bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine
Authors: Zou Xiuguo , Wang Chenyang , Luo Manman , Ren Qiaomu , Liu Yingying , Zhang Shikai , Bai Yungang , Meng Jiawei , Zhang Wentian , Su Steven W.
Published: April 2022

Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA–KNN-SVM classifier was 96.45%, and the LDA–KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage.
An Effective Gaze-Based Authentication Method with the Spatiotemporal Feature of Eye Movement
Authors: Yin Jinghui , Sun Jiande , Li Jing , Liu Ke
Published: April 2022

Eye movement has become a new behavioral feature for biometric authentication. In the eye movement-based authentication methods that use temporal features and artificial design features, the required duration of eye movement recordings are too long to be applied. Therefore, this study aims at using eye movement recordings with shorter duration to realize authentication. And we give out a reasonable eye movement recording duration that should be less than 12 s, referring to the changing pattern of the deviation degree between the gaze point and the stimulus point on the screen. In this study, the temporal motion features of the gaze points and the spatial distribution features of the saccade are using to represent the personal identity. Two datasets are constructed for the experiments, including 5 s and 12 s of eye movement recordings. On the datasets constructed in this paper, the open-set authentication results show that the Equal Error Rate of our proposed methods can reach 10.62% when recording duration is 12 s and 12.48% when recording duration is 5 s. The closed-set authentication results show that the Equal Error Rate of our proposed methods can reach 5.25% when recording duration is 12 s and 7.82% when recording duration is 5 s. It demonstrates that the proposed method provides a reference for the eye movements data-based identity authentication.
Effect of Temperature on Microwave Permeability of an Air-Stable Composite Filled with Gadolinium Powder
Authors: Starostenko Sergey N. , Petrov Dmitriy A. , Rozanov Konstantin N. , Shiryaev Artem O. , Lomaeva Svetlana F.
Published: April 2022

A composite containing about 30% volume of micrometer-size powder of gadolinium in paraffin wax is synthesized mechanochemically. The composite permittivity and permeability are measured within the frequency range from 0.01 to 15 GHz and the temperature range from ~0 °C to 35 °C. The permittivity is constant within the measured ranges. Curie temperature of the composite is close to 15.5 °C, the phase transition is shown to take place within a temperature range about ±10 °C. The effect of temperature deviation from Curie point on reflection and transmission of a composite layer filled with Gd powder is studied experimentally and via simulation. Constitutive parameters of the composite are measured in cooled coaxial lines applying reflection-transmission and open-circuit-short-circuit techniques, and the measured low-frequency permeability is in agreement with the values retrieved from the published magnetization curves. The effect of temperature on permeability spectrum of the composite is described in terms of cluster magnetization model based on the Wiener mixing formula. The model is applied to design a microwave screen with variable attenuation; the reflectivity attenuation of 4.5 mm-thick screen increases from about −2 dB to −20 dB at 3.5 GHz if the temperature decreases from 25 °C to 5 °C.
Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review
Authors: Marotta Luca , Scheltinga Bouke L. , van Middelaar Robbert , Bramer Wichor M. , van Beijnum Bert-Jan F. , Reenalda Jasper , Buurke Jaap H.
Published: April 2022

Physical exercise (PE) is beneficial for both physical and psychological health aspects. However, excessive training can lead to physical fatigue and an increased risk of lower limb injuries. In order to tailor training loads and durations to the needs and capacities of an individual, physical fatigue must be estimated. Different measurement devices and techniques (i.e., ergospirometers, electromyography, and motion capture systems) can be used to identify physical fatigue. The field of biomechanics has succeeded in capturing changes in human movement with optical systems, as well as with accelerometers or inertial measurement units (IMUs), the latter being more user-friendly and adaptable to real-world scenarios due to its wearable nature. There is, however, still a lack of consensus regarding the possibility of using biomechanical parameters measured with accelerometers to identify physical fatigue states in PE. Nowadays, the field of biomechanics is beginning to open towards the possibility of identifying fatigue state using machine learning algorithms. Here, we selected and summarized accelerometer-based articles that either (a) performed analyses of biomechanical parameters that change due to fatigue in the lower limbs or (b) performed fatigue identification based on features including biomechanical parameters. We performed a systematic literature search and analysed 39 articles on running, jumping, walking, stair climbing, and other gym exercises. Peak tibial and sacral acceleration were the most common measured variables and were found to significantly increase with fatigue (respectively, in 6/13 running articles and 2/4 jumping articles). Fatigue classification was performed with an accuracy between 78% and 96% and Pearson’s correlation with an RPE (rate of perceived exertion) between r = 0.79 and r = 0.95. We recommend future effort toward the standardization of fatigue protocols and methods across articles in order to generalize fatigue identification results and increase the use of accelerometers to quantify physical fatigue in PE.
Retention Effects of Long-Term Balance Training with Vibrotactile Sensory Augmentation in Healthy Older Adults
Authors: Bao Tian , Noohi Fatemeh , Kinnaird Catherine , Carender Wendy J. , Barone Vincent J. , Peethambaran Geeta , Whitney Susan L. , Seidler Rachael D. , Sienko Kathleen H.
Published: April 2022

