bluuebunny/crossref_metadata_2025 huggingface dataset:
Cultural Heritage and Religious Phenomenon between Urbicide and Cancel Culture: The Other Side of the Russian–Ukrainian Conflict
Authors: Botti Federica , Bianchi Cristina
Published: April 2023

The Russian–Ukrainian conflict, in addition to causing an unacceptable loss of human life, is straining the integrity of Ukraine’s cultural heritage, despite the fact that both countries involved are parties to the 1954 Hague Convention for the Protection of Cultural Property in the Event of Armed Conflict and its First Protocol. Churches are one of Ukraine’s most important historical assets, as well as symbolic places of Orthodox religious identity common to both the invaders and the invaded. The destruction of these places and their deliberate damage on the part of both sides appear to be part of a more general conflict concerning internal disagreements between Russian and Ukrainian Orthodoxy, which, in turn, reflect two different historical views of the Russian–Ukrainian relationship. A brief reconstruction of relations between the Orthodox Churches operating on the territory of Ukraine demonstrates how religious affiliation has affected the conflict, causing it to become decisive and deeply divisive, so much so that the Patriarchate of Moscow has become an active part of the conflict. This circumstance favours the hypothesis that it is precisely the religious cultural heritage that is most at risk of deliberate destruction. The Russians, by destroying the symbolic places of Ukrainian religious identity (urbicide), affirm the spiritual unity of the Russian and Ukrainian peoples. For their part, the Ukrainians attempt to erase the Russian presence and the common religious cultural roots by destroying buildings of worship dear to the tradition of the Moscow Patriarchate (cancel culture). They reject the imperial traditions of Russia and, at the same time, claim an independent Church. The question arises as to whether the reconstruction process following the war will take into account the original cultural–religious identities, or whether it will take the opportunity to adopt a new (also) religious identity instead, and whether the old and new instruments offered by law are adequate.
Editorial: Catholic Education and the Liberal Arts
Authors: Franchi Leonardo
Published: April 2023

This Special Issue of Religions offers a welcome opportunity to explore the argument that the Liberal Arts lie at the heart of the Catholic vision of education [...]
The Dispute around Same-Sex Marriage in Costa Rica: Arguments and Actions of Conservative Religious Activism (2017–2021)
Authors: León-Carvajal Arantxa , Pineda-Sancho Andrey
Published: April 2023

This article examines the role assumed by Costa Rican conservative religious activism during the public controversy that arose in the Central American country regarding the legalization of same-sex marriage. This public debate found its highest point during the presidential elections of 2018. The article is divided into two parts. Firstly, it identifies the arguments used by conservative actors during the controversy, with special emphasis on the conceptions of democracy and human rights that informed their political praxis. Secondly, it ponders the significance that the politicization of religion in a conservative direction has had on the dynamics and democratic institutions of contemporary Costa Rica.
Genomic Insults and their Redressal in the Eutopic Endometrium of Women with Endometriosis
Authors: Munshi Itti , Sachdeva Geetanjali
Published: April 2023

Endometrium, a highly dynamic tissue, is known for its remarkable ability to regenerate, differentiate, and degenerate in a non-conception cycle and transform into a specialized tissue to nurture and protect the embryo in a conception cycle. This plasticity of the endometrium endows the uterus to execute its major function, i.e., embryo implantation. However, this boon becomes a bane, when endometrium- or endometrium-like cells adhere, grow, and invade extrauterine sites, leading to endometriosis. Endometrial deposits at the extrauterine site lead to severe pelvic pain, painful menstruation, and infertility in endometriosis. Although benign, endometriotic lesions share several traits with cancerous cells, excessive proliferation, adhesion, invasion, and angiogenesis make endometriotic lesions analogous to cancer cells in certain aspects. There exists evidence to support that, akin to the cancer cell, endometriotic lesions harbor somatic mutations. These lesions are known to experience higher proliferative stress, oxidative stress, and inflammation, which may contribute to somatic mutations. However, it would be of more interest to establish whether in the eutopic endometriosis also, the mutational burden is higher or whether the DNA Damage Response (DDR) is compromised in the eutopic endometrium, in endometriosis. Such investigations may provide more insights into the pathobiology of endometriosis and may also unravel cellular events associated with the origin of the disease. This review compiles inferences from the studies conducted to assess DNA damage and DDR in endometriosis.
Crop Phenology Modelling Using Proximal and Satellite Sensor Data
Authors: Gobin Anne , Sallah Abdoul-Hamid Mohamed , Curnel Yannick , Delvoye Cindy , Weiss Marie , Wellens Joost , Piccard Isabelle , Planchon Viviane , Tychon Bernard , Goffart Jean-Pierre , Defourny Pierre
Published: April 2023

