bluuebunny/crossref_metadata_2025 huggingface dataset:
Substantial Effect of Water on Radical Melt Crosslinking and Rheological Properties of Poly(ε-Caprolactone)
Authors: Avella Angelica , Mincheva Rosica , Raquez Jean-Marie , Lo Re Giada
Published: February 2021

One-step reactive melt processing (REx) via radical reactions was evaluated with the aim of improving the rheological properties of poly(ε-caprolactone) (PCL). In particular, a water-assisted REx was designed under the hypothesis of increasing crosslinking efficiency with water as a low viscous medium in comparison with a slower PCL macroradicals diffusion in the melt state. To assess the effect of dry vs. water-assisted REx on PCL, its structural, thermo-mechanical and rheological properties were investigated. Water-assisted REx resulted in increased PCL gel fraction compared to dry REx (from 1–34%), proving the rationale under the formulated hypothesis. From dynamic mechanical analysis and tensile tests, the crosslink did not significantly affect the PCL mechanical performance. Dynamic rheological measurements showed that higher PCL viscosity was reached with increasing branching/crosslinking and the typical PCL Newtonian behavior was shifting towards a progressively more pronounced shear thinning. A complete transition from viscous- to solid-like PCL melt behavior was recorded, demonstrating that higher melt elasticity can be obtained as a function of gel content by controlled REx. Improvement in rheological properties offers the possibility of broadening PCL melt processability without hindering its recycling by melt processing.
Fabrication and Bonding of Refractive Index Matched Microfluidics for Precise Measurements of Cell Mass
Authors: Polanco Edward R. , Griffin Justin , Zangle Thomas A.
Published: February 2021

The optical properties of polymer materials used for microfluidic device fabrication can impact device performance when used for optical measurements. In particular, conventional polymer materials used for microfluidic devices have a large difference in refractive index relative to aqueous media generally used for biomedical applications. This can create artifacts when used for microscopy-based assays. Fluorination can reduce polymer refractive index, but at the cost of reduced adhesion, creating issues with device bonding. Here, we present a novel fabrication technique for bonding microfluidic devices made of NOA1348, which is a fluorinated, UV-curable polymer with a refractive index similar to that of water, to a glass substrate. This technique is compatible with soft lithography techniques, making this approach readily integrated into existing microfabrication workflows. We also demonstrate that this material is compatible with quantitative phase imaging, which we used to validate the refractive index of the material post-fabrication. Finally, we demonstrate the use of this material with a novel image processing approach to precisely quantify the mass of cells in the microchannel without the use of cell segmentation or tracking. The novel image processing approach combined with this low refractive index material eliminates an important source of error, allowing for high-precision measurements of cell mass with a coefficient of variance of 1%.
Fabrication of High-Quality Straight-Line Polymer Composite Frame with Different Radius Parts Using Fiber Winding Process
Authors: Mlýnek Jaroslav , Rahimian Koloor Seyed Saeid , Martinec Tomáš , Petrů Michal
Published: February 2021

The extraordinary features of fibrous composites enable advanced industries to design composite structures with superior performance compared to traditional structures. Composite frame structures have been designed frequently as components of mechanical systems to resist lateral and gravity loads. The manufacturing of high-quality composite frames depends primarily on the accurate fiber winding on frames with different pro-files and curved shapes. The optimal fiber winding process on a nonbearing composite frame with a circular cross-section is described in previous works by the same authors. As an extension to that, this study focuses on the manufacturing of straight-line composite frames with different profile radii at multiple locations. Such production procedure allows continuous winding of fibers gradually on individual parts of the frame and generally with different angles of fiber winding. The winding procedure is performed using fiber-processing head and industrial robot. The procedure for calculating the distance of the winding plane of fibers on the frame from the guide-line of the fiber-processing head is targeted. This distance depends on the required angle of fiber winding, the radius of the frame, and the geometric parameters of the fiber-processing head. The coordination of the speed of winding the fibers on the frame and the speed of the passage of the frame through the winding head is also considered. Determining the correct distance of winding the fibers from the corresponding guide-line of fiber-processing head and right coordination of the winding speed and the speed of passage of the frame through the fiber-processing head ensure compliance of the required angles of fiber windings on the frame and homogeneity of winding fibers, which are the two of the most important prerequisites for producing a quality composite frame. The derived theory is well verified on a practical experimental example.
AVILNet: A New Pliable Network with a Novel Metric for Small-Object Segmentation and Detection in Infrared Images
Authors: Song Ikhwan , Kim Sungho
Published: February 2021

