Tectonic and micro-tectonic studies at different scales are of primary importance in a modern metallogenic investigation. In the Ougnat massif of the eastern Anti-Atlas belt (Morocco), the barite ore deposit is hosted...
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Tectonic and micro-tectonic studies at different scales are of primary importance in a modern metallogenic investigation. In the Ougnat massif of the eastern Anti-Atlas belt (Morocco), the barite ore deposit is hosted within mafic to felsic rocks of the Ediacaran Saghro and Ouarzazate groups together with their folded Paleozoic cover. Here, we provide for the first time new field-based data on the tectonic control of barite mineralization, and their relation with regional tectonic events. The mineralization occurs in brittle-ductile structures as a veintype system, mainly hosted within NE-SW to E-W and NW-SE strike-slip-normal faults. Economic orebodies spread frequently along the Precambrian-Cambrian contact zone thus acting as open conduits for mineralized fluid flow. The geometry of hosted barite sigmoidal lenses corresponds to "pull-apart" or "tension gashes" openings, commonly arranged en echelon arrays along the bearing transcrustal faults. The occurrence of predominant massive and breccia internal texture implies a tectonic-mineralizing collapse process in an extensional tectonic context. Kinematic pattern, directional distribution and host-rock age relationship point to a syntectonic barite control probably occurred during the NW-SE Atlantic rifting, as already attributed to similar deposits in the neighboring Ougarta and Atlas-Meseta *** barite ore is quantitatively and qualitatively more preserved in competent host rocks than those with ductile behavior, thus offering a useful hint for further barite exploration in this part of the Anti-Atlas fold and thrust belt.
The proceedings contain 57 papers. The special focus in this conference is on Intelligent Systems. The topics include: A Computational Situationally Self-controlled Brain and Mind Interface Under Uncertainty;ethical C...
ISBN:
(纸本)9783031477201
The proceedings contain 57 papers. The special focus in this conference is on Intelligent Systems. The topics include: A Computational Situationally Self-controlled Brain and Mind Interface Under Uncertainty;ethical Concerns About Personhood, Responsibility, and Privacy in Active and Passive Brain-Computer Interfaces;The VesselAI Methodology for AI-Powered Decision Support Systems for the Maritime Industry;An Energy-Efficient Reconfigurable Autoencoder Implementation on FPGA;Convergence of the Mini-Batch SIHT Algorithm;graph Autoencoder-Based Detection of Unseen False data Injection Attacks in Smart Grids;causal analysis of Artificial Intelligence Adoption in Project Management;comparative Lightweight Scheme for Individual Identification Through Hand-Vein Patterns;URL Classification with Intrusion Detection System;designing Cancellation Intervention System with Sliding Lead Times;NP4G: Network Programming for Generalization;Blockchain and AI for Optimizing Bank data Security;streamlining Conceptual modeling;E-Step control: Solution for processing and analysis of IS Users Activities in the Context of Insider Threat Identification Based on Markov Chain;Machine Learning Based Intelligent Irrigation System Using WSN;comparison of Artificial Neural Networks Algorithms on datasets with Different Characteristics;boosting Federated Multitask Learning: Transfer Effects in Cross-Domain Drug-Target Interaction Prediction;Surveying Impacts of AI in Education and Creative Practices;active Risk Mitigation for Unmanned Aerial Systems;Towards Explainable AI: Relationship Between Twitter Sentiment, User Behaviour, and Bitcoin Price Prediction;automatic Generation of a Portuguese Land Cover Map with Machine Learning;Production Portfolio Theory ii—First Steps Towards a General Portfolio Theory and Numerical Examplifications;AI as a Threat to Education: Contrasting GPT-3 and Google in Answering Questions Along Bloom’s Taxonomy of Educational Objectives.
It is difficult for the existing workflow modeling technology to effectively deal with the complex and dynamic characteristics of business processes between enterprises, especially to support the ability of process co...
