Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes

With the increasingly widespread application of UAV intelligence, the need for autonomous navigation and positioning is becoming more and more important. To solve the problem that UAV cannot perform localization in complex scenes, a new multi-source fusion framework factor graph optimization algorithm is used for UAV localization state estimation in this paper, which is based on IMU/GNSS/VO multi-source sensors. Based on the factor graph model and the iSAM incremental inference algorithm, a multi-source fusion model of IMU/GNSS/VO is established, including the IMU pre-integration factor, IMU bias factor, GNSS factor, and VO factor. Mathematical simulations and validations on the EuRoC dataset show that, when the selected sliding window size is 30, the factor graph optimization (FGO) algorithm can not only meet the requirements of real time and accuracy at the same time, but it also achieves a plug-and-play function in the event of local sensor failures. Finally, compared with the traditional federated Kalman algorithm and the adaptive federated Kalman algorithm, the positioning accuracy of the FGO algorithm in this paper is improved by 1.5–2-fold, and can effectively improve autonomous navigation system robustness and flexibility in complex scenarios. Moreover, the multi-source fusion framework in this paper is a general algorithm framework that can satisfy other scenarios and other types of sensor combinations.
Influence of Career-Related Parental Support on Adolescents’ Career Maturity: A Two-Wave Moderated Mediation Model

Based on the career resources model (Hirschi, 2012), the current study examined the mechanism underlying the relationship between career-related parental support and adolescents’ career maturity by investigating the mediating role of future time perspective. In addition, the moderating role of core self-evaluation in the relationship between career-related parental support and future time perspective was explored. A two-wave survey was conducted with 225 Chinese middle school students. The results showed that career-related parental support positively affected future time perspective and career maturity. Moreover, future time perspective was found to play a completely mediating role in the relationship between career-related parental support and career maturity. Furthermore, the mediating effect of future time perspective was moderated by core self-evaluation; for higher levels of core self-evaluation, the mediating effect was more substantial. The results indicated that social and psychological resources could promote adolescents’ career development.
Exact solution of the plane problems for poroelastic rectangle and semi‐strip

The exact solutions of the plane problems of poroelasticity for a rectangle and a semi‐strip are derived in the article. The original problems are reduced to one‐dimensional problems with the help of finite and semi‐infinite Fourier transforms for a rectangle and a semi‐strip, respectively. The one‐dimensional boundary value problems are formulated in vector form, and the apparatus of matrix differential calculation is used. Analytical formulas for displacements, stress, and pore pressure are derived for the rectangle and semi‐strip. The investigation of stress and pore pressure for different load types and different poroelastic materials is done for the rectangle and semi‐strip.
The effect of plant-derived peptides on the hot-melt stickiness of herbal extracts during spray drying

Abstract The aim of this study was to explore plant-derived peptides as carrier material with the function of anti-hot-melt stickiness of traditional Chinese herbal extracts during spray drying. Crataegi fructus extract (CFE) with a severe hot-melt stickiness problem was used as a model drug, and soy peptides (SPT) were screened as the most effective and suitable carrier material among seven plant-derived proteins and peptides which include soy protein isolate, walnut protein and five kinds of peptides (corn peptides, wheat peptides, walnut peptides, rice peptides and SPT). The addition of 15% SPT also exhibited a strong anti-stickiness effect in other four hot-melt sticky herbal extracts from Chinese herbs (Lycii fructus, Corni fructus, Chaenomelis fructus and Mume fructus) since the obtained powder increased from completely sticking to the wall to more than 50% powder yield. The dynamic surface tensions (DST) of CFE decreased consistently with the increase of SPT from 5% to 20%, and DST1000ms had consistent reduction with the addition of SPT from zero to 20%. Meanwhile, the adhesive force of the single droplet at 28 min decreased below 18.5 N. The softening point of the spray-dried powder of CFE added 15% SPT increased by about 40 °C, and the addition of 15% SPT improved the stability which might be similar with the addition of 50% MD based on scanning electron microscope analysis but more efficiently. These findings confirmed that SPT plant-derived could serve as effective and stable carrier material with good anti-stickiness ability during spray drying.
Multilevel Noise Contrastive Network for Few-Shot Image Denoising

