Abstract
The concept of sustainable fashion is becoming more relevant in today's society. The purpose of this research is to identify the determinants of the purchase intention of sustainable fashion, and the relationship between price and the purchase of sustainable fashion. A questionnaire was administered, which made it possible to define the concept of sustainable fashion, to use PLS-SEM to identify the determinants, and to apply linear regression models and t-tests of two independent samples (two-tailed test). The concept of sustainable fashion comprises the dimensions of manufacturing with a reduced environmental impact, consuming second-hand fashion products, manufacturing in an environmentally friendly way, reusing fashion products, manufacturing to last longer, manufacturing according to fair trade principles, using recycled materials, and manufacturing from organic materials. The PLS-SEM results show that purchase intention is determined by consumer knowledge, environmental beliefs, and willingness to pay more. The research also revealed that there is a non-linear (quadratic or exponential) relationship between the price of the product and the price increase that consumers are willing to pay and that they value the dimensions of sustainable fashion differently. The purchase intention determinants of consumers and non-consumers of sustainable fashion are identical, yet the dimensions of sustainable fashion are valued differently by each group.

Abstract
This paper presents a simulation model of a Time of Flight distance sensor applying SimTwo robotics simulator in order to contribute to a mobile robotics application, in an educational context. The objective is to observe the sensor behavior, inside the simulation environment, face a set of experiments, such as an abrupt difference of distance, several angle inclinations and measurements to the maximum sensor range. The tests were performed using SimTwo being a high performance, open source, versatile, real time simulation environment, in which is possible to configure an specific sensor adding its features, which allows to achieve a realistic simulation. The results represented the expected sensor behavior for the proposed scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model's ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model's decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes.

Abstract
After the learning process, certain types of images may not be modeled correctly because they were not well represented in the training set. These failures can then be compensated for by collecting more images from the real-world and incorporating them into the learning process - an expensive process known as active learning. The proposed twist, called active supervision, uses the model itself to change the existing images in the direction where the boundary is less defined and requests feedback from the user on how the new image should be labeled. Experiments in the context of class imbalance show the technique is able to increase model performance in rare classes. Active human supervision helps provide crucial information to the model during training that the training set lacks.

Abstract
Lack of timeliness and capacity are seen as fundamental problems that jeopardise healthcare delivery systems everywhere. Many believe the shortage of medical providers is causing this timeliness problem. This action research presents how one doctor implemented the theory of constraints (TOC) to improve the throughput (quantity of patients treated) of his ophthalmology imaging practice by 64% in a few weeks with little to no expense. The five focusing steps (5FS) guided the TOC implementation - which included the drum-buffer-rope scheduling and buffer management - and occurred in a matter of days. The implementation provided significant bottom-line results almost immediately. This article explains each step of the 5FS in general terms followed by specific applications to healthcare services, as well as the detailed use in this action research. Although TOC successfully addressed the practice problems, this implementation was not sustained after the TOC champion left the organisation. However, this drawback provided valuable knowledge. The article provides insightful knowledge to help readers implement TOC in their environments to provide immediate and significant results at little to no expense.

Abstract
[No abstract available]