Original Data Research Articles

A road data assets revenue allocation model based on a modified Shapley value approach considering contribution evaluation

This paper constructs a two-layer road data asset revenue allocation model based on a modified Shapley value approach. The first layer allocates revenue to three roles in the data value realization process: the original data collectors, the data processors, and the data product producers. It fully considers and appropriately adjusts the revenue allocation to each role based on data risk factors. The second layer determines the correction factors for different roles to distribute revenue among the participants within those roles. Finally, the revenue values of the participants within each role are synthesized to obtain a consolidated revenue distribution for each participant. Compared to the traditional Shapley value method, this model establishes a revenue allocation evaluation index system, uses entropy weighting and rough set theory to determine the weights, and adopts a fuzzy comprehensive evaluation and numerical analysis to assess the degree of contribution of participants. It fully accounts for differences in both the qualitative and quantitative contributions of participants, enabling a fairer and more reasonable distribution of revenues. This study provides new perspectives and methodologies for the benefit distribution mechanism in road data assets, which aid in promoting the market-based use of road data assets, and it serves as an important reference for the application of data assetization in the road transportation industry.

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index

Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental governance. As a representative measure, the air quality index (AQI) is a single conceptual index value simplified by the concentrations of several routinely monitored air pollutants according to the proportion of various components in the air. With the gradual enhancement of awareness of environmental protection, air quality index forecasting is a key point of environment management. However, most of the traditional forecasting methods ignore the fuzziness of original data itself and the uncertainty of forecasting results which causes the unsatisfactory results. Thus, an innovative forecasting system combining data preprocessing technique, kernel fuzzy c-means (KFCM) clustering algorithm and fuzzy time series is successfully developed for air quality index forecasting. Concretely, the fuzzy time series that handle the fuzzy set is used for the main forecasting process. Then the complete ensemble empirical mode decomposition and KFCM are respectively developed for data denoising and interval partition. Furthermore, the interval forecasting method based on error distribution is developed to measure the forecasting uncertainty. Finally, the experimental simulation and evaluation system verify the great performance of proposed forecasting system and the promising applicability in a practical environment early warning system.

Spatial Resolution Enhancement of Vegetation Indexes via Fusion of Hyperspectral and Multispectral Satellite Data

The definition and calculation of a spectral index suitable for characterizing vegetated landscapes depend on the number and widths of the bands of the imaging instrument. Here, we point out the advantages of performing the fusion of hyperspectral (HS) satellite data with the multispectral (MS) bands of Sentinel-2 to calculate such vegetation indexes as the normalized area over reflectance curve (NAOC) and the red-edge inflection point (REIP), which benefit from the availability of quasi-continuous pixel spectra. Unfortunately, MS data may be acquired from satellite platforms with very high spatial resolution; HS data may not. Despite their excellent spectral resolution, satellite imaging spectrometers currently resolve areas not greater than 30 × 30 m2, where different thematic classes of landscape may be mixed together to form a unique pixel spectrum. A way to resolve mixed pixels is to perform the fusion of the HS dataset with the same dataset produced by an MS scanner that images the same scene with a finer spatial resolution. The HS dataset is sharpened from 30 m to 10 m by means of the Sentinel-2 bands that have all been previously brought to 10 m. To do so, the hyper-sharpening protocol, that is, m:n fusion, is exploited in two nested steps: the first one to bring the 20 m bands of Sentinel-2 all to 10 m, the second one to sharpen all the 30 m HS bands to 10 m by using the Sentinel-2 bands previously hyper-sharpened to 10 m. Results are presented on an agricultural test site in The Netherlands imaged by Sentinel-2 and by the satellite imaging spectrometer recently launched as a part of the environmental mapping and analysis program (EnMAP). Firstly, the excellent match of statistical consistency of the fused HS data to the original MS and HS data is evaluated by means of analysis tools, existing and developed ad hoc for this specific case. Then, the spatial and radiometric accuracy of REIP and NAOC calculated from fused HS data are analyzed on the classes of pure and mixed pixels. On pure pixels, the values of REIP and NAOC calculated from fused data are consistent with those calculated from the original HS data. Conversely, mixed pixels are spectrally unmixed by the fusion process to resolve the 10 m scale of the MS data. How the proposed method can be used to check the temporal evolution of vegetation indexes when a unique HS image and many MS images are available is the object of a final discussion.