In recent years, most denoising methods based on deep convolutional neural networks heavily rely on massive noisy–clean image pairs. Collecting massive noisy–clean image pairs is expensive and not practical in real scenes. Currently, few-shot learning has been applied to many areas to cope with the absence of data. The few-shot learning, however, in image denoising severely suffers from domain gap problems, including dataset domain gap and feature domain gap, especially for the real noisy images. Therefore, this article proposes a multilevel noise contrastive network (MNC-Net) performing few-shot image denoising. MNC-Net consists of two training stages: 1) using contrastive learning to self-supervise the training of multilevel noise contrastive learner (MNCL) on the pure synthetic noisy images with multiple Gaussian noise levels to ease the acute dataset domain gap and 2) features generated by the MNCL on limited data are fused to the second stage and alleviate the feature domain gap using our proposed denoising network. Specifically, the MNCL consists of a contrastive feature extractor (CFE) and a contrastive feature projector (CFP). MNCL learns the rich and complex content-invariant degradations and general multiple-level noise representations. The denoising network in the second stage is composed of guided feature encoder (GFE) and adaptive denoising decoder (ADD). The GFE uses contrast features from CFE to guide the produced representations on the specific input noisy images. Then, such output features are fed into the ADD to adaptively denoise the noisy images on the corresponding noise distribution. To the best of our knowledge, this work is the first attempt to jointly use the few-shot learning and contrastive learning in the deep denoising field. Extensive experiments on CBSD68, Kodak24, Set12, SIDD, and DND show that our method achieves promising denoising performances in the absence of data.
Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques

Remotely monitoring people’s healthcare is still among the most important research topics for researchers from both industry and academia. In addition, with the Wireless Body Networks (WBANs) emergence, it becomes possible to supervise patients through an implanted set of body sensors that can communicate through wireless interfaces. These body sensors are characterized by their tiny sizes, and limited resources (power, computing, and communication capabilities), which makes these devices prone to have faults and sensible to be damaged. Thus, it is necessary to establish an efficient system to detect any fault or anomalies when receiving sensed data. In this paper, we propose a novel, optimized, and hybrid solution between machine learning and statistical techniques, for detecting faults in WBANs that do not affect the devices’ resources and functionality. Experimental results illustrate that our approach can detect unwanted measurement faults with a high detection accuracy ratio that exceeds the 99.62%, and a low mean absolute error of 0.61%, clearly outperforming the existing state-of-art solutions.
Evaluation of primer pairs for eDNA‐based assessment of Ephemeroptera, Plecoptera, and Trichoptera across a biogeographically diverse region

Macroinvertebrates serve as key indicators in ecological assessments of aquatic eco systems, where the composition and richness of their communities are indicative of environmental and anthropogenic change. Established monitoring of macroinverte brates is expensive and time- consuming, and relies on expert taxonomic knowledge. In contrast, biomonitoring based on molecular tools can support faster characteriza tion of aquatic communities but needs validation for the target taxonomic groups and the study region. Here, we used data from a biomonitoring program covering a large biogeographic gradient to compare the routine kick- net method with eDNA metabarcoding. We used two primer pairs targeting COI, one targeting a broad meta zoan spectrum (mICOIintF/jgHCO2198) and another more recently developed primer pair optimized for the detection of freshwater invertebrates (fwhF2/EPTDr2n). We used the data of the macroinvertebrate monitoring with a focus on the orders of Ephemeroptera, Plecoptera, and Trichoptera across 92 rivers in Switzerland, covering four continental drainage basins and an elevational range from 198 to 1650 m a.s. l. Across all sample sites, the kick- net detected more distinct taxa than either of the metabarcoding approaches. At a site level, however, both primer pairs detected on average more species. Comparing both primer pairs, the fwhF2/EPTDr2n primer pair captured more species assigned to the indicator groups Ephemeroptera, Plecoptera, and Trichoptera, and showed a significantly larger overlap with the kick- net method. However, the community composition still varied significantly among the different metabarcoding approaches. Fewer Trichoptera species were recovered by eDNA, whereas the fwhF2/EPTDr2n primer pair detected more Plecopterans than the other two approaches. This study highlights the importance of the optimization and vali dation of novel molecular approaches under consideration of the target organismal group and the study area. net, we compared the total number of individuals with the number of sites at which this species was detected. The number of individuals detected per species was significantly associated with how widespread a species was over all the sampling sites linear regression. Species that were detected in higher numbers were also present at more sites. intervals of rank abundances detected by the different approaches.
Additive Manufacturing of Biomaterials—Design Principles and Their Implementation

Additive manufacturing (AM, also known as 3D printing) is an advanced manufacturing technique that has enabled progress in the design and fabrication of customised or patient-specific (meta-)biomaterials and biomedical devices (e.g., implants, prosthetics, and orthotics) with complex internal microstructures and tuneable properties. In the past few decades, several design guidelines have been proposed for creating porous lattice structures, particularly for biomedical applications. Meanwhile, the capabilities of AM to fabricate a wide range of biomaterials, including metals and their alloys, polymers, and ceramics, have been exploited, offering unprecedented benefits to medical professionals and patients alike. In this review article, we provide an overview of the design principles that have been developed and used for the AM of biomaterials as well as those dealing with three major categories of biomaterials, i.e., metals (and their alloys), polymers, and ceramics. The design strategies can be categorised as: library-based design, topology optimisation, bio-inspired design, and meta-biomaterials. Recent developments related to the biomedical applications and fabrication methods of AM aimed at enhancing the quality of final 3D-printed biomaterials and improving their physical, mechanical, and biological characteristics are also highlighted. Finally, examples of 3D-printed biomaterials with tuned properties and functionalities are presented.
Hallmarks of Cancer Applied to Oral and Oropharyngeal Carcinogenesis: A Scoping Review of the Evidence Gaps Found in Published Systematic Reviews

