Experiences show that boys are developing programming skills easier than girls in higher education. An analysis of programming skills by gender in secondary grammar schools was made with the help of a web based Informatics Test. After composing an on-line test on the base of the National Curriculum I analysed how effectively can students of different grades answer questions dealing with different subjects. From different towns of Hungary over 60 teachers used the test to see the knowledge level of more than 1000 students having answered these questions. After the evaluation of the test results the correctness of the original presumption emerged. First the Kolmogorov-Smirnov-test was used to see if the groups showed standard normal distribution in answering the questions. The means of the correct answers by gender were examined using a Z-test with two parameters and the deviation quotient was to calculated revealing how much gender influences the difference of means. Significance level was 5% through the analysis. Significant divergence by gender was found regarding programming skills showing the hypothesis correct in this two area. Students have to put everything in source code when learning programming. It seems this method is more favourable for boys. Girls have problems finding syntax errors in source code or understand it at all. I looks it would be better to teach girls with different tools using primitive building elements to write programs so when changing the parameters of these building elements no syntax errors would occur.

Autonomous vehicles rely on complex sensing systems to perceive their environment and ensure safe operation. This review analyses the main sensor technologies used in self-driving vehicles, including cameras, LiDAR, radar, ultrasonic sensors and GNSS/IMU-based localisation systems. A core set of 40 primary research articles was systematically analysed to compare the capabilities, limitations and integration challenges of sensing technologies used in autonomous vehicles. In addition to these primary studies, further references were included to provide background information and describe emerging developments in autonomous sensing systems. The review shows that no single sensor technology can provide reliable perception under all environmental conditions. Camera systems offer rich visual information but are sensitive to lighting and weather conditions, while LiDAR provides highly accurate three-dimensional geometry but suffers from signal attenuation in rain and fog. Radar sensors demonstrate superior robustness in adverse weather and enable direct velocity measurement, although their spatial resolution remains limited compared to optical sensors. As a result, modern autonomous vehicles rely on multi-sensor fusion architectures that combine complementary sensing modalities to improve reliability and safety. The analysis also identifies several key research gaps in the current literature. In particular, there is a lack of systematic evaluation of trade-offs between sensor performance, computational requirements and vehicle energy consumption. Furthermore, the safety certification of artificial intelligence-based perception systems and the integration of emerging technologies such as FMCW LiDAR and terahertz radar remain open research challenges. Overall, the results suggest that the future of autonomous vehicle perception will depend not only on improvements in individual sensors but also on robust sensor fusion architectures, safety-certified AI models and energy-efficient sensor processing platforms. These findings provide guidance for researchers and engineers developing next-generation sensing systems for autonomous driving.

In 2021 cybersecurity was again in spotlight. The number of data breaches until September 30, 2021, has already exceeded by 17 percent the total number of events in 2020 [1]. The banking sector marks another “worst year ever” for cyber-attacks. The sector was disproportionately affected by the attacks. In the first half of 2021 blackmail virus attacks increased by 1318 percent on a y-o-y basis [2]. Therefore, in addition to taking advantage of the development of information technology, new potential attack surfaces appeared. The threat of cyberattacks significantly increased. The sector will be exposed to more and more sophisticated attacks. Thus, cyber-security is becoming a priority in the future. With the development of digital technology hackers are also improving their skills. Building maximum protection against their malicious attacks is an increasing challenge for professionals. In this continuous war between the cybersecurity experts and cybercriminals the first step is to learn about the latest threat trends, the tools, and the techniques.

The rapid progress of sensing technologies, artificial intelligence, and embedded computing has significantly accelerated the development of autonomous vehicles. Among the core challenges of higher-level driving automation, reliable environmental perception remains one of the most critical. This review presents a systematic PRISMA-based analysis of multimodal sensor technologies and fusion architectures applied in autonomous driving, based on 66 peer-reviewed studies published between 2014 and 2025. The study examines the operational characteristics, advantages, and limitations of major sensing modalities, including cameras, LiDAR, radar, ultrasonic sensors, and GNSS/IMU-based localization systems. Particular attention is given to multimodal fusion strategies, covering early, mid-level, high-level, and transformer-based architectures that combine complementary sensor information to improve perception robustness and decision reliability. The review further synthesizes current evidence on performance under adverse environmental conditions, benchmark validation practices, real-time computational constraints, and the growing role of functional safety frameworks such as ISO 26262 and SOTIF. Emerging research directions, including 4D radar, self-supervised long-range fusion, foundation models, and cooperative V2X perception, are also discussed. The findings indicate that multimodal sensor fusion is a highly effective architectural strategy for improving scalability, fail-operational robustness, and certifiable safety in autonomous driving systems, particularly in higher-level automation scenarios. Future research should focus on uncertainty-aware fusion, explainable cross-modal reasoning, large-scale real-world validation, and efficient hardware–software co-design to support robust Level 4–5 vehicle autonomy.