The fast modeling of gamma-gamma density well logging is essential for the inversion techniques of formation properties, which is usually carried out jointly with other logging measurements such as electrical logging. It also can help to adjust the initial geological model in real time during geosteering. The Monte Carlo method is the foremost numerical technique to simulate gamma-gamma density logging measurement. But due to its slow speed, it is not sufficient for inversion or real-time forward modeling. An algorithm to achieve the fast simulation of density logging response is introduced. In the algorithm, a new approximation model is proposed to enable accurate forward modeling of density logging with better efficiency. The Monte Carlo simulation method is utilized as a benchmark to validate the performance of the fast simulation method. The density logging responses under vertical and high-angle well conditions are simulated. The results of the fast simulation show a good agreement with the Monte Carlo simulations in vertical and high-angle wells. In addition, the comparison of density imaging data also confirmed the accuracy of the fast simulation method.
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Sci Rep
December 2024
School of Vehicle and Energy, Yanshan University, 438 West Hebei Avenue, Qinhuangdao, 066004, People's Republic of China.
This study presents a strategy for an intelligent vehicle trajectory tracking system that employs an adaptive robust non-singular fast terminal sliding mode control (ARNFTSMC) approach to address the challenges of uncertain nonlinear dynamics. Initially, a path tracking error system based on mapping error is established, along with a speed tracking error system. Subsequently, a novel ARNFTSMC strategy is introduced to tackle the uncertainties and external perturbations encountered during actual vehicle operation.
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December 2024
School of Management, Shenyang University of Technology, Shenyang, 100870, China.
This study presents a novel framework for advancing sustainable urban logistics and distribution systems, with a pivotal focus on fast charging and power exchange modalities as the cornerstone of our research endeavors. Our central contribution encompasses the formulation of an innovative electric vehicle path optimization model, whose paramount objective is to minimize overall operational costs. Integrating V2G technology, we facilitate sophisticated slow charging and discharging management of EVs upon their return to distribution centers, enhancing resource utilization.
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December 2024
School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India.
The Internet of Things (IoT) network is a fast-growing technology, which is efficiently used in various applications. In an IoT network, the massive amount of connecting nodes is the existence of day-to-day communication challenges. The platform of IoT uses a cloud service as a backend for processing information and maintaining remote control.
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December 2024
Norwegian Institute for Nature Research, Postbox 5685, 7485, Trondheim, Norway.
The Atlantic salmon (Salmo salar) is an iconic species of significant ecological and economic importance. Their downstream migration as smolts represents a critical life-history stage that exposes them to numerous challenges, including passage through hydropower plants. Understanding and predicting fine-scale movement patterns of smolts near hydropower plants is therefore essential for adaptive and effective management and conservation of this species.
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December 2024
Saha Institute of Nuclear Physics, A CI of Homi Bhabha National Institute, Kolkata, 700064, India.
Antiferromagnetic materials offer potential for spintronic applications due to their resilience to magnetic field perturbations and lack of stray fields. Achieving exchange bias in these materials is crucial for certain applications; however, discovering such materials remains challenging due to their compensated spin structure. The quest for antiferromagnetic materials with exchange bias became a reality through our experimental study and theoretical simulation on and .
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