У цій статті проводиться дослідження в алгебрі кватерніонів. Зокрема, розглядаються функції кватерніонної змінної. Ми пропонуємо інший підхід до введення поняття диференційовної функції f(q). Також ми побудували деякі елементарні функції, диференційовні в рамках цього підходу.
Theory and practice of education, Information theory
Adrian Chmielewski, Piotr Piórkowski, Krzysztof Bogdziński
et al.
The development of energy storage systems is significant for solving problems related to climate change. A hybrid energy storage system (HESS), combining batteries with ultracapacitors, may be a feasible way to improve the efficiency of electric vehicles and renewable energy applications. However, most existing research requires comprehensive modelling of HESS components under different operating conditions, hindering optimisation and real-world application. This study proposes a novel approach to analysing the set of differential equations of a substitute model of HESS and validates a model-based approach to investigate the performance of an HESS composed of a Valve-Regulated Lead Acid (VRLA) Absorbent Glass Mat (AGM) battery and a Maxwell ultracapacitor in a parallel configuration. Consequently, the set of differential equations describing the HESS dynamics is provided. The dynamics of this system are modelled with a double resistive–capacitive (2-RC) scheme using data from Hybrid Pulse Power Characterisation (HPPC) and pseudo-random cycles. Parameters are identified using the Levenberg–Marquardt algorithm. The model’s accuracy is analysed, estimated and verified using Mean Square Errors (MSEs) and Normalised Root Mean Square Errors (NRMSEs) in the range of a State of Charge (SoC) from 0.1 to 0.9. Limitations of the proposed models are also discussed. Finally, the main advantages of HESSs are highlighted in terms of energy and open-circuit voltage (OCV) characteristics.
C.A. Orrico, M. van Berkel, T.O.S.J. Bosman
et al.
Reliable core density control with pellet fueling will be necessary to achieve required fusion power output in future fusion tokamaks such as ITER. The discrete nature of fuel pellets, however, complicates the density profile control problem significantly. As a solution, we propose a predictive density profile controller that considers fuel pellets as discrete actuators, while ensuring operation within prescribed density limits. The model predictive control (MPC) scheme we deploy combines the offset-free method to correct prediction model inaccuracies and our novel modified penalty term homotopy algorithm for real-time MPC (PTH-MPC). To demonstrate density profile control with discrete pellets, we couple the PTH-MPC density controller with JINTRAC integrated simulations of the ITER 15 MA/5.3 T scenario, using HPI2 to model discrete pellet ablation and deposition. We compare the density controller performance in integrated simulations using the Bohm/gyro-Bohm turbulent transport model against integrated simulations using the TGLF turbulent transport model. We highlight the necessity of treating pellets as discrete events for controller performance and for remaining within density limits. We conclude that PTH-MPC is a promising candidate for density profile control with pellets fueling in ITER and other future tokamaks and recommend further improvements using learning-based and robust MPC. We also note the limitations of quasi-linear turbulent transport models in simulations involving discrete pellets.
Nuclear and particle physics. Atomic energy. Radioactivity
Imbalanced class distributions pose a prevalent challenge in numerous classification problems, requiring effective strategies for learning from such skewed data. Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functions toward the majority class, resulting in suboptimal performance for minority classes. In our research, we propose a novel approach to address this challenge specifically tailored for Support Vector Machines (SVM), a well-established family of learning algorithms. Our method leverages a kernel trick to enhance the SVM’s classification capabilities on imbalanced datasets named KTI. It aims to streamline the classification process by incorporating adaptive data transformations within the algorithm itself, offering a more efficient and integrated solution for handling imbalanced data. Experimental evaluations conducted on diverse real-world datasets demonstrate the superior performance of our proposed strategy compared to existing methods, showcasing its potential for practical applications in classification tasks with skewed class distributions.