Marching Windows: Scalable Mesh Generation for Volumetric Data With Multiple Materials.

Volumetric data abounds in medical imaging and other fields. With the improved imaging quality and the increased resolution, volumetric datasets are getting so large that the existing tools have become inadequate for processing and analyzing the data. Here we consider the problem of computing tetrahedral meshes to represent large volumetric datasets with labeled multiple materials, which are often encountered in medical imaging or microscopy optical slice tomography. Such tetrahedral meshes are a more compact and expressive geometric representation so are in demand for efficient visualization and simulation of the data, which are impossible if the original large volumetric data are used directly due to the large memory requirement. Existing methods for meshing volumetric data are not scalable for handling large datasets due to their sheer demand on excessively large run-time memory or failure to produce a tet-mesh that preserves the multi-material structure of the original volumetric data. In this article we propose a novel approach, called Marching Windows, that uses a moving window and a disk-swap strategy to reduce the run-time memory footprint, devise a new scheme that guarantees to preserve the topological structure of the original dataset, and adopt an error-guided optimization technique to improve both geometric approximation error and mesh quality. Extensive experiments show that our method is capable of processing very large volumetric datasets beyond the capability of the existing methods and producing tetrahedral meshes of high quality.

Fruit recognition and classification based on tactile information of flexible hand

In the field of agricultural production, the sorting of agricultural products is a basic operation performed by robot manipulators. Agricultural products are fragile and easily damaged, and their shape, texture and size are different. In order to ensure grasping compliance and the integrity of agricultural products. In this study, firstly, a three-layer flexible tactile sensor was designed and fabricated. The performance of the sensor was tested. Three functions were used to fit the relationship between the sensor pressure and the output voltage, and the function with the best fitting degree was selected for sensor calibration. The sensor exhibits two kinds of sensitivity under the loading of 0 ∼ 4 N.When the applied pressure is 0 ∼ 2.75 N, the sensitivity is 1.21 V / N. When the applied pressure is 2.75 ∼ 4 N, the sensitivity is 0.24 V / N. Then, the sensor and the flexible gripper are combined to build a tactile information acquisition platform to collect the original data set of the tactile sequence generated when the flexible hand grabs the fruit, and the original data is preprocessed. Finally, we evaluated the performance of Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB) and Neural Network (NN) in fruit classification. The results show that the RF classifier achieves the highest accuracy in the training and testing stages, which are 98.64% and 93.06%, respectively, and the accuracy of other classifiers ranges from 66.43% to 95.83%. Our work is helpful to the realization of robotic tactile perception and non-invasive grasping in the field of agriculture, and can be helpful to fruit picking, handling, sorting and other related fields.

Investigating the interpretability of schizophrenia EEG mechanism through a 3DCNN-based hidden layer features aggregation framework

Background and objectiveElectroencephalogram (EEG) signals record brain activity, with growing interest in quantifying neural activity through complexity analysis as a potential biological marker for schizophrenia. Presently, EEG complexity analysis primarily relies on manual feature extraction, which is subjective and yields varied findings in studies involving schizophrenia and healthy controls. MethodsThis study aims to leverage deep learning methods for enhanced EEG complexity exploration, aiding early schizophrenia screening and diagnosis. Our proposed approach utilizes a three-dimensional Convolutional Neural Network (3DCNN) to extract enhanced data features for early schizophrenia identification and subsequent complexity analysis. Leveraging the spatiotemporal capabilities of 3DCNN, we extract advanced latent features and employ knowledge distillation to reintegrate these features into the original channels, creating feature-enhanced data. ResultsWe employ a 10-fold cross-validation strategy, achieving the average accuracies of 99.46% and 98.06% in subject-dependent experiments on Dataset 1(14SZ and 14HC) and Dataset 2 (45SZ and 39HC). The average accuracy for subject-independent is 96.04% and 92.67% on both datasets. Feature extraction and classification are conducted on both the re-aggregated data and the original data. Our results demonstrate that re-aggregated data exhibit superior classification performance and a more stable training process after feature extraction. In the complexity analysis of re-aggregated data, we observe lower entropy features in schizophrenic patients compared to healthy controls, with more pronounced differences in the temporal and frontal lobes. Analyzing Katz's Fractal Dimension (KFD) across three sub-bands of lobe channels reveals the lowest α band KFD value in schizophrenia patients. ConclusionsThis emphasizes the ability of our method to enhance the discrimination and interpretability in schizophrenia detection and analysis. Our approach enhances the potential for EEG-based schizophrenia diagnosis by leveraging deep learning, offering superior discrimination capabilities and richer interpretive insights.

