bluuebunny/crossref_metadata_2025 huggingface dataset:
Topsoil and Vegetation Dynamics 14 Years after Eucalyptus grandis Removal in Eastern Cape Province of South Africa
Authors: Mthethwa Kuhle , Ruwanza Sheunesu
Published: August 2023

A great deal of effort has been made to clear invasive alien plants in South Africa, yet it remains unclear if the clearing efforts are yielding positive soil and vegetation recovery trajectories. A few short-term studies have been conducted to monitor soil and vegetation recovery after alien plant removal in South Africa, but convincing, long-term monitoring studies are scarce yet needed. We investigated topsoil and vegetation recovery following Eucalyptus grandis removal 14 years ago by Working for Water in Makhanda, Eastern Cape province of South Africa. The detailed topsoil and vegetation surveys were conducted on forty 10 m × 10 m plots that were in paired cleared and natural sites. The results show no significant differences for the measured soil pH, total N, total C, K, Ca, and Na between the cleared and natural sites, an indication that the two sites are becoming similar. Similarly, the gravimetric soil moisture content shows no significant differences between the two sites, although monthly variations are observed. The topsoils in the cleared sites are hydrophobic as compared to those in the natural sites, which are wettable. We observed no significant vegetation diversity differences between the two sites, with native woody species, such as Crassula pellucida and Helichrysum petiolare, frequently occurring in the cleared sites. We recorded low reinvasion by E. grandis and other secondary invaders like Acacia mearnsii and Rubus cuneifolius in the cleared sites. Based on these results, we conclude that 14 years after E. grandis clearing, both topsoil and vegetation recovery are following a positive trajectory towards the natural sites. However, both reinvasion and secondary invasion have the potential to slow down soil and native vegetation recovery. Recommendations such as timeous follow-up clearing and incorporating restoration monitoring in the WfW clearing programme are discussed.
Exospheric Solar Wind Model Based on Regularized Kappa Distributions for the Electrons Constrained by Parker Solar Probe Observations
Authors: Pierrard Viviane , Péters de Bonhome Maximilien , Halekas Jasper , Audoor Charline , Whittlesey Phyllis , Livi Roberto
Published: August 2023

In the present work, the kinetic exospheric model of the solar wind is improved by considering regularized Kappa distributions that have no diverging moments through consideration of a cut-off at relativistic velocities. The model becomes valid even for kappa indices lower than 2, which is important since low values of kappa are observed in the fast solar wind. The exospheric model shows that the electric potential accelerates the wind to supersonic velocities. The presence of suprathermal Strahl electrons at the exobase can further increase the velocity to higher values, leading to profiles comparable to the observations in the fast and slow wind at all radial distances. The kappa index is not the only parameter that influences the acceleration of the wind: the difference in the altitude of the exobase also makes a significant difference between the fast and slow wind. The exobase is located at lower altitudes in the coronal holes where the density is smaller than in the other regions of the corona, allowing the wind originating from the holes to be accelerated to higher velocities. The new observations of Parker Solar Probe are used to constrain the model. The observations at low radial distances show suprathermal electrons already well present in the Strahl in the antisunward direction and a deficit in the sunward direction, confirming the exospheric feature of almost no incoming particles. For proton distributions, we observe that the proton tail parallel to the magnetic field is already present at 17.2 Rs.
Bioavailability, Sources, and Transfer Behavior of Heavy Metals in Soil–Crop Systems from a High Geological Background Area Impacted by Artisanal Zn Smelting in Guizhou Province, Southwest China
Authors: Ma Honghong , Zhao Chen , Zhang Li , Liu Zhizhuo , Zhang Fugui , Wang Huiyan , Guo Fei , Tang Shiqi , Yang Zheng , Peng Min
Published: August 2023

The environmental risk posed by heavy metals in agricultural soil is primarily influenced by their sources, bioavailability, and geochemical transfer behavior. This study focused on Weining County, a region in Guizhou province, Southwest China, with a high geological background and long-term impact from artisanal Zn smelting. Vertical soil profiles, crop, and rhizospheric soil samples were collected and analyzed for heavy metal concentration (As, Cd, Co, Cr, Cu, Hg, Ni, Pb, V, and Zn) including the total concentration and chemical fraction. The results revealed elevated concentrations of Cd (range: 0.7–6.9 mg·kg−1), Co (range: 19.3–120.0 mg·kg−1), Cu (range: 71.6–386.0 mg·kg−1), Ni (range: 51.0–121.0 mg·kg−1), and V (range: 310.0–721.0 mg·kg−1) in all soil samples compared to the background values of Guizhou Province. Chemical fractionation analysis indicated that Cr, Ni, As, Cu, and Zn were predominantly present in the residual fraction, while Hg and Pb were predominantly found in the potentially bioavailable fraction. Cd exhibited the highest bioavailability, accounting for 58.5% of its total concentration. Enrichment factor analysis suggested that artisanal Zn smelting activities were the main sources of Cd, Pb, and Zn contamination. Furthermore, Cd, Pb, and Zn were found to be highly accumulated in the surface soil layer (0–20 cm). Notably, 90.0% of potato and 9.4% of maize grain samples exceeded the food hygiene standards for Cd concentration, posing potential health risks to consumers. The bioconcentration factor (soil-to-root) and translocation factor (root-to-grain) analyses indicated that maize roots had a higher tendency to accumulate Cd from the soil, while Zn and Cu showed a significant transferability from roots to maize grains. These findings offer valuable insights for devising heavy metal remediation strategies in similar areas.
The Evolution of Paleolithic Hunting Weapons: A Response to Declining Prey Size
Authors: Ben-Dor Miki , Barkai Ran
Published: August 2023

