Miracle-dz/UniFAR-FTUs huggingface dataset:
Upper Ordovician chitinozoan biostratigraphy from the type Ashgill area (Cautley district) and the Pus Gill section (Dufton district, Cross Fell Inlier), Cumbria, Northern England

Seventy-five samples from the classic sections through the historical type area of the Ashgill Series in the Cautley district and along Pus Gill in the Cross Fell Inlier have been examined for chitinozoans. The results of this study allowed the recognition of five internationally recognized biozones and the definition of two new Avalonian chitinozoan zones. From bottom upwards, these are: the Fungochitina spinifera, the Tanuchitina bergstroemi? , the Conochitina rugata, the Spinachitina fossensis, the Bursachitina umbilicata sp. n. , the Ancyrochitina merga and the Belonechitina postrobusta zones. One new species is described: Bursachitina umbilicata sp. n. This biozonation enables a correlation between the Cautley district and the Baltoscandia and Gondwana palaeocontinents based on chitinozoans. The Baltoscandic chitinozoan zones are, therefore, now better correlated with the British chronostratigraphical scheme, which is still widely used. It is stratigraphically significant that the base of the Ashgill in its type area does not fall within the Tanuchitina bergstroemi Zone, as widely believed before, but in the Fungochitina spinifera Zone. In addition, chitinozoans from the Onnian (Caradoc) section of the Cross Fell Inlier provide a link with the type Caradoc section in Shropshire.
On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies

Abstract Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.
Impact of Clinical Parameters and Induction Regimens on Peripheral Blood Stem-Cell Mobilization and Collection in Multiple Myeloma Patients

Introduction: High-dose chemotherapy (HDCT) followed by autologous blood stem-cell transplantation (ABSCT) remains the standard consolidation therapy for newly diagnosed eligible multiple myeloma (MM) patients. As a prerequisite, peripheral blood stem cells (PBSCs) must be mobilized and collected by leukapheresis (LP). Many factors can hamper PBSC mobilization/collection. Here, we provide a comprehensive multiparametric assessment of PBSC mobilization/collection outcome parameters in a large cohort. Methods: In total, 790 MM patients (471 [60%] male, 319 [40%] female) who underwent PBSC mobilization/collection during first-line treatment were included. Evaluated PBSC mobilization/collection outcome parameters included the prolongation of PBSC mobilization, plerixafor administration, number of LP sessions, and overall PBSC collection goal/result. Results: 741 (94%) patients received cyclophosphamide/adriamycin/dexamethasone (CAD) and granulocyte-colony-stimulating factor (G-CSF) mobilization. Plerixafor was administered in 80 (10%) patients. 489 (62%) patients started LP without delay. 530 (67%) patients reached the PBSC collection goal at the first LP session. The mean overall PBSC collection result was 10.3 (standard deviation [SD] 4.4) × 106 CD34+ cells/kg. In a multiparametric analysis, variables negatively associated with PBSC mobilization/collection outcomes were female gender, age >60 years, an advanced ISS stage, and local radiation pre-/during induction, but not remission status postinduction. Notably, the identified risk factors contributed differently to each PBSC mobilization/collection outcome parameter. In this context, compared to all other induction regimens, lenalidomide-based induction with/without antibodies negatively affected only the number of LP sessions required to reach the collection goal, but no other PBSC mobilization/collection outcome parameters. In contrast, the probability of reaching a high collection goal of ≥6 × 106 CD34+ cells/kg body weight was higher after lenalidomide-based induction compared to VCD/PAD or VAD – taking into account – that a higher G-SCF dosage was given in approximately one-third of patients receiving lenalidomide-based induction with/without antibodies. Conclusion: Considering the identified risk factors in the clinical setting can contribute to optimized PBSC mobilization/collection. Moreover, our study demonstrates the necessity for a differentiated evaluation of PBSC mobilization/collection outcome parameters.
Lessons Learned Through Our Climate Change Professional Development Program for Middle and High School Teachers

The National Center for Atmospheric Research in Boulder, Colorado has been offering professional development on climate change for middle and high school teachers since the summer of 2001. Our experience with educators has shown that teachers feel ill-prepared to share the science and implications of climate change research with their students. As a result of the extensive exposure of this topic in the media and the controversy that sometimes surrounds it, teachers are highly motivated to engage in professional development on this subject. A variety of professional development approaches are needed to reach a wide spectrum of educators whose learning preferences and personal and professional constraints may preclude participation in some professional development opportunities. This paper provides lessons learned from successful climate change professional development for teachers, including the importance of inquiry-based curriculum activities, the importance of an opportunity to "learn by doing" and to share their perspectives with their colleagues. Finally, development and continuation of the learning community, both among participants as well as with instructors and scientists, provides critical support to classroom educators, who can at times experience a sense of professional isolation.
A prediction method of product machining accuracy based on domain adaption

