Hasil untuk "Cybernetics"

Ahmad Esmaeil Abbasi, Yassine Ouazene, Maria Pia Fanti

Robust perception and intelligent path planning are critical for reliable autonomous operation in robotics and autonomous vehicles (AVs), especially in complex environments. AVs must accurately analyze their surroundings, detect obstacles, and select safe routes, as even minor errors can cause collisions. Convolutional neural networks (CNNs) have greatly enhanced road understanding and real-time decision-making. This study integrates a CNN model into Stable Baselines 3 (SB3), enabling interaction with a SUMO simulation map to determine optimal routes using diverse metrics, including minimizing travel time, reducing congestion, avoiding traffic lights, preventing loops and dead ends, maintaining shorter distances to destination, and ensuring stable speeds. This addresses key limitations in prior research and supports more efficient and sustainable autonomous mobility. This work makes three main contributions. First, it introduces a multi-input CNN with a VGG-like backbone capable of fusing spatial traffic features with scalar speed data for path planning. Second, it identifies shortcomings in existing CNN-based approaches, including limited multi-modal fusion, insufficient handling of traffic-light congestion, and inadequate loop-avoidance mechanisms. Third, empirical evaluation in SUMO shows that multi-input fusion yields more stable predictions and superior routing compared to single-input CNN baselines, demonstrating the value of multi-modal deep learning for autonomous mobility.

Alina Yefimenko, Veronika Malaniuk, Murad Bagirzadeh et al.

In the context of Ukraine’s ongoing digital transformation and persistent governance challenges, understanding the role of digitalisation in combating corruption is both timely and essential. As the country intensifies efforts to modernise its public sector and attract international support, digital tools have emerged as critical instruments for enhancing transparency and institutional accountability. This study aims to assess the impact of digitalisation and technological development on corruption processes in Ukraine between 2005 and 2023. The analysis is based on data from the World Bank and the State Statistics Service of Ukraine. Using structural equation modelling and principal component analysis, the study constructs latent indicators for corruption, digitalisation, and technological development, and explores the relationships among these factors through a system of structural equations. Data were standardised, normalised, and tested for robustness and model adequacy using various statistical criteria. The findings reveal a strong inverse relationship between digitalisation and corruption: a one-unit increase in digitalisation reduces the corruption index by 0.646 units. Technological development indirectly contributes to corruption reduction by enhancing digital capacity, with a 1% rise in technological indicators leading to a 0.263-unit increase in digitalisation. Key drivers include ICT infrastructure, research and development expenditure, and innovation activity among enterprises. The model confirms that digital reforms in Ukraine are not only effective in curbing corruption but are also statistically sound and policy-relevant, reinforcing the importance of continued investment in digital governance as a core anti-corruption strategy.

Marin Milković, Dijana Vuković, Fani Kerum

Critical digital literacy is becoming a key skill in higher education, given the increasing integration of digital technologies into teaching and research. This paper explores the attitudes of students and professors in higher education institutions towards critical digital literacy, which includes technical skills, the ability to analyze and evaluate digital sources, and the active use of digital tools in an educational context. The research is based on an analysis of the perception of critical digital literacy as an important component of academic success and professional development. Through survey research and interviews with 900 students and 300 professors from all levels of study, attitudes were examined on the importance of digital technologies in education, online safety, and their role in developing critical thinking and recognizing disinformation. The results show that students and professors are mostly aware of the importance of critical digital literacy. Still, there are significant differences in the level of engagement and trust in digital tools among different groups. Students at lower levels of education show greater trust in technology, while professors highlight the challenges in integrating digital tools into teaching, especially in terms of assessment and maintaining academic ethics. This paper points to the need for further development of critical digital literacy in higher education institutions and suggests strategies for improving educational practices, including training for faculty and students in critical thinking, internet safety, and proper use of digital resources. In conclusion, the paper highlights the importance of continuous investment in developing digital skills, which are necessary for successfully facing the challenges of the digital age and preparing students for the labour market.

