bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling
Authors: Le Guillou Ronan , Schmoll Martin , Sijobert Benoît , Lobato Borges David , Fachin-Martins Emerson , Resende Henrique , Pissard-Gibollet Roger , Fattal Charles , Azevedo Coste Christine
Published: July 2021

Functional electrical stimulation (FES) is a technique used in rehabilitation, allowing the recreation or facilitation of a movement or function, by electrically inducing the activation of targeted muscles. FES during cycling often uses activation patterns which are based on the crank angle of the pedals. Dynamic changes in their underlying predefined geometrical models (e.g., change in seating position) can lead to desynchronised contractions. Adaptive algorithms with a real-time interpretation of anatomical segments can avoid this and open new possibilities for the automatic design of stimulation patterns. However, their ability to accurately and precisely detect stimulation triggering events has to be evaluated in order to ensure their adaptability to real-case applications in various conditions. In this study, three algorithms (Hilbert, BSgonio, and Gait Cycle Index (GCI) Observer) were evaluated on passive cycling inertial data of six participants with spinal cord injury (SCI). For standardised comparison, a linear phase reference baseline was used to define target events (i.e., 10%, 40%, 60%, and 90% of the cycle’s progress). Limits of agreement (LoA) of ±10% of the cycle’s duration and Lin’s concordance correlation coefficient (CCC) were used to evaluate the accuracy and precision of the algorithm’s event detections. The delays in the detection were determined for each algorithm over 780 events. Analysis showed that the Hilbert and BSgonio algorithms validated the selected criteria (LoA: +5.17/−6.34% and +2.25/−2.51%, respectively), while the GCI Observer did not (LoA: +8.59/−27.89%). When evaluating control algorithms, it is paramount to define appropriate criteria in the context of the targeted practical application. To this end, normalising delays in event detection to the cycle’s duration enables the use of a criterion that stays invariable to changes in cadence. Lin’s CCC, comparing both linear correlation and strength of agreement between methods, also provides a reliable way of confirming comparisons between new control methods and an existing reference.
A Flexible Coding Scheme Based on Block Krylov Subspace Approximation for Light Field Displays with Stacked Multiplicative Layers
Authors: Ravishankar Joshitha , Sharma Mansi , Gopalakrishnan Pradeep
Published: July 2021

To create a realistic 3D perception on glasses-free displays, it is critical to support continuous motion parallax, greater depths of field, and wider fields of view. A new type of Layered or Tensor light field 3D display has attracted greater attention these days. Using only a few light-attenuating pixelized layers (e.g., LCD panels), it supports many views from different viewing directions that can be displayed simultaneously with a high resolution. This paper presents a novel flexible scheme for efficient layer-based representation and lossy compression of light fields on layered displays. The proposed scheme learns stacked multiplicative layers optimized using a convolutional neural network (CNN). The intrinsic redundancy in light field data is efficiently removed by analyzing the hidden low-rank structure of multiplicative layers on a Krylov subspace. Factorization derived from Block Krylov singular value decomposition (BK-SVD) exploits the spatial correlation in layer patterns for multiplicative layers with varying low ranks. Further, encoding with HEVC eliminates inter-frame and intra-frame redundancies in the low-rank approximated representation of layers and improves the compression efficiency. The scheme is flexible to realize multiple bitrates at the decoder by adjusting the ranks of BK-SVD representation and HEVC quantization. Thus, it would complement the generality and flexibility of a data-driven CNN-based method for coding with multiple bitrates within a single training framework for practical display applications. Extensive experiments demonstrate that the proposed coding scheme achieves substantial bitrate savings compared with pseudo-sequence-based light field compression approaches and state-of-the-art JPEG and HEVC coders.
Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices
Authors: Zeng Xin , Zhang Xiaomei , Yang Shuqun , Shi Zhicai , Chi Chihung
Published: July 2021

Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.
A Study on the Failure Behavior of Sand Grain Contacts with Hertz Modeling, Image Processing, and Statistical Analysis
Authors: Li Siyue , Kasyap Sathwik S. , Senetakis Kostas
Published: July 2021

