bluuebunny/crossref_metadata_2025 huggingface dataset:
Special Issue “Analysis and Experimental Study on Natural Gas Hydrate Exploitation Processes”
Authors: Castellani Beatrice , Nicolini Andrea
Published: March 2023

Gas hydrates are crystalline structures formed by water molecule cages hosting gas molecules [...]
Toward to Hydrogen Energy of Electric Power: Characteristics and Main Case Studies in Shenzhen
Authors: Deng Zhijun , Du Jinqiao , Tian Jie , Gan Zhenning , Wang Bingjie , Zhao Chen
Published: March 2023

China has pledged that it will strive to achieve peak carbon emission by 2030 and realize carbon neutrality by 2060, which has spurred renewed interest in hydrogen for widespread decarbonization of the economy. Hydrogen energy is an important secondary clean energy with the advantage of high density, high calorific value, rich reserves, extensive sources and high conversion efficiency that can be widely used in power generation, transportation, fuel and other fields. In recent years, with the guidance of policies and the progress of technology, China’s hydrogen energy industry has developed rapidly. About 42% of China’s carbon emissions comes from the power system and Shenzhen has the largest urban power grid in China. Bringing the utilization of hydrogen energy into Shenzhen’s power system is an important method to achieve industry transformation, achieve the “double carbon” goal and promote sustainable development. This paper outlines the domestic and international development status of hydrogen energy, introduces the characteristics of Shenzhen new power system, the industrial utilization of hydrogen energy and the challenges of further integrating hydrogen energy into Shenzhen new power system and, finally, suggests on the integration of hydrogen energy into Shenzhen new power system in different dimensions.
Sense of Coherence as a Resource in Promoting Well-Being and Managing Type 1 Diabetes Mellitus: A Pilot Study
Authors: Manfredi Paola , Morotti Alberto , Tomasi Cesare
Published: March 2023

When investigating the feelings of caregivers to patients with T1DM, parental stress, anxiety, and depression are found to be most prevalent, especially in the diagnostic phase and in the first months after diagnosis. In this pilot study, we research various significant elements regarding the well-being of mothers with children between 10 and 15 years of age with a chronic condition. The study focuses on a period of at least three years after the child’s diagnosis. The aims of the study are to describe our sample’s levels of satisfaction (SWLS) and subjective happiness (SHS) and to evaluate possible associations. A sample of 40 mothers was offered a series of assessment tools about psychological skills that could play a role in improving mothers’ well-being: the use of specific coping mechanisms (CISS), the methods of narrating the experience of life with a son/daughter with diabetes, the sense of coherence (SOCS 29), health parameters vs. child’s disease (HbA1c, CBCL), and socio-demographic, such as education and work. The most significant associations with respect to subjective happiness are with the sense of coherence, as a unitary value and as distinct factors, and with task-oriented coping. Sense of coherence is also associated with satisfaction.
Approach for Preservation and Reconstruction of Two-Dimensional Wave Spectra and Its Application to Boundary Conditions in Nested Wave Modeling
Authors: Jiang Xingjie , Wang Daolong , Yang Yongzeng , Sun Meng , He Qianqian
Published: March 2023

Typically, storing a two-dimensional wave spectrum could occupy more than one thousand storage units, making saving and reading boundary spectra computationally burdensome in nested wave simulations. This paper proposes a new approach for preservation of a wave spectrum that can reduce the required number of storage units to dozens. Using a corresponding reconstruction approach, the spectrum can then be rebuilt with intact spectral characteristics. Experimental application confirmed that the reconstructed spectra could be adopted as boundary conditions in nested wave modeling. The newly proposed approach for preservation and reconstruction of spectra allows long-term spectral information covering the entire simulated domain to be saved with more acceptable storage consumption, and such information can then be adopted as nesting conditions for nested-child simulations without the limitations of predefined boundaries. The above-mentioned properties of the new method could help support engineering projects concerning wave environments, research focused on wave climatology, and studies associated with wave energy assessment.
Estimating Black Oat Biomass Using Digital Surface Models and a Vegetation Index Derived from RGB-Based Aerial Images
Authors: Trevisan Lucas Renato , Brichi Lisiane , Gomes Tamara Maria , Rossi Fabrício
Published: March 2023

