bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterizing the Distribution Pattern and a Physically Based Susceptibility Assessment of Shallow Landslides Triggered by the 2019 Heavy Rainfall Event in Longchuan County, Guangdong Province, China
Authors: Ma Siyuan , Shao Xiaoyi , Xu Chong
Published: August 2022

Rainfall-induced landslides pose a significant threat to the lives and property of residents in the southeast mountainous and hilly area; hence, characterizing the distribution pattern and effective susceptibility mapping for rainfall-induced landslides are regarded as important and necessary measures to remediate the damage and loss resulting from landslides. From 10 June 2019 to 13 June 2019, continuous heavy rainfall occurred in Longchuan County, Guangdong Province; this event triggered extensive landslide disasters in the villages of Longchuan County. Based on high-resolution satellite images, a landslide inventory of the affected area was compiled, comprising a total of 667 rainfall-induced landslides over an area of 108 km2. These landslides consisted of a large number of shallow landslides with a few flowslides, rockfalls, and debris flows, and the majority of them occurred in Mibei and Yanhua villages. The inventory was used to analyze the distribution pattern of the landslides and their relationship with topographical, geological, and hydrological factors. The results showed that landslide abundance was closely related to slope angle, TWI, and road density. The landslide area density (LAD) increased with the increase in the above three influencing factors and was described by an exponential or linear relationship. In addition, southeast and south aspect hillslopes were more prone to collapse than the northwest­–north aspect ones because of the influence of the summer southeast monsoon. A new open-source tool named MAT.TRIGRS(V1.0) was adopted to establish the landslide susceptibility map in landslide abundance areas and to back-analyze the response of the rainfall process to the change in landslide stability. The prediction results were roughly consistent with the actual landslide distribution, and most areas with high susceptibility were located on both sides of the river valley; that is, the areas with relatively steep slopes. The slope stability changes in different periods revealed that the onset of heavy rain on 10 June 2019 was the main triggering factor of these group‑occurring landslides, and the subsequent rainfall with low intensity had little impact on slope stability.
NLOS Identification- and Correction-Focused Fusion of UWB and LiDAR-SLAM Based on Factor Graph Optimization for High-Precision Positioning with Reduced Drift
Authors: Chen Zhijian , Xu Aigong , Sui Xin , Hao Yuting , Zhang Cong , Shi Zhengxu
Published: August 2022

In this study, we propose a tightly coupled integrated method of ultrawideband (UWB) and light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) for global navigation satellite system (GNSS)-denied environments to achieve high-precision positioning with reduced drift. Specifically, we focus on non-line-of-sight (NLOS) identification and correction. In previous work, we utilized laser point cloud maps to identify and exclude NLOS measurements in real time to attenuate their severe effects on the integrated system. However, the complete exclusion of NLOS measurements will likely lead to deterioration in the dilution of precision (DOP) for the remaining line-of-sight (LOS) anchors, counterproductively introducing large positioning errors into the integrated system. Therefore, this study considers the ranging accuracy and geometric distribution of UWB anchors and innovatively proposes an NLOS correction method using a grey prediction model. For a poor line-of-sight (LOS) anchor geometric distribution, the grey prediction model is used to fill in the gaps by predicting the NLOS measurements based on historical measurements. Including the corrected measurements effectively improves the original poor geometric configuration, improving the system positioning accuracy. Since conventional filtering-based fusion methods are exceedingly sensitive to measurement outliers, we use state-of-the-art factor graph optimization (FGO) to tightly integrate the UWB measurements (LOS and corrected measurements) with LiDAR-SLAM. The temporal correlation between measurements and the redundant system measurements effectively enhance the robustness of the integrated system. Experimental results show that the tightly coupled integrated method combining NLOS correction and FGO improves the positioning accuracy under a poor geometric distribution, increases the system availability, and achieves better positioning than filtering-based fusion methods with a root-mean-square error of 0.086 m in the plane direction, achieving subdecimeter indoor high-precision positioning.
Evaluation of HY-2 Series Satellites Mapping Capability on Mesoscale Eddies
Authors: Yu Fangjie , Qi Juanjuan , Jia Yongjun , Chen Ge
Published: August 2022

With the launches of HY-2B, HY-2C and HY-2D, the National Satellite Ocean Application Service (NSOAS) successfully established the first batch of HY-2 constellation on 19 May 2021, dedicated to detecting the marine dynamic environment. While the validation and calibration of HY-2 series satellites have been well studied, using both in situ and cross-track measurements, further evaluation is required of HY-2 series satellites mapping capability on mesoscale eddies. In this study, we conducted two groups of pseudo-observations obtained by ocean models output at positions and times of the satellite along ground tracks. The two altimetry satellite combinations are the Jason-3+Sentinel-3A/B configuration (reference group) and the Jason-3+Sentinel-3A/B+HY-2B/C/D configuration (evaluation group). Three regions at different latitudes were selected as the study areas. Then, we gridded the satellite along ground tracks into synthetic SSH products for each satellite configuration group. Finally, the attributes of mesoscale eddies extracted from gridded products were analyzed to evaluate the mapping capability of HY-2 series satellites. Our results suggest that the gridded products of evaluation groups are more similar to model data compared to reference groups, with a maximum difference reduced by 23%. In terms of eddy identification, the evaluation groups using HY-2 series satellites can alleviate eddy incorrect estimation and add more details to the spatial distribution of eddy radius and amplitude. Additionally, the evaluation groups displayed better performance in mesoscale eddy detection, with a maximum enhancement of 14% and 16% in eddy recognition rate and accuracy.
The Spectrum of Light Emitted by LED Using a CMOS Sensor-Based Digital Camera and Its Application
Authors: Park Hyeon-Woo , Choi Ji-Won , Choi Ji-Young , Joo Kyung-Kwang , Kim Na-Ri
Published: August 2022

