Numerical modelling of rope elasticity for heave compensation lifting in offshore crane operations by Bassey Asanga, Akanimo Ekpenyong, Aniekan Ikpe

“…The heave, roll and pitch motions were observed to be a regular wave pattern. The comparison of the variations of heave, roll and pitch (including their rates and accelerations) within resonating time (50 seconds) during WOC with WC revealed that the heave compensation system could reduce heave, roll and pitch motion.…”
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Evaluation of newly transplanted blackberry tolerance to a selection of preemergence herbicides by Kayla Knepp, Matthew B. Bertucci, Aaron J. Cato, Amanda L. McWhirt, Nilda R. Burgos

“…Results from these trials verify that flumioxazin, napropamide, oryzalin, and pendimethalin at the tested rates would be appropriate options for weed control in newly planted blackberries. …”
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DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model Framework by Arshak Rezvani, Nasrin Ravansalar, Mohammad Ali Akhaee, Andrew J. Greenshaw, Russell Greiner, Maryam S. Mirian, Muhammad Yousefnezhad, Martin J. McKeown

“…Assessing the severity of gait impairment in Parkinson’s disease (PD) using the Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is typically performed by clinical experts, but this process is time-consuming, subjective, and costly. …”
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SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN by Shengfeng He, Wenhu Qin, Zhonghua Yun, Chao Wu, Chongbin Sun

“…Next, a SOH estimation framework based on the CGKAN model is developed, where 1-Dimensional-Convolutional Neural Networks (1D-CNN) are used to extract deep features from the original data, Bidirectional Gated Recurrent Unit (BiGRU) captures the bidirectional dependencies of the time series, and Kolmogorov–Arnold Networks (KAN) enhances the modeling of complex nonlinear features through its nonlinear mapping capabilities, thereby improving the accuracy of SOH estimation. …”
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AEMS: Adaptive Ensemble GNNs for Multibehavior Stream Recommendation by Ritchie Natuan Caibigan, Punyaphol Horata, Pusadee Seresangtakul

“…The AEMS synergizes long-term preference patterns (derived from historical interactions) with real-time user intents and item attributes (captured through multibehavior signals), integrating them via an adaptive ensemble neural gating mechanism. …”
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Body mass change over winter is consistently sex-specific across roe deer (Capreolus capreolus) populations by Hewison, A.J. Mark, Bonnot, Nadège C., Gaillard, Jean-Michel, Kjellander, Petter, Lemaitre, Jean-François, Morellet, Nicolas, Pellerin, Maryline

“…We suggest that allocation to antler growth and territory establishment drives this pattern, providing a likely explanation to account for the lower rates of male adult survival that are consistently reported in this weakly dimorphic species.…”
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Deep learning driven prediction and comparative study of surrounding rock deformation in high speed railway tunnels by Zeping Yang, Zhikai Cheng, Da Wu

“…The methodology incorporates quadratic exponential smoothing for outlier mitigation, followed by sequential feature extraction using convolutional neural networks (CNNs) and bidirectional gated recurrent units (GRUs). Comparative experiments demonstrate the model’s superiority over conventional architectures including RNN, LSTM, GRU, and CNN-GRU. …”
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Forecasting Sales in Live-Streaming Cross-Border E-Commerce in the UK Using the Temporal Fusion Transformer Model by Qi Zhang, Xue Li, Pengbin Gao

“…Our multimodal approach integrates diverse time series data, including historical sales, key opinion leader (KOL) influence, and seasonal patterns. The Temporal Fusion Transformer (TFT) model demonstrated consistently lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE) across all forecasting horizons compared to other machine learning approaches, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Gated Recurrent Unit(GPU)-accelerated architectures. …”
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A clustering-based federated deep learning approach for enhancing diabetes management with privacy-preserving edge artificial intelligence by Xinyi Yang, Juan Li

“…We develop tailored models that enhance prediction accuracy by clustering patients based on carbohydrate (CHO) intake patterns. Utilizing Simple Recurrent Neural Network (SimpleRNN) and Gated Recurrent Unit (GRU) methods, the study evaluates the performance of local patients who contribute to training the cluster and global (non-cluster) models. …”
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Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning by Yanli Zhao, Zongduo Liu, Junjie Pang

“…Traditional detection approaches typically rely on statistical features while overlooking the interaction patterns and structural dependencies among traffic flows. …”
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Evaluation of the Effects of Anesthetics and Surgery on Sleep Quality in Patients Undergoing Posterior Spinal Instrumentation Surgery: A Prospective Randomised Clinical Trial by Selcan Akesen, Gökay Eken, Yücel Bilgin, Saltuk Buğra Güler, Elifgül Ulutaş