Responsible for food production and industry inputs, agriculture needs to adapt to worldwide increasing demands and environmental requirements. In this scenario, black oat has gained environmental and economic importance since it can be used in no-tillage systems, green manure, or animal feed supplementation. Despite its importance, few studies have been conducted to introduce more accurate and technological applications. Plant height (H) correlates with biomass production, which is related to yield. Similarly, productivity status can be estimated from vegetation indices (VIs). The use of unmanned aerial vehicles (UAV) for imaging enables greater spatial and temporal resolutions from which to derive information such as H and VI. However, faster and more accurate methodologies are necessary for the application of this technology. This study intended to obtain high-quality digital surface models (DSMs) and orthoimages from UAV-based RGB images via a direct-to-process means; that is, without the use of ground control points or image pre-processing. DSMs and orthoimages were used to derive H (HDSM) and VIs (VIRGB), which were used for H and dry biomass (DB) modeling. Results showed that HDSM presented a strong correlation with actual plant height (HREF) (R2 = 0.85). Modeling biomass based on HDSM demonstrated better performance for data collected up until and including the grain filling (R2 = 0.84) and flowering (R2 = 0.82) stages. Biomass modeling based on VIRGB performed better for data collected up until and including the booting stage (R2 = 0.80). The best results for biomass estimation were obtained by combining HDSM and VIRGB, with data collected up until and including the grain filling stage (R2 = 0.86). Therefore, the presented methodology has permitted the generation of trustworthy models for estimating the H and DB of black oats.
The Forest Fire Dynamic Change Influencing Factors and the Impacts on Gross Primary Productivity in China
Authors: Feng Lili , Zhou Wenneng
Published: March 2023

Forest fire as a common disturbance has an important role in the terrestrial ecosystem carbon cycling. However, the causes and impacts of longtime burned areas on carbon cycling need further exploration. In this study, we exploit Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to develop a quick and efficient method for large-scale forest fire dynamic monitoring in China. Band 2, band 4, band 6, and band 7 of MOD09A1 were selected as the most sensitive bands for calculating the Normalized Difference Fire Index (NDFI) to effectively estimate fire burned area. The Convergent Cross Mapping (CCM) algorithm was used to analyze the causes of the forest fire. A trend analysis was used to explore the impacts of forest fire on Gross Primary Productivity (GPP). The results show that the burned area has an increased tendency from 2009 to 2018. Forest fire is greatly influenced by natural factors compared with human factors in China. But only 30% of the forest fire causes GPP loss. The loss is mainly concentrated in the northeast forest region. The results of this study have important theoretical significance for vegetation restoration of the burned area.
Vegetation Cover Dynamics in the High Atlas Mountains of Morocco
Authors: Nguyen Thanh Thi , Aderdour Nacer , Rhinane Hassan , Buerkert Andreas
Published: March 2023

Since the 1990s, Morocco’s agriculture has been characterized by the co-existence and transformation of both modern and traditional smallholder systems. In the Atlas Mountains, the effects of rural–urban transformation have led to intensified irrigated agriculture in some agricultural areas, while others were abandoned. To better understand these effects, this study aimed at (1) analyzing the land use and land cover (LULC) changes, (2) assessing the structure and dynamics of vegetation, and (3) comparing a Support Vector Machine (SVM) classification approach with a seasonal rules-based approach. We, therefore, employed a semi-automatic supervised classification of LULC using Landsat data from the 1990s to the 2020s to distinguish between Open Canopy Vegetation, Bareland, Forest, and Water. Overall accuracies achieved ranged from 88% to 90% in 1990, 2000, 2010, and 2020. SVM results indicated the share of Bareland as >80% of the landscape in all periods. With the seasonal rules-based approach, 10% less Bareland was detected than with the SVM approach. Our findings indicate the limitation of detecting vegetation reflectance in semi-arid mountainous regions such as that prevailing in Morocco using a single machine learning method.
Characterizing Uncertainty and Enhancing Utility in Remotely Sensed Land Cover Using Error Matrices Localized in Canonical Correspondence Analysis Ordination Space
Authors: Wan Yue , Zhang Jingxiong , Zhang Wangle , Zhang Ying , Yang Wenjing , Wang Jianxu , Chukwunonso Okafor Somtoochukwu , Nadeeka Asurapplullige Milani Tharuka
Published: March 2023

In response to uncertainty in remotely sensed land cover products, there is continuing research on accuracy assessment and analysis. Given reference sample data, accuracy indicators are commonly estimated based on error matrices, from which areal extents of different cover types are also estimated. There are merits to explore the ways utilities of land cover products may be further enhanced beyond map face values and conventional area estimation. This paper presents an integrative method (CCAErrMat) for uncertainty characterization and utility enhancement. This works through reference-map cover type co-occurrence analyses based on error matrices localized in canonical correspondence analysis (CCA) ordination space rather than in geographic space to overcome the sparsity of reference sample data. The aforementioned co-occurrence analyses facilitate quantification of accuracy indicators, identification of correctly classified and perfectly misclassified pixels, and prediction of reference class probabilities, all at individual pixels. Moreover, these predicted reference class probabilities are used as auxiliary variables to formulate model-assisted area estimation, further enhancing map utilities. Extensions to CCAErrMat are also investigated as a way to bypass the pre-computing of map class occurrence pattern indices as candidate explanatory variables for CCAErrMat, leading to two variant methods: CCACCAErrMat and CNNCCAErrMat. A case study based in Wuhan municipality, central China was undertaken to compare the proposed method against alternative methods, including CCA-separate and CNN-separate. The advantages of CCAErrMat and CCACCAErrMat were confirmed. The proposed method is recommendable for characterizing uncertainty and enhancing utilities in land cover maps by analyzing locally constrained error matrices. The method is also cost-effective in terms of reference sample data, as requirements for them are similar to those for conventional accuracy assessments.
A Study on Spatial and Temporal Dynamic Changes of Desertification in Northern China from 2000 to 2020
Authors: Jiang Zhaolin , Ni Xiliang , Xing Minfeng
Published: March 2023

