Miracle-dz/UniFAR-FTUs huggingface dataset:
Urban Vulnerability Assessment for Pandemic surveillance

A Pandemic devastates the life of global citizens and causes significant economic, social, and political disruption. Evidence suggests that Pandemic's likelihood has increased over the past century because of increased global travel and integration, urbanization, and changes in land use. Further, evidence concerning the urban character of the Pandemic has underlined the role of cities in disease transmission. An early assessment of the severity of infection and transmissibility can help quantify the Pandemic potential and prioritize surveillance to control of urban areas in Pandemics. In this paper, an Urban Vulnerability Assessment (UVA) methodology is proposed. UVA investigates the possible vulnerable factors related to Pandemics to assess the vulnerability in urban areas. A vulnerability index is constructed by the aggregation of multiple vulnerability factors computed on each urban area (i.e., urban density, poverty index, informal labor, transmission routes). UVA provides insights into early vulnerability assessment using publicly available data. The applicability of UVA is shown by the identification of high-vulnerable areas where surveillance should be prioritized in the COVID-19 Pandemic in Bogota, Colombia.
Calcificación de las principales especies de corales constructoras de arrecifes en la costa del Pacífico del sur de México

El calentamiento global y la acidificacion del oceano influyen en la calcificacion de los corales. No obstante, existe poca informacion respecto a los parametros de crecimiento de las principales especies de corales constructores de arrecifes en areas de crecimiento marginales como la costa del Pacifico del sur de Mexico. Se obtuvieron los parametros de crecimiento de 8 especies de corales hermatipicos (especies con crec imiento masivo: Porites panamensis, Porites lobata, Pavona gigantea y Pavona varians ; especies con crecimiento ramificado: Pocillopora meandrina, Pocillopora damicornis , Pocillopora verrucosa y Pocillopora capitata ) en 2 zonas de la costa del Pacifico del sur de Mexico. Los corales ra mificados tuvieron ta sas de calcificacion ma s altas (2.99–5.23 g CaCO 3 cm –2 ano –1 ) que los corales masivos (0.34–1.13 g CaCO 3 cm –2 ano –1 ). Se observo una relacion significativa entre la temperatura superfi cial del mar (TSM) y la densidad del esqueleto para todas las especies de coral masivo. Ademas, 2 especies de coral masivo ( P. gigantea y P. varians ) mostraron una relacion significativa entre la tasa de calcificacion y la TSM. Las surg encias en la region del golfo de Tehuantepec acarrean aguas con bajo pH y baja saturacion de aragonita, y podrian estar afec tando negativamente la tasa de calcificacion de los corales en el area de estudio
The carbonic anhydrases : new horizons

The book deals with the role of both oxygen- and nitrogen-centred free radicals in inflammatory diseases such as rheumatoid arthritis. The well-known involvement of the superoxide anion radical in the bactericidal action of inflammatory cells suggests that radicals and the inflammatory response are inextricably linked. The widespread involvement of radicals in human disease seems inevitable, because inflammation is such a conspicuous component of human disease. For the first time, the present text integrates contributions from leading research groups who have been investigating the role of radicals within the context of all stages of inflammtion, such as the recruitment of inflammatory cells, their bactericidal action, inflammatory tissue destruction and inflammatory cell death by apoptosis. The chapters are broadly organised so that they trace the clinical course of the acute and chronic inflammtory response, emphasising the therapeutic implications of recent data on the contribution of nitric oxide and related nitrogen-centred species. The book will be of interest to academic and industrial researchers and clinicians with interests in the fields of inflammation or free radical biology.
Multishell Diffusion MRI Reflects Improved Physical Fitness Induced by Dance Intervention

Using multishell diffusion MRI and both tract-based spatial statistics (TBSS) and probabilistic tracking of specific tracts of interest, we evaluated the neural underpinnings of the impact of a six-month dance intervention (DI) on physical fitness and cognitive outcomes in nondemented seniors. The final cohort had 76 nondemented seniors, randomized into DI and control (life as usual) groups. Significant effects were observed between the DI and control groups in physical fitness measures and in attention. We detected associations between improved physical fitness and changes in diffusion tensor imagining (DTI) measures in the whole white matter (WM) skeleton and in the corticospinal tract and the superior longitudinal fascicle despite the fact that no significant differences in changes to the WM microstructure were found between the two groups.
Probability Density Functions Attached to Random Euler Products for Automorphic L-Functions

