Abstract
Gastric cancer (GC) is still a significant public health issue, among the most common and deadly cancers globally. The identification and characterization of precancerous lesions of the stomach using endoscopy are crucial for determining the risk of cancer and guiding appropriate surveillance. In this scenario, deep learning (DL)-based computer vision methods have the potential to help us classify and identify particular patterns in endoscopic images, leading to a more accurate classification of these types of lesions. The quantity and quality of the data used heavily influence the classification performance of DL networks. However, one of the major setbacks for developing high-performance DL classification models is the typical need for more available data in the medical field. This review explores the use of Generative Adversarial Networks (GANs) and classical data augmentation techniques for improving the classification of precancerous stomach lesions. GANs are DL models that have shown promising results in generating synthetic data, which can be used to augment limited medical datasets. This review discusses recent studies that have implemented GANs and classical data augmentation methods to improve the accuracy of cancerous lesion classification. The results indicate that GANs can effectively increase the dataset's size, enhance the classification models' performance. In specific applications, such as the augmentation of endoscopic images depicting gastrointestinal polyps and Barrett's esophagus Adenocarcinoma, our review reveals instances where GANs, including models like Deep Convolutional GANs and conditional GANs, outperform classical data augmentation methods. Furthermore, this review highlights the challenges and limitations of the recent works using GANs and classical data augmentation techniques in medical imaging analysis and proposes directions for future research.

Abstract
In industrial environments, the measurement and monitoring of filling levels (FL) in refilling stations (RS) are critical for quality control processes. Traditional methods used for this purpose, such as manual inspection and sensor-based techniques, have proven to be costly and time-consuming. As an alternative, this paper proposes a novel approach that leverages computer vision (CV) and advanced image processing techniques. This approach provides a more efficient and accurate method for monitoring filling levels in refilling stations, thereby reducing operational costs. The system operates through a comprehensive five-stage pipeline, including pre-processing, perspective transformation, thresholding and edge detection, post-processing and filling level calculation. The performance evaluation of this approach demonstrated promising results in accurately determining filling levels in most scenarios. However, we also identified challenges such as overlapping columns and occlusions in the camera’s field of view that require further improvements. By addressing these challenges, our research aims to develop a streamlined and automated method for filling level measurement in refilling stations, thereby enhancing productivity in industrial environments. Ultimately, this proposed approach holds potential to significantly improve the efficiency of refilling stations across multiple sectors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
A new algorithm based on the ant colony optimization (ACO) method for the multiple traveling salesman problem (mTSP) is presented and defined as ACO-BmTSP. This paper addresses the problem of solving the mTSP while considering several salesmen and keeping both the total travel cost at the minimum and the tours balanced. Eleven different problems with several variants were analyzed to validate the method. The 20 variants considered three to twenty salesmen regarding 11 to 783 cities. The results were compared with best-known solutions (BKSs) in the literature. Computational experiments showed that a total of eight final results were better than those of the BKSs, and the others were quite promising, showing that with few adaptations, it will be possible to obtain better results than those of the BKSs. Although the ACO metaheuristic does not guarantee that the best solution will be found, it is essential in problems with non-deterministic polynomial time complexity resolution or when used as an initial bound solution in an integer programming formulation. Computational experiments on a wide range of benchmark problems within an acceptable time limit showed that compared with four existing algorithms, the proposed algorithm presented better results for several problems than the other algorithms did.

Abstract
In this paper, it is presented an integration between a finishing device and a collaborative robot in order to automate the sanding process of a ceramic industry in Portugal. The finishing device and the collaborative robot are described as well as the communication between the devices. It was used a computer responsible for decision making and control of all the system. The system was able to control the position of the finishing device according to the force done in the sponge by the ceramic. The final system behavior was presented and discussed, which was satisfactory and performed well. The presented experimental setup is not intended for industrial use, but it is suitable for concept prove, in laboratory. The outputs that will emerge here will be applied in a future industrial application, with requisites compatible with the application environment, regarding robustness and repeatability. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Fluorescence sensing plays an important role in high sensitivity, selectivity, and real-time monitoring of biological and environmentally relevant species. Several classes of fluorescent dyes (fluorophores) including rhodamine, BODIPY, 1,8-naphthalimide, and coumarin-among others-when conveniently functionalized with reactive pyridyl receptors, have emerged as effective sensors to detect and quantify chemical species with high accuracy through fluorescent imaging and spectroscopy. Among the sensing targets, monitoring of harmful chemical species, e.g., metal ions (zinc, copper, iron, mercury, cadmium, lead, etc.) and anions (chloride, fluoride, sulfide, thiocyanate, etc.) can be used to understand their physiological and pathological role in live-cells and tissues, as well as to protect human health. This chapter focuses on recent advances in the molecular design of pyridyl-substituted fluorophores, their photophysical properties, and sensing applications.

Abstract
This paper presents an approach for a multi-agent-based cyber-physical system dedicated to operating the warehouse plant with a distributed approach. The recent technological evolution has improved the quality and robustness of the services for current warehouses. However, systems that operate warehouses do not follow this evolution, presenting predominantly central monolithic or hierarchical approaches, resulting in fragility related to flexibility, scalability, and robustness in the face of disturbances. In the proposed approach, each warehouse physical component has a computational unit associated, i.e. a cyber agent, with communication, negotiation, and data analysis capabilities. Agents contain all the information, algorithms, and functions necessary to operate the physical component, and instead of receiving orders from higher-layer agents, they negotiate and collaborate to perform the tasks. The proposed system was tested in a laboratory testbed, composed of six racks and up to eight robots for transporting products. Extensive experiments show the feasibility of the approach. Copyright (c) 2023 The Authors.