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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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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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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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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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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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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Geographical variation of overweight, obesity and related risk factors: Findings from the European Health Examination Survey in Luxembourg, 2013-2015. by Hanen Samouda, Maria Ruiz-Castell, Valery Bocquet, Andrea Kuemmerle, Anna Chioti, Frédéric Dadoun, Ngianga-Bakwin Kandala, Saverio Stranges

“…Large disparities in the prevalence of overweight and obesity were found between municipalities, with the highest rates of obesity found in 3 municipalities located in the South-West of the country. …”
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Fertility intentions among Chinese adults: a cross-sectional study using a national survey by Mingyan Hao, Chenxiao Zhang, Haofeng Fu, Jingting Zhang, Ziyi Zhang, Baotong Ma, Ruitong Xia, Yuanwei Lu, Jingwen Liu, Hanjia Xin, Yumeng Cao, Saier Yang, Runqing Li, Ji Zhao, Jin Zhang, Zheng Gao, Yaxin Zeng, Yixiao Ding, Zhuolun Ren, Yan Guan, Na Zhang, Jia Li, Yan Ma, Pei Wei, Jingjing Dong, Chen Chen, Yajing Zhou, Jianzhong Zheng, Wei Liu, Lizheng Wang

“…To tackle the challenge of decreasing fertility rates effectively, it is essential that interventions focus on younger women and highlight the advantages of living with multiple generations. …”
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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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Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs by Samir Abdaljalil, Hasan Kurban, Rachad Atat, Erchin Serpedin, Khalid Qaraqe

“…We introduce Temporal Structural Graph Anomaly Detection (<sc>T-StructGAD</sc>), an unsupervised framework that leverages Graph Convolutional Gated Recurrent Units (<monospace>GConvGRU</monospace>s) and Long Short-Term Memory networks (<monospace>LSTM</monospace>s) to jointly model both structural and temporal dynamics in graph node embeddings. …”
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A multi-voter multi-commission nearest neighbor classifier by Suyanto Suyanto, Prasti Eko Yunanto, Tenia Wahyuningrum, Siti Khomsah

“…Compared with two single-voter models: KNN and Bonferroni Mean Fuzzy k-Nearest Neighbors (BM-FKNN), the proposed MVMCNN also gives lower mean error rates as well as higher Precision, Recall, and F1 Score, indicating that the multi-voter model provides a better decision than the single-voter ones.…”
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Evaluation of knowledge, attitude, and response regarding transfusion-transmitted infections in blood donors sero-reactive for viral markers during postdonation counselling as an a... by Anju Dubey, Atul Sonker

“…Furthermore, the rates of donors’ response to notification and compliance to clinical referral were assessed to find the areas which require improvement. …”
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Modifying model life tables to derive mortality curves for countries with excess mortality by Lina María Sanchez-Cespedes

“…The method identifies the most appropriate model life table that reflects the mortality pattern without excess deaths and adjusts it to the actual one by considering external-cause mortality rates. …”
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Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022 by An Wang, Hua Su

“…This study demonstrates the ASTEN method is well suited for subsurface temperature reconstruction, and holds great potential in the gaps-filling of sparse ocean observations with high missing rates over a large scale. The new reconstruction of subsurface temperature can effectively reduce the uncertainty of subsurface ocean warming analysis.…”
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Remating and sperm competition in replicate populations of Drosophila melanogaster adapted to alternative environments. by Devin Arbuthnott, Aneil F Agrawal, Howard D Rundle

“…Using eight replicate populations of Drosophila melanogaster from a long-term evolution experiment, we measured remating rates and subsequent offspring production of females when housed with two separate males in sequence. …”
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Assessing the potential for carbon storage enhancement in forests of Xinjiang Uygur autonomous region, China by Zhizhong Chen, Mei Zan, Jingjing Kong

“…This study integrates multi-source remote sensing and forest inventory data to model Xinjiang’s forest age and carbon density (2019 baseline: 186.76 Mg/hm2 biomass, 93.38 Mg/hm2 carbon density, 46-year average age), revealing a south-to-north “low-high-low” spatial pattern. Using predictive models excluding anthropogenic and natural disturbances, we project forest carbon stock to reach 203.71 ± 2.31 Tg C by 2030 and 283.08 ± 4.23 Tg C by 2060, with declining carbon sink rates (3.67 ± 0.57 Tg C/a in 2019–2030 vs. 2.65 ± 0.56 Tg C/a in 2031–2060). …”
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Variable Aircraft Spacing Quadratic Bézier Curve Trajectory Planning for Cascading Delay Mitigation by Michael R. Variny, Travis W. Moleski, Jay P. Wilhelm

“…Specifically, uncrewed aerial systems utilizing near-maximum arrival rates would be greatly impacted when requested to move off their approach path and may interfere with others. …”
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