bluuebunny/crossref_metadata_2025 huggingface dataset:
When Mahāyāna Meets Theravāda: The Position of Chinese Bhikṣuṇīs in Contemporary Myanmar
Authors: Chiu Tzu-Lung
Published: July 2022

Mahāyāna and Theravāda are the two major traditions of Buddhism in contemporary Asia. Although they share many similar teachings, there are long-standing disputes between their respective sets of adherents, touching on doctrine, ritual, religious practices, and the ultimate goal, among other matters. Drawing on fieldwork conducted in Yangon and Mandalay, this study explores gender’s role in the position of Sino-Burmese Mahāyāna bhikṣuṇīs in the sociocultural context of Theravāda-majority Myanmar, where the full bhikṣuṇī lineage of Theravāda Buddhism has died out. Its findings, firstly, shed light on how the local Theravāda ethos inevitably affects Sino-Burmese Mahāyāna nuns’ positions and experiences of religious- and ethnic-minority status. Secondly, they demonstrate the gender dynamics of Sino-Burmese nuns’ interactions both with indigenous Burmese monks and Myanmar’s ethnic-Chinese laity. As such, this research opens up a fresh perspective on these nuns’ monastic lives, to which scant scholarly attention has hitherto been paid. Specifically, it argues that while Sino-Burmese nuns are subjected to “double suffering” on both gender and ethnoreligious minority grounds, they play an important role in shaping the future of Chinese Mahāyāna Buddhism by educating the next generation of monastics and serving the religious needs of the wider Sino-Burmese community in Myanmar.
Ultrasound Findings of Fetal Infections: Current Knowledge
Authors: Verteramo Rosita , Santi Erica , Ravennati Francesca , Scutiero Gennaro , Greco Pantaleo , Morano Danila
Published: July 2022

Infectious diseases during pregnancy are still a major cause of fetal mortality and morbidity worldwide. The most common teratogenic pathogens are cytomegalovirus (CMV), varicella-zoster virus (VZV), rubeovirus, parvovirus B19, herpes simplex virus (HSV), Toxoplasma gondii, Treponema pallidum and the emergent Zika virus (ZIKV). Ultrasound findings include cerebral anomalies, orbital defects, micrognathia, cardiac defects, hepatosplenomegaly, liver calcifications, abdominal anomalies, skin and limb anomalies, edema, placental and amniotic fluid anomalies and altered Doppler analyses. The classification of ultrasound markers of congenital infections by anatomical region is reported to guide differential diagnosis and prenatal care.
Monitoring Extractive Activity-Induced Surface Subsidence in Highland and Alpine Opencast Coal Mining Areas with Multi-Source Data
Authors: Wang Shuqing , Bai Zechao , Lv Yuepeng , Zhou Wei
Published: July 2022

Mining developments in alpine coal mining areas result in slow or rapid ground subsidence, which can lead to melting and collapse of permafrost. This paper integrated unmanned aerial vehicle (UAV) images and satellite-based SAR interferometry images to monitor intensive surface mining subsidence during reclamation. Digital Surface Model (DSM) acquired from UAV images was first used to evaluate the changes of the reclamation scheme on the microtopography carried out by slope and the Digital Elevation Model (DEM) of difference (DoD). The monitoring results showed that the slope had been reduced from over 30 degrees to under 15 degrees after the terrain had been reshaped. The DoD map revealed the distribution of main extraction areas and landfill areas. To further monitor the surface subsidence after local terrain adjustment, the Permanent Scatterer Interferometry (PS-InSAR) method was used to reveal the surface subsidence characteristics of the mine site before and after reclamation. The maximum cumulative subsidence ranged from −772.3 to 1183 mm based on 21 Sentinel-1A images in three years. Within a year of terrain reshaping, uplift and subsidence still occurred at hills and pit side slopes, following the nearly equal subsidence rate. The experimental results showed that the slope reshaping and vegetation recovery had a limited impact on the reduction of the ground subsidence in a short period. Therefore, on this basis, a combination of UAV and PS-InSAR methods can be used to continue monitoring time series subsidence in alpine mines.
Interrupted-Sampling Repeater Jamming-Suppression Method Based on a Multi-Stages Multi-Domains Joint Anti-Jamming Depth Network
Authors: He Xuesi , Liao Kuo , Peng Shupeng , Tian Zhenjie , Huang Jiyan
Published: July 2022

