Abstract

Abstract
Recycling of post-consumer wood waste into wood-based panels may be hindered by the presence of physical and chemical impurities in the waste stream. Therefore greater attention should be given to assessing the quality of wood waste and in particular to heavy metals contamination. One of the elements that poses concern is Chromium (Cr). Cr compounds can be toxic, particularly hexavalent chromium (Cr(VI)), which is a known human carcinogen. Hence, screening for Cr in wood waste plays a pivotal role in enhancing recycling facility operations and mitigating contamination before final product incorporation. In this study, a Laser-Induced Breakdown Spectroscopy (LIBS) methodology was optimized for screening wood waste for Cr and validated by X-ray Fluorescence (XRF) measurements. LIBS spectral complexity and sample matrix effects challenges were addressed through careful selection of Cr lines and tailored data analysis algorithms. The results showed that LIBS imaging successfully provided a straightforward timely output revealing the contaminated wood samples, crucial for quick decision-making in production lines.

Abstract
The aim of this work is to introduce a novel digital signal processing method for data acquired using low coherence interferometry (LCI) with a 1-kHz actuator oscillation frequency. Convolution and correlation operations are employed as efficient filters, reducing computational complexity for multilayer filtering. An envelope filtering technique is developed to address discrepancies in peak signal determination caused by nonlinear actuator motion. Additionally, a phase linearization method is presented to normalize the peak position relative to the actuator signal. Experimental results demonstrate a significant signal-to-noise ratio (SNR) improvement of 50 dB. Long-term measurements reveal an 11-dB noise reduction for frequencies below 1 mHz. This research enables LCI implementation at sampling rates of at least 1 kHz and expands its applicability to extreme measurement conditions.

Abstract

Abstract
The resource-constrained project scheduling problem is a widely studied problem in the literature. The goal is to construct a schedule for a set of activities, such that precedence and resource constraints are respected and that an objective function is optimized. In project scheduling literature, summary measures are often used as a tool to evaluate the performance of algorithms and to analyze instances and datasets. They can be classified in two groups, network measures describe the precedence constraints of a project, while resource measures focus on the resource constraints of the instance. In this manuscript we make an exhaustive evaluation of the summary measures for project scheduling. We provide an overview of the most prevalent measures and also introduce some new ones. For our tests we combine different datasets from the literature and generate a new set with diverse characteristics. We evaluate the performance of the summary measures on three dimensions: consistency, instance complexity and algorithm selection. We conclude by providing an overview of which measures are best suited for each of the three investigated dimensions.

Abstract
The methodology presented in this work allows for the creation of a real-time adjustment of Kalman Filter process noise for lithium battery state-of-charge estimation. This work innovates by creating a methodology for adjusting the process (Q) and measurement (R) Kalman Filter noise matrices in real-time. The filter algorithm with this adaptative mechanism achieved an average accuracy of 99.56% in real tests by comparing the estimated battery voltage and measured battery voltage. A cell-balancing strategy was also implemented, capable of guaranteeing the safety and efficiency of the battery pack in all conducted tests. This work presents all the methods, equations, and simulations necessary for the development of a battery management system and applies the system in a practical, real environment. The battery management system hardware and firmware were developed, evaluated, and validated on a battery pack with eight LiFePO4 cells, achieving excellent performance on all conducted tests.