Yaroslav M. Drin, Iryna I. Drin, Svitlana S. Drin et al.

In this paper, we study solvability of the Cauchy problem for a parabolic pseudodifferential equation with the deviation of the argument. Parabolic pseudodifferential operator with non-smooth symbols introduced by Eidel’man and Drin’ for the first time. For such equations, the initial condition is set on a certain interval. Technical and physical reasons for delays can be transport delays, delays in decision-making, delays in information transmission, etc. The most natural are delays when modeling objects in medicine, population dynamics, ecology, etc. Other physical and technical interpretations are also possible, for example, the molecular distribution of thermal energy in various media (liquids, solid bodies, etc.) is modeled by heat conduction equations. Features of the dynamics of vehicles in different environments (water, land, air) can also be taken into account by introducing a delay. The formula for the solution of the Cauchy problem is constructed for the nonlinear equation of heat conduction with a deviation of the argument, its properties are investigated.

Sonja Ehret

In this article, I will show how the method of intergenerational dialogue can promote transformative learning and fosters personal growth through education in both young and old people. Education is holistic in Humboldt's sense because it reflexively links the world and the theory with the individual's inner being. Students become researchers of their own educational process. The role of tacit knowledge in the context of experience, but also creativity, are key phenomena with which the learning and educational process can be made clear, both in old and young people. All theoretical terms are explained using a practical dialogue example. Differences between young and old people in the dialogue model contribute to refined insights.

Ruigang Ge, Guoyue Chen, Kazuki Saruta et al.

Breast cancer (BC) is the most common type of cancer among women globally and is one of the leading causes of cancer-related deaths among women. In the diagnosis of BC, histopathological assessment is the gold standard, where automated tumor detection technologies play a pivotal role. Utilizing Convolutional Neural Networks (CNNs) for automated analysis of image patches from Whole Slide Images (WSIs) enhances detection accuracy and alleviates the workload of pathologists. However, CNNs often face limitations in handling pathological patches due to a lack of sufficient contextual information and limited feature generation capabilities. To address this, we propose a novel Multi-scale Multi-head Self-attention Ensemble Network (MMSEN), which integrates a multi-scale feature generation module, a convolutional self-attention module, and an adaptive feature integration with an output module, effectively optimizing the performance of classical CNNs. The design of MMSEN optimizes the capture of key information and the comprehensive integration of features in WSIs pathological patches, significantly enhancing the precision of tumor detection. Validation results from a five-fold cross-validation experiment on the PatchCamelyon (PCam) dataset demonstrate that MMSEN achieves a ROC-AUC of 99.01% ± 0.02%, an F1-score of 98.00% ± 0.08%, a Balanced Accuracy (B-Acc) of 98.00% ± 0.08%, and a Matthews Correlation Coefficient (MCC) of 96.00% ± 0.16% (p<0.05). These results demonstrate the effectiveness and potential of MMSEN in detecting tumors from pathological patches in WSIs for BC.

Ali Akbar Khasseh, Sahar Naseri, Faramarz Soheili et al.

