bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of a Tropical Cyclone on Terrestrial Inputs and Bio-Optical Properties in Princess Charlotte Bay (Great Barrier Reef Lagoon)
Authors: Oubelkheir Kadija , Ford Phillip W. , Cherukuru Nagur , Clementson Lesley A. , Petus Caroline , Devlin Michelle , Schroeder Thomas , Steven Andrew D. L.
Published: January 2023

In January 2013, tropical cyclone Oswald caused widespread flooding in the North-East coast of Australia, and large and highly episodic inputs into Princess Charlotte Bay (PCB, northern Great Barrier Reef). Freshwater outflows from the Normanby and Kennedy rivers, the two main rivers draining the adjacent catchments, resulted in drastic changes in physical, biogeochemical and optical properties within PCB. On 31 January, 2 days after the peak riverine discharge from the Normanby river, nutrients and dissolved organic matter contents peaked under the influence of large outflows from the Kennedy river into the western section of the bay (5.8 μM for dissolved inorganic nitrogen, 6.9 g m−3 for dissolved organic carbon and 6.1 m−1 for the colored dissolved organic matter absorption coefficient at 412 nm). In the eastern section of the bay, the situation appeared more ‘mixed’, with a suspended solids concentration reaching 23.1 g m−3 close to the Normanby river mouth. The main phytoplankton bloom occurred in the transition zone between the Kennedy and Normanby flood plumes, and was dominated by diatoms with a chlorophyll a concentration reaching 14.6 mg m−3. This study highlights the need to better describe the critical spatial and temporal scales of variability of key biogeochemical and optical properties after a major flood event. The data collected is key to improve the accuracy of ocean color remote sensing algorithms and regional biogeochemical budgets following highly episodic inputs.
A Combination of Deep Autoencoder and Multi-Scale Residual Network for Landslide Susceptibility Evaluation
Authors: Wang Zhuolu , Xu Shenghua , Liu Jiping , Wang Yong , Ma Xinrui , Jiang Tao , He Xuan , Han Zeya
Published: January 2023

Landslide susceptibility evaluation can accurately predict the spatial distribution of potential landslides, which offers great usefulness for disaster prevention, disaster reduction, and land resource management. Aiming at the problems of insufficient samples for landslide compilation, difficulty in expanding landslide samples, and insufficient expression of nonlinear relationships among evaluation factors, this paper proposes a new evaluation method of landslide susceptibility combining deep autoencoder and multi-scale residual network (DAE-MRCNN). In the first step, a deep autoencoder network was used to learn the feature expression of the original landslide data in order to acquire effective features in the data. Next, a multi-scale residual network was constructed; specifically, the model was improved into a deep residual network model by adding skip connections in the convolutional layer. In addition, the multi-scale idea was utilized to fully extract the scale characteristics of the evaluation factors. Finally, the model was used for feature training, and the results were input into the Softmax classifier to complete the prediction of landslide susceptibility. For this purpose, a machine learning method and two state-of-the-art deep learning methods, namely SVM, CPCNN-ML, and 2D-CNN, were utilized to model landslide susceptibility in Hanzhong City, Shaanxi Province. The proposed method produced the highest model performance of 0.891, followed by 0.842, 0.869, and 0.873. The experimental results show that the DAE-MRCNN method can fully express the complex nonlinear relationships among the evaluation factors, alleviate the problem of insufficient samples in convolutional neural networks (CNN) training, and significantly improve the accuracy of susceptibility prediction.
Comparison and Assessment of Data Sources with Different Spatial and Temporal Resolution for Efficiency Orchard Mapping: Case Studies in Five Grape-Growing Regions
Authors: Yao Zhiying , Zhao Yuanyuan , Wang Hengbin , Li Hongdong , Yuan Xinqun , Ren Tianwei , Yu Le , Liu Zhe , Zhang Xiaodong , Li Shaoming
Published: January 2023

As one of the most important agricultural production types in the world, orchards have high economic, ecological, and cultural value, so the accurate and timely mapping of orchards is highly demanded for many applications. Selecting a remote-sensing (RS) data source is a critical step in efficient orchard mapping, and it is hard to have a RS image with both rich temporal and spatial information. A trade-off between spatial and temporal resolution must be made. Taking grape-growing regions as an example, we tested imagery at different spatial and temporal resolutions as classification inputs (including from Worldview-2, Landsat-8, and Sentinel-2) and compared and assessed their orchard-mapping performance using the same classifier of random forest. Our results showed that the overall accuracies improved from 0.6 to 0.8 as the spatial resolution of the input images increased from 58.86 m to 0.46 m (simulated from Worldview-2 imagery). The overall accuracy improved from 0.7 to 0.86 when the number of images used for classification was increased from 2 to 20 (Landsat-8) or approximately 60 (Sentinel-2) in one year. The marginal benefit of increasing the level of details (LoD) of temporal features on accuracy is higher than that of spatial features, indicating that the classification ability of temporal information is higher than that of spatial information. The highest accuracy of using a very high-resolution (VHR) image can be exceeded only by using four to five medium-resolution multi-temporal images, or even two to three growing season images with the same classifier. Combining the spatial and temporal features from multi-source data can improve the overall accuracies by 5% to 7% compared to using only temporal features. It can also compensate for the accuracy loss caused by missing data or low-quality images in single-source input. Although selecting multi-source data can obtain the best accuracy, selecting single-source data can improve computational efficiency and at the same time obtain an acceptable accuracy. This study provides practical guidance on selecting data at various spatial and temporal resolutions for the efficient mapping of other types of annual crops or orchards.
Use of Geostatistics for Multi-Scale Spatial Modeling of Xylella fastidiosa subsp. pauca (Xfp) Infection with Unmanned Aerial Vehicle Image
Authors: Belmonte Antonella , Gadaleta Giovanni , Castrignanò Annamaria
Published: January 2023

