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With the increasing sophistication of modern helicopter designs the problems arising from the interactional aerodynamic flow field around the helicopter has become more acute. Interactional aerodynamics are, by origin, of utmost complexity, because many of the interactions involve viscous processes, the flow usually is unsteady and the interactions are strongly interdependent.

EC-135P2+ helicopters operated by Polish Medical Air Rescue are highly exposed to environmental particles entering engines when performing helicopter emergency medical services. This paper aims to assess the effectiveness of inlet barrier filters installed to protect the engines, including their impact on maintenance.

The organisation adopted a comprehensive set of measures to predict and limit the impact of dust ingestion including visual inspections, health management and engine trend monitoring based on ground power checks’ (GPC) results. Three alternative particle separation solutions were considered. Finally, helicopter inlets were modified to allow the selected filter system to be installed, which reduced the number of particles ingested by the engine and prevented from premature overhauls.

The analyses carried out enabled not only the selection of the optimal filtration solution and its seamless implementation into the fleet but also confirmed its efficiency. After installing the filters, engines’ lifetime is extended from 500 to 4,500 flight hours while operating costs and the number of maintenance tasks was reduced significantly.

Lessons learned from operational experience show that a well-matched particle separation system can mitigate accelerated engine deterioration even if the platform is continuously exposed to environmental particles. The remaining useful life of engines can be predicted using performance models and data from GPC.

As an emerging technology marked by rapid advancements, artificial intelligence (AI) has the potential to profoundly transform higher education, particularly after the emergence of ChatGPT at the end of November 2022. Because of its immense power, it poses greater expectations and challenges than any technological advancement in the past. Thus, this paper aims to identify and discuss the need for AI-driven transformation, its challenges as well as policies and expectations of AI's successful integration into teaching and learning. This study examined recent literature and policy documents mostly published after ChatGPT's launch. This study may assist students and instructors in promoting awareness of the importance of ethical, positive and productive AI applications. Besides, the findings of this paper may aid in developing and adopting appropriate educational policies, improving curricula and training pre-service and in-service instructors in adopting knowledge, competencies and strategies for the efficient integration of AI in the teaching process. Thus, the study may help build a strategy for integrating AI systematically into higher education processes and contributing to the transformation of higher education.

The purpose of this paper is to present the design, analysis and experiments of the active trailing-edge flap (Seoul National University Flap, SNUF) for vibration reduction in the helicopter rotor prior to the small-scale blades planned to test in a whirl tower.

The predictions of the hinge moment in both steady and unsteady flows were obtained through computational fluid dynamics calculations. When compared with the results originated from analytical formulations, the proposed method showed improved prediction capabilities. To validate the deflection of the flap under the centrifugal load by rotating, static analysis was conducted using both contact and rotating condition of MSC NASTRAN. The corresponding experiment also was performed using the vertical frame for simulating the effect of the centrifugal force.

The hinge moment of the flap is predicted through unsteady analysis in the actuation frequency of 3/rev. The material of the guide in the flap mechanism was selected through static analysis under both contact and rotating condition. Finally, reduction of the deflection occurred because of the load in the axial direction of the hinge like the centrifugal load.

The important aspects, such as design, analysis, and experiments for the active trailing-edge flap were shown.

This paper showed the relationship of the displacement, block force and voltage of the piezo-actuator, combined with the hinge moment predicted. The methodology and the experiment were presented for simulating the centrifugal force acting on the flap.

The study aims to identify the practical borders of AI legal personality and accountability in human-centric services.

Using a framework tailored for AI studies, this research analyses structured interview data collected from auditors based in Poland.

The study identified new constructs to complement the taxonomy of arguments for AI legal personality: cognitive strain, consciousness, cyborg paradox, reasoning replicability, relativism, AI misuse, excessive human effort and substitution.

The insights presented herein are primarily derived from the perspectives of Polish auditors. There is a need for further exploration into the viewpoints of other key stakeholders, such as lawyers, judges and policymakers, across various global contexts.

The findings of this study hold significant potential to guide the formulation of regulatory frameworks tailored to AI applications in human-centric services. The proposed sui generis AI personality institution offers a dynamic and adaptable alternative to conventional legal personality models.

The outcomes of this research contribute to the ongoing public discourse on AI’s societal impact. It encourages a balanced assessment of the potential advantages and challenges associated with granting legal personality to AI systems.

This paper advocates for establishing a sui generis AI personality institution alongside a joint accountability model. This dual framework addresses the current uncertainties surrounding human, general AI and super AI characteristics and facilitates the joint accountability of responsible AI entities and their ultimate beneficiaries.