Hasil untuk "Cybernetics"

Emanuela Podda, Pol Hölzmer, Alexandre Amard et al.

The recent amendment to the European eIDAS Regulation has established the European Digital Identity Framework, which introduces electronic attestations of attributes. Technically, these attestations involve auxiliary information to ensure their verifiability, leading to the generation, processing, and storage of more than just personal data. In particular, this auxiliary information contains globally unique information that can be misused as personal identifiers and poses risks to the privacy of individuals engaging in transactions using a European Digital Identity Wallet. As such, they create tension with the principle of data minimisation under the General Data Protection Regulation (GDPR). On the positive side, privacy-enhancing technologies, especially zero-knowledge proofs (ZKPs), are rapidly advancing and capable of addressing this tension. In this paper, we analyse the impact of the availability of these techniques on legal compatibility in the European electronic identification context and explore the tension field between the technical requirements of the digital identity wallet and the GDPR’s data minimisation principle. We illustrate this dynamic through the specific examples of cryptographic data processed to ensure the authenticity and integrity of attributes' electronic attestations and shed light on how ZKPs can support legal compliance. This paper contributes to the privacy-oriented electronic identity management literature by providing policy and technical recommendations for achieving data minimisation compliance. We emphasise the necessity for regulatory bodies to enforce the use of advanced solutions like ZKPs to achieve unlinkability and unobservability. Accelerating the standardisation of these technologies is crucial for safeguarding user privacy and achieving seamless regulatory compliance in digital identity systems.

Mohammad Reza Fathi, Soraya Birami, Alireza Payvar et al.

The Viable System Model (VSM) is a foundational framework in organizational cybernetics, designed to manage complexity and ensure systemic viability in dynamic environments. Given the increasing importance of this model in addressing complex organizational challenges, the primary objective of this research is to conduct a comprehensive and systematic review of existing studies in the field of the Viable System Model. This review aims to identify and analyze the practical areas of this model, evaluate its challenges and opportunities in confronting contemporary systemic issues, and extract key insights from 21 peer-reviewed studies. By synthesizing and analyzing the findings of these studies, this paper intends to provide a clear and coherent picture of the Viable System Model's current state and future potential. The process of identifying relevant studies was conducted using the PRISMA method, which involved searching the Scopus database and performing manual searches. This study employs a bibliometric research design, utilizing a quantitative approach and combining bibliometric and network analysis to examine the landscape of VSM research. Key findings highlight VSM’s role in enhancing organizational resilience, improving decentralized decision-making, and enabling systemic adaptability. The integration of VSM with emerging technologies—such as artificial intelligence, digital twins, and big data analytics—demonstrates its potential to address contemporary organizational challenges. However, critical gaps remain, including limited empirical validation, insufficient applications in underrepresented sectors such as agriculture and education, and scalability issues for small and medium-sized enterprises (SMEs). The study emphasizes the need for longitudinal research, hybrid frameworks, and sector-specific models to enhance the theoretical and practical utility of VSM. By synthesizing recent applications and identifying research opportunities, this paper reinforces the significance of VSM as a robust approach to managing complexity and outlines pathways for future research.

Md Al Amin Sarker, Bharanidharan Shanmugam, Sami Azam et al.

Smart grid is a transformative advancement that modernized the traditional power system for effective electricity management, and involves optimized energy distribution by load forecasting. Precise load forecasting provides the best utilization of energy resources and increases sustainability. Dynamic changes of several connected factors, such as temporal and geographical variability, pose challenges to accurate load prediction. Integrating Artificial Intelligence (AI) in the smart grid can enhance the performance of the forecasting process by capturing these changes. This study investigated load forecasting tasks on four different datasets. Several preprocessing and augmentation techniques are applied to increase the data quality. An attention-based 1D-CNN-GRU model is proposed to capture the temporal patterns from the time-series data, and the hyperparameters of the model are optimized using a particle swarm optimization (PSO) algorithm that also accelerates the convergence and results in an efficient training session. Empirical evaluations highlight that the proposed model substantially minimizes the loss, reflecting the ability to make accurate predictions. It obtains MAE values of 0.12, 0.8, 16.48, and 82.64 for the four datasets. Moreover, the explainable AI (XAI) technique is applied using Shapley Additive explanations (SHAP) to interpret the model prediction, providing the feature ranking based on their prediction score. Moreover, this study utilizes federated learning, enables collaborative training, maintains the privacy of the grid data, and secures the process comprehensively. The aggregation mechanism in federated learning is modified using pruning-based methods that reduce the parameters and computational cost, resulting in a more efficient framework. Integrating all these approaches provides valuable insights for developing a load forecasting model and outlines potential contributions in the smart grid domain.

Lucie Gonsorcikova, Ales Prochazka, Alexandra Molcanova et al.

This paper focuses on the use of wearable sensors to acquire and process motion data, which is essential for monitoring physiological movement and identifying gait disorders. It is particularly relevant in pediatrics, neurology, and rehabilitation. The research evaluates body motion symmetry in children using accelerometric data, taking into account factors such as age, diagnosis, and gender. Signals were recorded from 35 children (average age 10.8 years) using mobile sensors and were analyzed using digital signal processing techniques and classification methods. The proposed methodology includes data acquisition by smartphone sensors, wireless data export to a remote drive, and data processing through a graphical user interface. The highest classification accuracy of walking features, at 92.0%, was achieved with a two-layer neural network. The findings underscore the effectiveness of these tools in rehabilitation, fitness monitoring, and neurological studies.

