Assessment of the Symmetry of the Ground Reaction Forces in Patient with ACL Rupture during Vertical Jump by Keyvan Sharifmoradi, Arash Raji

“…There were no significant differences between loading and unloading rates between both side in take-off and landing phases (p<0.05). …”
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Multi-scale spatio-temporal graph neural network for urban traffic flow prediction by Hui Chen, Jian Huang, Yong Lu, Jijie Huang

“…On the four datasets, the average improvement rates of the three prediction accuracy metrics, namely the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), reach 17.69%, 15.65%, and 10.30% respectively.…”
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Deep RNA sequencing of pectoralis muscle transcriptomes during late-term embryonic to neonatal development in indigenous Chinese duck breeds. by Chunhong Zhu, Weitao Song, Zhiyun Tao, Hongxiang Liu, Wenjuan Xu, Shuangjie Zhang, Huifang Li

“…A total of 393 differentially expressed genes (DEGs) were identified, which showed higher or lower expression levels at E27 compared with E21 and 5 dph, reflecting the pattern of PM growth rates. Among these, 43 DEGs were common to all three time points in both duck breeds. …”
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Feedback-Driven Dynamical Model for Axonal Extension on Parallel Micropatterns by Kyle Cheng, Udathari Kumarasinghe, Cristian Staii

“…In these environments, axons preferentially align with the pattern direction, form bundles, and advance at constant speed. …”
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Evaluation of effectiveness of transcranial mesodiencephalic modulation within comprehensive treatment of pulmonary tuberculosis patients with various comorbidities by O. A. Serov, O. A. Smolentseva

“…Two months after the start of the study, in Group 1, positive radiological changes were recorded more often – in 91,7% (22/24), in Group 2, positive changes were observed in 62,5% (15/24) of patients (p=0,047; χ2); the same pattern was observed with positive changes of laboratory rates – 91,7% (22/24) versus 66,7% (16/24), (p=0,033; χ2), respectively. …”
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The Use of 3D Convolutional Autoencoder in Fault and Fracture Network Characterization by Feng Xu, Zhiyong Li, Bo Wen, Youhui Huang, Yaojun Wang

“…Conventional pattern recognition methods directly use 1D poststack data or 2D prestack data for the statistical pattern recognition of fault and fracture network, thereby ignoring the spatial structure information in 3D seismic data. …”
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Prevalence of Moraxella Catarrhalis as a Nasal Flora among Healthy Kindergarten Children in Bhaktapur, Nepal by Neetu Amatya, Govinda Paudel, Bhuvan Saud, Sunita Wagle, Vikram Shrestha, Bibhav Adhikari

“…Using bivariate and multivariate analysis, the associated risk factors with significantly high carriage rates were age group of 3–4 years, classroom occupancy with 15–30 children, and antibiotic consumption within 6 months, with a p value of ≤0.05 in each of the cases. …”
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A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction by Bao Li, Quan Yang, Jianjiang Chen, Dongjin Yu, Dongjing Wang, Feng Wan

“…The temporal features are extracted with the gated recurrent unit (GRU). Then, we adaptively fuse spatial and temporal features with the gating mechanism to get the final spatio-temporal features for lane-level traffic prediction. …”
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Integrated Feature-Temporal GAN for Imbalanced Transaction Fraud Detection by Yicen Zheng, Yu Xie, Jiamin Yao

“…IFT-GAN introduces two key innovations: 1) A Feature-Aware Sample Selection (FASS) module that employs Fisher score-based feature ranking and weighted k-means clustering to create a balanced subset enriched with discriminative fraud signatures; and 2) A Temporal Dependency GAN (TD-GAN) architecture employing a novel Time-aware Gated Recurrent Unit (TaGRU) in both the generator and discriminator, explicitly modeling the temporal intervals between transactions through adaptive temporal gating. …”
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Evaluating the Clinico-biochemical Association between Stress and Chronic Periodontitis by Estimation of Serum Cortisol and Serum Chromogranin A Levels by Shilpa Duseja, Hiral Akshat Parikh, Sareen Duseja