In the age of fast digital transformation, Docker containers have become one of the central technologies for flexible and scalable application deployment. However, this has opened a new dimension of challenges in security, which are skyrocketing with increased technology adoption. This paper discerns these challenges through a manifold approach: first, comprehensive static analysis by Trivy, and second, real-time dynamic analysis by Falco in order to uncover vulnerabilities in Docker environments pre-deployment and during runtime. One can also find similar challenges in security within the Internet of Things (IoT) sector, due to the huge number of devices connected to WiFi networks, from simple data breaches such as brute force attacks and unauthorized access to large-scale cyber attacks against critical infrastructure, which represent only a portion of the problems. In connection with this, this paper is calling for the execution of robust AWS cloud security solutions: IoT Core, CloudWatch, and GuardDuty. IoT Core provides a secure channel of communication for IoT devices, and CloudWatch offers detailed monitoring and logging. Additional security is provided by GuardDuty’s automatized threat detection system, which continuously seeks out potential threats across network traffic. Armed with these technologies, we try to build a more resilient and privacy-oriented IoT while ensuring the security of our digital existence. The result is, therefore, an all-inclusive work on security in both Docker and IoT domains, which might be considered one of the most important efforts so far to strengthen the digital infrastructure against fast-evolving cyber threats, combining state-of-the-art methods of static and dynamic analyses for Docker security with advanced, cloud-based protection for IoT devices.
Objective: The purpose of this study was to investigate the level of readiness of Iranian academic libraries for sustainable management to realize green libraries.Method: In terms of the type of objective, the present research is applied research, and it was carried out by a descriptive survey method. The statistical population of this research includes different departments of university libraries under the supervision of the Iranian Ministry of Science, Research and Technology. In this research, a researcher-made checklist was used to collect the data, which was prepared based on the components of the ISO 14000, and a Delphi panel was used to determine its validity, and SPSS 22 software was applied to analyze the research data.Results: According to the obtained results, it can be concluded that the libraries of Iranian universities are far from the indicators of readiness to become green libraries, and in order to reach the desired level, it seems necessary to review the design of their infrastructure. The results also showed compliance with rules such as waste management, optimal energy consumption and use of renewable energies, attention to the optimal consumption of natural resources such as paper, the use of environmentally friendly raw materials and equipment in the library, and so forth. There are those who help the libraries in obtaining the necessary standards to become a green library.Conclusions: Considering the importance of paying attention to environmental issues, this research is the first work done in this direction in this society and its scope.
Information theory, Bibliography. Library science. Information resources
Sentiment analysis has become a highly effective research field in the natural language domain and has a large scope of real-world implementations. An existing active study concentration for sentiment analysis is the development of graininess at the document level, appearing with two featured objectives: subjectivity classification, which determines whether a document is objective or subjective and sentiment detection which defines whether or not a document has a sentiment. Deep learning approaches have featured as a chance for developing these objectives with their ability to present both syntactic and semantic characteristics of text without demands for high-level attribute engineering. In this paper, we focus to produce a systematic literature review of deep learning methods for document-based sentiment analysis to determine different features in the text. In addition, this systematic literature review presents a brief survey, evaluation, enhancement of recent developments in the field of sentiment analysis techniques and applications of documents for deep learning, starting with the Convolutional Neural Network, continues to cover the Recurrent Neural Network, including Long Short-Term Memory and Gated Repetitive Units. This review also contains the implementation and application of Recursive Neural Network, Deep Belief Network, Domain-Adversarial Network Models and Hybrid Neural Network. This work considers most of the papers published when the history of deep learning began, and specifically the sentiment analysis of the documents.
Natalia Zaretskaya, Jonas Bause, Jonathan R. Polimeni
et al.
