Abstract
This work proposes a signal processing algorithm to analyse the optical signal from a Low Coherence Interferometric (LCI) system. The system uses a Mach-Zehnder (MZ) interferometer to interrogate a Fabry-Perot cavity, working as an optical sensor. This algorithm is based on the correlation and convolution operations, which allows the signal to be reconstructed based on itself, as well as, on the linearization of the signal phase, allowing the non-linearities of the actuator incorporated on the MZ interferometer to be compensated. The results show a noise reduction of 30 dB in the signal acquired. As a result, a reduction of 8.2 dB in the uncertainty of the measurement of the physical measurand is achieved. It is also demonstrated that the phase linearization made it possible to obtain a coefficient of determination (namely, R-squared) higher than 0.999.

Abstract
As lithium-bearing minerals become critical raw materials for the field of energy storage and advanced technologies, the development of tools to accurately identify and differentiate these minerals is becoming essential for efficient resource exploration, mining, and processing. Conventional methods for identifying ore minerals often depend on the subjective observation skills of experts, which can lead to errors, or on expensive and time-consuming techniques such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) or Optical Emission Spectroscopy (ICPOES). More recently, Raman Spectroscopy (RS) has emerged as a powerful tool for characterizing and identifying minerals due to its ability to provide detailed molecular information. This technique excels in scenarios where minerals have similar elemental content, such as petalite and spodumene, by offering distinct vibrational information that allows for clear differentiation between such minerals. Considering this case study and its particular relevance to the lithium- mining industry, this manuscript reports the development of an unsupervised methodology for lithium-mineral identification based on Raman Imaging. The deployed machine-learning solution provides accurate and interpretable results using the specific bands expected for each mineral. Furthermore, its robustness is tested with additional blind samples, providing insights into the unique spectral signatures and analytical features that enable reliable mineral identification.

Abstract
As social media (SM) becomes increasingly prevalent, its impact on society is expected to grow accordingly. While SM has brought positive transformations, it has also amplified pre-existing issues such as misinformation, echo chambers, manipulation, and propaganda. A thorough comprehension of this impact, aided by state-of-the-art analytical tools and by an awareness of societal biases and complexities, enables us to anticipate and mitigate the potential negative effects. One such tool is BERTopic, a novel deep-learning algorithm developed for Topic Mining, which has been shown to offer significant advantages over traditional methods like Latent Dirichlet Allocation (LDA), particularly in terms of its high modularity, which allows for extensive personalization at each stage of the topic modeling process. In this study, we hypothesize that BERTopic, when optimized for Twitter data, can provide a more coherent and stable topic modeling. We began by conducting a review of the literature on topic-mining approaches for short-text data. Using this knowledge, we explored the potential for optimizing BERTopic and analyzed its effectiveness. Our focus was on Twitter data spanning the two years of the 117th US Congress. We evaluated BERTopic's performance using coherence, perplexity, diversity, and stability scores, finding significant improvements over traditional methods and the default parameters for this tool. We discovered that improvements are possible in BERTopic's coherence and stability. We also identified the major topics of this Congress, which include abortion, student debt, and Judge Ketanji Brown Jackson. Additionally, we describe a simple application we developed for a better visualization of Congress topics.

Abstract
Chest radiography has been widely used for automatic analysis through deep learning (DL) techniques. However, in the manual analysis of these scans, comparison with images at previous time points is commonly done, in order to establish a longitudinal reference. The usage of longitudinal information in automatic analysis is not a common practice, but it might provide relevant information for desired output. In this work, the application of longitudinal information for the detection of cardiomegaly and change in pairs of CXR images was studied. Multiple experiments were performed, where the inclusion of longitudinal information was done at the features level and at the input level. The impact of the alignment of the image pairs (through a developed method) was also studied. The usage of aligned images was revealed to improve the final mcs for both the detection of pathology and change, in comparison to a standard multi-label classifier baseline. The model that uses concatenated image features outperformed the remaining, with an Area Under the Receiver Operating Characteristics Curve (AUC) of 0.858 for change detection, and presenting an AUC of 0.897 for the detection of pathology, showing that pathology features can be used to predict more efficiently the comparison between images. In order to further improve the developed methods, data augmentation techniques were studied. These proved that increasing the representation of minority classes leads to higher noise in the dataset. It also showed that neglecting the temporal order of the images can be an advantageous augmentation technique in longitudinal change studies.

Abstract
Pickup and delivery problems are frequently encountered problems in transport companies. This paper presents a variant of the homogeneous vehicle, single-to-single Pickup and Delivery Problem with Time Windows, where several vehicles must fulfill transport requests from pickup nodes to delivery nodes, called missions, with associated service level agreements (SLA). A mathematical programming model is proposed to tackle this variant, focused on optimizing the allocation and sequencing of missions to be executed by autonomous vehicles. Numerical experiments are performed comparing instances with missions with long and short SLAs. The results show that the model takes longer to find the optimal solution when the missions have short SLAs and increased difficulty in meeting them if the number of vehicles is limited.

Abstract
Although implementing technologies is a continuous practice observed in organisations, many need help to achieve successful implementations and recognise its impact on their operations and outcomes. Therefore, this review paper aims to present the critical success factors that organisations consider when implementing technology in the Talent Management field. A comprehensive understanding of the technological implementation phenomenon requires adopting a strategic perspective. Consequently, this literature review centres on three clusters: challenges organisations are addressing (Challenges), the technological capabilities and the implementation/adoption process (Technology) and the expected impact (Impact). Findings indicate that a central area of research is the integration of technology in recruitment and, particularly, in the context of Small and Medium Enterprises. Digital Transformation, the Industrial Revolution, and a more diverse workforce are challenges that organisations face. Organisations aim to streamline Human Resources Management (HRM) practices, prioritising data-driven decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.