Synthetic Data Improve Survival Status Prediction Models in Early-Onset Colorectal Cancer.

In artificial intelligence-based modeling, working with a limited number of patient groups is challenging. This retrospective study aimed to evaluate whether applying synthetic data generation methods to the clinical data of small patient groups can enhance the performance of prediction models. A data set collected by the Cancer Registry Library Project from the Yonsei Cancer Center (YCC), Severance Hospital, between January 2008 and October 2020 was reviewed. Patients with colorectal cancer younger than 50 years who started their initial treatment at YCC were included. A Bayesian network-based synthesizing model was used to generate a synthetic data set, combined with the differential privacy (DP) method. A synthetic population of 5,005 was generated from a data set of 1,253 patients with 93 clinical features. The Hellinger distance and correlation difference metric were below 0.3 and 0.5, respectively, indicating no statistical difference. The overall survival by disease stage did not differ between the synthetic and original populations. Training with the synthetic data and validating with the original data showed the highest performances of 0.850, 0.836, and 0.790 for the Decision Tree, Random Forest, and XGBoost models, respectively. Comparison of synthetic data sets with different epsilon parameters from the original data sets showed improved performance >0.1%. For extremely small data sets, models using synthetic data outperformed those using only original data sets. The reidentification risk measures demonstrated that the epsilons between 0.1 and 100 fell below the baseline, indicating a preserved privacy state. The synthetic data generation approach enhances predictive modeling performance by maintaining statistical and clinical integrity, and simultaneously reduces privacy risks through the application of DP techniques.

Retraction: "Mesoglycan exerts its fibrinolytic effect through the activation of annexin A2".

Retraction: "Mesoglycan exerts its fibrinolytic effect through the activation of annexin A2" by Raffaella Belvedere, Elva Morretta, Emanuela Pessolano, Nunzia Novizio, Alessandra Tosco, Amalia Porta, James Whiteford, Mauro Perretti, Amelia Filippelli, Maria Chiara Monti, Antonello Petrella, J Cell Physiol. 2020, 236: 4926-4943. The above article, published online on 7 December 2020 in Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/jcp.30207) has been retracted by agreement between the journal's Editor in Chief, Alexander Hutchison, and Wiley Periodicals LLC. The retraction has been agreed due to concerns raised by third parties. Some flaws and inconsistencies between results presented and experimental methods described were found. Specifically, manipulations have been detected affecting the Western Blot experiment presented in Figure 3a, and the microscopy images presented in Figure 5 and Figure S7. The corresponding author, Antonello Petrella, acknowledged not having noticed the anomalies at earlier stages and collaborated with the investigation. The original experimental data related to the concerns identified were no longer available; the authors have performed new Western Blot experiments. However, based on the editorial evaluation, the provided material failed to adequately address the concerns. For instance, the data intended to replicate the findings in Figure 3a were provided at a quality too low to ensure its accuracy. Furthermore, the concerns regarding the manipulation of microscopy images in Figure 5 and Figure S7 remained entirely unaddressed. Accordingly, the editors no longer have confidence in the integrity of the data presented in the article and consider the conclusions to be invalid. Antonello Petrella, on behalf of all authors, believes that the conclusions of the article are not affected by the identified issues, in part also corroborated by the new data; all authors disagree with the decision to retract the article.