In this paper, we study the value distributions of L-functions of holomorphic primitive cusp forms in the level aspect. We associate such automorphic L-functions with probabilistic models called the random Euler products. First, we prove the existence of probability density functions attached to the random Euler products. Then various mean values of automorphic L-functions are expressed as integrals involving the density functions. Moreover, we estimate the discrepancies between the distributions of values of automorphic L-functions and those of the random Euler products.
Functional Age Predicted by Electronic Short Physical Performance Battery Can Detect Frailty Status in Older Adults

Purpose The importance of evaluating frailty status of older adults in clinical practice has been gaining attention with cumulative evidence showing its relevance in clinical outcomes and decision-making. We aimed to develop and validate whether the functional age predicted by an electronic continuous short physical performance battery (eSPPB) could predict frailty status. Patients and Methods We reviewed medical records of outpatients (N=834) of Asan Medical Center, aged 51–95 years. We used the eSPPB data of 717 patients as a development cohort, and that of 117 patients, who also underwent comprehensive geriatric assessments, as a validation cohort. Frailty index was calculated by counting deficits of 45 geriatric items including comorbidities, daily functions, mobility, mood, and cognition. For functional age, we used balance score (0–4), gait speed (m/s), and stand-up time (s) measured 5 times in the chair rise test. Results From the development cohort, we established a functional age using the formula (83.61 − 1.98*[balance score] − 5.21*[gait speed] + 0.23*[stand-up time]), by multivariate linear regression analysis with chronological age as a dependent variable (R2 = 0.233). In the validation cohort, the functional age positively correlated with frailty index (p < 0.001). C-statistics classifying frailty (defined as frailty index ≥0.25) was higher (p < 0.001) with functional age (0.912) than that with chronological age (0.637). A cut-off functional age of ≥77.2 years maximized Youden’s J when screening for frailty, with sensitivity of 94.4% and specificity of 80.8%. Conclusion A newly developed functional age predictor using eSPPB parameters can predict the frailty status as defined by the deficit accumulation method and may serve as a physical biomarker of human aging.
Automatic Dysarthric Speech Detection Exploiting Pairwise Distance-Based Convolutional Neural Networks

Automatic dysarthric speech detection can provide reliable and cost-effective computer-aided tools to assist the clinical diagnosis and management of dysarthria. In this paper we propose a novel automatic dysarthric speech detection approach based on analyses of pairwise distance matrices using convolutional neural networks (CNNs). We represent utterances through articulatory posteriors and consider pairs of phonetically-balanced representations, with one representation from a healthy speaker (i.e., the reference representation) and the other representation from the test speaker (i.e., test representation). Given such pairs of reference and test representations, features are first extracted using a feature extraction front-end, a frame-level distance matrix is computed, and the obtained distance matrix is considered as an image by a CNN-based binary classifier. The feature extraction, distance matrix computation, and CNN-based classifier are jointly optimized in an end-to-end framework. Experimental results on two databases of healthy and dysarthric speakers for different languages and pathologies show that the proposed approach yields a high dysarthric speech detection performance, outperforming other CNN-based baseline approaches.
Towards Collaborative Optimization of Cluster Configurations for Distributed Dataflow Jobs

Analyzing large datasets with distributed dataflow systems requires the use of clusters. Public cloud providers offer a large variety and quantity of resources that can be used for such clusters. However, picking the appropriate resources in both type and number can often be challenging, as the selected configuration needs to match a distributed dataflow job’s resource demands and access patterns. A good cluster configuration avoids hardware bottlenecks and maximizes resource utilization, avoiding costly overprovisioning.We propose a collaborative approach for finding optimal cluster configurations based on sharing and learning from historical runtime data of distributed dataflow jobs. Collaboratively shared data can be utilized to predict runtimes of future job executions through the use of specialized regression models. However, training prediction models on historical runtime data that were produced by different users and in diverse contexts requires the models to take these contexts into account.
Nonparametric goodness‐of‐fit testing for parametric covariate models in pharmacometric analyses

The characterization of covariate effects on model parameters is a crucial step during pharmacokinetic/pharmacodynamic analyses. Although covariate selection criteria have been studied extensively, the choice of the functional relationship between covariates and parameters, however, has received much less attention. Often, a simple particular class of covariate‐to‐parameter relationships (linear, exponential, etc.) is chosen ad hoc or based on domain knowledge, and a statistical evaluation is limited to the comparison of a small number of such classes. Goodness‐of‐fit testing against a nonparametric alternative provides a more rigorous approach to covariate model evaluation, but no such test has been proposed so far. In this manuscript, we derive and evaluate nonparametric goodness‐of‐fit tests for parametric covariate models, the null hypothesis, against a kernelized Tikhonov regularized alternative, transferring concepts from statistical learning to the pharmacological setting. The approach is evaluated in a simulation study on the estimation of the age‐dependent maturation effect on the clearance of a monoclonal antibody. Scenarios of varying data sparsity and residual error are considered. The goodness‐of‐fit test correctly identified misspecified parametric models with high power for relevant scenarios. The case study provides proof‐of‐concept of the feasibility of the proposed approach, which is envisioned to be beneficial for applications that lack well‐founded covariate models.
Numerical study of molten metal melt pool behaviour during conduction-mode laser spot melting