We introduced a digital photo image analysis in color space to estimate the spectrum of fluor components dissolved in a liquid scintillator sample through the hue and wavelength relationship. Complementary metal oxide semiconductor (CMOS) image sensors with Bayer color filter array (CFA) technology in the digital camera were used to reconstruct and decode color images. Hue and wavelength are closely related. To date, no literature has reported the hue and wavelength relationship measurements, especially for blue or close to the UV region. The non-linear hue and wavelength relationship in the blue region was investigated using a light emitting diode source. We focused on this wavelength region, because the maximum quantum efficiency of the bi-alkali photomultiplier tube (PMT) is around 430 nm. It is necessary to have a good understanding of this wavelength region in PMT-based experiments. The CMOS Bayer CFA approach was sufficient to estimate the fluor emission spectrum in the liquid scintillator sample without using an expensive spectrophotometer.
A Fully Integrated, Power-Efficient, 0.07–2.08 mA, High-Voltage Neural Stimulator in a Standard CMOS Process
Authors: Palomeque-Mangut David , Rodríguez-Vázquez Ángel , Delgado-Restituto Manuel
Published: August 2022

This paper presents a fully integrated high-voltage (HV) neural stimulator with on-chip HV generation. It consists of a neural stimulator front-end that delivers stimulation currents up to 2.08 mA with 5 bits resolution and a switched-capacitor DC-DC converter that generates a programmable voltage supply from 4.2 V to 13.2 V with 4 bits resolution. The solution was designed and fabricated in a standard 180 nm 1.8 V/3.3 V CMOS process and occupied an active area of 2.34 mm2. Circuit-level and block-level techniques, such as a proposed high-compliance voltage cell, have been used for implementing HV circuits in a low-voltage CMOS process. Experimental validation with an electrical model of the electrode–tissue interface showed that (1) the neural stimulator can handle voltage supplies up to 4 times higher than the technology’s nominal supply, (2) residual charge—without passive discharging phase—was below 0.12% for the whole range of stimulation currents, (3) a stimulation current of 2 mA can be delivered with a voltage drop of 0.9 V, and (4) an overall power efficiency of 48% was obtained at maximum stimulation current.
Automatic Extraction of Power Lines from Aerial Images of Unmanned Aerial Vehicles
Authors: Song Jiang , Qian Jianguo , Li Yongrong , Liu Zhengjun , Chen Yiming , Chen Jianchang
Published: August 2022

Automatic power line extraction from aerial images of unmanned aerial vehicles is one of the key technologies of power line inspection. However, the faint power line targets and complex image backgrounds make the extraction of power lines a greater challenge. In this paper, a new power line extraction method is proposed, which has two innovative points. Innovation point one, based on the introduction of the Mask RCNN network algorithm, proposes a block extraction strategy to realize the preliminary extraction of power lines with the idea of “part first and then the whole”. This strategy globally reduces the anchor frame size, increases the proportion of power lines in the feature map, and reduces the accuracy degradation caused by the original negative anchor frames being misclassified as positive anchor frames. Innovation point two, the proposed connected domain group fitting algorithm solves the problem of broken and mis-extracted power lines even after the initial extraction and solves the problem of incomplete extraction of power lines by background texture interference. Through experiments on 60 images covering different complex image backgrounds, the performance of the proposed method far exceeds that of commonly used methods such as LSD, Yolact++, and Mask RCNN. DSCPL, TPR, precision, and accuracy are as high as 73.95, 81.75, 69.28, and 99.15, respectively, while FDR is only 30.72. The experimental results show that the proposed algorithm has good performance and can accomplish the task of power line extraction under complex image backgrounds. The algorithm in this paper solves the main problems of power line extraction and proves the feasibility of the algorithm in other scenarios. In the future, the dataset will be expanded to improve the performance of the algorithm in different scenarios.
Weighted Iterative CD-Spline for Mitigating Occlusion Effects on Building Boundary Regularization Using Airborne LiDAR Data
Authors: dos Santos Renato César , Habib Ayman F. , Galo Mauricio
Published: August 2022