Vibrotactile sensory augmentation (SA) decreases postural sway during real-time use; however, limited studies have investigated the long-term effects of training with SA. This study assessed the retention effects of long-term balance training with and without vibrotactile SA among community-dwelling healthy older adults, and explored brain-related changes due to training with SA. Sixteen participants were randomly assigned to the experimental group (EG) or control group (CG), and trained in their homes for eight weeks using smart-phone balance trainers. The EG received vibrotactile SA. Balance performance was assessed before, and one week, one month, and six months after training. Functional MRI (fMRI) was recorded before and one week after training for four participants who received vestibular stimulation. Both groups demonstrated significant improvement of SOT composite and MiniBESTest scores, and increased vestibular reliance. Only the EG maintained a minimal detectable change of 8 points in SOT scores six months post-training and greater improvements than the CG in MiniBESTest scores one month post-training. The fMRI results revealed a shift from activation in the vestibular cortex pre-training to increased activity in the brainstem and cerebellum post-training. These findings showed that additional balance improvements were maintained for up to six months post-training with vibrotactile SA for community-dwelling healthy older adults.
Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning
Authors: Garibaldi-Márquez Francisco , Flores Gerardo , Mercado-Ravell Diego A. , Ramírez-Pedraza Alfonso , Valentín-Coronado Luis M.
Published: April 2022

Crop and weed discrimination in natural field environments is still challenging for implementing automatic agricultural practices, such as weed control. Some weed control methods have been proposed. However, these methods are still restricted as they are implemented under controlled conditions. The development of a sound weed control system begins by recognizing the crop and the different weed plants presented in the field. In this work, a classification approach of Zea mays L. (Crop), narrow-leaf weeds (NLW), and broadleaf weeds (BLW) from multi-plant images are presented. Moreover, a large image dataset was generated. Images were captured in natural field conditions, in different locations, and growing stages of the plants. The extraction of regions of interest (ROI) is carried out employing connected component analysis (CCA), whereas the classification of ROIs is based on Convolutional Neural Networks (CNN) and compared with a shallow learning approach. To measure the classification performance of both methods, accuracy, precision, recall, and F1-score metrics were used. The best alternative for the weed classification task at early stages of growth and in natural corn field environments was the CNN-based approach, as indicated by the 97% accuracy value obtained.
An Intra-Vehicular Wireless Multimedia Sensor Network for Smartphone-Based Low-Cost Advanced Driver-Assistance Systems
Authors: Fourie Christiaan M. , Myburgh Hermanus Carel
Published: April 2022

Advanced driver-assistance system(s) (ADAS) are more prevalent in high-end vehicles than in low-end vehicles. Wired solutions of vision sensors in ADAS already exist, but are costly and do not cater for low-end vehicles. General ADAS use wired harnessing for communication; this approach eliminates the need for cable harnessing and, therefore, the practicality of a novel wireless ADAS solution was tested. A low-cost alternative is proposed that extends a smartphone’s sensor perception, using a camera-based wireless sensor network. This paper presents the design of a low-cost ADAS alternative that uses an intra-vehicle wireless sensor network structured by a Wi-Fi Direct topology, using a smartphone as the processing platform. The proposed system makes ADAS features accessible to cheaper vehicles and investigates the possibility of using a wireless network to communicate ADAS information in a intra-vehicle environment. Other ADAS smartphone approaches make use of a smartphone’s onboard sensors; however, this paper shows the application of essential ADAS features developed on the smartphone’s ADAS application, carrying out both lane detection and collision detection on a vehicle by using wireless sensor data. A smartphone’s processing power was harnessed and used as a generic object detector through a convolution neural network, using the sensory network’s video streams. The network’s performance was analysed to ensure that the network could carry out detection in real-time. A low-cost CMOS camera sensor network with a smartphone found an application, using Wi-Fi Direct, to create an intra-vehicle wireless network as a low-cost advanced driver-assistance system.
Automatic Speech Recognition Performance Improvement for Mandarin Based on Optimizing Gain Control Strategy
Authors: Wang Desheng , Wei Yangjie , Zhang Ke , Ji Dong , Wang Yi
Published: April 2022

