Abstract
Haemoglobin (Hb) concentration is a key biomarker for several diseases. Traditional laboratory methods often have limitations due to their time-consuming nature, the need for skilled personnel, or the use of high-cost instrumentation. This work presents a sensing strategy for developing new point-of-care tests (POCTs) for Hb detection via a proof of concept. The proposed sensing approach is implemented using plasmonic plastic optical fiber (POF) sensor chips that integrate an electropolymerized molecularly imprinted polymer (eMIP) film on the plasmonic surface for Hb-selective detection. The developed sensor system demonstrates an ultra-low detection limit of 80 fM in buffer, about five orders of magnitude lower than that of other comparable Hb sensors. Selectivity tests against common interfering proteins, such as bovine serum albumin (BSA) and immunoglobulin G (IgG), confirmed high specificity towards the target analyte. Moreover, the sensor’s performance was tested using a whole-blood sample, yielding results consistent with those of standard haematology analysis. The proposed sensor system, based on simple equipment, provides a quick (about 10 min) and cost-effective (about 10 euros per chip) label-free diagnostic tool for POCTs in real-world scenarios, such as finger-prick sampling, offering a less invasive alternative to traditional laboratory methods, towards devices useful for Internet of Medical Things (IoMT).

Abstract

Abstract
Existing noisy-label hyperspectral image classification (HSIC) methods usually address clean sample selection and representation regularization as separate problems, although the reliability of observed labels varies substantially across samples in hyperspectral data. This issue is amplified by mixed pixels, boundary ambiguity, spectral overlap, and limited labeled samples, which make hard clean samples difficult to distinguish from mislabeled ones. We therefore propose PDAM, a sample-reliability-guided training framework for noisy-label HSIC. The method first estimates feature-space class consistency by comparing each sample with the prototype of its observed class and converting this consistency into a reliability probability with a Gaussian mixture model. To reduce conservative false negatives, matched high-confidence selection is further used to recover hard but correctly labeled samples. The resulting reliability estimate then determines how strongly the observed label is trusted through target refinement and how strongly the input is perturbed through reliability-guided masking. Finally, masked reconstruction provides label-independent structural regularization so that uncertain samples can still contribute to spectral–spatial representation learning. Under the evaluated synthetic symmetric noise settings on the University of Pavia (UP), Salinas Valley (SV), and Kennedy Space Center (KSC) datasets, PDAM achieves the best OA and Kappa in most reported comparisons and improves robustness under both moderate and severe noise. At 30% noise, PDAM reaches 97.30% OA on UP, 98.13% OA on SV, and 95.37% OA on KSC. Ablation studies further support the necessity of reliability estimation, hard clean sample recovery, and reliability-guided supervision and regularization within this unified training mechanism.

Abstract

Abstract
Electric vehicles (EVs) are expected to accelerate the decarbonization of transport while also becoming a highly distributed and flexible resource for power systems. By coupling substantial battery storage with long parking times, EVs can support higher shares of renewable generation through controlled charging and, where available, bidirectional operation (e.g., V1G/V2G and related concepts). At the same time, large-scale EV uptake can increase peak demand, aggravate congestion and losses, and trigger voltage issues (particularly if charging remains unmanaged) potentially leading to costly network reinforcements. This chapter reviews the main EV types, charging modes and technologies (including fast and emerging wireless solutions), and the underlying storage technologies. It then discusses grid-integration architectures and operational strategies, from uncontrolled charging and time-of-use incentives to coordinated “smart charging” and V2G, highlighting their impacts on distribution networks and the requirements for communication, aggregation and system operator interaction. Finally, it outlines a future vision where EV flexibility is integrated with other distributed energy resources to provide local voltage support (active and reactive power), congestion management and frequency regulation services, enabled by appropriate standards, market mechanisms, and regulatory frameworks. © Springer Nature Switzerland AG 2026.

Abstract
As cities grow and sustainability becomes a key driver of urban policy, active modes of transport such as walking and cycling are increasingly promoted. However, current route planning applications rarely consider factors beyond time and distance. This paper presents the design and evaluation of a mobile application prototype that supports multicriteria route planning for active transport modes. The proposed solution incorporates user-defined weights for dimensions such as safety, comfort, accessibility, and environmental quality. To ensure adaptability and up-to-date information, the study also explores the feasibility of crowdsourcing as a complementary data source. A mixed-method approach was followed, including literature review, user surveys (n=242), interface prototyping, and usability testing with real users. The results demonstrate strong user interest in contributing to data updates, especially when motivated by non-monetary incentives such as gamified rankings. The final prototype was positively evaluated for usability and interface quality. This research confirms the potential of user-centered, crowdsourcing-enhanced route planning to improve the experience of active mobility users and support sustainable urban mobility goals. Copyright © 2025. Published by Elsevier B.V.