In recent years, the use of Unmanned Aerial Vehicles (UAVs) has been spreading widely, as in plant pest control. The collection of huge amounts of spatial data raises various issues including that of scale. Data from UAVs generally explore multiple scales, so the problem arises in determining which one(s) may be relevant for a given application. The objective of this work was to investigate the potential of UAV images in the fight against the Xylella pest for olive trees. The data were a multiband UAV image collected on one date in an olive grove affected by Xylella. A multivariate geostatistics approach was applied, consisting firstly of estimating the linear coregionalization model to detect the scales from the data; and secondly, of using multiple factor kriging to extract the sets of scale-dependent regionalized factors. One factor was retained for each of the two selected scales. The short-range factor could be used in controlling the bacterium infection while the longer-range factor could be used in partitioning the field into three management zones. The work has shown the UAV data potential in Xylella control, but many problems still need to be solved for the automatic detection of infected plants in the early stages.
A Single-Hydrophone Coherent-Processing Method for Line-Spectrum Enhancement
Authors: Zhao Zhenxing , Li Qi , Xia Zhi , Shang Dajing
Published: January 2023

Improving the line-spectrum detection capability of a single hydrophone is of great significance for the passive detection of small underwater platforms. In this paper, we propose a single-hydrophone cross-power spectrum (SHCS) method based on time-domain coherence. This method uses the coherence of the line spectrum and the non-coherence of the continuous spectrum noise to obtain coherent gain and improve the signal-to-noise ratio (SNR) of the line spectrum. The effects of the input SNR, number of averaging operations, and overlap ratio on the performance of the SHCS method under a background of Gaussian white noise are simulated and analyzed. The results show that when the overlap ratio is 0 and the number of averaging operations reaches saturation, the SHCS method can achieve the best performance and about 15 dB coherence gain is obtained. The performance of the SHCS method was verified by sea experiments. Under the extremely low input SNR, in which the line spectrum was almost completely submerged in the marine environmental noise, the SHCS method can obtain about 10 dB coherence gain. Under the conventional input SNR, in which the line spectrum could be observed, the SHCS method can obtain about 13 dB coherence gain. The results of processing the radiated noise from an actual cargo ship also demonstrate the effectiveness of the SHCS method.
Spatiotemporal Characteristics and Hazard Assessments of Maize (Zea mays L.) Drought and Waterlogging: A Case Study in Songliao Plain of China
Authors: Wang Rui , Rong Guangzhi , Liu Cong , Du Walian , Zhang Jiquan , Tong Zhijun , Liu Xingpeng
Published: January 2023

The Songliao Plain is the largest maize (Zea mays L.) cropland area in China and, thus, is most influenced by water stress. To mitigate the adverse impact of water stress on maize yield and quality, various agricultural irrigation strategies have been implemented. Based on land surface temperature and an enhanced vegetation index, this study constructed the temperature vegetation dryness index (TVDI) and combined the Hurst index and Sen trend to analyze the spatiotemporal characteristics of drought and waterlogging. From the correlation between TVDI and gross primary productivity, the weight coefficients of different growth cycles of maize were derived to determine the drought and waterlogging stresses on maize in Songliao Plain for 2000–2020. The drought hazard on the western side of Songliao Plain was high in the west and low in the east, whereas the waterlogging hazard was high in the east. Waterlogging likely persisted according to the spatiotemporal trends and patterns of drought and waterlogging. During the second growth cycle, maize was most severely affected by water stress. There was a spatial heterogeneity in the severity of the hazards and the stress degree of maize. For the reason that precipitation in the study area was concentrated between mid-late July and early August, maize was susceptible to drought stress during the first two growth stages. Irrigation concentrated in the early and middle stages of maize growth and development in the western part of the Songliao Plain reduced the drought stress-induced damage. Spatiotemporally-detected drought and waterlogging couplings and hazards for maize in the Songliao Plain for 2000–2020 provide actionable insights into the prevention and mitigation of such disasters and the implementation of water-saving irrigation practices at the regional scale.
Radar Reflectivity Assimilation Based on Hydrometeor Control Variables and Its Impact on Short-Term Precipitation Forecasting
Authors: Zheng Hong , Chen Yaodeng , Zheng Shiwei , Meng Deming , Sun Tao
Published: January 2023