Molten metal melt pools are characterised by highly non-linear responses, which are very sensitive to imposed boundary conditions. Temporal and spatial variations in the energy flux distribution are often neglected in numerical simulations of melt pool behaviour. Additionally, thermo-physical properties of materials are commonly changed to achieve agreement between predicted melt-pool shape and experimental post-solidification macrograph. Focusing on laser spot melting in conduction mode, we investigated the influence of dynamically adjusted energy flux distribution and changing thermo-physical material properties on melt pool oscillatory behaviour using both deformable and non-deformable assumptions for the gas-metal interface. Our results demonstrate that adjusting the absorbed energy flux affects the oscillatory fluid flow behaviour in the melt pool and consequently the predicted melt-pool shape and size. We also show that changing the thermo-physical material properties artificially or using a non-deformable surface assumption lead to significant differences in melt pool oscillatory behaviour compared to the cases in which these assumptions are not made.
Long-term evolution of surviving companion stars of Type Iax supernovae

The nature of the progenitors and explosion mechanism of Type Iax supernovae (SNe Iax) remain a mystery. The single-degenerate (SD) systems that involve the incomplete pure deflagration explosions of near-Chandrasekhar-mass white dwarfs (WDs) have recently been proposed for producing SNe Iax in which non-degenerate companions are expected to survive from SN explosions. In this work, we concentrate on the main-sequence (MS) donor SD progenitor systems. By mapping the computed companion models from three-dimensional hydrodynamical simulations of ejecta-companion interaction into a one-dimensional stellar evolution code mesa, we investigate the long-term appearance and observational signatures of surviving MS companions of SNe Iax by tracing their post-impact evolution. Depending on different MS companion models, it is found that the shocked surviving companion stars can significantly expand and evolve to be more luminous (5–$500\, L_{\odot }$) for a time-scale of 10–104 yr. Comparing with the late-time light curve of an observed SN Iax (SN 2005hk), it is suggested that surviving MS companions of SNe Iax would expect to be visible about 1000 d after the explosion when SN itself has been faded.
Hyper-Doped Silicon Nanoantennas and Metasurfaces for Tunable Infrared Plasmonics

We present the experimental realization of ordered arrays of hyper-doped silicon nanodisks, which exhibit a localized surface plasmon resonance. The plasmon is widely tunable in a spectral window between 2 and 5 $\mu$m by adjusting the free carrier concentration between 10$^{20}$ and 10$^{21}$ cm$^{-3}$. We show that strong infrared light absorption can be achieved with all-silicon plasmonic metasurfaces employing nano-structures with dimensions as low as 100\,nm in diameter and 23 nm in height. Our numerical simulations show an excellent agreement with the experimental data and provide physical insights on the impact of the nanostructure shape as well as of near-field effects on the optical properties of the metasurface. Our results open highly promising perspectives for integrated all-silicon-based plasmonic devices for instance for chemical or biological sensing or for thermal imaging.
Intention-Based Lane Changing and Lane Keeping Haptic Guidance Steering System

Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle. However, current haptic guidance steering systems demonstrate some deficiencies in assisting lane changing. This study explored a new steering interaction method, including the design and evaluation of an intention-based haptic shared steering system. Such an intention-based method can support both lane keeping and lane changing assistance, by detecting a driver's lane change intention. By using a deep learning-based method to model a driver's decision timing regarding lane crossing, an adaptive gain control method was proposed for realizing a steering control system. An intention consistency method was proposed to detect whether the driver and the system were acting towards the same target trajectories and to accurately capture the driver's intention. A driving simulator experiment was conducted to test the system performance. Participants were required to perform six trials with assistive methods and one trial without assistance. The results demonstrated that the supporting system decreased the lane departure risk in the lane keeping tasks and could support a fast and stable lane changing maneuver.
Creation of radiocarbon C-14 at thunderstorms