Desertification is of significant concern as one of the world’s most serious ecological and environmental problems. China has made great achievements in afforestation and desertification control in recent years. The climate varies greatly across northern China. Using a long-time series of remote sensing data to study the effects of desertification will further the understanding of China’s desertification control engineering and climate change mechanisms. The moist index was employed in this research to determine the climate type and delineate the potential occurrence range of desertification in China. Then, based on the Google Earth Engine platform, MODIS data were used to construct various desertification monitoring indicators and applied to four machine learning models. By comparing different combinations of indicators and machine learning models, it was concluded that the random forest model with four indicator combinations had the highest accuracy of 86.94% and a Kappa coefficient of 0.84. Therefore, the random forest model with four indicator combinations was used to monitor desertification in the study area from 2000 to 2020. According to our studies, the area of desertification decreased by more than 237,844 km2 between 2000 and 2020 due to the impact of human activities and in addition to climatic factors such as the important role of precipitation. This research gives a database for the cause and control of desertification as well as a reference for national-scale desertification monitoring.
A Parallel InSAR Phase Unwrapping Method Based on Separated Continuous Regions
Authors: Gao Jian , Jiang Houjun , Sun Zhongchang , Wang Ruisheng , Han Youmei
Published: March 2023

Phase unwrapping is an imperative step in interferometry processing that has a significant influence on the quality of subsequent products. Many existing phase unwrapping algorithms have been designed to solve for the unwrapped phase under the assumption that noisy areas with discontinuities are small or that reliable continuity can be recovered there. They attempt to restore the unwrapped phase by using continuity and data quality measures, such as residues. However, when the observing field is divided into separate zones of continuous phase due to a large range of noise, such as those caused by rivers or mountains, it is difficult to use traditional phase unwrapping techniques to recover global continuity in these noisy areas. To address this challenge, we present a two-dimensional parallel phase unwrapping method that is designed to handle cases where the continuity of the phase is separated by closed noisy loops. Based on continuity distances, this method aims to identify continuous regions that are free of hidden phase discontinuities and restore phase continuity between the separated regions. A heterogeneous residual diffusion scheme is used to restore the unwrapped phase outside continuous regions. The parallel algorithm for extracting continuous regions, restoring continuity between the regions, and diffusing residuals was implemented on a GPU device to increase the processing efficiency. We applied our method to typical TanDEM-X data covering rivers, islands, and mountains and demonstrated that it is a promising solution for large-scale, heavily noisy phase unwrapping problems.
Land Surface Greening and CO2 Fertilization More than Offset the Gross Carbon Sequestration Decline Caused by Land Cover Change and the Enhanced Vapour Pressure Deficit in Europe
Authors: Wu Qiaoli , Wang Xinyao , Chen Shaoyuan , Wang Li , Jiang Jie
Published: March 2023

Satellite observations have revealed strong land surface “greening” (i.e., increases in vegetation greenness or leaf area index (LAI)) in the Northern Hemisphere over the past few decades. European terrestrial ecosystems are a greening hotspot, but how they respond to land surface greening, climate change, CO2 fertilization, land use and land cover change (LULCC) and other factors is unclear. Here, we assessed how these interacting factors might be combined to alter terrestrial gross primary production (GPP) throughout Europe during the period of 2001 to 2016 using a process-based Farquhar GPP model (i.e., FGM). We found a more productive European terrestrial ecosystem and most of the GPP enhancement in Europe was explained by increases in LAI (62%) and atmospheric CO2 concentration (29%). Spatially, the spatial signature of the LAI and GPP trends both suggested widespread (72–73% of the vegetated area) greening phenomena across Europe, among which 23.7% and 13.3% were statistically significant (p < 0.05). The interannual trend of GPP estimated by the FGM (0.55% yr−1) was reasonable compared with other GPP products (0.47% yr−1 to 0.92% yr−1) and the observed LAI increasing rate (0.62% yr−1). FGM factorial simulations suggested that land surface greening (+35.5 Pg C yr−2, p < 0.01), CO2 fertilization (+16.9 Pg C yr−2, p < 0.01), temperature warming (+3.7 Pg C yr−2, p < 0.05), and enhanced downwards solar radiation (+1.2 Pg C yr−2, p > 0.05) contributed to the GPP enhancement, while the enhanced vapour pressure deficit (−5.6 Tg C yr−2, p < 0.01) had significant negative impacts on GPP, especially in 2006 and 2012, when extreme droughts struck south-eastern Europe. Meanwhile, approximately 1.8% of the total area of Europe experienced LULCC from 2001 to 2016 and LULCC exerted a small but significant (−1.3 Tg C yr−2, p < 0.01) impact on GPP due to decreases in the total number of vegetated pixels (−159 pixels yr−1). Although the LULCC effect was negative, the largest increase occurred in forested land (+0.9% of total area). In addition, the increasing trends for the annual mean LAI (0.01 m2 m−2 yr−1, p < 0.001) and total GPP (22.2 Tg C yr−2, p < 0.001) of forests were more significant and higher than those of other vegetation types, suggesting that European forests may continue to play important roles in combating climate change in the future with long-lasting carbon storage potential. These results provide the first systematic quantitative analysis of the driving force of enhanced gross carbon assimilation by European ecosystems by considering variations in leaf physiological traits with environmental adaptations.
Efficient and Accurate Hierarchical SfM Based on Adaptive Track Selection for Large-Scale Oblique Images
Authors: Liang Yubin , Yang Yang , Fan Xiaochang , Cui Tiejun
Published: March 2023