Automatic speech recognition (ASR) is an essential technique of human–computer interactions; gain control is a commonly used operation in ASR. However, inappropriate gain control strategies can lead to an increase in the word error rate (WER) of ASR. As there is a current lack of sufficient theoretical analyses and proof of the relationship between gain control and WER, various unconstrained gain control strategies have been adopted on realistic ASR systems, and the optimal gain control with respect to the lowest WER, is rarely achieved. A gain control strategy named maximized original signal transmission (MOST) is proposed in this study to minimize the adverse impact of gain control on ASR systems. First, by modeling the gain control strategy, the quantitative relationship between the gain control strategy and the ASR performance was established using the noise figure index. Second, through an analysis of the quantitative relationship, an optimal MOST gain control strategy with minimal performance degradation was theoretically deduced. Finally, comprehensive comparative experiments on a Mandarin dataset show that the proposed MOST gain control strategy can significantly reduce the WER of the experimental ASR system, with a 10% mean absolute WER reduction at −9 dB gain.
Improving Data Security with Blockchain and Internet of Things in the Gourmet Cocoa Bean Fermentation Process
Authors: Abijaude Jauberth , Sobreira Péricles , Santiago Levy , Greve Fabíola
Published: April 2022

Brazil was one of the largest cocoa producers in the world, mainly supported by the South of Bahia region. After the 1980s, the witch-broom disease demolished plantations, and farmers were forced into bankruptcy. The worldwide search for gourmet cocoa has rekindled interest in this production, whose fermentation process is a key step in obtaining fine cocoa, thanks to the fact that many processing properties and sensory attributes are developed in this phase. This article presents a blockchain-IoT-based system for the control and monitoring of these events, aiming to catalog in smart contracts valuable information for improvement of the cocoa fermentation process, and future research. Blockchain is used as a distributed database that implements an application-level security layer. A proof of concept was modeled and the performance of the emulated system was evaluated in the OMNet simulator, where a technique based on the SNMP protocol was applied to reduce the amount of data exchanged and resources served/consumed in this representation. Then, a physical platform was developed and preliminary experiments were performed on a real farm, as a way to verify the improvement of the cocoa fermentation process when using a technological approach.
Real-Time System for Daily Modal Split Estimation and OD Matrices Generation Using IoT Data: A Case Study of Tartu City
Authors: Khoshkhah Kaveh , Pourmoradnasseri Mozhgan , Hadachi Amnir , Tera Helen , Mass Jakob , Keshi Erald , Wu Shan
Published: April 2022

In recent years, we have witnessed the emergence of the implementation and integration of significant working solutions in transportation, especially within the smart city concept. A lot of cities in Europe and around the world support this initiative of making their cities smarter for enhanced mobility and a sustainable environment. In this paper, we present a case study of Tartu city, where we developed and designed a daily real-time system for extracting and performing a modal split analysis. Our web-based platform relied on an optimization approach for calibrating our simulation in order to perform the analysis with the use of real data streams from IoT devices installed around the city. The results obtained from our system demonstrated acceptable performance versus the quality of the available data source. In addition, our platform provides downloadable OD matrices for each mode of mobility for the community.
Video Person Re-Identification with Frame Sampling–Random Erasure and Mutual Information–Temporal Weight Aggregation
Authors: Li Jiayue , Piao Yan
Published: April 2022

Partial occlusion and background clutter in camera video surveillance affect the accuracy of video-based person re-identification (re-ID). To address these problems, we propose a person re-ID method based on random erasure of frame sampling and temporal weight aggregation of mutual information of partial and global features. First, for the case in which the target person is interfered or partially occluded, the frame sampling–random erasure (FSE) method is used for data enhancement to effectively alleviate the occlusion problem, improve the generalization ability of the model, and match persons more accurately. Second, to further improve the re-ID accuracy of video-based persons and learn more discriminative feature representations, we use a ResNet-50 network to extract global and partial features and fuse these features to obtain frame-level features. In the time dimension, based on a mutual information–temporal weight aggregation (MI–TWA) module, the partial features are added according to different weights and the global features are added according to equal weights and connected to output sequence features. The proposed method is extensively experimented on three public video datasets, MARS, DukeMTMC-VideoReID, and PRID-2011; the mean average precision (mAP) values are 82.4%, 94.1%, and 95.3% and Rank-1 values are 86.4%, 94.8%, and 95.2%, respectively.
Data-Driven EEG Band Discovery with Decision Trees
Authors: Talebi Shawhin , Waczak John , Fernando Bharana A. , Sridhar Arjun , Lary David J.
Published: April 2022

Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta. While these bands have been shown to be useful for characterizing various brain states, their utility as a one-size-fits-all analysis tool remains unclear. The goal of this work is to outline an objective strategy for discovering optimal EEG bands based on signal power spectra. A two-step data-driven methodology is presented for objectively determining the best EEG bands for a given dataset. First, a decision tree is used to estimate the optimal frequency band boundaries for reproducing the signal’s power spectrum for a predetermined number of bands. The optimal number of bands is then determined using an Akaike Information Criterion (AIC)-inspired quality score that balances goodness-of-fit with a small band count. This data-driven approach led to better characterization of the underlying power spectrum by identifying bands that outperformed the more commonly used band boundaries by a factor of two. Additionally, key spectral components were isolated in dedicated frequency bands. The proposed method provides a fully automated and flexible approach to capturing key signal components and possibly discovering new indices of brain activity.
A Tightly Coupled LiDAR-Inertial SLAM for Perceptually Degraded Scenes
Authors: Yang Lin , Ma Hongwei , Wang Yan , Xia Jing , Wang Chuanwei
Published: April 2022

