PAPR Reduction in DCO-OFDM Visible Light Communication Systems Using Optimized Odd and Even Sequences Combination by Wei-Wen Hu

“…Even and odd sequences are obtained by adopting an inverse fast Fourier transform on the even and odd parts of a Hermitian symmetry encoded signal, respectively. Furthermore, a deterministic and efficient selection method of cyclic shift and phase rotation is derived to improve PAPR reduction performance and ensure that each candidate is unique, so that the receiver can recover the transmitted candidate signal without the use of SI. …”
Get full text

Optimization of Thermal Insulation Parameters for Vertical Perimeters in Buildings with Single-Layer Walls by Walery Jezierski, Paula Szczepaniak, Cezary Leszczyński

“…Using THERM 7.6 software for computational experiments, data were obtained to develop deterministic mathematical models of these relationships. …”
Get full text

Reinforcement Learning-Based Control for Robotic Flexible Element Disassembly by Benjamín Tapia Sal Paz, Gorka Sorrosal, Aitziber Mancisidor, Carlos Calleja, Itziar Cabanes

“…Traditional control systems struggle to handle these tasks efficiently, requiring adaptable solutions that can operate in unstructured environments that provide online adaptation. …”
Get full text

Cost-effectiveness of Triple Therapy with Telaprevir for Chronic Hepatitis C Virus Patients in Germany by Jona T. Stahmeyer, Svenja Schauer, Siegbert Rossol, Hans Heinrich Wedemeyer, Daniel Wirth, Florence Bianic, Christian Krauth

“…Model structure and inputs were discussed with clinical experts. Deterministic and probabilistic sensitivity analyses were performed to verify the robustness of results…”
Get full text

A Deep Reinforcement Learning Framework for Adaptive Resiliency Enhancement in Smart Power Grids by Boyu Wang, Aliasghar Baziar, Mohammad Reza Askari

“…To overcome these limitations, we propose a deep reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm, optimized using the Root Mean Square Propagation (RMSprop) method for stable and efficient training. …”
Get full text

Robust Traffic Signal Retiming Based on Queue Service Time Estimation Using Low-Penetration Connected Vehicle Data by Chengchuan An, Weihua Zhang, Yinpu Wang, Siping Ke, Jingxin Xia

“…However, the existing methods are mostly deterministic and do not well consider the uncertainty in both traffic demand and capacity. …”
Get full text

Analyzing measles spread through a Markovian SEIR model by Yousef Alnafisah, M. A. Sohaly

“…We employ the state reduction method to simplify complex computations and develop a Mathematica-based algorithm to efficiently determine steady-state probabilities. …”
Get full text

Research on Ship Heave Motion Compensation Control Under Complex Sea State Environment Based on Improved Reinforcement Learning by ZHANG Qin, ZHOU Jingyi, WANG Xingyue, HU Xiong

“…Therefore, this study presents a compensation control method for ship heave under complex sea conditions using an improved reinforcement learning approach.MethodsThis novel method imparts fresh insights into addressing heavy compensation in offshore operations and heralds a new trajectory for the evolution of future offshore operation technologies. …”
Get full text

Determinate arbitrary quantum state engineering through one-dimensional quantum walks by Yu Wang, Meng Li, Yuan-Yuan Zhao

“…This efficient method for preparing quantum superposition states lays a foundation for advancing quantum technologies, with broad applications in quantum information processing.…”
Get full text

Reliability Analysis of Three-dimensional Soil Slopes Considering Spatial Variability of Soil Parameters by Wan Yukuai, Zhou Yuqi, Shao Linlan, Wang Yuke, Zhang Fei

“…To address these limitations, this study employs the covariance matrix decomposition method to generate 3D lognormal random fields for soil parameters, enabling efficient modeling of spatial variability. …”
Get full text

Geological modeling of carbonate fracture-cavity reservoir: case study of Shunbei fault zone No. 5 by Bolin Li, Yunfeng He, Wen Chen, Haojie Shang, Lixin Wang

“…First, deterministic modeling methods were employed to establish fault-controlled body contour models through seismic attribute fusion (structure tensor, energy gradient, and variance attributes) calibrated with well-log data. …”
Get full text

