Principal Components of Superhigh-Dimensional Statistical Features and Support Vector Machine for Improving Identification Accuracies of Different Gear Crack Levels under Different... by Dong Wang, Kwok-Leung Tsui, Peter W. Tse, Ming J. Zuo

“…First, superhigh-dimensional statistical features are extracted from continuous wavelet transform at different scales. The number of the statistical features extracted by using the proposed method is 920 so that the extracted statistical features are superhigh dimensional. …”
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Implementation of Fuzzy Logic Scheme for Assessment of Power Transformer Oil Deterioration Using Imprecise Information by Zuhaib Nishter, Fangzong Wang

“…In this case, a fuzzy logic-based scheme is developed based upon the membership function, a rule-based and defuzzification method that works with imprecision and the implementation of uncertainty in assessing the condition of transformer oils. …”
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DFST-UNet: Dual-Domain Fusion Swin Transformer U-Net for Image Forgery Localization by Jianhua Yang, Anjun Xie, Tao Mai, Yifang Chen

“…DFST-UNet is built on a U-shaped encoder–decoder architecture. Swin Transformer blocks are integrated into the U-Net architecture to capture long-range context information and perceive forged regions at different scales. …”
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Laclau and Mouffe's Radical Hegemony and Democracy Thought: The Transformation of the Free Aceh Movement into the Aceh Party by Anandita Azzahra, Cinta Rasullillah, Davina Shafa Andira, Jihan Rahmadani, Aniqotul Ummah

“…The focus of this paper is on analyzing GAM's transformation into the Aceh Party, which is different from other radical movements in Indonesia. …”
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Transforming Fe-Cu alloys with unprecedented BCC Cu precipitates toward dual mechanical functionalities by Jeong Rae Kim, Hyun-Su Kang, Tae-Wan Ko, Young Jae Hwang, Gun Hee Kim, Seon-Jin Choi, Kwangchoon Lee, Hyuk-Su Han, Hyung Giun Kim, Taeg Woo Lee

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Liveness Detection in Computer Vision: Transformer-Based Self-Supervised Learning for Face Anti-Spoofing by Arman Keresh, Pakizar Shamoi

“…Numerous tests on standard datasets show that the ViT model performs better than other models in terms of accuracy and resistance to different spoofing methods. Our model’s superior performance, particularly in APCER (1.6%), the most critical metric in this domain, underscores its improved ability to detect spoofing relative to other models. …”
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Study on the Characteristics of Crystal Formation and Transformation of Alkali-Activated Slag Minerals Induced by Weak Alkali by Guodong Huang, Yonghao Huang, Xiaojun Zheng, Fengan Zhang, Jiahao Xu, Jun Qi, Zijian Chen

“…In this study, the effects of both strong and weak alkaline activators on the activation performance of alkali slag were examined, using a combination of X-ray diffraction, scanning electron microscopy, and Fourier-transform infrared spectroscopy analysis methods. The reaction mechanism was analyzed under different alkaline conditions, and the preparation cost could be significantly reduced without significantly degrading mechanical properties. …”
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Development and validation of an artificial intelligence-based pipeline for predicting oral epithelial dysplasia malignant transformation by Adam J. Shephard, Hanya Mahmood, Shan E. Ahmed Raza, Anna Luíza Damaceno Araújo, Alan Roger Santos-Silva, Marcio Ajudarte Lopes, Pablo Agustin Vargas, Kris D. McCombe, Stephanie G. Craig, Jacqueline James, Jill Brooks, Paul Nankivell, Hisham Mehanna, Syed Ali Khurram, Nasir M. Rajpoot

“…Manual grading of OED is subject to substantial variability and does not reliably predict prognosis, potentially resulting in sub-optimal treatment decisions. Method We developed a Transformer-based artificial intelligence (AI) pipeline for the prediction of malignant transformation from whole-slide images (WSIs) of Haematoxylin and Eosin (H&E) stained OED tissue slides, named ODYN (Oral Dysplasia Network). …”
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Study on Mercury Exposure and Different Approaches for the Management of Mercury Toxicity by Praveen Kumar Maddheshiya, Priyanka Bharti, Sarad Kumar Mishra, Kapil Gupta

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Ensemble models enhanced estimates of leaf chlorophyll from fractional order derivatives transformed spectra by Guangman Song, Yi Gan, Lingfeng Mao, Zhiwei Ge, Jiangshan Lai, Quan Wang

“…To address these challenges, we applied the fractional order derivatives (FOD) transformed spectra into ensemble models, consisting of two popular data-oriented approaches, to refine the spectral response characteristics of leaf chlorophyll using a composite dataset (two public datasets: LOPEX and ANGERS; two collected datasets: Naeba and Nakakawane) containing different species samples. …”
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Preparation of bainite phase microstructure by laser-electromagnetic induction hybrid solid-state phase transformation by Qunli Zhang, Lian You, Yi Yu, Zhijun Chen, Wenjian Wang, Mykola Anyakin, Jianhua Yao

