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Software Self-hosting: A systematic review of quantitative research

JOURNAL OF SYSTEMS AND SOFTWARE 2025年 226卷

作者： Hrgarek, Luka Zlatolas, Lili Nemec Univ Maribor Fac Elect Engn & Comp Sci Maribor Slovenia

In an era marked by heightened concerns surrounding personal privacy and data security, software self-hosting has gained significance as a means for individuals and organizations to reclaim control over their digital assets. This systematic review aims to identify relevant research gaps in the quantitative analysis of self-hosting, primarily focusing on studies employing Structural Equation modeling (SEM) and regression techniques. Employing a refined version of the Systematic Mapping process, we analyzed 49 quantitative research papers whose concepts were grouped into 12 substantive groups. The findings reveal a predominant concentration on constructs related to the Technology Acceptance Model (TAM), with limited exploration of self-hosting specifically, overshadowed by an emphasis on cloud computing, the Internet of Things (IoT), and privacy aspects. Our review provides a comprehensive overview of the existing literature and highlights the need for more focused research on self-hosting itself. This systematic review serves as a foundational resource for researchers and practitioners aimed at advancing the discourse on self-hosting.

关键词： Self-hosting Privacy Quantitative data sovereignty Cloud computing Technology Acceptance Model Systematic literature review

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A novel co-modulation and hybrid resolution strategy (CHRS) for fault diagnosis of planetary gearboxes

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MECHANICAL SYSTEMS AND SIGNAL processING 2025年 230卷

作者： Zhang, Mian Xie, Ruitong Kang, Tianbo Chen, Jiwei Wang, Yongshan Feng, Xu Zhao, Mengxiong Tianjin Univ Technol Tianjin Key Lab Adv Mechatron Syst Design & Intell Tianjin 300384 Peoples R China Tianjin Univ Technol Natl Demonstrat Ctr Expt Mech & Elect Engn Educ Tianjin Peoples R China Xian Modern Control Technol Res Inst Xian 710065 Peoples R China MEM Tianjin Fire Sci & Technol Res Inst Tianjin 300381 Peoples R China

Planetary gearboxes (PGs) serve as vital transmission links in rotating machinery, and diagnosing faults within them is crucial for effective maintenance. Traditional deep learning methods often operate as "black boxes," offering limited transparency in interpreting results, especially when analyzing the complex vibration signals of PGs. To address this issue, this paper proposes a co-modulation model combined with a hybrid resolution strategy (CHRS), leveraging amplitude modulation (AM) and frequency modulation (FM) intensities, to enhance the interpretability of fault diagnosis. First, a more comprehensive and adaptable expression of the co-modulation model is developed to describe gear faults. Second, CHRS links the model's generated signal with the actual monitoring data, establishing an intrinsic connection between the mathematical model and the data. An updating mechanism based on partial differential analysis is established for model parameter estimation. A partial differential-based updating mechanism is employed for model parameter estimation, enabling the quantitative analysis of model coefficients (including AM and FM), even with a limited number of training samples. Finally, the support vector machine (SVM) is employed to train and test these model parameters, facilitating the identification of different fault types through experimental data, thus validating the effectiveness of CHRS. In summary, CHRS significantly improves the interpretability of PG fault diagnosis by enhancing both the modeling process and quantitative analysis of vibration signals.

关键词： Planetary gearboxes Modulation effect Co-modulation signal model Hybrid resolution strategy Partial differential

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Limit Behavior of a Semi-Markov process Depending on a Small Parameter for Telecommunication Systems

Limit Behavior of a Semi-Markov Process Depending on a Small...

