bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis on Water Inrush Prevention Mechanism of Paste-Filled Floor above Confined Water
Authors: Chang Qingliang , Yao Xingjie , Qin Jianzhuang , Li Mengda , Wang Yizhe , Zhou Huaqiang , Xu Ying , Sun Yuantian
Published: January 2022

To study the influence of filling step and advancing distance on the deformation and failure of a working face floor, a mechanical model based on elastic foundation beam theory is established. The deflection and bending moment curves of the floor under different filling steps and advancing distance are obtained by Maple. Then, a fluid–solid coupling model of paste-filling mining on confined water is established by FLAC3D. The effects of different filling steps and advancing distance on the floor displacement, stress, and plastic zone of the floor are analyzed. The results show that there is a “concave” quadratic relationship between the filling step and the maximum displacement of the floor, and there is a “convex” quadratic relationship between the advancing distance and the maximum displacement of the floor. The maximum stress of the floor increases linearly with the increase in filling distance and tends to be stable with the increase in advancing distance. Moreover, the increase in filling steps will lead to the continuous increase in longitudinal failure. This study could guide paste-filling mining above confined water.
Prosthetic Surgical Templates and Dental Implant Site Time Preparation: An In Vitro Study
Authors: Zadrożny Łukasz , Czajkowska Marta , Tallarico Marco , Wagner Leopold , Markowski Jarosław , Mijiritsky Eitan , Cicciù Marco
Published: January 2022

Numerous types of prosthodontics surgical guides, with and without metallic sleeves, have been found to be useful in clinical studies. The aim of this in vitro research was to compare the time required to complete the surgical procedure with two differently designed surgical prosthetic templates. Ten identical prototype models of mandible based on a CBCT and optical scan of a partially edentulous patient with missing teeth numbers 37, 46, and 47 were prepared and then printed. Five of these models were used for implant site preparation with a surgical guide without metal sleeves and dedicated surgical kit, and the other five models were used for the same procedure performed with a surgical guide with metal sleeves and a dedicated surgical kit. The time of implant site preparation was measured and recorded. Statistical analysis was performed using Student’s t-test for independent samples. Differences between groups were found to be statistically significant (t = −9.94; df = 28; p = 0.0000) with a lower value in favor of the surgical templates without metallic sleeves. Different types of prosthodontics surgical guides, with or without metallic sleeves, seemed to be an important factor which can significantly impact the time of implant site preparation and, therefore, the overall surgical procedure.
Acknowledgment to Reviewers of Psychiatry International in 2021
Authors:   Psychiatry International Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Acknowledgment of Reviewers of Reactions in 2021
Authors:   Reactions Editorial Office
Published: January 2022

Rigorous peer reviews are the basis of high-quality academic publishing [...]
From Nuṣayrīs to ʿAlawīs: The Religiography of Muḥammad Kurd ʿAlī
Authors: Kearney Jonathan
Published: January 2022

A disproportionate emphasis on the work of Western European and North American scholars has been a feature of investigations into the development of the academic study of religion. This article seeks to examine how a non-European intellectual, the Syrian Muḥammad Kurd ʿAlī (1876–1953), produced and transmitted knowledge about religions in his encyclopedic historical topography of ‘Greater Syria’—the Khiṭaṭ al-Shām (1925–1928). Kurd ʿAlī was a leading figure in the Nahḍa, an intellectual movement that sought to revivify Arab (and for some, Islamic) culture through a rediscovery of its classical heritage and was a proponent of a reformist tendency within Sunnī Islam known as Salafism—often associated with the thought of Jamāl al-Dīn al-Afghānī and Muḥammad ʿAbduh. Kurd ʿAlī’s religiography in the Khiṭaṭ, though grounded in traditional Islamic discourse on the religious other, moves beyond that discourse to privilege the experiences and accounts of insiders. This move from heresiography to religiography is best seen through a close reading of Kurd ʿAlī’s writing on the ʿAlawīs (formerly known as Nuṣayrīs). Kurd ʿAlī’s writing on the ʿAlawīs is also an important witness to a vital phase in the development of that group’s articulation of its own identity in an environment that had been at best indifferent and at worst hostile to its existence.
Phycoremediation as a Strategy for the Recovery of Marsh and Wetland with Potential in Colombia
Authors: Aranguren Díaz Yani , Monterroza Martínez Edy , Carillo García Laura , Serrano María C. , Machado Sierra Elwi
Published: January 2022

