Heart failure (HF) remains a major cause of morbidity, mortality, and increased healthcare costs worldwide, and is recognized as the result of progressive cardiovascular-kidney-metabolic (CKM) syndrome. Obesity, type 2 diabetes, chronic kidney disease, hypertension, and atherosclerotic cardiovascular disease frequently coexist and interact through shared pathophysiological mechanisms that promote HF onset, especially with preserved ejection fraction (HFpEF). This European Journal of Heart Failure (EJHF) Expert Consensus Statement synthesizes contemporary evidence supporting pharmacological strategies to prevent HF across the CKM spectrum. Foundational therapies, including renin-angiotensin system inhibitors, sodium-glucose cotransporter 2 inhibitors, non-steroidal mineralocorticoid receptor antagonists, and incretin-based agents, demonstrate complementary benefits in reducing HF events, cardiovascular death, and chronic kidney disease (CKD) progression in high-risk populations. Early, parallel initiation of these therapies may modify disease trajectory and attenuate progression from at-risk and pre-HF stages to overt clinical HF. This document further emphasizes the need for an up-front, accelerated implementation strategy supported by structured monitoring, multidisciplinary CKM care pathways, and proactive management of laboratory abnormalities. Overcoming barriers such as clinical inertia, safety misperceptions, polypharmacy, cost constraints, and inequities in access is essential to translate evidence into population-level impact. Harmonization of prevention frameworks across cardiology, nephrology, endocrinology, and primary care societies is urgently required. Integrated, cross-disciplinary implementation of evidence-based CKM management offers a realistic opportunity to reduce incident HF and mitigate its growing global burden.

Genetic variants in superoxide dismutase 1 (SOD1) cause the progressive neurodegenerative disease Amyotrophic Lateral Sclerosis (ALS). Treatments for this disease are limited, and the unique vulnerability of cholinergic and glutamatergic motor neurons to degeneration seen in ALS patients is not clearly understood. Identifying genetic modifiers of neurodegeneration can provide insight into the selective vulnerability of motor neurons in ALS and may accelerate identification of novel therapeutic targets. Here, we describe a forward genetic screen for suppressors of the stress-induced glutamatergic neuron degeneration in a single-copy C. elegans model of sod-1G85R. Used two different screening strategies, we identified 53 suppressor lines that decreased glutamatergic neuron degeneration to varying degrees. To rapidly identify suppressor genes, we sequenced suppressor lines and identified candidate suppressor genes based on frequency of de novo exonic mutations and previously published work. Two candidates were tested and excluded as suppressor genes: erh-1 and imph-1. For a subset of unidentified suppressor lines, we determined if cholinergic neurodegeneration was also suppressed; roughly one-third of suppressor lines showed no corresponding cholinergic neuron rescue, suggesting that mechanisms involved in cholinergic and glutamatergic degeneration are at least partially divergent. This study outlines the first unbiased genetic screen in a knock-in model of SOD1 neurodegeneration, describes a cohort of candidate suppressor lines with decreased SOD1-dependent neurodegeneration, excludes two candidates and discusses approaches for suppressor mutation isolation and gene identification.

Ether-based electrolytes exhibit excellent anode compatibility and fast ion kinetics, yet their practical application is hindered by continuous cathode interfacial decomposition due to low oxidation resistance. Traditional solvation-regulation strategies face a trade-off between stabilizing the interface and maintaining bulk ion transport, making synergistic optimization challenging. Herein, we propose an in situ solvent polymerization strategy to develop a liquid polymer electrolyte (NEDL) that concurrently achieves fast ion transport and high interfacial stability. Specifically, trace LiPF6 initiates the partial ring-opening polymerization of 1,3-dioxolane (DOL) in diethylene glycol dimethyl ether (DEGDME), generating PDOL with abundant coordination sites to reconstruct the Na+ solvation structure. This polymer-modulated environment lowers the Na+ desolvation energy barrier, accelerating interfacial kinetics, and drives the formation of an inorganic-enriched, robust interphase that suppresses solvent decomposition. Consequently, under a 16 mg cm-2 mass loading, the Na3Fe2(PO4)P2O7 (NFPP)||Na coin cell delivers 94.76% capacity retention after 500 cycles at 2 C. Moreover, practical NFPP||hard carbon (HC) pouch cells retain 96.59% capacity over 500 cycles at 0.5 C. This work provides fundamental insights into how polymer-chain solvation behavior regulates ion transport kinetics and interfacial properties, offering a viable pathway for designing high-performance sodium-ion battery electrolytes.

