Miracle-dz/UniFAR-FTUs huggingface dataset:
Interprofessional Care Team, Staffing, and Setting Characteristics that Impact Patient Outcomes: A Review of Reviews

Background The purpose of this study was to identify research methods and evidence pertaining to the relationship of interprofessional acute care teams and hospital characteristics on patient outcomes in hospital-based acute care. Methods A review was completed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The search strategy was executed across PubMed, CINAHL, and Embase. The review included 12 systematic reviews from 2012 to 2023 that examine the impact of acute care staffing characteristics on patient outcomes. Results Workforce characteristics primarily focused on nurse staffing, with a limited number of studies assessing the impact of interprofessional teams or non-clinical workers on care quality. There is limited data describing the context of care delivery via potential relationships between hospital characteristics, interprofessional team staffing levels, and patient outcomes. Conclusions To promote comparability across studies, future workforce research should include a comprehensive analytic approach that includes clearly defined variables representing interprofessional care teams, community factors, and staffing and patient characteristics.
Contrasting environmental effects of astronomically driven climate change on three Eocene hemipelagic successions from the Basque–Cantabrian Basin

Several processes can contribute to the formation of hemipelagic limestone–marl alternations as a consequence of astronomically driven climate change. The aim of this study was to decipher which environmental factors governed the formation of three Eocene hemipelagic successions of the Basque–Cantabrian Basin using a comprehensive set of physical and bulk carbonate geochemical data (bed thickness, mineralogy, %CaCO3, δ13C and δ18O). The results show that the significance of several environmental processes varied depending on the palaeogeographic setting and eccentricity‐modulated precessional seasonality. In the Sopelana starved deep‐sea basin, limestones were formed as a consequence of high pelagic carbonate productivity during periods of warm seawater and sluggish circulation, which corresponded with periods of low seasonality (summers at aphelion); conversely, marls accumulated when pelagic carbonate productivity decreased during periods with cooler waters and more vigorous circulation, which occurred when seasonality was higher (summers at perihelion). In the Gorrondatxe submarine fan fringe, marls accumulated when high seasonality produced significant continental rainfall and run‐off, causing the dilution of pelagic carbonate sedimentation with terrigenous supplies. In the Oyambre upper slope, marls also accumulated when seasonality was high, as pelagic carbonate productivity decreased due to both the expansion of low‐salinity waters on the ocean surface and the increase in continentally derived nutrients, which caused detrimental seawater conditions for calcareous plankton. Both in Gorrondatxe and Oyambre, limestones accumulated when boreal summer at aphelion caused low seasonality, which allowed relatively stable conditions to prevail. At minimum eccentricity, when precession‐driven seasonality contrast diminished, changes in pelagic carbonate productivity were significant in the three sections. On the contrary, at maximum eccentricity, when seasonality peaked due to summers occurring at perihelion, the effects of other environmental processes, such as continental and oceanic currents, became influential. However, the influence of these processes minimized when summertime coincided with aphelion at maximum eccentricity and seasonality was weakest.
Bioévaluation des structures benthiques en rade de Brest

Les communautes de la macrofaune benthique en rade de Brest ont ete etudiees sur plusieurs annees. Les stations d'echantillonnage se situent sur les banquettes vaseuses peu profondes (< 5 m) du pourtour de la rade. Un certain nombre de stations ont ete selectionnees afin d'estimer l'impact des principales sources de pollutions urbaines et industrielles sur la macrofaune. Les autres stations se situent theoriquement a l'abri de toute atteinte anthropique notable. La methode d'evaluation de la sante des structures benthiques utilisee au laboratoire est basee sur la reconnaissance de groupes ecologiques de polluo-sensibilite differente. C'est leur abondance relative a chaque station qui permet d'identifier sept etapes de perturbation des communautes. Ainsi, les zones fortement polluees sont aisement identifiees, mais cette methode permet aussi de detecter les premiers signes de desequilibre lie notament a l'eutrophisation. A cet egard, des situations opposees apparaissent entre le bassin nord et le bassin sud de la rade.
Biogeochemical Cycling And Nutrient Control Strategies For Groundwater At Stormwater Infiltration Basins