Building occlusions usually decreases the accuracy of boundary regularization. Thus, it is essential that modeling methods address this problem, aiming to minimize its effects. In this context, we propose a weighted iterative changeable degree spline (WICDS) approach. The idea is to use a weight function for initial building boundary points, assigning a lower weight to the points in the occlusion region. As a contribution, the proposed method allows the minimization of errors caused by the occlusions, resulting in a more accurate contour modeling. The conducted experiments are performed using both simulated and real data. In general, the results indicate the potential of the WICDS approach to model a building boundary with occlusions, including curved boundary segments. In terms of Fscore and PoLiS, the proposed approach presents values around 99% and 0.19 m, respectively. Compared with the previous iterative changeable degree spline (ICDS), the WICDS resulted in an improvement of around 6.5% for completeness, 4% for Fscore, and 0.24 m for the PoLiS metric.
A New Approach for Detecting Fundus Lesions Using Image Processing and Deep Neural Network Architecture Based on YOLO Model
Authors: Santos Carlos , Aguiar Marilton , Welfer Daniel , Belloni Bruno
Published: August 2022

Diabetic Retinopathy is one of the main causes of vision loss, and in its initial stages, it presents with fundus lesions, such as microaneurysms, hard exudates, hemorrhages, and soft exudates. Computational models capable of detecting these lesions can help in the early diagnosis of the disease and prevent the manifestation of more severe forms of lesions, helping in screening and defining the best form of treatment. However, the detection of these lesions through computerized systems is a challenge due to numerous factors, such as the characteristics of size and shape of the lesions, noise and the contrast of images available in the public datasets of Diabetic Retinopathy, the number of labeled examples of these lesions available in the datasets and the difficulty of deep learning algorithms in detecting very small objects in digital images. Thus, to overcome these problems, this work proposes a new approach based on image processing techniques, data augmentation, transfer learning, and deep neural networks to assist in the medical diagnosis of fundus lesions. The proposed approach was trained, adjusted, and tested using the public DDR and IDRiD Diabetic Retinopathy datasets and implemented in the PyTorch framework based on the YOLOv5 model. The proposed approach reached in the DDR dataset an mAP of 0.2630 for the IoU limit of 0.5 and F1-score of 0.3485 in the validation stage, and an mAP of 0.1540 for the IoU limit of 0.5 and F1-score of 0.2521, in the test stage. The results obtained in the experiments demonstrate that the proposed approach presented superior results to works with the same purpose found in the literature.
Measuring Respiratory Motion for Supporting the Minimally Invasive Destruction of Liver Tumors
Authors: Spinczyk Dominik
Published: August 2022

Objective: Destroying liver tumors is a challenge for contemporary interventional radiology. The aim of this work is to compare different techniques used for the measurement of respiratory motion, as this is the main hurdle to the effective implementation of this therapy. Methods: Laparoscopic stereoscopic reconstruction of point displacements on the surface of the liver, observation of breathing using external markers placed on the surface of the abdominal cavity, and methods for registration of the surface of the abdominal cavity during breathing were implemented and evaluated. Results: The following accuracies were obtained: above 4 mm and 0.5 mm, and below 8 mm for laparoscopic, skin markers, and skin surface registration methods, respectively. Conclusions: The clinical techniques and accompanying imaging modalities employed to destroy liver tumors, as well as the advantages and limitations of the proposed methods, are presented. Further directions for their development are also indicated.
Software Defined Radio-Based Wireless Sensing System
Authors: Xu Yihan , Amineh Reza K. , Dong Ziqian , Li Fang , Kirton Kayla , Kohler Michael
Published: August 2022

In this paper, we investigate the application of using software-defined radio (SDR) and surface acoustic wave (SAW) device for wireless measurement of the response of in situ sensors. SDR uses software to realize different communication functions. After collecting the magnitude and phase of the response at discrete frequencies, we apply inverse Fourier transform to analyze the time domain responses which, in turn, allows for monitoring the changes of the response of the in situ sensor. We employ microwave signal flow graph concepts to improve the quality of the received signals. Comparing the normalized results obtained by SDR with those obtained from a commercial vector network analyzer (VNA), we demonstrate that the results are sufficiently close, and the SDR-based experiments can provide satisfactory measurement of the in-situ sensors. The objective is to eventually employ this wireless measurement system for soil nutrient sensing.
VibroTouch: Active Tactile Sensor for Contact Detection and Force Sensing via Vibrations
Authors: Sandykbayeva Danissa , Kappassov Zhanat , Orazbayev Bakhtiyar
Published: August 2022

Accurate and fast contact detection between a robot manipulator and objects is crucial for safe robot–object and human–robot interactions. Traditional collision detection techniques relied on force–torque sensors and Columb friction cone estimation. However, the strain gauges used in the conventional force sensors require low-noise and high-precision electronics to deliver the signal to the final user. The Signal-to-Noise Ratio (SNR) in these devices is still an issue in light contact detection. On the other hand, the Eccentric Rotating Mass (ERM) motors are very sensitive to subtle touch as their vibrating resonant state loses immediately. The vibration, in this case, plays a core role in triggering the tactile event. This project’s primary goal is to use generated and received vibrations to establish the scope of object properties that can be obtained through low-frequency generation on one end and Fourier analysis of the accelerometer data on the other end. The main idea behind the system is the phenomenon of change in vibration propagation patterns depending on the grip properties. Moreover, the project’s original aim is to gather enough information on vibration feedback on objects of various properties and compare them. These data sets are further analyzed in terms of frequency and applied grip force correlations in order to prepare the ground for pattern extraction and recognition based on the physical properties of an object.
An On-Demand TDMA Approach Optimized for Low-Latency IoT Applications
Authors: Prapas Antonios , Kantelis Konstantinos F. , Nicopolitidis Petros , Papadimitriou Georgios I.
Published: August 2022