Jamming will seriously affect the detection ability of radar, so it is essential to suppress the jamming of radar echoes. Interrupted-sampling repeater jamming (ISRJ) based on a digital-radio-frequency-memory (DRFM) device can generate false targets at the victim radar by the interception and repeating of the radar transmission signal, which is highly correlated with the true target signal. ISRJ can achieve main lobe jamming and has both deception and oppressive jamming effects, so it is difficult for the existing methods to suppress this jamming effectively. In this paper, we propose a deep-learning-based anti-jamming network, named MSMD-net (Multi-stage Multi-domain joint anti-jamming depth network), for suppressing ISRJ main lobe jamming in the radar echo. In the first stage of MSMD-net, considering that the target signal is difficult to detect under a high jamming-to-signal ratio (JSR), we propose a preprocessing method of limiting filtering on the time-frequency domain to reduce the JSR using the auxiliary knowledge of radar. In the second stage, taking advantage of the discontinuity of the jamming in the time domain, we propose a UT-net network that combines the U-net structure and the transformer module. The UT-net performs target feature extraction and signal reconstruction in the signal time-frequency domain and preliminarily realizes the suppression of the jamming component. In the third stage, combined with phase information, a one-dimensional complex residual convolution U-net network (ResCU-net) is constructed in the time domain to realize jamming filtering and signal recovery further. The experimental results show that MSMD-net can obtain the best jamming suppression effect under different transmitted signals, different jamming modes, and different jamming parameters.
Evaluation of Diverse Convolutional Neural Networks and Training Strategies for Wheat Leaf Disease Identification with Field-Acquired Photographs
Authors: Jiang Jiale , Liu Haiyan , Zhao Chen , He Can , Ma Jifeng , Cheng Tao , Zhu Yan , Cao Weixing , Yao Xia
Published: July 2022

Tools for robust identification of crop diseases are crucial for timely intervention by farmers to minimize yield losses. Visual diagnosis of crop diseases is time-consuming and laborious, and has become increasingly unsuitable for the needs of modern agricultural production. Recently, deep convolutional neural networks (CNNs) have been used for crop disease diagnosis due to their rapidly improving accuracy in labeling images. However, previous CNN studies have mostly used images of single leaves photographed under controlled conditions, which limits operational field use. In addition, the wide variety of available CNNs and training options raises important questions regarding optimal methods of implementation of CNNs for disease diagnosis. Here, we present an assessment of seven typical CNNs (VGG-16, Inception-v3, ResNet-50, DenseNet-121, EfficentNet-B6, ShuffleNet-v2 and MobileNetV3) based on different training strategies for the identification of wheat main leaf diseases (powdery mildew, leaf rust and stripe rust) using field images. We developed a Field-based Wheat Diseases Images (FWDI) dataset of field-acquired images to supplement the public PlantVillage dataset of individual leaves imaged under controlled conditions. We found that a transfer-learning method employing retuning of all parameters produced the highest accuracy for all CNNs. Based on this training strategy, Inception-v3 achieved the highest identification accuracy of 92.5% on the test dataset. While lightweight CNN models (e.g., ShuffleNet-v2 and MobileNetV3) had shorter processing times (<0.007 s per image) and smaller memory requirements for the model parameters (<20 MB), their accuracy was relatively low (~87%). In addition to the role of CNN architecture in controlling overall accuracy, environmental effects (e.g., residual water stains on healthy leaves) were found to cause misclassifications in the field images. Moreover, the small size of some target symptoms and the similarity of symptoms between some different diseases further reduced the accuracy. Overall, the study provides insight into the collective effects of model architecture, training strategies and input datasets on the performance of CNNs, providing guidance for robust CNN design for timely and accurate crop disease diagnosis in a real-world environment.
Relative Effects of the Greenhouse Gases and Stratospheric Ozone Increases on Temperature and Circulation in the Stratosphere over the Arctic
Authors: Hu Dingzhu , Guan Zhaoyong
Published: July 2022

Using a stratosphere-resolving general circulation model, the relative effects of stratospheric ozone and greenhouse gases (GHGs) increase on the temperature and circulation in the Arctic stratosphere are examined. Results show that stratospheric ozone or GHGs increase alone could result in a cooling and strengthening extratropical stratosphere during February, March and April. However, the contribution of stratospheric ozone increases alone on the cooling and strengthening Arctic stratosphere is approximately 2 fold that of the GHGs increase alone. Model simulations suggested that the larger responses of the Arctic stratosphere to the ozone increase alone are closely related to the wave fluxes in the stratosphere, rather than the wave activity in the stratosphere. In response to the ozone increase, the vertical propagation of planetary waves from the troposphere into the mid-latitude stratosphere weakens, mainly contributed by its wavenumber-1 component. The impeded planetary waves tend to result from the larger zonal wind shear and vertical gradient of the buoyancy frequency. The magnitudes of anomalies in the zonal wind shear and buoyancy frequency in response to GHGs increase alone are smaller than in response to the ozone increase, which is in accordance with the larger contribution of stratospheric ozone to the temperature and circulation in the Arctic stratosphere.
LGB-PHY: An Evaporation Duct Height Prediction Model Based on Physically Constrained LightGBM Algorithm
Authors: Chai Xingyu , Li Jincai , Zhao Jun , Wang Wuxin , Zhao Xiaofeng
Published: July 2022