This paper examines the hypothesis that changes in hunting weapons during the Paleolithic were a direct response to a progressive decline in prey size. The study builds upon a unified hypothesis that explains Paleolithic human evolutionary and behavioral/cultural phenomena, including improved cognitive capabilities, as adaptations to mitigate declined energetic returns due to a decline in prey size. Five selected case studies in Africa and Europe were analyzed to test this hypothesis, focusing on the relative presence of megaherbivores (>1000 kg) in the transition between the Acheulean/Early Stone Age and the Middle Paleolithic/Middle Stone Age. The findings indicate a decline in megaherbivores’ presence and biomass contribution in the studied transition period associated with the introduction of Levallois technology. We review the evolution of hunting weapons, including wooden-tipped and stone-tipped spears and bows and arrows. Analysis of tip size and breakage patterns indicate a reduction in point size over time, aligning with the declining prey size. We propose that changes in hunting weapons and strategies were driven by the practical and ontological incentives presented by the availability and size of prey. Developing smaller, more precise weapons required increased cognitive capacities, leading to the parallel evolution of human cognitive abilities.
Teaching, Learning and the Buddha: Educative Principles from the Nidāna-Kathā
Authors: Ashcraft Jessica , Calvert Isaac
Published: August 2023

This article investigates core ideas and principles of teaching and learning as found in the Nidāna-Kathā, a sacred Buddhist text that describes the lives and learning journeys of the Buddha. A three-tiered, exhaustive textual analysis revealed the following themes: resolve and responsibility, supererogatory effort, pabbajja, tradition, becoming, pedagogy and joy. Due in part to the noteworthy openness of a Buddhist conceptualization of canonicity, teachers have seldom drawn upon Buddhist sources (either living or written) to establish what might be termed Buddhist approaches to teaching and learning. While the purpose of this article is not to essentialize Buddhism nor its approach to education, by focusing on a single, primary source text detailing the Buddha’s teaching and learning journeys, we seek to put forth a few Buddhist educative principles that, though not representative of all Buddhisms, are at least grounded historically, mythically and archetypically. We hope this article acts as a call for future research to further explore Buddhist approaches to education based on either historical sources or the lived experiences of its generations of inheritor–practitioners.
Assessing the Performance of Multipath Mitigation for Multi-GNSS Precise Point Positioning Ambiguity Resolution
Authors: Zheng Kai , Tan Lingmin , Liu Kezhong , Chen Mozi , Zeng Xuming
Published: August 2023

Real-time GNSS PPP is commonly used for high-precision positioning, but its utility is constrained by factors that necessitate extended convergence periods for a dependable accuracy. Multipath, as an unmodeled error, significantly curtails PPP performance in time-constrained scenarios. Approximately 31 consecutive days of multi-GNSS data from the satellite positioning service of the German national survey (SAPOS) network were collected to evaluate the effectiveness of multipath correction for real-time PPP ambiguity resolution (AR). Using principal component analysis (PCA) to extract the common-mode error (CME) from observation residuals prior to multipath modeling, a multipath hemispherical map (MHM) and sidereal filtering (SF) approach were employed to alleviate the effects of multipath and assess the efficacy of multipath correction in real-time PPP-AR. The average RMS reductions of the carrier-phase and pseudorange residual of multi-GNSS were 25.5% and 20.1% with MHM 0.5, while being 24.4% and 18.3% using SF. With MHM 0.5 correction, the TTFF reductions were approximately 7.0%, 17.7%, 37.5%, and 23.7% for G/GE/GC/GEC kinematic PPP-AR, respectively; and the convergence times for G/GE/GC PPP-AR were reduced to 18.2, 11.7, and 8.6 min, while GEC achieved an average convergence time of 7.1 min; a remarkable improvement compared to the multipath-uncorrected result (18 min). Moreover, 80% of the stations achieved convergence within 10 min, while 40% achieved convergence within 5 min. The kinematic positioning accuracy for the GEC solution improved from 0.97, 0.88, and 2.07 cm, to 0.94, 0.70, and 1.72 cm. In the static results, the TTFF shortened by 30.1%, 19.1%, and 20.1% for G/GE/GC, and the GEC decreased from 10.5 to 9.7 min; the average convergence time for G/GE/GC shortened to 13.0, 10.0, and 11.3 min, and for GEC shortened from 12.5 to 8.3 min. For the GPS-only solution, 78.3% of stations achieved convergence within 15 min. Similarly, for the GE scheme, the convergence time was primarily concentrated within 10 min, and for GC and GEC, with the application of enhanced multipath error correction, some of the stations even achieved convergence of PPP-AR within 5 min. The static positioning accuracy for GEC PPP was 0.50, 0.30, and 0.71 cm for the east, north, and up components.
Optimized Extraction Method of Fruit Planting Distribution Based on Spectral and Radar Data Fusion of Key Time Phase
Authors: Zhao Guobing , Wang Lei , Zheng Jianghua , Tuerxun Nigela , Han Wanqiang , Liu Liang
Published: August 2023

