issues addressed in each phase and key points to consider in new implementations. For both approaches, items present satisfactory difficulty and discrimination indicators. Validity was ensured through expert validation, and the Rasch analysis revealed good EAP/PV reliabilities. Therefore, the tests have sound psychometric properties that make them reliable and valid instruments for measuring digital competence. This paper contributes to an increasing number of tools designed to evaluate digital competence and highlights the necessity of measuring higher-order cognitive skills.
Ecotourism as a Resource Sharing Strategy: Case Study of Community-Based Ecotourism at the Tangkahan Buffer Zone of Leuser National Park, Langkat District, North Sumatra, Indonesia
Authors: Wiratno Wiratno , Withaningsih Susanti , Gunawan Budhi , Iskandar Johan
Published: March 2022

The local community is an essential and key partner in managing protected areas, especially for national parks in Indonesia. Therefore, there is a need to establish adaptive collaborative management (ACM) between the park authorities and the local community. In 2000, several local leaders established a new organization to develop an ecotourism package called the Tangkahan Ecotourism Organization or Lembaga Pariwisata Tangkahan (LPT) and set up the Community Tour Operator to manage the ecotourism activities. Our study used a strengths, weaknesses, opportunities, and threats (SWOT) analysis through focus group discussions (FGDs), interviews with related stakeholders and key informants, and carried out a literature review. It was found that ensuring local community could generate alternative income from ecotourism was an effective way to protect the park from any illegal activities. Additionally, the results about sustainability from the FGDs show that all three categories: Social Process, Adaptive Natural Resource Management, and Impact/Condition are interrelated, meaning that the collaboration and adaptive management in Tangkahan have resulted in high levels of humanistic well-being and the maintenance of ecological values, supporting collaboration processes and adaptive levels. Finally, our study can be used as a basis for a model of national parks focusing on ACM.
Procedural Fairness and Expected Outcome Evaluations in the Public Acceptance of Sustainability Policymaking: A Case Study of Multiple Stepwise Participatory Programs to Develop an Environmental Master Plan for Sapporo, Japan
Authors: Ohnuma Susumu , Yokoyama Miki , Mizutori Shogo
Published: March 2022

Measures of sustainability-related participatory programs vary according to social and cultural contexts. Thus, this study proposed a stepwise participatory program in which stakeholders and randomly chosen citizens (citizen panels) were repeatedly and sequentially involved, and the citizen panels discharged discrete functions through all the planning stages. Procedural and outcome fairness was focal to the evaluation of the participatory program because these criteria are widely deemed essential for public acceptance. Evaluation by nonparticipants was imperative because of the limited number of participants, but sustainability plans affect and mandate the cooperation of the general public. Therefore, this study undertaken during the revision of the city of Sapporo’s environmental master plan compared evaluations of nonparticipants with those of participants from three stages of the stepwise participatory program applying backcasting scenario workshops. A two-wave mailout survey was administered to test two hypotheses: (a) workshop participants would evaluate the acceptance, process, outcome, and antecedent factors more positively than nonparticipants, and (b) procedural fairness and evaluation of expected outcomes would affect acceptance. The results supported these hypotheses. Procedural fairness was associated with acceptance most robustly and consistently. The study’s primary contribution to the extant literature entails accumulating empirical evidence on stepwise participatory programs.
Social Learning of Sustainability in a Pandemic—Changes to Sustainability Understandings, Attitudes, and Behaviors during the Global Pandemic in a Higher Education Setting
Authors: Weder Franzisca , Yarnold Jennifer , Mertl Stefanie , Hübner Renate , Elmenreich Wilfried , Sposato Robert
Published: March 2022

When people learn from each other and change their behavior accordingly, this is called social learning. COVID-19 not only taught us new habits to limit contagion, imposed restrictions also limited people’s everyday practices and behavior. Our study aims to analyze how (much) the pandemic may have incidentally fostered social learning of sustainability (SLS), representing a shift from rule-based behavior and forced behavior changes to more associative and potentially long-lasting sustainable behavior. To answer this question, we analyzed data from two mixed-method surveys with which we approached two customized samples in a higher education setting in Australia (n = 100) and Austria (n = 264). The findings show that in a higher education and, specifically, a university context, there are less sustainable practices evaluated as stable and “new normal” than assumed. Still, sustainability is more rule-related and less associative, predominantly in Australia. Nevertheless, a certain degree of awareness of what sustainable practices are can be observed mainly in Austria. As a broader implication, the study at hand leads to the conclusion that through COVID-19, sustainability at least became more tangible. Furthermore, universities as a specific institution have the potential to put sustainability higher on their agenda and take responsibility for social change. In the conclusion and outlook of the paper, limitations of the study as well as future research potential on social learning processes for sustainable development are presented and discussed.
Symmetry between Structure–Antibacterial Effect of Polymers Functionalized with Phosphonium Salts
Authors: Nemeş Nicoleta , Ardean Cristina , Davidescu Corneliu , Negrea Adina , Ciopec Mihaela , Musta Virgil , Duţeanu Narcis , Negrea Petru , Muntean Delia
Published: March 2022