Infrared small-object segmentation (ISOS) has a persistent trade-off problem—that is, which came first, recall or precision? Constructing a fine balance between of them is, au fond, of vital importance to obtain the best performance in real applications, such as surveillance, tracking, and many fields related to infrared searching and tracking. F1-score may be a good evaluation metric for this problem. However, since the F1-score only depends upon a specific threshold value, it cannot reflect the user’s requirements according to the various application environment. Therefore, several metrics are commonly used together. Now we introduce F-area, a novel metric for a panoptic evaluation of average precision and F1-score. It can simultaneously consider the performance in terms of real application and the potential capability of a model. Furthermore, we propose a new network, called the Amorphous Variable Inter-located Network (AVILNet), which is of pliable structure based on GridNet, and it is also an ensemble network consisting of the main and its sub-network. Compared with the state-of-the-art ISOS methods, our model achieved an AP of 51.69%, F1-score of 63.03%, and F-area of 32.58% on the International Conference on Computer Vision 2019 ISOS Single dataset by using one generator. In addition, an AP of 53.6%, an F1-score of 60.99%, and F-area of 32.69% by using dual generators, with beating the existing best record (AP, 51.42%; F1-score, 57.04%; and F-area, 29.33%).
NASA’s MODIS/VIIRS Global Water Reservoir Product Suite from Moderate Resolution Remote Sensing Data
Authors: Li Yao , Zhao Gang , Shah Deep , Zhao Maosheng , Sarkar Sudipta , Devadiga Sadashiva , Zhao Bingjie , Zhang Shuai , Gao Huilin
Published: February 2021

Global reservoir information can not only benefit local water management but can also improve our understanding of the hydrological cycle. This information includes water area, elevation, and storage; evaporation rate and volume values; and other characteristics. However, operational wall-to-wall reservoir storage and evaporation monitoring information is lacking on a global scale. Here we introduce NASA’s new MODIS/VIIRS Global Water Reservoir product suite based on moderate resolution remote sensing data—the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). This product consists of 8-day (MxD28C2 and VNP28C2) and monthly (MxD28C3 and VNP28C3) measurements for 164 large reservoirs (MxD stands for the product from both Terra (MOD) or Aqua (MYD) satellites). The 8-day product provides area, elevation, and storage values, which were generated by first extracting water areas from surface reflectance data and then applying the area estimations to the pre-established Area–Elevation (A–E) relationships. These values were then further aggregated to monthly, with the evaporation rate and volume information added. The evaporation rate and volume values were calculated after the Lake Temperature and Evaporation Model (LTEM) using MODIS/VIIRS land surface temperature product and meteorological data from the Global Land Data Assimilation System (GLDAS). Validation results show that the 250 m area classifications from MODIS agree well with the high-resolution classifications from Landsat (R2 = 0.99). Validation of elevation and storage products for twelve Indian reservoirs show good agreement in terms of R2 values (0.71–0.96 for elevation, and 0.79–0.96 for storage) and normalized root-mean-square error (NRMSE) values (5.08–19.34% for elevation, and 6.39–18.77% for storage). The evaporation rate results for two reservoirs (Lake Nasser and Lake Mead) agree well with in situ measurements (R2 values of 0.61 and 0.66, and NRMSE values of 16.25% and 21.76%). Furthermore, preliminary results from the VIIRS reservoir product have shown good consistency with the MODIS based product, confirming the continuity of this 20-year product suite. This new global water reservoir product suite can provide valuable information with regard to water-sources-related studies, applications, management, and hydrological modeling and change analysis such as drought monitoring.
Diamond Grinding Wheel Condition Monitoring Based on Acoustic Emission Signals
Authors: Bi Guo , Liu Shan , Su Shibo , Wang Zhongxue
Published: February 2021

Acoustic emission (AE) phenomenon has a direct relationship with the interaction of tool and material which makes AE the most sensitive one among various process variables. However, its prominent sensitivity also means the characteristics of random and board band. Feature representation is a difficult problem for AE-based monitoring and determines the accuracy of monitoring system. It is knottier for the situation of using diamond wheel grinding optical components, not only because of the complexity of grinding process but also the high requirement on surface and subsurface quality. This paper is dedicated to AE-based condition monitoring of diamond wheel during grinding brittle materials and feature representation is paid more attention. AE signal of brittle-regime grinding is modeled as a superposition of a series of burst-type AE events. Theory analysis manifested that original time waveform and frequency spectrum are all suitable for feature representation. Considering the convolution form of b-AE in time domain, a convolutional neural network with original time waveform of AE signals as the input is built for multi-class classification of wheel state. Detailed state division in a wheel’s whole life cycle is realized and the accuracy is over 90%. Different from the overlapping in time domain, AE components of different crack mechanisms are probably separated in frequency domain. From this point of view, AE spectrums are more suitable for feature extraction than the original time waveform. In addition, the time sequence of AE samples is essential for the evaluation of wheel’s life elapse and making use of sequential information is just the idea behind recurrent neural network (RNN). Therefore, long short-term memory (LSTM), a special kind of RNN, is used to build a regression prediction model of wheel state with AE spectrums as the model input and satisfactory prediction accuracy is acquired on the test set.
Wearable Devices Suitable for Monitoring Twenty Four Hour Heart Rate Variability in Military Populations
Authors: Hinde Katrina , White Graham , Armstrong Nicola
Published: February 2021