Realizing robust six degrees of freedom (6DOF) state estimation and high-performance simultaneous localization and mapping (SLAM) for perceptually degraded scenes (such as underground tunnels, corridors, and roadways) is a challenge in robotics. To solve these problems, we propose a SLAM algorithm based on tightly coupled LiDAR-IMU fusion, which consists of two parts: front end iterative Kalman filtering and back end pose graph optimization. Firstly, on the front end, an iterative Kalman filter is established to construct a tightly coupled LiDAR-Inertial Odometry (LIO). The state propagation process for the a priori position and attitude of a robot, which uses predictions and observations, increases the accuracy of the attitude and enhances the system robustness. Second, on the back end, we deploy a keyframe selection strategy to meet the real-time requirements of large-scale scenes. Moreover, loop detection and ground constraints are added to the tightly coupled framework, thereby further improving the overall accuracy of the 6DOF state estimation. Finally, the performance of the algorithm is verified using a public dataset and the dataset we collected. The experimental results show that for perceptually degraded scenes, compared with existing LiDAR-SLAM algorithms, our proposed algorithm grants the robot higher accuracy, real-time performance and robustness, effectively reducing the cumulative error of the system and ensuring the global consistency of the constructed maps.
Head and Trunk Kinematics during Activities of Daily Living with and without Mechanical Restriction of Cervical Motion
Authors: Weston Angela R. , Loyd Brian J. , Taylor Carolyn , Hoppes Carrie , Dibble Leland E.
Published: April 2022

Alterations in head and trunk kinematics during activities of daily living can be difficult to recognize and quantify with visual observation. Incorporating wearable sensors allows for accurate and measurable assessment of movement. The aim of this study was to determine the ability of wearable sensors and data processing algorithms to discern motion restrictions during activities of daily living. Accelerometer data was collected with wearable sensors from 10 healthy adults (age 39.5 ± 12.47) as they performed daily living simulated tasks: coin pick up (pitch plane task), don/doff jacket (yaw plane task), self-paced community ambulation task [CAT] (pitch and yaw plane task) without and with a rigid cervical collar. Paired t-tests were used to discern differences between non-restricted (no collared) performance and restricted (collared) performance of tasks. Significant differences in head rotational velocity (jacket p = 0.03, CAT-pitch p < 0.001, CAT-yaw p < 0.001), head rotational amplitude (coin p = 0.03, CAT-pitch p < 0.001, CAT-yaw p < 0.001), trunk rotational amplitude (jacket p = 0.01, CAT-yaw p = 0.005), and head–trunk coupling (jacket p = 0.007, CAT-yaw p = 0.003) were captured by wearable sensors between the two conditions. Alterations in turning movement were detected at the head and trunk during daily living tasks. These results support the ecological validity of using wearable sensors to quantify movement alterations during real-world scenarios.
Ultrasonic Sound Guide System with Eyeglass Device for the Visually Impaired
Authors: Kim Kevin , Kim Saea , Choi Anthony
Published: April 2022

The ultrasonic sound guide system presents the audio broadcasting system based on the inaudible ultrasonic sound to assist the indoor and outdoor navigation of the visually impaired. The transmitters are placed at the point of interest to propagate the frequency modulated voice signal in ultrasonic sound range. The dual channel receiver device is carried by the visually impaired person in the form of eyeglasses to receive the ultrasonic sound for the voice signal via demodulation. Since the ultrasonic sound demonstrates the acoustic properties, the velocity, directivity, attenuation, and superposition of ultrasonic sound provide the acoustic clue to the user for localizing the multiple transmitter positions by binaural localization capability. The visually impaired hear the designated voice signal and follow the signal attributions to arrive at the specific location. Due to the low microphone gain from side addressing, the time delay between the receiver channels demonstrates the high variance and high bias in end directions. However, the perception experiment shows the further prediction accuracy in end directions as compared to the center direction outcomes. The overall evaluations show the precise directional prediction for narrow- and wide-angle situations. The ultrasonic sound guide system is a useful device to localize places in the near field without touching braille.
Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages
Authors: Hussain Iqram , Hossain Md Azam , Jany Rafsan , Bari Md Abdul , Uddin Musfik , Kamal Abu Raihan Mostafa , Ku Yunseo , Kim Jik-Soo
Published: April 2022

Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data. We investigated the three-channel EEG sleep recordings of 154 individuals (mean age of 53.8 ± 15.4 years) from the Haaglanden Medisch Centrum (HMC, The Hague, The Netherlands) open-access public dataset of PhysioNet. The power of fast-wave alpha, beta, and gamma rhythms decreases; and the power of slow-wave delta and theta oscillations gradually increases as sleep becomes deeper. Delta wave power ratios (DAR, DTR, and DTABR) may be considered biomarkers for their characteristics of attenuation in NREM sleep and subsequent increase in REM sleep. The overall accuracy of the C5.0, Neural Network, and CHAID machine-learning models are 91%, 89%, and 84%, respectively, for multi-class classification of the sleep stages. The EEG-based sleep stage prediction approach is expected to be utilized in a wearable sleep monitoring system.
Indoor Localization Using Uncooperative Wi-Fi Access Points
Authors: Horn Berthold K. P.
Published: April 2022

Indoor localization using fine time measurement (FTM) round-trip time (RTT) with respect to cooperating Wi-Fi access points (APs) has been shown to work well and provide 1–2 m accuracy in both 2D and 3D applications. This approach depends on APs implementing the IEEE 802.11-2016 (also known as IEEE 802.11mc) Wi-Fi standard (“two-sided” RTT). Unfortunately, the penetration of this Wi-Fi protocol has been slower than anticipated, perhaps because APs tend not to be upgraded as often as other kinds of electronics, in particular in large institutions—where they would be most useful. Recently, Google released Android 12, which also supports an alternative “one-sided” RTT method that will work with legacy APs as well. This method cannot subtract out the “turn-around” time of the signal, and so, produces distance estimates that have much larger offsets than those seen with two-sided RTT—and the results are somewhat less accurate. At the same time, this method makes possible distance measurements for many APs that previously could not be used. This increased accessibility can compensate for the decreased accuracy of individual measurements. We demonstrate here indoor localization using one-sided RTT with respect to legacy APs that do not support IEEE 802.11-2016. The accuracy achieved is 3–4 m in cluttered environments with few line-of-sight readings (and using only 20 MHz bandwidths). This is not as good as for two-sided RTT, where 1–2 m accuracy has been achieved (using 80 MHz bandwidths), but adequate for many applications A wider Wi-Fi channel bandwidth would increase the accuracy further. As before, Bayesian grid update is the preferred method for determining position and positional accuracy, but the observation model now is different from that for two-sided RTT. As with two-sided RTT, the probability of an RTT measurement below the true distance is very low, but, in the other direction, the range of measurements for a given distance can be much wider (up to well over twice the actual distance). We describe methods for formulating useful observation models. As with two-sided RTT, the offset or bias in distance measurements has to be subtracted from the reported measurements. One difference is that here, the offsets are large (typically in the 2400–2700 m range) because of the “turn-around time” of roughly 16 μs (i.e., about two orders of magnitude larger than the time of flight one is attempting to measure). We describe methods for estimating these offsets and for minimizing the effort required to do so when setting up an installation with many APs.
A Covariance Matrix Reconstruction Approach for Single Snapshot Direction of Arrival Estimation
Authors: Muhammad Murdifi , Li Minghui , Abbasi Qammer , Goh Cindy , Imran Muhammad Ali
Published: April 2022

Achieving accurate single snapshot direction of arrival (DOA) information significantly improves communication performance. This paper investigates an accurate and high-resolution DOA estimation technique by enabling single snapshot data collection and enhancing DOA estimation results compared to multiple snapshot methods. This is carried out by manipulating the incoming signal covariance matrix while suppressing undesired additive white Gaussian noise (AWGN) by actively updating and estimating the antenna array manifold vector. We demonstrated the estimation performance in simulation that our proposed technique supersedes the estimation performance of existing state-of-the-art techniques in various signal-to-noise ratio (SNR) scenarios and single snapshot sampling environments. Our proposed covariance-based single snapshot (CbSS) technique yields the lowest root-mean-squared error (RMSE) against the true DOA compared to root-MUSIC and the partial relaxation (PR) approach for multiple snapshots and a single signal source environment. In addition, our proposed technique presents the lowest DOA estimation performance degradation in a multiple uncorrelated and coherent signal source environment by up to 25.5% with nearly negligible bias. Lastly, our proposed CbSS technique presents the best DOA estimation results for a single snapshot and single-source scenario with an RMSE of 0.05° against the true DOA compared to root-MUSIC and the PR approach with nearly negligible bias as well. A potential application for CbSS would be in a scenario where accurate DOA estimation with a small antenna array form factor is a limitation, such as in the intelligent transportation system industry and wireless communication.
Detection of Surface and Subsurface Flaws with Miniature GMR-Based Gradiometer
Authors: Nguyen Huu-Thang , Jeng Jen-Tzong , Doan Van-Dong , Dinh Chinh-Hieu , Trinh Xuan Thang , Dao Duy-Vinh
Published: April 2022