With China’s fruit tree industry becoming the largest in the world, accurately understanding the spatial distribution of fruit tree growing areas is crucial for promoting socio-economic development and rural revitalization. Remote sensing offers unprecedented opportunities for fruit tree monitoring. However, previous research has mainly focused on UAV and near-ground remote sensing, with limited accuracy in obtaining fruit tree distribution information through satellite remote sensing. In this study, we utilized the Google Earth Engine (GEE) remote sensing cloud platform and integrated data from Sentinel-1, Sentinel-2, and SRTM sources. We constructed a feature space by extracting original band features, vegetation index features, polarization features, terrain features, and texture features. The sequential forward selection (SFS) algorithm was employed for feature optimization, and a combined machine learning and object-oriented classification model was used to accurately extract fruit tree crop distributions by comparing key temporal phases of fruit trees. The results revealed that the backscatter coefficient features from Sentinel-1 had the highest contribution to the classification, followed by the original band features and vegetation index features from Sentinel-2, while the terrain features had a relatively smaller contribution. The highest classification accuracy for jujube plantation areas was observed in November (99.1% for user accuracy and 96.6% for producer accuracy), whereas the lowest accuracy was found for pear tree plantation areas in the same month (93.4% for user accuracy and 89.0% for producer accuracy). Among the four different classification methods, the combined random forest and object-oriented (RF + OO) model exhibited the highest accuracy (OA = 0.94, Kappa = 0.92), while the support vector machine (SVM) classification method had the lowest accuracy (OA = 0.52, Kappa = 0.31). The total fruit tree plantation area in Aksu City in 2022 was estimated to be 64,000 hectares, with walnut, jujube, pear, and apple trees accounting for 42.5%, 20.6%, 19.3%, and 17.5% of the total fruit tree area, respectively (27,200 hectares, 13,200 hectares, 12,400 hectares, and 11,200 hectares, respectively). The SFS feature optimization and RF + OO-combined classification model algorithm selected in this study effectively mapped the fruit tree planting areas, enabling the estimation of fruit tree planting areas based on remote sensing satellite image data. This approach facilitates accurate fruit tree industry and real-time crop monitoring and provides valuable support for fruit tree planting management by the relevant departments.
Hierarchical Feature Association and Global Correction Network for Change Detection
Authors: Lu Jinquan , Meng Xiangchao , Liu Qiang , Lv Zhiyong , Yang Gang , Sun Weiwei , Jin Wei
Published: August 2023

Optical satellite image change detection has attracted extensive research due to its comprehensive application in earth observation. Recently, deep learning (DL)-based methods have become dominant in change detection due to their outstanding performance. Remote sensing (RS) images contain different sizes of ground objects, so the information at different scales is crucial for change detection. However, the existing DL-based methods only employ summation or concatenation to aggregate several layers of features, lacking the semantic association of different layers. On the other hand, the UNet-like backbone is favored by deep learning algorithms, but the gradual downscaling and upscaling operation in the mainstream UNet-like backbone has the problem of misalignment, which further affects the accuracy of change detection. In this paper, we innovatively propose a hierarchical feature association and global correction network (HFA-GCN) for change detection. Specifically, a hierarchical feature association module is meticulously designed to model the correlation relationship among different scale features due to the redundant but complementary information among them. Moreover, a global correction module on Transformer is proposed to alleviate the feature misalignment in the UNet-like backbone, which, through feature reuse, extracts global information to reduce false alarms and missed alarms. Experiments were conducted on several publicly available databases, and the experimental results show the proposed method is superior to the existing state-of-the-art change detection models.
Classification of Hyperspectral and LiDAR Data Using Multi-Modal Transformer Cascaded Fusion Net
Authors: Wang Shuo , Hou Chengchao , Chen Yiming , Liu Zhengjun , Zhang Zhenbei , Zhang Geng
Published: August 2023