Heart rate variability (HRV) measurements provide information on the autonomic nervous system and the balance between parasympathetic and sympathetic activity. A high HRV can be advantageous, reflecting the ability of the autonomic nervous system to adapt, whereas a low HRV can be indicative of fatigue, overtraining or health issues. There has been a surge in wearable devices that claim to measure HRV. Some of these include spot measurements, whilst others only record during periods of rest and/or sleep. Few are capable of continuously measuring HRV (≥24 h). We undertook a narrative review of the literature with the aim to determine which currently available wearable devices are capable of measuring continuous, precise HRV measures. The review also aims to evaluate which devices would be suitable in a field setting specific to military populations. The Polar H10 appears to be the most accurate wearable device when compared to criterion measures and even appears to supersede traditional methods during exercise. However, currently, the H10 must be paired with a watch to enable the raw data to be extracted for HRV analysis if users need to avoid using an app (for security or data ownership reasons) which incurs additional cost.
On the Impact of Biceps Muscle Fatigue in Human Activity Recognition
Authors: Elshafei Mohamed , Costa Diego Elias , Shihab Emad
Published: February 2021

Nowadays, Human Activity Recognition (HAR) systems, which use wearables and smart systems, are a part of our daily life. Despite the abundance of literature in the area, little is known about the impact of muscle fatigue on these systems’ performance. In this work, we use the biceps concentration curls exercise as an example of a HAR activity to observe the impact of fatigue impact on such systems. Our dataset consists of 3000 biceps concentration curls performed and collected from 20 volunteers aged between 20–35. Our findings indicate that fatigue often occurs in later sets of an exercise and extends the completion time of later sets by up to 31% and decreases muscular endurance by 4.1%. Another finding shows that changes in data patterns are often occurring during fatigue presence, causing seven features to become statistically insignificant. Further findings indicate that fatigue can cause a substantial decrease in performance in both subject-specific and cross-subject models. Finally, we observed that a Feedforward Neural Network (FNN) showed the best performance in both cross-subject and subject-specific models in all our evaluations.
Searching and Tracking an Unknown Number of Targets: A Learning-Based Method Enhanced with Maps Merging
Authors: Yan Peng , Jia Tao , Bai Chengchao
Published: February 2021

Unmanned aerial vehicles (UAVs) have been widely used in search and rescue (SAR) missions due to their high flexibility. A key problem in SAR missions is to search and track moving targets in an area of interest. In this paper, we focus on the problem of Cooperative Multi-UAV Observation of Multiple Moving Targets (CMUOMMT). In contrast to the existing literature, we not only optimize the average observation rate of the discovered targets, but we also emphasize the fairness of the observation of the discovered targets and the continuous exploration of the undiscovered targets, under the assumption that the total number of targets is unknown. To achieve this objective, a deep reinforcement learning (DRL)-based method is proposed under the Partially Observable Markov Decision Process (POMDP) framework, where each UAV maintains four observation history maps, and maps from different UAVs within a communication range can be merged to enhance UAVs’ awareness of the environment. A deep convolutional neural network (CNN) is used to process the merged maps and generate the control commands to UAVs. The simulation results show that our policy can enable UAVs to balance between giving the discovered targets a fair observation and exploring the search region compared with other methods.
Life Prediction of Battery Using a Neural Gaussian Process with Early Discharge Characteristics
Authors: Yin Aijun , Tan Zhibin , Tan Jian
Published: February 2021

The state of health (SOH) prediction of lithium-ion batteries (LIBs) is of crucial importance for the normal operation of the battery system. In this paper, a new method for cycle life and full life cycle capacity prediction is proposed, which combines the early discharge characteristics with the neural Gaussian process (NGP) model. The cycle data sets of commercial LiFePO4(LFP)/graphite cells generated under different operating conditions are analyzed, and the power characteristic P is extracted from the voltage and current curves of the early cycles. A Pearson correlation analysis shows that there is a strong correlation between P and cycle life. Our model achieves 8.8% test error for predicting cycle life using degradation data for the 20th to 110th cycles. Based on the predicted cycle life, capacity degradation curves for the whole life cycle of the cells are predicted. In addition, the NGP method, combined with power characteristics, is compared with other classical methods for predicting the remaining useful life (RUL) of LIBs. The results demonstrate that the proposed prediction method of cycle life and capacity has better battery life and capacity prediction. This work highlights the use of early discharge characteristics to predict battery performance, and shows the application prospect in accelerating the development of electrode materials and optimizing battery management systems (BMS).
EVM Loss: A Loss Function for Training Neural Networks in Communication Systems
Authors: Stainton Scott , Johnston Martin , Dlay Satnam , Haigh Paul Anthony
Published: February 2021