The never-ending evolution of the Internet of Things ecosystem is reshaping the arena of wireless communications and competing against conventional networking solutions in fields such as battery life, device and deployment cost, coverage, and support for an immense number of devices. Inspired by this phenomenon, this paper presents a novel Medium Access Control protocol utilizing long-range technology, based on a Time Division Multiple Access communication protocol variant, adjusted to make better use of each device’s hardware. Focusing on Low Power Wide Area Network applications, this implementation improves data latency and offers amplified performance due to better network awareness and dynamic time slot rescheduling. Various simulation scenarios were contrived to evaluate the protocol’s performance. The results instate the proposed algorithm as a promising access scheme for the IoT field.
An Augmented Reality-Assisted Prognostics and Health Management System Based on Deep Learning for IoT-Enabled Manufacturing
Authors: Wang Liping , Tang Dunbing , Liu Changchun , Nie Qingwei , Wang Zhen , Zhang Linqi
Published: August 2022

With increasingly advanced Internet of Things (IoT) technology, the composition of workshop equipment has become more and more complex. Based on this, the rate of system performance degradation and the probability of fault have both increased. Owing to this, not only has the difficulty of constructing the remaining useful life (RUL) model increased but also the improvement in speed of maintenance personnel cannot keep up with the speed of equipment replacement. Therefore, an augmented reality (AR)-assisted prognostics and health management system based on deep learning for IoT-enabled manufacturing is proposed in this paper. Firstly, the feature extraction model based on Convolutional Neural Network-Particle Swarm Optimization (PSO-CNN) is proposed with the purpose of excavating the internal associations in large amounts of production data. Based on this, the high-accuracy RUL prediction is accomplished by Gate Recurrent Unit (GRU)-attention, which can capture the long-term and short-term dependencies of time series and successfully solve the gradient disappearance problem of RNN. Moreover, more attention will be paid to important content with the help of the attention mechanism. Additionally, high-efficiency maintenance guidance and visible instructions can be accomplished by AR. On top of this, the remote expert can offer help when maintenance personnel encounters tough problems. Finally, a real case was implemented in a typical IoT-enabled workshop, which validated the effectiveness of the proposed approach.
Low-Complexity Joint 3D Super-Resolution Estimation of Range Velocity and Angle of Multi-Targets Based on FMCW Radar
Authors: Li Yingchun , Long Qi , Wu Zhongjie , Zhou Zhiquan
Published: August 2022

Multi-dimensional parameters joint estimation of multi-targets is introduced to implement super-resolution sensing in range, velocity, azimuth angle, and elevation angle for frequency-modulated continuous waveform (FMCW) radar systems. In this paper, a low complexity joint 3D super-resolution estimation of range, velocity, and angle of multi-targets is proposed for an FMCW radar with a uniform linear array. The proposed method firstly constructs the size-reduced 3D matrix in the frequency domain for the system model of an FMCW radar system. Secondly, the size-reduced 3D matrix is established, and low complexity three-level cascaded 1D spectrum estimation implemented by applying the Lagrange multiplier method is developed. Finally, the low complexity joint 3D super-resolution algorithms are validated by numerical experiments and with a 77 GHz FMCW radar built by Texas Instruments, with the proposed algorithm achieving significant estimation performance compared to conventional algorithms.
Study on Spatial and Temporal Characteristics of Surface Albedo at the Northern Edge of the Badain Jaran Desert Based on C + STNLFFM Model
Authors: He Peng , Bi Rutian , Xu Lishuai , Yang Fan , Wang Jingshu , Cao Chenbin
Published: August 2022

Obtaining surface albedo data with high spatial and temporal resolution is essential for measuring the factors, effects, and change mechanisms of regional land-atmosphere interactions in deserts. In order to obtain surface albedo data with higher accuracy and better applicability in deserts, we used MODIS and OLI as data sources, and calculated the daily surface albedo data, with a spatial resolution of 30 m, of Guaizi Lake at the northern edge of the Badain Jaran Desert in 2016, using the Spatial and Temporal Non-Local Filter-based Fusion Model (STNLFFM) and topographical correction model (C model). We then compared the results of STNLFFM and C + STNLFFM for fusion accuracy, and for spatial and temporal distribution differences in surface albedo over different underlying surfaces. The results indicated that, compared with STNLFFM surface albedo and MODIS surface albedo, the relative error of C + STNLFFM surface albedo decreased by 2.34% and 3.57%, respectively. C + STNLFFM can improve poor applicability of MODIS in winter, and better responds to the changes in the measured value over a short time range. After the correction of the C model, the spatial difference in surface albedo over different underlying surfaces was enhanced, and the spatial differences in surface albedo between shifting dunes and semi-shifting dunes, fixed dunes and saline-alkali land, and the Gobi and saline-alkali land were significant. C + STNLFFM maintained the spatial and temporal distribution characteristics of STNLFFM surface albedo, but the increase in regional aerosol concentration and thickness caused by frequent dust storms weakened the spatial difference in surface albedo over different underlying surfaces in March, which led to the overcorrection of the C model.
BRefine: Achieving High-Quality Instance Segmentation
Authors: Yu Jimin , Yang Xiankun , Zhou Shangbo , Wang Shougang , Hu Shangguo
Published: August 2022

