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Modeling and Optimal control for Resource Allocation in the Epidemic Monitoring of a Multi-group Population

SN computer Science 2024年第1期5卷 121页

作者： Di Giamberardino, Paolo Iacoviello, Daniela Papa, Federico Department of Computer Control and Management Engineering “A. Ruberti” Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy Institute for System Analysis and Computer Science “A. Ruberti” CNR Via dei Taurini 19 Rome 00185 Italy

In the last 3 years, the entire world has been facing the sanitary emergency due to the SARS-CoV2;it has been stressed the mutual interdependence of the human populations, as well as the strong impact of specific conditions, such as age, work, habits, on the disease spread among subgroups of a given population. Moreover, the high percentage of asymptomatic individuals, especially among young subjects, improves the infection spread among groups with higher probability of fatal consequences. The vaccination campaign has strongly contributed to reduce mortality, so bringing the epidemic scenario probably closer to the endemic state. Nevertheless, the loss of the vaccination protection, the appearance of new variants and the mixing of moving people require a surveillance action to reduce new virus waives. By suitable modeling and optimal control design, this paper addresses the planning of a swab test campaign for the surveillance of the disease spread within a given multi-group population, when resource limitation and distinctive epidemiological characteristics of the subgroups are present. The proposed approach, applied on a case study in which the population is split into four groups, stresses the importance of a suitable tuned swab test campaign, saving a significant number of infections (and therefore of human lives), by scheduling the surveillance effort on the different populations. The determination of this kind of control is useful not only in the endemic condition, but also as general surveillance strategy at the beginning of an epidemic spread, with limitations in material and logistic resources. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Epidemic monitoring Mathematical modeling of epidemics Optimal control Resource allocation

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Beyond the network-based multi-omics data integration in COPD: A pathway-centric analysis

Beyond the network-based multi-omics data integration in COP...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Sibilio, Pasquale Conte, Federica Paci, Paola Sapienza University of Rome Department of Computer Control and Management Engineering Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Karolinska Institutet Stockholm17177 Sweden

ISBN: (纸本)9798350386226

Chronic obstructive pulmonary disease (COPD) is a lung disease causing hundred thousand of death each year worldwide and defined as a respiratory and airflow impairment majorly due to large and small airways dysfunctions. COPD could be considered as a syndrome that includes disease axes of variable pathological and clinical conditions. Despite recent advances, the comprehension of the molecular mechanisms responsible for COPD disease spectrum is far to be reached. Moreover, we should also consider continuous smoking exposure, which could lead to variability in the disease mechanisms and progression. Network-based multi-omics data integration can help to study the association between molecular determinants on diverse biomolecular layers in a disease context. In a previous study, we leveraged lung RNA-seq and DNA-methylation data of a COPD-control cohort to build a correlation-based integrated network (called coupled network), that helped to unveil genes involved in immune and inflammatory modulation of COPD. Therefore, in our previous study we highlighted the most important genes of the coupled network and inspected their single contribution to disease-related pathways. In this study we aim to overcome this limitation by performing a pathway activity analysis by considering the expression and DNA methylation profiles of coupled network genes. Moreover, we exploit this analysis to study the possible contribution of coupled network genes to the differential disease progression between current and former smokers patients. © 2024 IEEE.

关键词： Lung cancer

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Large Language Models for topic analysis: insights from robotic accident narratives

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Procedia computer Science 2025年 253卷 1462-1472页

作者： Francesco Costantino Nicolò Sabetta Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 00185 Rome Italy

The integration of robotics in manufacturing has significantly enhanced productivity and safety by performing hazardous tasks. However, it also introduces new risks and accident profiles that require thorough analysis. This study explores the application of Large Language Models (LLMs) for topic modeling on accident narratives from the Occupational Safety and Health Administration (OSHA) database, focusing on incidents involving robots. Advanced Natural Language Processing (NLP) techniques, specifically LLMs, were employed to uncover prevalent themes, common risk factors, and patterns in these narratives. The methodology includes data collection, text processing, statistical analysis, and various topic modeling techniques. The analysis identifies six topics, and key themes including maintenance-related accidents, types of machinery involved, and specific body parts frequently affected. The findings highlight critical areas for improving safety measures, such as rigorous lockout/tagout procedures and enhanced training for maintenance personnel. This study provides a novel approach to analyze accident narratives.

