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Hydrological imbalance in Nam Co Lake, the third-largest lake on the Tibetan Plateau
No full textLakes on the Tibetan Plateau (TP), as key components of the Asian Water Tower, play a vital role in regional water resources and hydrologic cycle. Groundwater is an important but often ignored factor in the lake’s hydrologic cycle, largely due to challenges in quantifying its contribution. In the case of the Nam Co Lake, the third-largest lake on the TP, research on basin groundwater remains notably limited. This paper presents, for the first time, an analysis of groundwater in the water balance of Nam Co Lake, based on a comprehensive hydrogeological investigation. Results revealed lacustrine groundwater discharge into the lake was about 10.31 ± 0.31 × 108 m3 during May to October 2018. Daily lake level change showed that the rising lake level resulted in a volume increase of 13.06 ± 1.53 × 108 m3 during the same period. The hydrometeorological observations revealed that during the observation period, precipitation over the lake, recorded by the automatic weather station, totaled 7.51 ± 0.75 × 108 m3, while evaporation from the lake, measured by the eddy covariance system, amounted to 9.56 ± 0.12 × 108 m3. Additionally, runoff of surrounding rivers into the lake reached 22.83 ± 2.28 × 108 m3. Thus, a lake water balance analysis revealed a surplus input of 18.03 ± 2.42 × 108 m3 compared to the output during the water balance duration. The only plausible explanation for the water imbalance is seepage, most likely occurring along the region’s known subsurface fault system. These findings underscore the significant role of groundwater and highlight the magnitude of lake seepage, offering new insights into the hydrological cycle of Nam Co Lake and the broader TP region
Introducing a method to monitor the impact of introducing value-based payment models; using a Dutch pilot project for coronary artery disease as an example
No full textThe adoption of value-based payment (VBP) models is expanding globally as healthcare systems seek to align financial incentives with patient outcomes. However, empirical research on both the intended and unintended consequences of VBP implementation remains limited. This study introduces a systematic approach to monitor the effects of VBP models and presents findings from its first application, using a Dutch outcome-based payment model for coronary artery disease (VBP-CAD) as a case study. The developed methodology integrates online questionnaires and semi-structured interviews to assess the impact of an outcome-based payment model on treatment decision-making, organizational dynamics, and the role of clinical outcomes in hospital management. Additionally, case-mix analyses were conducted using real-world nationwide data to evaluate potential shifts in patient risk profiles following the implementation of the payment model. The results of the case-study demonstrate that the proposed approach was successfully implemented, generating valuable insights into both positive and unintended effects of the VBP model. The systematic approach contributes to the ongoing discourse on optimizing value-based payment structures and ensuring equitable, high-quality care delivery. By applying this methodology in different healthcare settings, stakeholders can enhance their understanding of VBP outcomes.</p
Unravelling Sleep Apnea Dynamics: Quantifying Loop Gain Using Dynamical Modelling of Ventilatory Control
No full textStudy Objectives: Loop gain (LG) is a critical parameter for assessing ventilatory control stability in sleep apnea, with implications for personalized treatment. Existing LG estimation methods are hindered by complex processing and specialized equipment, limiting clinical applicability. This study aims to develop an automated method to quantify LG from respiratory inductance plethysmography (RIP) signals to enhance precision management of sleep apnea.Methods: Polysomnography data from Massachusetts General Hospital, high-altitude studies at Beth Israel Deaconess Medical Centre, and heart failure patients were analysed. Cases included an apnea-hypopnea index >15 and ≥4 hours of recorded sleep. RIP signals were filtered, normalized, and segmented into 8-minute windows. LG estimation employed an augmented Mackey-Glass equation and an expectation-maximization algorithm. Simulation experiments on synthetic breathing data with known parameter values quantified the accuracy of our parameter estimates.Results: Data from 465 patients were analysed, including 400 patients from the Massachusetts General Hospital dataset and 65 heart failure patients. The method accurately estimated LG across diverse apnea phenotypes. Patients with a higher central apnea index, high self-similarity or heart failure exhibited significantly higher median LG values (0.19, 0.27 and 0.41 respectively) compared to those with obstructive apnea (median LG = 0.11-0.14; p <0.001). In addition, LG was significantly elevated during non-rapid eye movement sleep and at higher altitudes.Conclusions: The automated LG estimation method developed in this study provides a scalable, non-invasive tool for endotyping in sleep apnea. By accurately modelling patient-specific ventilatory control, this approach supports personalized management strategies in apnea and broader clinical contexts.<br/
Tailoring Ankle-Foot Orthoses Stiffness to End-Users' Needs:Which Performance Variables Matter?