Neural networks and their application in communication systems are receiving growing attention from both academia and industry. The authors note that there is a disconnect between the typical objective functions of these neural networks with regards to the context in which the neural network will eventually be deployed and evaluated. To this end, a new loss function is proposed and shown to increase the performance of neural networks when implemented in a communication system compared to previous methods. It is further shown that a ‘split complex’ approach used by many implementations can be improved via formalisation of the ‘concatenated complex’ approach described herein. Experimental results using the orthogonal frequency division multiplexing (OFDM) and spectrally efficient frequency division multiplexing (SEFDM) modulation formats with varying bandwidth compression factors over a wireless visible light communication (VLC) link validate the efficacy of the proposed method in a real system, achieving the lowest error vector magnitude (EVM), and thus bit error rate (BER), across all experiments, with a 5 dB to 10 dB improvement in the received symbols EVM overall compared to the baseline implementation, with bandwidth compressions down to 40% compared to OFDM, resulting in a spectral efficiency gain of 67%.
Digital Twin Generation: Re-Conceptualizing Agent Systems for Behavior-Centered Cyber-Physical System Development
Authors: Stary Christian
Published: February 2021

Cyber-Physical Systems (CPS) form the new backbone of digital ecosystems. Upcoming CPS will be operated on a unifying basis, the Internet of Behaviors (IoB). It features autonomous while federated CPS architectures and requires corresponding behavior modeling for design and control. CPS design and control involves stakeholders in different roles with different expertise accessing behavior models, termed Digital twins. They mirror the physical CPS part and integrate it with the digital part. Representing role-specific behaviors and provided with automated execution capabilities Digital twins facilitate dynamic adaptation and (re-)configuration. This paper proposes to conceptualize agent-based design for behavior-based Digital twins through subject-oriented models. These models can be executed and, thus, increase the transparency at design and runtime. Patterns recognizing environmental factors and operation details facilitate the configuration of CPS. Subject-oriented runtime support enables dynamic adaptation and the federated use of CPS components.
An Efficient Broadband Adaptive Beamformer without Presteering Delays
Authors: Zhang Ming , Wang Xiaojian , Zhang Anxue
Published: February 2021

Broadband adaptive beamformers have been widely used in many areas due to their ability of filtering signals in space domain as well as in frequency domain. However, the space-time array employed in broadband beamformers requires presteering delays to align the signals coming from a specific direction. Because the presteering delays are direction dependent, it is difficult to make precise delays in practice. A common way to eliminate the presteering delays is imposing constraints on the weight vector of the space-time array. However, the structure of the constraint matrix is not taken into account in the existing methods, leading to a computational complexity of O(N2) when updating the weight vector. In this paper, we describe a new kind of constraint method in time domain that preserves the block diagonal structure of the constraint matrix. Based on this structure, we design an efficient weight vector update algorithm that has a computational complexity of O(N). In addition, the proposed algorithm does not contain matrix operations (only scalar and vector operations are involved), making it easy to be implemented in chips such as FPGA. Moreover, the constraint accuracy of the proposed method is as high as the frequency constraint method when the fractional bandwidth of the signal is smaller than 10%. Numerical experiments show that our method achieves the same performance of the state-of-the-art methods while keeping a simpler algorithm structure and a lower computational cost.
Visualization of Keratin with Diffuse Reflectance and Autofluorescence Imaging and Nonlinear Optical Microscopy in a Rare Keratinopathic Ichthyosis
Authors: Anker Pálma , Fésűs Luca , Kiss Norbert , Noll Judit , Becker Krisztina , Kuroli Enikő , Mayer Balázs , Bozsányi Szabolcs , Lőrincz Kende , Lihacova Ilze , Lihachev Alexey , Lange Marta , Wikonkál Norbert , Medvecz Márta
Published: February 2021

Keratins are one of the main fluorophores of the skin. Keratinization disorders can lead to alterations in the optical properties of the skin. We set out to investigate a rare form of keratinopathic ichthyosis caused by KRT1 mutation with two different optical imaging methods. We used a newly developed light emitting diode (LED) based device to analyze autofluorescence signal at 405 nm excitation and diffuse reflectance at 526 nm in vivo. Mean autofluorescence intensity of the hyperkeratotic palmar skin was markedly higher in comparison to the healthy control (162.35 vs. 51.14). To further assess the skin status, we examined samples from affected skin areas ex vivo by nonlinear optical microscopy. Two-photon excited fluorescence and second-harmonic generation can visualize epidermal keratin and dermal collagen, respectively. We were able to visualize the structure of the epidermis and other skin changes caused by abnormal keratin formation. Taken together, we were able to show that such imaging modalities are useful for the diagnosis and follow-up of keratinopathic diseases.
Structural Design and Physical Mechanism of Axial and Radial Sandwich Resonators with Piezoelectric Ceramics: A Review
Authors: Wang Wenjie , Jiang Yi , Thomas Peter J.
Published: February 2021