Colombia is the country with the sixth highest amount of water reserves in the world, and 25% of its territory is covered by wetlands. However, approximately 50% of the country’s water is estimated to exhibit some type of contamination related to anthropic activities. An alternative for the treatment and the recovery of its bodies of water is the use of microalgae, unicellular, and mixotrophic microorganisms, as these bioreactors are highly adaptable to the environment, and their maintenance costs are minimal, because they feed on almost any substrate. In fact, different countries have already reported using microalgae as bioremediators for bodies of water. The use of these microphytes is efficient because they metabolize, degrade, or bioaccumulate heavy metals, pesticides, emerging pollutants, and antibiotics. In general, strategies relying on microalgae to eliminate pollutants are very similar to one another. For example, the first stage often includes a process of bioadsorption, consumption, degradation, and accumulation, wherein the microalgae use molecules generated from their own cellular metabolism. Some pilot studies focusing on the phycoremediation of marshes and other bodies of water have already been conducted in Colombia; however, more studies on process optimization, effectively leveraging the biodiversity of the existing microalgae, and better adapting microalgae to the region are still required.
Ultrasound Features across Subtypes of Juvenile Idiopathic Arthritis
Authors: Mosa Doaa Mosad , Abdelrahman Ashraf M. , El-Bahnasawy Amany S.
Published: January 2022

Objective: The aim of this study was to evaluate musculoskeletal ultrasound (MSUS) features across categories of juvenile idiopathic arthritis (JIA). Methods: In this cross-sectional study, all patients were subjected to full history taking, clinical examination including disease assessment parameters and laboratory investigations. In addition, all children were examined by both grayscale (GS) and power Doppler (PD) MSUS images. Results: By MSUS, the number of joints with synovial effusion was 697 of a total 2400 examined joints (29%) and joints with synovial thickening counted 673 (28%). The number of joints with positive PD signals was 446 (18.6%). There was a significant difference among JIA subtypes as regards different MSUS features. Moreover, there was a discrepancy regarding synovial effusion (p = 0.018), hypertrophy scores (p = 0.013), and the total US severity score (p = 0.026). This divergence was attributed to the significant difference between systemic juvenile idiopathic arthritis (SJIA) and other categories. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of MSUS in JIA and its subtypes were calculated. Conclusion: MSUS is a highly sensitive method for detecting synovitis, tenosynovitis, and erosive bone disease, and it helps to make proper therapeutic decisions. There was a significant difference among JIA subtypes regarding MSUS features.
Investigation of the Mounting Position of a Wearable Robot Arm
Authors: Kojima Akimichi , Tran Dinh Tuan , Lee Joo-Ho
Published: January 2022

In a wearable robot arm, the minimum joint configuration and link length must be considered to avoid increasing the burden on the user. This work investigated how the joint configuration, length of arm links, and mounting position of a wearable robot arm affect the cooperative and invasive workspaces of the overall workspace. We considered the joint configurations and link lengths of passive and active joints in our proposed wearable robot arm, which is called the Assist Oriented Arm (AOA). In addition, we comprehensively studied the position of the arm on the user. As a result, three locations around the shoulders and two around the waist were chosen as potential mounting sites. Furthermore, we evaluated the weight burden when the user mounted the wearable robot arm at those positions.
Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation
Authors: Nasr Ali , Hashemi Arash , McPhee John
Published: January 2022

The closed-loop human–robot system requires developing an effective robotic controller that considers models of both the human and the robot, as well as human adaptation to the robot. This paper develops a mid-level controller providing assist-as-needed (AAN) policies in a hierarchical control setting using two novel methods: model-based and fuzzy logic rule. The goal of AAN is to provide the required extra torque because of the robot’s dynamics and external load compared to the human limb free movement. The human–robot adaptation is simulated using a nonlinear model predictive controller (NMPC) as the human central nervous system (CNS) for three conditions of initial (the initial session of wearing the robot, without any previous experience), short-term (the entire first session, e.g., 45 min), and long-term experiences. The results showed that the two methods (model-based and fuzzy logic) outperform the traditional proportional method in providing AAN by considering distinctive human and robot models. Additionally, the CNS actuator model has difficulty in the initial experience and activates both antagonist and agonist muscles to reduce movement oscillations. In the long-term experience, the simulation shows no oscillation when the CNS NMPC learns the robot model and modifies its weights to simulate realistic human behavior. We found that the desired strength of the robot should be increased gradually to ignore unexpected human–robot interactions (e.g., robot vibration, human spasticity). The proposed mid-level controllers can be used for wearable assistive devices, exoskeletons, and rehabilitation robots.
3D Sensor Based Pedestrian Detection by Integrating Improved HHA Encoding and Two-Branch Feature Fusion
Authors: Tan Fang , Xia Zhaoqiang , Ma Yupeng , Feng Xiaoyi
Published: January 2022

Pedestrian detection is vitally important in many computer vision tasks but still suffers from some problems, such as illumination and occlusion if only the RGB image is exploited, especially in outdoor and long-range scenes. Combining RGB with depth information acquired by 3D sensors may effectively alleviate these problems. Therefore, how to utilize depth information and how to fuse RGB and depth features are the focus of the task of RGB-D pedestrian detection. This paper first improves the most commonly used HHA method for depth encoding by optimizing the gravity direction extraction and depth values mapping, which can generate a pseudo-color image from the depth information. Then, a two-branch feature fusion extraction module (TFFEM) is proposed to obtain the local and global features of both modalities. Based on TFFEM, an RGB-D pedestrian detection network is designed to locate the people. In experiments, the improved HHA encoding method is twice as fast and achieves more accurate gravity-direction extraction on four publicly-available datasets. The pedestrian detection performance of the proposed network is validated on KITTI and EPFL datasets and achieves state-of-the-art performance. Moreover, the proposed method achieved third ranking among all published works on the KITTI leaderboard. In general, the proposed method effectively fuses RGB and depth features and overcomes the effects of illumination and occlusion problems in pedestrian detection.
Effectiveness of Semi-Supervised Learning and Multi-Source Data in Detailed Urban Landuse Mapping with a Few Labeled Samples
Authors: Sun Bo , Zhang Yang , Zhou Qiming , Zhang Xinchang
Published: January 2022

