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An optimization-based approach to track the Asian summer monsoon anticyclone across daily and interannual variability
No full textGlobal groundwater modeling: Proof-of-concept of 3D variably saturated flow simulation at kilometer resolution
No full textAdditive-Engineered CsPbBr 3 -Based Perovskite Memristors for Neuromorphic Computing and Associative Learning Applications
No full textPerovskite memristors have emerged as promising candidates for neuromorphic computing due to their simple fabrication process and mixed ionic and electronic properties. Among them, all-inorganic CsPbBr3 perovskites have garnered significant interest due to their excellent stability. However, the low solubility of cesium bromide (CsBr) in most common solvents poses a major challenge in fabricating high-quality, pinhole-free CsPbBr3 films for memory device applications using a convenient one-step solution method. In this work, a facile one-step spin-coating approach was employed to fabricate CsPbBr3-based memristors, incorporating a carbohydrazide (CBH) additive into the perovskite precursor to enhance device performance. The modified device exhibited an improved ON/OFF ratio, enhanced endurance, and longer retention time. Furthermore, it successfully emulated key synaptic functions, including excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation/depression, and learning–forgetting–relearning behaviors, effectively mimicking biological synapses. Additionally, an associative learning experiment inspired by Pavlov’s dog experiment was conducted, demonstrating memory formation and extinction under optical and electrical stimuli. The fabricated perovskite memristor was further evaluated in a convolutional neural network for Fashion MNIST classification, achieving a high recognition accuracy of 89.07%, confirming its potential for neuromorphic computing applications. This study highlights the effectiveness of additive engineering as a strategy for developing high-performance perovskite-based neuromorphic electronics
When legs and bodies synchronize: Two-level collective dynamics in dense crowds
No full textUltra-dense crowds, in which physical contact between people cannot be avoided, pose major safety concerns. Nevertheless, the underlying dynamics driving their collective behaviours remain poorly understood. Existing dense crowd models, mostly two-dimensional and contact-based, overlook biomechanical mechanisms that govern individual balance motion. In this study, we introduce a minimal two-level pedestrian model that couples upper body and legs dynamics, allowing us to capture transitions between balanced and unbalanced states at the individual scale. Whereas previous models fail to achieve it, this coupling gives rise to emergent collective behaviours observed empirically, such as self-organized waves and large-scale rotational motion within the crowd. The model bridges basic individual biomechanical concepts and macroscopic flow dynamics, offering a new framework for modelling and understanding collective motions in ultra-dense crowds
Solar-induced chlorophyll fluorescence (SIF) tracks variations in the soil-plant available water (PAW): a multiyear analysis on three crops
No full textRestrictions in the soil water availability can strongly impact crop productivity. The increasing frequency andseverity of drought events, as a result of global warming, has made the assessment of drought stress effects onvegetation of utmost importance for meeting humanity's agricultural production needs. Recent advances inremote sensing of solar-induced chlorophyll fluorescence (SIF) provide a basis for new approaches to directlyassess crop water status, since SIF is closely related to photosynthesis and, thus, to early plant physiologicalprocesses triggered by limitations in the water supply. This study provides new insights into the effect of varyinglevels of plant available water (PAW) in the soil on SIF emissions. We used several SIF datasets acquired with thehigh-performance airborne imaging spectrometer HyPlant during five subsequent vegetation periods (2018,2019, 2020, 2021 and 2022), each having a different precipitation regime. We normalized the SIF maps for theunderlying effects of canopy structure, calculated SIF emission efficiency (eSIF) and selected various crop fieldsincluding sugar beet, wheat and potato. Maps of eSIF were compared with spatial PAW patterns, which werederived from a forward soil infiltration model. Our results show positive correlation between eSIF and PAW inrainfed sugar beet fields at early growing stage, which remained consistent when accounting for variations in theleaf area index (LAI). This suggests that eSIF variations in sugar beet reflect the spatial reduction of photosynthesiscaused by reduced PAW. In irrigated potato fields, conversely, no eSIF-PAW correlations were found. Thisindicates the absence of leaf-level water stress in these well-irrigated fields. In rainfed winter wheat fields thatwere already in a late developmental stage, the variations in the SIF signal were dominated by locally differentripening, i.e., chlorophyll degradation, and therefore not representative of changing PAW. With this study, wecould demonstrate that normalized airborne SIF measurements are related to the functional water stress responsein different crops. This study supports future investigations on the development of SIF-based tools for theimprovement of water management in agriculture
Neurobiological Predictors of Hand Grip Strength as a Global Health Marker: Methodological Foundations and Interpretable Brain-Behaviour Prediction in Large-Scale Neuroimaging
No full textAging populations worldwide are widening the gap between lifespan and healthspan, underscoring theneed for early, scalable markers of organismal health and intervention targets before overt clinicaldecline. Hand grip strength (HGS) has emerged as a highly reliable and low-cost predictor of systemwidefactors such as frailty, cognitive decline, and mortality. Despite its simplicity and clearmusculoskeletal determinants, explaining its system-wide predictive value requires a deeperunderstanding of underlying brain-level neurobiological architectures, which is currently lacking. Thisthesis addresses this gap by investigating generalizable neural predictors of HGS using machine learning(ML) with large-scale, multi-modal neuroimaging data, grounded in methodological foundations forinterpretable brain-behaviour prediction.A critical review of methodological constraints and potential mitigation strategies inobservational neuroimaging-based ML studies was conducted to promote more reliable andgeneralizable brain-behaviour predictions and interpretations (Study 1). Such studies can be hamperedby pitfalls including data leakage, site-effects in multi-site datasets, misleading post-hoc modelinterpretations arising from feature multicollinearity, and model bias due to confounding. Strict out-ofsampleevaluation and clustering-based interpretation to deal with feature multicollinearity wereidentified as suitable solutions. To support principled confounder selection, a theoretically informed butempirically pragmatic 3-step approach was developped (Study 2). The proposed approach integratesmethdology from causal inference - domain knowledge, directed acyclic graphs, and respective graphrules - with associative data-driven modeling.Building on these foundations, a comprehensive, interpretable multi-modal predictive workflowrevealed generalizable, system-level neuroimaging predictors of HGS in a large, healthy cohort fromthe UK Biobank (Study 3). Across modeling approaches, microstructural integrity – particularly inascending medial lemniscus, thalamic radiations, and associative white-matter pathways – as well assubcortical gray matter volume (GMV), mainly in the anterior globus pallidus, emerged as relevantcontributors. In contrast, cortical structural measures and functional imaging features contributed littleto predictive performance. Collectively, these findings position HGS as a behavioural readout of thebrain’s capacity to coordinate, and integrate information across motor, sensory, cognitive, andmotivational systems, rather than a purely peripheral muscle measure or an isolated motor output.In sum, this thesis establishes a framework for neurobiologically interpretable large-scale brainbehaviourprediction and applies it to elucidate why HGS functions as a powerful marker of globalhealth. By intergating methodological rigor with system-level neuroimaging, it demonstrates howsimple behavioural phenotypes can serve as informative windows into the functioning and integrity ofdistributed neural architectures. Future work should determine whether identified HGS-linked neuralsignatures provide better or earlier prognostic and interventional value than the behavioural measureitself