Cost and effectiveness of differentiated ART service delivery strategies in Zambia: a modelling analysis using routine data by Nkgomeleng A. Lekodeba, Sydney Rosen, Bevis Phiri, Sithabiso D. Masuku, Caroline Govathson, Aniset Kamanga, Prudence Haimbe, Hilda Shakwelele, Muya Mwansa, Priscilla Lumano‐Mulenga, Amy N. Huber, Sophie J. S. Pascoe, Lise Jamieson, Brooke E. Nichols

“…Methods We developed an Excel‐based mathematical model using retrospective retention and viral suppression data from a national cohort of ART clients (≥15 years) in Zambia between January 2018 and March 2022 stratified by age, sex, setting (urban/rural) and model of ART delivery. …”
Get full text

Optimization of distribution networks using quantum annealing for loss reduction and voltage improvement in electrical vehicle parking management by Naser Rashnu, Babak Mozafari, Reza Sharifi

“…Overall, this study presents a robust and scalable optimization strategy for building more efficient, resilient, and sustainable smart distribution systems.…”
Get full text

THE TASK OF MINIMAX ADAPTIVE MANAGEMENT OF INNOVATIVE PROCESSES AT AN ENTERPRISE WITH RISK ASSESSMENT by Vitalina Babenko, Elena Alisejko, Zoya Kochuyeva

“…In view of this, it is suggested to use the deterministic approach based of the methods of the theory of optimal management and dynamic optimization. …”
Get full text

Enhancing Security With Hybrid Active- Passive RIS: A DRL Approach Against Eavesdropping and Jamming by Abdul Wahid, Syed Zain Ul Abideen, Nouman Imtiaz, Mian Muhammad Kamal, Abdullah Alharbi, Amr Tolba, M. A. Al-Khasawneh, Inam Ullah

“…To tackle this, we employ a Deep Reinforcement Learning (DRL) approach using the Deep Deterministic Policy Gradient (DDPG) algorithm, enabling efficient and dynamic optimization. …”
Get full text

Two-stage multi-timescale optimal scheduling for electricity-hydrogen coupling systems based on scenario approach and deep reinforcement learning by CHEN Zhe, WEI Meijia, LIN Da, LI Zhihao, CHEN Jian

“…The short-term model focuses on minimizing the systems’ operational costs and is solved using the deep deterministic policy gradient （DDPG） algorithm. Finally， case study simulations demonstrate that the proposed method effectively facilitates hydrogen energy shifting， smooths fluctuations in wind and solar output， verifying the method’s effectiveness.…”
Get full text

Advances in Hosting Capacity Assessment and Enhancement Techniques for Distributed Energy Resources: A Review of Dynamic Operating Envelopes in the Australian Grid by Naveed Ali Brohi, Gokul Thirunavukkarasu, Mehdi Seyedmahmoudian, Kafeel Ahmed, Alex Stojcevski, Saad Mekhilef

“…This paper reviews state-of-the-art HC assessment methods, including deterministic, stochastic, time-series, and AI-based approaches. …”
Get full text

Energy Management of Industrial Energy Systems via Rolling Horizon and Hybrid Optimization: A Real-Plant Application in Germany by Loukas Kyriakidis, Rushit Kansara, Maria Isabel Roldán Serrano

“…To solve the resulting nonlinear and constrained optimization problem at each RHA iteration, we propose a novel hybrid algorithm that combines Bayesian optimization (BO) with the Interior Point OPTimizer (IPOPT). While global deterministic and stochastic optimization methods are frequently used in practice, they often suffer from high computational costs and slow convergence, particularly when applied to large-scale, nonlinear problems with complex constraints. …”
Get full text

Uncertainty CNNs: A path to enhanced medical image classification performance by Vasileios E. Papageorgiou, Georgios Petmezas, Pantelis Dogoulis, Maxime Cordy, Nicos Maglaveras

“…Uncertainty quantification (UQ) is important as it helps decision-makers gauge their confidence in predictions and consider variability in the model inputs. Numerous deterministic deep learning (DL) methods have been developed to serve as reliable medical imaging tools, with convolutional neural networks (CNNs) being the most widely used approach. …”
Get full text

An Improved Toeplitz Measurement Matrix for Compressive Sensing by Xu Su, Yin Hongpeng, Chai Yi, Xiong Yushu, Tan Xue

“…In this paper, Toeplitz matrix is employed and optimized as a deterministic measurement matrix. A hardware platform for signal efficient acquisition and reconstruction is built by field programmable gate arrays (FPGA). …”
Get full text