Abstract
The main aim of the paper was to estimate the impact of older employees aged 55-74 on productivity compared with other age groups. The research (foundation of the paper) was conducted in 72 countries in the period 2000-2021. Countries were divided into three groups composed of 24 economies according to their GDP pc in 2021, i.e. the lowest GDP pc, the middle GDP pc, and the highest GDP pc countries. The study covered the five age and sex groups of employees: 25-34, 35-44, 45-54, 55-64, and 65-74. The Productive Capacities Index (PCI) built by UNCTAD and its selected categories were assumed to be dependent variables. The research results obtained for three groups of countries indicate that older employees had stronger positive impact on improving PCI than younger employees, especially on human capital development and private sector productivity. Our outcomes also suggest that older employees are better integrated than younger age groups with ICT tools that increase work productivity. To sum up, we can state that older employees can be a key factor in economic development due to their knowledge and experience, provided that others are willing to learn from them and they receive relevant organizational support.

Abstract
Background: Most previous studies assessing multimorbidity in asthma assessed the frequency of individual comorbid diseases. Objective: We aimed to assess the frequency and clinical and economic impact of co-occurring groups of comorbidities (comorbidity patterns using the Charlson Comorbidity Index) on asthma hospitalizations. Methods: We assessed the dataset containing a registration of all Portuguese hospitalizations between 2011-2015. We applied three different approaches (regression models, association rule mining, and decision trees) to assess both the frequency and impact of comorbidities patterns in the length-of-stay, in-hospital mortality and hospital charges. For each approach, separate analyses were performed for episodes with asthma as main and as secondary diagnosis. Separate analyses were performed by participants' age group. Results: We assessed 198340 hospitalizations in patients >18 years old. Both in hospitalizations with asthma as main or secondary diagnosis, combinations of diseases involving cancer, metastasis, cerebrovascular disease, hemiplegia/paraplegia, and liver disease displayed a relevant clinical and economic burden. In hospitalizations having asthma as a secondary diagnosis, we identified several comorbidity patterns involving asthma and associated with increased length-of-stay (average impact of 1.3 [95%CI=0.6-2.0]-3.2 [95%CI=1.8-4.6] additional days), in-hospital mortality (OR range=1.4 [95%CI=1.0-2.0]-7.9 [95%CI=2.6-23.5]) and hospital charges (average additional charges of 351.0 [95%CI=219.1-482.8] to 1470.8 [95%CI=1004.6-1937.0]) Euro compared with hospitalizations without any registered Charlson comorbidity). Consistent results were observed with association rules mining and decision tree approaches. Conclusions: Our findings highlight the importance not only of a complete assessment of patients with asthma, but also of considering the presence of asthma in patients admitted by other diseases, as it may have a relevant impact on clinical and health services outcomes.

Abstract
Oceans all over the world are an important way of sustainability in the lives of many people and have a high impact on the economy of most of the coastal countries. With the growth of underwater activity provided by the development of autonomous and remotely controlled vehicles and with the appearance of new underwater sensors, there is also a need to develop and design more robust underwater wireless networks to provide better and faster communications among the devices connected to the network. Nowadays several technologies provide wireless underwater communications. In this work, we address acoustic technology and the implementation of an acoustic communication system which applies a version of frequency modulation. The main goal of this work is to study the 4-FSK modulation technique and verify the efficiency of the communication system according to variables such as communication distance and baud rate. This implementation uses FPGA systems and Xilinx Vitis Model Composer software and MATLAB Simulink software for simulation. The developed communication system was tested in a controlled environment at two stages: aquarium and pool. The tests were carried out transmitting at 3 different baud rates (40, 100 and 200 kbps) in a distance of 100 cm in the aquarium and 5 meters in the pool.

Abstract
Fast and precise identification of minerals in geological samples is of paramount importance for the study of rock constituents and for technological applications in the context of mining. However, analyzing samples based only on the extrinsic properties of the minerals such as color can often be insufficient, making additional analysis crucial to improve the accuracy of the methods. In this context, Laser-induced breakdown spectroscopy mapping is an interesting technique to perform the study of the distribution of the chemical elements in sample surfaces, thus allowing deeper insights to help the process of mineral identification. In this work, we present the development and deployment of a processing pipeline and algorithm to identify spatial regions of the same mineralogical composition through chemical information in a fast and automatic way. Furthermore, by providing the necessary labels to the results on a training sample, we can turn this unsupervised methodology into a classifier that can be used to generalize and classify minerals in similar but unseen samples. The results obtained show good accuracy in reproducing the expected mineral regions and extend the interpretability of previous unsupervised methods with a visualization tool for cluster assignment, thus paving for future applications in contexts requiring high-throughput mineral identification systems, such as mining.

Abstract
Stress is a common feeling in people's day-to-day life, especially at work, being the cause of several health problems and absenteeism. Despite the difficulty in identifying it properly, several studies have established a correlation between stress and perceivable human features. The problem of detecting stress has attracted significant attention in the last decade. It has been mainly addressed through the analysis of physiological signals in the execution of specific tasks in controlled environments. Taking advantage of technological advances that allow to collect stress-related data in a non-invasive way, the goal of this work is to provide an alternative approach to detect stress in the workplace without requiring specific controlled conditions. To this end, a video-based plethysmography application that analyses the person's face and retrieves several physiological signals in a non-invasive way was used. Moreover, in an initial phase, additional information that complements and labels the physiological data was obtained through a brief questionnaire answered by the participants. The data collection pilot took place over a period of two months, having involved 28 volunteers. Several stress detection models were developed; the best trained model achieved an accuracy of 86.8% and a F1 score of 87% on a binary stress/non-stress prediction.

Abstract