The eddy-current (EC) testing method is frequently utilized in the nondestructive inspection of conductive materials. To detect the minor and complex-shaped defects on the surface and in the underlying layers of a metallic sample, a miniature eddy-current probe with high sensitivity is preferred for enhancing the signal quality and spatial resolution of the obtained eddy-current images. In this work, we propose a novel design of a miniature eddy-current probe using a giant magnetoresistance (GMR) sensor fabricated on a silicon chip. The in-house-made GMR sensor comprises two cascaded spin-valve elements in parallel with an external variable resistor to form a Wheatstone bridge. The two elements on the chip are excited by the alternating magnetic field generated by a tiny coil aligned to the position that balances the background output of the bridge sensor. In this way, the two GMR elements behave effectively as an axial gradiometer with the bottom element sensitive to the surface and near-surface defects on a conductive specimen. The performance of the EC probe is verified by the numerical simulation and the corresponding experiments with machined defects on metallic samples. With this design, the geometric characteristics of the defects are clearly visualized with a spatial resolution of about 1 mm. The results demonstrate the feasibility and superiority of the proposed miniature GMR EC probe for characterizing the small and complex-shaped defects in multilayer conductive samples.
A Transformer-Based Bridge Structural Response Prediction Framework
Authors: Li Ziqi , Li Dongsheng , Sun Tianshu
Published: April 2022

Structural response prediction with desirable accuracy is considerably essential for the health monitoring of bridges. However, it appears to be difficult in accurately extracting structural response features on account of complex on-site environment and noise disturbance, resulting in poor prediction accuracy of the response values. To address this issue, a Transformer-based bridge structural response prediction framework was proposed in this paper. The framework contains multi-layer encoder modules and attention modules that can precisely capture the history-dependent features in time-series data. The effectiveness of the proposed method was validated with the use of six-month strain response data of a concrete bridge, and the results are also compared with those of the most commonly used Long Short-Term Memory (LSTM)-based structural response prediction framework. The analysis indicated that the proposed method was effective in predicting structural response, with the prediction error less than 50% of the LSTM-based framework. The proposed method can be applied in damage diagnosis and disaster warning of bridges.
Application of a Validated Method for the Identification and Quantification of Mycotoxins in Wines Using UPLC-MS/MS
Authors: Louppis Artemis P. , Constantinou Michalis S.
Published: April 2022

The aim of the present study was to develop a rapid, simple and reliable method for the identification and quantification of six mycotoxins in wine using liquid chromatography with electrospray ionization tandem mass spectrometry. The analytical method was fully validated, and calibration curves were made with correlation coefficients >0.9970. A short analysis time and acceptable extraction efficiency were achieved by a direct extraction method of analytes (ochratoxin A, aflatoxin B1, B2, G1, G2 and Zearalenone) with acetonitrile. LOD values were from 0.03 to 0.27 μg kg−1, and LOQ values were from 0.08 to 0.81 μg kg−1, with recoveries at various values from 77 to 108%. The expanded uncertainty was 5–21% expressed at a coverage level of k = 2, at a confidence level of approximately 95%. The performance criteria of the method were fully met according to European legislation (EC) 401/2006. The method was successfully applied to wine samples from Cyprus. The method was simple, low cost, quick, accurate, and sensitive.
Thai SMEs’ Response in the Digital Economy Age: A Case Study of Community-Based Tourism Policy Implementation
Authors: Tongdaeng Wannapa , Mahakanjana Chandra-nuj
Published: April 2022

The goals of this study were to identify factors affecting the responses of small- and medium-sized enterprises (SMEs) in the digital economy age, to examine the impact of policy implementation and stakeholders’ roles in the promotion of SMEs in the service sector of community-based tourism (CBT), to analyze the competitiveness of CBT, and to reveal the gap in SMEs’ service quality. The research design consisted of both qualitative and quantitative methods. It was found that independent variables, i.e., the strategic management, the decision-making process, the structural contingency, perception, and the SMEs’ potential, together in the multiple regression model, could predict dependent variables. Policy actors can be divided into three levels. Politicians are at the national policy level, high-level bureaucrats are at the implementation level, and stakeholders in the community are at the local level. Policy instruments include projects to support SMEs. Local-level actors play a role in promoting SMEs through collaborative public management. The competitiveness analysis of CBT can be divided into five forces: the rivalry of CBT, the threat of new entrants, the threat of substitutes, the bargaining power of suppliers, and the bargaining power of customers. Meanwhile, there are some problems in assuring service quality.
A Molecular Dynamics Study of Tungsten’s Interstitial Dislocation Loops Formation Induced by Irradiation under Local Strain
Authors: Bany Salman Mohammad , Park Minkyu , Banisalman Mosab Jaser
Published: April 2022

