Miracle-dz/UniFAR-FTUs huggingface dataset:
Modification of hydrophobic electrospun polyurethane fibers for efficient wound healing activity

The intricate process of wound healing demands an atmosphere that is conducive to quick healing. In this situation, creating biocompatible and antibacterial fibers may be a good choice. In this study, the unmodified polyurethane and polyurethane modified with cellulose acetate and rosemary oil were electrospun into fibers. These fibers were subsequently adsorbed using silver nanoparticles. The FE-SEM was used to characterize the fibers, revealing their defect-free morphology and the presence of nanoparticles on them. Additionally, the XRD findings demonstrated that silver nanoparticles were successfully incorporated into the spun fiber mats. The contact angle measurements indicated a decreasing behavior starting from 100.4±1° to 28.4±3° in the case of unmodified polyurethane and modified fibers. The antibacterial assay revealed that although unmodified fiber mats have negligible antibacterial action, the modified fibers exhibited considerable antibacterial activity against E. coli and S. aureus. In-vitro studies revealed that Human embryonic kidney 293T (HEK 293T) cell proliferation was enhanced on the surface of modified fibers compared to unmodified fibers. In conclusion, our research presents unique multifunctional fibrous scaffolds that are appropriate and optimal for wound healing applications.
Restoration and enhancement optimization of blurred images based on SRGAN

Blurred images pose a significant challenge in many applications, including medical imaging, remote sensing, and surveillance systems. These images suffer from low resolution, noise, and missing data, which can hinder their interpretation and analysis. Traditional methods for image restoration and enhancement have their limitations, such as low quality and slow processing times. To overcome these challenges, this paper proposes an innovative method using Super-resolution Generative Adversarial Networks (SRGANs) to enhance image quality and fidelity. The proposed method employs adversarial training, perceptual loss, residual learning, and feature reconstruction to generate visually realistic and high-quality super-resolution (SR) images from low-resolution (LR) inputs. The SRGANs approach outperforms traditional methods, demonstrating its potential to advance image restoration and enhancement techniques. The paper also discusses possible improvements and future directions for this technique.
Evaluating the predictive performance of presence–absence models: Why can the same model appear excellent or poor?

Abstract When comparing multiple models of species distribution, models yielding higher predictive performance are clearly to be favored. A more difficult question is how to decide whether even the best model is “good enough”. Here, we clarify key choices and metrics related to evaluating the predictive performance of presence–absence models. We use a hierarchical case study to evaluate how four metrics of predictive performance (AUC, Tjur's R 2, max‐Kappa, and max‐TSS) relate to each other, the random and fixed effects parts of the model, the spatial scale at which predictive performance is measured, and the cross‐validation strategy chosen. We demonstrate that the very same metric can achieve different values for the very same model, even when similar cross‐validation strategies are followed, depending on the spatial scale at which predictive performance is measured. Among metrics, Tjur's R 2 and max‐Kappa generally increase with species' prevalence, whereas AUC and max‐TSS are largely independent of prevalence. Thus, Tjur's R 2 and max‐Kappa often reach lower values when measured at the smallest scales considered in the study, while AUC and max‐TSS reaching similar values across the different spatial levels included in the study. However, they provide complementary insights on predictive performance. The very same model may appear excellent or poor not only due to the applied metric, but also how predictive performance is exactly calculated, calling for great caution on the interpretation of predictive performance. The most comprehensive evaluation of predictive performance can be obtained by evaluating predictive performance through the combination of measures providing complementary insights. Instead of following simple rules of thumb or focusing on absolute values, we recommend comparing the achieved predictive performance to the researcher's own a priori expectations on how easy it is to make predictions related to the same question that the model is used for.
Estimation of Catchment Nutrient Loads in New Zealand Using Monthly Water Quality Monitoring Data

Causes of variation between loads estimated using alternative calculation methods and their repeatability were investigated using 20 years of daily flow and monthly concentration samples for 77 rivers in New Zealand. Loads of dissolved and total nitrogen and phosphorus were calculated using the Ratio, L5, and L7 methods. Estimates of loads and their precision associated with short‐term records of 5, 10, and 15 years were simulated by subsampling. The representativeness of the short‐term loads was quantified as the standard deviation of the 20 realizations. The L7 method generally produced more realistic loads with the highest precision and representativeness. Differences between load estimates were shown to be associated with poor agreement between the data and the underlying model. The best method was shown to depend on the match between the model and functional and distributional characteristics of the data, rather than on the contaminant. Short‐term load estimates poorly represented the long‐term load estimate, and deviations frequently exceeded estimated imprecision. The results highlight there is no single preferred load calculation method, the inadvisability of “unsupervised” load estimation and the importance of inspecting concentration‐flow, unit load‐flow plots and regression residuals. Regulatory authorities should be aware that the precision of loads estimated from monthly data are likely to be “optimistic” with respect to the actual repeatability of load estimates.
3D characteristics of ADCP echo intensity anomaly observed over the Snail, Archean, and Pika hydrothermal vent fields in the South Mariana Trough