Understanding crop phenology is crucial for predicting crop yields and identifying potential risks to food security. The objective was to investigate the effectiveness of satellite sensor data, compared to field observations and proximal sensing, in detecting crop phenological stages. Time series data from 122 winter wheat, 99 silage maize, and 77 late potato fields were analyzed during 2015–2017. The spectral signals derived from Digital Hemispherical Photographs (DHP), Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent. Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation) demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover), with the best model fits obtained for maize, followed by wheat and potato. S2-derived fAPAR showed decreasing variability as the growing season progressed. The use of a double sigmoid model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid) and senescence (downward sigmoid), while the upward endpoint corresponded to canopy closure and the maximum values to flowering and fruit development. Furthermore, increasing the frequency of sensor revisits is beneficial for detecting short-duration crop phenological stages. The results have implications for data assimilation to improve crop yield forecasting and agri-environmental modeling.
Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality Evaluation
Authors: Wang Baohang , Zhao Chaoying , Zhang Qin , Liu Xiaojie , Lu Zhong , Liu Chuanjin , Zhang Jianxia
Published: April 2023

Today, synthetic aperture radar (SAR) satellites provide large amounts of SAR data at unprecedented temporal resolutions, which promotes hazard dynamic monitoring and disaster mitigation with interferometric SAR (InSAR) technology. This study focuses on big InSAR data dynamical processing in areas of serious decorrelation and large gradient deformation. A new stepwise temporal phase optimization method is proposed to alleviate the decorrelation, customized for deformation parameter dynamical estimation. Subsequently, the sequential estimation theory is introduced to the intermittent small baseline subset (ISBAS) approach to dynamically obtain deformation time series with dense coherent targets. Then, we analyze the reason for the unstable accuracy of deformation parameters using sequential distributed scatterers-ISBAS technology, and construct five indices to describe the quality of deformation parameters pixel-by-pixel. Finally, real data of the post-failure Baige landslide at the Jinsha River in China is used to demonstrate the validity of the proposed approach.
Multi-Temporal Trend Analysis of Coastal Vegetation Using Metrics Derived from Hyperspectral and LiDAR Data
Authors: Suir Glenn M. , Jackson Sam , Saltus Christina , Reif Molly
Published: April 2023

Monitoring and modeling of coastal vegetation and wetland systems are considered major challenges, especially when considering environmental response to hazards, disturbances, and management activities. Remote sensing applications can provide alternatives and complementary approaches to the often costly and laborious field-based collection methods traditionally used for coastal ecosystem monitoring. New and improved sensors and data analysis techniques have become available, making remote sensing applications attractive for evaluation and potential use in monitoring coastal vegetation properties and ecosystem conditions and change. This study involves the extraction of vegetation metrics from airborne LiDAR (Light Detection and Ranging) and hyperspectral imagery (HSI) to quantify coastal dune vegetation characteristics and assesses landscape-level trends from those derived metrics. HSI- and LiDAR-derived elevation (digital elevation model) and vegetation metrics (canopy height model, leaf area index, and normalized difference vegetation index) were used in conjunction with per-pixel linear regression and hot spot analyses to evaluate hurricane-induced spatial and temporal changes in elevation and vegetation properties. These assessments showed areas with greatest decreases in vegetation metric values were associated with direct tropical storm energies and processes (i.e., overwashing events eroding beach and dune features), while those with the greatest increases in vegetation metric values were in areas where overwashed sediments were distributed. This study narrows existing gaps in dune vegetation data by advancing new methodologies to classify, quantify, and estimate critical coastal vegetation metrics. The tools and methods developed in this study will ultimately improve future estimates and predictions of nearshore dynamics and impacts from disturbance events.
State-Level Mapping of the Road Transport Network from Aerial Orthophotography: An End-to-End Road Extraction Solution Based on Deep Learning Models Trained for Recognition, Semantic Segmentation and Post-Processing with Conditional Generative Learning
Authors: Cira Calimanut-Ionut , Manso-Callejo Miguel-Ángel , Alcarria Ramón , Bordel Sánchez Borja , González Matesanz Javier
Published: April 2023

Most existing road extraction approaches apply learning models based on semantic segmentation networks and consider reduced study areas, featuring favorable scenarios. In this work, an end-to-end processing strategy to extract the road surface areas from aerial orthoimages at the scale of the national territory is proposed. The road mapping solution is based on the consecutive execution of deep learning (DL) models trained for ① road recognition, ② semantic segmentation of road surface areas, and ③ post-processing of the initial predictions with conditional generative learning, within the same processing environment. The workflow also involves steps such as checking if the aerial image is found within the country’s borders, performing the three mentioned DL operations, applying a p=0.5 decision limit to the class predictions, or considering only the central 75% of the image to reduce prediction errors near the image boundaries. Applying the proposed road mapping solution translates to operations aimed at checking if the latest existing cartographic support (aerial orthophotos divided into tiles of 256 × 256 pixels) contains the continuous geospatial element, to obtain a linear approximation of its geometry using supervised learning, and to improve the initial semantic segmentation results with post-processing based on image-to-image translation. The proposed approach was implemented and tested on the openly available benchmarking SROADEX dataset (containing more than 527,000 tiles covering approximately 8650 km2 of the Spanish territory) and delivered a maximum increase in performance metrics of 10.6% on unseen, testing data. The predictions on new areas displayed clearly higher quality when compared to existing state-of-the-art implementations trained for the same task.
Millimeter-Wave Radar Monitoring for Elder’s Fall Based on Multi-View Parameter Fusion Estimation and Recognition
Authors: Feng Xiang , Shan Zhengliang , Zhao Zhanfeng , Xu Zirui , Zhang Tianpeng , Zhou Zihe , Deng Bo , Guan Zirui
Published: April 2023