关键词： Robot collaborative robot health safety NLP LLM topic modelling

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Comparison of Traffic control with Model Predictive control and Deep Reinforcement Learning 9

Comparison of Traffic Control with Model Predictive Control ...

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9th International Conference on control, Decision and Information Technologies, CoDIT 2023

作者： Imran, Muhammad Izzo, Riccardo Tortorelli, Andrea Liberati, Francesco University of Rome 'La Sapienza' Department of Computer Control and Management Engineering 'A. Ruberti' Via Ariosto 25 RM Rome00185 Italy

ISBN: (纸本)9798350311402

Traffic congestion is among the worst causes of pollution, and the time spent in traffic can cost the world tens of billions of dollars every year. Solutions to mitigate this problem are at hand thanks to the advent of advanced control techniques and artificial intelligence (AI). Traditional traffic light control strategies based on fixed timing of the green, yellow and red phases are simple to implement, but at the same time very inefficient, in particular for busy intersections. This paper discusses both a model predictive control (MPC) approach and a model-free deep reinforcement learning (DRL) algorithm for controlling the traffic lights at a single intersection, with the aim of improving the traffic flow. Firstly, a detailed linear mathematical model of an intersection is formulated and successively tested in a MPC framework;secondly, a DRL algorithm is proposed and verified by comparing it with the currently implemented baseline controller. Finally, the results for the three approaches, MPC, DRL and the baseline controller, are validated through the SUMO (Simulation of Urban Mobility) microscopic traffic simulator. © 2023 IEEE.

关键词： Model predictive control

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Sensor Fault Estimation and Ftc Design for Fractional Linear Systems Using the Conformable Derivative 22

Sensor Fault Estimation and Ftc Design for Fractional Linear...

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22nd IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2025

作者： Alsharif, Ali Omar M. Jmal, A. Naifar, O. Ahmed, Ausama H. Gassara, H. Makhlouf, A. Ben Sfax University Engineering National School Control and Energy Management Laboratory (CEM Lab) Tunisia Elmergib University Faculty of Engineering Department of Electrical & Computer Engineering Alkhums Libya Monastir University Control and Energy Management Laboratory (CEM Lab) Tunisia National School of Engineering of Sfax University of Sfax Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering Tunisia Faculty of Sciences of Sfax Department of Mathematics Sfax 3000 Tunisia

ISBN: (纸本)9798331542726

In this paper, Fault Tolerant control (FTC) for fractional-order systems (FOS) has not received as much attention in the literature as it has for standard integer-order systems. This work aims to bridge that gap. We propose a practical approach to fault estimation and fault-tolerant control (FTC) specifically designed for linear (FOS) with sensor failures. Our method leverages fractional exponential stability within the Lyapunov framework and employs the Conformable formulation of fractional derivatives. Notably, the primary contribution of this paper is the first application of the Conformable fractional derivative (CFD) in the context of (FTC). To accurately estimate states and faults, the proposed approach utilizes an observer coupled with a customized adaptation law. To validate our theoretical contributions, we present a detailed simulation of a numerical case study. © 2025 IEEE.

关键词： Conformable derivative Fault Tolerant control Observer. Fractional-order systems Sensor Faults

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A Cooperative Feature Removal Mechanism for Cell Outage Detection in Wireless Telecommunication Networks 19th

A Cooperative Feature Removal Mechanism for Cell Outage Det...

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19th International Conference on Critical Information Infrastructures Security, CRITIS 2024

作者： Wrona, Andrea Gentile, Simone De Santis, Emanuele Giuseppi, Alessandro Pietrabissa, Antonio Delli Priscoli, Francesco Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome via Ariosto 25 Lazio Rome00185 Italy Department of Electrical and Information Engineering Polytechnic of Bari via Re David 200 Puglia Bari70125 Italy

ISBN: (纸本)9783031842597

In the domain of crisis management for telecommunications infrastructures, the autonomous detection of cell outages within cellular networks is of paramount importance for prompt identification and resolution in ensuring uninterrupted connectivity to users. Traditional methods usually involve data aggregation at the core network, which is responsible for identifying cell failures. Proposing a novel approach, we leverage a Machine Learning-based distributed and cooperative feature removal mechanism in order to preserve the privacy of data and avoid any degradation in classification performance. Simulations carried out on a dataset retrieved from a real 4G-LTE rollout demonstrate that the proposed approach, through a cooperation among agents, maintains or even slightly improves accuracy, precision, recall, and F1-score in outage prediction compared to other conventional methods, showcasing its efficacy for cell outage detection purposes while maintaining data privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Customer Attitudes Toward Circular Economy in the E-Waste Context: A Survey Assessing Sustainable Consumption Dynamics

