Abstract
In this study, different configurations based on linear fiber lasers were proposed and experimentally demonstrated to measure the concentration of liquid solutions. Samples of paracetamol liquid solutions with different concentrations, in the range from 52.61 to 201.33 g/kg, were used as a case-study. The optical gain was provided by a commercial bidirectional Erbium-Doped Fiber Amplifier (EDFA) and the linear cavity was obtained using two commercial Fiber Bragg Gratings (FBGs). The main difference of each configuration was the coupling ratio of the optical coupler used to extract the system signal. The sensing head corresponded to a Single-Mode Fiber (SMF) tip that worked as an intensity sensor. The results reveal that, despite the optical coupler used (50:50, 60:40, 70:30 or 80:20), all the configurations reached the laser condition, however, the concentration sensing was only possible using a laser drive current near to the threshold value. The configurations using a 70:30 and an 80:20 optical coupler allowed paracetamol concentration measurements with a higher sensitivity of (-3.00 +/- 0.24) pW/(g/kg) to be performed. In terms of resolution, the highest value obtained was 1.75 g/kg, when it was extracted at 20% of the output power to the linear cavity fiber laser configuration.

Abstract
The paper introduces Opportunistic Federated Learning (OFL) as an approach to enhance the efficiency of distributed learning in intelligent IoT systems. OFL allows any node in the network to initiate a learning task and collaboratively use local resources. The framework enables nodes to adapt configurations based on circumstances, optimizing resource utilization. Hence, this paper proposes a reliable node selection mechanism that accommodates the dynamic nature of local data and computing resources. Incentives for participating nodes are explored through a peer-to-peer communication using the Bertrand game to determine optimal pricing strategies. Results demonstrate the Nash equilibrium of the game-based incentive mechanism in a realistic FL setup.

Abstract
Contrastive Analysis (CA) regards the problem of identifying patterns in images that allow distinguishing between a background (BG) dataset (i.e. healthy subjects) and a target (TG) dataset (i.e. unhealthy subjects). Recent works on this topic rely on variational autoencoders (VAE) or contrastive learning strategies to learn the patterns that separate TG samples from BG samples in a supervised manner. However, the dependency on target (unhealthy) samples can be challenging in medical scenarios due to their limited availability. Also, the blurred reconstructions of VAEs lack utility and interpretability. In this work, we redefine the CA task by employing a self-supervised contrastive encoder to learn a latent representation encoding only common patterns from input images, using samples exclusively from the BG dataset during training, and approximating the distribution of the target patterns by leveraging data augmentation techniques. Subsequently, we exploit state-of-the-art generative methods, i.e. diffusion models, conditioned on the learned latent representation to produce a realistic (healthy) version of the input image encoding solely the common patterns. Thorough validation on a facial image dataset and experiments across three brain MRI datasets demonstrate that conditioning the generative process of state-of-the-art generative methods with the latent representation from our self-supervised contrastive encoder yields improvements in the generated image quality and in the accuracy of image classification. The code is available at https://github.com/CristianoPatricio/unsupervised-contrastive-cond-diff.

Abstract
At a time when European countries try to cope with escalating energy prices while decarbonizing their economies, waste heat recovery and reuse arises as part of the solution for sustainable energy transitions. The lack of appropriate assessment tools has been pointed out as one of the main barriers to the wider deployment of waste heat recovery projects and as a reason why its potential remains largely untapped. The EMB3Rs platform emerges as an online, open-source, comprehensive and novel tool that provides an integrated assessment of different types of waste heat recovery solutions, (e.g. internal or external) and comprises several analysis dimensions (e.g. physical, geographical, technical, market, and business models). It has been developed together with stakeholders, and tested in a number of representative contexts, covering both industrial and heat network applications. This has demonstrated the enormous potential of the tool in dealing with complex simulations, while delivering accurate results within a significantly lower time-frame than traditional analysis. The EMB3Rs tool removes important barriers such as analysis costs, time and complexity for the user, and aims at supporting a wider investment in waste heat recovery and reuse by providing an integrated estimation of the costs and benefits of such projects. This paper describes the tool and illustrates how it can be applied to help unlock the potential of waste heat recovery across European countries.

Abstract
This paper presents an approach to enhancing sensitivity in optical sensors by integrating self-image theory and graphene oxide coating. The sensor is specifically engineered to quantitatively assess glucose concentrations in aqueous solutions that simulate the spectrum of glucose levels typically encountered in human saliva. Prior to sensor fabrication, the theoretical self-image points were rigorously validated using Multiphysics COMSOL 6.0 software. Subsequently, the sensor was fabricated to a length corresponding to the second self-image point (29.12 mm) and coated with an 80 mu m/mL graphene oxide film using the Layer-by-Layer technique. The sensor characterization in refractive index demonstrated a wavelength sensitivity of 200 +/- 6 nm/RIU. Comparative evaluations of uncoated and graphene oxide-coated sensors applied to measure glucose in solutions ranging from 25 to 200 mg/dL showed an eightfold sensitivity improvement with one bilayer of Polyethyleneimine/graphene. The final graphene oxide-based sensor exhibited a sensitivity of 10.403 +/- 0.004 pm/(mg/dL) and demonstrated stability with a low standard deviation of 0.46 pm/min and a maximum theoretical resolution of 1.90 mg/dL.

Abstract
Software performance concerns have been attracting research interest at an increasing rate, especially regarding energy performance in non-wired computing devices. In the context of mobile devices, several research works have been devoted to assessing the performance of software and its underlying code. One important contribution of such research efforts is sets of programming guidelines aiming at identifying efficient and inefficient programming practices, and consequently to steer software developers to write performance-friendly code.Despite recent efforts in this direction, it is still almost unfeasible to obtain universal and up-to-date knowledge regarding software and respective source code performance. Namely regarding energy performance, where there has been growing interest in optimizing software energy consumption due to the power restrictions of such devices. There are still many difficulties reported by the community in measuring performance, namely in large-scale validation and replication. The Android ecosystem is a particular example, where the great fragmentation of the platform, the constant evolution of the hardware, the software platform, the development libraries themselves, and the fact that most of the platform tools are integrated into the IDE's GUI, makes it extremely difficult to perform performance studies based on large sets of data/applications. In this paper, we analyze the execution of a diversified corpus of applications of significant magnitude. We analyze the source-code performance of 1322 versions of 215 different Android applications, dynamically executed with over than 27900 tested scenarios, using state-of-the-art black-box testing frameworks with different combinations of GUI inputs. Our empirical analysis allowed to observe that semantic program changes such as adding functionality and repairing bugfixes are the changes more associated with relevant impact on energy performance. Furthermore, we also demonstrate that several coding practices previously identified as energy-greedy do not replicate such behavior in our execution context and can have distinct impacts across several performance indicators: runtime, memory and energy consumption. Some of these practices include some performance issues reported by the Android Lint and Android SDK APIs. We also provide evidence that the evaluated performance indicators have little to no correlation with the performance issues' priority detected by Android Lint. Finally, our results allowed us to demonstrate that there are significant differences in terms of performance between the most used libraries suited for implementing common programming tasks, such as HTTP communication, JSON manipulation, image loading/rendering, among others, providing a set of recommendations to select the most efficient library for each performance indicator. Based on the conclusions drawn and in the extension of the developed work, we also synthesized a set of guidelines that can be used by practitioners to replicate energy studies and build more efficient mobile software.