Miracle-dz/UniFAR-FTUs huggingface dataset:
Escolarização da criança hospitalizada sob a ótica da família

This study aimed to investigate the meaning of the experience of the educational process of children hospitalized with a chronic disease from a family perspective. It is a qualitative research, conducted at the pediatric clinic of a teaching hospital. The study included ten families of hospitalized children between seven and 16 years of age. We used semi-structured interviews and the principles of thematic interpretation for analysis. Results showed that families feel distressed by the situation of child' distancing from school due to the periodic hospitalizations and they think that going to school is a desire the children reveal. It was concluded that the hospital class represents an important care strategy. Through the maintenance of the educational process, the children reframe their hospital experience and have preserved their development, improving their self-esteem and quality of life. DESCRIPTORS: Chronic disease. Hospitalized children. Education. Family.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL) Use of protection motivation theory in non-compliance research Use of protection motivation theory in non-compliance research

There is a rich stream of research focusing on employee non-/compliance with information security policies. However, this stream suffers from inconsistent, even contradicting results and lack of theorical congruence. Attempts to explain such inconsistencies have included investigation of possible moderating effects of contextual variables. We further investigate these inconsistencies by analytically disentangling the consistency in the implementation of the four most used variables of Protection Motivation Theory— Perceived severity , Perceived susceptibility , Response efficacy , and Self-efficacy —across the research field. Specifically, we address the following research question; what inconsistencies, if any, are there in the use of Protection motivation theory in non-/compliance research? We find that three of the variables analyzed have been ascribed more than one theoretical property across the seven studies reviewed, thereby making it problematic to fully understand their cause-and-effect relationships. That is, it is unclear which property that explains employees’ intention to comply with IS policies, whether they have the same effects, or have an increased effect when applied in conjunction. This study contributes to the literature by proposing that inconsistent results may not only be due to omitted moderating factors, but also to theoretical properties of key variables being inconsistently defined and measured.
A Generic Model for the Resuspension of Multilayer Aerosol Deposits by Turbulent Flow

Abstract An idealized lattice structure is considered of multilayer aerosol deposits, where every particle at the deposit surface is associated with a resuspension rate constant depending on a statistically distributed particle parameter and on flow conditions. The response of this generic model is represented by a set of integrodifferential equations. As a first application of the general formalism, the behavior of Fromentin’s multilayer model is analyzed, and the model parameters are adapted to experimental data. In addition, improved relations between model parameters and physical input parameters are proposed. As a second application, a method is proposed for building multilayer models by using resuspension rate constants of existing monolayer models. The method is illustrated by a sample of monolayer data resulting from the model of Reeks, Reed, and Hall. Also discussed is the error to be expected if a monolayer resuspension model, which works well for thin aerosol deposits, is applied to thick deposits under the classical monolayer assumption that all deposited particles interact with the fluid at all times.
TITLE: How the South was won: current and potential range expansion of the 1 crested porcupine in Southern Italy 2

Since the 1970s, the crested porcupine has expanded his range into Northern regions in 27 Italy, where it was historically absent, helped by climatic change, legal protection, and 28 forest re-expansion. In the last ten years, a remarkable range expansion has also been 29 observed in the southernmost Italian regions. The aim of our work is to summarize the 30 distribution of this species in Southern Italy and assess potential range expansion under 31 multiple future scenarios of global warming. We collected 1783 occurrence records of 32 the crested porcupine through ad-hoc web pages, online platforms, and some data 33 directly collected by authors (N = 976 occurrences between 1998 and 2008; N = 807 34 between 2008 and 2019). A remarkable increase in occurrences occurred in Lucania, 35 Campania, and Apulia regions, in Southern Italy, mostly along the coastlines. Species 36 distribution models showed that porcupine presence is associated with warm 37 temperatures and an intermediate level of precipitation. Although land-cover showed 38 lower importance compared to climate, the species was positively associated with 39 forests and negatively associated with agricultural, grasslands and shrublands, and 40 urban landscapes. Model projections suggested that future global warming can improve 41 suitability for porcupines in the Apennine ridge, including the Southern Calabria and the 42 Aspromonte National Park. However, increase in drought and urbanization may reduce 43 the habitat suitability for the crested porcupine in the Salento peninsula, limiting the 44 success of the range expansion by this large rodent, and in Eastern Calabria, wih 45 possible range contractions along the Tyrrhenian coast.
Fast Algorithm for Low-rank Tensor Completion in Delay-embedded Space

