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Cloud-Computing Trend Analysis of Urbanization Impacts on Land Surface Temperature and Electricity Demand in Gaborone, Botswana

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 283-306页

作者： Moyo, Boineelo Schröder, Dietrich Moreri, Kealeboga Institute for living conditions in rural areas Johann Heinrich von Thuenen-Institut Bundesallee 50 Lower Saxony Braunschweig 38116 Germany Department of Geomatics Computer Science and Mathematics Stuttgart University of Applied Sciences Schellingstr. 24 Baden-Wuerttemberg Stuttgart 70174 Germany Department of Civil Engineering University of Botswana Notwane Rd Gaborone Botswana

Land use land cover (LULC) changes are critical drivers of global environmental changes, profoundly impacting sustainability in rapidly urbanizing regions. In urban areas, where vegetation cover is reduced, LULC changes significantly affect the distribution and intensity of Land Surface Temperature (LST), which in turn influences electricity demand and consumption. This study explores the relationship between urbanization, vegetation loss, surface temperature and electricity demand in Gaborone, Botswana. The study utilized the Landsat data to conduct a 15-year (2005–2020) spatio-temporal analysis of LULC changes and LST dynamics, identified surface urban heat islands (SUHI) using the Getis-Ord Gi* hot-spot analysis and assessed the correlation between LST and key spectral indices adopted in the study. The analysis employed Google Earth Engine (GEE) cloud computing platform and the Random Forest (RF) machine learning algorithm, known for their accuracy and efficiency in classification and change detection studies. Findings reveal substantial LULC transformations in Gaborone with approximately 12 km² of land converted to Built-Up areas, driving a 2.36% annual urban growth rate and shrinking green and bare spaces. Consequently, surface temperatures rose by about 2.1 °C, impacting energy demand, particularly for cooling during hot summers. This demand surge, coupled with urban expansion, has fueled higher electricity prices, posing new challenges in climate adaptation and mitigation. The study provides critical insights into the interplay between urbanization, rising temperatures, and energy consumption in Gaborone. These insights highlight the urgent need for developing sustainable urban planning and energy strategies to address the impacts of climate change, land degradation, and energy poverty. © The Author(s) 2024.

关键词： Climate change Electricity demand Google earth engine Land surface temperature Land use land cover Random forest

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Modeling the One Class of Radionuclide Migration Processes in Soils under Non-Isothermal Conditions Using Perturbation Theory Methods 13

Modeling the One Class of Radionuclide Migration Processes i...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Bomba, Andrii Ostapchuk, Oksana Vovk, Roman Kopnicky, Miroslav National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Software Engineering Ivano-Frankivsk Ukraine Catholic University in Ružomberok Department of Catholic Theology Spišská Kapitula Slovakia

Based on the synthesis of complex analysis methods, perturbation theory, and characteristics, a new approach has been developed for accounting for osmosis and temperature in predicting the migration processes of radionuclides under non-isothermal conditions in quasi-ideal porous media (in curved regions) bounded by streamlines and equipotential lines. The solution to the corresponding degenerate problem was obtained based on the developed method of nonlinear inversion of boundary value problem solutions into quasi-conformal mappings. An algorithm for calculating a uniformly dynamic grid, streamlines, and speed of ideal filtration field was constructed. With the developed method of characteristics, formulas for the approximate solution of diffusion-convection mass transfer problems in perturbed osmosis and heat filtration fields were obtained. © 2023 IEEE.

关键词： Porous materials

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An Enhanced Levenberg-Marquardt Method via Gram Reduction 39

An Enhanced Levenberg-Marquardt Method via Gram Reduction

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Chengchang Luo, Luo Lui, John C.S. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Data Science Fudan University China Shanghai Key Laboratory for Contemporary Applied Mathematics China

ISBN: (纸本)157735897X

This paper studies the problem of solving the system of nonlinear equations. We propose the Gram-reduced Levenberg-Marquardt method, which reuses the Gram matrix. Our method has a global convergence guarantee without relying on any step of line-search or solving sub-problems. We show that our method takes a smaller computational complexity than existing Levenberg-Marquardt methods to find the stationary point of the square norm of the equations. We also show that the proposed method enjoys a local superlinear convergence rate under the non-degenerate assumption. Experiments are conducted on real-world applications in scientific computing and machine learning, which validate the efficiency of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： e-science