The evaporation duct is a special atmospheric stratification that significantly influences the propagation path of electromagnetic waves at sea, and hence, it is crucial for the stability of the radio communication systems. Affected by physical parameters that are not universal, traditional evaporation duct theoretical models often have limited accuracy and poor generalization ability, e.g., the remote sensing method is limited by the inversion algorithm. The accuracy, generalization ability and scientific interpretability of the existing pure data-driven evaporation duct height prediction models still need to be improved. To address these issues, in this paper, we use the voyage observation data and propose the physically constrained LightGBM evaporation duct height prediction model (LGB-PHY). The proposed model integrates the Babin–Young–Carton (BYC) physical model into a custom loss function. Compared with the eXtreme Gradient Boosting (XGB) model, the LGB-PHY based on a 5-day voyage data set of the South China Sea provides significant improvement where the RMSE index is reduced by 68%, while the SCC index is improved by 6.5%. We further carried out a cross-comparison experiment of regional generalization and show that in the sea area with high latitude and strong adaptability of the BYC model, the LGB-PHY model has a stronger regional generalization performance than that of the XGB model.
Inundation Resilience Analysis of Metro-Network from a Complex System Perspective Using the Grid Hydrodynamic Model and FBWM Approach: A Case Study of Wuhan
Authors: Sun Hai , Li Meixin , Jiang Hui , Ruan Xuejing , Shou Wenchi
Published: July 2022

The upward trend of metro flooding disasters inevitably brings new challenges to urban underground flood management. It is essential to evaluate the resilience of metro systems so that efficient flood disaster plans for preparation, emergency response, and timely mitigation may be developed. Traditional response solutions merged multiple sources of data and knowledge to support decision-making. An obvious drawback is that original data sources for evaluations are often stationary, inaccurate, and subjective, owing to the complexity and uncertainty of the metro station’s actual physical environment. Meanwhile, the flood propagation path inside the whole metro station network was prone to be neglected. This paper presents a comprehensive approach to analyzing the resilience of metro networks to solve these problems. Firstly, we designed a simplified weighted and directed metro network module containing six characteristics by a topological approach while considering the slope direction between sites. Subsequently, to estimate the devastating effects and details of the flood hazard on the metro system, a 100-year rainfall–flood scenario simulation was conducted using high-precision DEM and a grid hydrodynamic model to identify the initially above-ground inundated stations (nodes). We developed a dynamic node breakdown algorithm to calculate the inundation sequence of the nodes in the weighted and directed network of the metro. Finally, we analyzed the resilience of the metro network in terms of toughness strength and organization recovery capacity, respectively. The fuzzy best–worst method (FBWM) was developed to obtain the weight of each assessment metric and determine the toughness strength of each node and the entire network. The results were as follows. (1) A simplified three-dimensional metro network based on a complex system perspective was established through a topological approach to explore the resilience of urban subways. (2) A grid hydrodynamic model was developed to accurately and efficiently identify the initially flooded nodes, and a dynamic breakdown algorithm realistically performed the flooding process of the subway network. (3) The node toughness strength was obtained automatically by a nonlinear FBWM method under the constraint of the minimum error to sustain the resilience assessment of the metro network. The research has considerable implications for managing underground flooding and enhancing the resilience of the metro network.
GRACE Combined with WSD to Assess the Change in Drought Severity in Arid Asia
Authors: Liu Jiawei , Zhu Guofeng , Zhao Kailiang , Jiao Yinying , Liu Yuwei , Yang Mingyue , Zhang Wenhao , Qiu Dongdong , Lin Xinrui , Ye Linlin
Published: July 2022

Gravity Recovery and Climate Experiment (GRACE) satellite data are widely used in drought studies. In this study, we quantified drought severity based on land terrestrial water storage (TWS) changes in GRACE data. We used the water storage deficit (WSD) and water storage deficit index (WSDI) to identify the drought events and evaluate the drought severity. The WSDI calculated by GRACE provides an effective assessment method when assessing the extent of drought over large areas under a lack of site data. The results show a total of 22 drought events in the central Asian dry zone during the study period. During spring and autumn, the droughts among these incidents occurred more frequently and severely. The longest and most severe drought occurred near the Caspian Sea. In the arid area of central Asia, the north of the region tended to be moist (the WSDI value was 0.04 year−1), and the south, east, and Caspian Sea area tended to be drier (the WSDI values were −0.07 year−1 in the south, −0.11 year−1 in the east, and −0.19 year−1 in the Caspian Sea). These study results can provide a key scientific basis for agricultural development, food security, and climate change response in the Asian arid zone.
Rapid Linear Frequency Swept Frequency-Modulated Continuous Wave Laser Source Using Iterative Pre-Distortion Algorithm
Authors: Li Peng , Zhang Yating , Yao Jianquan
Published: July 2022

