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4th International conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Zhukova, Aleksandra Flerova, Anna Chernov, Alexey Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Vavilov str 44 bld. 2 Moscow Russia Moscow Institute of Physics and Technology Institutki per. 9 Dolgoprudny141701 Russia

ISBN: (纸本)9783031812408

The formal description of the assets and liabilities management (ALM) by a bank within regulatory requirements leads to an optimal control problem with phase constraints. The phase constraints arise from the restrictions on banking activities, which are set by law to maintain the banking process. Solving such model explicitly is rarely possible, and numerical calculations enable to study its properties. The indirect method of the solution involves formulating the system of necessary optimality conditions and then computation of the ordinary differential equations system combined with specific equations for dual variables. This paper provides the details of application of the numerical methods and its specifics in the proposed economic model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Banking

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8th International conference on Construction, Architecture and Technosphere Safety, ICCATS 2024

8th International Conference on Construction, Architecture a...

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8th International conference on Construction, Architecture and Technosphere Safety, ICCATS 2024

ISBN: (纸本)9783031804816

The proceedings contain 67 papers. The special focus in this conference is on Construction, Architecture and Technosphere Safety. The topics include: Lime Compositions with the Additive of Polysilicate Solution for Restoration of Building Walls;durability of Lime Coatings with Polysaccharides Additives;study of the Structure and Properties of the Heat-Affected Area of Welded Joints;quickly Constructed Connection of Precast Concrete Elements with Dismantling Possibility;use of Dispersion analysis to Assess Significance of Effect of Production process Factors on Construction Plywood Parameters;strength of a Bending Reinforced Concrete Element in the Zone of Shear Forces;the Influence of the Properties of Permafrost Soils on the Seismic Resistance of Civil Buildings;study of the Influence of Carbon Nanotubes on the Characteristics of Fine-Grained Concrete;selection of the Climatic Information processing Period for Calculating Design Climate data;on the Shear Resistance of Mortars for Precast Piles in Permafrost;features of Solving Problems of Calculating Systems with Ideal Elastoplastic Material Behavior Using the Finite Element Method in the Form of a Classical Mixed Method;activated Cement-Sand Composition as a Nanomodifying Additive in Concrete;constructive Solutions of Energy-Active External Fences with an Estimated Justification of Heat Gain from Solar Radiation into the Premises;specifics of the Influence of Quartz and Glauconite Sand on the Concrete Strength;modeling of Nodal Elements of Spatial Rod Structures;development and Improvement of Energy Efficiency of a Heat Utilization System from the Ice Arena Refrigeration Machine;influence of Preliminary Electric Curing Parameters on Physical and Mechanical Properties of Concrete.

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A Novel Software Tool for Automated and Integrated Building Energy Model Calibration 11th

A Novel Software Tool for Automated and Integrated Building ...

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11th International conference of Ar.Tec.

作者： Maracchini, Gianluca D'Orazio, Marco Di Giuseppe, Elisa Revel, Gian Marco Univ Trento Dept Civil Environm & Mech Engn I-38123 Trento Italy Marche Polytech Univ Dept Construct Civil Engn & Architecture I-60131 Ancona Italy Marche Polytech Univ Dept Ind Engn & Math Sci I-60131 Ancona Italy

ISBN: (纸本)9783031718656;9783031718632;9783031718625

In the EU, the building sector significantly impacts energy consumption and greenhouse gas emissions, accounting for 40% of the total energy use and 35% of emissions, mainly due to the energy inefficiency of the building stock. With energy demand expected to increase over the next decade, improving building energy efficiency is essential for meeting EU sustainability goals. Building Energy Models (BEMs) are crucial for evaluating and enhancing building performance throughout their lifecycle. However, a notable "energy performance gap" usually exists between predicted and actual energy use, exacerbated by challenges in accurately inputting numerous variables and the simplifications inherent in modeling. BEM calibration (BC) approaches are often adopted to reduce these discrepancies, aimed at adjusting model inputs to match output with the observed data. Yet, there is not a universal consensus on which is the best calibration method, with manual and automated approaches offering different benefits. Automated methods, especially those using optimization algorithms, have gained prominence for their efficiency and ability to handle uncertainties. However, BC still significantly depends on the energy modelers' expertise. This paper introduces a novel software tool for automated BC, aiming to simplify the process by integrating expert knowledge, sensitivity analysis, and optimization algorithms techniques in a unique workflow. This tool reduces the dependence of BC success on modeler expertise, representing a significant step towards more accessible automated BC in the research field and engineering practice, thence allowing a more effective design of energy conservation measures.

