Abstract
A flexible wearable sensor utilizing a balloon-shaped interferometer structure, created from a bent standard single-mode fiber and a 3D-printed piece, was introduced and shown for respiratory monitoring. The interferometer is a compact, cost-effective, and easily fabricated sensor. The fiber's curvature causes interference between the core and cladding modes, which in turn results in the sensor operation. In the balloon-shaped curving section, light traversing the core partially escapes and interacts with the cladding. The preliminary results demonstrate an average displacement of 9.3 nm and the capability to evaluate breathing rate.

Abstract
This paper presents a Local Energy Market (LEM) model based on Walrasian Auctions for near real-time energy trading among peers in an Energy Community. The market operates with minimal information exchange, where peers only indicate trade decisions and quantities. The auctioneer updates prices iteratively to balance supply and demand. Two core algorithms support the LEM: (1) the Auctioneer Price Decision Algorithm, which adjusts prices based on past imbalances, and (2) a real-time bidding optimization algorithm, which optimizes peers' energy dispatch and local energy trading decisions based on expected demand, generation, storage, and opportunity costs of external trading. This work details the design and implementation of the bidding optimization algorithm and evaluates its performance through simulations. The results compare the LEM to a centralized pool-based market and individual optimizations, assessing its efficiency and imbalance control. The findings support the development of innovative and decentralized energy markets and smart grid applications.

Abstract

Abstract
The proliferation of disinformation on social media platforms poses a significant challenge to the reliability of online information ecosystems and the protection of public discourse. This study investigates the role of emotional sequences in detecting intentionally misleading messages disseminated on social networks. To this end, we apply a methodological pipeline that combines semantic segmentation, automatic emotion recognition, and sequential pattern mining. Emotional sequences are extracted at the subsentence level, preserving each message’s temporal order of emotional cues. Comparative analyses reveal that disinformation messages exhibit a higher prevalence of negative emotions, particularly fear, anger, and sadness, interspersed with neutral segments. Moreover, false messages frequently employ complex emotional progressions—alternating between high-intensity negative emotions and emotionally neutral passages—designed to capture attention and maximize engagement. In contrast, messages from reliable sources tend to follow simpler, more linear emotional trajectories, with a greater prevalence of positive emotions such as joy. Our dataset encompasses multiple categories of disinformation, enabling a fine-grained analysis of how emotional sequencing varies across different types of misleading content. Furthermore, we validate our approach by comparing it against a publicly available disinformation dataset, demonstrating the generalizability of our findings. The results highlight the importance of analyzing temporal emotional patterns to distinguish disinformation from verified content, reinforcing the value of integrating emotional sequences into machine learning pipelines to enhance disinformation detection. This work contributes to the growing body of research emphasizing the relationship between emotional manipulation and the virality of misleading content online.

Abstract
Contracts and assertions are effective methods to enhance software quality by enforcing preconditions, postconditions, and invariants. Previous research has demonstrated the value of contracts in traditional software development. However, the adoption and impact of contracts in the context of mobile app development, particularly of Android apps, remain unexplored. To address this, we present the first large-scale empirical study on the use of contracts in Android apps, written in Java or Kotlin. We consider contract elements divided into five categories: conditional runtime exceptions, APIs, annotations, assertions, and other. We analyzed 2,390 Android apps from the F-Droid repository and processed 52,977 KLOC to determine 1) how and to what extent contracts are used, 2) which language features are used to denote contracts, 3) how contract usage evolves from the first to the last version, and 4) whether contracts are used safely in the context of program evolution and inheritance. Our findings include: 1) although most apps do not specify contracts, annotation-based approaches are the most popular; 2) apps that use contracts continue to use them in later versions, but the number of methods increases at a higher rate than the number of contracts; and 3) there are potentially unsafe specification changes when apps evolve and in subtyping relationships, which indicates a lack of specification stability. Finally, we present a qualitative study that gathers challenges faced by practitioners when using contracts and that validates our recommendations.

Abstract