The crushing behavior of particles is encountered in a large number of natural and engineering systems, and it is important for it to be examined in problems related to hydraulic fracturing, where proppant–proppant and proppant–rock interactions are essential to be modeled as well as geotechnical engineering problems, where grains may crush because the transmitted stresses at their contacts exceed their tensile strength. Despite the interest in the study of the crushing behavior of natural particles, most previous experimental works have examined the single-grain or multiple-grain crushing configurations, and less attention has been given in the laboratory investigation of the interactions of two grains in contact up to their failure as well as on the assessment of the methodology adopted to analyze the data. In the present study, a quartz sand of 1.18–2.36 mm in size was examined, performing a total of 244 grain-to-grain crushing tests at two different speeds, 0.01 and 1 mm/min. In order to calculate stresses from the measured forces, Hertz modeling was implemented to calculate an approximate contact area between the particles based on their local radii (i.e., the radius of the grains in the vicinity of their contact). Based on the results, three different modes of failure were distinguished as conservative, fragmentary, and destructive, corresponding to micro-scale, meso-scale, and macro-scale breakage, respectively. From the data, four different classes of curves could be identified. Class-A and class-B corresponded to an initially Hertzian behavior followed by a brittle failure with a distinctive (single) peak point. The occurrence of hardening prior to the failure point distinguished class-B from class-A. Two additional classes (termed as class-C and class-D) were observed having two or multiple peaks, and much larger displacements were necessary to mobilize the failure point. Hertz fitting, Weibull statistics, and clustering were further implemented to estimate the influence of local radius and elastic modulus values. One of the important observations was that the method of analysis adopted to estimate the local radius of the grains, based on manual assessment (i.e., eyeball fitting) or robust Matlab-based image processing, was a key factor influencing the resultant strength distribution and m-modulus, which are grain crushing strength characteristics. The results from the study were further compared with previously reported data on single- and multiple-grain crushing tests.
Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs
Authors: Mao Yi-Cheng , Chen Tsung-Yi , Chou He-Sheng , Lin Szu-Yin , Liu Sheng-Yu , Chen Yu-An , Liu Yu-Lin , Chen Chiung-An , Huang Yen-Cheng , Chen Shih-Lun , Li Chun-Wei , Abu Patricia Angela R. , Chiang Wei-Yuan
Published: July 2021

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.
The Analysis of Emotion Authenticity Based on Facial Micromovements
Authors: Park Sung , Lee Seong Won , Whang Mincheol
Published: July 2021

People tend to display fake expressions to conceal their true feelings. False expressions are observable by facial micromovements that occur for less than a second. Systems designed to recognize facial expressions (e.g., social robots, recognition systems for the blind, monitoring systems for drivers) may better understand the user’s intent by identifying the authenticity of the expression. The present study investigated the characteristics of real and fake facial expressions of representative emotions (happiness, contentment, anger, and sadness) in a two-dimensional emotion model. Participants viewed a series of visual stimuli designed to induce real or fake emotions and were signaled to produce a facial expression at a set time. From the participant’s expression data, feature variables (i.e., the degree and variance of movement, and vibration level) involving the facial micromovements at the onset of the expression were analyzed. The results indicated significant differences in the feature variables between the real and fake expression conditions. The differences varied according to facial regions as a function of emotions. This study provides appraisal criteria for identifying the authenticity of facial expressions that are applicable to future research and the design of emotion recognition systems.
Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra
Authors: Barton Sinead , Alakkari Salaheddin , O’Dwyer Kevin , Ward Tomas , Hennelly Bryan
Published: July 2021

Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky–Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky–Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.
Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications
Authors: Toichoa Eyam Aitor , Mohammed Wael M. , Martinez Lastra Jose L.
Published: July 2021

The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capabilities, communication systems, and the information systems of the manufacturing systems which is reflected in the concept of Industry 4.0. Furthermore, the robotics systems are continuously required to address new challenges in the industrial and manufacturing domain, like keeping humans in the loop, among other challenges. Briefly, the keeping humans in the loop concept focuses on closing the gap between humans and machines by introducing a safe and trustworthy environment for the human workers to work side by side with robots and machines. It aims at increasing the engagement of the human as the automation level increases rather than replacing the human, which can be nearly impossible in some applications. Consequently, the collaborative robots (Cobots) have been created to allow physical interaction with the human worker. However, these cobots still lack of recognizing the human emotional state. In this regard, this paper presents an approach for adapting cobot parameters to the emotional state of the human worker. The approach utilizes the Electroencephalography (EEG) technology for digitizing and understanding the human emotional state. Afterwards, the parameters of the cobot are instantly adjusted to keep the human emotional state in a desirable range which increases the confidence and the trust between the human and the cobot. In addition, the paper includes a review on technologies and methods for emotional sensing and recognition. Finally, this approach is tested on an ABB YuMi cobot with commercially available EEG headset.
Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor
Authors: Chow Daniel Hung Kay , Tremblay Luc , Lam Chor Yin , Yeung Adrian Wai Yin , Cheng Wilson Ho Wu , Tse Peter Tin Wah
Published: July 2021