We present a simple iterative pre-distortion algorithm for achieving a rapid linear frequency sweep of semiconductor lasers. The algorithm achieves the desired frequency swept linearity with only four iterations. We derive a general formula for iterative pre-distortion by establishing the relationship between the laser output frequency and the drive current. The linear frequency-swept laser source obtained by this algorithm can be used in FMCW LiDAR systems. Experimentally, we investigated the algorithm using a 1550 nm distributed feedback (DFB) laser, achieving frequency swept excursion of 30.26 GHz, and frequency swept slope of 504 THz/s. We analyzed the linearity of the frequency swept results for the fourth iteration, achieving less than 5 MHz root mean square (RMS) value of frequency swept nonlinearity.
Learning the Link between Albedo and Reflectance: Machine Learning-Based Prediction of Hyperspectral Bands from CTX Images
Authors: Stepcenkov Sergej , Wilhelm Thorsten , Wöhler Christian
Published: July 2022

The instruments of the Mars Reconnaissance Orbiter (MRO) provide a large quantity and variety of imagining data for investigations of the Martian surface. Among others, the hyper-spectral Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) captures visible to infrared reflectance across several hundred spectral bands. However, Mars is only partially covered with targeted CRISM at full spectral and spatial resolution. In fact, less than one percent of the Martian surface is imaged in this way. In contrast, the Context Camera (CTX) onboard the MRO delivers images with a higher spatial resolution and the image data cover almost the entire Martian surface. In this work, we examine to what extent machine learning systems can learn the relation between morphology, albedo and spectral composition. To this end, a dataset of 67 CRISM-CTX image pairs is created and different deep neural networks are trained for the pixel-wise prediction of CRISM bands solely based on the albedo information of a CTX image. The trained models enable us to estimate spectral bands across large areas without existing CRISM data and to predict the spectral composition of any CTX image. The predictions are qualitatively similar to the ground-truth spectra and are also able to recover finer grained details, such as dunes or small craters.
A Newly Developed Tool for the Post-Processing of GPR Time-Slices in A GIS Environment
Authors: De Angeli Stefano , Serpetti Matteo , Battistin Fabiana
Published: July 2022

Ground-penetrating radar (GPR) is a precious and reliable research tool broadly used in archaeology because of its capacity to produce three-dimensional data about features preserved underground, such as buildings, infrastructures, and burials, as well as building rubble. GPR data (time-slices) management and exploitation in Geographic Information Systems (GIS) is mostly limited to the visualization and the manual interpretation and mapping of separate single time-slices. This study presents a newly developed plug-in designed to automatically post-process GPR time-slices in a GIS environment, to identify anomalies, and to produce a synchronic view of them. This map product, when combined with a DTM, results in a 2D map of subsurface anomalies which shows the absolute height of features above sea level, thus offering a comprehensive view of the three-dimensional configuration of the subsurface features identified. The paper illustrates the pixel-based processing chain of the plug-in and the results of the tests carried out in the case study of the Roman town of Falerii Novi (Italy), on the basis of high-resolution open access GPR data recently collected by the University of Cambridge and Ghent.
Impact of Snowpack on the Land Surface Phenology in the Tianshan Mountains, Central Asia
Authors: Yang Tao , Li Qian , Zou Qiang , Hamdi Rafiq , Cui Fengqi , Li Lanhai
Published: July 2022

The accumulation and ablation processes of seasonal snow significantly affect the land surface phenology in a mountainous ecosystem. However, the ability of snow to regulate the alpine land surface phenology in the arid regions is not well described in the context of climate change. The impact of snowpack changes on land surface phenology and its driving factors were investigated in the Tianshan Mountains using the land surface phenology metrics derived from satellited products and a snow dataset from downscaled regional climate model simulations covering the period from 1983 to 2015. The results demonstrated that the annual mean start of growing season (SOS) and length of growing season (LOS) experienced a significant (p < 0.05) decrease and increase with a rate of −2.45 days/decade and 2.98 days/decade, respectively. The significantly advanced SOS and increased LOS were mainly seen in the Western Tianshan Mountains and Ili Valley regions with elevations from 2500 to 3500 m a.s.l and below 3000 m a.s.l, respectively. During the early spring, the significant decline in snow cover fraction (SCF) could advance the SOS. In contrast, snowmelt amount and annual maximum snow water equivalent (SWE) have an almost equally substantial positive correlation with annual maximum vegetation greenness. In particular, the SOS of grassland was the most sensitive to variations of snow cover fraction during early spring than that of other vegetation types, and their strong relationship was mainly located at elevations from 1500 to 2500 m a.s.l. Its greenness was significantly controlled by the annual maximum snow water equivalent in all elevation bands. Both decreased SCF and increased temperature in the early spring caused a significant advance of the SOS, consequently prolonging the LOS. Meanwhile, more SWE and snowmelt amount could significantly promote vegetation greenness by regulating the soil moisture. The results can improve the understanding of the snow ecosystem services in the alpine regions under climate change.
Spatial Modelling of Vineyard Erosion in the Neszmély Wine Region, Hungary Using Proximal Sensing
Authors: Takáts Tünde , Mészáros János , Albert Gáspár
Published: July 2022

