Topology Unveiled: A New Horizon for Economic and Financial Modeling by Yicheng Wei, Junzo Watada, Zijin Wang

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Fluid Brilliance: Expanding the Horizons in MIMO Diversity Using Liquid Antenna by Viswanadh Raviteja Gudivada, Yi Huang

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ARIMA-Based Forecasting of Wastewater Flow Across Short to Long Time Horizons by Jiawen Ye, Xulai Meng, Haiying Wang, Qingdao Zhou, Siwei An, Tong An, Pooria Ghorbani Bam, Diego Rosso

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Optimal Consumption, Leisure, and Investment with Partial Borrowing Constraints over a Finite Horizon by Geonwoo Kim, Junkee Jeon

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The Rosencrantz Coin: Predictability and Structure in Non-Ergodic Dynamics—From Recurrence Times to Temporal Horizons by Dimitri Volchenkov

“…Instead, sequence structure is characterized by block probabilities and Stirling numbers of the second kind, peaking at block size <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mo>/</mo><mi>log</mi><mi>n</mi></mrow></semantics></math></inline-formula>. …”
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Long-Horizon Direct Model Predictive Control for Medium-Voltage Converters Connected to a Distorted Grid by Andrei Tregubov, Petros Karamanakos, Ludovico Ortombina

“…Long-horizon finite control set model predictive control (FCS-MPC) is known for its superior performance, particularly when applied to complex, higher order systems, such as grid-connected converters with <inline-formula><tex-math notation="LaTeX">$LCL$</tex-math></inline-formula> filters. …”
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Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation by Yuksel Rudy Alkarem, Kimberly Huguenard, Richard W. Kimball, Stephan T. Grilli

“…We introduce the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>τ</mi></semantics></math></inline-formula>-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. …”
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Robust Invariant Set Design for Reliable Stopping in Adaptive Cruise Control via Model Predictive Control by Shila Alizadehghobadi, Reza Ehsani

“…Additionally, we analyze and compare the feasibility and energy efficiency of the system for different terminal set strategies as control invariant set (<inline-formula> <tex-math notation="LaTeX">${\mathrm {C}}_{\infty }$ </tex-math></inline-formula>) and positive invariant set (<inline-formula> <tex-math notation="LaTeX">${\mathrm {O}}_{\infty }$ </tex-math></inline-formula>). …”
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ZAS-F: A Zero-Shot Abstract Sub-Goal Framework Empowers Robots for Long Horizontal Inventory Tasks by Yongshuai Wu, Jian Zhang, Shaoen Wu, Shiwen Mao, Ying Wang

“…Our experiments demonstrated that ZAS-F achieves a learning efficiency 30 times higher than previous methods, requiring only <inline-formula> <tex-math notation="LaTeX">$8{k}$ </tex-math></inline-formula> demonstrations. …”
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Comparative Study of Sphere Decoding Algorithm and FCS-MPC for PMSMs in Aircraft Application by Joseph O. Akinwumi, Yuan Gao, Xin Yuan, Sergio Vazquez, Harold S. Ruiz

“…In this study, we propose a long prediction horizon finite control set model predictive control (FCS-MPC) framework for PMSMs. …”
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Black Holes with a Cloud of Strings and Quintessence in a Non-Linear Electrodynamics Scenario by Francinaldo Florencio do Nascimento, Valdir Barbosa Bezerra, Jefferson de Morais Toledo

“…The solutions have four parameters, namely <i>m</i>, <i>k</i>, <i>a</i>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>, corresponding to the physical mass of the black hole, the nonlinear charge of a self-gravitating magnetic field, the cloud of strings, and the intensity of the quintessential fluid. …”
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A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder by Xingfa Zi, Feiyi Liu, Mingyang Liu, Yang Wang

“…In the 5 min ahead forecasting test, TiDE demonstrated high short-term accuracy with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> of 0.952, MAE of 0.150, and RMSE of 0.349, though performance declines for longer horizons, such as the 1 h ahead forecast, compared to other algorithms. …”
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Multi-Sensor Temporal Fusion Transformer for Stock Performance Prediction: An Adaptive Sharpe Ratio Approach by Jingyun Yang, Pan Li, Yiwen Cui, Xu Han, Mengjie Zhou

“…Empirical results show that TFT-ASRO achieves a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>18</mn><mo>%</mo></mrow></semantics></math></inline-formula> improvement in Sharpe ratio prediction accuracy compared to state-of-the-art baselines, with particularly strong performance in volatile market conditions. …”
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Application of Dual-Stage Attention Temporal Convolutional Networks in Gas Well Production Prediction by Xianlin Ma, Long Zhang, Jie Zhan, Shilong Chang

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Implementation of Principal Component Analysis (PCA)/Singular Value Decomposition (SVD) and Neural Networks in Constructing a Reduced-Order Model for Virtual Sensing of Mechanical... by M. A. Melgarejo, A. Pérez, D. Ruiz, A. Casas, F. González, V. González de Lena Alonso

“…The ANN model achieves a prediction error of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>MAE</mi><mi>test</mi></msub><mo>=</mo><mrow><mo>(</mo><mn>0.04</mn><mo>±</mo><mn>0.06</mn><mo>)</mo></mrow><mo> </mo><mi>MPa</mi></mrow></semantics></math></inline-formula> for the instantaneous mechanical stress predictions, evaluated over the entire range of stress values (0 to 5.32 MPa) across the component structure. …”
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A Multi-Modal Approach Using a Hybrid Vision Transformer and Temporal Fusion Transformer Model for Stock Price Movement Classification by Ibanga Kpereobong Friday, Sarada Prasanna Pati, Debahuti Mishra

“…Specifically, the proposed framework converts the historical data into candlestick charts of <inline-formula> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula>-pixel images where both modalities are effectively integrated and processed. …”
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Nonlinear Neutral Delay Differential Equations: Novel Criteria for Oscillation and Asymptotic Behavior by Belal Batiha, Nawa Alshammari, Faten Aldosari, Fahd Masood, Omar Bazighifan

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High-resolution Holocene record based on detailed tephrochronology from Torfdalsvatn, north Iceland, reveals natural and anthropogenic impacts on terrestrial and aquatic environme... by D. J. Harning, D. J. Harning, C. R. Florian, C. R. Florian, C. R. Florian, Á. Geirsdóttir, T. Thordarson, G. H. Miller, Y. Axford, S. Ólafsdóttir

“…We then apply this robust age constraint to support a complete tephrochronology (<span class="inline-formula">&gt;</span> 2200 grains analyzed in 33 tephra horizons) and sub-centennial geochemical (MS, TOC, C <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mo>/</mo></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="8pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="e653eaf840568ee76bb20ba3bf368ae0"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="cp-21-795-2025-ie00001.svg" width="8pt" height="14pt" src="cp-21-795-2025-ie00001.png"/></svg:svg></span></span> N, <span class="inline-formula"><i>δ</i>¹³</span>C, and BSi) and algal pigment records (chlorins, lutein, diatoxanthin, and canthaxanthin). …”
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A Hybrid GAS-ATT-LSTM Architecture for Predicting Non-Stationary Financial Time Series by Kevin Astudillo, Miguel Flores, Mateo Soliz, Guillermo Ferreira, José Varela-Aldás

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Bounce Cosmology in a Locally Scale Invariant Physics with a U(1) Symmetry by Meir Shimon

“…The scalar field <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ϕ</mi></semantics></math></inline-formula> is complex. …”
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