Detailed urban landuse information plays a fundamental role in smart city management. A sufficient sample size has been identified as a very crucial pre-request in machine learning algorithms for urban landuse classification. However, it is often difficult to recognize and label landuse categories from remote sensing images alone. Alternatively, field investigation is time-consuming with a high demand in human resources and monetary cost. Therefore, previous studies on urban landuse classification have often relied on a small size of labeled samples with very uneven spatial distribution. This study aims to explore the effectiveness of a semi-supervised classification framework with multi-source data for detailed urban landuse classification with a few labeled samples. A disagreement-based semi-supervised learning approach, the Co-Forest, was employed and compared with traditional supervised methods (e.g., random forest and XGBoost). Multi-source geospatial data were utilized including optical and nighttime light remote sensing and geospatial big data, which present the physical and socio-economic features of landuse categories. Taking urban landuse classification in Shenzhen City as a case, results show that the classification accuracy of the semi-supervised method are generally on par with that of traditional supervised methods, and less labeled samples are needed to achieve a comparable result under different training set ratios. Given a small sample size, the accuracy tends to be stable with training samples no less than 5% in total. Our results also indicate that the classification accuracy by using multi-source data is significantly higher than that with any single data source being applied. Among these data, map POI and high-resolution optical remote sensing data make larger contributions on the classification, followed by mobile data and nighttime light remote sensing data.
Comparative Analysis of Multi-Platform, Multi-Resolution, Multi-Temporal LiDAR Data for Forest Inventory
Authors: Lin Yi-Chun , Shao Jinyuan , Shin Sang-Yeop , Saka Zainab , Joseph Mina , Manish Raja , Fei Songlin , Habib Ayman
Published: January 2022

LiDAR technology is rapidly evolving as various new systems emerge, providing unprecedented data to characterize forest vertical structure. Data from different LiDAR systems present distinct characteristics owing to a combined effect of sensor specifications, data acquisition strategies, as well as forest conditions such as tree density and canopy cover. Comparative analysis of multi-platform, multi-resolution, and multi-temporal LiDAR data provides guidelines for selecting appropriate LiDAR systems and data processing tools for different research questions, and thus is of crucial importance. This study presents a comprehensive comparison of point clouds from four systems, linear and Geiger-mode LiDAR from manned aircraft and multi-beam LiDAR on unmanned aerial vehicle (UAV), and in-house developed Backpack, with the consideration of different forest canopy cover scenarios. The results suggest that the proximal Backpack LiDAR can provide the finest level of information, followed by UAV LiDAR, Geiger-mode LiDAR, and linear LiDAR. The emerging Geiger-mode LiDAR can capture a significantly higher level of detail while operating at a higher altitude as compared to the traditional linear LiDAR. The results also show: (1) canopy cover percentage has a critical impact on the ability of aerial and terrestrial systems to acquire information corresponding to the lower and upper portions of the tree canopy, respectively; (2) all the systems can obtain adequate ground points for digital terrain model generation irrespective of canopy cover conditions; and (3) point clouds from different systems are in agreement within a ±3 cm and ±7 cm range along the vertical and planimetric directions, respectively.
Performance Analysis of Zero-Difference GPS L1/L2/L5 and Galileo E1/E5a/E5b/E6 Point Positioning Using CNES Uncombined Bias Products
Authors: Zhao Lei , Blunt Paul , Yang Lei
Published: January 2022

The modernization of Global Navigation Satellite System (GNSS) including the transmission of signals on multiple frequencies has greatly promoted the development of the popular PPP (Precise Point Positioning) technique. A key issue of multi-frequency PPP is the handling of the observable-specific signal biases in order to allow for carrier-phase ambiguity resolution (AR). As a result, PPP modeling at a user side in the multi-frequency case varies depending on the definition of the applied phase bias products. In this study, we investigate the positioning performance of GPS L1/L2/L5 and Galileo E1/E5a/E5b/E6 undifferenced ionosphere-float model in the conventional PPP mode and the single-epoch mode using the uncombined code and phase bias products generated at the French CNES (Centre National D’Etudes Spatiales). A series of widelane ambiguities are configured in our multi-frequency PPP functional model instead of forming the classical Melbourne–Wübbena (MW) combination. The best integer equivariant (BIE) estimator is used for the ambiguity resolution in a conventional cascading scheme according to the wavelength of the combined ambiguities for each constellation. Real data collected at IGS stations with a 30-s sampling interval is applied to evaluate the above models. For the conventional kinematic PPP configuration, a significant accuracy improvement of 63% on the east component of the fixed solution is obtained with respect to the ambiguity-float solution. The PPP convergence is accelerated by 17% after the AR. Regarding the single-epoch positioning, an accuracy of 32 and 31 cm for north and east components can be achieved, respectively, (68th percentile) with the instantaneous widelane-ambiguity resolution, which is improved by 13% and 16% compared to multi-frequency code-based or float solution.
A Novel High-Squint Spotlight SAR Raw Data Simulation Scheme in 2-D Frequency Domain
Authors: Guo Zhengwei , Fu Zewen , Chang Jike , Wu Lin , Li Ning
Published: January 2022

