Abstract
Accurate evaluation of forecasting models is essential for ensuring reliable predictions. Current practices for evaluating and comparing forecasting models focus on summarising performance into a single score, using metrics such as SMAPE. We hypothesize that averaging performance over all samples dilutes relevant information about the relative performance of models. Particularly, conditions in which this relative performance is different than the overall accuracy. We address this limitation by proposing a novel framework for evaluating univariate time series forecasting models from multiple perspectives, such as one-step ahead forecasting versus multi-step ahead forecasting. We show the advantages of this framework by comparing a state-of-the-art deep learning approach with classical forecasting techniques. While classical methods (e.g. ARIMA) are long-standing approaches to forecasting, deep neural networks (e.g. NHITS) have recently shown state-of-the-art forecasting performance in benchmark datasets. We conducted extensive experiments that show NHITS generally performs best, but its superiority varies with forecasting conditions. For instance, concerning the forecasting horizon, NHITS only outperforms classical approaches for multi-step ahead forecasting. Another relevant insight is that, when dealing with anomalies, NHITS is outperformed by methods such as Theta. These findings highlight the importance of evaluating forecasts from multiple dimensions.

Abstract
The detection of Pulmonary Hypertension (PH) from the computer analysis of digitized heart sounds is a low-cost and non-invasive solution for early PH detection and screening. We present an extensive cross-domain evaluation methodology with varying animals (humans and porcine animals) and varying auscultation technologies (phonocardiography and seisomocardiography) evaluated across four methods. We introduce PH-ELM, a resource-efficient PH detection model based on the extreme learning machine that is smaller (300x fewer parameters), energy efficient (532x fewer watts of power), faster (36x faster to train, 44x faster at inference), and more accurate on out-of-distribution testing (improves median accuracy by 0.09 area under the ROC curve (auROC)) in comparison to a previously best performing deep network. We make four observations from our analysis: (a) digital auscultation is a promising technology for the detection of pulmonary hypertension; (b) seismocardiography (SCG) signals and phonocardiography (PCG) signals are interchangeable to train PH detectors; (c) porcine heart sounds in the training data can be used to evaluate PH from human heart sounds (the PH-ELM model preserves 88 to 95% of the best in-distribution baseline performance); (d) predictive performance of PH detection can be mostly preserved with as few as 10 heartbeats and capturing up to approximately 200 heartbeats per subject can improve performance.

Abstract
The International Symposium on Applied Reconfigurable Computing (ARC) is an annual forum for the discussion and dissemination of research, notably applying the Reconfigurable Computing (RC) concept to real-world problems. The first edition of ARC took place in 2005, and in 2024, ARC celebrated its 20th edition. During those 20 years, the field of reconfigurable computing saw a tremendous growth in its underlying technology. ARC contributed very significantly to the presentation and dissemination of new ideas, innovative applications, and fruitful discussions, all of which have resulted in the shaping of novel lines of research. Here, we present selected papers from the first 20 years of ARC, that we believe represent the corpus of work and reflect the ARC spirit by covering a broad spectrum of RC applications, benchmarks, tools, and architectures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Concrete structures require precise temperature and humidity monitoring during curing to ensure optimal strength and prevent defects like cracking. A compact optical sensing system was developed using a single fiber that can be embedded directly within the concrete. The system functions as both a temperature and humidity sensor when paired with a spectral interrogation unit operating in the 1500-1600 nm range. Temperature monitoring is achieved through a Fiber Bragg Grating, while humidity sensing is facilitated by a Fabry-Perot interferometer at the fiber tip. The interferometer cavity is formed with a layer of polyvinylpyrrolidone (PVP). Initial air humidity sensor tests showed a significant change in the interference period with RH, demonstrating low hysteresis and high reproducibility. Calibration of one sensor revealed an approximately 3 nm period decrease when RH increased from 55% to 95%, with results suggesting a quadratic relationship between the interference period and RH values.

Abstract

Abstract
Digital twins enable real-time modeling, simulation, and monitoring of complex systems, driving advancements in automation, robotics, and industrial applications. This study presents a large-scale digital twin-testing facility for evaluating mobile robots and pilot robotic systems in a research laboratory environment. The platform integrates high-fidelity physical and environmental models, providing a controlled yet dynamic setting for analyzing robotic behavior. A key feature of the system is its comprehensive data collection framework, capturing critical parameters such as position, orientation, and velocity, which can be leveraged for machine learning, performance optimization, and decision-making. The facility also supports the simulation of discrete operational systems, using predictive modeling to bridge informational gaps when real-time data updates are unavailable. The digital twin was validated through a matrix manufacturing system simulation, with an Augmented Reality (AR) interface on the HoloLens 2 to overlay digital information onto mobile platform controllers, enhancing situational awareness. The main contributions include a digital twin framework for deploying data-driven robotic systems and three key AR/VR integration optimization methods. Demonstrated in a laboratory setting, the system is a versatile tool for research and industrial applications, fostering insights into robotic automation and digital twin scalability while reducing costs and risks associated with real-world testing.