The primary visual cortex of humans contains patches of neurons responding preferentially to stimulation of one eye (the ocular dominance columns). Multiple previous studies attempted to detect their activity using fMRI. The majority of these fMRI studies used magnetic field strengths of 4 T and higher. However, there have been reports of reliable eye-selective activations at 3 T as well. In this study we investigated the possibility of detecting eye-selective V1 activity using high-resolution GE-EPI fMRI at 3 T and sub-millimeter resolution fMRI at ultrahigh 9.4 T magnetic field strengths with acquisition parameters optimized for each field strength. High-resolution fMRI at 9.4 T also allowed us to examine the eye-selectivity responses across the cortical depth, which are expected to be strongest in the middle layers.We observed a substantial increase in the percentage of eye-selective voxels, as well as a doubling in run-to-run consistency of eye preference at ultrahigh field compared to 3 T. We also found that across cortical depth, eye selectivity increased towards the superficial layers, and that signal contrast increased while noise remained nearly constant towards the surface. The depth-resolved results are consistent with a distortion of spatial specificity of the GE-EPI signal by ascending venules and large draining veins on the cortical surface. The effects of larger vessels cause increasing signal amplitude, but also displacement of the maximum BOLD signal relative to neural activity.In summary, our results show that increase in spatial resolution, reduced partial volume effects, and improved sensitivity at 9.4 T allow for better detection of eye-selective signals related to ocular dominance columns. However, although ultrahigh field yields higher sensitivity to the ocular dominance signal, GE-EPI still suffers from specificity issues, with a prominent signal contribution at shallow depths from larger cortical vessels.
Critical infrastructure protection faces increasing challenges, both in quality and in quantity. Most of the present security systems fully rely on automated mechanisms, which replace human operators, in order to perform computation intensive tasks and/or to work in extreme conditions. However, this solution presents some drawbacks with respect to the system performance. In order to provide effective measures against the pressure of new and sophisticated threats, an interdisciplinary approach, based on suitably coupling machine learning with human judgment, results as the right choice. In fact, this solution is particularly helpful for implementing efficient solutions capable of controlling critical scenarios and reacting effectively towards sophisticated threats. This paper discusses the proposed approach and demonstrates that this approach is the best choice for the effective protection of critical infrastructures.
David Cuesta-Frau, Daniel Novák, Vacláv Burda
et al.
Diabetes is a disease of great and rising prevalence, with the obesity epidemic being a significant contributing risk factor. Duodenal–jejunal bypass liner (DJBL) is a reversible implant that mimics the effects of more aggressive surgical procedures, such as gastric bypass, to induce weight loss. We hypothesized that DJBL also influences the glucose dynamics in type II diabetes, based on the induced changes already demonstrated in other physiological characteristics and parameters. In order to assess the validity of this assumption, we conducted a quantitative analysis based on several nonlinear algorithms (Lempel–Ziv Complexity, Sample Entropy, Permutation Entropy, and modified Permutation Entropy), well suited to the characterization of biomedical time series. We applied them to glucose records drawn from two extreme cases available of DJBL implantation: before and after 10 months. The results confirmed the hypothesis and an accuracy of 86.4% was achieved with modified Permutation Entropy. Other metrics also yielded significant classification accuracy results, all above 70%, provided a suitable parameter configuration was chosen. With the Leave–One–Out method, the results were very similar, between 72% and 82% classification accuracy. There was also a decrease in entropy of glycaemia records during the time interval studied. These findings provide a solid foundation to assess how glucose metabolism may be influenced by DJBL implantation and opens a new line of research in this field.
A solution for the problems of the finger spelling alphabet of gesture language modelling and recognition based on cross-platform technologies is proposed. Modelling and recognition performance can be flexible and adjusted, based on the hardware it operates or based on the availability of an internet connection. The proposed approach tunes the complexity of the 3D hand model based on the CPU type, amount of available memory and internet connection speed. Sign recognition is also performed using cross-platform technologies and the tradeoff in model size and performance can be adjusted. the methods of convolutional neural networks are used as tools for gestures of alphabet recognition. For the gesture recognition experiment, a dataset of 50,000 images was collected, with 50 different hands recorded, with almost 1,000 images per each person. The experimental researches demonstrated the effectiveness of proposed approaches.