Piezoelectric ceramics are inexpensive functional materials which are widely used in sonar detection, home appliances, meteorological detection, telemetry and environmental protection and other applications. Sensors fabricated from these materials are compact and have fast response characteristics. Their underlying functional methodology is based on the direct piezoelectric effect whereby very small mechanical vibration signals are converted into electrical signals. Piezoelectric resonators are based on the reverse piezoelectric effect and they are widely used for the control of precision instruments and precision machinery, microelectronic components, bioengineering devices and other in applications requiring components to provide precision control of the relevant functional mechanism. In this paper, the structural evolution and design mechanism of sandwich resonators based on piezoelectric materials are reviewed, and the advantages and disadvantages of different structures are compared and analyzed. The goal is to provide a comprehensive reference for the selection, application and promotion of piezoelectric resonators and for future structural innovation and mechanism research relevant to sandwich resonators.
Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods
Authors: Zhong Ming , Zhou Yajin , Chen Gang
Published: February 2021

IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network infrastructures from various attacks. IDS (Intrusion Detection System) is the invisible guardian for IoT servers. Many machine learning methods have been applied in IDS. However, there is a need to improve the IDS system for both accuracy and performance. Deep learning is a promising technique that has been used in many areas, including pattern recognition, natural language processing, etc. The deep learning reveals more potential than traditional machine learning methods. In this paper, sequential model is the key point, and new methods are proposed by the features of the model. The model can collect features from the network layer via tcpdump packets and application layer via system routines. Text-CNN and GRU methods are chosen because the can treat sequential data as a language model. The advantage compared with the traditional methods is that they can extract more features from the data and the experiments show that the deep learning methods have higher F1-score. We conclude that the sequential model-based intrusion detection system using deep learning method can contribute to the security of the IoT servers.
CFD Modelling and Optimization Procedure of an Adhesive System for a Modular Climbing Robot
Authors: Hernando Miguel , Gómez Virgilio , Brunete Alberto , Gambao Ernesto
Published: February 2021

Adhesion systems are very important in robots for infrastructure inspection (especially in vertical walls). They present the challenge of optimizing the ratio vacuum/power consumption in battery-powered robots. In this paper, a CFD (computer fluid dynamics) modelling and optimization process of a robot adhesion system is carried out to determine the best performing configuration in terms of vacuum and power consumption. Analytical and numerical models were developed to predict the behaviour of the system for different configurations. The models were validated, using test rig measurements, by calibrating an arbitrary defined inlet height that simulates the leakage flow. Then, different geometric parameters were varied to determine the best performing configuration based on the vacuum/power consumption ratio value. The model presented in the paper was capable of predicting the behaviour of the system for different configurations, with a margin of error of 15% for the vacuum prediction and 25% for the motor power calculation. Finally, the model was used to optimize parameters of the system, like the number of blades of the impeller. The adhesion system was conceived for the modular autonomous climbing legged robot ROMERIN.
Design, Synthesis and Antibacterial Studies of Novel Cationic Amphipathic Cyclic Undecapeptides and Their Linear Counterparts against Virulent Bacterial Strains
Authors: Farrag Hisham N. , Maeda Toshinari , Kato Tamaki
Published: February 2021

Bacteria have acquired resistance against almost all antibiotics because of the misuse of antibacterial agents and long periods of treatment. Antimicrobial peptides (AMPs) are one of the most encouraging candidates to solve this problem, as they possess high prokaryotic selectivity, and affect the bacteria by a unique mode of action. Novel cyclic undecapeptides (QNRNFYFNRNQ and QNRNFHFNRNQ) and their linear counterparts were investigated for their antibacterial activity against virulent strains. The minimal inhibitory concentration (MIC) values showed that tyrosine and histidine AMPs have promising antibacterial activity against virulent bacteria. The MIC values against the P. aeruginosa PA14, E. coli O157:H7 CR3, S. aureus 209P, and B. subtilis ATCC 6633 bacterial strains were evaluated for the cyclic peptide containing tyrosine, and their values were 6.25, 12.5, 12.5, and 12.5 µM, respectively. Meanwhile, for the linear form, they were 9.3, 12.5, 12.5, and 12.5 µM, respectively. The cyclic-peptide–containing histidines’ MIC values were 6.25, 3.1, 6.25, and 3.1 µM, respectively. Meanwhile, for the linear form, they were 3.1, 3.1, 3.1, and 6.25 µM, respectively. The antibacterial activities of the new AMPs were compared with that of gentamicin sulfate, and showed relatively higher potencies. Time-inhibition studies demonstrated the rapid antibacterial effects of the novel AMPs, which were more likely to be concentration-dependent, rather than time-dependent. At double the MIC concentration, all of the tested peptides exhibited relatively stable antibacterial effects up to 24 h, especially the peptides containing tyrosine, which showed an improved antibacterial effect.
Understanding the Complexity of Regional Innovation Capacity Dynamics in China: From the Perspective of Hidden Markov Model
Authors: Liu Shuai , Xu Xiao-Yu , Zhao Kai , Xiao Li-Ming , Li Qi
Published: February 2021