“…In this paper, the surface heat treatment of 42CrMo steel is carried out by using a laser heat source to achieve complete austenitization, then with electromagnetic induction heating for several times to implement insulation, and eventually to promote bainite phase transformation. In conjunction with the temperature history curves, OM, SEM, and EBSD are used to characterize the bainite morphology and percentage of bainite via different parameters of the hybrid process. …”
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Harmonizing musculoskeletal health: transformative effects of 8-week intervention program on posture in music students by Stanislav Azor, Michal Marko, Štefan Adamčák, Pavol Bartik

“…Material and Methods. The 8-week intervention program with transformative effects was conducted over 8 weeks (from September 18 to November 12) on Mondays and Thursdays, for 45 minutes each session. …”
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CoastVisionNet: transformer with integrated spatial-channel attention for coastal land cover classification by Li Yang, Liu Yijun, Wenhao Deng

“…These limitations stem from rigid spatial encoding schemes, insufficient spectral differentiation, and a lack of dynamic reasoning capabilities.MethodsTo overcome these challenges, we introduce CoastVisionNet, a transformer-based framework with integrated spatial-channel attention tailored for coastal land cover classification. …”
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Thyroid nodule segmentation in ultrasound images using transformer models with masked autoencoder pre-training by Yi Xiang, Rajendra Acharya, Quan Le, Jen Hong Tan, Chiaw-Ling Chng

“…The difficulty arises from factors such as the absence of prior knowledge about the thyroid region, low contrast between anatomical structures, and speckle noise, all of which obscure boundary detection and introduce variability in nodule appearance across different images.MethodsTo address these challenges, we propose a transformer-based model for thyroid nodule segmentation. …”
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LMVT: A hybrid vision transformer with attention mechanisms for efficient and explainable lung cancer diagnosis by Jesika Debnath, Al Shahriar Uddin Khondakar Pranta, Amira Hossain, Anamul Sakib, Hamdadur Rahman, Rezaul Haque, Md.Redwan Ahmed, Ahmed Wasif Reza, S M Masfequier.Rahman Swapno, Abhishek Appaji

“…Furthermore, we integrate attention mechanisms based on the Convolutional Block Attention Module (CBAM) and feature selection techniques derived from the Simple Gray Level Difference Method (SGLDM) to improve discriminative focus and minimize redundancy. …”
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General Solution of Linear Fractional Neutral Differential Difference Equations by Hai Zhang, Jinde Cao, Wei Jiang

“…This paper is concerned with the general solution of linear fractional neutral differential difference equations. The exponential estimates of the solution and the variation of constant formula for linear fractional neutral differential difference equations are derived by using the Gronwall integral inequality and the Laplace transform method, respectively. …”
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MISTIC: a novel approach for metastasis classification in Italian electronic health records using transformers by Livia Lilli, Mario Santoro, Valeria Masiello, Stefano Patarnello, Luca Tagliaferri, Fabio Marazzi, Nikola Dino Capocchiano

“…In this paper, we introduce MISTIC, a Natural Language Processing (NLP) approach to classify the presence or absence of metastasis in Italian EHRs, in the breast cancer domain. Methods Our approach consists of a transformer-based framework designed for few-shot learning, requiring a small labelled dataset and minimal computational resources for training. …”
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PERSPECTIVE DIRECTIONS OF PATRIOTIC EDUCATION by I. Ya. Murzina, S. V. Kazakova

“…However, in the modern sociocultural situation, characterised by diversity, discrepancy, multi-vector nature of development of society and polystylicity of culture, it is necessary to revise the forms of patriotic education in order to adjust them in accordance with the  requirements and mental features of new young generation – so-called generation Z, which is considerably different from the previous generation of peers.The aims of the research were the following: to identify promising areas and vectors of patriotic education, to demonstrate its resources and to highlight the most relevant means and possibilities, which are appropriate to present-day realities.Methodology and research methods. …”
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Contrastive learning with transformer for adverse endpoint prediction in patients on DAPT post-coronary stent implantation by Fang Li, Zenan Sun, Ahmed abdelhameed, Tiehang Duan, Laila Rasmy, Xinyue Hu, Jianping He, Yifang Dang, Jingna Feng, Jianfu Li, Yichen Wang, Tianchen Lyu, Naomi Braun, Si Pham, Michael Gharacholou, DeLisa Fairweather, Degui Zhi, Jiang Bian, Cui Tao

“…Traditional prognostic tools, such as rule-based methods or Cox regression, despite their widespread use and ease, tend to yield moderate predictive accuracy within predetermined timeframes. …”
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Multiscale feature fusion and enhancement in a transformer for the fine-grained visual classification of tree species by Yanqi Dong, Zhibin Ma, Jiali Zi, Fu Xu, Feixiang Chen

“…To address these challenges, we propose multiscale feature fusion (MFF) and enhancement in transformers to improve recognition performance. The method consists of a Swin transformer backbone, an MFF module, a discriminative feature enhancement (DFE) module, and a texture feature enhancement (TFE) module. …”
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