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Generalized Linear Models in Infectious Disease Analysis and Surveillance: Methods for Correlated Data
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Qualitative and Quantitative Use-Wear Analysis of Percussive Stone Tools from Nyayanga (Homa Peninsula, Kenya)
Full text linkThis study presents a comprehensive examination of the function of 26 percussive stone tools (PSTs) from Nyayanga, an Oldowan site located on the Homa Peninsula in southwestern Kenya. These artifacts, dating between 3.032 to 2.581 million years ago, were found together with hominin remains and animal fossils with stone tool butchery damage. To determine the function of the PSTs, we adopted a multiscale approach that combines qualitative use-wear analysis using microscopic techniques at low and high power approaches with quantitative analysis, employing 3D surface models generated with profilometry. These analyses indicate that Nyayanga hominins used PSTs to access both plant (e.g., USOs) and animal (bone marrow) nutrients.
The inferred multifunctionality of these tools hints at diverse dietary strategies and contributes to our understanding of human technological evolution
A geometrical approach to the sharp Hardy inequality in Sobolev–Slobodeckiĭ spaces
No full textWe give a partial negative answer to a question left open in a previous work by Brasco and the first and third-named authors concerning the sharp constant in the fractional Hardy inequality on convex sets. Our approach has a geometrical flavor and equivalently reformulates the sharp constant in the limit case p=1 as the Cheeger constant for the fractional perimeter and the Lebesgue measure with a suitable weight. As a by-product, we obtain new lower bounds on the sharp constant in the 1-dimensional case, even for non-convex sets, some of which optimal in the case p=1
deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics
Full text linkExcessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, and may not be standardized or spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map overstory tree mortality in a consistent manner over space and time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Low altitude observation platforms (e.g., drones or airplanes) provide a cost-effective source of training data by capturing high-resolution orthophotos of overstory tree mortality events at centimeter-scale resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than two thousand centimeter-resolution orthophotos, covering more than 1,000,000 ha, of which more than 58,000 ha are manually annotated with live/dead tree classifications. This community-sourced and rigorously curated dataset can serve as a comprehensive reference dataset to uncover tree mortality patterns from local to global scales using space-based Earth observation data and machine learning models. This will provide the basis to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. The open nature of deadtrees.earth, together with its curation of high-quality, spatially representative, and ecologically diverse data will continuously increase our capacity to uncover and understand tree mortality dynamics
Pyro-ecophysiology of 11 woody Karst species: Leaf flammability analysis reveals fire-safe species for green firebreaks development
Full text linkClimate change is intensifying wildfire frequency and severity in Mediterranean ecosystems, creating urgent needs for effective fire management strategies. Green firebreaks based on fire-resistant vegetation represent a promising approach, but require species-specific flammability assessments. We evaluated leaf moisture-dependent flammability of 11 dominant woody species in a Mediterranean karst ecosystem to identify potential candidates for green firebreak establishment. For each species, we conducted laboratory leaf flammability tests by measuring ignition vulnerability curves (IVCs), which relate leaf ignition probability at different moisture content levels, quantified as Live Fuel Moisture Content (LFMC) and Relative Water Content (RWC). Contemporarily, we monitored species-specific LFMC variation during summer drought in the field. Species-specific IVCs revealed critical LFMC thresholds corresponding to different ignition probability. Results showed marked interspecific variability in flammability, with Specific Leaf Area (SLA) being positively correlated with both maximum moisture content and LFMC at 50 % ignition probability. Species clustered into distinct vulnerability groups: high-risk species (Robinia pseudoacacia, Acer campestre, Acer monspessulanum) required minimal dehydration for ignition, while fire-resistant species (Quercus pubescens, Prunus mahaleb, Pistacia terebinthus, Ailanthus altissima) maintained high moisture content under drought and also required severe dehydration for ignition. These findings provide quantitative tools for species selection in fire management and demonstrate how plant hydraulic strategies determine landscape-scale fire vulnerability in Mediterranean karst ecosystems