Image-based 3D modeling has been widely used in many areas. Structure from motion is the key to image-based reconstruction. However, the rapid growth of data poses challenges to current SfM solutions. A hierarchical SfM reconstruction methodology for large-scale oblique images is proposed. Firstly, match pairs are selected using positioning and orientation (POS) data and the terrain of the survey area. Then, images are divided to image groups by traversing the selected match pairs. After pairwise image matching, tracks are decimated using an adaptive track selection method. Thirdly, submaps are reconstructed from the image groups in parallel based on incremental SfM in the object space. A novel method based on statistics of the positional difference between common tracks is proposed to detect the outliers in submap merging. Finally, the reconstructed submaps are incrementally merged and optimized. The proposed methodology was used on a large oblique image set. The proposed methodology was compared with the state-of-the-art image-based reconstruction systems COLMAP and Metashape for SfM reconstruction. Experimental results show that the proposed methodology achieved the highest accuracy on the experimental dataset, i.e., about 22.37, and 3.52 times faster than COLMAP and Metashape, respectively. The experimental results demonstrate that the proposed hierarchical SfM methodology is accurate and efficient for large-scale oblique images.
Continental-Scale Investigation of Underlying Electrical Conductivity Structure in Mainland China Using Geomagnetic Data
Authors: Mao Zhiqiang , Chen Chieh-Hung , Yisimayili Aisa , Chen Bin , Yuan Jiehao , Gao Yongxin , Sun Yang-Yi , Lin Kai
Published: March 2023

The magnetotelluric method has been used to fully study regional electrical conductivity structures in different areas in mainland China; however, there is a lack of overall understanding of the electrical structure distribution. A novel insight for the study of continental-scale underlying conductivity structures was proposed in this work via geomagnetic data recorded by permanent stations. To study the underlying electrical structure distribution in mainland China, we mapped the conductors and resistors at a depth range of 4–100 km beneath mainland China using Parkinson vectors through magnetic transfer function. Three-component geomagnetic data within a low artificial disturbance period (local time 23:00–05:00) from 98 stations in 2019 were collected and processed to derive Parkinson vectors in the frequency band of 0.001–0.5 Hz. The distribution of subsurface electrical structures at distinct depths was constructed using corresponding frequency through the skin depth. We compare the consistent results herein with previous magnetotelluric studies, which indicated the reliability of our method. Combining previous multiple geophysical inversion results, we found that large-scale plastic bodies are distributed along the east of the Qinghai-Tibet Plateau and extend to the west of Yunnan. In central mainland China, the areas are mainly highly resistive, indicating that the structures are overall rigid. In north China, there exist high-low-high-low conductive structures from west to east. The separate high- and low-conductive electrical bodies in the North China Craton provide geophysical evidence that the Craton is composed of multiple blocks. The distributions of the underlying electrical structures in this work can provide an overall perspective for studying tectonic evolution and geodynamics in mainland China.
Red Palm Weevil Detection in Date Palm Using Temporal UAV Imagery
Authors: Delalieux Stephanie , Hardy Tom , Ferry Michel , Gomez Susi , Kooistra Lammert , Culman Maria , Tits Laurent
Published: March 2023