With the continuous development of surface observation methods and technologies, we can acquire multiple sources of data more effectively in the same geographic area. The quality and availability of these data have also significantly improved. Consequently, how to better utilize multi-source data to represent ground information has become an important research question in the field of geoscience. In this paper, a novel model called multi-modal transformer cascaded fusion net (MMTCFN) is proposed for fusion and classification of multi-modal remote sensing data, Hyperspectral Imagery (HSI) and LiDAR data. Feature fusion and feature extraction are the two stages of the model. First, in the feature extraction stage, a three-branch cascaded Convolutional Neural Network (CNN) framework is employed to fully leverage the advantages of convolutional operators in extracting shallow-level local features. Based on this, we generated multi-modal long-range integrated deep features utilizing the transformer-based vectorized pixel group transformer (VPGT) module during the feature fusion stage. In the VPGT block, we designed a vectorized pixel group embedding that preserves the global features extracted from the three branches in a non-overlapping multi-space manner. Moreover, we introduce the DropKey mechanism into the multi-head self-attention (MHSA) to alleviate overfitting caused by insufficient training samples. Finally, we employ a probabilistic decision fusion strategy to integrate multiple class estimations, assigning a specific category to each pixel. This model was experimented on three HSI-LiDAR datasets with balanced and unbalanced training samples. The proposed model outperforms the other seven SOTA approaches in terms of OA performance, proving the superiority of MMTCFN for the HSI-LiDAR classification task.
Lane Crack Detection Based on Saliency
Authors: Zhang Shengyuan , Fu Zhongliang , Li Gang , Liu Aoxiang
Published: August 2023

Lane cracks are one of the biggest threats to pavement conditions. The automatic detection of lane cracks can not only assist the evaluation of road quality and quantity but can also be used to develop the best crack repair plan, so as to keep the road level and ensure driving safety. Although cracks can be extracted from pavement images because the gray intensity of crack pixels is lower than the background gray intensity, it is still a challenge to extract continuous and complete cracks from the three-lane images with complex texture, high noise, and uneven illumination. Different from threshold segmentation and edge detection, this study designed a crack detection algorithm with dual positioning. An image-enhancement method based on crack saliency is proposed for the first time. Based on Bayesian probability, the saliency of each pixel judged as a crack is calculated. Then, the Fréchet distance improvement triangle relationship is introduced to determine whether the key point extracted is the fracture endpoint and whether the fast-moving method should be terminated. In addition, a complete remote-sensing process was developed to calculate the length and width of cracks by inverting the squint images collected by mobile phones. A large number of images with different types, noise, illumination, and interference conditions were tested. The average crack extraction accuracy of 89.3%, recall rate of 87.1%, and F1 value of 88.2% showed that the method could detect cracks in pavement well.
A Lightweight Winter Wheat Planting Area Extraction Model Based on Improved DeepLabv3+ and CBAM
Authors: Zhang Yao , Wang Hong , Liu Jiahao , Zhao Xili , Lu Yuting , Qu Tengfei , Tian Haozhe , Su Jingru , Luo Dingsheng , Yang Yalei
Published: August 2023

This paper focuses on the problems of inaccurate extraction of winter wheat edges from high-resolution images, misclassification and omission due to intraclass differences as well as the large number of network parameters and long training time of existing classical semantic segmentation models. This paper proposes a lightweight winter wheat planting area extraction model that combines the DeepLabv3+ model and a dual-attention mechanism. The model uses the lightweight network MobileNetv2 to replace the backbone network Xception of DeepLabv3+ to reduce the number of parameters and improve the training speed. It also introduces the lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism to extract winter wheat feature information more accurately and efficiently. Finally, the model is used to complete the dataset creation, model training, winter wheat plantation extraction, and accuracy evaluation. The results show that the improved lightweight DeepLabv3+ model in this paper has high reliability in the recognition extraction of winter wheat, and its recognition results of OA, mPA, and mIoU reach 95.28%, 94.40%, and 89.79%, respectively, which are 1.52%, 1.51%, and 2.99% higher than those for the original DeepLabv3+ model. Meanwhile, the model’s recognition accuracy was much higher than that of the three classical semantic segmentation models of UNet, ResUNet and PSPNet. The improved lightweight DeepLabv3+ also has far fewer model parameters and training time than the other four models. The model has been tested in other regions, and the results show that it has good generalization ability. The model in general ensures the extraction accuracy while significantly reducing the number of parameters and satisfying the timeliness, which can achieve the fast and accurate extraction of winter wheat planting sites and has good application prospects.
An Algorithm for Building Exterior Facade Corner Point Extraction Based on UAV Images and Point Clouds
Authors: Zhang Xinnai , Sun Jiuyun , Gao Jingxiang
Published: August 2023

The high-precision building exterior facade corner point (BEFCP) is an essential element in topographic and cadastral surveys. However, current extraction methods rely on the interactions of humans with the 3D real-scene models produced by unmanned aerial vehicle (UAV) oblique photogrammetry, which have a high workload, low efficiency, poor precision, and cannot satisfy the requirements of automation. The dense point cloud contains discrete 3D building structure information. Still, it is challenging to accurately filter out the partial point cloud characterizing the building structure from it in order to achieve BEFCP extraction. The BEFCPs are always located on the plumb line of the building’s exterior wall. Thus, this paper back-calculated the plumb line from the image and designed a photographic ray corresponding to the image point and point cloud intersection point calculation algorithm to recover its approximate spatial position in order to successfully extract the accurate point cloud in the building structure neighborhood. It then utilized the high signal-to-noise ratio property of the point cloud as a base to eliminate the noise points and, finally, accurately located the building exterior façade corner points by recovering the building structure through segmental linear fitting of the point cloud. The proposed algorithm conducted automated building exterior facade corner point extraction via both of planar-to-stereo and rough-to-precise strategies, reached a 92.06% correctness rate and ±4.5 cm point mean square location error in the experiment, and was able to extract and distinguish the building exterior facade corner points under eaves obstruction and extreme proximity. It is suitable for all high-precision surveying and mapping tasks in building areas based on oblique photogrammetry, which can effectively improve the automation of mapping production.
Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle–Pedestrian Interactions
Authors: Alghodhaifi Hesham , Lakshmanan Sridhar
Published: August 2023