“…Introduction: The aim of the study is to evaluate the impact of the anesthesia methods and surgical procedure on the sleep patterns and sleep quality of patients undergoing posterior spinal instrumentation, using the Pittsburgh Insomnia Rating Scale-20 (PIRS-20). …”
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AI-DBS study: protocol for a longitudinal prospective observational cohort study of patients with Parkinson’s disease for the development of neuronal fingerprints using artificial... by Martijn Beudel, Rick Schuurman, Rob M A de Bie, Deborah Hubers, Mariëlle J Stam, Martijn G J de Neeling, Bart J Keulen, Arthur W G Buijink, Bernadette C M van Wijk

“…Correlations will be tested between spectral features of LFP data and symptom severity, which will be assessed using (1) inertial sensor data from a wearable smartwatch, (2) clinical rating scales and (3) patient diaries and analysed using conventional descriptive statistics and artificial intelligence algorithms. …”
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Dermatoscopic Criteria of Non-pigmented Basal Cell Carcinoma by Kiladze N., Shulaia T., Bulinska A., Abrahamovych L.

“…Our findings, regarding vessels are in accordance with the currently available literature; some minor differences concerning the rates of vessels morphology are due to the lack of metaphoric terminology which is the main methodological disadvantage of dermatoscopy. …”
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Planned Liver Stereotactic Body Radiotherapy for Residual Colorectal Cancer Liver Metastases After Surgery: A Single-Arm Retrospective Study by Sixuan Li, Dezuo Dong, Xuan Zheng, Hongzhi Wang, Kun Wang, Baocai Xing, Weihu Wang

“…The 1-, and 2-year cumulative incidence rates of LF after SBRT were 7.7%, and 9.6%, respectively. …”
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Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs by Samir Abdaljalil, Hasan Kurban, Rachad Atat, Erchin Serpedin, Khalid Qaraqe

“…Our method successfully captures temporal patterns, making it robust against subtle anomalies and structural changes. …”
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Potential profile analysis of financial toxicity and its related factors among lung cancer patients by Xu Zhang, Liwei Zhang, Ziyi Geng, Meimei Shang, Aijun Wang, Xing Zheng, Chao Li, Tingting Zhang, Hailing Yang, Yuanyuan Chen

“…General data questionnaires, FT scale for reported outcomes of cancer patients, Chinese version of the Quality of Life Scale for lung cancer patients, Social Support Rating Scale and simplified version of the Mental Resilience Scale were used. …”
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Taking a look at your speech: identifying diagnostic status and negative symptoms of psychosis using convolutional neural networks by Gleb Melshin, Anthony DiMaggio, Nadia Zeramdini, Michael MacKinley, Lena Palaniyappan, Alban Voppel

“…Modified ResNet-18 convolutional neural networks (CNNs) performed three classification tasks; (1) schizophrenia-spectrum vs healthy controls, within 179 clinically-rated patients, (2) individuals with more severe vs less severe negative symptom burden, and (3) clinically obvious vs subtle blunted affect. …”
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Behavioural insights in the underuse of cardiac resynchronisation therapy in heart failure: a pilot survey on incentive perceptions among referring cardiologists by Joan Costa-i-Font, Georgiana Miler-Raicu, Elena Arbelo, Ruben Casado-Arroyo, Aya Sami, Eric Wei Seong Tee, Joseph Hazel, Laurent Roten, Tobias Reichlin, Haran Burri, Khaled Albouaini, Nikola Kozhuharov

“…A survey was conducted with 51 physicians from six European countries, including electrophysiologists, heart failure specialists, and general cardiologists, recruited through cardiology networks and personal contacts. Participants rated their perceptions of five incentive strategies using a Likert scale (1–5). …”
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Influence of exercise interventions on functional movement screen scores in athletes: a systematic review and meta-analysis by Ali Asghar Maleki, Seyed Hamed Mousavi, Mohamad Ali Biabangard, Hooman Minoonejad

“…Abstract The functional movement screen (FMS) is a widely recognized tool for evaluating movement patterns and identifying potential injury risks in athletes. …”
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Laboratory Analysis of Soil Physicochemical Properties Based on Agricultural Fertilizer Input Requirement Application: A Case of Haramaya University, Ethiopia by Turyasingura, Benson, Chavula, Petros, Mohammed, Yasin, Eliyas, Abdi Alic, Girmac , Tesfasilassie, Sadeso, Kumale, Shentema, Solomon, Abebed, Aschalew, Katel, Shambhu, Timsina, Sandipa

“…Exchangeable calcium ranged 4.99 cmol (+)/kg to 18.34 cmol (+)/kg with a mean of 12.02 cmol (+)/kg which is rated as high. The potassium value of (0.05, 0.82 meq/100gm), 0.06 meq/100gm and (0.13, 0.46 meq/100gm) of soil respectively. …”
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