Radar reflectivity assimilation is often used to initialize hydrometeors, to which Numerical Weather Prediction (NWP) is highly sensitive. To better initialize hydrometeors, this study further developed the background error covariance (BEC) with vertical and multivariable correlations of hydrometeor control variables (H-BEC) in the WRF three-dimensional variational data assimilation system (WRFDA-3DVar). The impacts of the H-BEC are discussed using single radar reflectivity tests and series of cycling data assimilation and forecasting experiments for five multi-type convective rainfall cases. The conclusions are summarized as follows: (1) The vertical correlations can speed up the minimization of the cost function, whereas the multivariable correlations further accelerate this minimization; (2) The vertical correlations slightly improve the precipitation forecasting and only in the first hour, while multivariate correlations lead to a larger improvement and persist into the third hour; (3) The application of H-BEC leads to a more reasonable thermodynamic and dynamical structure of the initial field, thereby improving the capability of short-term precipitation forecasting.
Arbitrary-Oriented Ship Detection Method Based on Long-Edge Decomposition Rotated Bounding Box Encoding in SAR Images
Authors: Jiang Xinqiao , Xie Hongtu , Chen Jiaxing , Zhang Jian , Wang Guoqian , Xie Kai
Published: January 2023

Due to the limitations of the horizontal bounding boxes for locating the oriented ship targets in synthetic aperture radar (SAR) images, the rotated bounding box (RBB) has received wider attention in recent years. First, the existing RBB encodings suffer from boundary discontinuity problems, which interfere with the convergence of the model, and then lead to some problems, such as the inaccurate location of the ship targets in the boundary state. Thus, from the perspective that the long-edge features of the ships are more representative of their orientation, the long-edge decomposition RBB encoding has been proposed in this paper, which can avoid the boundary discontinuity problem. Second, the problem of the positive and negative samples imbalance is serious for the SAR ship images because only a few ship targets exist in the vast background of these images. Since the ship targets of different sizes are subject to varying degrees of interference caused by this problem, a multiscale elliptical Gaussian sample balancing strategy has been proposed in this paper, which can mitigate the impact of this problem by labeling the loss weights of the negative samples within the target foreground area with multiscale elliptical Gaussian kernels. Finally, experiments based on the CenterNet model were implemented on the benchmark SAR image dataset SSDD (SAR ship detection dataset). The experimental results demonstrate that our proposed long-edge decomposition RBB encoding outperforms other conventional RBB encodings in the task of oriented ship detection in SAR images. In addition, our proposed multiscale elliptical Gaussian sample balancing strategy is effective and can improve the model performance.
Acknowledgment to the Reviewers of Ruminants in 2022
Authors:   Ruminants Editorial Office
Published: January 2023

High-quality academic publishing is built on a rigorous peer review system [...]
Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors
Authors: Wagner Jakub , Szymański Marcin , Błażkiewicz Michalina , Kaczmarczyk Katarzyna
Published: January 2023

Gait analysis may serve various purposes related to health care, such as the estimation of elderly people’s risk of falling. This paper is devoted to gait analysis based on data from depth sensors which are suitable for use both at healthcare facilities and in monitoring systems dedicated to household environments. This paper is focused on the comparison of three methods for spatiotemporal gait analysis based on data from depth sensors, involving the analysis of the movement trajectories of the knees, feet, and centre of mass. The accuracy of the results obtained using those methods was assessed for different depth sensors’ viewing angles and different types of subject clothing. Data were collected using a Kinect v2 device. Five people took part in the experiments. Data from a Zebris FDM platform were used as a reference. The obtained results indicate that the viewing angle and the subject’s clothing affect the uncertainty of the estimates of spatiotemporal gait parameters, and that the method based on the trajectories of the feet yields the most information, while the method based on the trajectory of the centre of mass is the most robust.
Moisture Determination for Fine-Sized Copper Ore by Computer Vision and Thermovision Methods
Authors: Buchczik Dariusz , Budzan Sebastian , Krauze Oliwia , Wyzgolik Roman
Published: January 2023

The moisture of bulk material has a significant impact on the energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. This research aims to develop computer vision and thermovision techniques for the on-site estimation of moisture content in copper ore, for use, e.g., in dry grinding installations. The influence of particle size on the results of moisture estimation is also studied. The tested granular material was copper ore of particle size 0–2 mm and relative moisture content of 0.5–11%. Both vision and thermovision images were taken at standard and macro scales. The results suggest that median-intensity vision images monotonically reflect copper ore moisture in the range of about 0.5–5%. Suitable models were identified and cross-validated here. In contrary, thermograms should not be analyzed simply for their mean temperature but treated with computer vision processing algorithms.
Analysis and Suppression of Nonlinear Error of Pendulous Integrating Gyroscopic Accelerometer at Instrument Level
Authors: Zhou Xiaojun , Yang Gongliu , Niu Wentao , Tu Yongqiang
Published: January 2023