A molecular dynamics simulation was used to investigate the effect of applied strain on the formation of primary defects and the probability of interstitial dislocation loops (IDLs) formation of tungsten (W) during a collision cascade event. The research investigated primary knock-on atom energies of 1, 6, 10, and 14 keV, applied on a deformed W structure (form −1.4~1.6%). The peak and surviving number of Frenkel pairs (FPs) increased with increasing tension; however, these increases were more pronounced under higher strain due to the formation of IDLs. For 10 self-interstitial atoms (SIA) lengths, the strain effect reduces the clustering energy of the IDLs by about 7 eV. In general, the current findings suggest that strain effects should be carefully considered in radiation-damaged environments, particularly in low-temperature, high-radiation-energy environments. The compressed condition may advantage materials used in high-radiation-damage devices and power systems.
The Cut-Off Value for Classifying Active Italian Children Using the Corresponding National Version of the Physical Activity Questionnaire
Authors: Lupo Corrado , Boccia Gennaro , Ungureanu Alexandru Nicolae , Mulasso Anna , De Pasquale Paolo , Mancini Annamaria , Buono Pasqualina , Rainoldi Alberto , Brustio Paolo Riccardo
Published: April 2022

The present study aimed to determine a cut-off value following the filling in of a questionnaire (PAQ-C-It) to identify active Italian children. One-hundred-twenty-nine primary school children (5 Piedmont schools; 47.3% female; mean age = 10 ± 1 years) wore an accelerometer (Actigraph wGT3X-BT) to objectively quantify individual moderate-to-vigorous physical activity during one week. Afterwards, the PAQ-C-It was filled in by participants. A ROC curve procedure was applied to obtain an active/non-active cut-off point. Spearman’s correlation coefficient was also applied to establish the relationship between the two parameters. According to the ROC analysis, the PAQ-C-It cut-off point value is identifiable at >2.75 to indicate active children (area under the curve = 0.62; standard error = 0.05; p = 0.025; coefficient intervals = 0.518–0.716; sensitivity = 0.592, specificity = 0.382), determining that 65 participants (55%) were non-active (mean PAQ-C-It value = 2.3 ± 0.4; active mean PAQ-C-It value = 3.3 ± 0.4). Spearman’s correlation coefficient results were significant but with a small effect size (rho = 0.214; p = 0.008). In conclusion, the present results suggest that the PAQ-C-It can be cautiously used as tool to practically classify active Italian children because of a non-solid relationship between respective accelerometer data and MVPA daily data.
Early Sport Specialization and Relative Age Effect: Prevalence and Influence on Perceived Competence in Ice Hockey Players
Authors: Huard Pelletier Vincent , Lemoyne Jean
Published: April 2022

The relative age effect (RAE) and early sport specialization (ESS) have been of growing interest in the sports world, especially in ice hockey, because of their potential adverse effects. However, little is known about their distribution within each level of play in Canadian minor ice hockey, or whether they influence young people’s perceived competence, a variable of interest in long-term sports development. A sample of elite adolescent players (N = 204) and a sample of recreational and competitive players (N = 404) were used to measure these constructs, and chi-square tabulations were conducted to compare their distribution. Our results reveal that RAE (χ2 = 20.03, p < 0.01, Cramer’s V = 0.13) and ESS (χ2 = 66.14, p < 0.001, Cramer’s V = 0.24) are present, but there are apparently no gender differences in their distributions. Neither the level of RAE nor ESS seems to affect the perceived competence of the players, regardless of gender. The results of this study highlight the presence of RAE and ESS in Canadian minor ice hockey, especially at the elite level, but indicate that they do not affect the self-perception of ice hockey players. Additional research on these concepts is needed to obtain a complete picture of their potential impact on sports development.
Composted Sewage Sludge Application Reduces Mineral Fertilization Requirements and Improves Soil Fertility in Sugarcane Seedling Nurseries
Authors: Silva Rafael dos Santos , Jalal Arshad , Nascimento Raimunda Eliane Nascimento do , Elias Nathércia Castro , Kawakami Karen Cossi , Abreu-Junior Cassio Hamilton , Oliveira Fernando Carvalho , Jani Arun Dilipkumar , He Zhenli , Zhao Fengliang , Filho Marcelo Carvalho Minhoto Teixeira , Rossetto Raffaela , Capra Gian Franco , Nogueira Thiago Assis Rodrigues
Published: April 2022

Sugarcane demands large amounts of nutrients to reach a high level of productivity. Nutrients are generally supplied by mineral fertilizers, but their high costs and negative environmental impacts have generated interest in greater use of organic nutrient sources such as composted sewage sludge (CSS). In this study, we evaluated changes in soil chemical properties after the application of CSS or CSS/mineral fertilizer (MF) combinations to soil containing sugarcane seedlings under nursery conditions. Treatments included: T1: conventional mineral fertilization (MF) without application of CSS, T2: 100% of the recommended MF (06–30–24); T3: application of 2.5 Mg−1 CSS; T4: 5.0 Mg−1 CSS, T5: 7.5 Mg−1 CSS, T6: 2.5 Mg−1 CSS and 50% MF, T7: 5.0 Mg−1 CSS and 50% MF, T8: 7.5 Mg−1 CSS and 50% MF, T9: 2.5 Mg−1 CSS and 100% MF, T10: 5.0 Mg−1 CSS and 100% MF, T11: 7.5 Mg−1 CSS and 100% MF. Soil chemical properties were evaluated from the soil surface (0.0–0.25 m) and subsurface (0.25–0.50 m) horizons. The results showed that the increase in CSS application did not affect soil organic matter content at either depth, while Zn concentrations increased in the soil subsurface horizon. The application of CSS at 5.0 Mg ha−1 with or without 50% MF resulted in the highest pH values, sum of bases, cation-exchange capacity, P, K, Ca, Mg, Cu, and Zn in surface horizons. The use of CSS as an organic fertilizer in sugarcane nurseries improves soil fertility, reduces mineral fertilizer requirements, and, thus, facilitates the sustainable disposal of sewage sludge.
Household Migration and Intentions for Future Migration in the Climate Change Vulnerable Lower Meghna Estuary of Coastal Bangladesh
Authors: Paul Bimal Kanti , Rahman Munshi Khaledur , Lu Max , Crawford Thomas W.
Published: April 2022