Instance segmentation has been developing rapidly in recent years. Mask R-CNN, a two-stage instance segmentation approach, has demonstrated exceptional performance. However, the masks are still very coarse. The downsampling operation of the backbone network and the ROIAlign layer loses much detailed information, especially from large targets. The sawtooth effect of the edge mask is caused by the lower resolution. A lesser percentage of boundary pixels leads to not-fine segmentation. In this paper, we propose a new method called Boundary Refine (BRefine) that achieves high-quality segmentation. This approach uses FCN as the foundation segmentation architecture, and forms a multistage fusion mask head with multistage fusion detail features to improve mask resolution. However, the FCN architecture causes inconsistencies in multiscale segmentation. BRank and sort loss (BR and S loss) is proposed to solve the problems of segmentation inconsistency and the difficulty of boundary segmentation. It is combined with rank and sort loss, and boundary region loss. BRefine can handle hard-to-partition boundaries and output high-quality masks. On the COCO, LVIS, and Cityscapes datasets, BRefine outperformed Mask R-CNN by 3.0, 4.2, and 3.5 AP, respectively. Furthermore, on the COCO dataset, the large objects improved by 5.0 AP.
ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods
Authors: Li Chenghao , Fu Yuhui , Ouyang Ruihong , Liu Yu , Hou Xinwen
Published: August 2022

The incidence of maritime accidents can be significantly reduced by identifying the deck officer’s fatigue levels. The development of car driver fatigue detectors has employing electroencephalogram (EEG)-based technologies in recent years and made it possible to swiftly and accurately determine the level of a driver’s fatigue. However, individual variability and the sensitivity of EEG signals reduce the detection precision. Recently, another type of video-based technology for detecting driver fatigue by recording changes in the drivers’ eye characteristics has also been explored. In order to improve the classification performance of EEG-based approaches, this paper introduces the ADTIDO (Automatic Detect the TIred Deck Officers) algorithm, an EEG-based classification method of deck officers’ fatigue level, which combines a video-based approach to record the officer’s eye closure time for each time window. This paper uses a Discrete Wavelet Transformer (DWT) and decomposes the EEG signals into six sub-signals, from which we extract various EEG-based features, e.g., MAV, SD, and RMS. Unlike the traditional video-based method of calculating the Eyelid Closure Degree (ECD), this paper then obtains the ECD values from the EEG signals. The ECD-EEG fusion features are then created and used as the inputs for a classifier by combining the ECD and EEG feature sets. In addition, the present work develops the definition of “fatigue” at the individual level based on the real-time operational reaction time of the deck officer. To verify the efficacy of this research, the authors conducted their trials by using the EEG signals gathered from 21 subjects. It was found that Bidirectional Gated Recurrent Unit (Bi-GRU) networks outperform other classifiers, reaching a classification accuracy of 90.19 percent, 1.89 percent greater than that of only using EEG features as inputs. By combining the ADTIDO channel findings, the classification accuracy of deck officers’ fatigue levels finally reaches 95.74 percent.
Development and Testing of the A1 Volumetric Air Sampler, an Automatic Pollen Trap Suitable for Long-Term Monitoring of eDNA Pollen Diversity
Authors: Khan Gulzar , Hegge Albrecht , Gemeinholzer Birgit
Published: August 2022

Airborne pollen surveys provide information on various aspects of biodiversity and human health monitoring. Such surveys are typically conducted using the Burkard Multi-Vial Cyclone Sampler, but have to be technically optimized for eDNA barcoding. We here developed and tested a new airborne pollen trap, especially suitable for autonomous eDNA-metabarcoding analyses, called the A1 volumetric air sampler. The trap can sample pollen in 24 different tubes with flexible intervals, allowing it to operate independently in the field for a certain amount of time. We compared the efficiency of the new A1 volumetric air sampler with another automated volumetric spore trap, the Burkard Multi-Vial Cyclone Sampler, which features shorter and fewer sampling intervals to evaluate the comparability of ambient pollen concentrations. In a sterile laboratory environment, we compared trap performances between the automated volumetric air samplers by using pure dry pollen of three species—Fagus sylvatica, Helianthus annuus and Zea mays—which differ both by exine ornamentation and pollen size. The traps had a standard suction flow rate of 16.5 L/min, and we counted the inhaled pollen microscopically after a predefined time interval. Our results showed that though we put three different pollen types in the same container, both the traps inhaled all the pollens in a statistically significant manner irrespective of their size. We found that, on average, both traps inhaled equal an number of pollens for each species. We did not detect any cross-contamination between tubes. We concluded that the A1 volumetric air sampler has the potential to be used for longer and more flexible sampling intervals in the wild, suitable for autonomous monitoring of eDNA pollen diversity.
Rethinking Gradient Weight’s Influence over Saliency Map Estimation
Authors: Fahim Masud An Nur Islam , Saqib Nazmus , Siam Shafkat Khan , Jung Ho Yub
Published: August 2022

Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural network’s prediction. Gradient-based methods are generally faster than other branches of vision interpretability and independent of human guidance. The performance of CAM-like studies depends on the governing model’s layer response and the influences of the gradients. Typical gradient-oriented CAM studies rely on weighted aggregation for saliency map estimation by projecting the gradient maps into single-weight values, which may lead to an over-generalized saliency map. To address this issue, we use a global guidance map to rectify the weighted aggregation operation during saliency estimation, where resultant interpretations are comparatively cleaner and instance-specific. We obtain the global guidance map by performing elementwise multiplication between the feature maps and their corresponding gradient maps. To validate our study, we compare the proposed study with nine different saliency visualizers. In addition, we use seven commonly used evaluation metrics for quantitative comparison. The proposed scheme achieves significant improvement over the test images from the ImageNet, MS-COCO 14, and PASCAL VOC 2012 datasets.
Barriers to Governmental Income Supports for Sex Workers during COVID-19: Results of a Community-Based Cohort in Metro Vancouver
Authors: Pearson Jennie , Shannon Kate , Krüsi Andrea , Braschel Melissa , McDermid Jennifer , Bingham Brittany , Goldenberg Shira M.
Published: August 2022

The COVID-19 pandemic has brought into stark focus the economic inequities faced by precarious, criminalized and racialized workers. Sex workers have been historically excluded from structural supports due to criminalization and occupational stigma. Given emerging concerns regarding sex workers’ inequitable access to COVID-19 income supports in Canada and elsewhere, our objective was to identify prevalence and correlates of accessing emergency income supports among women sex workers in Vancouver, Canada. Data were drawn from a longstanding community-based open cohort (AESHA) of cis and trans women sex workers in Metro Vancouver from April 2020–April 2021 (n = 208). We used logistic regression to model correlates of access to COVID-19 income supports. Among 208 participants, 52.9% were Indigenous, 6.3% Women of Colour (Asian, Southeast Asian, or Black), and 40.9% white. Overall, 48.6% reported accessing income supports during the pandemic. In adjusted multivariable analysis, non-injection drug use was associated with higher odds of accessing COVID-19 income supports (aOR: 2.58, 95% CI: 1.31–5.07), whereas Indigenous women faced reduced odds (aOR 0.55, 95% CI 0.30–1.01). In comparison with other service workers, access to income supports among sex workers was low overall, particularly for Indigenous sex workers, demonstrating the compounding impacts of colonization and disproportionate criminalization of Indigenous sex workers. Results highlight the need for structural supports that are low-barrier and culturally-safe to support sex workers’ health, safety and dignity.
Green Synthesis of Immobilized CuO Photocatalyst for Disinfection of Water
Authors: Matoh Lev , Žener Boštjan , Genorio Boštjan
Published: August 2022

A green method for depositing a CuO layer with good adhesion and a large surface area on a support of activated alumina (Al2O3) was evaluated. The relatively simple method consists of adsorption of a copper salt on the surface of Al2O3, formation of Cu(OH)2, and subsequent decomposition of the hydroxide to CuO. The XRD confirmed that the deposited photocatalyst crystalized at low temperatures (80 °C). Furthermore, BET measurements show a surface area of about 90 m2/g. The large surface area is the result of the speed of the conversion and decomposition reactions. The photokilling properties of the prepared photocatalyst were evaluated using E. coli cells and the leaching of copper ions was determined using ICP-MS. The photocatalytic efficiency was also evaluated by the degradation of an organic azo dye. The prepared photocatalyst shows good activity in the purification and disinfection of treated water. The described method is economical, fast, and can be considered green, since the only byproducts are water and NaCl.
Buriti Fabric Reinforced Epoxy Composites as a Novel Ballistic Component of a Multilayered Armor System
Authors: Demosthenes Luana Cristyne da Cruz , Luz Fernanda Santos da , Nascimento Lucio Fabio Cassiano , Monteiro Sergio Neves
Published: August 2022

