Abstract
This paper provides an overview on recent work on Team Automata, whereby a network of automata interacts by synchronising actions from multiple senders and receivers. We further revisit this notion of synchronisation in other well known concurrency models, such as Reo, BIP, Choreography Automata, and Multiparty Session Types. We address realisability of Team Automata, i.e., how to infer a network of interacting automata from a global specification, taking into account that this realisation should satisfy exactly the same properties as the global specification. In this analysis we propose a set of interesting directions of challenges and future work in the context of Team Automata or similar concurrency models.

Abstract
Collaborative robots (cobots) are emerging in manufacturing as a response to the current mass customization production paradigm and the fifth industrial revolution. Before adopting this technology in production processes and benefiting from its advantages, manufacturers need to analyze the investment. Therefore, this study aims to develop a decision -making framework for cobot adoption, incorporating a comprehensive set of quantitative and qualitative criteria, to be used by decision -makers in manufacturing companies. To achieve that objective, a qualitative study was conducted by collecting data through interviews with key actors in the cobot (or advanced manufacturing technologies) adoption decision process in manufacturing companies. The main findings of this study include, firstly, an extensive list of decision criteria, as well as some indicators to be used by decisionmakers, some of which are new to the literature. Secondly, a decision -making framework for cobot adoption is proposed, as well as a set of guidelines to use it. The framework is based on a weighted scoring method and can be customizable by the manufacturing company depending on its specific context, needs, and resources. The main contribution of this study consists in assisting decision -makers of manufacturing companies in performing more complete and sustained decision analyses regarding cobots adoption.

Abstract
Logging has long been a pillar for monitoring and troubleshooting software systems. From server and infrastructure to application-specific data, logs are an easy and quick way to collect information that may prove useful in diagnosing future issues. When systems become distributed, as is common on the cloud, logs are harder to collect and process. This paper presents three design patterns for logging in cloud-native applications. Standard Logging advises using a standard format for logs across all services and teams so they are easier to process by humans and machines. Audit Logging suggests that important user actions and system changes are recorded in a data store to ensure regulatory compliance or help investigate user-reported issues. Lastly, Log Sampling is about prioritizing logs to maintain a manageable amount of storage. These patterns were mined from existing literature on logging and cloud best practices to make them simpler to communicate, more detailed, and easier for all practitioners to understand.

Abstract
Background: ROP Plus Form is an eye disease that can lead to blindness, and diagnosing it requires medical experts to manually examine the retinal condition. This task is challenging due to its subjective nature and poor image quality. Therefore, developing automatic tools for Retinal Blood Vessel Segmentation in fundus images could assist healthcare experts in diagnosing, monitoring, and prognosing the disease. Objective: This study focuses on developing a novel pipeline for automatically segmenting retinal blood vessels. The main requirements are that it can correctly identify the blood vessels in fundus images and perform well on different systems used for newborn evaluation. Methods: The pipeline uses different methods, including CIELAB Enhancement, Background Normalization, BellShaped Gaussian Matched Filtering, Modified Top-Hat operation, and a combination of vesselness filtering composed of Frangi and Jerman Filters. The segmentation is done by determining a threshold using the Triangle Threshold algorithm. A novel filter is also proposed to remove the Optical Disc artifacts from the primary segmentation based on the Circular Hough Transform. The segmentation pipeline is combined with different pretrained Convolution Neural Network architectures to evaluate its automatic classification capabilities. Results: The pipeline was tested with newborn fundus images acquired with Clarity RetCam3 and Phoenix ICON systems. The results were compared against annotations from three ophthalmologic experts. Clarity RetCam3 images achieved an accuracy of 0.94, specificity of 0.95, and sensitivity of 0.81, while Phoenix ICON images achieved an accuracy of 0.94, specificity of 0.97, and sensitivity of 0.83. The pipeline was also tested for the DRIVE Database, achieving an accuracy of 0.95, specificity of 0.97, and sensitivity of 0.82. For the classification task, the best results were achieved with the DenseNet121 architecture with an accuracy of 0.946. Conclusion: The segmentation scores were auspicious and confirmed the clinical relevance of the proposed pipeline. It has also proven to have a good generalization performance, essential for easier clinic integration. Finally, preliminary results on using CNNs showed how our work can be used to develop fully automatic tools for diagnosing ROP Plus form disease.

Abstract
The first scientific observations with adaptive optics (AO) at W. M. Keck Observatory (WMKO) began in 1999. Through 2023, over 1200 refereed science papers have been published using data from the WMKO AO systems. The scientific competitiveness of AO at WMKO has been maintained through a continuous series of AO and instrument upgrades and additions. This tradition continues with AO being a centerpiece of WMKO's scientific strategic plan for 2035. We will provide an overview of the current and planned AO projects from the context of this strategic plan. The current projects include implementation of new real-time controllers, the KAPA laser tomography system and the HAKA high-order deformable mirror system, the development of multiple advanced wavefront sensing and control techniques, the ORCAS space-based guide star project, and three new AO science instruments. We will also summarize steps toward the future strategic directions which are centered on ground-layer, visible and high-contrast AO.

Abstract
Manufacturing industry is experiencing another revolution towards the digitalization of industrial processes. Different value chain actors must share specific and sensitive data according to business and data requirements. Digital architectures must ensure seamless and comprehensive communications between actors according to agreed-upon vocabularies. The digital representation of machines and other types of equipment, including crucial information about their static and dynamic operational data, is made possible by the ontological modelling of Asset Administration Shells (AAS), which is proposed in this paper as modular and semantically interoperable resources. These Cognitive Digital Twins are herein defined with de facto domain ontologies that model the semantics of the current operation, status and configurations of assets. This paper reports a proof-of-concept technical implementation that demonstrates an innovative digital architecture that connects and communicates active and modular Digital Twin of a machine in a bi-directional, connecting this asset to a digital manufacturing service provider.