Abstract
This research addresses the Frequency Setting Problem (FSP) together with vehicle technology selection for bus fleet sizing and management. A decision support tool was developed that combines a multi-criteria decision analysis, using the Analytic Hierarchy Process (AHP), and an enumeration procedure. The tool assists transportation operators in selecting optimal frequencies and vehicle technologies, considering economic, social, and environmental criteria. Computational experiments performed in the city of Niterói, Brazil, demonstrate the effectiveness of the tool. Scenarios with different criteria prioritizations highlight the flexibility of the approach and emphasize the need for a balance between all the sustainability dimensions. This approach positively impacts public transportation system performance, favouring higher-capacity vehicles while considering demand, and contributing to sustainable urban mobility. © 2024 The Authors.

Abstract

Abstract
This work analyzes the sensitivity of an optical system consisting of two fiber Fabry-Perot ( FP) interferometers and the apparent increase in sensitivity due to the harmonics of the Vernier effect. Two scenarios are examined: (1) when the larger FP cavity acts as the sensor, and (2) when the smaller FP cavity acts as the sensor. The computation analysis reveals that in the first scenario, higher-order spectral harmonics yield greater sensitivity for maxima and minima of the same order. In the second scenario, however, the sensitivity remains constant and does not depend on the harmonic order. Moreover, it is demonstrated that the sensitivity curve is identical for both scenarios, regardless of the harmonic order. This outcome occurs because the use of spectral harmonics simply reduces the free-spectral range in certain situations, bringing the extrema closer to the maximum sensitivity condition (i.e., Delta L = 0) and thereby increasing sensitivity. Consequently, if points on the envelope other than maxima or minima are used, the sensitivity achieved is the same for both scenarios.

Abstract

Abstract
As energy systems evolve, protecting and empowering consumers is vital, enabling participation in decentralized electricity markets and maximizing benefits from energy resources. The integration of Distributed Energy Resources (DER) and Renewable Energy Sources (RES) fosters new energy communities, shifting from centralized systems to distributed structures. Consumers can sell excess production to neighbors, increasing income, reducing bills, and advancing energy transition goals. This paper proposes a community-based peer-to-peer (P2P) energy market model that reduces costs while respecting network constraints. Using the Alternating Direction Method of Multipliers (ADMM), ensures privacy enhancement, decentralization, and scalability. The Relaxed Branch Flow Model (RBFM) manages constraints, and Electric Vehicles (EVs) reduce imports and costs through strategic discharging. Tested on a 33-bus distribution network, the ADMM-based approach aligns closely with a centralized benchmark, showing minor discrepancies while maintaining system reliability. This model underscores the potential of decentralized markets for consumer-centric, flexible, and efficient energy trading.

Abstract
Breast cancer locoregional treatment can cause significant and long-lasting alterations to a patient's body. As various surgical options may be available to a patient and considering the impact that the aesthetic outcome may have on the patient's self-esteem, it is critical for the patient to be adequately informed of the possible outcomes of each treatment when deciding on the treatment plan. With the purpose of simulating how a patient may look like after treatment, we propose a deep generative model to transfer asymmetries caused by treatment from post-operative breast patients into pre-operative images, taking advantage of the inherent symmetry of breast images. Furthermore, we disentangle asymmetries related with the breast shape from the nipple within the latent space of the network, enabling higher control over the alterations to the breasts. Finally, we show the proposed model's wide applicability in medical imaging, by applying it to generate counterfactual explanations for cardiomegaly and pleural effusion prediction in chest radiographs.