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International conference on Applied Innovations in IT, ICAIIT 2024

作者： Degtyar, Sergii Kopiika, Oleh Shusharin, Yurii Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Institute of Applied Control Systems National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9783031892950

The limit behavior of a semi-Markov process, depending on a small parameter, is important for the analysis and optimization of telecommunication systems. Semi-Markov processes are an extension of Markov processes that allow modeling systems with preservation of some previous states. Such processes are important, for example, for modeling data transmission over wireless channels or communication networks. The limit behavior of a semi-Markov process is determined by a small parameter, which is usually denoted as Ε (epsilon). Various important characteristics of the system depend on the limit value of Ε, such as the probability of blocking, data transfer rate, average waiting time, etc. Markov renewal theorems are used as analytical tool for studying the limiting behavior of a semi-Markov process that depends on a small parameter Ε>0 at Ε→0,t→∞,Εt→u. This theorem makes it possible to study transient phenomena arising in telecommunication systems, such as the asymptotic behavior of additive functionals from ergodic processes, boundary value problems for random walks and processes with independent increments, branched processes close to critical ones, and others. Analyzing the limit behavior of a semi-Markov process with respect to a small parameter is a valuable tool for understanding and optimizing telecommunication systems, ensuring their reliability, and meeting performance requirements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data transfer rates

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Graph Neural Networks: Architectures, Applications, and Future Directions

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IEEE ACCESS 2025年 13卷 62870-62891页

作者： Ponzi, Valerio Napoli, Christian Sapienza Univ Rome Dept Comp Control & Management Engn I-00185 Rome Italy Italian Natl Res Council Inst Syst Anal & Comp Sci I-00185 Rome Italy Silesian Tech Univ Dept Math Applicat & Methods Artificial Intelligen PL-44100 Gliwice Poland

In recent years, deep learning has revolutionized fields such as computer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. However, many real world applications involve data from non-Euclidean domains, where graphs naturally represent entities and their complex interdependencies. Traditional machine learning methods have often struggled to process such data in an effective manner. Graph Neural Networks represent a crucial advance in the use of deep learning to interpret and extract knowledge from graph-based data. They have opened up new possibilities for tasks such as node categorization, link inference, and comprehensive graph analysis. This paper provides a detailed analysis of Graph Neural Network (GNN) methodologies, emphasizing their architectural diversity and wide ranging applications. GNN models are systematically categorized into fundamental frameworks such as message passing paradigms, spectral and spatial methods, and advanced extensions such as hypergraph neural networks and multigraph approaches. This paper also explores domains such as social network analysis, molecular biology, traffic forecasting, and recommendation systems. In addition, it emphasizes some critical open challenges, including scalability, dynamic graph modeling, and robustness against noisy or incomplete data. The paper concludes with a proposal for future research directions to improve the scalability, interpretability, and adaptability of GNNs in this fast-evolving field.

关键词： Deep learning Graph neural networks Social networking (online) Scalability data models Feature extraction Analytical models Vectors Roads Message passing Graph neural network deep learning message passing spectral methods spatial methods hypergraph neural networks multigraph neural networks scalability

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Surrogate modeling for control of Microbial Biopolymer Production process 11

Surrogate Modeling for Control of Microbial Biopolymer Produ...

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11th Vienna International conference on Mathematical Modelling (MATHMOD)

作者： Duerr, Robert Otto, Eric Kok, Rudolph L. Duvigneau, Stefanie Kienle, Achim Bueck, Andreas Magdeburg Stendal Univ Appl Sci D-39114 Magdeburg Germany Otto Guericke Univ D-39106 Magdeburg Germany Max Planck Inst Dynam Complex Tech Syst D-39106 Magdeburg Germany Friedrich Alexander Univ Erlangen Nuremberg D-39058 Erlangen Germany

In this contribution, the Dynamic Mode Decomposition with control (DMDc) is used to derive a surrogate model of a continuous PHA biopolymer production process based on a recently published complex process model. Here, snapshot simulation data of the original model is processed to obtain a linear surrogate model formulation using delay coordinates. The quality of the surrogate is statistically validated within simulation studies. Additionally, the influence of the of the order of delay coordinates is investigated. It is shown, that the highly nonlinear dynamics of the PHA-manufacturing process can be approximated accurately by the DMD-based model even for large variations of initial conditions and control variables. This offers the opportunity to apply well-studied and established tools from robust and optimal control in future investigations. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Dynamic Mode Decomposition Black-Box-Identification Surrogate modeling Polymerisation

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A systematic review of discretisation methods for time-delay systems

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JOURNAL OF control AND DECISION 2025年第3期12卷 335-350页