A Virtual Supply Airflow Rate Sensor Based on Original Equipment Manufacturer Data for Rooftop Air Conditioners

A Virtual Supply Airflow Rate Sensor Based on Original Equipment Manufacturer Data for Rooftop Air Conditioners

Social Media and the DIY Politics in Thailand's 2023 Election

This article argues that Thailand's 2023 parliamentary election was the first election in which social media played a decisive factor in the electoral outcomes. Prior to this election, social media was an important campaign tool, but it was unclear whether it made a difference in the electoral results. Based on our original post-election survey data (n = 1,249), social media was the most important media in governing vote choice. Social media was a crucial space for activation and conversion—motivating the undecided to become partisans and converting partisan voters to shift their allegiances. Thailand's 2023 election was also marked by a rise in the personalization of political campaigning, wherein citizens felt free to decide how and what their political participation looked like, and parties that encouraged inclusive and open engagement with politics were best poised to win in the electoral arena. Drawing on social network analysis of social media data, this article demonstrates how the Move Forward Party's (MFP) loosely structured and inclusive social media campaigns allowed both their candidates and supporters to mobilize individualized large-scale collective action, in contrast to their rivals who focused on traditional top-down style campaigning. Despite the MFP's winning social media campaigning that produced electoral victories, the party was unable to come to power due to an entrenched authoritarian political system designed to maintain the power of the country's autocratic elites. The Thai case demonstrates powerfully how autocrats might lose an election due to social media, yet still manage to hang on to power through entrenched authoritarian institutions.

On the emergence of argument encoding in causatives constructions in Romance

Causative constructions in Romance show complex variation regarding the case of the subject of the non-finite clause (causee) and to its position with respect to the main causative verb and the embedded non-finite verb. It has been argued that the variation reflects the diachronic evolution of the causative construction in Romance and also documents an ongoing process of further development. We present original empirical data from a comparative corpus of Bible translations that provide new insights into diachronic evolution as well as to the synchronic variation. Our comparative approach allows us to generalize over additional semantic and pragmatic parameters for the choice of a particular construction. Our data support the assumption of the extension of ECM from clitic to nominal causee constructions in French, but they do not support this for Italian. For Spanish we see some ECM constructions in the most recent translation only.

Data Augmentation of a Corrosion Dataset for Defect Growth Prediction of Pipelines Using Conditional Tabular Generative Adversarial Networks.

Due to corrosion characteristics, there are data scarcity and uneven distribution in corrosion datasets, and collecting high-quality data is time-consuming and sometimes difficult. Therefore, this work introduces a novel data augmentation strategy using a conditional tabular generative adversarial network (CTGAN) for enhancing corrosion datasets of pipelines. Firstly, the corrosion dataset is subjected to data cleaning and variable correlation analysis. The CTGAN is then used to generate external environmental factors as input variables for corrosion growth prediction, and a hybrid model based on machine learning is employed to generate corrosion depth as an output variable. The fake data are merged with the original data to form the synthetic dataset. Finally, the proposed data augmentation strategy is verified by analyzing the synthetic dataset using different visualization methods and evaluation indicators. The results show that the synthetic and original datasets have similar distributions, and the data augmentation strategy can learn the distribution of real corrosion data and sample fake data that are highly similar to the real data. Predictive models trained on the synthetic dataset perform better than predictive models trained using only the original dataset. In comparative tests, the proposed strategy outperformed other data generation methods.