The error coefficients of the pendulous integrating gyroscopic accelerometer (PIGA) mainly include the bias, scale factor, and nonlinear error. Previous works have fully studied and suppressed the bias and scale factor of PIGAs. At present, the nonlinear error is the most critical factor restricting the measurement accuracy of PIGAs. To address this barrier, a study on the analysis and suppression of the nonlinear error of PIGAs at the instrument level was carried out. Firstly, the error model of a PIGA is established by kinematics and dynamics analyses. Then, nonlinear error is analyzed based on the established model. Finally, a suppression method for the nonlinear error is proposed based on the analysis results. The nonlinear error analysis found that (1) the nonlinear error includes a quadratic term error caused by unequal inertia and the inertia product, cross-coupling error is caused by lateral accelerations, and error is caused by unequal stiffness; (2) unequal inertia and the inertia product were the most critical factors resulting in nonlinear error. Based on the results in the nonlinear error analysis, the suppression method for error focuses on unequal inertia and the inertia product. The proposed method of analysis and suppression was validated experimentally as the quadratic term coefficient was reduced by an order of magnitude from 1.9 × 10−6/g0 to 1.91 × 10−7/g0.
Predicting Injuries in Football Based on Data Collected from GPS-Based Wearable Sensors
Authors: Piłka Tomasz , Grzelak Bartłomiej , Sadurska Aleksandra , Górecki Tomasz , Dyczkowski Krzysztof
Published: January 2023

The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting non-contact lower body injuries coming from over- or undertraining. The purpose of this analysis was to create decision-making models based on data collected during both training and match, which will enable the preparation of a tool to model the load and report the increased risk of injury for a given player in the upcoming microcycle. For this purpose, three decision-making methods were implemented. Rule-based and fuzzy rule-based methods were prepared based on expert understanding. As a machine learning baseline XGBoost algorithm was considered. Taking into account the dataset used containing parameters related to the external load of the player, it is possible to predict the risk of injury with a certain precision, depending on the method used. The most promising results were achieved by the machine learning method XGBoost algorithm (Precision 92.4%, Recall 96.5%, and F1-score 94.4%).
An Improved ResNet-1d with Channel Attention for Tool Wear Monitor in Smart Manufacturing
Authors: Dong Liang , Wang Chensheng , Yang Guang , Huang Zeyuan , Zhang Zhiyue , Li Cen
Published: January 2023

Tool wear is a key factor in the machining process, which affects the tool life and quality of the machined work piece. Therefore, it is crucial to monitor and diagnose the tool condition. An improved CaAt-ResNet-1d model for multi-sensor tool wear diagnosis was proposed. The ResNet18 structure based on a one-dimensional convolutional neural network is adopted to make the basic model architecture. The one-dimensional convolutional neural network is more suitable for feature extraction of time series data. Add the channel attention mechanism of CaAt1 to the residual network block and the channel attention mechanism of CaAt5 automatically learns the features of different channels. The proposed method is validated on the PHM2010 dataset. Validation results show that CaAt-ResNet-1d can reach 89.27% accuracy, improving by about 7% compared to Gated-Transformer and 3% compared to Resnet18. The experimental results demonstrate the capacity and effectiveness of the proposed method for tool wear monitor.
Application of the Model of Spots for Inverse Problems
Authors: Simonov Nikolai A.
Published: January 2023

This article proposes the application of a new mathematical model of spots for solving inverse problems using a learning method, which is similar to using deep learning. In general, the spots represent vague figures in abstract “information spaces” or crisp figures with a lack of information about their shapes. However, crisp figures are regarded as a special and limiting case of spots. A basic mathematical apparatus, based on L4 numbers, has been developed for the representation and processing of qualitative information of elementary spatial relations between spots. Moreover, we defined L4 vectors, L4 matrices, and mathematical operations on them. The developed apparatus can be used in Artificial Intelligence, in particular, for knowledge representation and for modeling qualitative reasoning and learning. Another application area is the solution of inverse problems by learning. For example, this can be applied to image reconstruction using ultrasound, X-ray, magnetic resonance, or radar scan data. The introduced apparatus was verified by solving problems of reconstruction of images, utilizing only qualitative data of its elementary relations with some scanning figures. This article also demonstrates the application of a spot-based inverse Radon algorithm for binary image reconstruction. In both cases, the spot-based algorithms have demonstrated an effective denoising property.
A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network
Authors: Guesbaya Mounir , García-Mañas Francisco , Rodríguez Francisco , Megherbi Hassina
Published: January 2023