Red palm weevil (RPW) is widely considered a key pest of palms, creating extensive damages to the date palm trunk that inevitably leads to palm death if no pest eradication is done. This study evaluates the potential of a remote sensing approach for the timely and reliable detection of RPW infestation on the palm canopy. For two consecutive years, an experimental field with infested and control palms was regularly monitored by an Unmanned Aerial Vehicle (UAV) carrying RGB, multispectral, and thermal sensors. Simultaneously, detailed visual observations of the RPW effects on the palms were made to assess the evolution of infestation from the initial stage until palm death. A UAV-based image processing chain for nondestructive RPW detection was built based on segmentation and vegetation index analysis techniques. These algorithms reveal the potential of thermal data to detect RPW infestation. Maximum temperature values and standard deviations within the palm crown revealed a significant (α = 0.05) difference between infested and non-infested palms at a severe infestation stage but before any visual canopy symptoms were noticed. Furthermore, this proof-of-concept study showed that the temporal monitoring of spectral vegetation index values could contribute to the detection of infested palms before canopy symptoms are visible. The seasonal significant (α = 0.05) increase of greenness index values, as observed in non-infested trees, could not be observed in infested palms. These findings are of added value for steering management practices and future related studies, but further validation of the results is needed. The workflow and resulting maps are accessible through the Mapeo® visualization platform.
Atmospheric Boundary Layer Height: Inter-Comparison of Different Estimation Approaches Using the Raman Lidar as Benchmark
Authors: Summa Donato , Vivone Gemine , Franco Noemi , D’Amico Giuseppe , De Rosa Benedetto , Di Girolamo Paolo
Published: March 2023

This work stems from the idea of improving the capability to measure the atmospheric boundary layer height (ABLH) in variable or unstable weather conditions or in the presence of turbulence and precipitation events. A new approach based on the use of rotational and roto-vibrational Raman lidar signals is considered and tested. The traditional gradient approach based on the elastic signals at wavelength 532 nm is also considered. Lidar data collected by the University of Basilicata Raman lidar (BASIL) within the Special Observation Period 1 (SOP 1) in Cardillargues (Ceveninnes–CV supersite) during the Hydrological Cycle in the Mediterranean Experiment (HyMeX) were used. Our attention was specifically focused on the data collected during the period 16–21 October 2012. ABLH estimates from the Raman lidar were compared against other innovative methods, such as the recently established Morphological Image Processing Approach (MIPA) and the temperature gradient technique applied to potential temperature obtained from radio-sounding data. For each considered methodology, a statistical analysis was carried out. In general, the results from the different methodologies are in good agreement. Some deviations have been observed in correspondence with quite unstable weather conditions.
New Investigation of a Tropical Cyclone: Observational and Turbulence Analysis for the Faraji Hurricane
Authors: Ciardullo Giuseppe , Primavera Leonardo , Ferrucci Fabrizio , Lepreti Fabio , Carbone Vincenzo
Published: March 2023

In the general framework of the atmosphere, the in-depth study of extreme events of complex nature such as tropical cyclones remains an open problem. Nowadays, there are different useful studies aiming to enlarge the current knowledge of these events. Under this perspective, the search for mechanisms and geographical areas of formation and dynamics of tropical cyclones at different latitudes needs better constraints on their preliminary results. This work focuses on a diagnostic analysis of a tropical cyclone, with the aim of identifying key points that characterize its evolution, from a dynamic and observational point of view. The study is applied to Hurricane Faraji, the most powerful tropical cyclone of the 2021 Indian Ocean season, classified as fifth-category on the Saffir–Simpson intensity scale. The study develops in three main sections, all related to each other. The starting point is a large set of satellite products from both polar and geostationary platforms. From the data acquired by the polar instruments, an accurate study of the evolution of the hurricane is carried out, focused on the extraction of physical information related to the system (temperature and altitude of the associated cloudy system, temperature gradient, pressure in the different regions) at moderate resolutions. From the data acquired by the geostationary instruments, it is possible to obtain very high temporal resolution pictures of the temperature field of the cyclone, from which a study of the turbulent dynamics is carried out. In particular, to investigate the maximum energy content in the different regions of the cyclone, the Proper Orthogonal Decomposition (POD) technique is used to extract the associated spectra of both the spatial and temporal components, studied separately on three different ranges of scales.
A Depth-Wise Separable U-Net Architecture with Multiscale Filters to Detect Sinkholes
Authors: Alshawi Rasha , Hoque Md Tamjidul , Flanagin Maik C.
Published: March 2023

