Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cluster-Randomized Evaluation of the SuperShelf Intervention in Choice-Based Food Pantries.

BACKGROUND Interventions in food pantry settings have the potential to improve health among clients at risk of diet-related disease. PURPOSE This study evaluates whether a cluster-randomized, behavioral intervention in food pantries resulted in improved client outcomes. METHODS Sixteen Minnesota food pantries were randomized to an intervention (n = 8) or control condition (n = 8). The intervention offered pantries technical assistance to improve healthy food supply and implement behavioral economics strategies to promote healthy food selection. A convenience sample of adult clients were enrolled (paired sample, 158 intervention, 159 control) and followed for 1 year. Additional clients were enrolled at follow-up to assess food selection (follow-up sample, 85 intervention, 102 control). Analysis was limited to data from 11 pantries (5 intervention, 6 control) due to COVID-19. Outcome measures included Healthy Eating Index-2015 (HEI-2015) total and subcomponent scores for 24-hr dietary recalls and client cart selections, and Life's Simple 7 (LS7) total and subcomponent scores. Multilevel mixed-effects models tested whether client outcomes differed by intervention condition. RESULTS In adjusted models, there were no statistically significant differences by intervention condition in HEI-2015 or LS7 scores. Clients in intervention food pantries had improved Refined Grain subcomponent scores (p = .004); clients in control pantries had worsened Saturated Fat subcomponents scores (p = .019) and improved physical activity scores (p = .007). CONCLUSIONS The intervention did not result in improved diet quality or cardiovascular health as measured by HEI-2015 or LS7. Coordinated efforts across settings are needed to address health risks facing this population.
Statistical correction of the Winner’s Curse explains replication variability in quantitative trait genome-wide association studies

Genome-wide association studies (GWAS) have identified hundreds of SNPs responsible for variation in human quantitative traits [1]. However, genome-wide-significant associations often fail to replicate across independent cohorts, in apparent inconsistency with their apparent strong effects in discovery cohorts. This limited success of replication raises pervasive questions about the utility of the GWAS field. We identify all 332 studies of quantitative traits from the NHGRI-EBI GWAS Database [1, 2] with attempted replication. We find that 70% of studies provide insufficient data to evaluate replication rates. The remaining papers replicate significantly worse than expected (p < 10 −14), even when adjusting for regression-to-the-mean of effect size between discovery-and replication-cohorts termed the Winner’s Curse [3] (p < 10 −16). We show this is due in part to misreporting replication cohort-size as a maximum number, rather than per-locus one. In 39 studies accurately reporting per-locus cohort-size for attempted replication of 707 loci in samples with similar ancestry, replication rate matched expectation (predicted 458, observed 457, p = 0:94). In contrast, ancestry differences between replication and discovery (13 studies, 385 loci) cause the most highly-powered decile of loci to replicate worse than expected, due to difference in linkage disequilibrium. Author Summary The majority of associations between common genetic variation and human traits come from genome-wide association studies, which have analyzed millions of single-nucleotide polymorphisms in millions of samples. These kinds of studies pose serious statistical challenges to discovering new associations. Finite resources restrict the number of candidate associations that can brought forward into validation samples, introducing the need for a significance threshold. This threshold creates a phenomenon called the Winner’s Curse, in which candidate associations close to the discovery threshold are more likely to have biased overestimates of the variant’s true association in the sampled population. We survey all human quantitative trait association studies that validated at least one signal. We find the majority of these studies do not publish sufficient information to actually support their claims of replication. For studies that did, we computationally correct the Winner’s Curse and evaluate replication performance. While all variants combined replicate significantly less than expected, we find that the subset of studies that (1) perform both discovery and replication in samples of the same ancestry; and (2) report accurate per-variant sample sizes, replicate as expected. This study provides strong, rigorous evidence for the broad reliability of genome-wide association studies. We furthermore provide a model for more efficient selection of variants as candidates for replication, as selecting variants using cursed discovery data enriches for variants with little real evidence for trait association.
Design of triple band antenna with improved gain and low profile using triple band artificial magnetic conductor

In this paper, a tri-band low profile antenna based on Artificial Magnetic Conductor (AMC) is designed. The antenna consists of a tri-band slot antenna and AMC reflector. The Coplanar Waveguide(CPW)-fed tri-band antenna generate multiple resonant frequencies from slots and parasitic elements. To improve the CPW antenna’s radiation performance, an AMC structure with three zero-phases of the reflection coefficient is designed and employed as the reflector. An AMC unit with three zero-phase reflection coefficients is designed and arranged in 3×3 as the reflector of the slot antenna. The distance between the slot antenna and AMC structure is 0. 056λ (λ is the wavelength of 2.4 GHz in free space). Simulation results show that the antenna impedance bandwidth after loading the AMC reflector are 2.23-2.49 GHz, 5.45-5.89 GHz and 7.45-7.91GHz. Meanwhile, the peak gain are improved from 1.9 to 3.4 dBi at 2.4 GHz, 3.45 to 6.2 dBi at 5.8GHz, 3.92 to 7.6 dBi at 7.6 GHz respectively.
Defect detection algorithm for steel surface based on improved YOLOv5