In greenhouses, sensors are needed to measure the variables of interest. They help farmers and allow automatic controllers to determine control actions to regulate the environmental conditions that favor crop growth. This paper focuses on the problem of the lack of monitoring and control systems in traditional Mediterranean greenhouses. In such greenhouses, most farmers manually operate the opening of the vents to regulate the temperature during the daytime. Therefore, the state of vent opening is not recorded because control systems are not usually installed due to economic reasons. The solution presented in this paper consists of developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) as a soft sensor to estimate vent opening using the measurements of different inside and outside greenhouse climate variables as input data. A dataset from a traditional greenhouse located in Almería (Spain) was used. The data were processed and analyzed to study the relationships between the measured climate variables and the state of vent opening, both statistically (using correlation coefficients) and graphically (with regression analysis). The dataset (with 81 recorded days) was then used to train, validate, and test a set of candidate LSTM-based networks for the soft sensor. The results show that the developed soft sensor can estimate the actual opening of the vents with a mean absolute error of 4.45%, which encourages integrating the soft sensor as part of decision support systems for farmers and using it to calculate other essential variables, such as greenhouse ventilation rate.
A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things
Authors: Rodríguez Eva , Otero Beatriz , Canal Ramon
Published: January 2023

Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, autonomous vehicles, and eHealth. As such, IoT devices are ubiquitously connected, transferring sensitive and personal data without requiring human interaction. Consequently, it is crucial to preserve data privacy. This paper presents a comprehensive survey of recent Machine Learning (ML)- and Deep Learning (DL)-based solutions for privacy in IoT. First, we present an in depth analysis of current privacy threats and attacks. Then, for each ML architecture proposed, we present the implementations, details, and the published results. Finally, we identify the most effective solutions for the different threats and attacks.
A Composite-Type MEMS Pirani Gauge for Wide Range and High Accuracy
Authors: Chen Shuo , Feng Liuhaodong , Guo Song , Ji Yucheng , Zeng Shuwen , Peng Xinlin , Xu Yang , Hu Tianbao , Wu Zhenyu , Wang Shinan
Published: January 2023

To achieve a wide range and high accuracy detection of the vacuum level, for example, in an encapsulated vacuum microcavity, a composite-type MEMS Pirani gauge has been designed and fabricated. The Pirani gauge consists of two gauges of different sizes connected in series, with one gauge having a larger heat-sensitive area and a larger air gap for extending the lower measurable limit of pressure (i.e., the high vacuum end) and the other gauge having a smaller heat-sensitive area and a smaller air gap for extending the upper measurable limit. The high-resistivity titanium metal was chosen as the thermistor; SiNx was chosen as the dielectric layer, considering the factors relevant to simulation and manufacturing. By simulation using COMSOL Multiphysics and NI Multisim, a range of measurement of 2 × 10−2 to 2 × 105 Pa and a sensitivity of 52.4 mV/lgPa were obtained in an N2 environment. The performance of the fabricated Pirani gauge was evaluated by using an in-house made vacuum test system. In the test, the actual points of measurement range from 6.6 × 10−2 to 1.12 × 105 Pa, and the highest sensitivity is up to 457.6 mV/lgPa. The experimental results are better in the range of measurement, sensitivity, and accuracy than the simulation results. The Pirani gauge proposed in this study is simple in structure, easy to manufacture, and suitable for integration with other MEMS devices in a microcavity to monitor the vacuum level therein.
Automatic Thinning Detection through Image Segmentation Using Equivalent Array-Type Lamp-Based Lock-in Thermography
Authors: Lee Seungju , Chung Yoonjae , Kim Chunyoung , Kim Wontae
Published: January 2023

Among the non-destructive testing (NDT) techniques, infrared thermography (IRT) is an attractive and highly reliable technology that can measure the thermal response of a wide area in real-time. In this study, thinning defects in S275 specimens were detected using lock-in thermography (LIT). After acquiring phase and amplitude images using four-point signal processing, the optimal excitation frequency was calculated. After segmentation was performed on each defect area, binarization was performed using the Otsu algorithm. For automated detection, the boundary tracking algorithm was used. The number of pixels was calculated and the detectability using RMSE was evaluated. Clarification of defective objects using image segmentation detectability evaluation technique using RMSE was presented.
An Inexpensive Unmanned Aerial Vehicle-Based Tool for Mobile Network Output Analysis and Visualization
Authors: Buggiani Vittorio , Ortega Julio César Úbeda , Silva Guillermo , Rodríguez-Molina Jesús , Vilca Diego
Published: January 2023

Usage of Unmanned Aerial Vehicles (UAVs) for different tasks is widespread, as UAVs are affordable, easy to manoeuvre and versatile enough to execute missions in a reliable manner. However, there are still fields where UAVs play a minimal role regardless of their possibilities. One of these application domains is mobile network testing and measurement. Currently, the procedures used to measure the main parameters of mobile networks in an area (such as power output or its distribution in a three-dimensional space) rely on a team of specialized people performing measurements with an array of tools. This procedure is significantly expensive, time consuming and the resulting outputs leave a higher degree of precision to be desired. An open-source UAV-based Cyber-Physical System is put forward that, by means of the Galileo satellite network, a Mobile Data Acquisition System and a Graphical User Interface, can quickly retrieve reliable data from mobile network signals in a three-dimensional space with high accuracy for its visualization and analysis. The UAV tested flew at 40.43 latitude and −3.65 longitude degrees as coordinates, with an altitude over sea level of around 600–800 m through more than 40 mobile network cells and signal power displayed between −75 and −113 decibels.
Metal Artifact Reduction in Dental CBCT Images Using Direct Sinogram Correction Combined with Metal Path-Length Weighting
Authors: Hegazy Mohamed A. A. , Cho Myung Hye , Cho Min Hyoung , Lee Soo Yeol
Published: January 2023