High-throughput discovery of functional MXenes requires efficient strategies to identify promising candidates within vast compositional spaces. In this work, we develop a reinforcement-inspired active learning framework to accelerate the search for MXenes with desirable electronic properties. Using a curated dataset of 4448 MXenes, including 946 semiconducting compositions with PBE0 hybrid-functional band gaps, we demonstrate two complementary tasks: (i) identifying semiconductors within a targeted bandgap window (1.65-3.35 eV) corresponding approximately to the visible region relevant for photovoltaic applications, and (ii) identifying the maximum-bandgap material within the semiconducting subset as a materials optimization problem. These tasks reflect two core objectives in high-throughput materials discovery. Task (i) corresponds to practical screening for device-compatible bandgaps (e.g., photovoltaics and optoelectronics), while task (ii) captures optimization-driven discovery, where the goal is to identify the best-performing semiconductor in a large library using as few expensive evaluations as possible. For the first task, we initialize with only 1% of the data as labeled input and apply a suite of exploration-exploitation policies, including ε-greedy, uncertainty sampling, entropy, upper confidence bound (UCB), Thompson sampling, and diversity-augmented strategies. Remarkably, with a budget of only 500 queries, the framework retrieves 130 of the 150 possible target MXenes, substantially outperforming random selection. For the second task, we restrict to 946 semiconductors and employ batch-mode UCB active learning to efficiently identify the material with the maximum bandgap. Starting with 2% labeled data and examining only 60 candidates in total, the algorithm recovers the top-bandgap MXene within six iterations across multiple batch sizes. The proposed framework provides a generalizable approach for sequential materials discovery and optimization while substantially reducing the number of expensive hybrid-functional DFT evaluations required for large-scale materials screening.

Timely feedback on whole-body vibration (WBV) exposure requires standardised metrics during measurement, yet many workflows record raw acceleration for offline processing, delaying feedback and increasing the data burden for bandwidth-constrained deployment. This paper presents a sensing node computing ISO 2631-1/AS 2670.1 exposure metrics on-device and transmitting metric records, not waveforms. The node combines an LIS2DH microelectromechanical systems (MEMS) accelerometer with an RP2040 microcontroller to calculate weighted root-mean-square (RMS) acceleration, daily exposure A(8), vibration dose value, maximum transient vibration value and crest factor. Algorithm-level verification showed that the implemented Wk filter reproduced the tabulated ISO 2631-1 response within 1.1% across 0.5-80 Hz. The node was compared under laboratory conditions with a CEM DT-178A datalogger whose 20 Hz recordings were reprocessed through a method-matched causal pipeline. Across ten trials per configuration under vertical excitation in the 6.3 Hz one-third-octave band, mean Z-axis A(8) differences were -4.5% (wired) and -7.9% (wireless). The datalogger's 20 Hz sampling was included within the comparison chain, and the lower realised Wk gain was consistent with the direction and approximate magnitude of the observed offset. Metric-level telemetry reduced the sustained payload rate by up to four orders of magnitude at the summary cadence, supporting bandwidth-constrained uplinks.