Buriti Fibers extracted from the leafstalk of palm tree, Mauritia flexuosa, native to the Amazon region, have been investigated as a reinforcement of polymer matrix composites. Recently, the fabric made from buriti fibers was also studied as a possible reinforcement of epoxy composites. In particular, the preliminary results of a 10 vol% buriti fabric epoxy composite in a multilayered armor system (MAS) displayed a satisfactory backface signature (BFS) but the composite target was not able to preserve its integrity after the ballistic impact. This motivated the present work, in which we carry out a complete statistical investigation of the ballistic performance of 10, 20, and 30 vol% buriti fabric epoxy composites as a MAS second layer against 7.62 mm rifle ammunition. BFS, associated with the depth of penetration in a clay witness simulating a human body, disclosed values of 18.9 to 25 mm, statistically similar and well below the lethal value of 44 mm specified by the international standard. Absorbed energy in stand-alone ballistic tests of 163–190 J for armor perforation were also found to be statistically higher than 58 ± 29 J obtained for the conventionally applied synthetic aramid fabric. The 30 vol% buriti fabric composites maintained the integrity of the MAS second layer, as required for use in body armor. Failure mechanisms found for the 10 vol% and 20 vol% buriti fabric composites by macro analysis and scanning electron microscopy confirmed the importance of a higher amount such as 30 vol% in order to achieve effective ballistic protection.
Fast InSAR Time-Series Analysis Method in a Full-Resolution SAR Coordinate System: A Case Study of the Yellow River Delta
Authors: Duan Huizhi , Li Yongsheng , Li Bingquan , Li Hao
Published: August 2022

Ground deformation is a major determinant of delta sustainability. Sentinel-1 Terrain Observation by Progressive Scans (TOPS) data are widely used in interferometric synthetic aperture radar (InSAR) applications to monitor ground subsidence. Due to the unparalleled mapping coverage and considerable data volume requirements, high-performance computing resources including graphics processing units (GPUs) are employed in state-of-the-art methodologies. This paper presents a fast InSAR time-series processing approach targeting Sentinel-1 TOPS images to process massive data with higher efficiency and resolution. We employed a GPU-assisted InSAR processing method to accelerate data processing. Statistically homogeneous pixel selection (SHPS) filtering was used to reduce noise and detect features in scenes with minimal image resolution loss. Compared to the commonly used InSAR processing software, the proposed method significantly improved the Sentinel-1 TOPS data processing efficiency. The feasibility of the method was investigated by mapping the surface deformation over the Yellow River Delta using SAR datasets acquired between January 2021 and February 2022. The findings indicate that several events of significant subsidence have occurred in the study area. Combined with the geological environment, underground brine and hydrocarbon extraction as well as sediment consolidation and compaction contribute to land subsidence in the Yellow River Delta.
Gender and Equity Considerations for Building Climate Resilience: Lessons from Rural and Periurban Botswana
Authors: Makwatse Kutlwano , Modie Leatile , Mpalo Morati , Blaser Mapitsa Caitlin
Published: August 2022

Botswana’s new national climate-adaptation plan framework acknowledges the fundamental challenges climate change is posing to household resilience. While the plan aims to be gender-responsive, there is limited empirical data on the current gender dynamics around household-level climate-adaptive priorities and practices. This study aims to understand the gendered variations of how people understand resilience to climate change in both rural and a periurban areas. The authors then consider how these views are reflected in current climate-adaptation policies and responses. A household-level baseline survey with 141 participants was conducted in Ramotswa and Xhumaga, using participant-coded narratives to understand how people understand resilience to climate change. This study found that planning for the shocks and stressors of climate change is gendered, and these variations have important implications for how equity should be reflected in a policy response.
Health Care Financing and Economic Performance during the Coronavirus Pandemic, the War in Ukraine and the Energy Transition Attempt
Authors: Piech Kornelia
Published: August 2022

On account of the current epidemiological situation in the world, which results from propagation of the coronavirus, research related to the impact of health on economic growth is becoming especially important. Health capital is an important variable in economic growth models. The method of financing health care has a significant impact on both the health of the population and the level of income. Therefore, this article attempts to analyze the relationship between these values. The way in which health care is financed has a significant impact both on the state of health of the population and on the level of financial resources allocated to health care (e.g., health contributions according to income). The analysis covered the period 2000–2021. On the basis of official reports, available literature and own studies, health expenditure has been divided into three income groups: low-income countries, middle-income countries and high-income countries. On the basis of statistical and economic analyses, it has been found that GDP (Gross Domestic Product) has an impact on public health expenditure in upper- and middle-income groups, but not in low-income countries. The analysis presented is becoming more important in the context of the coronavirus pandemic, the war in Ukraine and energy expenditure related to health care.
Participatory Evaluation of Rice Varieties for Specific Adaptation to Organic Conditions in Italy
Authors: De Santis Giuseppe , Ponzini Daniela , Stentella Rachele , Gaifami Tommaso , Bussi Bettina , Caimo-Duc Rosalia , Stocchi Ugo , Cuneo Marco , Paravicini Marco , Bocci Riccardo , Petitti Matteo , Ceccarelli Salvatore
Published: August 2022