Coastal residents of Bangladesh are now confronted with the increased incidence, variability, and severity of weather-related hazards and disasters due to climate change-induced sea level rise (SLR). Many researchers hold the view that as a consequence residents of such area have either already migrated to inland locations or intend to so in the near future. We examine the migration of households following a flash flood event that took place in August 2020 and address intentions for future migration in the Lower Meghna Estuary of coastal Bangladesh. The data obtained for this study include 310 household surveys, field observations, and informal discussions with respondents and local people. Based on the analysis of the field data, this empirical research found one household migrated to other district within one year after the event. When the respondents were asked about their future migration intensions, only a tiny proportion, namely 21 (6.77%) households, likely will leave the study area to settle in other districts while the remaining 289 households likely will stay in the Lakshmipur district. This finding challenges the existing narratives about vulnerability to environmentally induced migration. Moreover, it provides evidence of non-migration, which is a new as well as thriving area of investigation in relation to coastal Bangladesh.
A Cross-Cultural Comparison of New Implemented Sustainable Wine Tourism Strategies during the COVID-19 Crisis
Authors: Szolnoki Gergely , Bail Susan , Tafel Maximilian , Feher Aron , Veith Cristina
Published: April 2022

To compensate for loss of business during the COVID-19 crisis, wineries in the tourism industry had to apply new strategies. In order to collect and compare these newly developed sustainable strategies, a cross-cultural study has been conducted in 2021. This study is based on a qualitative survey using purposeful sampling with key decision-makers of 70 wineries from the U.S., Australia, Germany, Hungary, and Romania covering wine growing countries both from the Old and New World. The aim was to identify new and sustainable initiatives and resilience strategies implemented to deal with the disruption caused by the COVID-19 pandemic, noting any cultural differences in each country’s response and to analyse the perspectives of wine tourism in the future. The findings highlight the wineries’ impressive focus on creativity and flexibility while also bringing attention to cultural differences. The insights form a preliminary suggestion for best practice strategies that businesses within wine tourism may consider helpful in their future business planning.
COVID-19, Economic Policies and Public Debt Sustainability in Italy
Authors: Della Posta Pompeo , Marelli Enrico , Signorelli Marcello
Published: April 2022

We analyze the conditions for public debt-to-GDP ratio stability extending the Domar’s approach by including the interaction between the government’s reaction function and the private sector’s reaction function, and considering the impact of 2020–2021 pandemic shock and the monetary and fiscal policy responses, with simulations applied to the Italian case. The outcomes of the numerical simulations show the crucial importance of ECB extraordinary monetary policies, of the NGEU and, to lesser extent, of national expansionary fiscal policies adopted during pandemic shock; both European wide monetary and fiscal policies actually increase the sustainability area avoiding the high risk of sovereign debt crisis in Italy (and other peripheral Eurozone countries). The stabilizing effect of GDP growth, hopefully resulting from the NGEU policy, is also simulated in the paper.
What about the Chief Digital Officer? A Literature Review
Authors: Raković Lazar , Marić Slobodan , Đorđević Milutinović Lena , Sakal Marton , Antić Slobodan
Published: April 2022

Digital transformation is becoming more ingrained into everyday life and is a talking point among researchers today. It has been evident from the growing number of publications that have focused on various aspects of digital transformation in the last few years. Based on the number of papers published, one of them is of much less interest to researchers: the managerial role of Chief Digital Officer (CDO) inherent in digital transformation. Having this in mind, a systematic literature review was conducted to elucidate the CDO’s role, duties, and required competencies. In this paper, the results and conclusions of 22 papers are presented based on the search criteria outlined by the Web of Science and Scopus index databases. It is obvious that managing digital transformation is becoming more important as digital transformation affects almost every sector of business. The purpose of this study is to examine the position of Chief Digital Officer in terms of its position, role, and responsibilities as well as its necessary competencies. The results of this study could serve as the basis for future research in this area, which judging by the relevance of the topic, will certainly be more intensive.