Utilization of Neural Network in the Diagnosis of Pes Planus and Pes Cavus with a Smartphone Camera by Samir Ghandour MD, Anton Lebedev BS, Wei Shao Tung BS, Konstantin Semianov BS, Artem Semyanov MS, Daniel Guss MD, MBA, Gregory R. Waryasz MD, John Y. Kwon MD, Christopher W. DiGiovanni MD, Soheil Ashkani-Esfahani MD, Lorena Bejarano-Pineda MD

“…Conclusion: Our smartphone-based CNN model is highly accurate as a decision-support tool, and it is reliable and accessible for predicting Pes planus and Pes cavus deformities. This tool may be very useful in underserved healthcare settings and for patients with limited access to expert clinical assessment. …”
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Chemometric-assisted UV spectrophotometric methods for determination of miconazole nitrate and lidocaine hydrochloride along with potential impurity and dosage from preservatives by Esraa S. Ashour, Ghada M. El-Sayed, Maha A. Hegazy, Nermine S. Ghoniem

“…The obtained results revealed that PLS algorithm was superior to PCR depending on the lowest root mean square error of prediction (RMSEP) and correlation coefficient values (r). …”
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A Scale‐Adaptive Urban Hydrologic Framework: Incorporating Network‐Level Storm Drainage Pipes Representation by Taher Chegini, Hong‐Yi Li, Y. C. Ethan Yang, Günter Blöschl, L. Ruby Leung

“…Comparisons with the National Water Model show better performance in predicting flood peaks and overall water balance, underscoring the promises of our new framework for urban hydrologic modeling at large scales. …”
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Joint classification and regression with deep multi task learning model using conventional based patch extraction for brain disease diagnosis by Padmapriya K., Ezhumalai Periyathambi

“…Magnetic resonance imaging (MRI) is increasingly used in clinical score prediction and computer-aided brain disease (BD) diagnosis due to its outstanding correlation. …”
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Securing the economic management and service infrastructure of banks via the use of artificial intelligence (MO-ILSTM) by Xintong Wu

“…The platform can also effectively forecast financial risks, with a prediction accuracy of 75.6 % due to information exchange and interaction. …”
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Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysisRes... by Zijie Zhang, Yang Ding, Kaibin Lin, Wenli Ban, Luyue Ding, Yudong Sun, Chuanliang Fu, Yihang Ren, Can Han, Xue Zhang, Xiaoer Wei, Shundong Hu, Yuwu Zhao, Li Cao, Jun Wang, Saman Nazarian, Ying Cao, Lan Zheng, Min Zhang, Jianliang Fu, Jingbo Li, Xiang Han, Dahong Qian, Dong Huang

“…Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were calculated for model evaluation. …”
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Unmanned Aerial Vehicle Remote Sensing for Monitoring Fractional Vegetation Cover in Creeping Plants: A Case Study of <i>Thymus mongolicus</i> Ronniger by Hao Zheng, Wentao Mi, Kaiyan Cao, Weibo Ren, Yuan Chi, Feng Yuan, Yaling Liu

“…FVC growth rates exhibited distinct variations across phenological stages, indicating high consistency between predicted and actual growth trends. This study highlights the feasibility of UAV-based FVC monitoring for <i>T. mongolicus</i> and indicates its potential for tracking creeping plants.…”
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Dynamic Evacuation Shelter Allocation in Response to Human Mobility: A Case Study of Taipei City by Chang-Hung Shih, Cheng-Yun Wu, Shu-Ping Tseng, Yi-Lin Huang, Rong-Pu Jhuang, Yi-Chung Chen, Tien-Yi Yang, Wei-Ting Chen

“…This study developed a system for the targeted assignment of evacuation sites during air raids. The DBSCAN algorithm was used to group data based on pedestrian flow patterns and an LSTM model was used to enhance the prediction speed and accuracy. …”
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THE RELATIONSHIP BETWEEN THE INDIVIDUAL GENETICALLY DETERMINED FACIAL PROFILE OF PATIENTS WITH A DISTAL OCCLUSION (ENGLE CLASS II) AND THE TYPE OF GROWTH OF THEIR FACIAL SKULL AND... by S.I. Doroshenko, O.Y. Opekha, V.V. Volkova