“…The analysis of stress levels using the Social Readjustment Rating Scale and lifestyle evaluation using the Health Practice Index was done. …”
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Principles for Achieving Legibility in Residential Spaces: A Synthesis of Cognitive and Perceptual Approaches by Slobodan Marković, Đorđe Alfirević, Sanja Simonović Alfirević, Sanja Nikolić

“…The experimental component examines cognitive navigation and subjective perception, assessed via the recognition of a 3D interior from a 2D plan and bipolar rating scales. Participants explored regular and irregular layouts through animated simulations. …”
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Evolution of public broadcasting: TeleJurnal’s viewership trends and strategic implications for business sustainability by Săseanu Ramona, Stanomir Ruxandra, Bădulescu Andreea-Alexandra

“…By employing a time-series autoregressive moving-average model, this article assesses the stationarity and autocorrelation of audience data to evaluate the consistency of ratings amidst external factors such as changes in programming strategy, political events, and market competition. …”
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A GPT-Based Approach for Cyber Threat Assessment by Fahim Sufi

“…Notably, anomaly detection identified six significant deviations during the monitored timeframe, starting from 25 September 2023 to 25 November 2024, with a sensitivity of 75%, revealing critical insights into unusual activity patterns. The fully deployed automated model also identified 11 correlated factors and five unique clusters associated with high-rated cyber incidents. …”
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What Motivates Companies to Take the Decision to Decarbonise? by Stefan M. Buettner, Werner König, Frederick Vierhub-Lorenz, Marina Gilles

“…For instance, long-term cost risks were rated more relevant than reputational gains or regulatory compliance. …”
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Machine learning models for predicting multimorbidity trajectories in middle-aged and elderly adults by Li Yao, Qiaoxing Li, Zihan Zhou, Jiajia Yin, Tingrui Wang, Yan Liu, Qinqin Li, Lu Xiao, Dongliang Yang

“…The study identified three key factors—baseline disease counts, self-rated Activities of Daily Living (ADL), and self-rated health status—as critical predictors of multimorbidity trajectories.…”
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Enhancing Sensitivity across Scales with Highly Sensitive Hall Effect‐Based Auxetic Tactile Sensors by Youngheon Yun, Dongchan Lee, Soyeon Lee, Salvador Pané, Josep Puigmartí‐Luis, Sungwoo Chun, Bumjin Jang

“…HEATS exhibits a remarkable 20‐fold and 10‐fold improvement at tensile rates of 0.9% and 30%, respectively, compared to non‐auxetic sensors. …”
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Perception of quality and adoption of health-preventive measures in gym-goers after the COVID-19 pandemic peak in Italy by L Falese, E Langiano, M Ferrara, P Diotaiuti, A Rodio, T Di Libero, A Andrade, E De Vito

“…Participants generally rated the gym positively, with 34.49% considering it "Good" and 39.23% rating it "Very good." …”
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Two-choice discrimination learning in African elephants (Loxodonta africana) by Charles Hyatt

“… A study of two-choice discrimination learning across 22 pattern pairs was conducted with three African elephants (Loxodonta africana) at the Atlanta/Fulton County Zoo with rates of acquisition and retention observed to be similar to those earlier reported for the Asian species (Elephas maximus). …”
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From Margin to Mainstream: Informal Street Vendors and Local Politics in Kolkata, India by Tathagata Chatterji, Souvanic Roy

“…A new, fragmented spatial structure has started emerging, with the middle classes and the globalizing segments of the economy, moving away to gated enclaves at the urban fringe. These changes are not isolated phenomenon, but rather symptomatic with the larger process of urban transformation and entwined with the political-economic change in the state level.…”
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Emoji-Driven Sentiment Analysis for Social Bot Detection with Relational Graph Convolutional Networks by Kaqian Zeng, Zhao Li, Xiujuan Wang

“…Subsequently, seven sentiment-based features are extracted to quantify statistical disparities in emotional expression patterns between bot and human accounts. An attention gating mechanism is further designed to dynamically fuse these sentiment features with user description, tweet content, numerical attributes, and categorical features. …”
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