Raw data simulation is the front-end work of synthetic aperture radar (SAR), which is of great significance. For high-squint spotlight SAR, the frequency domain simulation algorithm is invalid because of the range-azimuth coupling effect. In order to realize high-squint spotlight SAR raw data simulation in the frequency domain, an algorithm based on coordinate transformation and non-uniform fast Fourier transform (NUFFT) is proposed. This algorithm generates broadside raw data using a two-dimensional (2-D) frequency simulation algorithm; then, coordinate transformation is used by analyzing the characteristics of broadside and high-squint spotlight SAR. After coordinate transformation, NUFFT is carried out to realize the coupling relation in the 2-D frequency domain. Since the coordinate transformation ignores the influence of range walk, the range walk is compensated after NUFFT. As a result, compared with the traditional squint spotlight SAR frequency domain simulation algorithm, the proposed algorithm can improve the accuracy of point and distributed target imaging results, and the efficiency of the proposed algorithm can be significantly improved in contrast the traditional time domain algorithm.
Signatures of Equatorial Plasma Bubbles and Ionospheric Scintillations from Magnetometer and GNSS Observations in the Indian Longitudes during the Space Weather Events of Early September 2017
Authors: Vankadara Ram Kumar , Panda Sampad Kumar , Amory-Mazaudier Christine , Fleury Rolland , Devanaboyina Venkata Ratnam , Pant Tarun Kumar , Jamjareegulgarn Punyawi , Haq Mohd Anul , Okoh Daniel , Seemala Gopi Krishna
Published: January 2022

Scintillation due to ionospheric plasma irregularities remains a challenging task for the space science community as it can severely threaten the dynamic systems relying on space-based navigation services. In the present paper, we probe the ionospheric current and plasma irregularity characteristics from a latitudinal arrangement of magnetometers and Global Navigation Satellite System (GNSS) stations from the equator to the far low latitude location over the Indian longitudes, during the severe space weather events of 6–10 September 2017 that are associated with the strongest and consecutive solar flares in the 24th solar cycle. The night-time influence of partial ring current signatures in ASYH and the daytime influence of the disturbances in the ionospheric E region electric currents (Diono) are highlighted during the event. The total electron content (TEC) from the latitudinal GNSS observables indicate a perturbed equatorial ionization anomaly (EIA) condition on 7 September, due to a sequence of M-class solar flares and associated prompt penetration electric fields (PPEFs), whereas the suppressed EIA on 8 September with an inverted equatorial electrojet (EEJ) suggests the driving disturbance dynamo electric current (Ddyn) corresponding to disturbance dynamo electric fields (DDEFs) penetration in the E region and additional contributions from the plausible storm-time compositional changes (O/N2) in the F-region. The concurrent analysis of the Diono and EEJ strengths help in identifying the pre-reversal effect (PRE) condition to seed the development of equatorial plasma bubbles (EPBs) during the local evening sector on the storm day. The severity of ionospheric irregularities at different latitudes is revealed from the occurrence rate of the rate of change of TEC index (ROTI) variations. Further, the investigations of the hourly maximum absolute error (MAE) and root mean square error (RMSE) of ROTI from the reference quiet days’ levels and the timestamps of ROTI peak magnitudes substantiate the severity, latitudinal time lag in the peak of irregularity, and poleward expansion of EPBs and associated scintillations. The key findings from this study strengthen the understanding of evolution and the drifting characteristics of plasma irregularities over the Indian low latitudes.
Dolines and Cats: Remote Detection of Karst Depressions and Their Application to Study Wild Felid Ecology
Authors: Čonč Špela , Oliveira Teresa , Portas Ruben , Černe Rok , Breg Valjavec Mateja , Krofel Miha
Published: January 2022