Tensor completion using multiway delay-embedding transform (MDT) (or Hankelization) suffers from the large memory requirement and high computational cost in spite of its high potentiality for the image modeling. Recent studies have shown high completion performance with a relatively small window size, but experiments with large window sizes require huge amount of memory and cannot be easily calculated. In this study, we address this serious computational issue, and propose its fast and efficient algorithm. Key techniques of the proposed method are based on two properties: (1) the signal after MDT can be diagonalized by Fourier transform, (2) an inverse MDT can be represented as a convolutional form. To use the properties, we modify MDT-Tucker [26], a method using Tucker decomposition with MDT, and introducing the fast and efficient algorithm. Our experiments show more than 100 times acceleration while maintaining high accuracy, and to realize the computation with large window size.
Understanding Uncertainty Maps in Vision with Statistical Testing

Quantitative descriptions of confidence intervals and uncertainties of the predictions of a model are needed in many applications in vision and machine learning. Mechanisms that enable this for deep neural network (DNN) models are slowly becoming available, and occasionally, being integrated within production systems. But the literature is sparse in terms of how to perform statistical tests with the uncertainties produced by these overparameterized models. For two models with a similar accuracy profile, is the former model's uncertainty behavior better in a statistically significant sense compared to the second model? For high resolution images, performing hypothesis tests to generate meaningful actionable information (say, at a user specified significance level $\alpha=0.05)$ is difficult but needed in both mission critical settings and elsewhere. In this paper, specifically for uncertainties defined on images, we show how revisiting results from Random Field theory (RFT) when paired with DNN tools (to get around computational hurdles) leads to efficient frameworks that can provide a hypothesis test capabilities, not otherwise available, for uncertainty maps from models used in many vision tasks. We show via many different experiments the viability of this framework.
Sparse and Complete Latent Organization for Geospatial Semantic Segmentation

Geospatial semantic segmentation on remote sensing images suffers from large intra-class variance in both fore-ground and background classes. First, foreground objects are tiny in the remote sensing images and are represented by only a few pixels, which leads to large foreground intra-class variance and undermines the discrimination between foreground classes (issue firstly considered in this work). Second, background class contains complex context, which results in false alarms due to large background intra-class variance. To alleviate these two issues, we construct a sparse and complete latent structure via prototypes. In particular, to enhance the sparsity of the latent space, we design a prototypical contrastive learning to have prototypes of the same category clustering together and prototypes of different categories to be far away from each other. Also, we strengthen the completeness of the latent space by modeling all foreground categories and hardest (nearest) background objects. We further design a patch shuffle augmentation for remote sensing images with complicated contexts. Our augmentation encourages the semantic information of an object to be correlated only to the limited context within the patch that is specific to its category, which further reduces large intra-class variance. We conduct extensive evaluations on a large scale remote sensing dataset, showing our approach significantly outperforms state-of-the-art methods by a large margin.
Hydroxy‐Al polymers block the frayed edge sites of illitic minerals in acid soils: studies in southwestern Japan at various weathering stages

The transformation of hydroxy‐interlayered vermiculite (HIV) to vermiculite in humid temperate soils may involve the gradual alteration of illitic minerals. However, it is difficult to detect such minor and progressive changes using conventional methods. We measured the amount of the frayed edge site (i.e. the weathering front of illitic minerals) in soil clays using Radiocaesium Interception Potential (RIP) methodology, and elucidated the effect of hydroxy‐Al polymers on the frayed edge site that may be occluded within HIV structures in the acidic soils of southwestern Japan. X‐ray diffraction patterns showed the progressive transformation of HIV to vermiculite, or further, to smectite in soil clays located in upper horizons and therefore subjected to more intensive podzolization. In this process, the amount of hydroxy‐Al polymers (represented by hot‐citrate Al) gradually decreased, while the vermiculitic charges increased (represented by Cs‐fixing capacity). However, the amount of the frayed edge site (represented by the RIP) firstly increased but then reversed and decreased in the upper layers of podzolic soils. After hot‐citrate extraction to remove the hydroxy‐Al polymers from HIV, the amount of the frayed edge site increased in HIV‐dominated clays, whereas there was a negligible increase in vermiculitic‐ or smectitic‐clays, thus indicating the blockage effect of hydroxy‐Al polymers on the frayed edge site. The sequential increase followed by the decrease that we documented in the amount of the frayed edge site along with the HIV‐vermiculite‐smectite transformation, suggested that the weathering front of illitic minerals was exposed as the HIV layers lose hydroxy‐Al polymers. Thereafter, the charges in the exposed frayed edge site might decline under the very acidic conditions of the upper layers of podzolic soils.
Understanding Business Analysts’ Needs for Data Report Authoring