关键词： Building Energy Model Model calibration Optimization algorithm Sensitivity analysis Software

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analysis of Graphical Responses in Energy Efficiency Simulators

Analysis of Graphical Responses in Energy Efficiency Simulat...

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International Joint conference on Mechanics, Design Engineering and Advanced Manufacturing, JCM 2024

作者： García, Ruth Fernández Fernández, Marta Menéndez Diez, Rafael Rodríguez-Galindo Gero, María Belén Prendes Miguel, Diego José Guerrero Cerra, Pablo Pando Mining Tecnology Topography and Structures Department University of León León24071 Spain Construction and Manufacturing Engineering Department University of Oviedo Gijón33203 Spain

ISBN: (纸本)9783031728280

Year by year, compliance with energy efficiency standards in buildings becomes more and more demanding in order to carry out the different energy transitions established by international institutions to implement the increasingly demanding energy plans for this sector. These objectives affect all the areas involved, not only the building sector, but also the energy sector, administrations and even the end user. For this reason, the control of all the principles of sustainable building is complex due to the amount of data generated in the process, making necessary a series of tools capable of managing all this information, looking for the maximum optimization in the results, which is consumption. One of these data and the basis of the whole process is the geometry of the building, a priority requirement in the process. The work of more than 20 years with the calculation and energy management software supported by the Spanish administration shows a commitment to calculation and estimation programs, to the clear detriment of geometric definitions, which leads to an energy over-dimensioning contrary to the final and real objective sought. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Green buildings

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Enhanced Business Location Intelligence by Forecasting Innovation Adopter Types and Predictive Space-Time Locations: An Integrated Approach 58

Enhanced Business Location Intelligence by Forecasting Innov...

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58th Hawaii International conference on System Sciences, HICSS 2025

作者： Franklin, Christopher G. Sreedharan, Jayashree Bezak, Victor J. University of Texas Dallas United States University of Redlands United States

ISBN: (纸本)9780998133188

The modeling-ensemble consists of three components;the (1) Bass temporal forecasting model, (2) Rogers' Innovation Adopter Theory and (3) Hierarchical Bayes Model (HBM). The modeling-ensemble's HBM updates priors and posterior distributions when new spatial 'presence' data evidence is available in the form of REA's (random empirical adoptions) or geocoded sales. In other words, (1) Bass tells us (WHEN) to expect 'innovators and imitators' to adopt, (2) Rogers informs us (WHO) the adopter types are likely to be (i.e., Innovators, Early Adopters, Early Majority, Late Majority or Laggards) and (3) Bayes informs us (WHERE) the adopters are likely located, based on REA geocoded addresses, within census units-of-analysis, across a retail Store Level Trade Area. Formally, we refer to this modeling-ensemble as the “Bass-Rogers-Bayes-Temporal-Spatial Extension” or BRBTSE. An earlier prototype called the “Bass-Bayes-Spatial-Extension or BBSE (Franklin, 2018) was the foundation for this “WHEN-WHO-WHERE” approach. Implementing the novel BRBTSE allows the practitioner or researcher to (1) discover the simpatico relationships between “Bass Innovators” and “Rogers' Innovators” and (2) how the Bass “Imitator” forecast cascades to each of Rogers' Adopter types after Innovators (i.e., Early Adopter, Early Majority, Late Majority and Laggards). By integrating and synchronizing Bass and Rogers in this way, we discover new information about WHEN, WHO and WHERE Adopter types diffuse into the market for strategic marketing purposes. Timing and spatiality guide potentially unique marketing mixes evolving during the innovation diffusion process, based on Rogers' Innovation Adopter Behavioral Type profiles. Value Chain Managers and their Demand Chain Managers, in particular, will benefit dynamically from this parsimonious combination of geospatial big data and enhanced Location Intelligence. Bayesian spatial inference prediction, over Census areal units-of-analysis, allows strategic marketin

关键词： Bass Bayes Location-Intelligence Presence data Rogers

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Modelling System for Decision Support of Epidemic Diseases in Ukraine 19th

Modelling System for Decision Support of Epidemic Diseases i...