The synthesis of isotopes is possible under conditions of power electric discharge in the atmosphere. Knowledge of the radioactive 14C yield under flash conditions (as additional channel of 14C production relative to the main - cosmogenic one) is important for radiocarbon analysis. It is proposed the simple model for evaluation of the upper limit of the 14C yield, which creation was simulated for the altitudes up to 13 km. Along with 14C synthesis the radioactive isotope 41Ar is created also. The relevant decay parameters of 41Ar allow to advise it as a appropriate tracer of radiocarbon 14C generation under the flash conditions. The upper limit of the 14C creation on the Earth per year during the thunderstorms is evaluated as 2% of cosmic yield in the atmosphere.
Grid Skyrme functional obtained using a committee of multilayer neural networks

We present two new mass models based on a Skyrme energy density functional. The parameters of these models were adjusted using a coordinate representation on a three-dimensional grid, allowing for both axial and triaxial deformation during the fitting process. In order to manage its computational complexity, we have employed a committee of multilayer neural networks to accelerate the adjustment process. The resulting models, GSk1 and GSk2, achieve an rms error on the 2408 known masses of 661 and 731 keV respectively, with GSk2 achieving a better description of the pairing properties of finite nuclei. The 884 measured nuclear charge radii are well reproduced with rms errors of 0.025 and 0.024 fm, respectively, as are the properties of nuclear matter, as predicted by ab-initio calculations. Both models predict significant numbers of nuclei to exhibit triaxial deformation in their ground states.
Risk-Constrained Thompson Sampling for CVaR Bandits

The multi-armed bandit (MAB) problem is a ubiquitous decision-making problem that exemplifies the exploration-exploitation tradeoff. Standard formulations exclude risk in decision making. Risk notably complicates the basic reward-maximising objective, in part because there is no universally agreed definition of it. In this paper, we consider a popular risk measure in quantitative finance known as the Conditional Value at Risk (CVaR). We explore the performance of a Thompson Sampling-based algorithm CVaR-TS under this risk measure. We provide comprehensive comparisons between our regret bounds with state-of-the-art L/UCB-based algorithms in comparable settings and demonstrate their clear improvement in performance. We also include numerical simulations to empirically verify that CVaR-TS outperforms other L/UCB-based algorithms.
Expertise and confidence explain how social influence evolves along intellective tasks

Discovering the antecedents of individuals' influence in collaborative environments is an important, practical, and challenging problem. In this paper, we study interpersonal influence in small groups of individuals who collectively execute a sequence of intellective tasks. We observe that along an issue sequence with feedback, individuals with higher expertise and social confidence are accorded higher interpersonal influence. We also observe that low-performing individuals tend to underestimate their high-performing teammate's expertise. Based on these observations, we introduce three hypotheses and present empirical and theoretical support for their validity. We report empirical evidence on longstanding theories of transactive memory systems, social comparison, and confidence heuristics on the origins of social influence. We propose a cognitive dynamical model inspired by these theories to describe the process by which individuals adjust interpersonal influences over time. We demonstrate the model's accuracy in predicting individuals' influence and provide analytical results on its asymptotic behavior for the case with identically performing individuals. Lastly, we propose a novel approach using deep neural networks on a pre-trained text embedding model for predicting the influence of individuals. Using message contents, message times, and individual correctness collected during tasks, we are able to accurately predict individuals' self-reported influence over time. Extensive experiments verify the accuracy of the proposed models compared to baselines such as structural balance and reflected appraisal model. While the neural networks model is the most accurate, the dynamical model is the most interpretable for influence prediction.
A robust statistical method for Genome-wide association analysis of human copy number variation

Conducting genome-wide association studies (GWAS) in copy number variation (CNV) level is a field where few people involves and little statistical progresses have been achieved, traditional methods suffer from many problems such as batch effects, heterogeneity across genome, leading to low power or high false discovery rate. We develop a new robust method to find disease-risking regions related to CNV's disproportionately distributed between case and control samples, even if there are batch effects between them, our test formula is robust to such effects. We propose a new empirical Bayes rule to deal with overfitting when estimating parameters during testing, this rule can be extended to the field of model selection, it can be more efficient compared with traditional methods when there are too much potential models to be specified. We also give solid theoretical guarantees for our proposed method, and demonstrate the effectiveness by simulation and realdata analysis.
Agri‐environmental schemes affect the trophic niche size and diet of common carabid species in agricultural landscapes

1. Carabids are abundant in agroecosystems and provide biocontrol services by feeding on weed seeds and pests. We compare the effects of two fundamentally different agri‐environmental schemes (AES) on the numeric and trophic responses of four common carabid species: Anchomenus dorsalis, Poecilus cupreus, Pseudoophonus rufipes and Pterostichus melanarius.