This study aimed to explore the state transition of regional innovation capacity (RIC) and analyze the heterogeneous effects of determinants in an innovative subject and environment of RIC state transition based on the data collected from 30 provinces in China during 2000–2017. By applying a hidden Markov model (HMM), this study identified three RIC states: low, medium, and high. The results suggested that (1) the overall state of RIC rapidly improved but with a significant disparity across regions in China; (2) the lock-in effect of RIC is most significant in regions with a medium state, while the enterprise-dominated mode of regional innovation helps RIC transition from a medium state to a high state or to remain in a high state; and (3) the interaction and collaboration between universities and enterprises in a region can stimulate RIC to higher states for all regions. Intellectual property administrative protection exerts positive impacts on RIC transitions to higher states. Intellectual property judicial protection only exerts positive impacts on an RIC’s transition from a medium state to a high state or remaining in a high state, while these positive impacts are not significant when RIC is in a low state. Highlighting the dynamic nature of RIC evolution and the heterogeneity of determinants affecting RIC state transition, the findings provide policymakers a roadmap to identify RIC states and make precise policies based on the current RIC state.
Variability of Higher Education Students’ Learning Styles Depending on Gender, Course, Degree and Institutional Context
Authors: Alonso-Martín Pilar , Cruz-Díaz Rocío , Granado-Alcón Carmen , Lago-Urbano Rocío , Martínez-García Concha
Published: February 2021

In higher education it is important to consider learning styles of students to facilitate the teaching–learning process. The aims guiding the research were to describe the learning styles predominating among students in the field of the social sciences, to analyse the results with respect to gender, year of study, degree course and institution, and to perform correlation analysis between these variables. The data analyses were carried out with non-parametric statistics with a confidence level of 95%. The sample was composed of 636 students at the Universities of Huelva (UHU), Cádiz (UCA), and Pablo de Olavide of Seville (UPO), who completed the Honey–Alonso Learning Styles Questionnaire and reported sociodemographic and educational data. The results showed a significant preference for the Reflector style. Significant correlations were found in most variables highlighting that the courses showed an inverse correlation with the learning styles, the Activist, Theorist, and Pragmatist styles being less preferred as they progressed in the career. It is worth noting the significant direct correlation between Reflector, Theorist, and Pragmatist styles, but the Activist style inversely correlates with all three. As a complementary contribution, a proposal for intervention in classrooms with a sustainable perspective is offered. It is important to attend to the evolution in the preference of the learning styles that students acquire as they advance in higher education courses in order to facilitate a more optimal and sustainable teaching–learning process.
Nature on a Plate: Linking Food and Tourism within the Ecosystem Services Framework
Authors: Derek Marta
Published: February 2021

This paper explores the links between local resources (the natural environment) and the local food on offer; i.e., what parts of nature reach the tourist’s plate. It is based on a content analysis of restaurant menus in the Great Masurian Lakes region, which is considered to be one of the main nature-based tourism destinations in Poland. The concept of Ecosystem Services (ES), closely interlinked with the idea of sustainable development, is employed as it recognizes the relationships between humans and the natural environment. The results indicate that food representing the region’s history is almost non-existent. Instead, the local food heritage is being reinvented based on the area’s iconic ES, i.e., wild food. In this way, the supply side of the ES cascade (ecosystem resources and potential) and its demand side (the benefits for tourists and the costs they pay to obtain these benefits) are encompassed on a tourist’s plate in Masurian restaurants. The findings also reveal that, in many cases, the localness of “local” food is an illusion. This, in turn, poses an important challenge for the sustainability of food (and) tourism in the studied area.
Understanding the Government Responsibility and Role of Enterprises’ Participation in Disaster Management in China
Authors: Kong Feng , Sun Shao
Published: February 2021

The natural advantages of enterprises in capital, technology, and equipment make them have great potential in disaster management. How to ensure enterprises participate in disaster prevention and mitigation efficiently is a responsibility that the government must undertake, on the other hand, it can also relieve the pressure of the government. This paper first introduces the continuous improvement of enterprises’ role in disaster management. Then, this paper analyzes the political responsibility, legal responsibility, social responsibility, and economical responsibility of the government in an enterprises’ participation in disaster management. This paper further analyzes enterprises’ deficiencies in disaster management and the multi role of the government in enterprises’ participation in disaster management. Finally, this paper puts forward the pathways of the Chinese government to promote enterprises to participate in disaster management.
Assessment of the Odour Quality of the Air Surrounding a Landfill Site: A Case Study
Authors: Szałata Łukasz , Zwoździak Jerzy , Majerník Milan , Cierniak-Emerych Anna , Jarossová Malgorzata A. , Dziuba Szymon , Knošková Ľubica , Drábik Peter
Published: February 2021