Steel is frequently used in manufacturing equipment, and detecting steel surface flaws is critical to the proper operation of steel equipment in manufacturing workshops. In the process of industrial steel defect detection, there are problems such as low detection accuracy of multi-category defects, low detection speed and high missed detection rate of small targets. An improved YOLOv5 algorithm has been proposed to address the aforementioned problems. This algorithm adds the SE attention module to the backbone network of YOLOv5, which improves the feature representation ability. The bidirectional feature pyramid network (BIFPN) is then utilized to increase the model's feature fusion capabilities and detection accuracy. Finally, a small object detection layer is introduced to the model prediction layer for small objects in the data set to lower the missed detection rate of small objects. The algorithm proposed in this research increases object detection accuracy through experimental verification on the NEU-DET data set, when compared to a variety of object detection algorithms. Compared with the original model of YOLOv5s, the speed is increased by three frames, and the mAP value is increased by 5.1%.
Research on tree barrier measurement method based on monocular image depth estimation

Tree barrier plays a vital role in transmission lines, and its existence directly affects the stable supply of electricity. Therefore, it is necessary to measure and inspect the tree barrier. For this purpose, this paper proposes a measurement method based on monocular image depth estimation. In this method, the LSD (Line Segment Detector) algorithm is used to detect the straight line, and the RANSAC (RANdom SAmple Consensus) algorithm is used to iteratively detect the vanishing point, and the vanishing point coordinates are successfully obtained. The camera calibration is carried out to determine the pixel coordinates of the object in the image and to obtain the internal and external parameters and rotation matrix of the camera. Through coordinate system transformation, the depth estimation of the target in the world coordinate system and the distance between any tree lines can be accurately calculated. Finally, the height of the camera is used as the reference height to complete the measurement of the tree barrier around the wire, and the experimental results show that the method has certain feasibility and the error is controlled within a reasonable range.
Corrosion prediction of iron-based metals by the prophet model

This paper proposes the prophet model for corrosion prediction of iron-based materials. The corrosive degree of iron-based materials under different conditions is assessed though the weight loss method and the experimental results are fed to the proposed model as the training data. It indicated that HT200, Q345B, and 45 steel are easily and rapidly corroded in the periodic immersion condition. The predicted results from the Prophet model are compared with the experimental results to evaluate the performance of the prediction model. The highest RMSE is 2.35×10-4 and the largest MAPE is 2.90×10- 4%. That means the proposed model is a reliable machine learning model for corrosion prediction of iron-based materials.
Visual servoing for wheeled cable-climbing robots with deflection correction

Wheeled cable-climbing robots that can detect cable damages are widely used to liberate human resources and reduce bridge hazards. Most wheeled cable-climbing robots have poor stability and will deflect along the cable under uneven loading. In this paper, a visual servoing method for wheeled cable-climbing robots is proposed with applications in antideflection. The proposed method integrates visual localization and trajectory tracking control, enabling robots to precisely estimate the deflection angles and stably correct deflection in dynamic and complex environments. For visual localization, a deflection angle estimation algorithm that combines object detection and coplanar feature points matching is developed. Then, a reference image update strategy is used to overcome the visual servoing errors caused by images distortion. On the controlling side, the trajectory tracking of the wheeled cable-climbing robots is implemented under the framework of model predictive control (MPC) with constraint functions to achieve expected position while avoiding wheel slipping. Based on the Multibody and V-Realm simulation platforms, the visual servoing method of wheeled cable-climbing robots is demonstrated, and its effectiveness and accuracy are verified by experiments.
Design of single cavity filter antenna array based on substrate integrated waveguide

In this paper, a filtering antenna array based on substrate integrated waveguide technology is designed. The antenna works in the Sub 6G band of 5G, and the array effect is formed by etching slots on the upper surface of the cavity at specific locations to excite the patch, while the filtering effect is generated because of the SIW cavity and patch coupling, forming a high gain filtering antenna. Simulation results show that the filter antenna has 3% bandwidth, peak gain greater than 10dBi in the operating band, and front-to-back ratio greater than 20dB.
Recognition and positioning of vehicle nameplate in inspection scene based on binocular vision