Lorenzo Nannipieri

Il presente contributo analizza, a prima lettura, le previsioni del DDL 1717 (nella versione proposta all'esito dell'esame in commissione in sede referente) in materia di appalti pubblici. In particolare, l'attenzione è rivolta all'art. 10 del DDL (divenuto art. 13 dopo l'esame in commissione) e al difficile raccordo con le previsioni normative, già vigenti, introdotte dal d.lgs. 36/2023.

Zisen Nie, Qingrui Zhang, Xiaohan Wang et al.

Abstract The problem of triangular lattice formation in robot swarms has been investigated extensively in the literature, but the existing algorithms can hardly keep comparative performance from swarm simulation to real multi‐robot scenarios, due to the limited computation power or the restricted field of view (FOV) of robot sensors. Eventually, a distributed solution for triangular lattice formation in robot swarms with minimal sensing and computation is proposed and developed in this study. Each robot is equipped with a sensor with a limited FOV providing only a ternary digit of information about its neighbouring environment. At each time step, the motion command is directly determined by using only the ternary sensing result. The circular motions with a certain level of randomness lead the robot swarms to stable triangular lattice formation with high quality and robustness. Extensive numerical simulations and multi‐robot experiments are conducted. The results have demonstrated and validated the efficiency of the proposed approach. The minimised sensing and computation requirements pave the way for massive deployment at a low cost and implementation within swarms of miniature robots.

Jodie A. Yuwono, Xinyu Li, Tyler D. Doležal et al.

Abstract Multi principal element alloys (MPEAs) comprise an atypical class of metal alloys. MPEAs have been demonstrated to possess several exceptional properties, including, as most relevant to the present study a high corrosion resistance. In the context of MPEA design, the vast number of potential alloying elements and the staggering number of elemental combinations favours a computational alloy design approach. In order to computationally assess the prospective corrosion performance of MPEA, an approach was developed in this study. A density functional theory (DFT) – based Monte Carlo method was used for the development of MPEA ‘structure’; with the AlCrTiV alloy used as a model. High-throughput DFT calculations were performed to create training datasets for surface activity/selectivity towards different adsorbate species: O2-, Cl- and H+. Machine-learning (ML) with combined representation was then utilised to predict the adsorption and vacancy energies as descriptors for surface activity/selectivity. The capability of the combined computational methods of MC, DFT and ML, as a virtual electrochemical performance simulator for MPEAs was established and may be useful in exploring other MPEAs.

Iza Gigauri, Mirela Panait, Simona Andreea Apostu et al.

The attention to the phenomenon of social entrepreneurship has been especially enhanced during the current turbulent times, as social enterprises have a key role to play in solving social problems caused by the pandemic. Currently, social entrepreneurship is still an undeveloped area in Georgia, but it has the potential to contribute to the country’s economy and improve the social, ecological, and economic conditions of society. This paper analyses the concept in Georgia and explores social entrepreneurship from the social entrepreneurs’ perspective. A qualitative interview method was applied to collect the data for this study, and semi-structured interviews were conducted with the seventeen Georgian social entrepreneurs from May–June 2021. The research reveals the drivers of social entrepreneurs and investigates the financial sources of social enterprises. It also discusses the practice of social entrepreneurship in terms of preventing and supportive factors while adopting the concept in transition economies, particularly in Georgia. The research demonstrated an urgent need for legislation to regulate the field of social entrepreneurship and formalize it.

E.Yu. Shchetinin

Early detection of patients with COVID-19 coronavirus infection is essential in ensuring an adequate treatment and reducing the burden on the health care system. An effective method of detecting COVID-19 is computer analysis of chest X-rays. The paper proposes a methodology that consists of stages of formatting X-ray images to the size (224, 224) size, their classification using deep convolutional neural networks, such as Xception, InceptionResnetV2, MobileNetV2, DenseNet121, ResNet50 and VGG16, which are pre-trained on the ImageNet dataset and then fine-tuned on a set of chest X-rays. The results of computer experiments showed that the VGG16 model with fine-tuning of parameters demonstrated the best performance in the COVID-19 classification with accuracy = 99.09 %, recall = 99.483 %, precision = 99.08 % and f1_score = 99.281 %.

Victor Krasnobayev, Sergey Koshman, Dmytro Kovalchuk

The subject of the article is the development of a method for implementing the arithmetic operation of adding the residuals of numbers, which are represented in the system of residual classes (RNS). This method is based on the use of positional binary adders. The purpose of the article is to improve the performance of computer systems (CS) and their components by introducing new ways of organizing calculations based on the use of RNS. Tasks: to analyze and identify the shortcomings of the existing number systems that are used in the construction of computer systems and components; explore possible ways to eliminate the identified deficiencies; explore the structure of binary positional adders, taking into account the scheme for adding two residues of numbers modulo RNS; to develop a method for constructing adders modulo RNS, which is based on the use of a set of binary single-digit positional adders. Research methods: methods of analysis and synthesis of computer systems, number theory, coding theory in RNS. The following results are obtained. The paper shows that one of the promising ways to improve the performance of the CS is the use of RNS. The mathematical basis of RNS is the Chinese remainder theorem, which states that an integer operation on one large modulus can be replaced by a set of operations on coprime small modules. This opens up broad prospects for optimizing calculations. On the one hand, it is possible to significantly simplify the performance of complex and cumbersome calculations, including on low-resource computing platforms. On the other hand, calculations for different modules can be performed in parallel, which increases the performance of the CS. Conclusions. The article considers the operation of adding two numbers. This operation is the basis for both traditional positional number systems and RNS, i.e. forms the computational basis of all existing CS components. A new method for calculating the sum of the residuals of numbers modulo an arbitrary is proposed, and examples are given that clearly demonstrate the effectiveness of the proposed method. This method can be used in various computer applications, including for improving computing performance, ensuring fault tolerance, etc.