Automatic methods for detecting and delineating relief features allow remote and low-cost mapping, which has an outstanding potential for wildlife ecology and similar research. We applied a filled-DEM (digital elevation model) method using LiDAR (Light Detection and Ranging) data to automatically detect dolines and other karst depressions in a rugged terrain of the Dinaric Mountains, Slovenia. Using this approach, we detected 9711 karst depressions in a 137 km2 study area and provided their basic morphometric characteristics, such as perimeter length, area, diameter, depth, and slope. We performed visual validation based on shaded relief, which indicated 83.5% accordance in detecting depressions. Although the method has some drawbacks, it proved suitable for detection, general spatial analysis, and calculation of morphometric characteristics of depressions over a large scale in remote and forested areas. To demonstrate its applicability for wildlife research, we applied it in a preliminary study in combination with GPS-telemetry data to assess the selection of these features by two wild felids, the Eurasian lynx (Lynx lynx) and the European wildcat (Felis silvestris). Both species selected for vicinity of karst depressions, among which they selected for larger karst depressions. Lynx also regularly killed ungulate prey near these features, as we found more than half of lynx prey remains inside or in close vicinity of karst depressions. These results illustrate that karstic features could play an important role in the ecology of wild felids and warrant further research, which could be considerably assisted with the use of remote detection of relief features.
Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area
Authors: Florath Janine , Keller Sina
Published: January 2022

Bushfires pose a severe risk, among others, to humans, wildlife, and infrastructures. Rapid detection of fires is crucial for fire-extinguishing activities and rescue missions. Besides, mapping burned areas also supports evacuation and accessibility to emergency facilities. In this study, we propose a generic approach for detecting fires and burned areas based on machine learning (ML) approaches and remote sensing data. While most studies investigated either the detection of fires or mapping burned areas, we addressed and evaluated, in particular, the combined detection on three selected case study regions. Multispectral Sentinel-2 images represent the input data for the supervised ML models. First, we generated the reference data for the three target classes, burned, unburned, and fire, since no reference data were available. Second, the three regional fire datasets were preprocessed and divided into training, validation, and test subsets according to a defined schema. Furthermore, an undersampling approach ensured the balancing of the datasets. Third, seven selected supervised classification approaches were used and evaluated, including tree-based models, a self-organizing map, an artificial neural network, and a one-dimensional convolutional neural network (1D-CNN). All selected ML approaches achieved satisfying classification results. Moreover, they performed a highly accurate fire detection, while separating burned and unburned areas was slightly more challenging. The 1D-CNN and extremely randomized tree were the best-performing models with an overall accuracy score of 98% on the test subsets. Even on an unknown test dataset, the 1D-CNN achieved high classification accuracies. This generalization is even more valuable for any use-case scenario, including the organization of fire-fighting activities or civil protection. The proposed combined detection could be extended and enhanced with crowdsourced data in further studies.
Spatially Downscaling a Global Evapotranspiration Product for End User Using a Deep Neural Network: A Case Study with the GLEAM Product
Authors: Long Xunjian , Cui Yaokui
Published: January 2022

High spatiotemporal resolution evapotranspiration (ET) data are very important for end users to manage water resources. The global ET product always has a high temporal resolution, but the spatial resolution is too low to meet the requirements of most end users. In this study, we developed a deep neural network (DNN)-based global ET product downscaling algorithm by combining remotely sensed and meteorological data sets as the input data. The relationship between global ET product and input data was built at a low spatial resolution using the DNN. Then, this relationship was applied at high spatial resolution to generate high spatial resolution ET derived from the input data with high spatial resolution. Taking the Global Land Evaporation Amsterdam Model (GLEAM) ET product as an example, downscaled ET was found to be highly consistent with the original GLEAM ET product, but to have high spatial resolution. Field validations showed that the overall coefficient of correlation and root mean square error (bias, Nash–Sutcliffe efficiency coefficient) of the downscaled GLEAM ET is 0.90 and 0.87 mm/d (−0.32 mm/d, 0.62), respectively, indicating high quality. The proposed method bridged the gaps between the global ET product and the requirements of local end users. This will benefit end users in charge of water resources management.
Quantitatively Assessing the Contributions of Dust Aerosols to Direct Radiative Forcing Based on Remote Sensing and Numerical Simulation
Authors: Wang Jinyan , Su Shixiang , Yin Zelun , Sun Caixia , Xie Xiangshan , Wang Tianyu , Yasheng Dilinuer , Chen Jinche , Zhang Xin , Yang Yi
Published: January 2022

