MODERN FAMILY VALUES AND THEIR FEATURES by NESMEIANOVA I.A.

“…Methods used. In addition to general scientific methods, when preparing the article, such special research methods as the comparative legal method and the formal legal method were used. …”
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Multimodal multi-instance evidence fusion neural networks for cancer survival prediction by Hui Luo, Jiashuang Huang, Hengrong Ju, Tianyi Zhou, Weiping Ding

“…Multimodal data, such as histopathological images, genomic data, and clinical information, provide complementary and comprehensive information, significantly enhancing the accuracy of this task. However, existing methods, despite achieving some promising results, still exhibit two significant limitations: they fail to effectively utilize global context and overlook the uncertainty of different modalities, which may lead to unreliable predictions. …”
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Determination of pre-Darcy/Darcy/non-Darcy seepage patterns in coal around extraction boreholes by Mingkun PANG, Hongyu PAN, Shipeng ZHU, Xiang JI, Tianjun ZHANG

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DADNet: text detection of arbitrary shapes from drone perspective based on boundary adaptation by Jun Liu, Jianxun Zhang, Ting Tang, Shengyuan Wu

“…However, the unique perspective and complex environment during drone photography lead to various challenges in text detection, including diverse text shapes, large-scale variations, and background interference, making traditional methods inadequate. To address this issue, we propose a drone-based text detection method based on boundary adaptation. …”
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A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images by Oguzhan Katar, Ozal Yildirim, Ru-San Tan, U Rajendra Acharya

“…In the proposed method, each feature matrix is flattened separately and then combined to form a comprehensive feature vector. …”
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An Improved Small Target Segmentation Model Based on Mask Dino by Jun Yang, Xu Chen, Yun Guan, Yixuan Hu, Gang Ge

“…Initially, we introduce scaled cosine attention and the log-cpb method into the Swin Transformer backbone network. …”
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TS2GNet: A temporal–spatial–spectral multidomain guided network for classifying hyperspectral tree species using multiseason satellite imagery by Kaijian Xu, Henghui Han, Shuzhou Wang, Ping Zhao, Jun Geng, Hailan Jiang, Anxin Ding

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LSV-MAE: A Masked-Autoencoder Pre-Training Approach for Large-Scale 3D Point Cloud Data by Nuo Cheng, Chuanyu Luo, Han Li, Sikun Ma, Shengguang Lei, Pu Li

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Thermo-mechanical-metallurgical and damage modeling for hot stamping and warm-cutting of boron steel by Yongfeng Li, Hui Zhang, Shuhui Li

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Hybrid Wavelet-Attention Model for Detecting Changes in High-Resolution Remote Sensing Images by Lhuqita Fazry, MGS M. Luthfi Ramadhan, Alif Wicaksana Ramadhan, Muhammad Febrian Rachmadi, Aprinaldi Jasa Mantau, Lukito Edi Nugroho, Chi-Hung Chi, Wisnu Jatmiko

“…Besides reducing the data, DWT decomposition also serves to extract important features that represent images so that the initial data can be approximated through the Inverse Discrete Wavelet Transform (IDWT) process. On the CDD dataset, WaveCD outperforms the state-of-the-art CD method, SwinSUNet, by 12.3% on IoU and 7.3% on F1 score. …”
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Development and validation of digital leadership skills scale by Nadire Cavus, Seyedali Aghamiri, Nuriye Sancar

“…Contribution & Value Added: In this study, we constructed a novel scale to determine the digital leadership skills of the leaders who will lead the digital transformation process in enterprises. Moreover, we introduced to the literature a different model that can evaluate the effects of digital leadership skills on organisational factors, DT, and financial performance of enterprises in the digitalization process in enterprises by proposing a model that includes the constructed scale. …”
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Abnormal Effect of Al on the Phase Stability and Deformation Mechanism of Ti-Zr-Hf-Al Medium-Entropy Alloys by Penghao Yuan, Lu Wang, Ying Liu, Xidong Hui

“…In the present work, we adjust the phase constitution and microstructure by Al addition in a series of (Ti₂ZrHf)_100−xAl_x (x = 12, 14, 16, 18, 20, at.%, named Al_x) MEAs. Different from traditional titanium, Al shows a β-stabilizing effect, and the phase follows the evolution of α′(α)→α″→β + ω + B2 with Al increasing from 12 to 20 at.%, which could not be predicted by the CALPHAD (Calculate Phase Diagrams) method or the Bo-Md diagram because of the complex interactions among composition elements. …”
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A Research on the Relationship of Social Intelligence and Cultural Intelligence with Leadership Styles by Serap Yalçınyigit, Hakkı Aktaş

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Design and prediction of high optical density photovoltaic polymers using machine learning-DFT studies by Aljaafreh Mamduh J., Hassan Abrar U.

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Academics’ Leadership Styles and Their Motivation to Participate in a Leadership Training Program in the Digital Era by Zhao Cheng, Chang Zhu

“…Based on survey data from 761 participants directly involved in a leadership development project, this study adopted a path model analysis method and provides novel empirical evidence on whether participants’ leadership styles influence their motivation to participate in leadership training programs. …”
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CoMSeC: A Comparative Analysis of Various Service Classification Techniques by Malabika Das, Ansh Sarkar, Sujata Swain

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Graines d'histoire by Claire Newton, Jean-Frédéric Terral, Sarah Ivorra, Muriel Gros-Balthazard, Claire Tito de Morais, Sandrine Picq, Margareta Tengberg, Jean-Christophe Pintaud

“…For that purpose, the morphological diversity of the date palm was studied through seed shape analysis, using the Elliptic Fourier Transforms (EFT) method.The biological material used comprises seeds of uncultivated Phoenix individuals from isolated Oman populations, cultivated date palm varieties, and other related Phoenix species. …”
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Automatic Speech Recognition: A survey of deep learning techniques and approaches by Harsh Ahlawat, Naveen Aggarwal, Deepti Gupta

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RMTSE: A Spatial-Channel Dual Attention Network for Driver Distraction Recognition by Junyi He, Chang Li, Yang Xie, Haotian Luo, Wei Zheng, Yiqun Wang

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Leveraging deep learning for robust EEG analysis in mental health monitoring by Zixiang Liu, Juan Zhao

“…Despite the potential of these approaches, they often fall short in capturing the intricate spatiotemporal relationships within EEG data, leading to lower classification accuracy and poor adaptability across various populations and mental health scenarios.MethodsTo overcome these limitations, we introduce the EEG Mind-Transformer, an innovative deep learning architecture composed of a Dynamic Temporal Graph Attention Mechanism (DT-GAM), a Hierarchical Graph Representation and Analysis (HGRA) module, and a Spatial-Temporal Fusion Module (STFM). …”
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