In actual context, when the terms of biomass and bioenergy are extensively used, it becomes clear that the comparative study of some biopolymers, such as cellulose and chitosan, can offer a large usage range, based on the scientific progress obtained in the biomaterials field. Starting from the structural similarity of these two polymers, we synthesized composite materials by grafting on their surface biocide substances (phosphonium salts). After testing the biocidal effect, we can conclude that the antibacterial effect depends on the ratio of support to phosphonium salt, influenced by the interaction between the cationic component of the biocides and by the anionic component of the bacterial cellular membrane. It was also observed that for the materials obtained by cellulose functionalization with tri-n-butyl-hexadecyl phosphonium bromide, the bacterial effect on E. coli strain was much better when chitosan was used as the support material.
Solving a System of Fractional-Order Volterra Integro-Differential Equations Based on the Explicit Finite Difference Approximation via the Trapezoid Method with Error Analysis
Authors: Ahmed Shazad Shawki
Published: March 2022

The well-known central finite difference approximation was combined with the trapezoid quadrature method in this study to provide a numerical solution of the linear system of Volterra integro-fractional differential equations (LSVI-FDEs) of arbitrary orders, where the fractional derivative is described in the Caputo sense and the orders are between zero and one. The method works by first using the central finite difference approximation to approximate the Caputo derivative at any fixed point and then using the trapezoidal rule to obtain a finite difference expression for our fractional equation, while recalling the linear spline approximation for the first steps. This new, more efficient method involves converting sets of equations and conditions into matrix relationships, from which symmetry matrices can be created in some cases. We also present a new approach for error analysis of the discrete numerical scheme and the explicit numerical technique for LSVI-FDEs. The multi-level explicit finite difference approximation’s stability and convergence were explored, and a MatLab application was created to explain the results. Finally, several numerical examples are offered to demonstrate the technique’s application.
The Wheelchair Propulsion Wheel Rotation Angle Function Symmetry in the Propelling Phase: Motion Capture Research and a Mathematical Model
Authors: Wieczorek Bartosz
Published: March 2022

The movement of a wheelchair with manual propulsion depends on the kinematics of the human body and the forces exerted by the muscles. To design innovative wheelchair propulsion systems, the biomechanical parameters resulting from human interaction in this anthropotechnical system must be formalised. The research objectives were thus adopted: an analysis of the propulsion wheel angle of rotation resulting from the hand movement’s trajectory and the mathematical formalisation of the propulsion wheel angle of rotation described as a function of the propelling phase’s duration. The research was carried out using three variants of manually propelled wheelchairs on a group of 10 patients representing the same group (C50) of anthropometric dimensions. The research demonstrated that the function of the propulsion wheel angle of rotation shows the features of central symmetry occurring at an angle of rotation of φ 52° and a propelling phase duration of 58%. Moreover, the measurements were averaged and a mathematical model of the propulsion wheel rotation function during the propulsion phase was developed, depending on the percentage of duration.
The Effect of Sealer Application Methods on Voids Volume after Aging of Three Calcium Silicate-Based Sealers: A Micro-Computed Tomography Study
Authors: Atmeh Amre R. , Alharbi Rakan , Aljamaan Ibrahim , Alahmari Abdulrahman , Shetty Ashwin C. , Jamleh Ahmed , Farooq Imran
Published: March 2022