Wearable sensors facilitate running kinematics analysis of joint kinematics in real running environments. The use of a few sensors or, ideally, a single inertial measurement unit (IMU) is preferable for accurate gait analysis. This study aimed to use a convolutional neural network (CNN) to predict level-ground running kinematics (measured by four IMUs on the lower extremities) by using treadmill running kinematics training data measured using a single IMU on the anteromedial side of the right tibia and to compare the performance of level-ground running kinematics predictions between raw accelerometer and gyroscope data. The CNN model performed regression for intraparticipant and interparticipant scenarios and predicted running kinematics. Ten recreational runners were recruited. Accelerometer and gyroscope data were collected. Intraparticipant and interparticipant R2 values of actual and predicted running kinematics ranged from 0.85 to 0.96 and from 0.7 to 0.92, respectively. Normalized root mean squared error values of actual and predicted running kinematics ranged from 3.6% to 10.8% and from 7.4% to 10.8% in intraparticipant and interparticipant tests, respectively. Kinematics predictions in the sagittal plane were found to be better for the knee joint than for the hip joint, and predictions using the gyroscope as the regressor were demonstrated to be significantly better than those using the accelerometer as the regressor.
Efficient FPGA Implementation of a Dual-Frequency GNSS Receiver with Robust Inter-Frequency Aiding
Authors: Huang Kuan-Ying , Juang Jyh-Ching , Tsai Yung-Fu , Lin Chen-Tsung
Published: July 2021

Multiple frequency global navigation satellite system (GNSS) has become more complex due to the existence of extra channels. Typically, auxiliary methods are used to synchronize the second signals at other bands by aiding the acquired channel parameters. However, there are critical limitations because the reception of GNSS signals is subject to uncertainties due to noise carrier injection or circuit interference. The relationship between the two Doppler frequencies can be affected by uncertainties. Therefore, we aimed to implement an efficient dual-frequency field-programmable gate array (FPGA), performing a direct aid tracking method for the secondary channel to achieve resource efficiency and inner aid robustness. A robust estimator that directly links two loops in the two bands is proposed. In this scheme, (1) a robust estimator able to cope with uncertainty; (2) a primary tracking scheme to obtain the error boundary, and (3) a tracked bit-boundary for the initial code phase of the second channel are used. Based on experiments on the FPGA, the robust channel link can achieve direct aid tracking, and 31.02% of the original hardware resources from the aided acquisition module were released satisfactorily.
Modeling and Imaging of Ultrasonic Array Inspection of Side Drilled Holes in Layered Anisotropic Media
Authors: Anand Chirag , Groves Roger M. , Benedictus Rinze
Published: July 2021

There has been an increase in the use of ultrasonic arrays for the detection of defects in composite structures used in the aerospace industry. The response of a defect embedded in such a medium is influenced by the inherent anisotropy of the bounding medium and the layering of the bounding medium and hence poses challenges for the interpretation of the full matrix capture (FMC) results. Modeling techniques can be used to understand and simulate the effect of the structure and the defect on the received signals. Existing modeling techniques, such as finite element methods (FEM), finite difference time domain (FDTD), and analytical solutions, are computationally inefficient or are singularly used for structures with complex geometries. In this paper, we develop a novel model based on the Gaussian-based recursive stiffness matrix approach to model the scattering from a side-drilled hole embedded in an anisotropic layered medium. The paper provides a novel method to calculate the transmission and reflection coefficients of plane waves traveling from a layered anisotropic medium into a semi-infinite anisotropic medium by combining the transfer matrix and stiffness matrix methods. The novelty of the paper is the developed model using Gaussian beams to simulate the scattering from a Side Drilled Hole (SDH) embedded in a multilayered composite laminate, which can be used in both immersion and contact setups. We describe a method to combine the scattering from defects with the model to simulate the response of a layered structure and to simulate the full matrix capture (FMC) signals that are received from an SDH embedded in a layered medium. The model-assisted correction total focusing method (MAC-TFM) imaging is used to image both the simulated and experimental results. The proposed method has been validated for both isotropic and anisotropic media by a qualitative and quantitative comparison with experimentally determined signals. The method proposed in this paper is modular, computationally inexpensive, and is in good agreement with experimentally determined signals, and it enables us to understand the effects of various parameters on the scattering of a defect embedded in a layered anisotropic medium.
Non-Contact Measurement of Motion Sickness Using Pupillary Rhythms from an Infrared Camera
Authors: Park Sangin , Mun Sungchul , Ha Jihyeon , Kim Laehyun
Published: July 2021