Numerous variants of the basic deep segmentation model—U-Net—have emerged in recent years, achieving reliable performance across different benchmarks. In this paper, we propose an improved version of U-Net with higher performance and reduced complexity. This improvement was achieved by introducing a sparsely connected depth-wise separable block with multiscale filters, enabling the network to capture features of different scales. The use of depth-wise separable convolution significantly reduces the number of trainable parameters, making the training faster, while reducing the risk of overfitting. We used our developed sinkhole dataset and the available benchmark nuclei dataset to assess the proposed model’s performance. Pixel-wise annotation is laborious and requires a great deal of human expertise; therefore, we propose a fully deep convolutional autoencoder network that utilizes the proposed block to automatically annotate the sinkhole dataset. Our segmentation model outperformed the state-of-the-art methods, including U-Net, Attention U-Net, Depth-Separable U-Net, and Inception U-Net, achieving an average improvement of 1.2% and 1.4%, respectively, on the sinkhole and the nuclei datasets, with 94% and 92% accuracy, as well as a reduced training time. It also achieved 83% and 80% intersection-over-union (IoU) on the two datasets, respectively, which is an 11.8% and 9.3% average improvement over the above-mentioned models.
High-Precision 3D Reconstruction Study with Emphasis on Refractive Calibration of GelStereo-Type Sensors
Authors: Zhang Chaofan , Cui Shaowei , Wang Shuo , Hu Jingyi , Huangfu Yipeng , Zhang Boyue
Published: March 2023

GelStereo sensing technology is capable of performing three-dimensional (3D) contact shape measurement under various contact structures such as bionic curved surfaces, which has promising advantages in the field of visuotactile sensing. However, due to multi-medium ray refraction in the imaging system, robust and high-precision tactile 3D reconstruction remains a challenging problem for GelStereo-type sensors with different structures. In this paper, we first propose a universal Refractive Stereo Ray Tracing (RSRT) model for GelStereo-type sensing systems to realize 3D reconstruction of the contact surface. Moreover, a relative geometry-based optimization method is presented to calibrate multiple parameters of the proposed RSRT model, such as the refractive indices and structural dimensions. Furthermore, extensive quantitative calibration experiments are performed on four different GelStereo sensing platforms; the experimental results show that the proposed calibration pipeline can achieve less than 0.35 mm in Euclidean distance error, based on which we believe that the proposed refractive calibration method can be further applied in more complex GelStereo-type and other similar visuotactile sensing systems. Such high-precision visuotactile sensors can facilitate the study of robotic dexterous manipulation.
Multimodal Sentiment Analysis Representations Learning via Contrastive Learning with Condense Attention Fusion
Authors: Wang Huiru , Li Xiuhong , Ren Zenyu , Wang Min , Ma Chunming
Published: March 2023

Multimodal sentiment analysis has gained popularity as a research field for its ability to predict users’ emotional tendencies more comprehensively. The data fusion module is a critical component of multimodal sentiment analysis, as it allows for integrating information from multiple modalities. However, it is challenging to combine modalities and remove redundant information effectively. In our research, we address these challenges by proposing a multimodal sentiment analysis model based on supervised contrastive learning, which leads to more effective data representation and richer multimodal features. Specifically, we introduce the MLFC module, which utilizes a convolutional neural network (CNN) and Transformer to solve the redundancy problem of each modal feature and reduce irrelevant information. Moreover, our model employs supervised contrastive learning to enhance its ability to learn standard sentiment features from data. We evaluate our model on three widely-used datasets, namely MVSA-single, MVSA-multiple, and HFM, demonstrating that our model outperforms the state-of-the-art model. Finally, we conduct ablation experiments to validate the efficacy of our proposed method.
Exploring Self-Supervised Vision Transformers for Gait Recognition in the Wild
Authors: Cosma Adrian , Catruna Andy , Radoi Emilian
Published: March 2023

The manner of walking (gait) is a powerful biometric that is used as a unique fingerprinting method, allowing unobtrusive behavioral analytics to be performed at a distance without subject cooperation. As opposed to more traditional biometric authentication methods, gait analysis does not require explicit cooperation of the subject and can be performed in low-resolution settings, without requiring the subject’s face to be unobstructed/clearly visible. Most current approaches are developed in a controlled setting, with clean, gold-standard annotated data, which powered the development of neural architectures for recognition and classification. Only recently has gait analysis ventured into using more diverse, large-scale, and realistic datasets to pretrained networks in a self-supervised manner. Self-supervised training regime enables learning diverse and robust gait representations without expensive manual human annotations. Prompted by the ubiquitous use of the transformer model in all areas of deep learning, including computer vision, in this work, we explore the use of five different vision transformer architectures directly applied to self-supervised gait recognition. We adapt and pretrain the simple ViT, CaiT, CrossFormer, Token2Token, and TwinsSVT on two different large-scale gait datasets: GREW and DenseGait. We provide extensive results for zero-shot and fine-tuning on two benchmark gait recognition datasets, CASIA-B and FVG, and explore the relationship between the amount of spatial and temporal gait information used by the visual transformer. Our results show that in designing transformer models for processing motion, using a hierarchical approach (i.e., CrossFormer models) on finer-grained movement fairs comparatively better than previous whole-skeleton approaches.
Towards a Broad-Persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments
Authors: Nguyen Hung Son , Cruz Francisco , Dazeley Richard
Published: March 2023