Metal artifacts in dental computed tomography (CT) images, caused by highly X-ray absorbing objects, such as dental implants or crowns, often more severely compromise image readability than in medical CT images. Since lower tube voltages are used for dental CTs in spite of the more frequent presence of metallic objects in the patient, metal artifacts appear more severely in dental CT images, and the artifacts often persist even after metal artifact correction. The direct sinogram correction (DSC) method, which directly corrects the sinogram using the mapping function derived by minimizing the sinogram inconsistency, works well in the case of mild metal artifacts, but it often fails to correct severe metal artifacts. We propose a modified DSC method to reduce severe metal artifacts, and we have tested it on human dental images. We first segment the metallic objects in the CT image, and then we forward-project the segmented metal mask to identify the metal traces in the projection data with computing the metal path length for the rays penetrating the metal mask. In the sinogram correction with the DSC mapping function, we apply the weighting proportional to the metal path length. We have applied the proposed method to the phantom and patient images taken at the X-ray tube voltage of 90 kVp. We observed that the proposed method outperforms the original DSC method when metal artifacts were severe. However, we need further extensive studies to verify the proposed method for various CT scan conditions with many more patient images.
Simulating IoT Workflows in DISSECT-CF-Fog
Authors: Markus Andras , Al-Haboobi Ali , Kecskemeti Gabor , Kertesz Attila
Published: January 2023

The modelling of IoT applications utilising the resources of cloud and fog computing is not straightforward because they have to support various trigger-based events that make human life easier. The sequence of tasks, such as performing a service call, receiving a data packet in the form of a message sent by an IoT device, and managing actuators or executing a computational task on a virtual machine, are often associated with and composed of IoT workflows. The development and deployment of such IoT workflows and their management systems in real life, including communication and network operations, can be complicated due to high operation costs and access limitations. Therefore, simulation solutions are often applied for such purposes. In this paper, we introduce a novel simulator extension of the DISSECT-CF-Fog simulator that leverages the workflow scheduling and its execution capabilities to model real-life IoT use cases. We also show that state-of-the-art simulators typically omit the IoT factor in the case of the scientific workflow evaluation. Therefore, we present a scalability study focusing on scientific workflows and on the interoperability of scientific and IoT workflows in DISSECT-CF-Fog.
Preparation of Tryptanthrin Derivates Bearing a Thiosemicarbazone Moiety to Inhibit SARS-CoV-2 Replication
Authors: Veselá Kateřina , Mělková Zora , Abramenko Nikita , Kejík Zdeněk , Kaplánek Robert , Dytrych Petr , Sinica Alla , Vozniuk Oleksandra , Martásek Pavel , Jakubek Milan
Published: January 2023

SARS-CoV-2 is a serious viral pathogen, and agents that inhibit its replication are in high demand. In the present work, we prepared two novel tryptanthrin derivates bearing a thiosemicarbazone moiety as potential antiviral agents. Both compounds displayed potent chelation activity against Fe(III/II) ion-associated COVID-19. The molecular docking results suggest that the compounds can display significant affinity towards SARS-CoV-2 papain-like proteases and SARS-CoV-2 main proteases. In addition, administering T8H-TSC can repress viral replication in the used model (Vero cells). Moreover, the therapeutic potential of the prepared compounds was predicted and analysed in terms of Lipinski’s rules, drug-likeness and drug score.
#NotDying4Wallstreet: A Discourse Analysis on Health vs. Economy during COVID-19
Authors: Genç Merve
Published: January 2023

This paper combines political/poststructuralist discourse theory with actor–network theory to explore dystopian visions in the context of a discourse around the hashtag #NotDying4Wallstreet. The call for protest against former US president Donald Trump’s demand to reopen the economy during lockdown dominates the discourse. The tweets were analyzed with quantitative discourse analysis and network analysis to identify key terms and meaning clusters leading to two main conclusions. The first (A) is an imaginary dystopic future with an accelerated neoliberal order. Human lives, especially elderly people, are sacrificed for a well-functioning economy in this threat scenario. The second (B) includes the motive of protest and the potential of the people’s demands to unite and rally against this threat. Due to the revelation of populist features, this (online) social movement seems to be populist without a leader figure. The empirical study is used to propose a research approach toward a mixed-methods design based on a methodological discussion and the enhancement of PDT with ANT. Thus, the article has a double aim: an update of contemporary approaches to social media analysis in discourse studies and its empirical demonstration with a study.
Mapping and Measuring the Phenomenon of Precariousness in the Labour Market: Challenges and Implications
Authors: Kosmas Petros , Theocharous Antonis , Ioakimoglou Elias , Giannoulis Petros , Panagopoulou Maria , Andreev Hristo , Vatikioti Aggeliki
Published: January 2023