In modern machine tool processing, predicting the precision of processed products has become an important research issue in the field of machine tool processing. Based on the analysis of the displacement data, a prediction model optimization method (CRLSTM-TCN) based on the combination of domain-adaptive long short-term memory network and temporal convolutional neural network is proposed. Experiments show that this method solves the problem of processing accuracy prediction in the case of insufficient sample size.
A 3D finite element analysis for the influence of different marginal finish line design on stress distribution in posterior monolithic zirconia crown prosthetic system

The purpose of this paper was to evaluate the effect of four kinds of marginal finish line design on stress distribution in posterior zirconia all-porcelain crown prosthetic system. Four 3D finite element models with different margin designs of mandibular first molar restorated by zirconia all-porcelain crown were developed. Each model was designed with one kind of finish line margin respectively (SC0.5: shallow concave finish line with a width of 0.5 mm; SC1.0: shallow concave finish line with a width of 1.0 mm; RA0.5: right angle finish line with a width of 0.5 mm; RA1.0: right angle finish line with a width of 1.0 mm). A load of 670 N which equals the greatest masticatory pressure in centric occlusion was applied to the zirconia all-porcelain crown vertically. The other load of 220 N simulating the lateral occlusion was applied at 45 degrees to the long axis of the abutment tooth. Then FE analysis was performed in each situation to observe stress distributions and Von Mises stress values in each layer of the zirconia prosthetic system which consisted of zirconia crown, cement layer, and abutment tooth. For all models, stress was localized in the occlusal loading area and the boundary of the crown. The minimum stress values were obtained in model SC1.0, the greatest stress values occurred in model RA0.5. Furthermore, the Von Mises stress values of the SC group were apparently higher than that of the RA group. The FEA method can effectively evaluate the stress in finish line. The results suggested that a combination of shallow concave finish line with a width of 0.5 mm potentially have more suitable geometry to reduce stress concentration.
Modeling of polarized radiation transmission of skylight and simulation software design

This essay, that is based on the Mie scattering Monte Carlo methodology, simulates the actual atmospheric light transmission process by constructing various parameters, including atmosphere, cloud layer, and surface albedo, and then generates the simulated polarized radiation transmission model of sky light. The sky polarization simulation software was developed using the Qt graphics library based on this model. By data entry as with atmospheric conditions, the degree of polarization and the angle of polarization are evaluated, and the meridian degree of polarization curve and the all-sky polarization diagram would then be drawn. For the purpose of polarization measurement under actual weather circumstances, it offers data assistance and verification.
Weakly supervised part segmentation of 3D models based on sphere node graph

In this paper, a weakly supervised 3D model part segmentation method based on Sphere Node Graph (SN-Graph) is proposed. For a given 3D model (usually in the form of mesh), we extract the sphere node graph from its interior space as the discrimination criterion of surface points segmentation, since SN-Graph explicitly represents the global and local structures. These sphere nodes are unsupervised, while the weakly supervised information for part segmentation comes from a small number of labelled point clouds for each model in the training set. Utilizing these small number of labelled points, we train the bisection segmentation information for each sphere by parameterizing the orientation of the plane through the center of the sphere and classification features. Experiments demonstrate that the proposed method significantly improves the segmentation accuracy of surface points at part junctions and achieves similar overall accuracy to fully supervised methods (utilizing more than 1K labelled points).
Importance evaluation method for complex network nodes based on third-order K-shell

The KPN algorithm refines the importance of peer nodes in K-shell to identify important nodes in complex networks, but it does not distinguish the removal order of peer nodes from the proportion weight of K-shell values and fails to fully consider the impact of local domain characteristics of nodes. To solve the above problems, a node importance evaluation algorithm based on third-order neighborhood is presented. The algorithm assigns proportional weights to the removal order and K-shell values of nodes at the same level, and then evaluates the significance of nodes by using the influence within their third-order neighborhoods based on the three-degree separation of influence propagation. The simulation results on six real social networks using epidemic model and linear threshold model demonstrate that the method can identify key nodes in complex networks more accurately than the other six algorithms.
Sentiment analysis using multi neighborhood representation learning embeddings: graph convolutional neural networks