Retraction: “Implementation of deep neural networks for classifying electroencephalogram signal using fractional S‐transform for epileptic seizure detection”

Retraction: Ashokkumar SR, Anupallavi S, Premkumar M, Jeevanantham V. Implementation of deep neural networks for classifying electroencephalogram signal using fractional S‐transform for epileptic seizure detection. Int J Imaging Syst Technol. 2021;31:895–908. https://doi.org/10.1002/ima.22565The above article, published online on 02 March 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor‐in‐Chief Dr. Mohamed Seghier, and Wiley Periodicals, LLC. The retraction has been agreed due to major overlap between this article and the following article: Liu G, Zhou W, Geng M. Automatic Seizure Detection Based on S‐Transform and Deep Convolutional Neural Network. Int. J. Neural Syst. 2020;30:1950024. https://doi.org/10.1142/S0129065719500242. Following a complaint from a third party, an investigation by the editorial office and Wiley concluded that Figures 5 and 10 were duplicated from the previously‐published article and that the degree of overlap between the articles' topics, preprocessing methods, and proposed AI tools constitutes plagiarism. The authors responded to an inquiry by the publisher, but did not provide original data. The authors were notified about the retraction but did not respond.

A Galaxy Image Augmentation Method Based on Few-shot Learning and Generative Adversarial Networks

Galaxy morphology classifications based on machine learning are a typical technique to handle enormous amounts of astronomical observation data, but the key challenge is how to provide enough training data for the machine learning models. Therefore this article proposes an image data augmentation method that combines few-shot learning and generative adversarial networks. The Galaxy10 DECaLs data set is selected for the experiments with consistency, variance, and augmentation effects being evaluated. Three popular networks, including AlexNet, VGG, and ResNet, are used as examples to study the effectiveness of different augmentation methods on galaxy morphology classifications. Experiment results show that the proposed method can generate galaxy images and can be used for expanding the classification model’s training set. According to comparative studies, the best enhancement effect on model performance is obtained by generating a data set that is 0.5–1 time larger than the original data set. Meanwhile, different augmentation strategies have considerably varied effects on different types of galaxies. FSL-GAN achieved the best classification performance on the ResNet network for In-between Round Smooth Galaxies and Unbarred Loose Spiral Galaxies, with F1 Scores of 89.54% and 63.18%, respectively. Experimental comparison reveals that various data augmentation techniques have varied effects on different categories of galaxy morphology and machine learning models. Finally, the best augmentation strategies for each galaxy category are suggested.

Proximate node aware optimal and secure data aggregation in wireless sensor network based IoT environment

<span>Internet of things (IoT) has become one of the eminent phenomena in human life along with its collaboration with wireless sensor networks (WSNs), due to enormous growth in the domain; there has been a demand to address the various issues regarding it such as energy consumption, redundancy, and overhead. Data aggregation (DA) is considered as the basic mechanism to minimize the energy efficiency and communication overhead; however, security plays an important role where node security is essential due to the volatile nature of WSN. Thus, we design and develop proximate node aware secure data aggregation (PNA-SDA). In the PNA-SDA mechanism, additional data is used to secure the original data, and further information is shared with the proximate node; moreover, further security is achieved by updating the state each time. Moreover, the node that does not have updated information is considered as the compromised node and discarded. PNA-SDA is evaluated considering the different parameters like average energy consumption, and average deceased node; also, comparative analysis is carried out with the existing model in terms of throughput and correct packet identification.</span>

CorrDiff: Corrective Diffusion Model for Accurate MRI Brain Tumor Segmentation.

Accurate segmentation of brain tumors in MRI images is imperative for precise clinical diagnosis and treatment. However, existing medical image segmentation methods exhibit errors, which can be categorized into two types: random errors and systematic errors. Random errors, arising from various unpredictable effects, pose challenges in terms of detection and correction. Conversely, systematic errors, attributable to systematic effects, can be effectively addressed through machine learning techniques. In this paper, we propose a corrective diffusion model for accurate MRI brain tumor segmentation by correcting systematic errors. This marks the first application of the diffusion model for correcting systematic segmentation errors. Additionally, we introduce the Vector Quantized Variational Autoencoder (VQ-VAE) to compress the original data into a discrete coding codebook. This not only reduces the dimensionality of the training data but also enhances the stability of the correction diffusion model. Furthermore, we propose the Multi-Fusion Attention Mechanism, which can effectively enhances the segmentation performance of brain tumor images, and enhance the flexibility and reliability of the corrective diffusion model. Our model is evaluated on the BRATS2019, BRATS2020, and Jun Cheng datasets. Experimental results demonstrate the effectiveness of our model over state-of-the-art methods in brain tumor segmentation.