Intense soil erosion in the northern part of the Gerecse Hills, Hungary, is causing significant damage to vineyards in the area. Three vineyards in the Neszmély Wine Region were investigated to quantify the amount of eroded soils. The method was based on monitoring vineyards for one-year between July 2019 and June 2020. Every season, a set of photographs of the vineyards were taken from an unmanned aerial vehicle. The images were processed in a photogrammetric workflow to produce high-resolution digital terrain models (DTMs) and orthophotos, which were used to estimate the soil loss using the Universal Soil Loss Equation (USLE) model. Particular attention was paid to the effect of seasonal variation in vegetation cover and rainfall, and the erosion control effect of the inter-row grassing already applied in the vineyards was also modelled. The results confirm and quantify the extent to which intense summer rainfall has a more significant effect on erosion compared to autumn or winter rainfall.
Sonar Dome Geometry Design Using CFD to Reduce Ship Resistance at Cruise Speed
Authors: Wu Ping-Chen , Chen Jiun-Yu , Liu Xuan-Hong , Wu Chen-I , Lu Chien-Chung
Published: July 2022

The objective of this study is to design the hull-mounted sonar dome of a ship. The goal is to reduce the ship total resistance and improve the flow field around the sonar dome for the ship design speed. OpenFOAM 6 was applied to analyze the viscous flow around the ship bow and then optimize the sonar dome geometry. The length, width and depth of the original geometry were maintained. Only the local geometry was fine-tuned considering the back slope and front tip by using Rhinoceros 6. The verification and validation was performed for the original hull form against towing tank resistance data. The grid independence was checked for the optimal design in different design stages. To ensure less influence on the interior equipment installation and to be able to re-use the non-steel dome part, the best resistance reduction is almost 2%. With a larger allowance of shape deformation, the maximal reduction could reach slightly higher than 3%. The flow field is improved for smaller flow separation and vortex, and less fluid nose in sonar detection is expected. The main reason of the resistance reduction is the decrease of the pressure component. In conclusion, a sonar dome design procedure is proposed, and an optimal geometry is suggested.
Study on a Detection Technique for Scholte Waves at the Seafloor
Authors: Liang Minshuai , Wang Liang , Yu Gaokun , Ren Yun , Peng Linhui
Published: July 2022

Scholte waves at the seafloor have significant potential for underwater detection and communication, so a study about detecting Scholte waves is very meaningful in practice. In this paper, the detection of Scholte waves at the seafloor is researched theoretically and experimentally. Acoustic models with the multilayer elastic bottom are established according to the ocean environment, and a tank experiment is designed and carried out to detect Scholte waves. Different from detecting Scholte waves in the seismic wavefield, a technique for detecting Scholte waves in the sound pressure field is proposed in this paper. The experimental results show that the proposed technique can detect Scholte waves effectively, and there are no problems such as seabed coupling and the effect of wave speeds. Furthermore, the results also show that this detection technique is still effective in conditions with a sediment layer. The existence of sediment layers changes the acoustic field conditions and affects the excitation of Scholte waves.
Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals
Authors: Pei Dingyi , Olikkal Parthan , Adali Tülay , Vinjamuri Ramana
Published: July 2022

Brain-machine interfaces (BMIs) have become increasingly popular in restoring the lost motor function in individuals with disabilities. Several research studies suggest that the CNS may employ synergies or movement primitives to reduce the complexity of control rather than controlling each DoF independently, and the synergies can be used as an optimal control mechanism by the CNS in simplifying and achieving complex movements. Our group has previously demonstrated neural decoding of synergy-based hand movements and used synergies effectively in driving hand exoskeletons. In this study, ten healthy right-handed participants were asked to perform six types of hand grasps representative of the activities of daily living while their neural activities were recorded using electroencephalography (EEG). From half of the participants, hand kinematic synergies were derived, and a neural decoder was developed, based on the correlation between hand synergies and corresponding cortical activity, using multivariate linear regression. Using the synergies and the neural decoder derived from the first half of the participants and only cortical activities from the remaining half of the participants, their hand kinematics were reconstructed with an average accuracy above 70%. Potential applications of synergy-based BMIs for controlling assistive devices in individuals with upper limb motor deficits, implications of the results in individuals with stroke and the limitations of the study were discussed.
Using Deep Learning with Thermal Imaging for Human Detection in Heavy Smoke Scenarios
Authors: Tsai Pei-Fen , Liao Chia-Hung , Yuan Shyan-Ming
Published: July 2022