Мета. Метою статті є розробка формальної методики теорії інтелекту, а саме розробка моделі і аксіоматики на мові алгебри кінцевих предикатів. Пропонується ввести систему операцій за відносинами для побудови алгебри відносин. Методи. У статті використані методи алгебри кінцевих предикатів, булева алгебра і аксіоматичний метод. Результати. У статті був розвинений математичний апарат теорії інтелекту. Розроблено моделі та аксіоматику відносин на мові алгебри кінцевих предикатів, введені операції над такими відносинами, як ін'єкція, еквівалентність, сюр'єкція, квазіпорядок, частковий порядок, циркуляція і добуток відносини. Побудована алгебра відносин. Аксіоматично призначається система операцій над предикатами в алгебрі скінченних предикатів, а саме: булеве заперечення, диз'юнкція, кон'юнкція, імплікація, еквівалентність. Вводяться основні предикати (предикати розпізнавання об'єктів). Висновки. Предикати різних порядків відповідають поняттям іншого рівня абстракції. Рішення рівнянь алгебри кінцевих предикатів можна інтерпретувати як творчу діяльність людини. Через наявність такої широкої і змістовної інтерпретації навіть чисто математичний розвиток алгебри кінцевих предикатів дозволяє в той же час стимулювати розвиток теорії інтелекту. Мінімізація, декомпозиція, рішення рівнянь, тотожне перетворення формул є важливими завданнями теорії інтелекту.
У статті описується правова OWL-онтологія, яка робить явним концептуальне знання в області судової справи, підтримує міркування про область, і може використовуватися для анотування тексту судової справи, якій в свою чергу можна використати, для поповнення онтології. Заповнена онтологія може використовуватися для інформаційного пошуку, видобування і доказової аргументації.
Special aspects of education, Electronic computers. Computer science
Background: Though subtle through the years, there has been a perceptible shift in competitive and market intelligence (CMI) practice from that of relying more heavily on sole operators to ones relying on collaboration. It happens within the nature of work performed inside intelligence functions, the larger organisation, and between organisations (i.e., intra-organisational). In this paper, the authors describe the change, develop a three-layered taxonomy for documenting it,and provide examples of how it impacts intelligence practice both now and possibly in the future.
Objective: To describe the increasingly evident role of collaboration and collaborative behaviour within insight producing functions in commercial, market-facing organisations. Identify evidence of collaborative intelligence practices in use across a range of different companies, industries, and geographies.
Method: The authors used a participant observation approach to developing this research. The discussion and frameworks in this study are based upon the authors’ current roles, experiences and observations in leading a CMI group for a successful provincially based yet globally focused research and technology organisation, and having led interactive workshops and courses for over 100 organisations and approximately 1800 CMI analysts in over a dozen countries.
Results: The authors identified an impressive array of collaborative practices for each of the three layers of organisational environments studied. These included ones in (1) intra-process (aka, intelligence cycle) collaboration, (2) intra-organisational collaboration (i.e. within the intelligence and broader organisation) and (3) inter-organisational collaboration (i.e. between discrete organisations). These are illustrated from actual, observed, and ongoing CMI practices and are shared as examples reinforcing our view of the movement away from independent practices and approaches toward purposeful, socialised ones.
Conclusion: The evidence we have amassed provides substantial evidence of a notable and beneficial shift from doing intelligence work independently, frequently within silos, towards doing it collaboratively and across multiple types of boundaries. Intelligence practitioners are growing in their capabilities by taking advantage of emerging technologies, adapting practices imported from adjacent fields and benefitting from academic and/or scholarly research that helps push ahead the working boundaries of the field and allows it to make progress. In our view, CMI practice has recently entered a third era of evolution, one in which collaboration will continue to feature prominently, if not centrally.
Management information systems, Information theory