Abstract
Cooperative robotics is exponentially gaining strength in scientific research, especially regarding the cooperation between ground mobile robots and Unmanned Aerial Vehicles (UAVs), where the remaining challenges are equipollent to its potential uses in different fields, such as agriculture and electrical tower inspections. Due to the complexity involved in the process, precision landing by UAVs on moving robotic platforms for tasks such as battery hot-swapping is a major open research question. This work explores the feasibility and accuracy of different fiducial markers to aid in the precision landing process by a UAV on a mobile robotic platform. For this purpose, a TelloUAV was used to acquire images at different positions, angles, and distances from ArUco, ARTag, and ArUco Board markers to evaluate their detection precision. The analyses demonstrate the highest reliability in the measurements performed through the ArUco marker. Future work will be devoted to using the ArUco marker to perform precision landing on a mobile robotic platform, considering the necessary adjustments to lessen the impact of errors intrinsic to detecting the fiducial marker during the landing procedure.

Abstract
The application of optical fibers in optogenetics is rapidly expanding due to their compactness, cost-effectiveness, sensitivity, and accuracy. This paper introduces a twin-core optical fiber (TCF) sensor employing a Mach-Zehnder interferometer (MZI) to monitor the optogenetic response of opsin-expressing human dental pulp stem cells (hDPSCs) based on refractive index (RI) measuring. In order to improve the RI sensitivity of the sensor, an in fiber Mach-Zeander modulator formed using TCF optics segments can detect changes in the RI in the surrounding medium, and in order to improve the RI sensitivity of the sensor, it is proposed to etch one side of the TCF cladding. The RI sensitivity of the sensor was obtained 233.62 nm/RIU in the range of 1.33-1.4 RIU and 870.01 nm/RIU in the range of 1.4-1.43 RIU, R2 = 0.99. simulation results show that in terms of sensor sensitivity and spectral response, there is a good agreement between the theoretical and experimental results, indicating that the TCF-MZI sensor can perform optical neural recording. In vitro experiments monitored wavelength changes in opsin-expressing and non-opsin-expressing in human dental pulp stem cells (hDPSCs) during optogenetic stimulation with 473 nm pulsed illumination. The results revealed that optical stimulation of ChR2 opsin-expressing hDPSCs leads to active the light sensitive ion channel and changing the effective RI of the surrounding medium. The neural activity is driven by changes in intracellular and extracellular ion concentrations, which lead to alterations in the RI of the cell medium RI variations detectable by the sensor. The novel sensor structure demonstrated its ability to detect RI changes in the cell medium during optogenetic stimulation and fiber optic sensors can be a good candidate for optical recording of the neural activity. Beyond these in vivo applications, label free fiber optic biosensors-based IR measurement can be used for all optical multifunctional probe in stimulation, recording, and sensing of neuroscience applications.

Abstract
Although the classification of chest radiographs has long been an extensively researched topic, interest increased significantly with the onset of the COVID-19 pandemic. Existing results are promising; however, the radiological similarities between COVID-19 and other types of respiratory diseases limit the success of conventional image classification approaches that focus on single instances. This study proposes a novel perspective that conceptualizes COVID-19 pneumonia as a deviation from a normative distribution of typical pneumonia patterns. Using a population- based approach, our approach utilizes distributional anomaly detection. This method diverges from traditional instance-wise approaches by focusing on sets of scans instead of individual images. Using an autoencoder to extract feature representations, we present instance-based and distribution-based assessments of the separability between COVID-positive and COVIDnegative pneumonia radiographs. The results demonstrate that the proposed distribution-based methodology outperforms conventional instance-based techniques in identifying radiographic changes associated with COVID-positive cases. This underscores its potential as an early warning system capable of detecting significant distributional shifts in radiographic data. By continuously monitoring these changes, this approach offers a mechanism for early identification of emerging health trends, potentially signaling the onset of new pandemics and enabling prompt public health responses.

Abstract
Accurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning solution employing Convolutional Neural Networks (CNN) to tackle the localization problem, specifically in the context of the RobotAtFactory 4.0 competition. The proposed approach leverages transfer learning from the pre-trained VGG16 model to capitalize on its existing knowledge. To validate the effectiveness of the approach, a simulated scenario was employed. The experimental results demonstrated an error within the millimeter scale and rapid response times in milliseconds. Notably, the presented approach offers several advantages, including a consistent model size regardless of the number of training images utilized and the elimination of the need to know the absolute positions of the fiducial markers.

Abstract
This research addresses the significant challenge of automating the annotation of medical images, with a focus on capsule endoscopy videos. The study introduces a novel approach that synergistically combines Deep Learning and Content-Based Image Retrieval (CBIR) techniques to streamline the annotation process. Two pre-trained Convolutional Neural Networks (CNNs), MobileNet and VGG16, were employed to extract and compare visual features from medical images. The methodology underwent rigorous validation using various performance metrics such as accuracy, AUC, precision, and recall. The MobileNet model demonstrated exceptional performance with a test accuracy of 98.4%, an AUC of 99.9%, a precision of 98.2%, and a recall of 98.6%. On the other hand, the VGG16 model achieved a test accuracy of 95.4%, an AUC of 99.2%, a precision of 97.3%, and a recall of 93.5%. These results indicate the high efficacy of the proposed method in the automated annotation of medical images, establishing it as a promising tool for medical applications. The study also highlights potential avenues for future research, including expanding the image retrieval scope to encompass entire endoscopy video databases.

Abstract
Exploring a rich administrative matched employer -employee longitudinal dataset over the 2002-2020 period and a task -based approach, this study investigates to what extent the recent paradigm shift in the electricity sector has affected the structure of employment and wages in the Portuguese case. Our results show that the liberalization in the sector led to the entry of new players and firms' downsizing of the workforce, most notably in occupations involving routine cognitive tasks and non -routine manual tasks. In two decades, the employment share of occupations involving non -routine cognitive tasks (abstract or interactive) doubled, from 29.7% in 2002 to 58.1% in 2020. Regarding wage premiums, the results reveal a clear positive trend in real hourly wages for all types of occupations in the sector. However, we observe a lower wage growth acceleration for workers employed in routine (cognitive or manual) occupations, when compared with similar workers employed in non -routine occupations (cognitive or manual). Our findings are partly consistent with the skill -biased and routine -biased technological change hypotheses in the sense that we observe, respectively, a skill up -grading translated into an increase in employment share in non -routine cognitive occupations and a substantial decline in employment share in routine cognitive occupations.