Ensuring that intelligent vehicles do not cause fatal collisions remains a persistent challenge due to pedestrians’ unpredictable movements and behavior. The potential for risky situations or collisions arising from even minor misunderstandings in vehicle–pedestrian interactions is a cause for great concern. Considerable research has been dedicated to the advancement of predictive models for pedestrian behavior through trajectory prediction, as well as the exploration of the intricate dynamics of vehicle–pedestrian interactions. However, it is important to note that these studies have certain limitations. In this paper, we propose a novel graph-based trajectory prediction model for vehicle–pedestrian interactions called Holistic Spatio-Temporal Graph Attention (HSTGA) to address these limitations. HSTGA first extracts vehicle–pedestrian interaction spatial features using a multi-layer perceptron (MLP) sub-network and max pooling. Then, the vehicle–pedestrian interaction features are aggregated with the spatial features of pedestrians and vehicles to be fed into the LSTM. The LSTM is modified to learn the vehicle–pedestrian interactions adaptively. Moreover, HSTGA models temporal interactions using an additional LSTM. Then, it models the spatial interactions among pedestrians and between pedestrians and vehicles using graph attention networks (GATs) to combine the hidden states of the LSTMs. We evaluate the performance of HSTGA on three different scenario datasets, including complex unsignalized roundabouts with no crosswalks and unsignalized intersections. The results show that HSTGA outperforms several state-of-the-art methods in predicting linear, curvilinear, and piece-wise linear trajectories of vehicles and pedestrians. Our approach provides a more comprehensive understanding of social interactions, enabling more accurate trajectory prediction for safe vehicle navigation.
A Cardinality Estimator in Complex Database Systems Based on TreeLSTM
Authors: Qi Kaiyang , Yu Jiong , He Zhenzhen
Published: August 2023

Cardinality estimation is critical for database management systems (DBMSs) to execute query optimization tasks, which can guide the query optimizer in choosing the best execution plan. However, traditional cardinality estimation methods cannot provide accurate estimates because they cannot accurately capture the correlation between multiple tables. Several recent studies have revealed that learning-based cardinality estimation methods can address the shortcomings of traditional methods and provide more accurate estimates. However, the learning-based cardinality estimation methods still have large errors when an SQL query involves multiple tables or is very complex. To address this problem, we propose a sampling-based tree long short-term memory (TreeLSTM) neural network to model queries. The proposed model addresses the weakness of traditional methods when no sampled tuples match the predicates and considers the join relationship between multiple tables and the conjunction and disjunction operations between predicates. We construct subexpressions as trees using operator types between predicates and improve the performance and accuracy of cardinality estimation by capturing the join-crossing correlations between tables and the order dependencies between predicates. In addition, we construct a new loss function to overcome the drawback that Q-error cannot distinguish between large and small cardinalities. Extensive experimental results from real-world datasets show that our proposed model improves the estimation quality and outperforms traditional cardinality estimation methods and the other compared deep learning methods in three evaluation metrics: Q-error, MAE, and SMAPE.
A Novel 6G Conversational Orchestration Framework for Enhancing Performance and Resource Utilization in Autonomous Vehicle Networks
Authors: Shahzadi Sonia , Chaudhry Nauman Riaz , Iqbal Muddesar
Published: August 2023

A vision of 6G aims to automate versatile services by eliminating the complexity of human effort for Industry 5.0 applications. This results in an intelligent environment with cognitive and collaborative capabilities of AI conversational orchestration that enable a variety of applications across smart Autonomous Vehicle (AV) networks. In this article, an innovative framework for AI conversational orchestration is proposed by enabling on-the-fly virtual infrastructure service orchestration for Anything-as-a-Service (XaaS) to automate a network service paradigm. The proposed framework will potentially contribute to the growth of 6G conversational orchestration by enabling on-the-fly automation of cloud and network services. The orchestration aspect of the 6G vision is not limited to cognitive collaborative communications, but also extends to context-aware personalized infrastructure for 6G automation. The experimental results of the implemented proof-of-concept framework are presented. These experiments not only affirm the technical capabilities of this framework, but also push into several Industry 5.0 applications.
Research on 3D Reconstruction of Binocular Vision Based on Thermal Infrared
Authors: Li Huaizhou , Wang Shuaijun , Bai Zhenpeng , Wang Hong , Li Sen , Wen Shupei
Published: August 2023