No full textAnkle Foot Orthoses (AFOs) are devices designed to assist or rehabilitate gait. The stiffness of the AFO is an important parameter that needs to be tailored to the enduser's characteristics. However, the lack of clinical guidelines for optimizing AFO stiffness has led to inconsistent outcomes and low user satisfaction in existing experimental approaches. This inconsistency may stem from two reasons: (1) the limited number of variables considered by these methods, which may fail to capture the full complexity of gait; and (2) the variability in the relationship between stiffness and gait variables among users. To address these challenges, we propose a user-tailored optimization framework that identifies and incorporates relevant gait variables based on individual needs and preferences. In this pilot study, we explore key performance variables in two participants with cerebral palsy (CP), analyzing the results using XGBoost and SHAP values. Our findings highlight the importance of multiple performance variables in capturing gait complexity and reveal that the most relevant variables differ between participants. This study underscores the potential of personalized approaches to improve AFO optimization and user outcomes.</p
Visual Grounding in 2D and 3D:A unified perspective and survey
No full textVisual Grounding (VG), the task of localizing specific image or scene regions based on natural language descriptions, is crucial for bridging the semantic gap between vision and language in Artificial Intelligence. Despite substantial progress in both 2D and 3D domains, existing surveys often focus narrowly on one dimension, lacking a unified perspective. This survey provides the first comprehensive review offering such a unified viewpoint, systematically integrating and analyzing research across both 2D and 3D Visual Grounding. We provide a structured categorization of core methodologies, detailing the evolution of two-stage and one-stage paradigms and their representative techniques. Furthermore, we review emerging trends, including the integration of Large Language Models (LLM) for enhanced semantic reasoning and strategies for cross-dimensional knowledge transfer between 2D and 3D VG, as well as the nascent area of monocular 3DVG. The survey also encompasses an overview of benchmark datasets, a discussion of evaluation metrics, an analysis of current performance levels, and an articulation of open challenges. By offering this holistic and systematically organized review, we aim to provide researchers with a clear understanding of the current landscape, facilitate the identification of promising research avenues, and inspire further innovation in this dynamic and impactful cross-modal research area.</p
An Overview of Sound and Modest Approaches to Quantitative Model Checking from Sea to Space
No full textQuantitative system properties such as resilience, response times, and throughput are crucial measures in the design and operation of complex cyber-physical systems. The formal methods community has developed a variety of approaches to evaluate and optimise such properties with clear correctness and optimality guarantees. In practice, however, every application poses new challenges that require adaptations and novel combinations of the “off-the-shelf” methods we usually present in scientific papers. In this extended abstract accompanying the author’s FMICS 2025 invited presentation, we use recent case studies ranging from water management for storm surge protection to routing in satellite constellations to (i) contrast the different demands on model expressiveness and tool capabilities of each application and (ii) highlight the capabilities of the Modest Toolset to solve these challenges with the varied modelling, simulation, and verification approaches it implements. In addition to these examples, we outline how quantitative verification tools can deliver the correctness and optimality guarantees we would like to see
Viscoelasticity reduces the droplet size in mucosalivary film fragmentation during intense respiratory events