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TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization

arXiv

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arXiv 2025年

作者： Zhang, Jiayi Zhang, Yiming Zheng, Yuan Wang, Yuchen You, Jinjiang Xu, Yuchen Jiang, Wenxing Dev, Soumyabrata School of Mathematics and Applied Mathematics University of Nottingham Ningbo China School of Computer Science University of Nottingham Ningbo China Department of Electrical and Computer Engineering University of Washington United States School of Computer Science Carnegie Mellon University United States School of Engineering University of Nottingham Ningbo China School of Computer Science University College Dublin Ireland The ADAPT SFI Research Centre Ireland

Urban traffic optimization is crucial for improving transportation efficiency and alleviating congestion. Traditional algorithms [1] like Dijkstra’s [2] and Floyd’s [3], along with Genetic Algorithms (GA) [4], are widely used for route planning but struggle with large-scale, dynamic traffic networks due to computational complexity and limited ability to capture nonlinear spatial-temporal dependencies [5]. To address these challenges, we propose a hybrid framework combining Kolmogorov-Arnold Networks (KAN) with Graph Convolutional Networks (GCN) [6, 7, 8] for urban traffic flow optimization. KAN’s ability to approximate nonlinear functions, along with GCN’s capacity to model spatial dependencies in graph-structured data, provides a more effective representation of traffic dynamics. We evaluate KAN-GCN on real-world datasets and compare it to traditional models like Multi-Layer Perceptrons (MLP) [9] and decision trees. While MLP-GCN shows higher accuracy, KAN-GCN excels in handling noisy and complex traffic patterns [7]. Results highlight challenges in applying KAN to large-scale networks and suggest potential improvements. Additionally, we explore extending the framework to Transformer models for scalable and dynamic traffic forecasting. Our findings indicate that combining KAN with GCN offers promising potential for intelligent traffic management, with further optimization possible for large-scale *** the spirit of reproducible research, the model code, dataset, and results of the experiments in this paper are available at: https://***/ZhangJiayi24/KAN_GCN_*** Codes 90B20, 68T07, 05C85, 90C90 © 2025, CC BY.

关键词： Decision trees

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Bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms

Bitcoin price prediction based on financial data, technical ...

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Distributed Computing and High Performance Computing (DCHPC), International Conference on

作者： Masoud Arjmand Saman Kazeminia Hedieh Sajedi Department of Computer Engineering Kish International Campus University of Tehran Kish Iran Department of Computer Vision and Data Science NHL Stenden University of Applied Science Leeuwarden Netherlands Department of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has had a lot of price fluctuations since its inception. In this paper, we are going to forecast the price of Bitcoin using news headline analysis, technical analysis indicators, and historical financial data. The news headlines used in this article are scraped from the Cointelegraph news website, which contains 3988 news headlines related to Bitcoin between 2/7/2020 and 3/8/2021. A transformer pre-trained model on cryptocurrency-related texts called CryptoBERT, which is a BERT-based sentiment analysis model, has been used to analyze the textual data. Also, a novel hybrid 2DCNN-GRU deep learning model has been used to predict the price. To adjust the parameters of this model, a parameter tuning method based on orthogonal arrays called the Taguchi method has been employed. Finally, to examine the proposed model’s efficiency, the obtained results have been compared with other deep learning models from the literature review that used text data to predict bitcoin prices. The results show that this model outperformed other models in terms of MAE criterion, while in the other three criteria, namely MSE, RMSE and MAPE, it still demonstrated good results.

