Abstract
The refractive index of the pigs heart was measured at wavelengths between 255 and 850 nm to calculate the dispersion. The total transmittance and total reflectance spectra of the pig heart were measured between 200 and 1000 nm to calculate the spectral absorption coefficient. Using Kramers-Kronig relations, the dispersion of the heart was matched to experimental refractive index values. © 2024 IEEE.

Abstract
Paraxial fluids of light have recently emerged as promising analog physical simulators of quantum fluids using laser propagation inside nonlinear optical media. In particular, recent works have explored the versatility of such systems for the observation of two-dimensional quantum-like turbulence regimes, dominated by quantized vortex formation and interaction that results in distinctive kinetic energy power laws and inverse energy cascades. In this manuscript, we explore a regime analog to Kelvin-Helmholtz instability to examine in further detail the qualitative dynamics involved in the transition from smooth laminar flow to turbulence at the interface of two fluids with distinct velocities. Both numerical and experimental results reveal the formation of a vortex sheet as expected, with a quantized number of vortices determined by initial conditions. Using an effective length transformation scale we get a deeper insight into the vortex formation phase, observing the appearance of characteristic power laws in the incompressible kinetic energy spectrum that are related to the single vortex structures. The results enclosed demonstrate the versatility of paraxial fluids of light and may set the stage for the future observation of distinct classes of phenomena recently predicted to occur in these systems, such as radiant instability and superradiance.

Abstract
This paper presents an efficient and secure method for updating firmware in IoT devices using LoRaWAN network resources and communication protocols. The proposed method involves dividing the firmware into fragments, storing them in the application server's database, and transmitting them to remote IoT devices via downlink messages, without necessitating any changes to the device's class. This approach can be replicated across any IoT LoRaWAN device, offering a robust and scalable solution for large-scale firmware updates while ensuring data security and integrity. The proposed method significantly reduces the downtime of IoT devices and enhances the energy efficiency of the update process. The method was validated by updating a block in the program memory, associated to a specific functionality of the IoT end device. The associated Intel Hex file was segmented into 17 LoRaWAN downlink frames with an average size of 46 bytes. Upon receiving the complete firmware update, the microcontroller employs self-programming techniques that restrict the update process to specific rows of the program memory, avoiding interruptions or reboots. The update process was successfully completed in 51.33 ms, resulting in a downtime of 16.88 ms. This method demonstrates improved energy efficiency compared to existing solutions while preserving the communication network's capacity, making it an adequate solution for remote devices in LoRaWAN networks.

Abstract
Here we define a new unification algorithm for terms interpreted in semantic domains denoted by a subclass of regular types here called deterministic regular types. This reflects our intention not to handle the semantic universe as a homogeneous collection of values, but instead, to partition it in a way that is similar to data types in programming languages. We first define the new unification algorithm which is based on constraint generation and constraint solving, and then prove its main properties: termination, soundness, and completeness with respect to the semantics. Finally, we discuss how to apply this algorithm to a dynamically typed version of Prolog. © 2025 Open Publishing Association. All rights reserved.

Abstract

Abstract
An automatic system for pathology classification in chest X-ray scans needs more than predictive performance, since providing explanations is deemed essential for fostering end-user trust, improving decision-making, and regulatory compliance. CLARE-XR is a novel methodology that, when presented with an X-ray image, identifies the associated pathologies and provides explanations based on the presentation of similar cases. The diagnosis is achieved using a regression model that maps an image into a 2D latent space containing the reference coordinates of all findings. The references are generated once through label embedding, before the regression step, by converting the original binary ground-truth annotations to 2D coordinates. The classification is inferred minding the distance from the coordinates of an inference image to the reference coordinates. Furthermore, as the regressor is trained on a known set of images, the distance from the coordinates of an inference image to the coordinates of the training set images also allows retrieving similar instances, mimicking the common clinical practice of comparing scans to confirm diagnoses. This inherently interpretable framework discloses specific classification rules and visual explanations through automatic image retrieval methods, outperforming the multi-label ResNet50 classification baseline across multiple evaluation settings on the NIH ChestX-ray14 dataset.