Human activity recognition plays a vital role in many applications, such as body falling surveillance and healthcare for elder’s in-home monitoring. Instead of using traditional micro-Doppler signals based on time-frequency distribution, we turn to another way and use the Relax algorithm to process the radar echo so as to obtain the required parameters. In this paper, we aim at the multi-view idea in which two radars at different views work synchronously and fuse the features extracted from each radar, respectively. Furthermore, we discuss the common estimated time-frequency features and time-varying spatial features of multi-view radar-echo and then formulate the parameters matrix via principal component analysis, and finally transform them into the machine learning classifiers to make further comparisons. Simulations and results show that our proposed multi-view parameter fusion idea could lead to relative-high accuracy and robust recognition performance, which would provide a feasible application for future human–computer monitoring scenarios.
Wavenumber-4 Structure in COSMIC-2 Observations: Vertical Plane Perspective
Authors: Joshi Lalit Mohan , Tsai Lung-Chih , Su Shin-Yi
Published: April 2023

High-rate radio occultation (RO) in COSMIC-2 (FORMOSAT7) enables us to investigate the finer details of the ionosphere owing to the measurements being made at a significantly high spatiotemporal resolution, which was unthinkable a decade ago. In the vertical plane, local-time ionospheric wavenumber-4 (WN4) structures display tilted phase-fronts over the equatorial ionization anomaly (EIA) belt. The longitudinal extent of a tilted WN4 phase-front approximates the zonal wavelength of nonmigrating DE3 tide in the local-time frame of reference, i.e., ~900. The WN4-filtered (residual) component indicates a greater tilt (when visible), with a larger longitudinal extent of a wavenumber structure in the vertical plane. The WN4 structure over the EIA crest region is found to be out of phase (in phase) with respect to that over the EIA trough region during daytime (nighttime), which also depended on the altitude under consideration. Intriguingly, above 400 km, the WN4 structures in the EIA crest and trough regions are seen to be in phase with each other at all local times. The phenomenon of the “longitudinal co-location” of WN4 over the EIA crest and trough regions at altitudes above ~400 km at all local times remains unexplained. Results also highlight that the formation of WN4 is governed by a complex interplay of direct forcing of nonmigrating tides and the zonal electric field whose characteristics within the EIA belt vary drastically with latitude and altitude under consideration.
Editorial for the Special Issue: “Radio Occultations for Numerical Weather Prediction, Ionosphere, and Space Weather”
Authors: Gorbunov Michael
Published: April 2023

Sounding of the Earth’s ionosphere is an important application of the RO technique [...]
SAR Image Quality Assessment: From Sample-Wise to Class-Wise
Authors: Yu Ziyi , Dong Ganggang , Liu Hongwei
Published: April 2023

Target recognition is the core application of radar image interpretation. In recent years, deep learning has become the mainstream solution. However, this family of methods is highly dependent on a great deal of training samples. Limited samples may lead to problems such as underfitting and poor robustness. To solve the problem, numerous generative models have been presented. The generated samples played an important role in target recognition. It is therefore needed to assess the quality of simulated images. However, few studies were performed in the preceding works. To fill the gap, a new evaluation strategy is proposed in this paper. The proposed method is composed of two schemes: the sample-wise assessment and the class-wise one. The simulated images can then be evaluated from two different perspectives. The sample-wise assessment combines the Fisher separability criterion, fuzzy comprehensive evaluation, analytic hierarchy process, and image feature extraction into a unified framework. It is used to evaluate whether the relative intensity of the speckle noise of the SAR image and the target backscattering coefficients are well simulated. Contrarily, the class-wise assessment is designed to compare the application capability of the simulated images holistically. Multiple comparative experiments are performed to verify the proposed method.
Lagged Linkage between the Kara–Barents Sea Ice and Early Summer Rainfall in Eastern China in Chinese CMIP6 Models
Authors: Yang Huidi , Rao Jian , Chen Haohan , Lu Qian , Luo Jingjia
Published: April 2023