This research article presents an empirical model that takes economic vulnerability into consideration to measure and address the phenomenon of precarious work and precariousness. In order to achieve this, three satisfactory indicators were formulated, consisting of both individual and institutional levels and taking into account the country-specific relationships among the variables, depending on country-specific conditions. Based on this, the choice of homeownership is introduced instead of the eligibility for employment benefits. In this way, precarity has been examined as a condition in which precariousness and economic vulnerability intersect and interact. In Cyprus, 9.5% of the workers in Cyprus were classified as precarious, while 4.4% were classified as being in precarity (i.e., precarious and economically vulnerable). The empirical findings revealed that precariousness was related to gender, migration, and the employment sector, which is consistent with the well-known literature. One of the most noteworthy findings was the high number of female migrant domestic workers in Cyprus. In this study, new variables and novel empirical approaches were introduced into the discussion of precarious work and precariousness, which may eventually lead to new theoretical and policy avenues for reducing or eliminating this phenomenon.
Online Microfluidic Production of Sustainable Cyrene™-Derived Porous Microparticles
Authors: El Itawi Hassan , Fadlallah Sami , Leephakphumphanich Wichapol , Ruscassier Nathalie , Zoghlami Aya , Allais Florent , Perré Patrick
Published: January 2023

The use of sustainable raw materials is now a necessity in all industries, including the production of porous microparticles. Cyrene™ is a cellulose-derived compound that is readily prepared through the reduction of the α,β-unsaturation of levoglucosenone (LGO)—a wood-based platform molecule. In this work, the importance of Cyrene™ as a potential bio-based molecule to produce sustainable porous microparticles is demonstrated. First, a methacrylic derivative of Cyrene™ (m-Cyrene) was synthesized. A microfluidic co-flow device was then established to produce m-Cyrene-based oil-in-water (O/W) controlled-size emulsions and to polymerize them by ultraviolet (UV) radiation in a vial. The continuous phase was a sodium dodecyl sulfate aqueous solution, and the dispersed phase was a mixture of m-Cyrene with methacrylic anhydride (MAN) at two different mass concentrations (i.e., 1 wt.% MAN and 92 wt.% MAN) and 2,2-dimethoxy-2-phenylacetophenone (DMPA) as a photoinitiator. The process used the lowest possible quantity of raw materials and avoided excessive purifications to produce homogeneous porous m-Cyrene-MAN microparticles. The controlled size and homogeneous size distribution of the produced polymer microparticles were confirmed by scanning electron microscope (SEM) images. The 3D microstructure as well as the porosity were determined using X-ray microtomography. The high-resolution 3D images produced indicate that the pores of the microparticles are homogeneous and that their porosity is controllable through the concentration of MAN in the monomer mixture (porosity of 30% for a 1 wt.% MAN ratio and 2% for a 92 wt.% MAN ratio). Such porosity control is very important for future potential encapsulation processes that require precise release control.
Sustainability and Organizational Performance in South Korea: The Effect of Digital Leadership on Digital Culture and Employees’ Digital Capabilities
Authors: Shin Jinkyo , Mollah Md Alamgir , Choi Jaehyeok
Published: January 2023

In the era of digital transformation, organizations are making efforts towards sustainability. In particular, leadership is transforming into digital leadership according to changes in management environments, which are deeply related to organizational performance. In this study, we focus on organizational performance and sustainability management and clarify the role of digital culture and employees’ digital capabilities in perspectives on digital leadership. We collected data from 149 employees who work in South Korean organizations using a survey based on digital leadership, digital culture, employees’ digital capabilities, and organizational performance, and we tested our hypotheses using structural equation modeling. The results show that digital leadership has a positive direct and indirect effect on organizational performance. Moreover, digital culture and employees’ digital capabilities partially mediate the relationship between digital leadership and sustainable organizational performance in South Korea. This study contributes to leadership and resource-based view (RBV) research by providing evidence for the role of digital leadership in sustainable organizational performance. As leadership continues to extend alongside verification of the RBV theory, the crucial role of digital leadership is changing, and the role of employees’ digital capabilities in organizational performance in South Korea needs to be considered.
Aerosol Characterization of Northern China and Yangtze River Delta Based on Multi-Satellite Data: Spatiotemporal Variations and Policy Implications
Authors: Luan Kuifeng , Cao Zhaoxiang , Hu Song , Qiu Zhenge , Wang Zhenhua , Shen Wei , Hong Zhonghua
Published: January 2023

