Abstract
Gastric Cancer (GC) and Colorectal Cancer (CRC) are some of the most common cancers in the world. The most common diagnostic methods are upper endoscopy and biopsy. Possible expert distractions can lead to late diagnosis. GC is a less studied malignancy than CRC, leading to scarce public data that difficult the use of AI detection methods, unlike CRC where public data are available. Considering that CRC endoscopic images present some similarities with GC, a CRC Transfer Learning approach could be used to improve AI GC detectors. This paper evaluates a novel Transfer Learning approach for real-time GC detection, using a YOLOv4 model pre-trained on CRC detection. The results achieved are promising since GC detection improved relatively to the traditional Transfer Learning strategy.

Abstract
The integration of explanation techniques promotes the comprehension of a model's output and contributes to its interpretation e.g. by generating heat maps highlighting the most decisive regions for that prediction. However, there are several drawbacks to the current heat map-generating methods. Probability by itself is not indicative of the model's conviction in a prediction, as it is influenced by multiple factors, such as class imbalance. Consequently, it is possible that a model yields two true positive predictions - one with an accurate explanation map, and the other with an inaccurate one. Current state-of-the-art explanations are not able to distinguish both scenarios and alert the user to dubious explanations. The goal of this work is to represent these maps more intuitively based on how confident the model is regarding the diagnosis, by adding an extra validation step over the state-of-the-art results that indicates whether the user should trust the initial explanation or not. The proposed method, Confident-CAM, facilitates the interpretation of the results by measuring the distance between the output probability and the corresponding class threshold, using a confidence score to generate nearly null maps when the initial explanations are most likely incorrect. This study implements and validates the proposed algorithm on a multi-label chest X-ray classification exercise, targeting 14 radiological findings in the ChestX-Ray14 dataset with significant class imbalance. Results indicate that confidence scores can distinguish likely accurate and inaccurate explanations. Code available via GitHub. © 2023 IEEE.

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.

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Blockchain technology is expected to play a pivotal role in innovation across various sectors in the near future, facilitating long-term and sustainable growth for businesses and the economy. The food retail sector, in particular, can greatly benefit from blockchain technology, as food safety remains a top priority for stakeholders, particularly consumers. This chapter examines the advantages of blockchain technology in the food retail sector and presents a case study of a Portuguese food retail corporation. The case study explores whether the company should expedite the adoption of blockchain technology in its supply chain management. The conclusions drawn from this study help managers in making informed decisions regarding future strategy implementation, intending to enhance supply chain efficiency, safety, and transparency. Furthermore, these findings will provide valuable insights for other players in the food retail sector grappling with similar challenges. © 2023, IGI Global.