作者： Sharma, Pooja Neeli, Satyanarayana MNIT Jaipur Rajasthan India 63 Krishi Anusandhan nagar Jaipur 302029 Rajasthan India

Time delay is an inherent characteristic of real-world phenomena which may affect the system's characteristic. The systems including delay are known as time-delay systems, they are represented using delay differential equations. modeling, discretisation, stability and control design for time-delay systems are still challenging in modern control theory. This paper systematically overviews available discretisation methods of linear and nonlinear time-delay systems. Emphasis is placed on illustrating fundamental results and recent progress on discretisation methods for delay systems. Numerous methods for the discretisation of linear and nonlinear systems considering input delays, state or output delays in the system's dynamics have been presented. A particular attention will be paid to illustrate effects of the discretisation process on the stability of discretised systems. Examples of mathematical descriptions, problems, and performance analysis for delay systems are presented. The presentation of discretisation methods is as easy as possible, focussing more on the main ideas and mathematical concepts by analogy. Finally, some possible future research directions to be tackled by researchers in this field are discussed.

关键词： Delay linear nonlinear discretisation method sample data control system

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On the right choice of data from popular datasets for Internet traffic classification

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COMPUTER COMMUNICATIONS 2025年 233卷

作者： Krupski, Jacek Iwanowski, Marcin Graniszewski, Waldemar Warsaw Univ Technol Inst Control & Ind Elect Ul Koszykowa 75 PL-00662 Warsaw Poland

Machine learning (ML) models used to analyze Internet traffic, similar to models in all other fields of ML, need to be fed by training datasets. Many such sets consist of labeled samples of the collected traffic data from harmful and benign traffic classes captured from the actual traffic. Since the traffic recording tools capture all the transmitted data, they contain much information related to the registration process that is irrelevant to the actual traffic class. Moreover, they are not fully anonymized. Thus, there is a need to preprocess the data before proper modeling, which should always be addressed in related studies, but often, this is not done. In our paper, we focus on the dependence of the efficiency of threat detection ML models by selecting the appropriate data samples from the training sets during preprocessing. We are analyzing three popular datasets: USTC-TFC2016, VPN-nonVPN, and TOR-nonTOR, which are widely used in traffic classification, security, and privacy-enhancing technologies research. We show that some choices of data sample pieces, although maximizing the model's efficiency, would not result in similar outcomes in the case of traffic data other than the learning set. The reason is that, in these cases, models are biased due to learning incidental correlations that appear in the datasets used for training the model, introduced by auxiliary data related to the network traffic capturing and transmission process. They are present in popular datasets but may never appear in traffic data. Consequently, the models trained on such datasets, without any preprocessing and anonymization, would never reach the accuracy levels of the training data. Our paper introduces five consecutive levels of anonymization of the traffic data and points out that only the highest provide correct learning results. We validate the results by applying decision trees, random forests, and extra tree models. Having found the optimal part of the header data that may safel

关键词： Network traffic classification Internet threats detection Traffic data anonymization Machine learning Decision trees

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IM-TD3: A Reinforcement Learning Approach for Liquid Rocket Engine Start-Up Optimization

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 2250-2262页

作者： Liu, Yuwei Li, Yang Cheng, Yuqiang Pan, Wei Wu, Jianjun Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 201210 Peoples R China Delft Univ Technol Dept Cognit Robot NL-2628 CD Delft Netherlands

With advancements in reusable liquid rocket engine technology to meet the diverse demands of space missions, engine systems have become increasingly complex. In most cases, these engines rely on stable open-loop control and closed-loop regulation systems. However, due to the high degree of coupling and nonlinear dynamics within the system, most transient adjustments still depend on open-loop control. Open-loop control often fails to provide the optimal control strategy when encountering external disturbances. To address this issue, we introduce the intrinsically motivated twin delayed deep deterministic (TD3) algorithm, specifically designed for the startup process of LOX/Kerosene high-pressure staged combustion engine. This approach leverages intrinsic motivation to enable the algorithm to adapt to the abrupt parameter changes during the start-up process. A series of comprehensive experiments were conducted to verify the effectiveness of our method. The experimental results demonstrate that our method outperforms both the PID method and previous researchers' reinforcement learning methods based on the TD3 algorithm and DDPG, achieving a faster and more stable start-up process and significantly enhancing engine performance.