Dust aerosols substantially impinge on the Earth’s climate by altering its energy balance, particularly over Northwest China, where dust storms occur frequently. However, the quantitative contributions of dust aerosols to direct radiative forcing (DRF) are not fully understood and warrant in-depth investigations. Taking a typical dust storm that happened during 9–12 April 2020 over Northwest China as an example, four simulation experiments based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) were designed, including a real scenario with dust emissions and three hypothetical scenarios without dust emissions, with dust emissions doubled, and with dust emissions reduced by half, to quantitatively evaluate the contributions of dust aerosols to DRF and then to surface temperature, with particular attention to the differences between daytime and nighttime. Moreover, multi-satellite observations were used to reveal the behavior of dust events and to evaluate the model performance. During the daytime, the net dust radiative forcing induced by dust aerosols was −3.76 W/m2 at the surface (SFC), 3.00 W/m2 in the atmosphere (ATM), and −0.76 W/m2 at the top of the atmosphere (TOA), and thus led to surface air temperature cooling by an average of −0.023 °C over Northwest China. During the nighttime, the net dust radiative forcing was 2.20 W/m2 at the SFC, −2.65 W/m2 in the ATM, and −0.45 W/m2 at the TOA, which then resulted in surface temperature warming by an average of 0.093 °C over Northwest China. These results highlight that the contribution of dust aerosols to DRF is greater during the daytime than that during the nighttime, while exhibiting the opposite impact on surface temperature, as dust can slow down the rate of surface temperature increases (decreases) by reducing (increasing) the surface energy during the daytime (nighttime). Our findings are critical to improving the understanding of the climate effects related to dust aerosols and provide scientific insights for coping with the corresponding disasters induced by dust storms in Northwest China.
Coupling Complementary Strategy to U-Net Based Convolution Neural Network for Detecting Lunar Impact Craters
Authors: Mao Yuqing , Yuan Rongao , Li Wei , Liu Yijing
Published: January 2022

Lunar crater detection plays an important role in lunar exploration, while machine learning (ML) exhibits promising advantages in the field. However, previous ML works almost all used a single type of lunar map, such as an elevation map (DEM) or orthographic projection map (WAC), to extract crater features; the two types of images have individual limitations on reflecting the crater features, which lead to insufficient feature information, in turn influencing the detection performance. To address this limitation, we, in this work, propose feature complementary of the two types of images and accordingly explore an advanced dual-path convolutional neural network (Dual-Path) based on a U-NET structure to effectively conduct feature integration. Dual-Path consists of a contracting path, bridging path, and expanding path. The contracting path separately extracts features from DEM and WAC images by means of two independent input branches, while the bridging layer integrates the two types of features by 1 × 1 convolution. Finally, the expanding path, coupled with the attention mechanism, further learns and optimizes the feature information. In addition, a special deep convolution block with a residual module is introduced to avoid network degradation and gradient disappearance. The ablation experiment and the comparison of four competitive models only using DEM features confirm that the feature complementary can effectively improve the detection performance and speed. Our model is further verified by different regions of the whole moon, exhibiting high robustness and potential in practical applications.
Mapping the Abundance of Multipurpose Agroforestry Faidherbia albida Trees in Senegal
Authors: Lu Tingting , Brandt Martin , Tong Xiaoye , Hiernaux Pierre , Leroux Louise , Ndao Babacar , Fensholt Rasmus
Published: January 2022

Multi-purpose Faidherbia albida trees represent a vital component of agroforestry parklands in West Africa as they provide resources (fodder for livestock, fruits and firewood) and support water lifting and nutrient recycling for cropping. Faidherbia albida trees are characterized by their inverse phenology, growing leaf flowers and pods during the dry season, thereby providing fodder and shedding leaves during the wet season, which minimizes competition with pastures and crops for resources. Multi-spectral and multi-temporal satellite systems and novel computational methods open new doors for classifying single trees and identifying species. This study used a Multi-Layer Perception feedforward artificial neural network to classify pixels covered by Faidherbia albida canopies from Sentinel-2 time series in Senegal, West Africa. To better discriminate the Faidherbia albida signal from the background, monthly images from vegetation indices were used to form relevant variables for the model. We found that NDI54/NDVI from the period covering onset of leaf senescence (February) until end of senescence (leaf-off in June) to be the most important, resulting in a high precision and recall rate of 0.91 and 0.85. We compared our result with a potential Faidherbia albida occurrence map derived by empirical modelling of the species ecology, which deviates notably from the actual species occurrence mapped by this study. We have shown that even small differences in dry season leaf phenology can be used to distinguish tree species. The Faidherbia albida distribution maps, as provided here, will be key in managing farmlands in drylands, helping to optimize economic and ecological services from both tree and crop products.
A Precision Efficient Method for Collapsed Building Detection in Post-Earthquake UAV Images Based on the Improved NMS Algorithm and Faster R-CNN
Authors: Ding Jiujie , Zhang Jiahuan , Zhan Zongqian , Tang Xiaofang , Wang Xin
Published: January 2022

