Due to the increase in data availability in urban and regional studies, various spatial panel models have emerged to model spatial panel data, which exhibit spatial patterns and spatial dependencies between observations across time. Although estimation is usually based on maximum likelihood or generalized method of moments, these methods may fail to yield unique solutions if researchers are faced with high-dimensional settings. This article proposes a model-based gradient boosting algorithm, which enables estimation with interpretable results that is feasible in low- and high-dimensional settings. Due to its modular nature, the flexible model-based gradient boosting algorithm is suitable for a variety of spatial panel models, which can include random and fixed effects. The general framework also enables data-driven model and variable selection as well as implicit regularization where the bias-variance trade-off is controlled for, thereby enhancing accuracy of prediction on out-of-sample spatial panel data. Monte Carlo experiments concerned with the performance of estimation and variable selection confirm proper functionality in low- and high-dimensional settings while real-world applications including non-life insurance in Italian districts, rice production in Indonesian farms and life expectancy in German districts illustrate the potential application.

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Explainable machine learning techniques have gained increasing attention in engineering applications, especially in aerospace design and analysis, where understanding how input variables influence data-driven models is essential. Partial Dependence Plots (PDPs) are widely used for interpreting black-box models by showing the average effect of an input variable on the prediction. However, their global sensitivity metric can be misleading when strong interactions are present, as averaging tends to obscure interaction effects. To address this limitation, we propose a global sensitivity metric based on Individual Conditional Expectation (ICE) curves. The method computes the expected feature importance across ICE curves, along with their standard deviation, to more effectively capture the influence of interactions. We provide a mathematical proof demonstrating that the PDP-based sensitivity is a lower bound of the proposed ICE-based metric under truncated orthogonal polynomial expansion. In addition, we introduce an ICE-based correlation value to quantify how interactions modify the relationship between inputs and the output. Comparative evaluations were performed on three cases: a 5-variable analytical function, a 5-variable wind-turbine fatigue problem, and a 9-variable airfoil aerodynamics case, where ICE-based sensitivity was benchmarked against PDP, SHapley Additive exPlanations (SHAP), and Sobol' indices. The results show that ICE-based feature importance provides richer insights than the traditional PDP-based approach, while visual interpretations from PDP, ICE, and SHAP complement one another by offering multiple perspectives.

Scores are calculated by giving a weight of 1 for applicable, 0.5 for conditionally applicable, and 0 for incorrect responses.

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Muhammad Hafil Nugraha Research Centre for Smart Mechatronics National Research and Innovation Agency Bandung, Indonesia muha167@brin.go.id Estiko Rijanto Research Centre for Smart Mechatronics National Research and Innovation Agency Bandung, Indonesia estiko.rijanto@brin.go.id Oka Mahendra Research Centre for Smart Mechatronics National Research and Innovation Agency Bandung, Indonesia oka.mahendra@brin.go.id Abstract -- Accurate localization is crucial for effectively operating mobile robots in indoor environments. This paper presents a comprehensive approach to mobile robot localization by integrating an ultrasound - based indoor positioning system (IPS) with wheel odometry data via sensor fusion techniques. The Extended Kalman Filter (EKF) fusion method combines the data from the IPS sensors and the robot's wheel odometry, providing a robust and relia ble localization solution. Extensive experiments in a controlled indoor environment reveal that the fusion - based localization system significantly enhances accuracy and precision compared to standalone systems.

I've been a freelance journalist for 10 years, usually writing for magazines and websites about cinema. I presented a morning show on Radio Kraków twice a week for about two years. It was only one part of my work, but I really enjoyed it. It was about culture and cinema, and featured a range of people, from artists to activists. I remember interviewing Ukrainians about the Russian invasion for the first programme I presented, back in 2022. I was let go in August 2024, alongside a dozen co-workers who were also part-time. We were told the radio station was having financial problems.