This paper reports the design, development and laboratory validation of a low-cost multi-sensor system for bridge Structural Health Monitoring with complementary scour-related environmental sensing. The proposed sensing apparatus is intended for continuous dynamic (vibration-based) and quasi-static structural monitoring, in particular for bridge applications. It employs a distributed wired architecture comprising two custom printed circuit boards, BridgeWatch and EnvironMonitor. BridgeWatch nodes integrate MEMS accelerometers and gyroscopes, enabling acceleration measurements, quasi-static inclination estimates from gravity components, angular-rate measurements and structural surface-temperature acquisition. The EnvironMonitor node acquires wind, rainfall, air temperature, humidity and bridge water level; these variables provide environmental and hydrological context relevant to scour risk, without directly measuring riverbed elevation or scour depth. A Raspberry Pi coordinates acquisition, synchronisation and communication through RS485 and RS232 links. The hardware was implemented through custom PCB design and dedicated firmware. The principal contribution is the system-level integration of structural and environmental measurements in one open architecture. Laboratory validation comprised communication and synchronisation checks, shaker tests, a scaled structural benchmark, climatic-cell tests and environmental-input simulations. The results demonstrate functional acquisition and relevant dynamic-feature extraction under controlled conditions. These outcomes enable future full-scale in situ validation and testing on full-size case studies.

Ephemeral leaflets are produced on emerging daughter cladodes of Opuntia ficus-indica, a constitutive crassulacean acid metabolism (CAM) species of major agronomic and ecological importance. Despite their ubiquity across Cactaceae, these transient organs have never been characterized as discrete photosynthetic units. Here we investigate whether leaflets operate via C3 or CAM photosynthesis and quantify their contribution to early cladode carbon (C) economy. Gas-exchange measurements, stomatal aperture analysis, titratable acidity assays, C isotope discrimination (δ13C), and 13CO2 pulse-labelling consistently indicate that leaflets operate via the C3 pathway: they exhibit daytime stomatal opening, minimal nocturnal acid accumulation, greater discrimination against 13CO2 (δ13C reaching -17‰ in mature leaflets compared with -10‰ to -11‰ in cladodes) and net daytime CO2 uptake at rates approximating 50 % of those in mature cladodes. Carbon-balance calculations indicate that leaflets supply ∼0.12 g C cladode-1 day-1, meeting approximately 20 % of the daily C demand of the expanding daughter cladode. Experimental leaflet removal and stomatal blockage reduced daughter cladode surface area by 28-34 % and dry weight by ∼38 % relative to controls at three weeks. These findings reveal an intra-plant co-ordination of C3 and CAM C fixation in spatially distinct organs and identify leaflets as adaptive source organs that accelerate biomass accumulation during pulsed periods of water availability in arid environments.

Drones have become a significant threat in both modern warfare and civilian environments, presenting unprecedented security challenges. While hard kill (physical destruction) defense methods exist to counter threat drones, they remain prohibitively expensive and often result in collateral damage. This study focuses on software-based peaceful neutralization as an anti-drone countermeasure, with particular emphasis on optimizing Wi-Fi credential recovery by leveraging exploitable vulnerabilities in threat drone wireless communications. The methodology included extensive performance evaluation experiments to assess vulnerabilities in drone Wi-Fi networks through GPU-accelerated credential recovery. Across the tested GPU platforms, the proposed implementation achieved an average throughput improvement of 8.78% over the Hashcat baseline, with platform-specific gains of 5.0% on H100, 8.77% on A100, 6.53% on RTX PRO 6000, and 14.8% on RTX 4060. The proposed approach represents a preliminary step toward a cost-effective and damage-minimizing alternative, applicable to Wi-Fi-dependent drone platforms under specific operational conditions. Our experimental results suggest the potential feasibility of credential-recovery-based approaches as an enabling step toward software-based neutralization of Wi-Fi-dependent drone systems. This paper contributes to the emerging field of counter-unmanned aerial systems (C-UAS) by establishing a methodology for credential recovery against Wi-Fi-based drone communications, which may contribute to reducing reliance on physical intervention in applicable scenarios.

