Abstract
Concept-based models naturally lend themselves to the development of inherently interpretable skin lesion diagnosis, as medical experts make decisions based on a set of visual patterns of the lesion. Nevertheless, the development of these models depends on the existence of concept-annotated datasets, whose availability is scarce due to the specialized knowledge and expertise required in the annotation process. In this work, we show that vision-language models can be used to alleviate the dependence on a large number of concept-annotated samples. In particular, we propose an embedding learning strategy to adapt CLIP to the downstream task of skin lesion classification using concept-based descriptions as textual embeddings. Our experiments reveal that vision-language models not only attain better accuracy when using concepts as textual embeddings, but also require a smaller number of concept-annotated samples to attain comparable performance to approaches specifically devised for automatic concept generation.

Abstract
The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost-benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.

Abstract
The phase response of all-pole (AP) models is known to be non-linear and highly dependent on the frequency response magnitude. The objective and perceptual impact of the group delay of AP models in the synthesis of vowel sounds has not been thoroughly addressed in the literature. In this paper, we use a dedicated frequency-domain framework so as to i) synthesize a plausible glottal excitation setting the ground-truth for the harmonic phase structure and replicating the fundamental frequency contour of natural vowels, ii) synthesize realistic vowel sounds through all-zero (AZ) and all-pole (AP) models sharing the same frequency response magnitude, and iii) assess the objective and perceptual impact of the group delay of AP models taking as a reference natural vowels and, in particular, the ground-truth harmonic phase structure of the glottal excitation. Our findings emphasize that the non-linear phase characteristics of AP models degrade the harmonic phase structure of synthetic vowels significantly beyond what is found in natural vowels, however, that is not always clearly audible.

Abstract
Purpose - This study investigates the barriers to the adoption of Virtual Reality (VR) in the tourism industry. Although VR has great potential to enhance the tourist experience, the adoption of this technology is still limited in the tourism sector. Building on the fundamental principles of the Technology -Organization -Environment (TOE) theory and its contribution to perceptions of technology adoption, this study aims to fill the knowledge gap regarding the specific barriers to VR adoption by tourism enterprises. Methodology - To achieve this objective, interviews were conducted with managers of tourism companies, and the data was analysed using qualitative methodology through MAXQDA 20 software. Conclusions - The results reveal that the main barriers identified by managers mainly include lack of knowledge about VR, particularly in the tourism sector. The perceived lack of usefulness, limited experience with the technology, and reluctance to invest in technological equipment also emerge as barriers to VR adoption. Originality of research - This study can help companies in the tourism sector to develop more effective strategies to overcome these barriers, thereby improving the tourist experience and increasing their competitiveness in the market using VR equipment.

Abstract
The deployment of energy communities (EC) will foster new business models contributing to the decentralization and democratization of energy access and a reduction in the energy bill of final consumers. This decentralization is only possible if investments are made in production and storage technologies, that must be installed near the locals of consumption, according to common rules of the regulatory frameworks of EC. In this paper we propose a methodology for the optimal sizing of production and shared storage assets, and we assess the cost reduction of considering shared storage assets. We then formulate seven business models (BM) that dictate how to share this benefit among the EC members, and we propose two indicators to assess them. Results show the difficulty in choosing a BM as well as the limitations of the BM and of the indicators.

Abstract
The optimal power flow (OPF) is one of the most noticeable and integral tools in the power system operation and control and aims to obtain the most economical combination of power plants to exactly serve the total demand of the system without any load shedding or islanding through adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, the successful implementation of an expeditious and reliable optimization algorithm is crucial. To solve this issue, this research proposes an enhanced democratic political algorithm (DPA), which can effectively solve multi-objective optimum power flow problems. The proposed method is a version of the democratic political optimization algorithm in which the search capability of this method to cover the borders of the Pareto frontier is enhanced. For the sake of practicality, the objectives with innate differences such as total emission, active power loss, and fuel cost are selected. Due to the practical limitations in real power systems, additional restrictions including valve-point effect, multi-fuel characteristics, and forbidden operational zones, are also considered. The proposed approach is tested and validated on IEEE 57 bus and IEEE 118-bus systems with different case studies. Simulation results are analyzed and compared with two popular and commonly used multi-objective-evolutionary algorithms namely, non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) on the problem. The study results illustrate the effectiveness of the proposed method in handling different scales, non-convex, and multi-objective optimization problems.