Both physiological and neurological mechanisms are reflected in pupillary rhythms via neural pathways between the brain and pupil nerves. This study aims to interpret the phenomenon of motion sickness such as fatigue, anxiety, nausea and disorientation using these mechanisms and to develop an advanced non-contact measurement method from an infrared webcam. Twenty-four volunteers (12 females) experienced virtual reality content through both two-dimensional and head-mounted device interpretations. An irregular pattern of the pupillary rhythms, demonstrated by an increasing mean and standard deviation of pupil diameter and decreasing pupillary rhythm coherence ratio, was revealed after the participants experienced motion sickness. The motion sickness was induced while watching the head-mounted device as compared to the two-dimensional virtual reality, with the motion sickness strongly related to the visual information processing load. In addition, the proposed method was verified using a new experimental dataset for 23 participants (11 females), with a classification performance of 89.6% (n = 48) and 80.4% (n = 46) for training and test sets using a support vector machine with a radial basis function kernel, respectively. The proposed method was proven to be capable of quantitatively measuring and monitoring motion sickness in real-time in a simple, economical and contactless manner using an infrared camera.
Constrained Multiple Planar Reconstruction for Automatic Camera Calibration of Intelligent Vehicles
Authors: Lee Sang Jun , Lee Jae-Woo , Lee Wonju , Jang Cheolhun
Published: July 2021

In intelligent vehicles, extrinsic camera calibration is preferable to be conducted on a regular basis to deal with unpredictable mechanical changes or variations on weight load distribution. Specifically, high-precision extrinsic parameters between the camera coordinate and the world coordinate are essential to implement high-level functions in intelligent vehicles such as distance estimation and lane departure warning. However, conventional calibration methods, which solve a Perspective-n-Point problem, require laborious work to measure the positions of 3D points in the world coordinate. To reduce this inconvenience, this paper proposes an automatic camera calibration method based on 3D reconstruction. The main contribution of this paper is a novel reconstruction method to recover 3D points on planes perpendicular to the ground. The proposed method jointly optimizes reprojection errors of image features projected from multiple planar surfaces, and finally, it significantly reduces errors in camera extrinsic parameters. Experiments were conducted in synthetic simulation and real calibration environments to demonstrate the effectiveness of the proposed method.
Formation of Humic-Like Substances during the Technological Process of Lignohumate® Synthesis as a Function of Time
Authors: Yakimenko Olga , Stepanov Andrey , Patsaeva Svetlana , Khundzhua Daria , Osipova Olesya , Gladkov Oleg
Published: July 2021

The composition, structure, and biological activity of humic-like substances (HLS) synthesized in the process of lignosulfonate conversion for the production of the humic product Lignohumate® (LH) were examined. It is shown that during the hydrolytic-oxidative process, the transformation of raw material and accumulation of HLS occur. Data on the chemical (elemental content, functional groups, FTIR) and spectral (absorbance and fluorescence) parameters and biological activity (in phytotest) combined with PCA show that the LH samples can be divided into three groups, depending on the duration of synthesis: initial raw material (0-time sample); “young” HLS (15–30 min), and “mature” HLS in 45–120 min of treatment. During the first 30 min, reactions similar to the ones that occur during lignin humification in nature take place: depolymerization, oxidative carboxylation, and further polycondensation with the formation and accumulation of HLS. After 45–60 min, the share of HLS reaches a maximum, and its composition stabilizes. Biological activity reaches a maximum after 45–60 min of treatment, and at that stage, the further synthesis process can be stopped. Further processing (up to 2 h and more) does not provide any added value to the humic product.
Chromatographic Analysis of Aflatoxigenic Aspergillus flavus Isolated from Malaysian Sweet Corn
Authors: Khan Rahim , Ghazali Farinazleen Mohamad , Mahyudin Nor Ainy , Samsudin Nik Iskandar Putra
Published: July 2021