No full textWe examine the fundamental fluid dynamical mechanisms dictating the generation of bioaerosols in the human trachea during intense respiratory events such as coughing and sneezing, with an emphasis on the role played by the mucosalivary fluid viscoelasticity. An experimental investigation of the shear-induced fragmentation of a mucosalivary-mimetic fluid in a confined geometry reveals that viscoelastic liquids undergo atomization in a manner akin to Newtonian liquids—via the formation of baglike structures—which ultimately rupture through the appearance of retracting holes on the bag surface. Droplets are produced via the unstable retraction of liquid rims bounding these holes. However, in comparison to Newtonian liquids, viscoelastic bags inflate to larger sizes—implying thinner sheets and, consequently smaller droplets upon rupture. Numerical simulations support that the smaller droplets can be attributed to the thinner sheets, with a more uniform thickness, for viscoelastic bags prior to rupture. Hence, we highlight the role of the viscoelasticity in determining the thickness of the intermediate baglike structures, which, in turn, govern the droplet size distribution of the expelled aerosol.</p
The Impact of AI-Based Collaborative Conversational Agents on Metacognitive Awareness
No full textThis study investigates how Clair, an AI-based Collaborative Conversational Agent (CCA), can support metacognitive awareness. Clair utilizes learning analytics and the Academically Productive Talk framework to monitor and intervene in discussions, encouraging deeper understanding and reflection. Seventeen dyads (N = 34) participated in discussions on climate change, first without and then with Clair’s intervention. We categorized chats into conversational patterns using the framework defined by the Program for International Student Assessment (PISA), which suggests the following metacognitive phases: Exploring and Understanding, Representing and Formulating, Planning and Executing, and Monitoring and Reflecting. Results indicate that Clair significantly improved metacognitive engagement, reducing unproductive conversational loops and strengthening transitions between metacognitive phases by using talk moves. Markov chain analysis showed that transitions to Monitoring and Reflection increased with Clair’s intervention. Specific talk moves, such as “expand reasoning” and “recapping,” were particularly effective in fostering deeper metacognitive processing. Findings suggest that AI-based CCAs can scaffold metacognitive regulation by promoting structured discourse and guiding learners toward effective cognitive strategies. This research underscores that CCAs such as Clair can facilitate meaningful learning interactions, suggesting further refinements to optimize their impact on metacognitive awareness in collaborative learning settings.</p
Chip-integrated ultra-narrow linewidth laser at 640 nm
No full textPhotonic integration of visible ultra-narrow tunable laser sources has great potential for a wide range of applications, including atomic clocks, and quantum sensing. The two opposing approaches are extended cavity diode lasers (ECDLs) or self-injection locking (SIL), e.g., of Fabry-Perot diode lasers [1]. Typically, ECDLs are preferred for wide tunability [2], while the narrowest linewidths have been achieved by self-injection locking [3] in the infrared.</p
Animating faces with emotions through a generative adversarial network preserving identity
No full textArtificially applying specific emotions to videos of people faces with a neutral expression, while preserving the identity of the subject is a challenging task. When parts of the face are synthetically moved to generate an emotion, it typically results in spatio-temporal artifacts in the generated videos, or inconsistency to preserve the identity of subjects. Existing methods that deploy spatio-temporal convolutions and de-convolutions to generate consecutive frames in a single step are not able to ensure proper motion dynamics, in the sense that the emotion may be not visible on the face or the facial features are distorted in the video. At the same time, approaches that generate motion and identity in two separate steps are not able to ensure the consistency of the subject identity after the generation of the emotion. In this paper we propose a novel method, Video Identity-Consistent Emotion GAN (VICEGAN), that improves the video generative capabilities of two-step methods. We decouple motion and content generation, thus ensuring the consistency of subject identity in the generated videos by using an encoder-decoder generator and a new identity-preserving loss in an adversarial framework. The proposed neural network architecture also guarantees the generation of proper motion of the target expressions, mitigating the presence of artifacts. We evaluated VICEGAN on the MUG dataset and compared it with a method based on a GAN, ImaGINator, demonstrating superior performance both quantitatively and qualitatively, and with a popular method based on a diffusion model, LFDM, showing a better capability to generate recognizable emotions