With the thriving of social networks, sentiment analysis at aspect-level has become a research hotspot in the field of natural language processing, aiming to extract user expressed sentiment from massive text data. To tackle the problem that current sentiment analysis methods cannot simultaneously consider the contextual and syntactic information of aspect terms, this paper proposes a targeted model for aspect-level sentiment analysis by embedding different neighborhood representations. By integrating aspect neighborhood information and node neighborhood information through convolutional neural networks, our proposed model achieves high effectiveness on five public datasets as demonstrated by the evaluation coefficients constructed in our study.
Lightweight image recognition of peach tree pests based on global context modeling

Timely and accurate identification of peach tree pests and assistance in pest control is the key to promoting the healthy development of the peach industry. A lightweight peach tree pest image recognition model based on global context modeling is proposed for existing pest image recognition models with high structural complexity, high dependence on computational resources at runtime, and deployment difficulties. First, a lightweight global context modeling module is embedded in the ShuffleNet V2 unit to aggregate all the location information of the input image, obtain global context features, correct the pest information, and improve the feature representation capability of the model. Then, the ordinary convolution layer of ShuffleNet V2 is optimized to add lightweight convolution to improve the feature extraction ability of the model at different stages. Finally, a softmax classifier is connected to obtain a lightweight peach tree pest image recognition model. After experimental verification, the proposed model outperforms neural networks such as AlexNet in terms of recognition accuracy, parameter computation and floating point operation, and the number of parameters is only one-tenth or even a few tenths of AlexNet, which is a lightweight and efficient neural network model that can be easily deployed.
Deep reinforcement learning for unmanned aerial vehicles cluster task allocation

Real-time task allocation for Unmanned Aerial Vehicle (UAV) clusters is a complex decision problem, particularly in dynamic and uncertain environments. In practice, it can be difficult to predict enemy information, which is often obtained gradually through UAV reconnaissance. Traditional algorithms may not perform satisfactorily in such scenarios. In this study, we develop a Deep Reinforcement Learning (DRL) algorithm that combines Proximal Policy Optimization (PPO) with Long Short-Term Memory (LSTM) to address this challenge. By predicting the enemy’s positions and behavior, the algorithm performs real-time task allocation using a pre-trained model. Comparative experiments with basic DRL algorithms validate the convergence, effectiveness, and scalability of our approach.
PointRendUNet: a model for fine boundary segmentation

Segmenting tumors from brain MRI sequences is critical for prognosis assessment, radiomics analysis, surgical planning, and pathological diagnosis. However, semantic segmentation based on convolutional neural networks is on the regular grid representation of the input and output of the network, which leads to the tendency of the predicted label edges to smooth. The infiltrating nature of malignant gliomas also poses challenges for the segmentation task. Therefore, we propose an automated, standardized method for fine-segmenting brain tumor boundaries. This method introduces the PointRend module based on 3D-UNet and trains a model with coarse-to-fine segmentation on the open dataset BraTS. The performance of our model on the public validation dataset is as follows: Dice of 0.84699, 0.84640, and 0.84661 for the whole tumor, tumor core, and enhancing tumor area, respectively. The evaluation shows that our model produces a good and balanced performance for different tumor subregions.
Topic opinion leader identification based on latent Dirichlet allocation topic model

This paper focuses on the indicator of professional authority for mining user proficiency within a specific topic domain, which contributes to Sina Weibo opinion leader identification. We construct an indicator system based on three dimensions comprising user attributes, interaction behaviors, and topic extraction. We quantify the professional authority indicator by applying the latent Dirichlet allocation topic model and information entropy. The grey relational analysis and the entropy-weight method are employed to calculate the opinion leader index and rank candidates accordingly, reflecting influence within a topic domain. And we validate the proposed method by analyzing a typical event of "Wei Ya's tax evasion" as an experimental case. Results demonstrate that the proposed model effectively identifies highly influential users with proficiency in this topic, these opinion leaders could play a guiding role in information diffusion. Moreover, it can serve as a reference for selecting opinion leaders in similar topic events as a prediction method. This paper supports regulatory agencies and Sina Weibo regarding online public opinion control, guidance, and response.
Optimization and implementation of vehicle detection algorithm for tiny objects