In this study, we propose using a thermal imaging camera (TIC) with a deep learning model as an intelligent human detection approach during emergency evacuations in a low-visibility smoky fire scenarios. We use low-wavelength infrared (LWIR) images taken by a TIC qualified with the National Fire Protection Association (NFPA) 1801 standards as input to the YOLOv4 model for real-time object detection. The model trained with a single Nvidia GeForce 2070 can achieve >95% precision for the location of people in a low-visibility smoky scenario with 30.1 frames per second (FPS). This real-time result can be reported to control centers as useful information to help provide timely rescue and provide protection to firefighters before entering dangerous smoky fire situations.
Force-Displacement Analysis in Diaphragm-Embedded Fiber Bragg Grating Sensors
Authors: Leal-Junior Arnaldo , Biazi Vitorino , Marques Carlos , Frizera Anselmo
Published: July 2022

This paper presented the force and displacement analyses of a diaphragm-embedded fiber Bragg grating (FBG) sensor. In the first step, a numerical analysis (via finite element method) was performed considering linear elastic materials, where there is a linear variation on the strain in the optical fiber for both displacement and force (or pressure). In the second step, the experimental analysis was performed using two approaches: (i) controlling the displacement applied in the diaphragm-embedded FBG (while the force is also measured). (ii) Controlling the force applied in the sensor (also with the measurement of the displacement). Results showed reflected optical power variations and wavelength shift following the application of displacement and force. The sensitivities of both wavelength shift and optical power were different (and non-proportional) when displacement and force were compared. However, a higher correlation, determination coefficient (R2) of 0.998, was obtained in the analysis of the wavelength shift as a function of the displacement, which indicated that the strain transmission in the optical fiber is directly related to the strain in the diaphragm, whereas the force has an indirect relation with the strain and depends on the material features. Then, the possibility of simultaneous estimation of force and displacement was investigated, where the linear relation of both parameters (displacement and force) with the wavelength shift and the optical power were obtained in a limited range of displacement and force. In this range, root mean squared errors of 0.37 N and 0.05 mm were obtained for force and displacement, respectively. In addition, the force variation with a step displacement input also shows the possibility of using the proposed FBG device for the characterization of the materials’ viscoelastic features such as phase delay, creep, and stress relaxation, which can be employed for in situ characterization of different viscoelastic materials.
Experimental Study on the Coupling Mechanism of Sensors under a Strong Electromagnetic Pulse
Authors: Cao Jie , Wei Minxiang , Zhou Dong
Published: July 2022

This paper presents a study on the electromagnetic effects of a main fuel control system under the action of a strong electromagnetic pulse. It assesses the electromagnetic sensitivity of a speed sensor and a linear variable differential transformer (LVDT) sensor. This assessment focuses on the control system’s electromagnetic effects and the sensors’ coupling signals. The effects and signals were determined using radiation and pulse current injection tests. Analysis of the signal at the system power port shows that it is the same as those for the two test methods. Based on analysis of the working mechanism and terminal signals of the sensors, the electromagnetic sensitivity of the system is different under the different electromagnetic pulse conditions. The electromagnetic sensitivity characteristics of the sensors were verified, and the electromagnetic effects of the main fuel regulation control system were analyzed. Meanwhile, the degree of sensor coupling mechanism caused by the electromagnetic coupling effects of the main fuel regulation control system under strong electromagnetic pulses were studied. These findings have clear practical implications for electromagnetic pulse protection of aero-engine control systems.
Heavy Rain Face Image Restoration: Integrating Physical Degradation Model and Facial Component-Guided Adversarial Learning
Authors: Son Chang-Hwan , Jeong Da-Hee
Published: July 2022

With the recent increase in intelligent CCTVs for visual surveillance, a new image degradation that integrates resolution conversion and synthetic rain models is required. For example, in heavy rain, face images captured by CCTV from a distance have significant deterioration in both visibility and resolution. Unlike traditional image degradation models (IDM), such as rain removal and super resolution, this study addresses a new IDM referred to as a scale-aware heavy rain model and proposes a method for restoring high-resolution face images (HR-FIs) from low-resolution heavy rain face images (LRHR-FI). To this end, a two-stage network is presented. The first stage generates low-resolution face images (LR-FIs), from which heavy rain has been removed from the LRHR-FIs to improve visibility. To realize this, an interpretable IDM-based network is constructed to predict physical parameters, such as rain streaks, transmission maps, and atmospheric light. In addition, the image reconstruction loss is evaluated to enhance the estimates of the physical parameters. For the second stage, which aims to reconstruct the HR-FIs from the LR-FIs outputted in the first stage, facial component-guided adversarial learning (FCGAL) is applied to boost facial structure expressions. To focus on informative facial features and reinforce the authenticity of facial components, such as the eyes and nose, a face parsing-guided generator and facial local discriminators are designed for FCGAL. The experimental results verify that the proposed approach based on a physical-based network design and FCGAL can remove heavy rain and increase the resolution and visibility simultaneously. Moreover, the proposed heavy rain face image restoration outperforms state-of-the-art models of heavy rain removal, image-to-image translation, and super resolution.
On a Blockchain-Based Security Scheme for Defense against Malicious Nodes in Vehicular Ad-Hoc Networks
Authors: Liu Guandong , Fan Na , Wu Chase Q. , Zou Xiaomin
Published: July 2022