关键词： Engines Rockets Space vehicles Transient analysis Aerospace engineering data models Combustion Training Liquids Computational modeling Deep reinforcement learning (DRL) gas generator cycle nonlinear control reinforcement learning (RL) rocket engine

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Joint probabilistic modeling analysis of head and eye behaviors in autism spectrum disorder children based on a social interaction paradigm

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BIOMEDICAL SIGNAL processING AND control 2025年 106卷

作者： Su, Tongxu Yang, Minqiang Zhou, Wei Tao, Yongfeng Ni, Minghui Zheng, Weihao Hu, Bin Lanzhou Univ Sch Informat Sci & Engn Lanzhou Peoples R China Beijing Inst Technol Sch Med Technol Beijing Peoples R China

Autism Spectrum Disorder (ASD), as a complex neurodevelopmental disorder, is closely associated with attention deficits that manifest through eye and head movements. Previous studies on the eye gaze and head posture of children with ASD have been somewhat limited by the contexts in which the children were observed, Exploring head and eye coordination in natural environments is crucial for developing effective intervention strategies applicable to everyday life. This paper aims to examine the perceptual and behavioral responses of children with ASD in simulated real-life social environments. Using asocial interaction paradigm based on real-life scenarios, we propose a joint probabilistic modeling method for head and eye behaviors. This method includes the influence of head posture on gaze direction in eye-tracking studies within social interaction contexts. For each participant, we establish a data-driven Markov chain model based on individual data, preserving the temporal nature of eye movement behavior and the highly individualized nature of visual behavior. We conducted experiments on a video dataset of children with ASD that we collected, achieving a classification accuracy of 79.66%, demonstrating the feasibility and effectiveness of our proposed method. Additionally, we found that one manifestation of attention deficits in children with ASD is an increased occurrence of head-eye counter movement. This finding provides new reference indicators for the early diagnosis and screening of ASD.

关键词： Autism spectrum disorder Eye movements Head gestures Joint probabilistic

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Inherently Interpretable Physics-Informed Neural Network for Battery modeling and Prognosis

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第1期36卷 1145-1159页

作者： Wang, Fujin Zhi, Quanquan Zhao, Zhibin Zhai, Zhi Liu, Yingkai Xi, Huan Wang, Shibin Chen, Xuefeng Xi An Jiao Tong Univ Natl Key Lab Aerosp Power Syst & Plasma Technol Xian Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Beijing Aerosp Control Ctr Beijing 100094 Peoples R China Xi An Jiao Tong Univ Key Lab ThermoFluid Sci & Engn Minist Educ Sch Energy & Power Engn Xian 710049 Peoples R China

\Lithium-ion batteries are widely used in modern society. Accurate modeling and prognosis are fundamental to achieving reliable operation of lithium-ion batteries. Accurately predicting the end-of-discharge (EOD) is critical for operations and decision-making when they are deployed to critical missions. Existing data-driven methods have large model parameters, which require a large amount of labeled data and the models are not interpretable. Model-based methods need to know many parameters related to battery design, and the models are difficult to solve. To bridge these gaps, this study proposes a physics-informed neural network (PINN), called battery neural network (BattNN), for battery modeling and prognosis. Specifically, we propose to design the structure of BattNN based on the equivalent circuit model (ECM). Therefore, the entire BattNN is completely constrained by physics. Its forward propagation process follows the physical laws, and the model is inherently interpretable. To validate the proposed method, we conduct the discharge experiments under random loading profiles and develop our dataset. analysis and experiments show that the proposed BattNN only needs approximately 30 samples for training, and the average required training time is 21.5 s. Experimental results on three datasets show that our method can achieve high prediction accuracy with only a few learnable parameters. Compared with other neural networks, the prediction MAEs of our BattNN are reduced by 77.1%, 67.4%, and 75.0% on three datasets, respectively. Our data and code will be available at: https://***/wang-fujin/BattNN.

关键词： End-of-discharge (EOD) inherently interpretable lithium-ion battery physics-informed neural network (PINN) prognosis

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