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Proximal methods for structured nonsmooth optimization over Riemannian submanifolds

arXiv

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arXiv 2024年

作者： Li, Qia Zhang, Na Yan, Hanwei Feng, Junyu School of Computer Science and Engineering Guangdong Province Key Laboratory of Computational Science Sun Yat-sen University Guangzhou China Department of Applied Mathematics College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

In this paper, we consider a class of structured nonsmooth optimization problems over an embedded submanifold of a Euclidean space, where the first part of the objective is the sum of a difference-of-convex (DC) function and a smooth function, while the remaining part is the square of a weakly convex function over a smooth function. This model problem has many important applications in machine learning and scientific computing, for example, the sparse generalized eigenvalue problem. We propose a manifold proximal-gradient-subgradient algorithm (MPGSA) and show that under mild conditions any accumulation point of the solution sequence generated by it is a critical point of the underlying problem. By assuming the Kurdyka-Lojasiewicz property of an auxiliary function, we further establish the convergence of the full sequence generated by MPGSA under some suitable conditions. When the second component of the DC function involved is the maximum of finite continuously differentiable convex functions, we also propose an enhanced MPGSA with guaranteed subsequential convergence to a lifted B-stationary points of the optimization problem. Finally, some preliminary numerical experiments are conducted to illustrate the efficiency of the proposed *** Codes 90C26, 90C30, 65K05 Copyright © 2024, The Authors. All rights reserved.

关键词： Discriminant analysis

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Study and prediction of prostate cancer using fuzzy inference system

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Materials Today: Proceedings 2022年 56卷 157-164页

作者： Boadh, Rahul Aarya, Deo Datta Dahiya, Mamta Rathee, Rakesh Rathee, Sarita Kumar, Ajay Jain, Sarika Rajoria, Yogendra Kumar Department of the Mathematics School of Basic and Applied Science K. R. Mangalam University Sohna Road Haryana Gurugram122103 India Department of the Mathematics Acharya Narendra Dev College University of Delhi India Department of Computer Science & Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India University Institute of Engineering & Technology Maharshi Dayanand University Haryana 124001 Rohtak India Department of Electrical Engineering DPG Institute of Technology and Management Haryana Gurgaon122001 India Department of Mechanical Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India Department of Mathematics Amity School of Applied Sciences Amity University Haryana Gurugram122413 India

The essential driver of passing among men on the planet is prostate disease. In the chief stage, for early identification of the prostate disease by applying Fuzzy Inference System (FIS) has been depicted in this study. The age of the patient, Prostate Volume (PV), % Free PSA (FPSA) and Prostate Specific Antigen (PSA) are utilized as input parameters in FIS for computing the risk factor of the prostate cancer. In this study Mamdani method is used for evaluation and analysis of the Prostate Cancer Risk (PCR). The present FIS additionally required the base estimation decreases horribleness lessens dismalness, mortality and more dependable from other system. It can likewise be considered in medicine to decrease manual assignments and human mistake. This review advocate that, this FIS might consequence on work on the productivity to recognized the distinctive kind of the malignant growths just as lessening the expense of care of the patients. © 2022

关键词： Risk assessment

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Pre- and Post-renovation Monitoring with Data Analysis as a Means to Determine Reasons for Performance Gaps 15

Pre- and Post-renovation Monitoring with Data Analysis as a ...

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15th International Conference on Thermal Performance of the Exterior Envelopes of Whole Buildings 2022

作者： Rode, Carsten Rasmussen, Christoffer Madsen, Henrik The Department of Environmental and Resource Engineering Technical University of Denmark Kgs. Lyngby Denmark The Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark

ISBN: (纸本)9781955516280

A significant fraction of current building activity in Europe is with renovation of the existing building stock. It is a well-known challenge that the realized energy performance and indoor climate of renovated buildings deviate from what was expected from the design of the renovation. Reasons may lie in poor technical performance of the renovated building envelope parts, such as failures in thermal insulation, leaks, unforeseen thermal bridges, poor workmanship, or insufficient performance of the delivered products. Deviations may also occur with the performance of the building services systems, and most notably with its operation and control. Finally, the influence from occupants of the buildings can be at least as decisive as the technical reasons related to the building products. The paper will outline a methodology whereby it is possible by use of frequently monitored data of energy use for heating, along with measurements of the indoor and outdoor environments to determine the main reasons for possible performance gaps. Key elements to the methodology are frequent measurements, few data points, time series analysis and mathematical modelling. So-called white-, black- or grey-box modelling techniques can be employed to distill the desired performance variables and explain the realized performance. This may in turn lead to guidelines on which technical issues should be rectified, if needed, or as input to feedback to occupants and caretakers that may guide them. The paper will outline the methodology and show examples of use. The method was developed as part of the project REBUS, "Renovating buildings sustainably" where social housing was used as case buildings for demonstration. © 2022 ASHRAE.