Business analysts often create static, data-driven reports to summarize and communicate findings from marketing dashboards. However, the requirements and workflow for creating data-driven reports in business analytics have not been fully investigated. In this work, we interviewed fifteen professional analysts to understand their unique needs for data-driven report authoring and identify gaps between their goals, technical skills, and existing reporting tools. Our findings suggest eight fundamental takeaways for report authoring, such as the need for persistent interactive experiences combined with more robust narrative authoring for linking story pieces and customizing the narrative layout. Based on these interviews, we synthesize the results into five design guidelines to direct future analytic reporting tools.
Detected fluctuations in SDSS LRG magnitudes: Bulk flow signature or systematic?

In this paper we search for a signature of a large scale bulk flow by looking for fluctuations in the magnitudes of distant LRGs. We take a sample of LRGs from the Sloan Digital Sky Survey with redshifts of z>0.08 over a contiguous area of sky. Neighboring LRG magnitudes are averaged together to find the fluctuation in magnitudes as a function of R.A. The result is a fluctuation of a few percent in flux across roughly 100 degrees. The source of this fluctuation could be from a large scale bulk flow or a systematic in our treatment of the data set, or the data set itself. A bulk flow model is fitted to the observed fluctuation, and the three bulk flow parameters, its direction and magnitude: alpha_b, delta_b, v_b are constrained. We find that the bulk flow direction is consistent with the direction found by other authors, with alpha_b 180, delta_b -50. The bulk flow magnitude however was found to be anomalously large with v_b>4000km/s. The LRG angular selection function cannot be sufficiently taken into account in our analysis with the available data, and may be the source of either the anomalous magnitude of the bulk flow signal, or possibly the entire fluctuation. However, the fluctuation indicates a bulk flow direction very close to those found using other data sets and analyses. Further investigation with upcoming data is required to confirm this detection.
Dynamic Blockchain Sharding

By supporting decentralized applications (DApps), modern blockchains have become the technology of choice for the Web3, a decentralized way for people to interact with each other. As the popularity of DApps is growing, the challenge is now to allocate shard or subnetwork resources to face the associated demand of individual DApps. Unfortunately, most sharding proposals are inherently static as they cannot be adjusted at runtime. Given that blockchains are expected to run for years without interruption, these proposals are insufficient to cope with the upcoming demand. In this paper, we present dynamic blockchain sharding, a new way to create and close shards on-demand, and adjust their size at runtime without requiring to hard fork (i.e., creating duplicated instances of the same blockchain). The novel idea is to reconfigure sharding through dedicated smart contract invocations: not only does it strengthen the security of the sharding reconfiguration, it also makes it inherently transparent as any other blockchain data. Similarly to classic sharding, our protocol relies on randomness to cope with shard-takeover attacks and on rotating nodes to cope with the bribery of a slowly-adaptive adversary. By contrast, however, our protocol is ideally suited for open networks as it does not require fully synchronous communications. To demonstrate its efficiency, we deploy it in 10 countries over 5 continents and demonstrate that its performance increases quasi-linearly with the number of shards as it reaches close to 14,000 TPS on only 8 shards. 2012 ACM Subject Classification Computing methodologies → Distributed algorithms
Wastewater surveillance of influenza activity: Early detection, 1 surveillance, and subtyping in city and neighbourhood communities 2