The lagged relationship between Kara–Barents sea ice and summer precipitation in eastern China is evaluated for Chinese models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). A previous study revealed a dipole rainfall structure in eastern China related to winter Arctic sea ice variability. Almost all Chinese CMIP6 models reproduce the variability and climatology of the sea ice in most of the Arctic well except the transition regions with evident biases. Further, all Chinese CMIP6 models successfully simulate the decreasing trend for the Kara–Barents sea ice. The dipole centers located in the Yangtze–Huai River Valley (YHRV) and South China (SC) related to Kara–Barents sea ice variability are simulated with different degrees of success. The anomalous dipole rainfall structure related to the winter Kara–Barents sea ice variability can roughly be reproduced by two models, while other models reproduce a shifted rainfall anomaly pattern or with the sign reversed. The possible delayed influence of sea ice forcing on early summer precipitation in China is established via three possible processes: the long memory of ice, the long-lasting stratospheric anomalies triggered by winter sea ice forcing, and the downward impact of the stratosphere as the mediator. Most Chinese models can simulate the negative Northern Hemisphere Annular Mode (NAM) phase in early winter but fail to reproduce the reversal of the stratospheric anomalies to a positive NAM pattern in spring and early summer. Most models underestimate the downward impact from the stratosphere to the troposphere. This implies that the stratospheric pathway is essential to mediate the winter sea ice forcing and rainfall in early summer over China for CMIP6 models.
A Multi-Scale Spatial Difference Approach to Estimating Topography Correlated Atmospheric Delay in Radar Interferograms
Authors: Yu Zhigang , Huang Guoman , Zhao Zheng , Huang Yingchun , Zhang Chenxi , Zhang Guanghui
Published: April 2023

The Interferometric Synthetic Aperture Radar (InSAR) has been widely used as a powerful technique for monitoring land surface deformations over the last three decades. InSAR observations can be plagued by atmospheric phase delays; some have a roughly linear relationship with the ground elevation, which can be approximated using a linear model. However, the estimation results of this linear relationship are sometimes affected by phase ramps such as orbital errors, tidal loading, etc. In this study, we present a new approach to estimate the transfer function of vertical stratification phase delays and the transfer function of phase ramps. Our method uses the idea of multi-scale spatial differences to decompose the atmospheric phase delay into the vertical stratification component, phase ramp component, and other features. This decomposition makes the correlation between the vertical stratification phase delays and topography more significant and stable. This can establish the correlation between the different scales and phase ramps. We demonstrate our approach using a synthetic test and two real interferograms. In the synthetic test, the transfer functions estimated by our method were closer to the design values than those estimated by the full interferogram–topography correlation approach and the band-pass filtering approach. In the first real interferogram, out of the 9 sub-regions corrected by the proposed method, 7 sub-regions were outperformed the full interferogram–topography correlation approach, and 8 sub-regions were superior to the band-pass filtering method. Our technique offers a greater correction effect and robustness for coseismic deformation signals in the second real interferogram.
Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method
Authors: Mi Pei , Zheng Chaolei , Jia Li , Bai Yu
Published: April 2023

Global, long-term, gap-free, high quality soil moisture products are extremely important for hydrological monitoring and climate change research. However, soil moisture products produced from satellite observations have data gaps due to the limited capabilities of satellite orbit/swath and retrieval algorithms, which limit the regional and global applications of soil moisture data in hydrology and agriculture studies. To solve this problem, we proposed a gap-filling method to reconstruct a global gap-free surface soil moisture product by applying the machine learning (Random Forest) algorithm on a pixel-by-pixel basis, taking into account the nonlinear relationship between surface soil moisture and the related surface environmental variables. The gap-filling method was applied to the NN-SM surface soil moisture product, which has a fraction of data gaps of around 50% globally on a multi-year average. A global daily gap-free surface soil moisture dataset from 2002 to 2020 was then generated. The reconstructed values of several sub-regions after manually eliminating the original values were cross-verified with the original data, and this clearly demonstrated the reliability of the reconstruction method with the correlation coefficient (R) ranging between 0.770 and 0.918, the Root Mean Square Error (RMSE) between 0.057 and 0.082 m3/m3, the unbiased Root Mean Square Error (ubRMSE) between 0.053 and 0.081 m3/m3, and Bias between −0.012 and 0.008 m3/m3. The accuracy of the reconstructed surface soil moisture dataset was evaluated using in situ observations of surface soil moisture at 12 sites from the International Soil Moisture Network (ISMN) and the Long-Term Agroecosystem Research (LTAR) network, and the results showed good accuracy in terms of R (0.610), RMSE (0.067 m3/m3), ubRMSE (0.045 m3/m3) and Bias (0.031 m3/m3). Overall, the reconstructed surface soil moisture dataset retained the characteristics of the NN-SM product, such as high accuracy and good spatiotemporal pattern. However, with the advantage of continuous spatiotemporal coverage, it is more suitable for further applications in the analysis of global surface soil moisture trends, land surface hydrological processes, and land-atmosphere energy and water exchanges, etc.
Cactus Pear Silage to Mitigate the Effects of an Intermittent Water Supply for Feedlot Lambs: Intake, Digestibility, Water Balance and Growth Performance
Authors: Nobre Ismael de Sousa , Araújo Gherman Garcia Leal de , Santos Edson Mauro , Carvalho Gleidson Giordano Pinto de , de Albuquerque Italo Reneu Rosas , Oliveira Juliana Silva de , Ribeiro Ossival Lolato , Turco Silvia Helena Nogueira , Gois Glayciane Costa , Silva Thieres George Freire da , Perazzo Alexandre Fernandes , Zanine Anderson de Moura , Ferreira Daniele de Jesus , de Sousa Santos Francisco Naysson , Campos Fleming Sena
Published: April 2023