Deep Reinforcement Learning (DeepRL) methods have been widely used in robotics to learn about the environment and acquire behaviours autonomously. Deep Interactive Reinforcement 2 Learning (DeepIRL) includes interactive feedback from an external trainer or expert giving advice to help learners choose actions to speed up the learning process. However, current research has been limited to interactions that offer actionable advice to only the current state of the agent. Additionally, the information is discarded by the agent after a single use, which causes a duplicate process at the same state for a revisit. In this paper, we present Broad-Persistent Advising (BPA), an approach that retains and reuses the processed information. It not only helps trainers give more general advice relevant to similar states instead of only the current state, but also allows the agent to speed up the learning process. We tested the proposed approach in two continuous robotic scenarios, namely a cart pole balancing task and a simulated robot navigation task. The results demonstrated that the agent’s learning speed increased, as evidenced by the rising reward points of up to 37%, while maintaining the number of interactions required for the trainer, in comparison to the DeepIRL approach.
Elliptic Curve Cryptography Considerations for Securing Automation and SCADA Systems
Authors: Tidrea Alexandra , Korodi Adrian , Silea Ioan
Published: March 2023

Securing critical infrastructures and manufacturing plants in the Industrial-Internet-Of-Things and Industry 4.0 is a challenge today due to the increased number of attacks against automation and SCADA systems. These systems were built without any security considerations in mind, so the evolution towards interconnection and interoperability makes them vulnerable in the context of exposing data to the outside network. Even though new protocols are considering built-in security, the widely spread legacy standard protocols need to be protected. Hence, this paper attempts to offer a solution for securing the legacy insecure communication protocols based on elliptic curve cryptography while fulfilling the time constraints of a real SCADA network. Elliptic curve cryptography is chosen to respond to the low memory resources available for the low level devices of a SCADA network (e.g., PLCs), and also because it can achieve the same level of security as other cryptographic algorithms using smaller sizes for the secure keys. Furthermore, the proposed security methods have the purpose of assuring that the data transmitted between entities of a SCADA and automation system are authentic and confidential. The experimental results showed good timing performance for the cryptographic operations executed on Industruino and MDUINO PLCs, demonstrating that our proposed concept is deployable for Modbus TCP communication in a real automation/SCADA network on existing devices from the industry.
Quantitative Detection of Tank Floor Defects by Pseudo-Color Imaging of Three-Dimensional Magnetic Flux Leakage Signals
Authors: Yang Zhijun , Yang Jiang , Cao Huaiqing , Sun Han , Zhao Yazhong , Zhang Bowen , Meng Changpeng
Published: March 2023

Highly integrated three-dimensional magnetic sensors have just been developed and have been used in some fields, such as angle measurement of moving objects. The sensor used in this paper is a three-dimensional magnetic sensor with three Hall probes highly integrated inside; 15 sensors are used to design the sensor array and then measure the magnetic field leakage of the steel plate; the three-dimensional component characteristics of the magnetic field leakage are used to determine the defect area. Pseudo-color imaging is the most widely used in the imaging field. In this paper, color imaging is used to process magnetic field data. Compared with analyzing the three-dimensional magnetic field information obtained directly, this paper converts the magnetic field information into color image information through pseudo-color imaging and then obtains the color moment characteristic values of the color image in the defect area. Moreover, the least-square support-vector machine and particle swarm optimization (PSO-LSSVM) algorithm are used to quantitatively identify the defects. The results show that the three-dimensional component of the magnetic field leakage can effectively determine the area range of defects, and it is feasible to use the color image characteristic value of the three-dimensional magnetic field leakage signal to identify defects quantitatively. Compared with a single component, the three-dimensional component can effectively improve the identification rate of defects.
Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging
Authors: Suliman Ahmad , Mowla Md Rakibul , Alivar Alaleh , Carlson Charles , Prakash Punit , Natarajan Balasubramaniam , Warren Steve , Thompson David E.
Published: March 2023

Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.
Dormancy Breaking of Teramnus labialis (L.f.) Spreng Seeds Is Affected by the Extent of Liquid Nitrogen Exposure
Authors: Acosta Fernández Yanier , Pérez Gómez Lianny , Fontes Marrero Dayami , Martinez Montero Marcos Edel
Published: March 2023

Teramnus labialis (L.f.) Spreng shows dormancy as a result of impermeability of the seed coat, which requires scarification treatment before sowing. Liquid nitrogen (LN) as a scarifying treatment has recently been used on this species, with excellent results. However, moisture content and immersion time on LN are factors that may affect seed germination and dormancy break. This report studies (i) the effects of dehydration on T. labialis seed viability and germination and (ii) the appropriate moisture content and extent of LN to make this scarification an effective treatment. Moisture contents of 4%, 6%, 8%, and 10% fresh weight basis (FWB) and extension to LN for 15, 30, 45, and 60 min were performed. Seed viability did not change after seed dehydration up to a moisture content of 4% FWB, whereas the percentage of germination decreased as a result of increasing the percentage of hard seeds. The seed moisture content did not affect germination after immersion in LN, but at least 30 min of exposure was required for dormancy break. The mean germination time, germination index, and time to 50% germination improved with the increasing germination from 33% to 91% and a greater duration of immersion in LN. The dormancy of T. labialis seeds with a moisture content between 4% and 10% FWB was only broken when exposed to LN for 30 to 60 min.
Athlete Perceptions of Governance-Related Issues to Sexual Abuse in Sport
Authors: Krieger Jörg , Pieper Lindsay Parks
Published: March 2023