Recurrent epidemics of influenza infection and its pandemic potential present a significant risk to global 14 population health. To mitigate hospitalizations and death, local public health relies on clinical surveillance to locate and 15 monitor influenza-like illnesses and/or influenza cases and outbreaks. At an international level, the global integration of 16 clinical surveillance networks is the only reliable method to report influenza types and subtypes and warn of an 17 emergent pandemic strain. During the COVID-19 pandemic, the demonstrated utility of wastewater surveillance (WWS) 18 in complementing or even replacing clinical surveillance, the latter a resource-intensive enterprise, was predicated on 19 the presence of stable viral fragments in wastewater. We show that influenza virus targets are stable in wastewaters 20 and partitions to the solids fraction. We subsequently quantify, type, and subtype influenza virus in municipal 21 wastewater and primary sludge throughout the course of a community outbreak. This research demonstrates the 22 feasibility of applying influenza virus WWS to city and neighbourhood levels; showing a 17-day lead time in forecasting 23 a citywide flu outbreak and providing population-level viral subtyping in near real-time using minimal resources and 24 infrastructure. Upon this background, and with the goal of advancing influenza WWS, we first investigated the partitioning 82 behavior of endogenous IAV in city and neighbourhood wastewater. The partitioning findings were used to optimize an 83 enrichment protocol for the quantification of influenza RNA in wastewater and subsequently apply the protocol to quantitate wastewater IAV and IBV RNA at the citywide level as well as within three distinct neighbourhoods in Ottawa, 85 Ontario, Canada. The wastewater viral signal was correlated with clinical case data available from the Ottawa public 86 health unit/agency during an out-of-season outbreak, determining the benefits and complementary attributes of 87 influenza virus WWS at the citywide and neighbourhood level. Finally, IAV positive citywide and neighbourhood 88 wastewater were tested for H1N1 and H3N2 to identify the circulating subtype responsible for community disease. To 89 the best of our knowledge, this study is the first to demonstrate the applicability of influenza virus WWS and subtyping 90 at the citywide and neighbourhood-level during an influenza community outbreak and the relation of influenza virus 91 WWS and influenza wastewater subtyping to clinical surveillance. level in Ottawa, from samples harvested from the sewer system. It was demonstrated that the normalized IAV viral signal differed between neighbourhood and with the citywide signal when population characteristic differed.
3D Scene Painting via Semantic Image Synthesis

We propose a novel approach to 3D scene painting using a configurable 3D scene layout. Our approach takes a 3D scene with semantic class labels as input and trains a 3D scene painting network that synthesizes color values for the input 3D scene. We exploit an off-the-shelf 2D seman-tic image synthesis method to teach the 3D painting net-work without explicit color supervision. Experiments show that our approach produces images with geometrically cor-rect structures and supports scene manipulation, such as the change of viewpoint, object poses, and painting style. Our approach provides rich controllability to synthesized images in the aspect of 3D geometry.
A Typology of Guidance Tasks in Mixed‐Initiative Visual Analytics Environments

Guidance has been proposed as a conceptual framework to understand how mixed‐initiative visual analytics approaches can actively support users as they solve analytical tasks. While user tasks received a fair share of attention, it is still not completely clear how they could be supported with guidance and how such support could influence the progress of the task itself. Our observation is that there is a research gap in understanding the effect of guidance on the analytical discourse, in particular, for the knowledge generation in mixed‐initiative approaches. As a consequence, guidance in a visual analytics environment is usually indistinguishable from common visualization features, making user responses challenging to predict and measure. To address these issues, we take a system perspective to propose the notion of guidance tasks and we present it as a typology closely aligned to established user task typologies. We derived the proposed typology directly from a model of guidance in the knowledge generation process and illustrate its implications for guidance design. By discussing three case studies, we show how our typology can be applied to analyze existing guidance systems. We argue that without a clear consideration of the system perspective, the analysis of tasks in mixed‐initiative approaches is incomplete. Finally, by analyzing matchings of user and guidance tasks, we describe how guidance tasks could either help the user conclude the analysis or change its course.
Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks

Dynamic neural networks have been shown as an encouraging alternative to traditional approaches for nonlinear temporal predictions. We use partially recurrent neural networks to study solar wind-magnetosphere coupling by predicting geomagnetic storms. The solar wind and Dst data used in this study are selected from the period 1963 to 1992. Statistical cross-correlation analyses and neural networks are applied to finding the best coupling functions. It is found that the results from both studies are consistent and that the coupling functions P 1/3 VB S , P 1/2 V B s , V 2 B S , VB S , and VB z are well suited for predicting geomagnetic storms. Also, the relative importance of solar wind parameters is investigated in detail, aimed at finding the optimal combinations of solar wind parameters for predicting geomagnetic storms. The two basic combinations giving accurate prediction are B s , n, V and B z , n, V. Addition of B, F, or B y can further improve predictions. We find that the best combination outperforms the best coupling functions in terms of prediction accuracy. Geomagnetic storms are very accurately predicted 1-2 hours in advance from the solar wind alone. The accurate predictions 1-2 hours ahead might well imply that the internal dynamic magnetospheric processes in forming geomagnetic storms occur on a timescale of about 1 hour. The predictions 3-5 hours ahead are useful in practical operation according to their acceptable accuracy. The prediction goodness is decreased with increasing prediction time. We consider this as a result of the fact that the internal magnetospheric dynamics limits the short-term prediction accuracy. The short-term predictability of geomagnetic storms and the validity of the results from cross-correlation analyses are discussed.