The aim of this study was to evaluate the intake, digestibility, water balance and growth performance of lambs receiving diets containing cactus silage under an intermittent water supply. Thirty-six male, uncastrated Santa Inês lambs with an initial weight of 19.8 ± 2.1 kg and age of 6 months were distributed in a 3 × 3 factorial arrangement, with three proportions of cactus pear in the diets (0 (control diet containing Tifton hay), 21% and 42% of dry matter) and three periods of intermittent water supply (0, 24 and 48 h), with four repetitions. Lambs that received diets non-isonitrogenous with cactus silage showed higher intakes of dry matter (p < 0.001), total digestible nutrients (p < 0.001), water excretion via faeces (p < 0.001) and water balance (p < 0.001). Lambs that received diets with cactus silage showed higher digestibility of total carbohydrates, non-fibre carbohydrates (p = 0.005), water intake via food (p < 0.001), total water intake (p < 0.001), water excretion via urine (p < 0.001) and water balance (p < 0.05), when compared to the control diet. Lambs that received diets with cactus silage promoted growth performance (p = 0.001). When using 42% forage cactus silage in place of Tifton hay and water offered at 48 h intervals, intake, digestibility, and performance of feedlot lambs were improved.
Run Your 3D Object Detector on NVIDIA Jetson Platforms:A Benchmark Analysis
Authors: Choe Chungjae , Choe Minjae , Jung Sungwook
Published: April 2023

This paper presents a benchmark analysis of NVIDIA Jetson platforms when operating deep learning-based 3D object detection frameworks. Three-dimensional (3D) object detection could be highly beneficial for the autonomous navigation of robotic platforms, such as autonomous vehicles, robots, and drones. Since the function provides one-shot inference that extracts 3D positions with depth information and the heading direction of neighboring objects, robots can generate a reliable path to navigate without collision. To enable the smooth functioning of 3D object detection, several approaches have been developed to build detectors using deep learning for fast and accurate inference. In this paper, we investigate 3D object detectors and analyze their performance on the NVIDIA Jetson series that contain an onboard graphical processing unit (GPU) for deep learning computation. Since robotic platforms often require real-time control to avoid dynamic obstacles, onboard processing with a built-in computer is an emerging trend. The Jetson series satisfies such requirements with a compact board size and suitable computational performance for autonomous navigation. However, a proper benchmark that analyzes the Jetson for a computationally expensive task, such as point cloud processing, has not yet been extensively studied. In order to examine the Jetson series for such expensive tasks, we tested the performance of all commercially available boards (i.e., Nano, TX2, NX, and AGX) with state-of-the-art 3D object detectors. We also evaluated the effect of the TensorRT library to optimize a deep learning model for faster inference and lower resource utilization on the Jetson platforms. We present benchmark results in terms of three metrics, including detection accuracy, frame per second (FPS), and resource usage with power consumption. From the experiments, we observe that all Jetson boards, on average, consume over 80% of GPU resources. Moreover, TensorRT could remarkably increase inference speed (i.e., four times faster) and reduce the central processing unit (CPU) and memory consumption in half. By analyzing such metrics in detail, we establish research foundations on edge device-based 3D object detection for the efficient operation of various robotic applications.
A Brief Survey of Recent Advances and Methodologies for the Security Control of Complex Cyber–Physical Networks
Authors: Wan Ying , Cao Jinde
Published: April 2023

Complex cyber–physical networks combine the prominent features of complex networks and cyber–physical systems (CPSs), and the interconnections between the cyber layer and physical layer usually pose significant impacts on its normal operation. Many vital infrastructures, such as electrical power grids, can be effectively modeled as complex cyber–physical networks. Given the growing importance of complex cyber–physical networks, the issue of their cybersecurity has become a significant concern in both industry and academic fields. This survey is focused on some recent developments and methodologies for secure control of complex cyber–physical networks. Besides the single type of cyberattack, hybrid cyberattacks are also surveyed. The examination encompasses both cyber-only hybrid attacks and coordinated cyber–physical attacks that leverage the strengths of both physical and cyber attacks. Then, special focus will be paid to proactive secure control. Reviewing existing defense strategies from topology and control perspectives aims to proactively enhance security. The topological design allows the defender to resist potential attacks in advance, while the reconstruction process can aid in reasonable and practical recovery from unavoidable attacks. In addition, the defense can adopt active switching-based control and moving target defense strategies to reduce stealthiness, increase the cost of attacks, and limit the attack impacts. Finally, conclusions are drawn and some potential research topics are suggested.
A Compact and High-Precision Three-Degree-of-Freedom Grating Encoder Based on a Quadrangular Frustum Pyramid Prism
Authors: Wang Shengtong , Liao Baiqi , Shi Ningning , Li Xinghui
Published: April 2023