Purpose: The purpose of this study is to determine the relationship between organizational citizenship behavior, leader-member exchange, and perceived supervisor support among employees of public libraries in Kurdistan province.Method: The present research method is a survey type in terms of practical purpose and data collection method. The statistical population of this study includes all employees of public libraries in Kurdistan province, totaling 99 people, which was determined using the census method due to the small population size. Three standard questionnaires were utilized to collect data on Organizational Citizenship Behavior (Kanuski, 1996), Leaders-Member Exchange (Gran and Joey Bain, 1995), and perceived support for the Supervisor (Rubin, 2013). The content validity of the questionnaire was confirmed by five professors of information science, and the reliability was assessed using Cronbach's alpha, resulting in values of 0.785, 0.845, and 0.938. Data analysis was conducted using statistical coefficients such as correlation coefficient and multiple linear regression in SPSS software version 22.Findings: The findings showed a significant relationship between leader-member exchange and all five dimensions of organizational citizenship behavior, as well as the perceived support of the supervisor. Additionally, a significant relationship was found between the perceived support of the supervisor and organizational citizenship behavior. The perceived support of the supervisor was identified to play a mediating role in the relationship between leader-member exchange and the dimensions of altruism, respect, and humility. Lastly, the gender of the respondents was found to be related to the altruistic dimension.Conclusion: Based on the results obtained, it can be said that employees are more likely to engage in organizational citizenship behavior in response to perceived support from the supervisor rather than as a tool for rewarding. In other words, employee's express organizational citizenship behavior because they feel satisfied with the support of their supervisor. What is certain is that it is not only the role of the supervisor that strengthens the behavior of organizational citizenship in the workplace; organizational citizenship behavior is a team effort.

Nain Tara, Zubair Ahmad

Decision-making practices in family business are crucial and usually involve several stakeholders. To determine the decision-making mechanism in the family business, qualitative research approach was carried out. 30 semi-structured interviews were conducted involving family owners and non-family managers. Thematic analysis was used to analyze data. Three themes such as Business sense, consultation with elders, and ceremonial approval of the board were generated. Study reveals that decision-making is informal and based on tacit knowledge. Study emphasizes the codification of tacit knowledge, the codification will make decision-making more formal and institutionalized.

Siamak Karimi, Mehdi Zadeh, Jon Are Suul

Abstract This paper proposes a three‐layer framework for energy efficiency evaluation of Shore‐to‐Ship Charging (S2SC) systems using load‐dependent loss models of the components. The considered S2SC system is supplied by the grid but is also supported by On‐Shore Batteries (OSB). The presented approach is then used to investigate the impact of the specific design and operational parameters on energy efficiency. Power system architectures for three general S2SC solutions for ac, dc, and inductive charging are defined and compared in terms of energy efficiency. Operational parameters are also considered in the analysis, namely, the grid power ratio, determining the load sharing between the grid and the OSB, as well as the OSB charging profile. A case study is performed with peak charging power of 1 MW, and the most efficient S2SC solutions are identified for both ac‐ and dc‐based onboard power systems. Moreover, it is shown that charging OSB with the highest available power from the grid between the charging breaks would often lead to higher energy efficiency than the maximum utilization of the available charging time. Field data from a real S2SC system is used to verify the estimated energy efficiency by the proposed framework. The analysis of the real case S2SC is then extended to include and verify a projected OSB.

Andy Amoordon, Virginie Deniau, Anthony Fleury et al.

Wireless networks are nowadays indispensable components of telecommunication infrastructures. They offer flexibility, mobility and rapid expansion of telecommunication infrastructures. In wireless networks, transmissions are unisolated and most commonly emitted using omnidirectional antennas. This makes wireless networks more vulnerable to some specific attacks as compared to wired networks. For instance, attacks such as fake access points, intentional jamming and deauthentication can be easily perpetrated against IEEE 802.11 networks using freely accessible software and cheap hardware. Intentional jamming and deauthentication attacks are standalone attacks, but they can be combined with the fake access point attack to increase the latter’s effectiveness. In our research, we work on methods to detect the three different attacks when they are perpetrated independently (one at a time) or concurrently (several at the same time). In this contribution, we present a model that can detect the three attacks, when perpetrated independently, by analysing a set of features (frame interval, Received Signal Strength Indicator, sequence number gap and management frame subtype) extracted from IEEE 802.11 management frame and radiotap headers. We have implemented the model using several supervised learning algorithms. The model with Random Forest and the K-Nearest Neighbour predictors have best detection precision (over 96 %) for fake access point and deauthentication attacks and perfectible detection precision for the intentional jamming attack (over 81%).