Elevated concentrations of nutrients, particularly nitrate, in groundwater and springs in Florida are a growing resource management concern. Stormwater infiltration basins, which are a common stormwater management practice in the well-drained karst terrain areas of Florida, are a potentially important source of nutrients to the groundwater system because stormwater exits the basin by only evaporation or infiltration. To better understand the biogeochemical processes integrating stormwater infiltration impacts on groundwater resources in a field-scale setting, a combination of hydrologic, soil chemistry, water chemistry, dissolved and soil gas, isotope, and microbiological data was collected from 2007 through 2010 at two stormwater infiltration basins receiving runoff from predominantly residential watersheds in north-central Florida. Substantially different biogeochemical processes affecting nitrogen fate and transport were observed beneath the two stormwater infiltration basins. Differences are related to soil textural properties that deeply link hydroclimatic conditions with soil moisture variations in a humid, subtropical climate. During 2008, shallow groundwater beneath the basin with predominantly clayey soils (median 41% silt+clay content) exhibited decreases in dissolved oxygen from 3.8 to 0.1 mg/L and decreases in nitrate-nitrogen from 2.7 mg/L to less than 0.016 mg/L, followed by manganese and iron reduction, sulfate reduction, and methanogenesis. In contrast, beneath the basin with predominantly sandy soils (median 2% silt+clay content), aerobic conditions persisted from 2007 through 2009 (dissolved oxygen of 5.0â€“7.8 mg/L), resulting in nitrate-nitrogen of 1. 3â€“3.3 mg/L in shallow groundwater. Soil extractable nitrate-nitrogen was significantly lower and the copper-containing nitrite reductase gene density was significantly higher beneath the clayey basin. Differences in moisture retention capacity between fine- and coarse-textured soils resulted in median volumetric gas-phase contents of 0.04 beneath the clayey basin and 0.19 beneath the sandy basin, inhibiting surface/subsurface oxygen exchange beneath the clayey basin. Subsurface biogeochemical processes at the clayey stormwater infiltration basin were further analyzed to better understand the effects of the highly variable hydrologic conditions common in humid, subtropical climates. Cyclic variations in biogeochemical processes generally coincided with wet and dry hydroclimatic conditions. Oxidizing conditions in the subsurface persisted for about one month or less at the beginning of wet periods with dissolved oxygen and nitrate showing similar temporal patterns. Reducing conditions in the subsurface evolved during prolonged flooding of the basin. At about the same time oxygen and nitrate reduction concluded, manganese, iron, and sulfate reduction began, with the onset of methanogenesis one month later. Reducing conditions persisted up to six months, continuing into subsequent dry periods until the next major oxidizing infiltration…
Effect of Exercise and Motor Interventions on Physical Activity and Motor Outcomes of Adults with Cerebral Palsy: A Systematic Review

ABSTRACT Purpose Systematically review the effect of exercise and motor interventions on physical activity and motor outcomes of adults with cerebral palsy (CP). Methods Eight databases were searched. Results Twenty-five studies were included, representing 439 adults with CP. Very low to low quality evidence supports that gait training is more effective than standard care or neurodevelopmental treatment for improving gait velocity, quality of gait, and ankle stiffness; balance training is more effective than seated therapeutic activities for improving walking self-confidence and perceived change in balance; whole-body vibration is no more effective than resistance training for improving strength or gait function; and resistance training is no more effective than a person’s typical exercise program for improving strength or gait function of adults with CP. Adverse events were reported for balance training, functional training, resistance training, and whole-body vibration. Discussion Further research is needed that is adequately powered and uses well-controlled study designs.
Biological Applications of Thermoplasmonics

Thermoplasmonics has emerged as an extraordinarily versatile tool with profound applications across various biological domains ranging from medical science to cell biology and biophysics. The key feature of nanoscale plasmonic heating involves remote activation of heating by applying laser irradiation to plasmonic nanostructures that are designed to optimally convert light into heat. This unique capability paves the way for a diverse array of applications, facilitating the exploration of critical biological processes such as cell differentiation, repair, signaling, and protein functionality, and the advancement of biosensing techniques. Of particular significance is the rapid heat cycling that can be achieved through thermoplasmonics, which has ushered in remarkable technical innovations such as accelerated amplification of DNA through quantitative reverse transcription polymerase chain reaction. Finally, medical applications of photothermal therapy have recently completed clinical trials with remarkable results in prostate cancer, which will inevitably lead to the implementation of photothermal therapy for a number of diseases in the future. Within this review, we offer a survey of the latest advancements in the burgeoning field of thermoplasmonics, with a keen emphasis on its transformative applications within the realm of biosciences.
Nonlinear functional regression by functional deep neural network with kernel embedding

With the rapid development of deep learning in various fields of science and technology, such as speech recognition, image classification, and natural language processing, recently it is also widely applied in the functional data analysis (FDA) with some empirical success. However, due to the infinite dimensional input, we need a powerful dimension reduction method for functional learning tasks, especially for the nonlinear functional regression. In this paper, based on the idea of smooth kernel integral transformation, we propose a functional deep neural network with an efficient and fully data-dependent dimension reduction method. The architecture of our functional net consists of a kernel embedding step: an integral transformation with a data-dependent smooth kernel; a projection step: a dimension reduction by projection with eigenfunction basis based on the embedding kernel; and finally an expressive deep ReLU neural network for the prediction. The utilization of smooth kernel embedding enables our functional net to be discretization invariant, efficient, and robust to noisy observations, capable of utilizing information in both input functions and responses data, and have a low requirement on the number of discrete points for an unimpaired generalization performance. We conduct theoretical analysis including approximation error and generalization error analysis, and numerical simulations to verify these advantages of our functional net.
Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cues