The results of collapsed building detection act as an important reference for damage assessment after an earthquake, which is crucial for governments in order to efficiently determine the affected area and execute emergency rescue. For this task, unmanned aerial vehicle (UAV) images are often used as the data sources due to the advantages of high flexibility regarding data acquisition time and flying requirements and high resolution. However, collapsed buildings are typically distributed in both connected and independent pieces and with arbitrary shapes, and these are generally more obvious in the UAV images with high resolution; therefore, the corresponding detection is restricted by using conventional convolutional neural networks (CNN) and the detection results are difficult to evaluate. In this work, based on faster region-based convolutional neural network (Faster R-CNN), deformable convolution was used to improve the adaptability to the arbitrarily shaped collapsed buildings. In addition, inspired by the idea of pixelwise semantic segmentation, in contrast to the intersection over union (IoU), a new method which estimates the intersected proportion of objects (IPO) is proposed to describe the degree of the intersection of bounding boxes, leading to two improvements: first, the traditional non-maximum suppression (NMS) algorithm is improved by integration with the IPO to effectively suppress the redundant bounding boxes; second, the IPO is utilized as a new indicator to determine positive and negative bounding boxes, and is introduced as a new strategy for precision and recall estimation, which can be considered a more reasonable measurement of the degree of similarity between the detected bounding boxes and ground truth bounding boxes. Experiments show that compared with other models, our work can obtain better precision and recall for detecting collapsed buildings for which an F1 score of 0.787 was achieved, and the evaluation results from the suggested IPO are qualitatively closer to the ground truth. In conclusion, the improved NMS with the IPO and Faster R-CNN in this paper is feasible and efficient for the detection of collapsed buildings in UAV images, and the suggested IPO strategy is more suitable for the corresponding detection result’s evaluation.
A Geomatics Approach in Scan to FEM Process Applied to Cultural Heritage Structure: The Case Study of the “Colossus of Barletta”
Authors: Alfio Vincenzo Saverio , Costantino Domenica , Pepe Massimiliano , Restuccia Garofalo Alfredo
Published: January 2022

Artistic, architectural and Cultural Heritage (CH) structures are often exposed to a high risk of damage caused by seismic events, natural disasters and more by negligence and poor state of preservation and conservation; the use of a series of technologies, based on digital acquisition and high-level data processing, allows the realisation of a three-dimensional model of high detail. In order to preserve structures of particular historical and architectural value, it is necessary to assess their structural stability. In addition, many structures, such as statues, have rather complex geometries. Therefore, it is necessary to identify a methodology able to transform the point cloud generated through a geomatics approach into a model suitable for FEM (Finite Element Analysis) analysis. This process, known as Scan to FEM, is addressed in this paper. The paper shows the case study of the “Colossus of Barletta”, a bronze statue dating back to the 5th century A.D. located in the city of Barletta, Italy. To analyse this structure, a suitable methodology has been developed which is based on the optimisation of the surface model of the structure; in this way, it is possible to obtain an efficient transformation from a digital photogrammetric model with complex geometry into a model suitable for structural finite element analysis. The digital photogrammetry technique was applied for the survey of the structure, which allowed us to obtain a very high-resolution dense point cloud and a geometrically accurate three-dimensional mesh model, i.e., in a TIN (Triangulated Irregular Network) model. Subsequently, the TIN was transformed into a quad mesh model (identifying a suitable reduction value) and finally into NURBS (Non-Uniform Rational Basis-Splines) to be optimised and imported into a finite element calculation software. This geomatics approach has validated an efficient Scan to FEM process; in fact, thanks to this methodology, it is possible to elaborate three-dimensional models with complex geometry and draw a series of considerations related to structural behaviour or specific restoration interventions.
Design and Control of a Series–Parallel Elastic Actuator for a Weight-Bearing Exoskeleton Robot
Authors: Wang Tianshuo , Zheng Tianjiao , Zhao Sikai , Sui Dongbao , Zhao Jie , Zhu Yanhe
Published: January 2022

Weight-bearing exoskeletons are robots that need to carry loads and interact with humans frequently. Therefore, the actuators of these exoskeletons are supposed to be capable of outputting sufficient force with high compliance and little weight. A series–parallel elastic actuator (SPEA) is designed, in this work, to meet the demanding requirements of an exoskeleton robot called PALExo. A gas spring is installed in parallel with an electric cylinder to adjust the force output range of the actuator according to the needs of the exoskeleton. A series elastic module (SEM) is installed in series with the electric cylinder and gas spring to improve the compliance of the actuator, the stiffness of which is variable to adapt to the different stiffness requirements of the exoskeleton’s legs in the standing phase and swinging phase. A force controller combining dynamic compensation and a cascade control with an inner velocity loop and a disturbance observer is designed for the SPEA. The performance of the force controller is verified by experiments and the results demonstrate that the controller has good adaptability to the stiffness of the SEM.
Multispectral Optical Remote Sensing for Water-Leak Detection
Authors: Krapez Jean-Claude , Sanchis Muñoz Javier , Mazel Christophe , Chatelard Christian , Déliot Philippe , Frédéric Yves-Michel , Barillot Philippe , Hélias Franck , Barba Polo Juan , Olichon Vincent , Serra Guillaume , Brignolles Céline , Carvalho Alexandra , Carreira Duarte , Oliveira Anabela , Alves Elsa , Fortunato André B. , Azevedo Alberto , Benetazzo Paolo , Bertoni Alessandro , Le Goff Isabelle
Published: January 2022