Vehicular ad-hoc networks (VANETs) aim to provide a comfortable driving experience. Sharing messages in VANETs can help with traffic management, congestion mitigation, and driving safety. However, forged or false messages may undermine the efficiency of VANETs. In this paper, we propose a security scheme based on blockchain technology, where two types of blockchain are constructed based on roadside units (RSUs) and Certificate Authorities (CAs), respectively. The proposed security scheme has multifold goals to identify malicious nodes and detect forged messages based on multiple factors, such as reputation of sender nodes, and time and distance effectiveness of messages. In addition, an incentive mechanism is introduced on the RSU blockchain to encourage RSUs to adopt active behaviors. Extensive simulations show that the proposed scheme exhibits superior performances to existing methods in detecting forged messages and identifying malicious nodes. Meanwhile, it provides privacy protection and improves the efficiency of vehicular networks.
Review of Optical Fiber Sensors for Temperature, Salinity, and Pressure Sensing and Measurement in Seawater
Authors: Liang Honglin , Wang Jing , Zhang Lihui , Liu Jichao , Wang Shanshan
Published: July 2022

Temperature, salinity, and pressure (TSP) are essential parameters for the ocean. Optical fiber sensors (OFSs) have rapidly come into focus as an ocean detection technology in recent years due to their advantages of electromagnetic interference, light weight, low cost, and no waterproof requirement. In this paper, the most recently developed TSP sensors for single parameter and multi-parameter TSP sensing and measurement based on different OFSs are reviewed. In addition, from the practical point of view, encapsulation methods that protect fibers and maintain the normal operation of OFSs in seawater, and the response time of the OFS, are addressed. Finally, we discuss the prospects and challenges of OFSs used in marine environments and provide some clues for future work.
The Time-of-Arrival Offset Estimation in Neural Network Atomic Denoising in Wireless Location
Authors: Hu Yunbing , Peng Ao , Tang Biyu , Ou Guojian , Lu Xianzhi
Published: July 2022

With the increasing demand for wireless location services, it is of great interest to reduce the deployment cost of positioning systems. For this reason, indoor positioning based on WiFi has attracted great attention. Compared with the received signal strength indicator (RSSI), channel state information (CSI) captures the radio propagation environment more accurately. However, it is necessary to take signal bandwidth, interferences, noises, and other factors into account for accurate CSI-based positioning. In this paper, we propose a novel dictionary filtering method that uses the direct weight determination method of a neural network to denoise the dictionary and uses compressive sensing (CS) to extract the channel impulse response (CIR). A high-precision time-of-arrival (TOA) is then estimated by peak search. A median value filtering algorithm is used to locate target devices based on the time-difference-of-arrival (TDOA) technique. We demonstrate the superior performance of the proposed scheme experimentally, using data collected with a WiFi positioning testbed. Compared with the fingerprint location method, the proposed location method does not require a site survey in advance and therefore enables a fast system deployment.
Occupational Risk of Technostress Related to the Use of ICT among Teachers in Spain
Authors: Rey-Merchán María del Carmen , López-Arquillos Antonio
Published: July 2022

Teachers are frequently exposed to several occupational risks. The continuous use of Information and Communication Technologies (ICT) represents a potential source of technostress among teachers. The aim of the current research is to evaluate the technostress levels of school and high school teachers in Spain. The methodology applied in the manuscript, namely the staticized group technique was based on an expert panel. Results showed high levels of technostress in both dimensions (techno-anxiety and techno-fatigue). Experts highlighted the relationship between administrative tasks and technostress. More rational and efficient distribution of the workload associated with administrative obligations of the teachers, could reduce the level of technostress in the education sector. Results from the current research can help to improve working conditions of teachers considering the findings to design preventive strategies adapted to the profile of teachers and their organization. Some policy interventions such as occupational safety training, institutional technical support, and computer training programs could reduce the levels of technostress and mitigate associated negative impacts concerning the occupational health of teachers.
A Review on Sustainability Characteristics Development for Wooden Furniture Design
Authors: Muhammad Suandi Mohd Effendi , Amlus Mohammad Harith , Hemdi Abdul Rahman , Abd Rahim Shayfull Zamree , Ghazali Mohd Fathullah , Rahim Nur Liza
Published: July 2022