Simple Summary This scoping review of systematic reviews aims to accurately assess the degree of existing scientific evidence on the cancer hallmarks proposed in 2011 by Hanahan and Weinberg, in the form of systematic reviews and meta-analyses, applied to oral potentially malignant disorders, oral cavity and oropharyngeal squamous cell carcinomas, in order to point out gaps in evidence and lines of research that should be implemented in the future to improve the malignant transformation prediction, diagnosis and/or prognosis of these diseases. Abstract In 2000 and 2011, Hanahan and Weinberg published two papers in which they defined the characteristics that cells must fulfil in order to be considered neoplastic cells in all types of tumours that affect humans, which the authors called “hallmarks of cancer”. These papers have represented a milestone in our understanding of the biology of many types of cancers and have made it possible to reach high levels of scientific evidence in relation to the prognostic impact that these hallmarks have on different tumour types. However, to date, there is no study that globally analyses evidence-based knowledge on the importance of these hallmarks in oral and oropharyngeal squamous cell carcinomas. For this reason, we set out to conduct this scoping review of systematic reviews with the aim of detecting evidence gaps in relation to the relevance of the cancer hallmarks proposed by Hanahan and Weinberg in oral and oropharyngeal cancer, and oral potentially malignant disorders, and to point out future lines of research in this field.
Photo-Protective and Anti-Inflammatory Effects of Antidesma thwaitesianum Müll. Arg. Fruit Extract against UVB-Induced Keratinocyte Cell Damage

The main cause of most skin cancers is damage from UVB from sunlight, which penetrate the skin surface and induce inflammation. For this reason, this study aims to identify natural products with photo-protection properties and their mode of action by using the UVB-irradiated HaCaT keratinocyte model. Antidesma thwaitesianum fruit extracts at 25, 50, and 100 µg/mL recovered cell viability following UVB exposure in a dose-dependent manner. Cell survival was associated with the reduction in intracellular ROS and NO. In addition, we showed that the pre-treatment with the fruit extract lowered the phosphorylation level of two MAPK-signaling pathways: p38 MAPKs and JNKs. The resulting lower MAPK activation decreased their downstream pro-inflammatory cascade through COX-2 expression and subsequently reduced the PGE2 proinflammatory mediator level. The photoprotective effects of the fruit extract were correlated with the presence of polyphenolic compounds, including cyanidin, ferulic acid, caffeic acid, vanillic acid, and protocatechuic acid, which have been previously described as antioxidant and anti-inflammation. Together, we demonstrated that the pre-treatment with the fruit extract had photo-protection by inhibiting oxidative stress and subsequently lowered stress-induced MAPK responses. Therefore, this fresh fruit is worthy of investigation to be utilized as a skincare ingredient for preventing UVB-induced skin damage.
Adaptive Context Exploration Network for Polyp Segmentation in Colonoscopy Images

Recently, automatic and accurate polyp segmentation has become an emerging yet challenging issue. Although convolutional neural networks (CNNs) exhibit a promising future modality to address this issue, most CNN-based methods highly require extensive labeled data. Unfortunately, there is a lack of large-scale public colorectal polyp segmentation datasets in the clinical community and academia. In this study, we construct a new benchmark dataset, which includes 2163 colonoscopy images and their pixel-wise annotations. Moreover, for intelligent polyp segmentation, we propose a novel adaptive context exploration network (ACENet). Our ACENet follows an encoder-decoder architecture and consists of two key modules, i.e., an attentional atrous spatial pyramid pooling (AASPP) module and an adaptive context extraction (ACE) module. The AASPP fuses semantic features from the encoder, and generates the global guidance information for the following decoder. The ACE captures multi-scale features and aggregates them by a branch-wise attention mechanism. Benefiting from these two modules, our ACENet is capable of adaptively exploring the context features to locate and detect the polyp regions effectively. Extensive experiments on the collected dataset and four publicly available datasets show that the proposed ACENet achieves superior performance on five evaluation metrics over three mainstream categories of the state-of-the-art methods.
Together–A couples’ program integrating relationship and financial education: A randomized controlled trial