“…Jarabak methods has enabled a more detailed diagnosis of distal occlusion (Class II malocclusion) and the development of a comprehensive treatment algorithm with improved outcome prediction. …”
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Determination of the Characteristics of the Molecular Structure of Trialkyl Phosphates by the Joint Application of Electronic Ionization Mass Spectra with Registration of Positive... by Yu. I. Morozik, A. A. Tsvetkov, А. R. Valiev, A. G. Terentyev

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Phase-Controlled Closing Strategy for UHV Circuit Breakers with Arc-Chamber Insulation Deterioration Consideration by Hao Li, Qi Long, Xu Yang, Xiang Ju, Haitao Li, Zhongming Liu, Dehua Xiong, Xiongying Duan, Minfu Liao

“…Compared with the least squares fitting, this algorithm achieves a reasonable balance between goodness of fit and complexity, with prediction deviations tending to be randomly distributed, no obvious systematic offset, and low dispersion degree. …”
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Design and Implementation of a CMOS-MEMS Out-of-Plane Detection Gyroscope by Huimin Tian, Zihan Zhang, Li Liu, Wenqiang Wei, Huiliang Cao

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Identifying and prioritizing appropriate strategies for customer loyalty in a mass environment (case study: clothing house) by Zahra Joorbonyan, Ali Sorourkhah, Seyyed Ahmad Edalatpanah

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An Improved YOLOP Lane-Line Detection Utilizing Feature Shift Aggregation for Intelligent Agricultural Machinery by Cundeng Wang, Xiyuan Chen, Zhiyuan Jiao, Shuang Song, Zhen Ma

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The problem of recurrent cystitis from the perspective of a gynecologist. Possible ways to overcome by I. V. Ignatko, K. R. Bakhtiyarov, A. A. Churganova, V. M. Anokhina

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Novel Russian guidelines for the assessment and modification of cardiovascular risk in non-cardiac surgery by A. N. Sumin, F. I. Belyalov

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Unlocking potent anti-tuberculosis natural products through structure–activity relationship analysis by Delfly Booby Abdjul, Fitri Budiyanto, Joko Tri Wibowo, Tutik Murniasih, Siti Irma Rahmawati, Dwi Wahyu Indriani, Masteria Yunovilsa Putra, Asep Bayu

“…Significant characteristics and relevant biological properties of each compound were analysed using a Random Forest, machine learning algorithm, to explore SAR. Using molecular docking, AutoDock Vina was utilised to assess molecular interactions with protein targets, and predictive accuracy was enhanced using the XGBoost machine learning model. …”
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Optimal design of high‐performance rare‐earth‐free wrought magnesium alloys using machine learning by Shaojie Li, Zaixing Dong, Jianfeng Jin, Hucheng Pan, Zongqing Hu, Rui Hou, Gaowu Qin

“…Abstract In this study, a small dataset of 370 datapoints of Mg alloys are selected for machine learning (ML), in which each datapoint includes five rare‐earth‐free alloying elements (Ca, Zn, Al, Mn and Sn), three extrusion parameters (extrusion speed, temperature and ratio), and three mechanical properties (yield strength [YS], ultimate tensile strength [UTS] and elongation [EL]). The ML algorithms, including support vector machine regression (SVR), artificial neural network, and other three methods, are employed, and the SVR has the best performance in predicting mechanical properties based on the components, and process parameters, with the mean absolute percentage error of YS, UTS, and EL being 6.34%, 4.19%, and 13.64% in the test set, respectively. …”
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A Self-Supervised Feature Point Detection Method for ISAR Images of Space Targets by Shengteng Jiang, Xiaoyuan Ren, Canyu Wang, Libing Jiang, Zhuang Wang

“…Feature point detection in inverse synthetic aperture radar (ISAR) images of space targets is the foundation for tasks such as analyzing space target motion intent and predicting on-orbit status. Traditional feature point detection methods perform poorly when confronted with the low texture and uneven brightness characteristics of ISAR images. …”
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An FPGA-accelerated multi-level AI-integrated simulation framework for multi-time domain power systems with high penetration of power converters by Chen Liu, Peng Su, Hao Bai, Xizheng Guo, Alber Filbà Martínez, Jose Luis Dominguez Garcia

“…The framework structures systems hierarchically using energy transmission functions and unified energy information flow-based surrogate models with defined ports, ensuring compatibility with artificial intelligence algorithms. By integrating AI techniques, such as back propagation neural networks, the framework predicts variables with high computational complexity, improving accuracy and simulation efficiency. …”
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