High-performance liquid chromatography (HPLC) provides a quick and efficient tool for accurately characterizing aflatoxigenic and non-aflatoxigenic isolates of Aspergillus flavus. This method also provides a quantitative analysis of AFs in Aspergillus flavus. The method’s recovery was assessed by spiking a mixture of AF at different concentrations to the testing medium. The validity of the method was confirmed using aflatoxigenic and non-aflatoxigenic strains of A. flavus. The HPLC system, coupled with a fluorescence detector and post-column photochemical reactor, showed high sensitivity in detecting spiked AFs or AFs produced by A. flavus isolates. Recovery from medium spiked with 10, 20, 60, and 80 ppb of AFs was found to be 73–86% using this approach. For AFB1 and AFB2, the limit of detection was 0.072 and 0.062 ppb, while the limit of quantification was 0.220 and 0.189 ppb, respectively. The AFB1 concentrations ranged from 0.09 to 50.68 ppb, while the AFB2 concentrations ranged between 0.33 and 9.23 ppb. The findings showed that six isolates produced more AFB1 and AFB2 than the acceptable limit of 5 ppb. The incidence of aflatoxigenic isolates of A. flavus in sweet corn and higher concentrations of AFB1 and AFB2 emphasize the need for field trials to explore their real potential for AF production in corn.
Predicting eWOM’s Influence on Purchase Intention Based on Helpfulness, Credibility, Information Quality and Professionalism
Authors: Chen Yen-Liang , Chang Chia-Ling , Sung An-Qiao
Published: July 2021

Product reviews co-written by many Internet reviewers can help consumers make purchase decisions and provide a basis for companies to improve their business strategies. For the company, the most important thing is to understand how the various factors of reviews influence the purchase intention. Therefore, we took this issue as the core and investigated the influence of eWOM on purchase intention based on helpfulness, credibility, information quality and professionalism. We adopted feature filtering algorithms and proposed an ensemble model to integrate these classification results to obtain the most accurate prediction. The empirical evaluation shows that the models based on the four importan aspects of reviews can effectively predict the degree of impact of reviews on purchase intention.
Local Food Campaign in a Globalization Context: A Systematic Review
Authors: Jia Susan (Sixue)
Published: July 2021

As a basic commodity, food has undergone thorough globalization, with the global food market totaling 1392 billion USD in 2019. Despite such a great amount of global food trade, the idea of favouring the consumption of local food, or local food campaigns, has won ever growing attention and advocacy in recent years as an effort to enhance social and environmental sustainability. This systematic review study draws wisdom from the extant literature and provides critical thinking on how local food differs from non-local food and whether the two are more antagonistic or more complementary. Results suggest that although the term “local food” has hardly been clearly defined, it is possible to accommodate different opinions in a set of common constructs in Eriksen’s “three domains of proximity”. Regarding the strengths of local food, researchers agree more on its strong personal connection, distinctive culture, and high quality, but less on its supporting local economy, reduced energy consumption, and environmental friendliness. Meanwhile, local food has its current weaknesses in terms of higher price and unsuccessful information communication; however, these are not without solutions. Overall, while food localization and globalization differ in purpose, they can well co-exist, promote collaboration rather than confrontation, and together accelerate the sustainable growth of the food market.
Impact of Climate Change on Wine Tourism: An Approach through Social Media Data
Authors: Sottini Veronica Alampi , Barbierato Elena , Bernetti Iacopo , Capecchi Irene
Published: July 2021

Wine tourism is one of the best opportunities for rural development, but because it is partially exposed to climatic conditions, it is a climate-vulnerable tourism activity. However, an understanding of the potential impacts of global climate change on this popular activity remains limited. This study proposes a new methodology that combines current daily gridded climate data from the E-OBS project with big spatiotemporal data from the Flickr photo-sharing platform through a generalized additive model This methodology was implemented to study the potential impacts on tourism flows due to climate change and to make predictions about the future using data from the CMIP5 project. We applied the methodology to 5 European wine tourism regions: Alsace (FR), Chianti (IT), La Rioja (SP), Langhe-Monferrato (IT), and Moselle (DE). Results show an increased probability of presence and increased deseasonalization of tourism in all study areas and an anticipation of peak presence from summer to spring in three of the five regions. We believe that these results can be useful for public and private stakeholders to adapt the offer of wine tourism services to changes in demand and to direct the organization of events such as festivals and thematic tours.
Effects of Pretreatment and Ratio of Solid Sago Waste to Rumen on Biogas Production through Solid-State Anaerobic Digestion
Authors: Sumardiono Siswo , Adisukmo Gebyar , Hanif Muthia , Budiyono Budiyono , Cahyono Heri
Published: July 2021