Cases of abuse in sport have emerged with frightening regularity over the past two decades. Scholarship has identified risk factors that can help facilitate abuse in sport and has explored athletes’ experiences with sexual abuse. However, less is known about athletes’ perceptions of the systematic organizational-level problems that fail to curtail sexual abuse. This article, therefore, explores what athletes believe to be the key issues in governance that facilitate sexual abuse in sport. An analysis of the lawsuits that athletes filed against US sport organizations and the testimonies they provided to the US Congress from 2017 to 2022 show four primary ways in which organizational culture, decisions and policies helped permit misconduct in sport. Athlete perspectives suggest governance issues related to monopolistic power structures, a lack of athlete representation, conflicts of interest, and commercialization facilitated an abuse-prone culture within Olympic and Paralympic sport in the United States. These findings show that athletes feel that the adjudication mechanisms that remain connected to sport bodies do not always curtail abuse.
Ageism in Nursing Education: Students’ Views of Ageing
Authors: Cerqueira Andreia Ferreri , Ramos Ana Lúcia , Palma José
Published: March 2023

The world is ageing, families are changing, and a new view of society is needed. Since nursing students will be the health professionals caring for older adults, working with nursing students from an early stage is critical. With this study, we intend to assess ageism levels among nursing students and analyse students’ views on ageing, improving nursing education and care for older adults, and promoting well-being in society. An exploratory mixed-methods study with first-year nursing degree students was conducted. Two instruments were used: the Fraboni Scale of Ageism and a sociodemographic questionnaire with an open question: what does ageing mean to you? Sixty-four students participated in the study. A mean total ageism score of 80.05 (SD = 12.23) was found, indicating a positive attitude towards older adults. In this study, no statistically significant relationship was found between the total score of ageism and the variables age, gender, and contact with older adults. Two visions emerged regarding the “meaning of ageing”: the subcategory “positive view”: experience and knowledge; psychological and social growth; skills development; and good feelings/emotions. The following subcategories emerged from the subcategory “negative view”: finitude; disabilities; physiological decline; and bad feelings/emotions. The results achieved allow for a more effective educational response, integrating personalised and innovative strategies in the training of nursing students. However, it is crucial to develop more studies about the educational strategies that promote a more positive view of ageing among nursing students, which may influence the way older adults are cared for in society.
Application of the FMEA Tool in an Accredited Testing Laboratory in the Context of the ISO/IEC 17025:2017 Standard
Authors: Blaško Peter , Šolc Marek , Petrík Jozef , Girmanová Lenka , Blašková Andrea
Published: March 2023

Organizations and laboratories tend to integrate systems based on risk management. Risk management helps to optimize laboratory processes and information flow, increase valid and reliable results, and make better decisions. This paper describes the development and present trends in risk management related to standard ISO/IEC 17025. This paper focuses on risk assessment in the accredited testing laboratories using the FMEA tool. In the basic eight defined laboratory areas, risks were identified and evaluated, and methods were proposed to minimize them.
Conflict: The Missing Ingredient for Sustainability in Complex Partnerships
Authors: Carpenter Ami
Published: March 2023

Addressing today’s most pressing challenges requires collaboration between professionals of different disciplines and the capacity to work effectively across sectors. Cross-sector partnerships (CSPs) are an increasingly common vehicle for doing so, but too often they fall short of achieving the desired social impact. Three years of research alongside a unique multi-sector partnership to prevent human trafficking identifies lack of shared understanding as the main problem, caused by conflict avoidance during early stages of partnership development. Counterintuitively, controversy is necessary to develop shared norms, power structure, and communication practices—all elements of participatory design—through a process of stakeholder dialogue. Effective dialogue requires people to explore, confront, and contest diverse perspectives; however, research finds that groups are more likely to avoid conflict and engage in consensus-confirming discussions, thereby undermining their effectiveness. Using the singular case study of a cross-sector partnership that formed to enact new anti-trafficking legislation, this study examines how conflict avoidance constrained the performance and sustainability of a cross-sector, multi-actor collaboration. The study finds that conflict avoidance stifles shared understanding of governance, norms, and administrative practices, negatively impacting multiple processes that are important to sustainable collaborations. The conclusion drawn is that conflict management should receive greater attention in the study and practice of cross-sector partnerships.