Abstract
This paper proposes an optimized Reconfigurable Internet of Things (RIoT) framework that integrates optical and radio wireless technologies with a focus on energy efficiency, scalability, and adaptability. To address the inherent complexity of hybrid optical-radio environments, a high-fidelity Digital Twin (DT) is developed within the Network Simulator 3 (NS-3) platform. The DT models deploy subsystems of the RIoT architecture, including Radio Frequency (RF) communication, Optical Wireless Communication (OWC), and energy harvesting and consumption mechanisms that enable autonomous operation. Real-time energy and power measurements from target hardware platforms are also incorporated to ensure accurate representation of physical behavior and enable runtime analysis and optimization. Building on this foundation, a proactive cross-layer optimization strategy is devised to balance energy efficiency and quality of service (QoS). The strategy dynamically reconfigures RIoT nodes by adapting transmission rates, wake/sleep scheduling, and access technology selection. Results demonstrate that the proposed framework, combining digital twin technology, hybrid optical-radio integration, and data-driven energy modeling, substantially enhances the performance, resilience, and sustainability of 6G IoT networks.

Abstract
In streaming scenarios, models must learn continuously, adapting to concept drifts without erasing previously acquired knowledge. However, existing research communities address these challenges in isolation. Continual Learning (CL) focuses on long-term retention and mitigating catastrophic forgetting, often without strict real-time constraints. Stream Learning (SL) emphasizes rapid, efficient adaptation to high-frequency data streams, but typically neglects forgetting. Recent efforts have tried to combine these paradigms, yet no clear algorithmic overlap exists. We argue that large in-context tabular models (LTMs) provide a natural bridge for Streaming Continual Learning (SCL). In our view, unbounded streams should be summarized on-the-fly into compact sketches that can be consumed by LTMs. This recovers the classical SL motivation of compressing massive streams with fixed-size guarantees, while simultaneously aligning with the experience-replay desiderata of CL. To clarify this bridge, we show how the SL and CL communities implicitly adopt a divide-to-conquer strategy to manage the tension between plasticity (performing well on the current distribution) and stability (retaining past knowledge), while also imposing a minimal complexity constraint that motivates diversification (avoiding redundancy in what is stored) and retrieval (re-prioritizing past information when needed). Within this perspective, we propose structuring SCL with LTMs around two core principles of data selection for in-context learning: (1) distribution matching, which balances plasticity and stability, and (2) distribution compression, which controls memory size through diversification and retrieval mechanisms. © 2026 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Abstract
Feline chronic kidney disease is a leading cause of mortality in geriatric cats, characterized by a progressive and irreversible loss of renal function. Despite its high prevalence, early diagnosis remains challenging due to nephron compensatory mechanisms and the limited sensitivity of traditional biomarkers, creating a diagnostic gap that necessitates the exploration of novel biomarkers for earlier detection. This review examines the complex pathophysiology of the disease, including renin-angiotensin-aldosterone system activation, tubulointerstitial fibrosis, and mineral metabolism disturbances. By analyzing recent scientific literature, this work evaluates current diagnostic landscape and clinical relevance of emerging biomarkers. Evidence indicates that symmetric dimethylarginine and fibroblast growth factor-23 improve detection of early metabolic and filtration changes, while urinary biomarkers like cystatin B and retinol-binding protein provide specific insights into tubular injury. Bridging the diagnostic gap requires a transition from a reactive, azotemia-based framework to a multi-parametric diagnostic approach that integrates novel biomarkers with serial clinical and laboratory monitoring. Although financial constraints and limited availability restrict widespread clinical implementation, incorporating these advances is essential for earlier prognostic stratification and timely therapeutic decision-making. This integrated strategy has the potential to slow disease progression and improve survival and quality of life in cats with chronic kidney disease.

Abstract
Accurate prediction of peak electricity demand at the substation level is critical for power system planning and operation. This study proposes an interpretable panel data framework with fixed effects to forecast substation-level peak demand in Portugal, combining robust event-based peak identification with climate-aware demand modeling.Compared to black-box machine learning approaches, in this application the proposed model maintains competitive predictive performance while providing clear insights into the structural drivers of demand. Results show that panel-data fixed effects specifications outperform Random Forest and Gradient Boosting methods when persistent heterogeneity across substations is explicitly accounted for.The study further introduces temperature-related variables capturing deviations from local climatic norms and short-term thermal dynamics around peak events. The results reveal a clear seasonal asymmetry: winter peak demand is significantly influenced by sustained temperature dynamics, whereas summer demand is largely insensitive to temperature effects within the observed range of conditions.Overall, the findings highlight the importance of explicitly modeling structural heterogeneity and demand responses in electricity demand forecasting. By combining interpretability with robust predictive performance, the proposed approach supports more informed operational forecasting and infrastructure planning decisions in modern distribution networks

Abstract
A key feature of model finding techniques allows users to enumerate and explore alternative solutions. However, it is challenging to guarantee that the generated instances are relevant to the user, representing effectively different scenarios. This challenge is exacerbated in quantitative modelling, where one must consider both the qualitative, structural part of a model, and the quantitative data on top of it. This results in a search space of possibly infinite candidate solutions, often infinitesimally similar to one another. Thus, research on instance enumeration in qualitative model finding is not directly applicable to the quantitative context, which requires more sophisticated methods to navigate the solution space effectively. The main goal of this paper is to explore a generic approach for navigating quantitative solution spaces and showcase different iteration operations, aiming to generate instances that differ considerably from those previously seen and promote a larger coverage of the search space. Such operations are implemented in QAlloy - a quantitative extension to Alloy - on top of Max-SMT solvers, and are evaluated against several examples ranging, in particular, over the integer and fuzzy domains.

Abstract
Diabetic retinopathy (DR) is a leading cause of vision loss in working-age adults. While screening reduces the risk of blindness, traditional imaging is often costly and inaccessible. Artificial intelligence (AI) algorithms present a scalable diagnostic solution, but concerns regarding fairness and generalization persist. This work evaluates the fairness and performance of image-trained models in DR prediction, as well as the impact of disentanglement as a bias mitigation technique, using the diverse mBRSET fundus dataset. Three models, ConvNeXt V2, DINOv2, and Swin V2, were trained on macula images to predict DR and sensitive attributes (SAs) (e.g., age and gender/sex). Fairness was assessed between subgroups of SAs, and disentanglement was applied to reduce bias. All models achieved high DR prediction performance in diagnosing (up to 94% AUROC) and could reasonably predict age and gender/sex (91% and 77% AUROC, respectively). Fairness assessment suggests disparities, such as a 10% AUROC gap between age groups in DINOv2. Disentangling SAs from DR prediction had varying results, depending on the model selected. Disentanglement improved DINOv2 performance (2% AUROC gain), but led to performance drops in ConvNeXt V2 and Swin V2 (7% and 3%, respectively). These findings highlight the complexity of disentangling fine-grained features in fundus imaging and emphasize the importance of fairness in medical imaging AI to ensure equitable and reliable healthcare solutions.