Thermal infrared imaging is less affected by lighting conditions and smoke compared to visible light imaging. However, thermal infrared images often have lower resolution and lack rich texture details, making them unsuitable for stereo matching and 3D reconstruction. To enhance the quality of infrared stereo imaging, we propose an advanced stereo matching algorithm. Firstly, the images undergo preprocessing using a non-local mean noise reduction algorithm to remove thermal noise and achieve a smoother result. Subsequently, we perform camera calibration using a custom-made chessboard calibration board and Zhang’s camera calibration method to obtain accurate camera parameters. Finally, the disparity map is generated using the SGBM (semi-global block matching) algorithm based on the weighted least squares method, enabling the 3D point cloud reconstruction of the object. The experimental results demonstrate that the proposed algorithm performs well in objects with sufficient thermal contrast and relatively simple scenes. The proposed algorithm reduces the average error value by 10.9 mm and the absolute value of the average error by 1.07% when compared with the traditional SGBM algorithm, resulting in improved stereo matching accuracy for thermal infrared imaging. While ensuring accuracy, our proposed algorithm achieves the stereo reconstruction of the object with a good visual effect, thereby holding high practical value.
Deep Learning-Based Evaluation of Ultrasound Images for Benign Skin Tumors
Authors: Lee Hyunwoo , Lee Yerin , Jung Seung-Won , Lee Solam , Oh Byungho , Yang Sejung
Published: August 2023

In this study, a combined convolutional neural network for the diagnosis of three benign skin tumors was designed, and its effectiveness was verified through quantitative and statistical analysis. To this end, 698 sonographic images were taken and diagnosed at the Department of Dermatology at Severance Hospital in Seoul, Korea, between 10 November 2017 and 17 January 2020. Through an empirical process, a convolutional neural network combining two structures, which consist of a residual structure and an attention-gated structure, was designed. Five-fold cross-validation was applied, and the train set for each fold was augmented by the Fast AutoAugment technique. As a result of training, for three benign skin tumors, an average accuracy of 95.87%, an average sensitivity of 90.10%, and an average specificity of 96.23% were derived. Also, through statistical analysis using a class activation map and physicians’ findings, it was found that the judgment criteria of physicians and the trained combined convolutional neural network were similar. This study suggests that the model designed and trained in this study can be a diagnostic aid to assist physicians and enable more efficient and accurate diagnoses.
The Use of Wearable Technology in Providing Assistive Solutions for Mental Well-Being
Authors: Alhejaili Reham , Alomainy Akram
Published: August 2023

The main goal of this manuscript is to provide an extensive literature review and analysis of certain biomarkers, which are frequently used to identify stress, anxiety, and other emotions, leading to potential solutions for the monitoring of mental wellness using wearable technologies. It is possible to see the impacts of several biomarkers in detecting stress levels and their effectiveness with an investigation into the literature on this subject. Biofeedback training has demonstrated some psychological effects, such as a reduction in anxiety and self-control enhancement. This survey demonstrates backed up by evidence that wearable devices are assistive in providing health and mental wellness solutions. Because physical activity tracing would reduce the stress stressors, which affect the subject’s body, therefore, it would also affect the mental activity and would lead to a reduction in cognitive mental load.
A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study
Authors: Othman Walaa , Hamoud Batol , Kashevnik Alexey , Shilov Nikolay , Ali Ammar
Published: August 2023

Driving behaviour analysis has drawn much attention in recent years due to the dramatic increase in the number of traffic accidents and casualties, and based on many studies, there is a relationship between the driving environment or behaviour and the driver’s state. To the best of our knowledge, these studies mostly investigate relationships between one vital sign and the driving circumstances either inside or outside the cabin. Hence, our paper provides an analysis of the correlation between the driver state (vital signs, eye state, and head pose) and both the vehicle maneuver actions (caused by the driver) and external events (carried out by other vehicles or pedestrians), including the proximity to other vehicles. Our methodology employs several models developed in our previous work to estimate respiratory rate, heart rate, blood pressure, oxygen saturation, head pose, eye state from in-cabin videos, and the distance to the nearest vehicle from out-cabin videos. Additionally, new models have been developed using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to classify the external events from out-cabin videos, as well as a Decision Tree classifier to detect the driver’s maneuver using accelerometer and gyroscope sensor data. The dataset used includes synchronized in-cabin/out-cabin videos and sensor data, allowing for the estimation of the driver state, proximity to other vehicles and detection of external events, and driver maneuvers. Therefore, the correlation matrix was calculated between all variables to be analysed. The results indicate that there is a weak correlation connecting both the maneuver action and the overtaking external event on one side and the heart rate and the blood pressure (systolic and diastolic) on the other side. In addition, the findings suggest a correlation between the yaw angle of the head and the overtaking event and a negative correlation between the systolic blood pressure and the distance to the nearest vehicle. Our findings align with our initial hypotheses, particularly concerning the impact of performing a maneuver or experiencing a cautious event, such as overtaking, on heart rate and blood pressure due to the agitation and tension resulting from such events. These results can be the key to implementing a sophisticated safety system aimed at maintaining the driver’s stable state when aggressive external events or maneuvers occur.
A Transformer-Optimized Deep Learning Network for Road Damage Detection and Tracking
Authors: Wang Niannian , Shang Lihang , Song Xiaotian
Published: August 2023