Solid sago waste is a potential source of producing renewable energy in the form of biogas. This study investigated the effects of solid sago waste particle size, biological pretreatment using a microbial consortium of lignocelluloses, pretreatment with NaOH, and the ratio between solid sago waste and cow rumen based on the biogas production rate. Several variations of these conditions were used to achieve this. The anaerobic digestion process was conducted over two months at 30.42 °C ± 0.05 °C, and the biogas production rate was measured every two days. The 1:1 ratio showed better results compared to the 2:1, because it allows the bacteria to achieve metabolic balance. The highest cumulative biogas production (27.91 mL/g TS) was generated when the sago waste underwent milling (±1 mm), pretreatment with 4% NaOH g/g TS, and treatment with microbial consortium 5% v/v at a 1:1 ratio of solid sago waste to the rumen.
The Circular Economy: A Study on the Use of Airbnb for Sustainable Coastal Development in the Vietnam Mekong Delta
Authors: He Jianjia , Mai Thi Hoai Thuong
Published: July 2021

The concept of the circular economy has become well known for its solution-oriented approach to transforming available resources into a closed-loop resource system. However, in the context of coastal areas, coastal resources seem to be ignored in the tourism production and consumption process. In relation to the 2030 Agenda for Sustainable Development Goals (SDGs), this article discusses how sharing economy practices may sustain coastal resources through ecotourism, applying a sharing-economy theory that emphasizes changes in the new form—a circular economy—rather than a single traditional Airbnb model or ecotourism model. This study proposes a coastal sustainable development structure model based on the integration between the sharing economy and ecotourism with three modes—positive economic effects, positive economic pressures, and sustainable coastal development—and uses coastal residents’ expectations of their living conditions as moderating factors to investigate the impact of the circular economy on coastal sustainability. We developed a survey-based model that included 303 samples from the indigenous residents of 13 provinces throughout the Vietnam Mekong Delta. The results show that the integration of ecotourism with the Airbnb model has a positive effect on residents’ living conditions, supporting sustainable local development. However, the advancement of technology and residents’ awareness involves barriers to coastal development because the process of modernization is still limited in coastal areas. More specifically, in the case of the Vietnam Mekong Delta, our results suggest that limited technical knowledge and language ability stand as barriers to coastal businesses, showing that the lack of inter-regional connectivity limits the magnitude of local tourism in coastal areas. These findings are useful for assessing residents’ living conditions so that coastal development can work towards poverty reduction. Finally, the establishment and expansion of policies by local authorities can be an indispensable part of coastal economic development by limiting the negative effects of the abuse of natural resources and facilitating family businesses in coastal zones in an effort towards the integration of economic development and social and environmental responsibility.
There Is No Place like Home! How Willing Are Young Adults to Move to Find a Job?
Authors: Weiss Julia , Ferrante Livio , Soler-Porta Mariano
Published: July 2021

The European Union (EU) has undergone significant economic crises in recent years. Therein, young people were amongst the hardest hit groups, with youth unemployment rising as high as 50% in some member states. Particularly high rates of youth unemployment were often observed in rural areas, where labour market supply in relation to demand were notably divergent. One of the core pillars of the EU’s agenda is to tackle the persistent problem of youth unemployment. Since the recent crisis, this has been via the “Youth on the Move” initiative, which involves the promotion of intra- and international mobility of young adults in order to gain access to job opportunities. However, what has received little attention so far is the question of what the general willingness of young adults to move is like, and to what extent this varies, for example, depending upon the area they live in. This paper therefore asks if rural youth differ from youth in urban areas in relation to their willingness to move for a job within their country or to another country. Moreover, what influences the general willingness to be mobile? Based on the Cultural Pathways to Economic Self-Sufficiency and Entrepreneurship (CUPESSE) Survey, which includes data on 18–35-year-olds in a sample of 11 European countries, it is shown that living in a rural area is strongly associated with the willingness to move. Furthermore, it shows that rural youth are more willing to move within the country but less willing to move to another country. Based on the presentation of the various factors, which promote or curb mobility readiness, the results make it clear that the success of EU initiatives depends on the preferences and willingness of the target group in question.
Cooperative Green Technology Innovation of an E-Commerce Sales Channel in a Two-Stage Supply Chain
Authors: Mu Zongyu , Zheng Yuangang , Sun Hao
Published: July 2021