Water losses from water distribution means have a high environmental impact in terms of natural resource depletion (water, energy, ecosystems). This work aims to develop an optical airborne surveillance service for the detection of water leaks (WADI—Water-tightness Airborne Detection Implementation) to provide water utilities with adequate and timely information on leaks in water transportation mains outside urban areas. Firstly, a series of measurement campaigns were performed with two hyperspectral cameras and a thermal infrared camera in order to select the most appropriate wavelengths and combinations thereof for best revealing high moisture areas, which are taken as a proxy for water leakage. The Temperature-Vegetation-Index method (T-VI, also known as Triangle/Trapezoid method) was found to provide the highest contrast-to-noise ratio. This preliminary work helped select the most appropriate onboard instrumentation for two types of aerial platforms, manned (MAV) and unmanned (UAV). Afterwards, a series of measurement campaigns were performed from 2017 to 2019 in an operational environment over two water distribution networks in France and Portugal. Artificial leaks were introduced and both remote sensing platforms successfully detected them when excluding the unfavorable situations of a recent rain event or high vegetation presence. With the most recent equipment configuration, known and unknown real leaks in the overflown part of a water transportation network in Portugal have been detected. A significant number of false alarms were also observed which were due either to natural water flows (groundwater exfiltration, irrigation runoff and ponds) or to vegetation-cover variability nearby water-distribution nodes. Close interaction with the water utilities, and ancillary information like topographic factors (e.g., slope orientation), are expected to reduce the false alarm rates and improve WADI’s methodology performance.
Minimization of Energy Losses in the BLDC Motor for Improved Control and Power Supply of the System under Static Load
Authors: Sikora Andrzej , Zielonka Adam , Woźniak Marcin
Published: January 2022

In this article we present the optimal method of controlling and supplying a BLDC motor under static load, proposed and implemented as a result of the research. A research infrastructure was developed to measure and analyze variants of the motor control. In the research we determine possible losses of electric energy released in the form of heat in the tested engine elements. The test results showed that the lowest energy losses are provided by the variant where the control signals are obtained from an external magnetic disc and the motor is powered by an additional DC/DC converter. The conclusions from the analyses allowed for the selection of the best variant of motor control and power supply, which minimizes energy losses during the BLDC motor operation.
A Smart Multi-Sensor Device to Detect Distress in Swimmers
Authors: Jalalifar Salman , Kashizadeh Afsaneh , Mahmood Ishmam , Belford Andrew , Drake Nicolle , Razmjou Amir , Asadnia Mohsen
Published: January 2022

Drowning is considered amongst the top 10 causes of unintentional death, according to the World Health Organization (WHO). Therefore, anti-drowning systems that can save lives by preventing and detecting drowning are much needed. This paper proposes a robust and waterproof sensor-based device to detect distress in swimmers at varying depths and different types of water environments. The proposed device comprises four main components, including heart rate, blood oxygen level, movement, and depth sensors. Although these sensors were designed to work together to boost the system’s capability as an anti-drowning device, each could operate independently. The sensors were able to determine the heart rate to an accuracy of 1 beat per minute (BPM), 1% SpO2, the acceleration with adjustable sensitivities of ±2 g, ±4 g, ±8 g, and ±16 g, and the depth up to 12.8 m. The data obtained from the sensors were sent to a microcontroller that compared the input data to adjustable threshold values to detect dangerous situations. Being in hazardous situations for more than a specific time activated the alarming system. Based on the comparison made in the program and measuring the time of submersion, a message indicating drowning or safe was sent to a lifeguard to continuously monitor the swimmer’ condition via Wi-Fi to an IP address reachable by a mobile phone or laptop. It is also possible to continuously monitor the sensor outputs on the device’s display or the connected mobile phone or laptop. The threshold values could be adjusted based on biometric parameters such as swimming conditions (swimming pool, beach, depth, etc.) and swimmers health and conditions. The functionality of the proposed device was thoroughly tested over a wide range of parameters and under different conditions, both in air and underwater. It was demonstrated that the device could detect a range of potentially hazardous aquatic situations. This work will pave the way for developing an effective drowning sensing system that could save tens of thousands of lives across the globe every year.
Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning
Authors: Mehdizadeh Dastjerdi Aliasghar , Morency Catherine
Published: January 2022

An important question in planning and designing bike-sharing services is to support the user’s travel demand by allocating bikes at the stations in an efficient and reliable manner which may require accurate short-time demand prediction. This study focuses on the short-term forecasting, 15 min ahead, of the shared bikes demand in Montreal using a deep learning approach. Having a set of bike trips, the study first identifies 6 communities in the bike-sharing network using the Louvain algorithm. Then, four groups of LSTM-based architectures are adopted to predict pickup demand in each community. A univariate ARIMA model is also used to compare results as a benchmark. The historical trip data from 2017 to 2021 are used in addition to the extra inputs of demand related engineered features, weather conditions, and temporal variables. The selected timespan allows predicting bike demand during the COVID-19 pandemic. Results show that the deep learning models significantly outperform the ARIMA one. The hybrid CNN-LSTM achieves the highest prediction accuracy. Furthermore, adding the extra variables improves the model performance regardless of its architecture. Thus, using the hybrid structure enriched with additional input features provides a better insight into the bike demand patterns, in support of bike-sharing operational management.