In the motor vehicle inspection scenario, accurate identification and positioning of vehicle nameplates is an important prerequisite for automated robotic inspection operations. In view of the current problems that vehicle nameplates are mainly checked manually and the level of intelligence is low, a target recognition and localisation method based on binocular vision combined with improved SURF algorithm is proposed. Firstly, feature points are extracted based on the SURF algorithm, and target recognition is achieved by combining the nearest neighbour ratio method with the RANSAC algorithm based on the gradient constraint of the parallax; then the edge contour of the target in the template image is obtained based on the Canny edge detection method, and the target shape centre on the template image is obtained based on the contour, and the left and right scene map target shape centres are obtained using the single-response matrix mapping; finally, target localisation is achieved by combining the principle of binocular vision 3D reconstruction. The experimental results show that the method has good recognition efficiency and accuracy, and can successfully identify and locate the target, which has certain application value.
Node2vec enhanced attributed graph matrix factorization algorithm

Community detection is an important tool to analyze and understand large-scale graph networks. Traditional non-negative matrix factorization method use the complete adjacency matrix, in which the redundant information may interfere with the learning of node features and bring high computational complexity. Thus, we propose the Node2vec enhanced attributed graph matrix factorization algorithm (N2V-AGMF) which combining the graph embedding and non-negative matrix factorization. The algorithm uses the Node2vec to extract node low-dimensional features based on topological information, then combines them with the attribute matrix for joint matrix factorization. The low dimensional embedding of the adjacency matrix enriches the representation of node features, and effectively reduces the high computational complexity caused by the factorization of the high dimensional matrix. Experiments were carried out on five real world datasets to verify the effectiveness of the algorithm.
Research on predicting trusted business relationships in autonomous system based on neural networks

Autonomous System (AS) business relationship data serves as an important trust foundation for detecting route leaks. Some AS's reluctant to disclose all of their business relationship data for various reasons has led to a decrease in routing detection accuracy, requiring AS business relationship prediction. The existing AS business relationship prediction schemes have problems with data tampering and the accuracy needs to be improved. This article proposes a trusted AS business relationship prediction method based on neural networks. By collecting and storing AS business relationship data on the blockchain, a feature library of AS business relationship data is constructed. Finally, a deep neural network is used for AS business relationship prediction to achieve reliable and accurate AS business relationship prediction.
Adaptive curvelet-domain primary-multiple separation

In many exploration areas, successful separation of primaries and multiples greatly determines the quality of seismic imaging. Despite major advances made by surface-related multiple elimination (SRME), amplitude errors in the predicted multiples remain a problem. When these errors vary for each type of multiple in different ways (as a function of offset, time, and dip), they pose a serious challenge for conventional least-squares matching and for the recently introduced separation by curvelet-domain thresholding. We propose a data-adaptive method that corrects amplitude errors, which vary smoothly as a function of location, scale (frequency band), and angle. With this method, the amplitudes can be corrected by an elementwise curvelet-domain scaling of the predicted multiples. We show that this scaling leads to successful estimation of primaries, despite amplitude, sign, timing, and phase errors in the predicted multiples. Our results on synthetic and real data show distinct improvements over conventional least-squares matching in terms of better suppression of multiple energy and high-frequency clutter and better recovery of estimated primaries.
Developing a personalized chat-based AI model for enhanced sports education: a case study

The chat-based AI model is an emerging technology that has shown great potential for enhancing sports education. The aim of this study is to develop a personalized chat-based AI model for sports education using machine learning algorithms. To achieve this aim, we collected sports-related data through web scraping and preprocessed it using natural language processing techniques. We then trained the General Pre-trained Transformer 2 (GPT-2) model on this data and fine-tuned it for sports education using PyTorch, Adam optimization, cross-entropy loss, and a validation set. The model was evaluated based on its accuracy, coherence, relevance, and ease of use using a pilot study. The results showed that the personalized chat-based AI model was effective in providing customized sports education, with an overall accuracy of 84.5%, coherence of 81%, relevance of 79%, and ease of use of 81%. This study demonstrates the potential of chat-based AI models in changing the way sports education is delivered and highlights the importance of data collection and preprocessing in building accurate and effective models.
The Effect of Different Weed Control Methods on Weed Infestation, Growth and Yield of Soybeans (Glycine max (L) Merril) in the Southern Guinea Savanna of Nigeria