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ISBN:
(数字)9798331519032
ISBN:
(纸本)9798331519049
It is difficult for the existing workflow modeling technology to effectively deal with the complex and dynamic characteristics of business processes between enterprises, especially to support the ability of process collaboration. This project intends to study the modeling theory and method of flexible workflow system based on business agent from the perspective of multi-agent cooperation and high flexibility requirements. Construct a flexible workflow architecture with multi-level and multi-authority control. In this paper, the process management and implementation of hierarchical management and control. The process is optimized using social network analysis and self-encoder method. The advantage is that the original input can be restored in the case of noise, while taking full advantage of the diversity and complexity of noise. The deep neural network is used to enhance the recovery ability of the initial features, so as to effectively suppress the external noise. Through a case study of the actual process of a cashmere enterprise, the corresponding model is established. The research proves that the model proposed in this paper can deal with the cooperative relationship between process flexibility and enterprise organization well.
The proceedings contain 57 papers. The special focus in this conference is on Intelligent Systems. The topics include: A Computational Situationally Self-controlled Brain and Mind Interface Under Uncertainty;ethical C...
ISBN:
(纸本)9783031477140
The proceedings contain 57 papers. The special focus in this conference is on Intelligent Systems. The topics include: A Computational Situationally Self-controlled Brain and Mind Interface Under Uncertainty;ethical Concerns About Personhood, Responsibility, and Privacy in Active and Passive Brain-Computer Interfaces;The VesselAI Methodology for AI-Powered Decision Support Systems for the Maritime Industry;An Energy-Efficient Reconfigurable Autoencoder Implementation on FPGA;Convergence of the Mini-Batch SIHT Algorithm;graph Autoencoder-Based Detection of Unseen False data Injection Attacks in Smart Grids;causal analysis of Artificial Intelligence Adoption in Project Management;comparative Lightweight Scheme for Individual Identification Through Hand-Vein Patterns;URL Classification with Intrusion Detection System;designing Cancellation Intervention System with Sliding Lead Times;NP4G: Network Programming for Generalization;Blockchain and AI for Optimizing Bank data Security;streamlining Conceptual modeling;E-Step control: Solution for processing and analysis of IS Users Activities in the Context of Insider Threat Identification Based on Markov Chain;Machine Learning Based Intelligent Irrigation System Using WSN;comparison of Artificial Neural Networks Algorithms on datasets with Different Characteristics;boosting Federated Multitask Learning: Transfer Effects in Cross-Domain Drug-Target Interaction Prediction;Surveying Impacts of AI in Education and Creative Practices;active Risk Mitigation for Unmanned Aerial Systems;Towards Explainable AI: Relationship Between Twitter Sentiment, User Behaviour, and Bitcoin Price Prediction;automatic Generation of a Portuguese Land Cover Map with Machine Learning;Production Portfolio Theory ii—First Steps Towards a General Portfolio Theory and Numerical Examplifications;AI as a Threat to Education: Contrasting GPT-3 and Google in Answering Questions Along Bloom’s Taxonomy of Educational Objectives.
This paper studies the problem of trajectory tracking control for the wearable exoskeleton system subject to unknown dynamics. An ultra-local model is adopted to describe the dynamic relationship between control input...
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The machining error of parts is the result of the coupling action of various error sources, such as the spatial error of machine tools in the processing system. Machining error modeling is to establish the machining e...
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This paper proposes an innovative Dynamic Principal Component analysis (DPCA) scheme to perform fault detection and identification (FDI) for systems affected by process faults. In this scheme, a new modeling method is...
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ISBN:
(数字)9798350361025
ISBN:
(纸本)9798350361032
This paper proposes an innovative Dynamic Principal Component analysis (DPCA) scheme to perform fault detection and identification (FDI) for systems affected by process faults. In this scheme, a new modeling method is proposed to fix the data lag and the number of principal components to retain. A new structuration method is introduced to identify the process fault. It is based on the computation of common angles between residual subspaces of different modes. This structuration yields a concise set of detection indices, specifically sensitive to certain process faults while remaining unsensitive to others. The proposed FDI scheme is successfully applied to a three tank affected by several process faults.