The potential broad market of green consumption has encouraged an increasing number of enterprises to carry out green technology innovation activities. This paper examines a two-stage supply chain of e-commerce sales channels under different cooperative models. We find that consumers’ green preferences are the main factor that affects green product market demand. The manufacturer and the retailer can raise the levels of green technology innovation and extend green promotional services to expand product market demand in online and offline channels. However, consumers’ e-commerce preferences and online free-riding behaviors affect the manufacturer’s sales channel choice. The retailer can improve the level of green promotional services to hold offline channel market demand, while promotional behaviors have a positive/negative spillover effect on online market demand if the level of free riding falls above/below consumers’ e-commerce preferences. The higher the cooperative level is, the later the manufacturer will open the online channel and close the offline channel to ensure a high level of green promotional service from the cooperative retailer. The results show that the stronger the level of cooperation among all members is, the better the economic, ecological, and social benefits will be. Therefore, we design a revenue-cost sharing contract that can effectively motivate green technology innovation and green promotional services and afford all members win-win profits.
Impact Assessment Analysis of Sea Level Rise in Denmark: A Case Study of Falster Island, Guldborgsund
Authors: Boest-Petersen Alexander , Michalak Piotr , Jokar Arsanjani Jamal
Published: July 2021

Anthropogenically-induced climate change is expected to be the contributing cause of sea level rise and severe storm events in the immediate future. While Danish authorities have downscaled the future oscillation of sea level rise across Danish coast lines in order to empower the coastal municipalities, there is a need to project the local cascading effects on different sectors. Using geospatial analysis and climate change projection data, we developed a proposed workflow to analyze the impacts of sea level rise in the coastal municipalities of Guldborgsund, located in Southeastern Denmark as a case study. With current estimates of sea level rise and storm surge events, the island of Falster can expect to have up to 19% of its landmass inundated, with approximately 39% of the population experiencing sea level rise directly. Developing an analytical workflow can allow stakeholders to understand the extent of expected sea level rise and consider alternative methods of prevention at the national and local levels. The proposed approach along with the choice of data and open source tools can empower other communities at risk of sea level rise to plan their adaptation.
Perceived Trip Time Reliability and Its Cost in a Rail Transit Network
Authors: Liu Jie , Schonfeld Paul , Chen Jinqu , Yin Yong , Peng Qiyuan
Published: July 2021

Time reliability in a Rail Transit Network (RTN) is usually measured according to clock-based trip time, while the travel conditions such as travel comfort and convenience cannot be reflected by clock-based trip time. Here, the crowding level of trains, seat availability, and transfer times are considered to compute passengers’ Perceived Trip Time (PTT). Compared with the average PTT, the extra PTT needed for arriving reliably, which equals the 95th percentile PTT minus the average PTT, is converted into the monetary cost for estimating Perceived Time Reliability Cost (PTRC). The ratio of extra PTT needed for arriving reliably to the average PTT referring to the buffer time index is proposed to measure Perceived Time Reliability (PTR). To overcome the difficulty of obtaining passengers’ PTT who travel among rail transit modes, a Monte Carlo simulation is applied to generated passengers’ PTT for computing PTR and PTRC. A case study of Chengdu’s RTN shows that the proposed metrics and method measure the PTR and PTRC in an RTN effectively. PTTR, PTRC, and influential factors have significant linear relations among them, and the obtained linear regression models among them can guide passengers to travel reliably.
Valorization of Spent Coffee by Caffeine Extraction Using Aqueous Solutions of Cholinium-Based Ionic Liquids
Authors: Ferreira Ana M. , Gomes Hugo M. D. , Coutinho João A. P. , Freire Mara G.
Published: July 2021