In the present study, the authors assessed the odour quality of the air in the vicinity of a landfill site using a case study of a waste management plant that processes non-hazardous and inert waste as an example. An analysis of the impact of the facility under study on the odour quality of the air was performed based on a mathematical modelling system used to, among other things, assess the impact of investments in air quality both in Poland and worldwide. The most important element of the system is the puff dispersion model CALPUFF. In conclusion, the analysis of the plant’s odour impact clearly indicates a significant impact on the air quality in the studied area. The range of the impact may even reach up to 1.5 km; in the nearest locality, the odour perceptibility threshold may be exceeded for more than 3% of the hours in a year. However, taking into account the fact that the landfill is located within an agricultural area, the incidental odour impact in this area may also be associated with periods of intensive fertilization and a roadside ditch collecting municipal sewage from roadside households.
Community Perception and Communication of Volcanic Risk from the Cotopaxi Volcano in Latacunga, Ecuador
Authors: Gomez-Zapata Juan , Parrado Cristhian , Frimberger Theresa , Barragán-Ochoa Fernando , Brill Fabio , Büche Kerstin , Krautblatter Michael , Langbein Michael , Pittore Massimiliano , Rosero-Velásquez Hugo , Schoepfer Elisabeth , Spahn Harald , Zapata-Tapia Camilo
Published: February 2021

The inhabitants of Latacunga living in the surrounding of the Cotopaxi volcano (Ecuador) are exposed to several hazards and related disasters. After the last 2015 volcanic eruption, it became evident once again how important it is for the exposed population to understand their own social, physical, and systemic vulnerability. Effective risk communication is essential before the occurrence of a volcanic crisis. This study integrates quantitative risk and semi-quantitative social risk perceptions, aiming for risk-informed communities. We present the use of the RIESGOS demonstrator for interactive exploration and visualisation of risk scenarios. The development of this demonstrator through an iterative process with the local experts and potential end-users increases both the quality of the technical tool as well as its practical applicability. Moreover, the community risk perception in a focused area was investigated through online and field surveys. Geo-located interviews are used to map the social perception of volcanic risk factors. Scenario-based outcomes from quantitative risk assessment obtained by the RIESGOS demonstrator are compared with the semi-quantitative risk perceptions. We have found that further efforts are required to provide the exposed communities with a better understanding of the concepts of hazard scenario and intensity.
Industrial Symbiosis: A Sectoral Analysis on Enablers and Barriers
Authors: Henriques Juan , Ferrão Paulo , Castro Rui , Azevedo João
Published: February 2021

Industrial Symbiosis (IS) around the world in the last 20 years had been characterized through an extensive analysis of scientific papers on the IS emerging process, with a special focus on its early stages. The literature suggests that in this process there are key factors (enablers, barriers, triggers, and challenges) that play a critical role in Industrial Symbiosis. From those factors, the enablers and barriers have been highlighted in most of the studies in their different dimensions (social, economic, policy, technological, management, or geographical, amongst others). Several implementation cases suggest that the relevance of these factors rely on the dominant economic sectors involved. This study aims to reveal the key enablers and barriers in various economic sectors and its behaviour according to each one. To accomplish this objective, a comprehensive assessment methodology was designed and performed. This methodology is divided in two sequential phases: the first, sectoral analysis, focuses on the identification of the more relevant dimensions per economic sectors; in the second phase, incidence analysis, the individual behaviour of the enablers and barriers per economic sector are identified. This new approach correlates the economic sectors and factors incidence in order to provide new insights on the key barriers, and enablers on different dimensions. The main result of this study consists in the identification of a set of recommendations that might be critical to reinforce the emerging synergies process and to help overcome the barriers in each economic sector analysed.
What Can Drive Consumers’ Dining-Out Behavior in China and Korea during the COVID-19 Pandemic?
Authors: Zhong Yongping , Oh Segu , Moon Hee Cheol
Published: February 2021