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19th International conference on Mathematical modeling and Simulation of Systems, MODS 2024

作者： Pylypenko, Oleksandr Kivva, Sergii Brovchenko, Igor Institute of Mathematical Machines and System Problems NAS of Ukraine Glushkov Prosp. 42 Kyiv03187 Ukraine

ISBN: (纸本)9783031907340

The aim of the article is to develop and apply a modelling system to support decision-making regarding the spread of infectious diseases. The recent course of the COVID-19 pandemic worldwide has emphasised the need for software tools to prevent, analyse, and manage the epidemic process. Although by the time the pandemic began, there were already quite a few mathematical tools for modelling infectious diseases, practical experience with their application revealed their weaknesses and limitations. One of the main shortcomings of mathematical models is the uncertainty of parameters in the case of an epidemic caused by a new, unknown infection. When there is not enough time to conduct medical experiments to determine the properties of the virus, the challenge arises to develop algorithms for calibrating models based on available observational data. This work develops a modelling system for managing the epidemic process, which allows for the prediction of the spread of the COVID-19 viral infection, taking into account the age groups of the population and their spatial heterogeneity. It also includes a parameter optimisation block that allows for flexible adjustment of the model as new information about the virus and new statistical data become available. Model parameter calibration is based on comparing the predicted results with observed data by solving a nonlinear constrained least squares problem. The modelling system was applied to simulate four waves of COVID-19 in Ukraine. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Uncertainty analysis

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Maximizing Efficiency and controlling Congestion in Interlinked Queueing Models for Integrated Voice and data Communication Networks

Maximizing Efficiency and Controlling Congestion in Interlin...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Amit Kumar Prabhdeep Singh Virk Neeraj Kumar Department of Mathematics Chandigarh University Mohali Punjab India Department of CSE (AI & ML) Raj Kumar Goel Institute of Technology Ghaziabad U.P India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

This paper introduces an interdependent tandem queueing model for communication networks. The model effectively transports voice and data traffic by adopting strong packet switching and multiplexing capabilities. Application of the bit-dropping scheme as the best congestion control mechanism in the network model encapsulates one of the primary thrusts of this study. Performance measures of statistical multiplexing are built in the research, e.g., approximating the correlated arrival and service processes to the Poisson process, an empirical model employed in the modeling of random events. Importantly, the study illustrates the practical advantages of interdependent communication networks. In particular, it shows their ability to decrease the average transmission delay, or the time for data to pass through the network, as well as buffer level fluctuation. These results show the potential of interdependent networks to optimize network efficiency, a significant issue in modern communication systems.

关键词： Multiplexing Power transmission lines Sensitivity analysis Computational modeling Transmission line measurements Throughput data models Delays Communication networks data communication

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2nd International conference on Smart Electrical Grid and Renewable Energy, SEGRE 2024

2nd International Conference on Smart Electrical Grid and Re...

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2nd International conference on Smart Electrical Grid and Renewable Energy, SEGRE 2024

ISBN: (纸本)9789819619641

The proceedings contain 76 papers. The special focus in this conference is on Smart Electrical Grid and Renewable Energy. The topics include: Point Cloud Denoising Method Based on Improved PointCleanNet;dynamic Dual-Strategy Update of Offline-to-Online Reinforcement Learning for Optimal Energy System Scheduling;knowledge Extraction Method of Electric Power Equipment Defects Based on Transfer Learning;a Secondary Bidding Strategy for the Charging Station Day-Ahead Market Based on the Evaluation of the Dispatchable Capacity of Electric Vehicles;primary Frequency Modulation control Strategy of Energy Storage System Based on State of Charge;building Integrated Energy System Expansion Planning Considering Reward-Punishment Ladder Carbon Mechanism and Load Uncertainty;Simulation analysis and Verification of Four-Air-Gap Current Transducer Based on TMR;optimal control of Large Number of Air Conditioners Driven by Dynamic Electric Carbon Emission Factor;application of a Bi-level Optimization Model for Energy Storage Capacity Allocation in Distribution Network with Renewable Energy Integration;power Load Disaggregation Method Based on Sparse Constraint;optimal Primary Frequency Support Demand Dispatch for Multiple Wind Turbines Considering Loss of Captured Wind Energy;promoting Eco-Friendly Power and Traffic Network Operations Using Machine Learning Techniques;Advanced-data Analytics for Household Power Consumption Forecasting Using CNN-LSTM Hybrid Network;research on the Interruption Performance of Environmentally Friendly 126 kV Circuit Breaker;research on Identification of Electricity Theft in Regional Power Grid Under New Energy Scenario;nonlinear modeling for One Pipe with Multiple Units Under Gird Connected of Pumped Storage Stations;a Methodology for the Evaluation of the Power Supply Capability of Subway Traction Power Supply System.