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IEEE engineering management Review 2023年第4期51卷 28-45页

作者： Jabbour, Charbel Jose Chiappetta Colasante, Annarita D'Adamo, Idiano Rosa, Paolo Sassanelli, Claudio NEOMA Business School Department Information Systems Supply Chain and Decision Making Mont-Saint-AignanCedex 76130 France Unitelma Sapienza-University of Rome Department of Law and Economics Rome00161 Italy Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy Politecnico di Milano Department of Management Economics and Industrial Engineering Milan20133 Italy Politecnico di Bari Department of Mechanics Mathematics and Management Bari70125 Italy

Waste from electrical and electronic equipment (WEEE) is a relevant concern in Europe. Even if relevant improvements have been made in terms of material recovery technologies and policy measures, there remains a significant gap between the amount of WEEE generated versus collected in the EU. This study is based on an online survey whose aim was to assess the attitude of students (i.e., individuals who dispose with a higher frequency electronic product) to buy/substitute three iconic product types (i.e., smartphones, notebooks, televisions), and their openness toward circular practices (i.e., recycling, reconditioning, reuse). Results have shown that: a) new products represent the greatest market share, b) reliability and quality were the most significant factors influencing the purchase decision in all product types, and c) circular premium was not recognized by students for any product type. From a social perspective, a responsible consumption pattern emerged, though a sustainability bias was evident with respect to products' residual value. From an economic perspective, these findings may inform companies about the orientation of young consumers and provide key insights for new business strategies. Results suggest that a proper waste management strategy also requires a behavioral change of people who should adopt it. © 1973-2011 IEEE.

关键词： Electronic Waste

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A Modular Approach for the Analysis of Blockchain Consensus Protocol Under Churn 5

A Modular Approach for the Analysis of Blockchain Consensus ...

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5th International Symposium on Foundations and Applications of Blockchain, FAB 2022

作者： Dinu, Floris Ciprian Bonomi, Silvia Department of Computer Control and Management Engineering Antonio Ruberti University of Rome La Sapienza Italy

ISBN: (纸本)9783959772488

Blockchain is an emerging technology that gained a lot of attention in the last years. Many different consensus protocols have been proposed to improve both the scalability and the resilience of existing blockchain. However, all these solutions have been defined for rather static settings. We propose a modular approach for analysing and comparing different consensus protocols used in blockchain under churn. © 2022 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Blockchain

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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

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Local and Remote Fog based Trade-offs for QOE in VR Applications by using CloudXR and Oculu Air Link

Local and Remote Fog based Trade-offs for QOE in VR Applicat...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Maiorano, Gabriele Mattia, Gabriele Proietti Beraldi, Roberto Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Rome Italy

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Virtual Reality (VR) is now a well-established technology which offers realistic and immersive virtual worlds to the user usually by means of Head-Mounted Displays (HMDs). Actually, these devices can also be backed by a cloud server which can host the game server or even directly render the virtual world, as in the well-known cloud gaming paradigm. However, due to the drastically low latencies that technology requires, it is more convenient, when possible, to use servers that are as close to the users as possible. As a consequence, implementing the game or the render server in the Fog Computing layer is a concrete possibility. A research investigation has been carried out by using the Meta Quest 2 device, which are the QoE trade-offs, in terms of graphic quality and network performance, both in the case in which the HMD performs the 3d rendering locally by using a sample game written with Unreal Engine and in the case in which the 3d rendering is done in the Fog by means of the nVidia CloudXR framework and Oculus Air Link. The results of the proposed experiments reveal that the remote rendering offers a stable frame rate against a higher quality image. Instead, local rendering sets the best possible graphics quality against the optimal frame rate. Additionally, the utilization of remote rendering to perform video compression in the case of decreasing the available bandwidth to adjust graphics quality and FPS has also been analyzed. The same does nothold for Motion-to-Photon latency, which increases with distance, reducing the general QoE. © 2022 IEEE.

关键词： Virtual reality

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