A compact and high-precision three-degrees-of-freedom (DOF; X, Y, and Z directions) grating encoder based on the quadrangular frustum pyramid (QFP) prisms is proposed in this paper to solve the insufficient installation space problem of the reading head of the multi-DOF in high-precision displacement measurement applications. The encoder is based on the grating diffraction and interference principle, and a three-DOF measurement platform is built through the self-collimation function of the miniaturized QFP prism. The overall size of the reading head is 12.3 × 7.7 × 3 cm3 and has the potential for further miniaturization. The test results show that three-DOF measurements can be realized simultaneously in the range of X-250, Y-200, and Z-100 μm due to the limitations of the measurement grating size. The measurement accuracy of the main displacement is below 500 nm on average; the minimum and maximum errors are 0.0708% and 2.8422%, respectively. This design will help further popularize the research and applications of multi-DOF grating encoders in high-precision measurements.
Privacy-Preserving Indoor Trajectory Matching with IoT Devices
Authors: Lu Bingxian , Wu Di , Qin Zhenquan , Wang Lei
Published: April 2023

With the rapid development of the Internet of Things (IoT) technology, Wi-Fi signals have been widely used for trajectory signal acquisition. Indoor trajectory matching aims to achieve the monitoring of the encounters between people and trajectory analysis in indoor environments. Due to constraints ofn the computation abilities IoT devices, the computation of indoor trajectory matching requires the assistance of a cloud platform, which brings up privacy concerns. Therefore, this paper proposes a trajectory-matching calculation method that supports ciphertext operations. Hash algorithms and homomorphic encryption are selected to ensure the security of different private data, and the actual trajectory similarity is determined based on correlation coefficients. However, due to obstacles and other interferences in indoor environments, the original data collected may be missing in certain stages. Therefore, this paper also complements the missing values on ciphertexts through mean, linear regression, and KNN algorithms. These algorithms can predict the missing parts of the ciphertext dataset, and the accuracy of the complemented dataset can reach over 97%. This paper provides original and complemented datasets for matching calculations, and demonstrates their high feasibility and effectiveness in practical applications from the perspective of calculation time and accuracy loss.
An Image Information-Based Objective Assessment Method of Technical Manipulation Skills for Intravascular Interventions
Authors: Guo Jin , Li Maoxun , Wang Yue , Guo Shuxiang
Published: April 2023

The clinical success of vascular interventional surgery relies heavily on a surgeon’s catheter/guidewire manipulation skills and strategies. An objective and accurate assessment method plays a critical role in evaluating the surgeon’s technical manipulation skill level. Most of the existing evaluation methods incorporate the use of information technology to find more objective assessment models based on various metrics. However, in these models, sensors are often attached to the surgeon’s hands or to interventional devices for data collection, which constrains the surgeon’s operational movements or exerts an influence on the motion trajectory of interventional devices. In this paper, an image information-based assessment method is proposed for the evaluation of the surgeon’s manipulation skills without the requirement of attaching sensors to the surgeon or catheters/guidewires. Surgeons are allowed to use their natural bedside manipulation skills during the data collection process. Their manipulation features during different catheterization tasks are derived from the motion analysis of the catheter/guidewire in video sequences. Notably, data relating to the number of speed peaks, slope variations, and the number of collisions are included in the assessment. Furthermore, the contact forces, resulting from interactions between the catheter/guidewire and the vascular model, are sensed by a 6-DoF F/T sensor. A support vector machine (SVM) classification framework is developed to discriminate the surgeon’s catheterization skill levels. The experimental results demonstrate that the proposed SVM-based assessment method can obtain an accuracy of 97.02% to distinguish between the expert and novice manipulations, which is higher than that of other existing research achievements. The proposed method has great potential to facilitate skill assessment and training of novice surgeons in vascular interventional surgery.
Sound-Based Localization Using LSTM Networks for Visually Impaired Navigation
Authors: Bakouri Mohsen , Alyami Naif , Alassaf Ahmad , Waly Mohamed , Alqahtani Tariq , AlMohimeed Ibrahim , Alqahtani Abdulrahman , Samsuzzaman Md , Ismail Husham Farouk , Alharbi Yousef
Published: April 2023