A field experiment was conducted during the 2012 and 2013 rainy season at the Kwara State University Teaching and Research Farm located in Malete. The aim was to determine the effect of different weed control methods on Weed infestation, growth and yield of soybeans (variety TGX 1448 – 2E). The experiment consisted of 8 treatments, namely, the application of metolachor at 1.5, 2.0 nd 2.5 kg a.i./ ha, pendimethalin at 1.5, 2.0 and 2.5 kg a.i./ha, a tank mixture of metolachlor + diuron at 1.5 + 0.5, 2.0 + 1.0 and 2.5 + 1.5 kg a.i./ha, pendimethalin + diuron at 1.5 + 0.5, 2.0 + 1.0 and 2.5 + 1.5 kg a.i./ha, metolachor at 2.0 kg a.i. /ha plus I supplementary hoe weeding (SHW) at 6 WAS, pendimethalin at 2.0 kg a.i. /ha plus supplimentary hoe weeding (SHW) at 6WAS, metolachlor + diuron at 1.0 +0.5 kg a.i. /ha plus ISHW, pendimethalin + diuron at 1.5 +0.5 plus ISHW at 6WAS, weeding at 3 and 6 WAS and a weedy check. Results show that all the herbicide treatments significantly reduced weed infestation compared with the weedy check. However, metolachlor + diuron integrated with ISHW was more effective than the application of only herbicides in the control of weeds throughout the crop life. This weed control method also resulted in significantly better growth and higher yield. Therefore for better growth and higher yields, metolachlor + diuron integrated with ISHW at 6 WAS is recommend to formers in the Southern Guinea Savanna of Nigeria.
Unsupervised change detection method for ground-based radar images based on convolutional neural network

Ground-based radar has been widely used for deformation monitoring and early warning of geological hazard potential areas. However, during long-term monitoring, ground-based radar images are vulnerable to human and environmental factors leading to severe decoherence. The study of ground-based radar image change detection can provide reference information for its long-term monitoring. Based on this, an unsupervised change detection method based on convolutional neural network (CNN) for ground-based radar images is proposed in this paper. First, the interference principle to extract change information is used for the first time for the change detection task, aiming to improve the accuracy of the initial extraction of change regions. Secondly, the fuzzy c-means clustering algorithm is used to obtain the pseudo-label matrix with categories, and the appropriate neighborhood image blocks with pseudo-labels are selected as training samples to train CNN. Finally, the change detection results of ground-based radar images are obtained using the trained CNN. Experiments were conducted using actual measurement data from ground-based radar in a monitoring task in a mining area in China and compared with other methods to verify the effectiveness of this paper's method and more accurate detection results.
Textile fabric image classification method based on enhanced deep learning features

In recent years, the deep learning features, which was extracted and coded by neural network, have been widely used in image classification and achieved significant results. Textile fabric industry usually classify the items according to image patterns of fabrics, in which the shape and color of image have more distinguish effect. So how to use the image characteristics of textile fabrics to enhance the discrimination of deep learning features is the key process to improve the accuracy of classification of textile fabric. This paper proposes a method of textile fabric classification based on the deep learning features enhanced by Histograms of Oriented Gradients (HOG) features and HS histogram statistical features extracted in HSV domain. By fusing more image shape and color information with deep learning features, the enhanced deep learning features have greater discrimination. The experiment shows that the proposed method has good classification result, achieve 92.4% accuracy on the corresponding dataset.
A review of research on vibration and noise reduction in switched reluctance motors

Switched reluctance motors have many advantages over traditional adjustable speed drives, but vibration and noise problems are particularly acute due to their double salient pole construction and switching power supply. Therefore, taking effective measures and means to optimize the switched reluctance motor system to improve vibration and noise has important theoretical and practical significance for its promotion and application. In order to provide a clear solution to the problem of SRM vibration and noise reduction, this paper introduces the sources and theoretical research status of vibration and noise of switched reluctance motors and focuses on a series of research methods to reduce vibration and noise of switched reluctance motors from the aspects of motor body structure, parameters, and control strategy technology.
Research on the evaluation of ink animation films based on ChipGAN

Ink painting is a typical representative of Chinese painting. With the acceleration of globalization and the continuous development of deep learning and other technologies, Chinese ink painting urgently needs to develop into digital development. Generated adversarial network has been widely used in the field of image style transfer, among which, ChipGAN model is specifically aimed at the study of Chinese ink painting style transfer. In previous studies, we have tried to stylish the animated film ink painting, but there is no transfer learning under the ChipGAN model, and the comprehensive transfer effect evaluation is lacking. This paper will be for the animated film "big fish haitang", select five different ink style style data set, using ChipGAN model for ink style transfer, and use the SSIM image quality evaluation index and HSV color difference value from the content and style of transfer image for a more comprehensive evaluation, to explore the suitable for Chinese ink painting image transfer evaluation method lay the foundation.