To address the issue of current target detection models struggling to detect small vehicles and complex depth models being ill-suited for embedded platforms with limited computing power and memory, NWD and IoU are combined for tiny object detection, and the lightweight backbone network EfficientFormer is used to replace the YOLOv5 model backbone, which finally improves the tiny object detection effect while reducing the model parameters to achieve the lightweight depth model. The experimental results show that on the actual data set collected from the project, the mAP reaches 82.1%, and the number of parameters is reduced by 8.5MB.
Target recognition method for Guangdong-Hong Kong-Macao Greater Bay Area port based on deep residual neural network

The traditional port target recognition algorithm has the problems of high time complexity and low recognition accuracy. In order to solve this problem, we propose a method of Guangdong-Hong Kong-Macao Greater Bay Area port target recognition based on deep residual neural network. By studying various network models with different deep and parameters, a new multi-strapdown residual network model (Mu-ResNet) with less memory occupation and lower time complexity is designed by combining two-layer residual learning module with three-layer residual learning module. The experimental results show that the recognition rate of our proposed method is better than the traditional recognition algorithm on the port target dataset.
Design of real-time object classification acceleration platform based on SDSoC

This study introduces a hardware acceleration design scheme for real-time object classification using SDSoC development software, and compares the performance advantages and disadvantages of different optimization strategies. Unlike the traditional RTL design and HLS design, SDSoC adopts the whole system optimization compiler, which can provide automatic data connection and other functions, so that developers can invest more energy in algorithm optimization, reduce the development difficulty and speed up the development time. This study uses a convolutional neural network to classify objects and optimizes the network according to hardware performance. A real-time video acquisition and display system was built in Vivado. This study builds a complete system in SDSoC, and focuses on optimizing convolution operation and data transmission path according to the running time of each function in the builtin TCF profiler. The effects of different acceleration function settings and network quantization on performance and resource utilization were studied. Finally, different optimization strategies are compared and evaluated, and the best design scheme achieves 3.599 W power consumption and 9.24 GOP/s per watt performance.
Research on dynamic and static grassland fire identification based on UAV images

China's grassland area is vast, once a grassland fire occurs, it will cause serious threat to people's life and property. At present, the domestic grassland fire monitoring means is mainly manual monitoring, this monitoring means of monitoring cost is large, low efficiency, real-time poor, and it is difficult to achieve complete detection, so the use of satellite, UAV images and other advanced means to identify grassland fire is the current research hot spot. In this paper, using UAV images, the detection is divided into two stages according to the burning process of grassland fires. In the early stage, the improved YOLOv5 network model is used to identify smoke due to the more obvious smoke characteristics; in the middle and late stage, the color characteristics of the fire area change obviously, and a flame detection method combining static and dynamic features is proposed. The C3 structure of the Neck part of the YOLOv5 model is improved, and the CBAM attention mechanism is introduced to make the model pay more attention to the target information; the loss function of YOLOv5 is improved, and DIoU is adopted as its loss function, and the experimental results show that the mAP of the improved YOLOv5 model is improved by 2.53% when trained on the self-built smoke dataset, and the recognition accuracy is effectively improved. When flame recognition is performed, the flame is affected by wind, vegetation coverage, humidity and terrain, etc., and spreads in all directions at different speeds, forming a dynamic outward-expanding circle with an increasing area and a distinct black color in the overfire area. Static color features are firstly used for pre-determination, and then the dynamic area and contour changes of the overfire are further discriminated. Experiments show that the algorithm reduces the false recognition rate of grassland fires and has good practicality.
Domain adaptation strategy for aging and damage classification model of high chromium martensitic heat-resistant steel based on transfer learning

Using deep learning to study the aging and damage classification of high-chromium martensite microscopic metallography requires a large amount of labeled data; usually a pre-trained deep learning model is used to deal with this problem, however, the pre-trained model contains a large number of available Features, although fine-tuning the entire pre-training model improves the model effect, it consumes a lot of computing power. In view of this, based on migration learning, this paper divides the VGG network into several parts according to the order of network reasoning from the perspective of domain adaptation, and divides and identifies the functions of each part from the perspective of down sampling; using high-chromium martensitic metallographic aging Each part of the network is fine-tuned separately with the impairment dataset. The research results show that the fine-tuning part of the model converges faster and saves more computing power than the fine-tuning of the entire model; different functional parts of the model have different effects on the final effect of the model, and fine-tuning the module closest to the input has less impact on the final effect of the model. In this way, a more efficient domain adaptation strategy for the aging and damage classification model of high-chromium martensitic heat-resistant steel is obtained.