Depression, a prominent contributor to global disability, affects a substantial portion of the population. Efforts to detect depression from social media texts have been prevalent, yet only a few works explored depression detection from user-generated video content. In this work, we address this research gap by proposing a simple and flexible multi-modal temporal model capable of discerning non-verbal depression cues from diverse modalities in noisy, real-world videos. We show that, for in-the-wild videos, using additional high-level non-verbal cues is crucial to achieving good performance, and we extracted and processed audio speech embeddings, face emotion embeddings, face, body and hand landmarks, and gaze and blinking information. Through extensive experiments, we show that our model achieves state-of-the-art results on three key benchmark datasets for depression detection from video by a substantial margin. Our code is publicly available on GitHub.
The categories of corings and coalgebras over a ring are locally countably presentable

For any commutative ring $R$, we show that the categories of $R$-coalgebras and cocommutative $R$-coalgebras are locally $\aleph_1$-presentable, while the categories of $R$-flat $R$-coalgebras are $\aleph_1$-accessible. Similarly, for any associative ring $R$, the category of $R$-corings is locally $\aleph_1$-presentable, while the category of $R$-$R$-bimodule flat $R$-corings is $\aleph_1$-accessible. The cardinality of the ring $R$ can be arbitrarily large. We also discuss $R$-corings with surjective counit and flat kernel. The proofs are straightforward applications of an abstract category-theoretic principle going back to Ulmer. For right or two-sided $R$-module flat $R$-corings, our cardinality estimate for the accessibility rank is not as good.
Mitigation Measures for Wildlife in Wind Energy Development, Consolidating the State of Knowledge — Part 2: Operation, Decommissioning

During this rapid development of wind energy aiming to combat climate change worldwide, there is greater need to avoid, reduce, and compensate for impacts on wildlife: Through the effective use of mitigation, wind energy can continue to expand while reducing impacts. This is a first broad step into discussing and understanding mitigation strategies collectively, identifying the current state of knowledge and be a beneficial resource for practitioners and conservationists.We review the current state of published knowledge, both land-based and offshore, with a focus on wind energy–wildlife mitigation measures. We state measures and highlight their objective and discuss at which project stage it is most effective (e.g. planning, construction, operation). Thereafter, we discuss key findings within current wind energy mitigation research, needing improved understanding into the efficacy of wildlife mitigation as well as research into the cost aspects of mitigation implementation. This review is divided into two articles; Part 1 focuses on mitigation measures during planning, siting, and construction, while Part 2 focuses on measures during operation and decommissioning.
Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery

In this paper, we provide the first convergence guarantee for the factorization approach. Specifically, to avoid the scaling ambiguity and to facilitate theoretical analysis, we optimize over the so-called left-orthogonal TT format which enforces orthonormality among most of the factors. To ensure the orthonormal structure, we utilize the Riemannian gradient descent (RGD) for optimizing those factors over the Stiefel manifold. We first delve into the TT factorization problem and establish the local linear convergence of RGD. Notably, the rate of convergence only experiences a linear decline as the tensor order increases. We then study the sensing problem that aims to recover a TT format tensor from linear measurements. Assuming the sensing operator satisfies the restricted isometry property (RIP), we show that with a proper initialization, which could be obtained through spectral initialization, RGD also converges to the ground-truth tensor at a linear rate. Furthermore, we expand our analysis to encompass scenarios involving Gaussian noise in the measurements. We prove that RGD can reliably recover the ground truth at a linear rate, with the recovery error exhibiting only polynomial growth in relation to the tensor order. We conduct various experiments to validate our theoretical findings.
Hyperparameter Estimation for Sparse Bayesian Learning Models

Sparse Bayesian Learning (SBL) models are extensively used in signal processing and machine learning for promoting sparsity through hierarchical priors. The hyperparameters in SBL models are crucial for the model's performance, but they are often difficult to estimate due to the non-convexity and the high-dimensionality of the associated objective function. This paper presents a comprehensive framework for hyperparameter estimation in SBL models, encompassing well-known algorithms such as the expectation-maximization (EM), MacKay, and convex bounding (CB) algorithms. These algorithms are cohesively interpreted within an alternating minimization and linearization (AML) paradigm, distinguished by their unique linearized surrogate functions. Additionally, a novel algorithm within the AML framework is introduced, showing enhanced efficiency, especially under low signal noise ratios. This is further improved by a new alternating minimization and quadratic approximation (AMQ) paradigm, which includes a proximal regularization term. The paper substantiates these advancements with thorough convergence analysis and numerical experiments, demonstrating the algorithm's effectiveness in various noise conditions and signal-to-noise ratios.
Anomalous symmetries of quantum spin chains and a generalization of the Lieb-Schultz-Mattis theorem