In this work, we developed a prototype that adopted sound-based systems for localization of visually impaired individuals. The system was implemented based on a wireless ultrasound network, which helped the blind and visually impaired to navigate and maneuver autonomously. Ultrasonic-based systems use high-frequency sound waves to detect obstacles in the environment and provide location information to the user. Voice recognition and long short-term memory (LSTM) techniques were used to design the algorithms. The Dijkstra algorithm was also used to determine the shortest distance between two places. Assistive hardware tools, which included an ultrasonic sensor network, a global positioning system (GPS), and a digital compass, were utilized to implement this method. For indoor evaluation, three nodes were localized on the doors of different rooms inside the house, including the kitchen, bathroom, and bedroom. The coordinates (interactive latitude and longitude points) of four outdoor areas (mosque, laundry, supermarket, and home) were identified and stored in a microcomputer’s memory to evaluate the outdoor settings. The results showed that the root mean square error for indoor settings after 45 trials is about 0.192. In addition, the Dijkstra algorithm determined that the shortest distance between two places was within an accuracy of 97%.
A Miniaturized 3D-Printed Quartz-Enhanced Photoacoustic Spectroscopy Sensor for Methane Detection with a High-Power Diode Laser
Authors: Chen Yanjun , Liang Tiantian , Qiao Shunda , Ma Yufei
Published: April 2023

In this invited paper, a highly sensitive methane (CH4) trace gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) technique using a high-power diode laser and a miniaturized 3D-printed acoustic detection unit (ADU) is demonstrated for the first time. A high-power diode laser emitting at 6057.10 cm−1 (1650.96 nm), with the optical power up to 38 mW, was selected as the excitation source to provide a strong excitation. A 3D-printed ADU, including the optical and photoacoustic detection elements, had a dimension of 42 mm, 27 mm, and 8 mm in length, width, and height, respectively. The total weight of this 3D-printed ADU, including all elements, was 6 g. A quartz tuning fork (QTF) with a resonant frequency and Q factor of 32.749 kHz and 10,598, respectively, was used as an acoustic transducer. The performance of the high-power diode laser-based CH4–QEPAS sensor, with 3D-printed ADU, was investigated in detail. The optimum laser wavelength modulation depth was found to be 0.302 cm−1. The concentration response of this CH4–QEPAS sensor was researched when the CH4 gas sample, with different concentration samples, was adopted. The obtained results showed that this CH4–QEPAS sensor had an outstanding linear concentration response. The minimum detection limit (MDL) was found to be 14.93 ppm. The normalized noise equivalent absorption (NNEA) coefficient was obtained as 2.20 × 10−7 cm−1W/Hz−1/2. A highly sensitive CH4–QEPAS sensor, with a small volume and light weight of ADU, is advantageous for the real applications. It can be portable and carried on some platforms, such as an unmanned aerial vehicle (UAV) and a balloon.
Pruning Points Detection of Sweet Pepper Plants Using 3D Point Clouds and Semantic Segmentation Neural Network
Authors: Giang Truong Thi Huong , Ryoo Young-Jae
Published: April 2023

Automation in agriculture can save labor and raise productivity. Our research aims to have robots prune sweet pepper plants automatically in smart farms. In previous research, we studied detecting plant parts by a semantic segmentation neural network. Additionally, in this research, we detect the pruning points of leaves in 3D space by using 3D point clouds. Robot arms can move to these positions and cut the leaves. We proposed a method to create 3D point clouds of sweet peppers by applying semantic segmentation neural networks, the ICP algorithm, and ORB-SLAM3, a visual SLAM application with a LiDAR camera. This 3D point cloud consists of plant parts that have been recognized by the neural network. We also present a method to detect the leaf pruning points in 2D images and 3D space by using 3D point clouds. Furthermore, the PCL library was used to visualize the 3D point clouds and the pruning points. Many experiments are conducted to show the method’s stability and correctness.
Multicriteria Decision Making in Supply Chain Management Using FMEA and Hybrid AHP-PROMETHEE Algorithms
Authors: Altubaishe Bandar , Desai Salil
Published: April 2023

In today’s global environment, supplier selection is one of the critical strategic decisions made by supply chain management. The supplier selection process involves the evaluation of suppliers based on several criteria, including their core capabilities, price offerings, lead times, geographical proximity, data collection sensor networks, and associated risks. The ubiquitous presence of internet of things (IoT) sensors at different levels of supply chains can result in risks that cascade to the upstream end of the supply chain, making it imperative to implement a systematic supplier selection methodology. This research proposes a combinatorial approach for risk assessment in supplier selection using the failure mode effect analysis (FMEA) with hybrid analytic hierarchy process (AHP) and the preference ranking organization method for enrichment evaluation (PROMETHEE). The FMEA is used to identify the failure modes based on a set of supplier criteria. The AHP is implemented to determine the global weights for each criterion, and PROMETHEE is used to prioritize the optimal supplier based on the lowest supply chain risk. The integration of multicriteria decision making (MCDM) methods overcomes the shortcomings of the traditional FMEA and enhances the precision of prioritizing the risk priority numbers (RPN). A case study is presented to validate the combinatorial model. The outcomes indicate that suppliers were evaluated more effectively based on company chosen criteria to select a low-risk supplier over the traditional FMEA approach. This research establishes a foundation for the application of multicriteria decision-making methodology for unbiased prioritization of critical supplier selection criteria and evaluation of different supply chain suppliers.
Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot
Authors: Hu Yang , Meng Jingyan , Li Guoning , Zhao Dazheng , Feng Guang , Zuo Guokun , Liu Yunfeng , Zhang Jiaji , Shi Changcheng
Published: April 2023