To solve the problems of low accuracy and false counts of existing models in road damage object detection and tracking, in this paper, we propose Road-TransTrack, a tracking model based on transformer optimization. First, using the classification network based on YOLOv5, the collected road damage images are classified into two categories, potholes and cracks, and made into a road damage dataset. Then, the proposed tracking model is improved with a transformer and a self-attention mechanism. Finally, the trained model is used to detect actual road videos to verify its effectiveness. The proposed tracking network shows a good detection performance with an accuracy of 91.60% and 98.59% for road cracks and potholes, respectively, and an F1 score of 0.9417 and 0.9847. The experimental results show that Road-TransTrack outperforms current conventional convolutional neural networks in terms of the detection accuracy and counting accuracy in road damage object detection and tracking tasks.
Optimal Distributed Finite-Time Fusion Method for Multi-Sensor Networks under Dynamic Communication Weight
Authors: Yu Hang , Dai Keren , Li Qingyu , Li Haojie , Zhang He
Published: August 2023

Aiming at the problem of distributed state estimation in sensor networks, a novel optimal distributed finite-time fusion filtering method based on dynamic communication weights has been developed. To tackle the fusion errors caused by incomplete node information in distributed sensor networks, the concept of limited iterations of global information aggregation was introduced, namely, fast finite-time convergence techniques. Firstly, a local filtering algorithm architecture was constructed to achieve fusion error convergence within a limited number of iterations. The maximum number of iterations was derived to be the diameter of the communication topology graph in the sensor network. Based on this, the matrix weight fusion was used to combine the local filtering results, thereby achieving optimal estimation in terms of minimum variance. Next, by introducing the generalized information quality (GIQ) calculation method and associating it with the local fusion result bias, the relative communication weights were obtained and embedded in the fusion algorithm. Finally, the effectiveness and feasibility of the proposed algorithm were validated through numerical simulations and experimental tests.
Flavonol Glycosides from Eugenia uniflora Leaves and Their In Vitro Cytotoxicity, Antioxidant and Anti-Inflammatory Activities
Authors: Oriola Ayodeji Oluwabunmi , Miya Gugulethu Mathews , Singh Moganavelli , Oyedeji Adebola Omowunmi
Published: August 2023

In view of the extensive use of Eugenia uniflora leaves for the management of tumours and other chronic inflammatory diseases in traditional medicine, an activity-guided fractionation of its leaf ethanolic extract led to the isolation of two flavonol glycosides. Cytotoxicity study was based on the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) viability assay against the non-tumourigenic human embryonic kidney (HEK-293) cells, and the cancerous liver (Hep-G2) and cervical (HeLa) cell lines. Antioxidant tests were carried out using 2,2-diphenyl-1-picrylhydrazyl (DPPH), nitric oxide (NO) and hydrogen peroxide (H2O2) radical scavenging assays, while an in vitro anti-inflammatory test was conducted using egg albumin denaturation (EAD) assay. Based on comprehensive spectroscopic and spectrometric evidence, the compounds were elucidated as myricitrin (1) and a newly described compound, 5,7-dihydroxy-3-(3,4,5-trihydroxy-6-methyltetrahydropyran-2-yloxy)-2-(2,4,5-trihydroxyphenyl)chromen-4-one, named “unifloratrin (2)”. The cytotoxicity of myricitrin (1) was comparable to 5-fluorouracil (standard drug), with a CC50 of 8.5 ± 2.2 µg/100 µL against HeLa cells. It also demonstrated better antioxidant activity, with an IC50 of 6.23 ± 1.09, 22.01 ± 2.59 and 30.46 ± 1.79 µM against DPPH, NO and H2O2 free radicals, respectively. At 20 µg/mL and an incubation time of 2 h, myricitrin was comparable to diclofenac (standard drug) in anti-inflammatory activity. This report may serve as a justification for the ethnomedicinal use of E. uniflora, while flavonol glycosides, such as myricitrin (1), could be further exploited as a candidate cytotoxic agent.
Carob Pulp Flour Extract Obtained by a Microwave-Assisted Extraction Technique: A Prospective Antioxidant and Antimicrobial Agent
Authors: Zahorec Jana , Šoronja-Simović Dragana , Kocić-Tanackov Sunčica , Bulut Sandra , Martić Nikola , Bijelić Katarina , Božović Danica , Pavlić Branimir
Published: August 2023

