MASSIVE: A scalable framework for agent-based scheduling of micro-grids using market mechanisms by Jakob M. Fritz, Lea Riebesel, André Xhonneux, Dirk Müller

“…It, however, is run multiple times a day to form a rolling horizon, making it less sensitive to forecasting errors. The market approach furthermore exhibits a nearly linear scalability with regard to the duration of the market clearing. …”
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Employment of a Radial Basis Function Model for Predicting the Heating Load of Construction by Yuxuan Dai

“…These validations also prove that the RBPV model is the most outstanding regarding real-world applicability and accuracy. It attains an outstanding maximum R^2 train value of 0.992 , indicating a high degree of explanatory power and an impressively low RMSE_train value of 0.896, signifying minimal prediction errors in comparison to other frameworks.…”
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Efficient Deep Learning Model Compression for Sensor-Based Vision Systems via Outlier-Aware Quantization by Joonhyuk Yoo, Guenwoo Ban

“…These results confirmed the potential of OAQ for optimizing deep learning models in sensor-based vision applications.…”
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What We Know About the Role of Large Language Models for Medical Synthetic Dataset Generation by Larissa Montenegro, Luis M. Gomes, José M. Machado

“…Structured consultation models, such as SOAP and Calgary–Cambridge, enhance coherence but do not fully prevent errors. Hybrid techniques that combine retrieval-based grounding with domain-specific fine-tuning improve factual accuracy and task performance. …”
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Neutrality as a judicial virtue: Essence, significance and related terms by Hadži Stević Brano

“…Thus, it is not up to a judge to correct the substantive errors of the legislator or another lawmaker. If a judge evaluates the positive law and departs from it due to his/her substantive or moral views, it is not considered legal reasoning. …”
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New glacier thickness and bed topography maps for Svalbard by W. van Pelt, T. Frank

“…We discuss the remaining sources of errors, differences from previous ice thickness maps of the region, and future applications of our results.…”
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Implementation of the FIFO System and Its Impact on Asset Stock Accuracy at the Nias Regency Statistics Office by Candry Yurlina Waruwu, Rina Novianti Telaumbanua, Agusniaman Zega, Osadikman Zega, Jurisman Waruwu, Arliyanto Zai

“…Previously, manual spreadsheet-based asset management led to the risk of recording errors, reporting delays, and a lack of efficiency. …”
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M.R.H. A New Era in Magnetic Head Design by Mazen R. Husni

“…Therefore, in tape system, and using an inductive head, it becomes necessary to use some form of electronic equalization in order to correct errors in the signal, resulting from any variations in the speed of the tape. …”
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6D Pose Estimation Algorithm Based on Improved YOLOv5 With Asymptotic Feature Pyramid Network and Attention Mechanism by Yan Zhang, Hanyu Ye

“…Inaccurate pose estimation can lead to augmented reality failures or robotic grasping errors, directly affecting system performance and safety. …”
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Design and Simulation of Detection Algorithm for QRS Wave of ECG Signal by WANG Hai-ying, WANG Yuan-yuan, YAO Zhi-lin

“…Considering the poor real-time performance and high error detection rate of conventional ECG detection algorithms, this paper designs a forward and reverse bidirectional adaptive differential threshold detection algorithm, and increases the R-wave misdetection miss detection compensation algorithm. …”
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Euler Operational Matrix of Integration and Collocation Method for Solving Functional Integral Equations by Sohrab Bazm, Fatemeh Pahlevani

“…One significant advantage of this method lies in its ability to avoid the need for direct integration to discretize the integral operator. Error estimates are provided and two illustrative examples are included to demonstrate the method’s effectiveness and practical applicability.…”
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On the Q-Convergence and Dynamics of a Modified Weierstrass Method for the Simultaneous Extraction of Polynomial Zeros by Plamena I. Marcheva, Ivan K. Ivanov, Stoil I. Ivanov

“…In the present paper, we prove a new local convergence theorem with initial conditions and error estimates that ensure the Q-quadratic convergence of a modification of the famous Weierstrass method. …”
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Evaluation of LLMs accuracy and consistency in the registered dietitian exam through prompt engineering and knowledge retrieval by Iman Azimi, Mohan Qi, Li Wang, Amir M. Rahmani, Youlin Li

“…Although state-of-the-art LLMs have shown superior performance in several conversational applications, evaluations within nutrition and diet applications are still insufficient. …”
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Incorporation of XAI and Deep Learning in Biomedical Imaging: A Review by Sushil K. Singh, Bal Virdee, Saurabh Aggarwal, Abhilash Maroju

“…Concerns about liability in autonomous car accidents are comparable to those associated with deep learning applications in medical imaging. Errors such as false positives and false negatives can negatively affect patients' health. …”
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A large-scale estimation method for beach slopes using ICESat-2 altimeter: A case study of New Zealand by Nan Xu, Hao Xu, Wenyu Li, Hui Lu, Yongze Song, Jiaqi Yao, Yue Ma, He Ren, Tingting He, Fan Mo, Peng Gong

“…Our results indicate that the inverted beach slopes from our method exhibit a high accuracy (root mean square error (RMSE) = 0.06, determination coefficient (R2) = 0.76). …”
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Spatiotemporal inhomogeneity of accuracy degradation in AI weather forecast foundation models: A GNSS perspective by Junsheng Ding, Wu Chen, Junping Chen, Jungang Wang, Yize Zhang, Lei Bai, Yuyan Wang, Xiaolong Mi, Tong Liu, Duojie Weng

“…We calculated the mean deviation, mean absolute error, and root mean square error of these data. …”
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Using multispectral information and machine learning to improve daytime fire radiative power estimation from METImage measurements by Yingxin Gu, Ivan Csiszar, Marina Tsidulko, Wei Guo

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Integration Method with Backpropagation by Nidhal AL-Assady, Jamal Majeed, Shahbaa Khaleel

“…In this research, a new method is discovered (combined method) to accelerate the backpropagation network by using the expected values of source units for updating weights, we mean the expected value of unit by the sum of the output of the unit and its error term multiplied by the factor Beta to accelerate the algorithm and also adjust the value of learning coefficient continuously if the value of energy function E decreases the learning rate is increased by a factor , if the value of the energy function E increases , the value of the learning rate is decreased by a factor . …”
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A cluster-based local modeling paradigm for high spatiotemporal resolution VPD prediction using multi-source data and machine learning by Mi Wang, Zhuowei Hu, Xiangping Liu, Wenxing Hou

“…Spatiotemporal distribution statistics reveals higher prediction errors in southwestern mountainous and plateau regions, primarily due to heterogeneous local features driven by complex topography and climate. …”
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Intelligent Resource Allocation via Hybrid Reinforcement Learning in 5G Network Slicing by Zahraa Zakariya Saleh, Maysam F. Abbod, Rajagopal Nilavalan

“…Beyond 5G (B5G) networks provide unprecedented communications performance and flexibility through virtualization and network slicing, which generates various logical partitions for particular applications with specific requirements. RAN slicing is an essential section of 5G network slicing due to its vulnerability to errors, affecting its ability to meet stringent reliability requirements. …”
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