Robot-assisted rehabilitation therapy has been proven to effectively improve upper-limb motor function in stroke patients. However, most current rehabilitation robotic controllers will provide too much assistance force and focus only on the patient’s position tracking performance while ignoring the patient’s interactive force situation, resulting in the inability to accurately assess the patient’s true motor intention and difficulty stimulating the patient’s initiative, thus negatively affecting the patient’s rehabilitation outcome. Therefore, this paper proposes a fuzzy adaptive passive (FAP) control strategy based on subjects’ task performance and impulse. To ensure the safety of subjects, a passive controller based on the potential field is designed to guide and assist patients in their movements, and the stability of the controller is demonstrated in a passive formalism. Then, using the subject’s task performance and impulse as evaluation indicators, fuzzy logic rules were designed and used as an evaluation algorithm to quantitively assess the subject’s motor ability and to adaptively modify the stiffness coefficient of the potential field and thus change the magnitude of the assistance force to stimulate the subject’s initiative. Through experiments, this control strategy has been shown to not only improve the subject’s initiative during the training process and ensure their safety during training but also enhance the subject’s motor learning ability.
Approach to the Quantitative Diagnosis of Rolling Bearings Based on Optimized VMD and Lempel–Ziv Complexity under Varying Conditions
Authors: Wang Haobo , Yang Tongguang , Han Qingkai , Luo Zhong
Published: April 2023

The quantitative diagnosis of rolling bearings is essential to automating maintenance decisions. Over recent years, Lempel–Ziv complexity (LZC) has been widely used for the quantitative assessment of mechanical failures as one of the most valuable indicators for detecting dynamic changes in nonlinear signals. However, LZC focuses on the binary conversion of 0–1 code, which can easily lose some effective information about the time series and cannot fully mine the fault characteristics. Additionally, the immunity of LZC to noise cannot be insured, and it is difficult to quantitatively characterize the fault signal under strong background noise. To overcome these limitations, a quantitative bearing fault diagnosis method based on the optimized Variational Modal Decomposition Lempel–Ziv complexity (VMD-LZC) was developed to fully extract the vibration characteristics and to quantitatively characterize the bearing faults under variable operating conditions. First, to compensate for the deficiency that the main parameters of the variational modal decomposition (VMD) have to be selected by human experience, a genetic algorithm (GA) is used to optimize the parameters of the VMD and adaptively determine the optimal parameters [k, α] of the bearing fault signal. Furthermore, the IMF components that contain the maximum fault information are selected for signal reconstruction based on the Kurtosis theory. The Lempel–Ziv index of the reconstructed signal is calculated and then weighted and summed to obtain the Lempel–Ziv composite index. The experimental results show that the proposed method is of high application value for the quantitative assessment and classification of bearing faults in turbine rolling bearings under various operating conditions such as mild and severe crack faults and variable loads.
An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts
Authors: Fernández-Alcalá Rosa M. , Jiménez-López José D. , Le Bihan Nicolas , Cheong Took Clive
Published: April 2023

This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of T1 and T2-properness, which entails a reduction in the dimension of the problem and, consequently, computational savings. The methodology proposed enables us to provide an optimal (in the least-mean-squares sense) linear fusion filtering algorithm for estimating the tessarine state with a lower computational cost than the conventional one devised in the real field. Simulation results illustrate the performance and advantages of the solution proposed in different settings.
Magneto-Optical Indicator Films: Fabrication, Principles of Operation, Calibration, and Applications
Authors: Dorosinskiy Lev , Sievers Sibylle
Published: April 2023

Magneto-optical indicator films (MOIFs) are a very useful tool for direct studies of the spatial distribution of magnetic fields and the magnetization processes in magnetic materials and industrial devices such as magnetic sensors, microelectronic components, micro-electromechanical systems (MEMS), and others. The ease of application and the possibility for direct quantitative measurements in combination with a straightforward calibration approach make them an indispensable tool for a wide spectrum of magnetic measurements. The basic sensor parameters of MOIFs, such as a high spatial resolution down to below 1 μm combined with a large spatial imaging range of up to several cm and a wide dynamic range from 10 μT to over 100 mT, also foster their application in various areas of scientific research and industry. The history of MOIF development totals approximately 30 years, and only recently have the underlying physics been completely described and detailed calibration approaches been developed. The present review first summarizes the history of MOIF development and applications and then presents the recent advances in MOIF measurement techniques, including the theoretical developments and traceable calibration methods. The latter make MOIFs a quantitative tool capable of measuring the complete vectorial value of a stray field. Furthermore, various scientific and industrial application areas of MOIFs are described in detail.