Pedestrian-future-trajectory prediction is a critical task in intelligent driving, traffic-scene understanding, and active safety decision-making. In real-world road environments, pedestrians' historical trajectories are frequently affected by occlusions from vehicles, other pedestrians, road infrastructure, and detector-missed detections, leading to missing values in the historical-observation sequence. Understanding and handling missing values in pedestrian-observation sequences is essential for improving the performance of prediction models; however, existing research has rarely considered this realistic scenario. This paper proposes RCN-MAMBA, a pedestrian-future-trajectory-prediction method for occlusion scenarios. The overall pipeline follows a de-occlusion completion first, future-trajectory-prediction second paradigm. Specifically, in the de-occlusion stage, based on linear interpolation for trajectory completion, a BiLSTM Residual-Correction Network is employed to further refine the occluded trajectory. This paper further proposes the C_MAMBA prediction architecture: first, explicit motion-state encoding is introduced to inject position, velocity, and acceleration information explicitly into the trajectory representation; then, multi-scale temporal convolution is used to extract local motion patterns across different time ranges; finally, a Temporal Bi-Mamba module is introduced to model long-term temporal dependencies in the completed trajectory from both forward and backward temporal directions. RCN-MAMBA achieves competitive performance on the JAAD dataset. Qualitative results show that its predicted future trajectories are consistent with actual motion trends. Quantitative analysis is conducted under six whole-frame random occlusion ratios: 5%, 10%, 15%, 20%, 30%, and 40%. RCN-MAMBA achieves optimal results under all these occlusion ratios. The paired-t test for each trajectory further confirms that the performance advantage of our proposed method over all baseline methods is statistically significant, demonstrating that the proposed method possesses good prediction accuracy and robustness under varying occlusion intensities.

Monolithic and laminated glass elements are increasingly used in structural applications, where their response to impact loading must be assessed to ensure safety. For soft impacts, simplified simulations based only on the initial impact velocity may be inaccurate because the transient coupling between the impactor and the glass depends on the impactor stiffness and mass. This study investigates the dynamic response of monolithic and laminated glass beams under soft impact loading using accelerometers, an instrumented impact hammer, strain gauges, operational modal analysis, and simplified finite element models. The influence of three impactor heads and three hammer masses is examined. Two approaches are assessed: a modal simulation driven by the measured impact-force history and a sensor-based method that reconstructs stresses from measured structural responses and experimentally identified mode shapes. Comparisons with experimental measurements yield approximate absolute-peak errors of 2.1% for the monolithic-beam acceleration and 1.5% for the laminated-beam stress, normalised by the maximum absolute measured response in each case, with corresponding Pearson correlation coefficients of 0.972 and 0.943. The results show that accounting for the measured impact-force history and combining response measurements at discrete locations with modal information can provide efficient estimates of dynamic response and stress without explicitly modelling the impactor-glass contact.

Electromagnetic vibration and noise issues of converter transformers under harmonic operating conditions have become increasingly prominent. To enable a systematic comparison of different filtering strategies, this paper establishes a multiphysical field coupling model incorporating electromagnetic, structural, and acoustic fields, and systematically investigates four filtering configurations: grid-side LC filtering, valve-side LC filtering, inductive filtering, and valve-side capacitive filtering through theoretical analysis, finite-element simulation, and experimental verification on a 12-pulse laboratory HVDC prototype platform. The simulation results show that the maximum vibration acceleration decreases from 3.06 m/s2 under the no-filter condition to 1.02 m/s2 under valve-side capacitive filtering. Experimental measurements further confirm the suppression effect, with the vibration acceleration at Point 8 decreasing from 2.28 m/s2 to 0.42 m/s2, corresponding to a reduction of approximately 82%, while the average A-weighted sound pressure level decreases from 66.1 dB to 58.1 dB. These results indicate that vibration and noise suppression cannot be evaluated solely by total harmonic distortion; the attenuation and distribution of individual harmonic components should also be considered. The findings provide a theoretical and engineering basis for the vibration and noise reduction design of converter transformers.