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Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment

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IEEE ACCESS 2025年 13卷 11280-11294页

作者： Al Mazroa, Alanoud Albogamy, Fahad R. Khairi Ishak, Mohamad Mostafa, Samih M. Princess Nourah bint Abdulrahman Univ PNU Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Taif Univ Turabah Univ Coll Comp Sci Program Taif 21944 Saudi Arabia Ajman Univ Coll Engn & Informat Technol Dept Elect & Comp Engn Ajman U Arab Emirates South Valley Univ Fac Comp & Informat Comp Sci Dept Qena 83523 Egypt New Assiut Technol Univ NATU Fac Ind & Energy Technol New Assiut City 71684 Egypt

The swift advancement of cyber-physical systems (CPSs) across sectors such as healthcare, transportation, critical infrastructure, and energy enhances the crucial requirement for robust cybersecurity measures to protect these systems from cyberattacks. The cyber-physical method is a hybrid of cyber and physical components, and a safety breach in the element is central to catastrophic consequences. Cyberattack recognition and mitigation techniques in CPSs include using numerous models like intrusion detection systems (IDSs), access control mechanisms, encryption, and firewalls. Cyberattack detection employing deep learning (DL) contains training neural networks to identify patterns indicative of malicious actions within system logs or network traffic, allowing positive classification and mitigation of cyber-attacks. By leveraging the integral ability of DL methods to learn complex representations, this technique enhances the accuracy and efficiency of detecting diverse and growing cyber-attacks. Thus, the study proposes an automated Cyberattack Detection using Binary Metaheuristics with Deep Learning (ACAD-BMDL) method in a CPS environment. The ACAD-BMDL method mainly focuses on enhancing security in the CPS environment via the cyberattack detection process. The ACAD-BMDL method uses Z-score normalization to scale the input dataset. In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. Moreover, the Enhanced Elman Spike Neural Network (EESNN) model detects cyber-attacks. Furthermore, the Archimedes Optimization Algorithm (AOA) model is employed to select the optimum hyperparameter for the EESNN model. The empirical analysis of the ACAD-BMDL technique is performed on a benchmark dataset. The experimental validation of the ACAD-BMDL technique portrayed a superior accuracy value of 99.12% and 99.36% under NSLKDD2015 and CICIDS2017 datasets in the CPS environment.

关键词： Cyberattack Optimization Feature extraction data models Accuracy Security Neural networks Adaptation models Deep learning Computational modeling Cyber-physical systems deep learning cyberattack detection metaheuristics Archimedes optimization algorithm

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17th International conference on the Simulation of Adaptive Behavior, SAB 2024

17th International Conference on the Simulation of Adaptive ...

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17th International conference on the Simulation of Adaptive Behavior, SAB 2024

ISBN: (纸本)9783031715327

The proceedings contain 26 papers. The special focus in this conference is on Simulation of Adaptive Behavior. The topics include: Vector-Based Navigation Inspired by Directional Place Cells;a Behavior-Based Model of Foraging Nectarivorous Echolocating Bats;benefit of Varying Navigation Strategies in Robot Teams;no-brainer: Morphological Computation Driven Adaptive Behavior in Soft Robots;cuttleBot: Emulating Cuttlefish Behavior and Intelligence in a Novel Robot Design;the Emergence of a Complex Representation of Touch Through Interaction with a Robot;analyzing Multi-robot Leader-Follower Formations in Obstacle-Laden Environments;spatio-Temporal Dynamics of Social Contagion in Bio-inspired Interaction Networks;behavioural Contagion in Human and Artificial Multi-agent Systems: A Computational modeling Approach;transient Milling Dynamics in Collective Motion with Visual Occlusions;extended Swarming with Embodied Neural Computation for Human control over Swarms;bio-Inspired Agent-Based Model for Collective Shepherding;DaNCES: A Framework for data-inspired Agent-Based Models of Collective Escape;the Role of Energy Constraints on the Evolution of Predictive Behavior;influence of the Costs of Acquisition of Private and Social Information on Animal Dispersal;integrated Information in Genetically Evolved Braitenberg Vehicles;neural Chaotic Dynamics for Adaptive Exploration control of an Autonomous Flying Robot;non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models;memory-Feedback controllers for Lifelong Sensorimotor Learning in Humanoid Robots;extracting Principles of Exploration Strategies with a Complex Ecological Task;the Cost of Behavioral Flexibility: Reversal Learning Driven by a Spiking Neural Network;"Value" Emerges from Imperfect Memory;the Role of Theory of Mind in Finding Predator-Prey Nash Equilibria;nonverbal Immediacy analysis in Education: A Multimodal Computational Model.

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