In this study, carob pulp flour (CF) extract was characterized as a high-value antioxidant and antimicrobial agent. CF extracts were obtained using microwave-assisted extraction (MAE) and optimization of the MAE process was accomplished using response surface methodology. The studied processing parameters of MAE were the liquid/solid (L/S) ratio (10–30 mL/g), extraction time (15–35 min), and ethanol concentration (40–80% w/w). The efficiency of the extraction of valuable compounds from CF was evaluated by the determining extraction yield (Y), total phenolic(TP), total flavonoid (TF) content, and antioxidant activity (DPPH, FRAP, and ABTS). The optimized MAE parameters for maximizing the yields of target compounds and antioxidant activity were the L/S ratio 30 mL/g, extraction time 35 min, and ethanol concentration 40%. The experimentally obtained values for TP, TF, DPPH, FRAP, and ABTS were 1609.92 GAE/100 g, 271.92 CE/100 g, 99.02 µMTE/g, 50.45 µM Fe2+/g, and 110.55 µMTE/g, respectively. The optimized CF extract was compared with the CF extracts obtained by conventional solid–liquid extraction (S/L) and ultrasound-assisted extraction (UAE) and was found to be more beneficial due to a 30% higher yield of TP and TF and 30–80% higher antioxidant activity. The phenolic profiles of the three extracts were quite similar. The microdilution method confirmed the antibacterial activity of MAE and S/L extracts while the antifungal effect was not observed.
Trans Abroad: American Transgender Students’ Experiences of Navigating Identity and Community While Studying Abroad
Authors: Michl Taylor , Stookey Alexandra , Wilson Jillian , Chiou Katie , Raque Trisha L. , Kracen Amanda
Published: August 2023

Despite significant and increasing numbers of students studying internationally, there are few data about the experiences of study abroad for various marginalized students, including transgender and gender expansive (TGE) students. Therefore, 15 TGE adults from the United States were interviewed about navigating gender and culture during undergraduate study abroad programs. Interviews were analyzed using consensual qualitative research (CQR). Participants shared how they benefited from international study and navigated intersecting social identities, including gender, which was complex and nuanced. They discussed how they actively managed issues of disclosure and its consequences, explored their identities and the influence of their social setting, and dealt with anticipated, deliberate, and unintentional harm from others. Relationships and community were priorities for participants when studying internationally; they explained how they determined whether to invest in relationships or not. Participants also clarified what their relationships looked like during study abroad, as well as unique considerations that arose from their marginalized identities. These findings can help inform the development of more inclusive, safe, and satisfying study abroad experiences for all students, especially TGE individuals; implications for future research and study abroad interventions are provided.
Edge Resolvability in Generalized Petersen Graphs
Authors: Iqbal Tanveer , Bokhary Syed Ahtsham Ul Haq , Hilali Shreefa O. , Alhagyan Mohammed , Gargouri Ameni , Azhar Muhammad Naeem
Published: August 2023

The generalized Petersen graphs are a type of cubic graph formed by connecting the vertices of a regular polygon to the corresponding vertices of a star polygon. This graph has many interesting graph properties. As a result, it has been widely researched. In this work, the edge metric dimensions of the generalized Petersen graphs GP(2l + 1, l) and GP(2l, l) are explored, and it is shown that the edge metric dimension of GP(2l + 1, l) is equal to its metric dimension. Furthermore, it is proved that the upper bound of the edge metric dimension is the same as the value of the metric dimension for the graph GP(2l, l).
Estimation of Hidden Logits Using Several Randomized Response Techniques
Authors: Halim Asma , Arshad Irshad Ahmad , Alomair Abdullah Mohammed , Alomair Mohammed Ahmed
Published: August 2023

In survey sampling, we aspire to obtain sound and consistent responses, which are not achieved while dealing with sensitive issues. Frequently, respondents give elusive responses to sensitive questions, so we employ randomized response techniques that facilitate finding an appropriate proportion of socially sensitive characteristics. In the present study, we proposed a hidden logit estimation method using Huang, Warner, and Mangat’s randomized response techniques. This study depicts that the estimates become closer to the standard logits as the values of p increase. We found that the hidden logit estimates obtained by the Huang randomized response technique were nearer to the parametric values, in contrast to the other existing techniques, and we demonstrate an increase in accuracy as well. The simulation-based AIC and SIC values are used to assess model performance. We found that the Huang model is the best model for the proposed hidden logit method. This paper contributes towards the application of logistic models in the case of sensitive or socially stigmatized issues.
A Framework for Predicting X-Nuclei Transmitter Gain Using 1H Signal
Authors: Vaeggemose Michael , Schulte Rolf F. , Hansen Esben S. S. , Miller Jack J. , Rasmussen Camilla W. , Pilgrim-Morris Jemima H. , Stewart Neil J. , Collier Guilhem J. , Wild Jim M. , Laustsen Christoffer
Published: August 2023

Commercial human MR scanners are optimised for proton imaging, containing sophisticated prescan algorithms with setting parameters such as RF transmit gain and power. These are not optimal for X-nuclear application and are challenging to apply to hyperpolarised experiments, where the non-renewable magnetisation signal changes during the experiment. We hypothesised that, despite the complex and inherently nonlinear electrodynamic physics underlying coil loading and spatial variation, simple linear regression would be sufficient to accurately predict X-nuclear transmit gain based on concomitantly acquired data from the proton body coil. We collected data across 156 scan visits at two sites as part of ongoing studies investigating sodium, hyperpolarised carbon, and hyperpolarised xenon. We demonstrate that simple linear regression is able to accurately predict sodium, carbon, or xenon transmit gain as a function of position and proton gain, with variation that is less than the intrasubject variability. In conclusion, sites running multinuclear studies may be able to remove the time-consuming need to separately acquire X-nuclear reference power calibration, inferring it from the proton instead.