Rice is the fourth most important crop in Italy with a growing area under organic management. We conducted a participatory evaluation of 21 rice cultivars (10 old, 10 modern and a mixture) in four organic/biodynamic farms, for two cropping seasons, to assess the extent of varieties × farms and varieties × years within farm interactions and farmers’ preferences. There were significant differences between farms and varieties, as well as large interactions between varieties and farms, particularly in the case of plant height and reactions to Fusarium fujikuroi Nirenberg (bakanae) and Magnaporthe oryzae B Cooke (leaf and neck blast), but also for grain yield. There were also large interactions between varieties and years, which resulted in considerable differences in stability among varieties with one of the old, one modern and the mixture combining high grain yield and stability. Farmers, regardless of gender, were able to visually identify the highest yielding varieties in a consistent way across years, and although accustomed to seeing uniform varieties, they scored the mixture higher than the mean. The results are discussed in the context of a decentralized-participatory breeding program, to serve the target population of heterogenous environments represented by organic and biodynamic farms.
Evaluation of Coal Supply and Demand Security in China and Associated Obstacle Factors
Authors: Zhang Xintong , Ning Yuncai , Lu Cuijie
Published: August 2022

In China, coal security issues are strongly linked to national energy security and economic and social stability. Facing environmental protection constraints, research on China’s coal security should analyze both supply and demand security. In this study, 19 criteria were selected for four subsystems (coal supply chain, coal market, economy and demographics, and social ecology) to construct an evaluation system for China‘s coal supply and demand security. Entropy and TOPSIS methods were used to evaluate coal security for 2002–2019. The obstacle factors affecting coal security for each subsystem were determined, and the grey model was used to predict the security and obstacle degree for each subsystem for 2020 and 2021. The results indicate that (1) China’s coal supply and use safety level in the period 2002–2019 was below a relatively safe level, i.e., at a safe early warning level during the period 2010–2014 and at a general safety level in the remaining years. (2) The basic coal reserves, the reserve-production ratio of the basic reserves, the balance of social coal stocks at the beginning and the end of the year, and the proportion of coal imports to consumption, urbanization rate, carbon dioxide emissions, and coal consumption in thermal power generation are the main obstacle factors that affect the safety of coal supply and use in China. (3) The obstacle degree of the coal production and supply evaluation subsystem is higher than that of the other three sub-systems, and the overall change trend during the study period showed a downward trend at first, followed by an upward trend.
Evaluation of Sustainable Water Resource Use in the Tarim River Basin Based on Water Footprint
Authors: Wang Yiding , Chen Yaning , Duan Weili , Jiao Li
Published: August 2022

Quantifying water use for agricultural production and accurate evaluation is important for achieving a balance between water supply and demand and sustainable use, especially in arid regions. This study quantifies the water footprint of food production in the Tarim River Basin (TRB) from 2000 to 2019 by conducting a sustainability evaluation using both the water footprint and DPSIR model as a theoretical framework, and by analyzing spatial and temporal changes. The results show that the water footprint of the TRB increased from 2.15 m3/kg to 2.86 m3/kg per unit during the study period. The average annual weighted water footprint of the basin is 2.59 m3/kg, of which 2.41 m3/kg is blue water and 0.18 m3/kg is green water. Blue water inputs contribute more than 94% to food production annually. Furthermore, although the level of sustainable water use increased, its score is low, with the most prominent stress assessment value indicating poor regional water use. Prior to 2010, the Tarim River Basin region’s sustainability was less than 0.4, indicating that water resources were at or below the level of basic unsustainability. By 2019, however, the sustainability of areas with better water use was greater than 0.4., and the sustainability of 80% of the region was above 0.2. In the future, we need to reduce the crop water footprint and improve water use efficiency to ensure the sustainable use of water resources and avoid further pressure on water use.
Reimbursement Costs of Auditing Financial Assets Measured by Fair Value Model in Jordanian Financial Firms’ Annual Reports
Authors: Alharasis Esraa Esam , Tarawneh Ahmad Saleem , Shehadeh Maha , Haddad Hossam , Marei Ahmad , Hasan Elina F.
Published: August 2022

This paper investigates the impact of financial assets measured by the fair value (F.V.) model, proxied by held-for-trading (H.F.T.), available-for-sale (A.F.S.), and the fair value option (F.V.O.), on reimbursement costs charged by external auditors in the context of the Jordanian finance industry. The study applied fixed-effects regression with a sample of 2408 firm-year observations of Jordanian firms from 2005 to 2018. The regression results confirmed that higher H.F.T. and A.F.S. of fair-valued assets were the primary cause of high audit fees; however, the F.V.O. had no significant impact. The impact of the F.V. model on audit reimbursement expenses has received little scholarly attention even in developing countries. The current study introduces an updated audit-fee model and new empirical evidence to provide more insights into this relationship and bridge a gap in the auditing literature. As a result, it investigates the impact of each fair-valued financial asset category on audit pricing for the first time. This research is unique in that it uses the F.V. model to investigate the association between each item of fair-valued financial assets and audit fees. The findings of this research provide current empirical information on the implication of the F.V. model in Jordan. The results contribute by guiding audit fee determinants in the context of Jordan, where there is no specific limit for audit fees determined by the government. The outcomes guide regulatory authorities in monitoring and regulating the audit profession and regulating the audit of F.V. model practices.