Spent coffee grounds (SCGs) are a waste product with no relevant commercial value. However, SCGs are rich in extractable compounds with biological activity. To add value to this coffee byproduct, water and aqueous solutions of cholinium-based ionic liquids (ILs) were studied to extract caffeine from SCGs. In general, all IL aqueous solutions lead to higher extraction efficiencies of caffeine than pure water, with aqueous solutions of cholinium bicarbonate being the most efficient. A factorial planning was applied to optimize operational conditions. Aqueous solutions of cholinium bicarbonate, at a temperature of 80 °C for 30 min of extraction, a biomass–solvent weight ratio of 0.05 and at an IL concentration of 1.5 M, made it possible to extract 3.29 wt% of caffeine (against 1.50 wt% obtained at the best conditions obtained with pure water). Furthermore, to improve the sustainability of the process, the same IL aqueous solution was consecutively applied to extract caffeine from six samples of fresh biomass, where an increase in the extraction yield from 3.29 to 13.10 wt% was achieved. Finally, the cholinium bicarbonate was converted to cholinium chloride by titration with hydrochloric acid envisioning the direct application of the IL-caffeine extract in food, cosmetic and nutraceutical products. The results obtained prove that aqueous solutions of cholinium-based ILs are improved solvents for the extraction of caffeine from SCGs, paving the way for their use in the valorization of other waste rich in high-value compounds.
Drag Coefficients of Irregularly Shaped Particles in Newtonian Fluids
Authors: Akanni Owolabi , Fu Chunkai , Guo Boyun
Published: July 2021

An accurate prediction of the settling velocities of drill cuttings is essential in effectively designing, running, and optimizing drilling operations. If there is no reliable process for modelling the drag coefficient, the settling velocity cannot be obtained. In most current literature, particles are assumed to be spherical, which can be easily modelled. However, this assumption may lead to inaccurate results for other irregular particle shapes. This paper studies the transport behavior of irregular particles by modelling these shapes as variants of a bow shape, with a numerical simulation approach for their drag coefficients. The drilling fluid around the particle is water (Newtonian). The drag coefficients of the non-spherical particle (grouped into three sub-shapes) were modelled. In addition, the inlet velocity of the fluid is varied to show the effects on the shape drag coefficients. The results of the simulations were compared to experimental results carried out by other researchers. It was observed that as the particles became less streamlined, their drag coefficient increased. A sensitivity analysis was carried out to investigate the effects of fluid properties on the drag coefficient. The results were consistent and logical. The results showed that Computational Fluid Dynamics analysis provided a reliable estimation of the drag coefficient, which can help optimize the transport of drill cuttings during drilling operations.
Board of Directors’ Remuneration, Employee Costs, and Layoffs: Evidence from Spain
Authors: González-Sánchez Mariano , Ibáñez Jiménez Eva M. , Segovia San Juan Ana I.
Published: July 2021

Most of the empirical studies on board remuneration have focused on finding explanatory performance measures. There are studies that analyze if the compensation contracts of directors reward managers in such a way that they strive to maximize firm performance and shareholders’ wealth; however, there are few studies on the social aspect of corporate governance, or agent–employee and principal–employee relationships. Thus, in this study, our aim is to test whether there is a causal relationship between the remuneration of the board of directors of listed companies and the personnel policies of the companies, expressed through the cost of personnel and layoffs. For that, we used a sample of Spanish listed companies, and we found that two performance measures (return on equity and earnings per share on market price) have a greater effect on the growth rate of board remuneration when layoffs occur. Additionally, we found that the sales revenue and cash flow on total assets subsequently influenced personnel management.
Application of Industry 4.0 in the Procurement Processes of Supply Chains: A Systematic Literature Review
Authors: Jahani Niloofar , Sepehri Arash , Vandchali Hadi Rezaei , Tirkolaee Erfan Babaee
Published: July 2021

The fourth industrial revolution has significantly changed the traditional way of managing supply chains. The applications of Industry 4.0 (I4.0) technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI) in different processes of supply chains have assisted companies to improve their performance. Procurement can be considered a critical process in supply chain management since it can provide novel opportunities for supply chains to improve their efficiency and effectiveness. However, I4.0 applications can be costly and may not be reasonably affordable. Therefore, the benefits of implementing these technologies should be clarified for procurement managers before investing in the digitalization of the procurement process. Despite the importance of this issue, few papers have attempted to address the effects of I4.0 technologies and smart systems in procurement. To fill this gap, a Systematic Literature Review (SLR) on the applications of I4.0 technologies in procurement has been used in this study. By reviewing 70 papers through appropriate keywords, a conceptual framework is developed to classify different value propositions provided by the different applications of I4.0 technologies in procurement processes. Results reveal nine value propositions that can provide a better understanding for the procurement department to analyze the benefits of implementing the related I4.0 technologies in different activities. Finally, findings and future study opportunities are concluded.