A review on model-based design of experiments for parameter precision – Open challenges, trends and future perspectives
No full textIn the last decades, the systematic use of mathematical models has become pervasive within the chemical process engineering context. Experiment design plays a critical role for the rapid calibration and validation of mathematical models – whether mechanistic, data-driven or semiempirical models – with the ultimate goal of driving process development and optimisation. Since data is often limited, and running additional experiments to gather more information for model calibration might be expensive or impractical, model-based design of experiments (MBDoE) techniques have become increasingly relevant to support the model calibration task on a broad range of process engineering applications. In this review, we aim to provide a comprehensive overview of the advances in the field that have occurred since the previous work presented by Franceschini and Macchietto (2008), Chemical Engineering Science, 63, 4846–4872. We first provide the theoretical foundations behind the standard MBDoE problem formulation and highlight recent advances; we then thoroughly analyse limitations, open challenges, and current trends by discussing about 250 contributions in the field. Finally, we highlight future research directions that may enlarge and further enhance the robustness and reliability of MBDoE implementation in real industrial scenarios
Probabilistic characterization of joint roughness coefficient through a novel Bayesian sequential updating framework
No full textThe probabilistic characteristics of joint roughness coefficient (JRC) are critical for risk assessment and reliability-based design in rock engineering involving jointed rock masses. Direct measurements are often laborious and limited, while empirical models using various topographic metrics typically yield inconsistent JRC estimates, posing challenges for reliable result selection. Thus, effectively combining multi-metric evaluations for reasonable probabilistic JRC characterization remains an urgent task. For this purpose, this paper proposes a novel Bayesian sequential updating (BSU) framework that considers the inherent uncertainties in various JRC estimation models and innovatively incorporates correlations among multi-source metrics using multivariate normal, Gaussian copula, and Vine copula models, respectively. Furthermore, the Bayesian model averaging (BMA) technique is employed for the first time to address the selection uncertainty in Vine copula-based dependence structures. Three real-life datasets of root mean square of the average local slope (Z2), ultimate slope of the profile (Rmax), and standard deviation of undulation angle (SDi) are sequentially integrated into the proposed BSU framework to generate massive equivalent JRC sample sets, through which the statistics and probability distribution of JRC are analyzed. The results show that the proposed BSU framework significantly outperforms the conventional BSU with independence assumptions. As more multi-source information is integrated, it achieves better BSU results with comparable or superior accuracy to individual empirical models, circumventing the model selection challenge. The proposed approach demonstrates enhanced adaptability to limited datasets and broad generality for probabilistic characterization of data-constrained geotechnical parameters with correlated multi-source indirect information
Distributed data-driven unknown-input observers
No full textUnknown inputs related to, e.g., sensor aging, modeling errors, or device bias, represent a major concern in wireless sensor networks, as they degrade the state estimation performance. To improve the performance, unknown-input observers (UIOs) have been proposed. Most of the results available to design UIOs are based on explicit system models, which can be difficult or impossible to obtain in real-world applications. Data-driven techniques, on the other hand, have become a viable alternative for the design and analysis of unknown systems using only data. In this context, a novel data-driven distributed unknown-input observer (D-DUIO) for unknown continuous-time linear time-invariant (LTI) systems is developed, which requires solely some data collected offline, without any prior knowledge of the system matrices. In the paper, first, a model-based approach to the design of a DUIO is presented. A sufficient condition for the existence of such a DUIO is recalled, and a new one is proposed, that is prone to a data-driven adaptation. Moving to a data-driven approach, it is shown that under suitable assumptions on the input/output/state data collected from the continuous-time system, it is possible to both claim the existence of a D-DUIO and to derive its matrices in terms of the matrices of pre-collected data. Finally, the efficacy of the D-DUIO is illustrated by means of numerical examples