Horizontal and vertical distributions of aerosol properties in the Taklimakan Desert (TD), North central region of China (NCR),North China Plain(NCP), and Yangtze River Delta (YRD) were investigated by statistical analysis using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) L3 data from 2007 to 2020, to identify the similarities and differences in atmospheric aerosols in different regions, and evaluate the impact of pollution control policies developed in China in 2013 on aerosol properties in the study area. The aerosol optical depth (AOD) distribution had substantial seasonal and spatial distribution characteristics. AOD had high annual averages in TD (0.38), NCP (0.49), and YRD (0.52). However, these rates showed a decline post-implementation of the long-term pollution control policies; AOD values declined by 5%, 13.8%, 15.5%, and 23.7% in TD, NCR, NCP, and YRD respectively when comparing 2014–2018 to 2007–2013, and by 7.8%, 11.5%, 16%, and 10.4% when comparing 2019–2020 to 2014–2018. The aerosol extinction coefficient showed a clear regional pattern and a tendency to decrease gradually as height increased. Dust and polluted dust were responsible for the changes in AOD and extinction coefficients between TD and NCR and NCP and YRD, respectively. In TD, with change of longitude, dust aerosol first increased and then decreased gradually, peaking in the middle. Similarly in NCP, polluted dust aerosol first increased and then decreased, with a maximum value in the middle. The elevated smoke aerosols of NCP and YRD were significantly higher than those observed in TD and NCR. The high aerosol extinction coefficient values (>0.1 km−1) were mainly distributed below 4 km, and the relatively weak aerosol extinction coefficients (>0.001 km−1) were mainly distributed between 5–8 km, indicating that the high-altitude long-range transport of TD and NCR dust aerosols affects NCP and YRD.
Multi-Source Data-Based Evaluation of Suitability of Land for Elderly Care and Layout Optimization: A Case Study of Changsha, China
Authors: Yang Jun , Lou Zhifei , Tang Xinglong , Sun Ying
Published: January 2023

This paper constructs an evaluation index system for the suitability of community home and institutional elderly care land development, respectively, from different elderly care modes with the data of urban POI, OSM road network, and expert questionnaires in Changsha urban area in 2021, in order to alleviate the pressure of insufficient land for elderly care brought on by the increasingly serious aging problem. The suitability evaluation index system is based on the intersection of Thiessen polygons with the current elderly care facilities as the center point as the supplementary land for the elderly and explores the optimization path of the land for the elderly in combination with the existing residential land in Changsha. The results show the following: ① The spatial variation of land suitability for both community home and institutional elderly facilities is significant, exhibiting a pattern of “high in the middle and low in the surroundings, with high-value areas clustered in the center of the city, decreasing in suitability toward the periphery, and occasional scattered clusters in the suburbs.” Among them, Furong District has the highest proportion of suitable areas for the elderly; ② Utilizing Changsha’s Tianxin and Yuhua districts as case studies, the optimal path of land use for the elderly are investigated to provide a foundation for land use planning for the elderly in Changsha.
The Impact of COVID-19 Process on Sustainability in Education: Work Alienation of Physical Education and Sports Teachers
Authors: Temel Cenk , Gökduman Çiğdem , Uğraş Sinan , Sağın Ahmet Enes , Yücekaya Mehmet Akif , Kartal Mehmet , Toros Turhan
Published: January 2023

This study aimed to examine physical education teachers’ perceptions of work alienation in Turkey according to different variables (including gender, marital status, school level, availability of a gym in the school, age, and years of service) during the COVID-19 pandemic, which affected sustainability in education on a global scale. The study employed the survey method and research data were collected from 442 volunteer physical education teachers working in different provinces of Turkey through the “Physical Education Teachers’ Alienation to Work Scale”. The results showed that physical education teachers had low levels of alienation in their work. The scale’s subdimensions ‘occupational isolation’ and ‘powerlessness’ indicated higher levels of work alienation compared to other subdimensions. Among teachers who had completed their graduate education, the level of work alienation was higher in the subdimensions ‘powerlessness’ and ‘occupational alienation’. Based on a comparison with prior research on sustainability in education, the COVID-19 pandemic could be said to have no significant impact on physical education teachers’ levels of work alienation. The cause of work alienation among physical education teachers was structural issues rather than specific time-bound events such as the COVID-19 pandemic.
From Natural Woods to High Density Materials: An Ecofriendly Approach
Authors: Gullo Francesca , Marangon Andrea , Croce Alessandro , Gatti Giorgio , Aceto Maurizio
Published: January 2023

Recently, different methods have been proposed to develop wood materials, termed “densified woods”, with density increment and improvement in mechanical proprieties. Almost all the proposed methods involve the use of reducing agents and strong bases. In this work, a new method has been developed involving the use of less polluting agents. The formation of densified woods is divided into two steps: delignification involves the removal of lignin, hemicelluloses, and shorter chains of cellulose, whereas densification involves the plastering of the delignified woods. The obtained materials showed a density increase of two to four times. The obtained densified woods were characterized by spectroscopic, microscopic, and thermogravimetric techniques and mechanical tests. The characterizations aimed at determining the variations of chemical and structural compositions of the samples after delignification and densification processes, showing, respectively, a decrease in lignin and a significant increase in the density and force necessary to bring the materials to yield. The final density of wood was two to three times higher and the force necessary to reach the yield point reached more than three times the initial one for some of the studied samples. These characterizations showed how different woods, with different properties, reach comparable densities and final mechanical properties after delignification and densification process. The increased mechanical properties of the materials allow their application in place of other composite woody materials.