关键词： Thermal insulation

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CLEARING SECTIONS OF LATTICE LIABILITY NETWORKS

arXiv

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arXiv 2025年

作者： Ghrist, Robert Gould, Julian Lopez, Miguel Riess, Hans Department of Mathematics and Electrical & Systems Engineering University of Pennsylvania Philadelphia PA Department of Mathematics University of Pennsylvania Philadelphia PA Applied Mathematics & Computational Science University of Pennsylvania Philadelphia PA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

Modern financial networks involve complex obligations that transcend simple monetary debts: multiple currencies, prioritized claims, supply chain dependencies, and more. We present a mathematical framework that unifies and extends these scenarios by recasting the classical Eisenberg-Noe model of financial clearing in terms of lattice liability networks. Each node in the network carries a complete lattice of possible states, while edges encode nominal liabilities. Our framework generalizes the scalar-valued clearing vectors of the classical model to lattice-valued clearing sections, preserving the elegant fixed-point structure while dramatically expanding its descriptive power. Our main theorem establishes that such networks possess clearing sections that themselves form a complete lattice under the product order. This structure theorem enables tractable analysis of equilibria in diverse domains, including multi-currency financial systems, decentralized finance with automated market makers, supply chains with resource transformation, and permission networks with complex authorization structures. We further extend our framework to chain-complete lattices for term structure models and multivalued mappings for complex negotiation systems. Our results demonstrate how lattice theory provides a natural language for understanding complex network dynamics across multiple domains, creating a unified mathematical foundation for analyzing systemic risk, resource allocation, and network *** Codes 91G40, 06B23, 91D30, 68Q85 Copyright © 2025, The Authors. All rights reserved.

关键词： Lattice theory

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Improved PIR Schemes using Matching Vectors and Derivatives

arXiv

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arXiv 2024年

作者： Ghasemi, Fatemeh Kopparty, Swastik Sudan, Madhu Department of Mathematics University of Toronto Canada Department of Mathematics Department of Computer Science University of Toronto Canada School of Engineering and Applied Sciences Harvard University CambridgeMA United States

In this paper, we construct new t-server Private Information Retrieval (PIR) schemes with communication complexity subpolynomial in the previously best known, for all but finitely many t. Our results are based on combining derivatives (in the spirit of Woodruff-Yekhanin [WY05]) with the Matching Vector based PIRs of Yekhanin [Yek08] and Efremenko [Efr09]. Previously such a combination was achieved in an ingenious way by Dvir and Gopi [DG15], using polynomials and derivatives over certain exotic rings, en route to their fundamental result giving the first 2-server PIR with subpolynomial communication. Our improved PIRs are based on two ingredients: • We develop a new and direct approach to combine derivatives with Matching Vector based PIRs. This approach is much simpler than that of Dvir-Gopi: it works over the same field as the original PIRs, and only uses elementary properties of polynomials and derivatives. • A key subproblem that arises in the above approach is a higher-order polynomial interpolation problem. We show how "sparse S-decoding polynomials", a powerful tool from the original constructions of Matching Vector PIRs, can be used to solve this higher-order polynomial interpolation problem using surprisingly few higer-order evaluations. Using the known sparse S-decoding polynomials from [Efr09, IS08, CFL+13] in combination with our ideas leads to our improved PIRs. Notably, we get a 3-server PIR scheme with communication (formula presented), improving upon the previously best known communication of 2Õ(√log n) due to Efremenko [Efr09]. Copyright © 2024, The Authors. All rights reserved.

关键词： Vectors

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