During obturation, air voids are undesirable as they may provide shelter for microorganisms or passage for fluids. This study aimed to compare the occurrence of voids between three calcium silicate-based sealers (CSBSs) (MTA-Fillapex, BioRoot-RCS, Bio-C) and the change in their volume after aging. In addition, we aimed to compare voids when using two sealer application methods: lentulo-spiral (LS) and gutta-percha (GP) cone. Thirty extracted mandibular premolars (n = 30) were endodontically prepared and obturated using single GP cone (SGPC) technique. Each sealer was applied to 10 teeth (n = 10) using LS or GP. Micro-computed tomography (micro-CT) was used to quantify the volume of root filling and voids before and after 8-week storage in a phosphate-rich medium. The percentage of root filling and voids were compared between the groups using a Mann–Whitney U test and Kruskal–Wallis test with a Bonferroni correction. Before aging, the percentages of root filling volume after obturation were comparable with no significant differences between sealers (p = 0.325) or application methods (p = 0.950). After aging, the voids’ volume increased significantly in all sealers (p ≤ 0.05). However, no significant differences were found between sealers (p = 0.302). In conclusion, voids in CSBSs may not reduce in size with aging; hence, SGPC should be carefully selected for suitable cases.
New Insights into the Toxin Diversity and Antimicrobial Activity of the “Fire Coral” Millepora complanata
Authors: Hernández-Elizárraga Víctor Hugo , Ocharán-Mercado Andrea , Olguín-López Norma , Hernández-Matehuala Rosalina , Caballero-Pérez Juan , Ibarra-Alvarado César , Rojas-Molina Alejandra
Published: March 2022

To date, few studies have been carried out aimed at characterizing the toxins synthesized by hydrocorals of the genus Millepora. The purpose of this study was to explore the toxin diversity and antibacterial activity of the “fire coral” M. complanata using a transcriptomic data mining approach. In addition, the cytolytic and antibacterial activities of the M. complanata nematocyst proteome were experimentally confirmed. Cytolysins were predicted from the transcriptome by comparing against the Animal Toxin Annotation Project database, resulting in 190 putative toxins, including metalloproteases, hemostasis-impairing toxins, phospholipases, among others. The M. complanata nematocyst proteome was analyzed by 1D and 2D electrophoresis and zymography. The zymograms showed different zones of cytolytic activity: two zones of hemolysis at ~25 and ~205 kDa, two regions corresponding to phospholipase A2 (PLA2) activity around 6 and 25 kDa, and a proteolytic zone was observed between 50 and 205 kDa. The hemolytic activity of the proteome was inhibited in the presence of PLA2 and proteases inhibitors, suggesting that PLA2s, trypsin, chymotrypsin, serine-proteases, and matrix metalloproteases are responsible for the hemolysis. On the other hand, antimicrobial peptide sequences were retrieved from their transcripts with the amPEPpy software. This analysis revealed the presence of homologs to SK84, cgUbiquitin, Ubiquicidin, TroTbeta4, SPINK9-v1, and Histone-related antimicrobials in the transcriptome of this cnidarian. Finally, by employing disk diffusion and microdilution assays, we found that the nematocyst peptidome of M. complanata showed inhibitory activity against both Gram-positive and Gram-negative bacteria including S. enteritidis, P. perfectomarina, E. coli, and C. xerosis, among others. This is the first transcriptomic data mining analysis to explore the diversity of the toxins synthesized by an organism of the genus Millepora. Undoubtedly, this work provides information that will broaden our general understanding of the structural richness of cnidarian toxins.
The Occurrence, Distribution, and Toxicity of High-Risk Ciguatera Fish Species (Grouper and Snapper) in Kiritimati Island and Marakei Island of the Republic of Kiribati
Authors: Zhu Jingyi , Lee Wai-Hin , Wu Jiajun , Zhou Shiwen , Yip Ki-Chun , Liu Xiaowan , Kirata Taratau , Chan Leo-Lai
Published: March 2022

Ciguatera is one of the most widespread food poisonings caused by the ingestion of fish contaminated by ciguatoxins (CTXs). Snapper and grouper with high palatable and economic value are the primary food source and fish species for exportation in the Republic of Kiribati, but they are highly suspected CTX-contaminated species due to their top predatory characteristics. In this study, 60 fish specimens from 17 species of snappers and groupers collected from the Kiritimati Island and Marakei Island of the Republic of Kiribati were analyzed using mouse neuroblastoma (N2a) assay and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to determine Pacific CTX-1, -2 and -3 (P-CTX-1, -2 and -3). The LC-MS/MS results show that CTXs were detected in 74.5% of specimens from Marakei Island and 61.5% of specimens from Kiritimati Island. The most toxic fish Epinephelus coeruleopunctatus from Marakei Island and Cephalopholis miniata from Kiritimati Island were detected as 53-fold and 28-fold P-CTX-1 equivalents higher than the safety level of 10 pg/g P-CTX-1 equivalents, respectively. CTX levels and composition profiles varied with species and location. The N2a results suggested that fish specimens also contain high levels of other CTX-like toxins or sodium channel activators. The distribution patterns for ciguatoxic fish of the two islands were similar, with fish sampled from the northwest being more toxic than the southwest. This study shows that groupers and snappers are high-risk species for ciguatera in the Republic of Kiribati, and these species can further be used as indicator species in ciguatera endemic areas for risk assessment.