Retraction: lncRNA ABHD11-AS1, regulated by the EGFR pathway, contributes to the ovarian cancer tumorigenesis by epigenetically suppressing TIMP2.

Xiang-Yang Zeng, Xiao-Yan Jiang, Jia-HuiYong, Hui Xie, Jing Yuan, Da Zeng, Ying-Yu Dou, Song-Shu Xiao "lncRNA ABHD11-AS1, regulated by the EGFR pathway, contributes to the ovarian cancer tumorigenesis by epigenetically suppressing TIMP2" Cancer Med. 2019; 8: 7074-7085 (https://doi.org/10.1002/cam4.2586) The above article, published online on 30 September 2019, in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal's Editor-in-Chief, Stephen Tait, and John Wiley and Sons Ltd. Following publication, concerns were raised by a third party regarding image overlap within Figure2b. Furthermore, several figure elements have been published elsewhere, identifying the cells as originating from different cell lines and subjected to different treatment. The authors did not respond to the concerns raised and did not provide the original data. The editors consider the results and conclusion reported in this article unreliable.

DMT-EV: An Explainable Deep Network for Dimension Reduction.

Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful properties of the original data. It is used in various applications, such as image recognition, single-cell sequencing analysis, and biomarker discovery. However, contemporary parametric-free and parametric DR techniques suffer from several significant shortcomings, such as the inability to preserve global and local features and the poor generalisation performance. On the other hand, regarding explainability, it is crucial to comprehend the embedding process, especially the contribution of each part to the embedding process, while understanding how each feature affects the embedding results that identify critical components and help diagnose the embedding process. To address these problems, we have developed a deep neural network method called DMT-EV, which provides not only excellent performance in structural maintainability but also explainability to the DR therein. DMT-EV starts with data augmentation and a manifold-based loss function to improve embedding performance. The explanation is based on saliency maps and aims to examine the trained DMT-EV parameters and contributions of components during the embedding process. The proposed techniques are integrated with a visual interface to help the user to adjust DMT-EV to achieve better DR performance and explainability. The interactive visual interface makes it easier to illustrate the data features, compare different DR techniques, and investigate DR. An in-depth experimental comparison shows that DMT-EV consistently outperforms the state-of-the-art methods in both performance measures and explainability.

Study on the Examination and Judgment of WeChat Record-type Evidence in Criminal Proceedings

In order to solve the problems existing in China at present in the examination and judgment of WeChat record evidence, this paper is based on the relevant laws and regulations in Chinese criminal procedure law, analyze the examination and judgment mechanism of WeChat record evidence, namely, the authenticity examination of the content, original data and storage medium, the relevance examination of WeChat record evidence to examine whether the content of WeChat record evidence is related to the facts to be proved and the legality review of the subject of the evidence, the way of the evidence and the procedure of the evidence collection. The legality review of the subject of the evidence collection, the way of evidence collection and the procedure of evidence collection. On this basis, it further analyzes the problems existing in the current mechanism, and provides certain methodological reference for improving the examination and judgment of WeChat record evidence.

Mean Square Error Estimation of Small Area Predictors by Use of Parametric and Nonparametric Bootstrap

In this article, we propose and compare some old and new parametric and nonparametric bootstrap methods for MSE estimation in small area estimation, restricting to the case of the widely used Fay-Herriot model. The parametric method consists of generating parametrically a large number of area bootstrap samples from the model fitted to the original data, re-estimating the model parameters for each bootstrap sample and then estimating the separate components of the MSE. The use of double-bootstrap is also considered. The nonparametric method generates the samples by bootstrapping standardized residuals, estimated from the original sample data. The bootstrap procedures are compared to other methods proposed in the literature in a simulation study, which also examines the robustness of the various methods to non-normality of the model error terms. A design-based MSE estimator for the Fay-Herriot model-dependent predictor is also described and its performance is investigated in a separate simulation study. AMS subject classification: 62F10, 62F40