The design of furniture products is influenced by increasing consumer interest in green products and sustainability values. However, although the demand for sustainable furniture products is high, the standardization of sustainability characteristics in furniture design has still not been achieved. A thorough literature review was conducted, which considered various sustainability characteristics that apply in industries that design furniture. This review paper aimed to identify common sustainability characteristics so that a new standard for furniture industries can be established. In this review, numerous themes were explored relating to design guidelines, design criteria, design preferences, design optimization, design evaluation and assessment, design decision making, strategic planning, design strategies, the integration of eco-design, and eco-design tools. A total of 137 articles were reviewed regarding their sustainability characteristics according to the triple bottom-line framework for a relevant product sector. Due to the limited reports on the sustainability characteristics of furniture design activities, this paper also tried to include common sustainability characteristics of non-furniture products that are available on the market. Through the review, 10 sustainability characteristics were identified for the environment, 17 for the economy, and 16 for the social dimension as being common among manufacturers when designing their products. A further in-depth analysis was conducted by mapping the characteristics to those that were significantly implemented in the design process, of which five (5) were environmental, two (2) were economic, and five (5) were social sustainability characteristics. This review is significant in helping furniture designers to use appropriate and effective sustainability standards in the design and manufacture of products that meet customers’ demands. Previous literature reviews have not clearly measured the triple bottom line. Furthermore, no definite characteristics were proposed in previous works regarding wooden furniture design, leaving a gap to be closed by future works.
Timing Decision for Active Remanufacturing Based on 3E Analysis of Product Life Cycle
Authors: Gong Qingshan , Xiong Yurong , Jiang Zhigang , Yang Jinghong , Chen Chen
Published: July 2022

Active remanufacturing is an important technique that is used to reduce the uncertainty of the quality of remanufactured cores. However, the implementation of active remanufacturing too early or late will lead to a reduction in economic benefits and an increase in environmental impact during the whole life cycle of the product. To this end, an active-remanufacturing-timing decision method is proposed based on an economic, energy and environmental (3E) analysis of product life cycle. In this method, the quantitative function of the cost, energy consumption and environmental emissions of used products in the manufacturing stage, service stage, and remanufacturing stage are firstly constructed based on life-cycle assessment (LCA) and life-cycle cost (LCC). Then, a multi-objective optimization method and the particle swarm algorithm are utilized to obtain active-remanufacturing timing with the optimal economic and environmental benefits of remanufacturing. Finally, a case study on remanufacturing on used engines is demonstrated to validate the proposed method.
Correction: Ahmad et al. Estimating the Total Volume of Running Water Bodies Using Geographic Information System (GIS): A Case Study of Peshawar Basin (Pakistan). Sustainability 2022, 14, 3754
Authors: Ahmad Naveed , Khan Sikandar , Ehsan Muhsan , Rehman Fayaz Ur , Al-Shuhail Abdullatif
Published: July 2022

The authors would like to make the following corrections to the published paper [...]
Landscape Architecture and the Green Deal Dare: Five Successful Experiences in Urban Open Spaces
Authors: Olivetti Maria Livia
Published: July 2022

The present work focuses on European cities’ environments and open spaces, aiming to demonstrate how, through landscape architecture, it is possible to respond effectively to many of the ecological and social hardships that the Green Deal aspires to alleviate. It was signed in 2019 by all of the member countries of the Union, seeks to reverse the climate change trend by establishing a series of goals for improving environmental and economic quality for 2030 and 2050 and also aims to enact social justice in rural areas and in the urban environment. Landscape architecture, which is the art of combining the physical and immaterial elements in cities’ open spaces, is taken in this work as the method of interpreting the existing environment. The description, together with the narrative analysis of five selected site-specific urban renovations projects carried out in the last 15 years by some of the main contemporary landscape architects, such as Micheal Desvigne, Peter Latz and Gilles Clément, demonstrates, by means of their empirical experiences, the benefits of the landscape design. It is able to match both the ecological need expressed in the Deal and to respond to the ambition of an open and rightful city, as called for by the theories of Sennet and Balmori. In order to reach the just transition and to leave no one behind, and to meet and to implement the Green Deal objectives, the new, positive and long-lasting explained transformations require the consideration of landscape design, in all its material and immaterial components, as a theoretical synthesis capable of obtaining a practical application in fighting climate change, and it should be considered and included in city management policies and in the Deal, too.
When Are Organizations Sustainable? Well-Being and Discomfort in Working Contexts: Old and New Form of Malaise
Authors: Gozzoli Caterina , Gazzaroli Diletta
Published: July 2022

The proposal for this Special Issue is related to a question that is increasingly present and debated by both researchers and practitioners [...]