For any locality-preserving action of a group $G$ on a quantum spin chain one can define an anomaly index taking values in the group cohomology of $G$. The anomaly index is a kinematic quantity, it does not depend on the Hamiltonian. We prove that a nonzero anomaly index prohibits any $G$-invariant Hamiltonian from having $G$-invariant gapped ground states. Lieb-Schultz-Mattis-type theorems are a special case of this result when $G$ involves translations. In the case when the symmetry group $G$ is a Lie group, we define an anomaly index which takes values in the differentiable group cohomology as defined by J.-L. Brylinski and prove a similar result.
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors

We propose denoising diffusion variational inference (DDVI), an approximate inference algorithm for latent variable models which relies on diffusion models as flexible variational posteriors. Specifically, our method introduces an expressive class of approximate posteriors with auxiliary latent variables that perform diffusion in latent space by reversing a user-specified noising process. We fit these models by optimizing a lower bound on the marginal likelihood inspired by the wake-sleep algorithm. Our method is easy to implement (it fits a regularized extension of the ELBO), is compatible with black-box variational inference, and outperforms alternative classes of approximate posteriors based on normalizing flows or adversarial networks. It increases the expressivity of flow-based methods via non-invertible deep recurrent architectures and avoids the instability of adversarial methods. We use DDVI on a motivating task in biology -- inferring latent ancestry from human genomes -- and we find that it outperforms strong baselines on the Thousand Genomes dataset.
Roughness Signature Functions

Inspired by the activity signature introduced by Todorov and Tauchen (2010), which was used to measure the activity of a semimartingale, this paper introduces the roughness signature function. The paper illustrates how it can be used to determine whether a discretely observed process is generated by a continuous process that is rougher than a Brownian motion, a pure-jump process, or a combination of the two. Further, if a continuous rough process is present, the function gives an estimate of the roughness index. This is done through an extensive simulation study, where we find that the roughness signature function works as expected on rough processes. We further derive some asymptotic properties of this new signature function. The function is applied empirically to three different volatility measures for the S&P500 index. The three measures are realized volatility, the VIX, and the option-extracted volatility estimator of Todorov (2019). The realized volatility and option-extracted volatility show signs of roughness, with the option-extracted volatility appearing smoother than the realized volatility, while the VIX appears to be driven by a continuous martingale with jumps.
Existence and Uniqueness for the SQG Vortex-Wave System when the Vorticity is Constant near the Point-Vortex

This article studies the vortex-wave system for the Surface Quasi-Geostrophic equation with parameter 0<s<1. We obtained local existence of classical solutions in H^4 under the standard"plateau hypothesis", H^2-stability of the solutions, and a blow-up criterion. In the sub-critical case s>1/2 we established global existence of weak solutions. For the critical case s = 1/2, we introduced a weaker notion of solution (V-weak solutions) to give a meaning to the equation and prove global existence.
The Vulnerability Is in the Details: Locating Fine-grained Information of Vulnerable Code Identified by Graph-based Detectors

Vulnerability detection is a crucial component in the software development lifecycle. Existing vulnerability detectors, especially those based on deep learning (DL) models, have achieved high effectiveness. Despite their capability of detecting vulnerable code snippets from given code fragments, the detectors are typically unable to further locate the fine-grained information pertaining to the vulnerability, such as the precise vulnerability triggering locations.In this paper, we propose VULEXPLAINER, a tool for automatically locating vulnerability-critical code lines from coarse-level vulnerable code snippets reported by DL-based detectors.Our approach takes advantage of the code structure and the semantics of the vulnerabilities. Specifically, we leverage program slicing to get a set of critical program paths containing vulnerability-triggering and vulnerability-dependent statements and rank them to pinpoint the most important one (i.e., sub-graph) as the data flow associated with the vulnerability. We demonstrate that VULEXPLAINER performs consistently well on four state-of-the-art graph-representation(GP)-based vulnerability detectors, i.e., it can flag the vulnerability-triggering code statements with an accuracy of around 90% against eight common C/C++ vulnerabilities, outperforming five widely used GNN-based explanation approaches. The experimental results demonstrate the effectiveness of VULEXPLAINER, which provides insights into a promising research line: integrating program slicing and deep learning for the interpretation of vulnerable code fragments.