The present study evaluated the effectiveness of the TOGETHER program through a randomized controlled trial. A sample of community couples, predominantly African-American and low-income, was randomly assigned to either a no-intervention control group (N = 147) or the intervention group (N = 145) that participated in the program consisting of a 20-hour integrated relationship and financial education workshop and referrals to community services for family needs. Both groups completed self-report instruments upon enrollment (T1), 8 weeks later (workshop end = T2), and 6 months later (T3) regarding general and financial individual coping and well-being, general and financial relationship functioning, and financial self-efficacy and management. Dyadic multilevel models controlling for demographic variables and baseline measurement of outcome variables were used to test T1 – T2, T2 – T3, and T1 – T3 mean differences. Improvements in the intervention group were statistically significantly different from the control group at 6-month follow-up in the following areas: Stress levels, use of positive individual and dyadic strategies to manage financial and non-financial stress, conflict management, relationship satisfaction and commitment, and budgeting. Considering the reciprocal influence of individual/relational emotional and financial aspects, TOGETHER may be a particularly useful relationship education program for financially distressed couples. However, given the absence of significant differences between the intervention and control groups in financial stress, financial shared goals and values, cash and credit management, and savings/investment, future studies should continue evaluating whether the financial literacy component of the program may have a positive impact in such outcomes. Limitations and implications are discussed.
Simulation of chemical and hydrometallurgical processes

The use of computer simulation, is now a basic tool for process design and optimization, as evidenced by its increasing use of practicing engineers through commercially available simulator software products. Engineers, in charge of designing equipment for the hydrometallurgical plants have to be able to select the most suitable equipment for each application and to predict the performances under different process conditions. With the increasing capacity of microcomputers, the software companies would like to provide computer tools for plant operators to obtain the best results from the equipment and process. In order to allow the process engineers to concentrate on their task, the simulation software has to offer a user-friendly interface combined with quick and accurate calculations.
Bimodal Learning Engagement Recognition from Videos in the Classroom

Engagement plays an essential role in the learning process. Recognition of learning engagement in the classroom helps us understand the student’s learning state and optimize the teaching and study processes. Traditional recognition methods such as self-report and teacher observation are time-consuming and obtrusive to satisfy the needs of large-scale classrooms. With the development of big data analysis and artificial intelligence, applying intelligent methods such as deep learning to recognize learning engagement has become the research hotspot in education. In this paper, based on non-invasive classroom videos, first, a multi-cues classroom learning engagement database was constructed. Then, we introduced the power IoU loss function to You Only Look Once version 5 (YOLOv5) to detect the students and obtained a precision of 95.4%. Finally, we designed a bimodal learning engagement recognition method based on ResNet50 and CoAtNet. Our proposed bimodal learning engagement method obtained an accuracy of 93.94% using the KNN classifier. The experimental results confirmed that the proposed method outperforms most state-of-the-art techniques.
Investigating the usefulness of green dual fuel Metrobuses for Sustainability in Johannesburg, through the 'New beginning for Metrobus' programme.

vii Dedication and Acknowledgements . viii List of figures and Tables . ix List of Abbreviations . xi Chapter One Introduction . I 1.1 Overview . 1 1.2 Situating argument for Sustainability . 2 1.3 CNG as Renewable Energy in Johannesburg . 2 1.4 Using the 'NBM' programme as a Case Study . 3 1.5 Research motivation and inspiration . 4 1.6 Origins of Case Study for this research . 4 1.7 Research Question. 4 1.8 Research Sub-Questions . 5 1.9 Research Methodology & Methods . 5 1.10 Ethical considerations . 6 1.11 Chapter Outline . 6 Chapter Two Literature Review & Conceptual Framework . II 2.1 Overview . 8 2.2 Sustainability & the Green Economy . 8 2.3 'Sustainable' & 'Compact' Cities . 10 2.4 Sustainable Transport . 12 2.5 Renewable Energy CNG vs Biogas . 12 2.6 Renewable Energy Technology Natural Gas Vehicles . 16 2.7 CNG – Strengths & Limitations . 17 2.8 Lessons of Conceptual Framework . 18 Chapter Three Research Methodology and Methods . III
A Survey on Visual Navigation and Positioning for Autonomous UUVs

Autonomous navigation and positioning are key to the successful performance of unmanned underwater vehicles (UUVs) in environmental monitoring, oceanographic mapping, and critical marine infrastructure inspections in the sea. Cameras have been at the center of attention as an underwater sensor due to the advantages of low costs and rich content information in high visibility ocean waters, especially in the fields of underwater target recognition, navigation, and positioning. This paper is not only a literature overview of the vision-based navigation and positioning of autonomous UUVs but also critically evaluates the methodologies which have been developed and that directly affect such UUVs. In this paper, the visual navigation and positioning algorithms are divided into two categories: geometry-based methods and deep learning-based. In this paper, the two types of SOTA methods are compared experimentally and quantitatively using a public underwater dataset and their potentials and shortcomings are analyzed, providing a panoramic theoretical reference and technical scheme comparison for UUV visual navigation and positioning research in the highly dynamic and three-dimensional ocean environments.