Currently, living with COVID-19 under certain protective measures still continues as the “new normal” for most of the world. The disruption that the pandemic has caused to societies and economies, especially to the restaurant industry, may last longer than some had thought. This study intended to find out the key drivers of consumers’ dining-out intentions and their internal relationships. We adopted the structural equation modeling (SEM) method with 508 surveys collected from China and Korea. The results were as follows: perceived psychological risks, subjective norms, and enjoyment are influential to consumers’ dining-out intentions but not restaurant precautionary measures or perceived physical risk; subjective norms have direct implications on both perceived psychological and physical risks, while restaurant precautionary measures can only significantly affect perceived physical risk; enjoyment can be negatively influenced by perceived psychological and physical risks; country can moderate the relationships between subjective norms, perceived physical/psychological risk, and enjoyment. This study provides further understanding of the current food consumption patterns, which will help restaurants set up strategies accordingly to sustain their businesses and get them more prepared for any future outbreaks.
Attention Modulated Multiple Object Tracking with Motion Enhancement and Dual Correlation
Authors: Wang Yifeng , Zhang Zhijiang , Zhang Ning , Zeng Dan
Published: February 2021

The one-shot multiple object tracking (MOT) framework has drawn more and more attention in the MOT research community due to its advantage in inference speed. However, the tracking accuracy of current one-shot approaches could lead to an inferior performance compared with their two-stage counterparts. The reasons are two-fold: one is that motion information is often neglected due to the single-image input. The other is that detection and re-identification (ReID) are two different tasks with different focuses. Joining detection and re-identification at the training stage could lead to a suboptimal performance. To alleviate the above limitations, we propose a one-shot network named Motion and Correlation-Multiple Object Tracking (MAC-MOT). MAC-MOT introduces a motion enhance attention module (MEA) and a dual correlation attention module (DCA). MEA performs differences on adjacent feature maps which enhances the motion-related features while suppressing irrelevant information. The DCA module focuses on decoupling the detection task and re-identification task to strike a balance and reduce the competition between these two tasks. Moreover, symmetry is a core design idea in our proposed framework which is reflected in Siamese-based deep learning backbone networks, the input of dual stream images, as well as a dual correlation attention module. Our proposed approach is evaluated on the popular multiple object tracking benchmarks MOT16 and MOT17. We demonstrate that the proposed MAC-MOT can achieve a better performance than the baseline state of the arts (SOTAs).
Qualitative Rating of Lossy Compression for Aerial Imagery by Neutrosophic WASPAS Method
Authors: Bausys Romualdas , Kazakeviciute-Januskeviciene Giruta
Published: February 2021

The monitoring and management of consistently changing landscape patterns are accomplished through a large amount of remote sensing data using satellite images and aerial photography that requires lossy compression for effective storage and transmission. Lossy compression brings the necessity to evaluate the image quality to preserve the important and detailed visual features of the data. We proposed and verified a weighted combination of qualitative parameters for the multi-criteria decision-making (MCDM) framework to evaluate the quality of the compressed aerial images. The aerial imagery of different contents and resolutions was tested using the transform-based lossy compression algorithms. We formulated an MCDM problem dedicated to the rating of lossy compression algorithms, governed by the set of qualitative parameters of the images and visually acceptable lossy compression ratios. We performed the lossy compression algorithms’ ranking with different compression ratios by their suitability for the aerial images using the neutrosophic weighted aggregated sum product assessment (WASPAS) method. The novelty of our methodology is the use of a weighted combination of different qualitative parameters for lossy compression estimation to get a more precise evaluation of the effect of lossy compression on the image content. Our methodology includes means of solving different subtasks, either by altering the weights or the set of aspects.
Doctors’ Perceptions, Attitudes and Practices towards the Management of Multidrug-Resistant Organism Infections after the Implementation of an Antimicrobial Stewardship Programme during the COVID-19 Pandemic
Authors: Spernovasilis Nikolaos , Ierodiakonou Despo , Spanias Christos , Mathioudaki Anna , Ioannou Petros , Petrakis Emmanouil C. , Kofteridis Diamantis P.
Published: February 2021

Background: Greece is among the European countries with the highest consumption of antibiotics, both in community and hospital settings, including last-line antibiotics, such as carbapenems. We sought to explore doctors’ perceptions, attitudes and practices towards the management of patients with multidrug-resistant organism (MDRO) infections after the implementation of an antimicrobial stewardship programme (ASP) in a tertiary academic hospital during the COVID-19 pandemic. Methods: A self-administered, internet-based questionnaire survey was completed by doctors of the University Hospital of Heraklion in Crete, Greece. Results: In total, 202 (59.1%) hospital doctors fully completed the questionnaire. Most of them agreed that the prospective audit and feedback ASP strategy is more effective and educational than the preauthorization ASP strategy. ASP implementation prompted most respondents to monitor the continuously evolving microbiological data of their patients more closely and affected them towards a multidisciplinary and personalised care of patients with infections caused by MDROs and towards a more rigorous implementation of infection prevention and control measures. The vast majority of participants (98.5%) stated that ASP must be continued and further developed during the COVID-19 pandemic. Conclusion: The ASP implementation in our hospital had a beneficial impact on doctors’ perceptions, attitudes and practices with regard to the management of infections due to MDROs.