University of Information Science and Technology St. Paul The Apostle

The increased usage of Information and Communication Technology (ICT) tools into the mechanical devices in manner of normal everyday use within the aviation industry raised the concerns about cybersecurity. The extent of common flaws and vulnerabilities in the software tools that power these systems grows as their level of integration increases. Moreover, these concerns are becoming even more acute as the implementation of modern electronic smart devices on aircraft and airports in the aviation industry is increasing. The data on cyber-attacks and threats to air traffic security over the past twenty years can help identify, map and analyze trends and insights that are important to maintaining the security and resilience of aviation systems. The main objective is to identify the subjects of the common threat, their motivations, types of attacks and mapping the vulnerabilities of those elements of the aviation critical infrastructure, which are most often the subject of constant attack campaigns. Such analyzes should enable improved understanding both the current and potential future challenges to cybersecurity protection in the aviation sector. The main threats to the industry come from Advance Persistent Threat (APT) groups, which in cooperation with certain criminal structures and state intelligence institutions, steal intellectual property data in order to advance their own national aviation capabilities, as well as to monitor, to infiltrate and undermine the capabilities of other sovereign nations. The most commonly attacked segment of the aviation industry is the information technology (IT) infrastructure, while the most prominent type of attack is malicious hacking with the intention of gaining unauthorized access to confidential and sensitive information. Analysis of the range of attacked platforms and existing threat dynamics is used as a basis for predicting future cyber-attacks trends. Insights arising from the review should support future definition and implementation of proactive measures that protect aviation critical infrastructures from cyber-incidents that erode customer confidence in a key service-oriented industry.

BACKGROUND: Coronary arterial disease (CAD) is a leading cause of morbidity and mortality worldwide. Chronic psycho-emotional stress is recognized as a major risk factor influencing the onset, progression, and prognosis of CAD. Contemporary rehabilitation increasingly emphasizes stress reduction and lifestyle modification as part of secondary prevention. AIM: To evaluate the effects of a specialized yoga program on stress reduction, modification of risk factors, and functional recovery in patients undergoing rehabilitation for coronary arterial disease. METHODS: A prospective observational study was conducted in 514 patients with CAD who regularly attended specialized yoga courses during a one-month inpatient rehabilitation program. The intervention included relaxation techniques, vyayama, pranayama, kriyas, theoretical education, and supervised walks. Clinical status, cardiovascular functional parameters (EKG, echocardiography, treadmill stress test), correction of risk factors, and subjective physical and psychological outcomes were assessed using standardized examinations and questionnaires, including perceived stress scales. RESULTS: After one month, patients showed a 20% improvement in overall functional cardiovascular capacity, with increases in exercise tolerance and oxygen consumption. Echocardiography demonstrated improvement in left ventricular function (EF +6%, FS +4%). Significant correction of risk factors was observed, particularly smoking (89.0%), psycho-emotional stress (73.8%), and arterial hypertension (76.7%). Subjectively, most patients reported improved physical condition, relaxation, reduced anxiety, better sleep quality, and enhanced stress resistance. A high proportion (78.2%) intended to continue yoga practice after discharge. CONCLUSIONS: Specialized yoga programs appear to be a safe and effective complementary approach in the rehabilitation and secondary prevention of coronary arterial disease, primarily through reduction of chronic psycho-emotional stress and improved self-control, lifestyle awareness, and adherence to preventive measures. Although findings are encouraging, further long-term, controlled, and multicentric studies are required to confirm sustained cardiovascular benefits.

This work proposes k-space filters with the option to fine-tune the filter's strength. The application is in MRI imaging. The novelty of this paper is the multiplication of the filtering functions by the Sinc Function so as to obtain the Sinc-shaped convolving function. The effect of this multiplication is visible in k-space where a central region is formed and it acts as a k-space filter. Worth noting in Figure 12 is the finesse of high-frequency components depicted by the intensity-curvature functional (ICF). Read the article here https://onlinelibrary.wiley.com/doi/full/10.1002/appl.202200109.

Water pollution is a global issue that has an impact on the entire ecosystems' life cycles. Traditional sampling and laboratory testing techniques are labor-intensive and error-prone, making them ineffective for quickly detecting changes in water quality. This paper presents the development of a low-cost, portable and efficient prototype sensor-based system for monitoring water quality in real-time. The system consists of a microcontroller, temperature, turbidity, pH, and distance sensors, and an application for a visual representation of the data. Extensive testing was carried out to ensure uninterrupted operation. The prototype is a user-friendly sensor system that can be positioned close to the target area in order to assist in preventing environmental and biological harm. This can ensure safe, healthy, and sustainable water supplies for the communities, environment, and the economy. Continuous monitoring of water parameters can also help avoid critical situations. The experimental results demonstrate a successful development of a smart water quality monitoring system with potential applications in various scenarios.

This electronic document describes one of the first full open-source frameworks for posture and face detection and classification using Kinect sensors, based entirely on an open-sourced software platform. It has been designed with the possibility of automatic monitoring of either an elderly person who is situated in a closed space (a room with furniture, appliances, and other rigid objects), or a patient who suffers from a physiological disorder. It is also based on an original hardware platform (the AmI-Platform), which includes more Kinect sensors, data acquisition stations, and crunches (data servers). The articles tackle two of the most important issues for ambient intelligence: accurate determination of the person and his or her facial landmarks (to understand emotions) and fast and reliable posture determination (to take appropriate measures in case of an accident).

Media production and consumption behaviors are changing in response to new technologies and demands, giving birth to a new generation of social applications. Among them, crowd journalism represents a novel way of constructing democratic and trustworthy news relying on ordinary citizens arriving at breaking news locations and capturing relevant videos using their smartphones. The ARTICONF project [1] proposes a trustworthy, resilient, and globally sustainable toolset for developing decentralized applications (DApps). Leveraging the ARTICONF tools, we introduce a new DApp for crowd journalism called MOGPlay. MOGPlay collects and manages audio-visual content generated by citizens and provides a secure blockchain platform that rewards all stakeholders involved in professional news production. Besides live streaming, MOGPlay offers a marketplace for audio-visual content trading among citizens and free journalists with an internal token ecosystem. We discuss the functionality and implementation of the MOGPlay DApp and illustrate three pilot crowd journalism live scenarios that validate the prototype.

Social media applications are essential for next-generation connectivity. Today, social media are centralized platforms with a single proprietary organization controlling the network and posing critical trust and governance issues over the created and propagated content. The ARTICONF project funded by the European Union's Horizon 2020 program researches a decentralized social media platform based on a novel set of trustworthy, resilient and globally sustainable tools that address privacy, robustness and autonomy-related promises that proprietary social media platforms have failed to deliver so far. This paper presents the ARTICONF approach to a car-sharing decentralized application (DApp) use case, as a new collaborative peer-to-peer model providing an alternative solution to private car ownership. We describe a prototype implementation of the car-sharing social media DApp and illustrate through real snapshots how the different ARTICONF tools support it in a simulated scenario.