This study combines the methods of dataanalysis and mathematical modeling to explore an effective solution about traffic flow control in well-known scenic towns. Firstly, the missing and abnormal data of license plat...
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ISBN:
(数字)9798331530334
ISBN:
(纸本)9798331530341
This study combines the methods of dataanalysis and mathematical modeling to explore an effective solution about traffic flow control in well-known scenic towns. Firstly, the missing and abnormal data of license plate number are eliminated through the open source dataset. Then, through hierarchical clustering analysis to divide the peak traffic flow hours, statistical traffic flow hour data, and using NSGA-ii algorithm to optimize the signal timing, and establish a service level model to analyze the results, and it is found that it can effectively improve the traffic efficiency and alleviate traffic congestion. The case used in this paper is the 2024 National Student Mathematical modeling Competition E question - traffic flow control of the topic for research and analysis, the results obtained and the actual review of the results of the information very much in line with the results.
Gene regulatory networks (GRNs) are large and complex dynamical systems often monitored through RNA sequencing or microarray technologies. Genomics studies often focus on a small subset of genes and analyze only these...
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ISBN:
(数字)9781665451963
ISBN:
(纸本)9781665451963
Gene regulatory networks (GRNs) are large and complex dynamical systems often monitored through RNA sequencing or microarray technologies. Genomics studies often focus on a small subset of genes and analyze only these genes due to the huge cost and time-limit constraints. Therefore, selecting a small subset of genes that carries the highest information about the underlying process of these complex systems is highly desired. The existing biomarker selection techniques rely on unrealistic assumptions such as direct observability of genes' states as well as the availability of perfect knowledge about the modelingprocess. To address the aforementioned issues, this paper models GRNs with uncertain regulatory models with the signal model of partially-observed Boolean dynamical systems (POBDS) and derives the optimal Bayesian biomarker selection framework given the noisy available gene-expression data. The proposed framework is built on the multiple-model adaptive estimation (MMAE) framework and the optimal minimum mean-square error (MMSE) state estimator for POBDS, called Boolean Kalman smoother (BKS). The proposed framework is an optimal solution relative to the uncertainty class, and its high performance is demonstrated using the mammalian cell-cycle Boolean network model and the p53-MDM2 negative feedback loop observed through gene-expression data.
In the modern world automation technologies of industrial processes have found wide application. Automated processcontrol systems have become an important part of enterprises that operate in different economic fields...
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ISBN:
(纸本)9783030942021;9783030942014
In the modern world automation technologies of industrial processes have found wide application. Automated processcontrol systems have become an important part of enterprises that operate in different economic fields and life support facilities all over the world. Nevertheless, the high growth of automation means raises the acute problem of providing APCs information security from external and internal threats. There are systematic reports in the media about new critical vulnerabilities in industrial equipment and attacks based on the exploitation of such vulnerabilities. When designing such systems it is necessary to estimate possible information security threats that already exist in the system or that are predicted to appear. That is why threat modeling is an important part of providing information security at industrial facilities using automation. In this work the author investigates the methodology of estimating threats to software security TRIKE. The research is aimed at finding general approaches to determine threat sources, tactics, and techniques for making implementation scenarios of threats to software information security, for applying them to provide cyber security of automated processcontrol systems. In the frameworks of the subject main-streaming, the research gives a description of cyber-attacks at big industrial facilities, and their basic vulnerabilities are emphasized. In the article the analysis of TRIKE methodology has been done. It is aimed at determining basic threat modeling stages applicable to industrial automation systems. The approach of TRIKE methodology to generating a list of threats to information security is formalized. To achieve the aims the author constructs DFD data flow diagrams with the decomposition of peculiar elements of the algorithm for modeling threats to information security of automated processcontrol systems using TRIKE methodology.
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