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The European Committee for Future Accelerators (ECFA) Early-Career Researcher (ECR) panel, which represents the interests of the ECR community to ECFA, presents in this document its initiatives and activities in the year 2023. This report summarises the process of the first big turnover in the panel composition at the start of 2023 and reports on the activities of the active working groups - either pursued from before or newly established. The overarching goal of the ECFA-ECR panel is to better understand and support the diverse interests of early-career researchers in the ECFA community and beyond.

The European Committee for Future Accelerators (ECFA) Early Career Researcher's (ECR) panel, which represents the interests of the ECR community to ECFA, officially began its activities in January 2021. In the first two years, the panel has defined its own internal structure, responded to ECFA requests for feedback, and launched its own initiatives to better understand and support the diverse interests of early career researchers. This report summarises the panel composition and structure, as well as the different activities the panel has been involved with during the first two years of its existence.

The European Committee for Future Accelerators (ECFA) Early-Career Researchers (ECR) Panel was invited by the ECFA Detector R&D Roadmap conveners to collect feedback from the European ECR community. A working group within the ECFA ECR panel held a Townhall Meeting to get first input, and then designed and broadly circulated a detailed survey to gather feedback from the larger ECR community. A total of 473 responses to this survey were received, providing a useful overview of the experiences of ECRs in instrumentation training and related topics. This report summarises the feedback received, and is intended to serve as an input to the ECFA Detector R&D Roadmap process.

This study examines how market integration affects the adoption of sustainable production practices among agritourist households in Serbia. Using a Propensity Score Matching approach supported by endogeneity tests, mediation analysis, and Double Machine Learning, data from 148 households were analyzed. Results show that participation in modern market channels significantly increases the use of green technologies, particularly drip irrigation, biological pest control, and crop residue management. The mediation model confirms that market integration fosters adoption partly by enhancing producers’ cognitive awareness of sustainability. Findings remain robust across different estimation methods. The study offers policy insights into promoting sustainable agritourism through support for market access and digital competencies.

In this paper we demonstrate a new conceptual framework in the application of multilayer perceptron (MLP) artificial neural networks (ANNs) to bankruptcy risk prediction using different time-delay neural network (TDNN) models to assess Altman’s EM Z″-score risk zones of firms for a sample of 100 companies operating in the hotel industry in the Republic of Serbia. Hence, the accuracies of 9580 forecasting ANNs trained for the period 2016 to 2021 are analyzed, and the impact of various input parameters of different ANN models on their forecasting accuracy is investigated, including Altman’s bankruptcy risk indicators, market and internal nonfinancial indicators, the lengths of the learning periods of the ANNs and of their input parameters, and the K-means clusters of risk zones. Based on this research, 11 stability indicators (SIs) for the years under analysis are formulated, which represent the generalization capabilities of ANN models, i.e., differences in the generalization errors between the preceding period and the year for which zone assessment is given; these are seen as a consequence of structural changes at the industry level that occurred during the relevant year. SIs are validated through comparison with the relative strength index (RSI) for descriptive indicators of Altman’s model, and high correlation is found. Special focus is placed on the identification of the stability in 2020 in order to assess the impact of the COVID-19 crisis during that year. It is established that despite the fact that the development of bankruptcy risk in the hotel industry in the Republic of Serbia is a highly volatile process, the largest changes in the analyzed period occurred in 2020, i.e., the potential applications of ANNs for forecasting zones in 2020 are limited.

This paper presents an innovative method for determining the distribution of the friction generated heat from the contact of a locomotive wheel and rail, as well as the heat partition factor, during wheel slipping of an accelerating locomotive. The new method combines the finite element analysis simulation and experimental determination of the temperature distribution in a downsized model of a wheel and rail. As a result of a virtual experiment by the finite element analysis, an empirical dependence between the temperature distribution and the heat partition factor was established. The determination of the dependence enabled finding of the exact value of the heat partition factor by the optimization procedure based on matching temperatures obtained by the virtual and real experiment. nema

The use of robots in various industries has increased significantly in recent years, with mobile robots playing a central role in automation. Their applications range from service robotics and automated material handling to bomb disposal and planetary exploration. A rapidly growing area of mobile robotics involves coordinated groups of autonomous robots, commonly referred to as swarms. However, only a limited number of studies have addressed systems in which ropes or wires physically connect robots. Connecting multiple autonomous robotic vehicles with a tensioned wire can form a movable fence, enabling coordinated motion as a single dynamic entity. This paper presents a real-time control approach for the off-road motion of physically connected skid-steering robotic vehicles. A numerical-simulation-driven artificial neural network is employed as a surrogate model to estimate wheel–ground load distribution online, enabling stable steering control and accurate trajectory tracking on rough terrain while accounting for wire-induced coupling effects.

Slider-crank mechanisms are used in many machines where there is a need to transform rotary motion into translation, and vice versa. Implementation of the control into a mechanical assembly of the slider-crank mechanism offers a wide range of applications of such controlled mechanism in mechatronic systems. This paper shows an example of the remote control of the angular velocity of the crank in a mechatronic redesigned slider-crank mechanism in order to achieve the desired motion of the slider. The remote control is achieved over the Internet connection and the appropriate software which is executed in the user’s internet browser. The aim of this paper is to present the applied control algorithm as well as to explain advantages of the possibility to remotely run a mechatronic redesigned slider-crank mechanism in service. This is done through an example of using a controlled slider-crank mechanism in a remote laboratory experiment.