Copyright © 2016 by The Geochemical Society of Japan. ture can be determined if the nature of the reflected objects is known. In a hydrothermal plume, Thomson et al. (1989, 1992) first analyzed the sound reflected by zooplankton by using plankton nets and acoustic observations from ADCPs during tow-yos over the Endeavour Ridge. They compared the ADCP sound beam and zooplankton density with regards to depth. Though the research focused on one dimension of depth, they effectively used acoustic technology to suggest the location and thickness of zooplankton in a plume. More recent platform evolutions, such as sensors mounted on autonomous underwater vehicles (AUVs), have succeeded in measuring the lateral distribution of plumes, including vent sites (German et al., 2008; Baker et al., 2012). This has enabled us to determine and understand plume distribution in much more detail. With this new platform technology, new features of hydrothermal plumes are being discovered by the application of acoustic sensors. Using side-scan sonars, the AUV “Urashima”, owned by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), observed 100-m-high filaments and fog-like structures above the seabed in the Iheya-North Knoll (Kumagai et al., 2010) and the South Mariana Trough (Asada et al., 2011; Nakamura et al., 2013), which were interpreted as hydrothermal plumes just spouted out from vents. In repeat observations using cameras mounted on the ROPOS remotely operated vehicle (ROV) and the Shinkai 6500 3D characteristics of ADCP echo intensity anomaly observed over the Snail, Archean, and Pika hydrothermal vent fields in the South Mariana Trough
Tackling upcoming projects: The development and efficacy of event previews an experimental study

Traditional performance management systems are increasingly seen as ill-conceived for today’s dynamic organizational landscape. Researchers and practitioners advocate for agile PM systems that emphasize continuous monitoring, learning, and feedback. We present the ‘event preview’, a novel approach that is designed to address several shortcomings of traditional performance management practices. Event previews consist of five fixed questions, which are discussed among team members before an event, instigating a detailed reflection and mental simulation of upcoming events or projects in order to achieve the desired outcomes. In doing so, event previews support teams to utilize their projects as learning opportunities. This study provides the theoretical basis for the event preview and empirically tests its effectiveness. A sample of 119 teams participated in the experiment in which they were asked to solve as many puzzles as possible within a fixed time frame. One condition conducted an event preview beforehand, the other condition did not. Our findings, which were based on a comparison of the averages of the two conditions, suggest that the event preview holds promise for improving team performance and communication. As such, the event preview presents an additional instrument to the changing performance management landscape. This simple practice can be incorporated in the performance management cycle, emphasizing adaptability and continuous improvement in organizations.
Opposing, spatially-determined epigenetic forces impose restrictions on stochastic olfactory receptor choice

Olfactory receptor (OR) choice represents an example of genetically hardwired stochasticity, where every olfactory neuron expresses one out of ~2000 OR alleles in the mouse genome in a probabilistic, yet stereotypic fashion. Here, we propose that topographic restrictions in OR expression are established in neuronal progenitors by two opposing forces: polygenic transcription and genomic silencing, both of which are influenced by dorsoventral gradients of transcription factors NFIA, B, and X. Polygenic transcription of OR genes may define spatially constrained OR repertoires, among which one OR allele is selected for singular expression later in development. Heterochromatin assembly and genomic compartmentalization of OR alleles also vary across the axes of the olfactory epithelium and may preferentially eliminate ectopically expressed ORs with more dorsal expression destinations from this ‘privileged’ repertoire. Our experiments identify early transcription as a potential ‘epigenetic’ contributor to future developmental patterning and reveal how two spatially responsive probabilistic processes may act in concert to establish deterministic, precise, and reproducible territories of stochastic gene expression.
Level and influencing factors of transition shock among new nurses in China: A multicenter cross‐sectional study

New nurses are an important part of nursing teams. The failure of new nurses to successfully transition seriously affects personal career development and nursing work quality, and important influencing factors deserve the attention of nursing managers. At present, multicenter, large‐sample investigations of transition shock among new nurses are lacking in China. This study aims to investigate the current level and influencing factors of transition shock among new nurses in China.
Qualitative evaluation of a Salutogenic Healthy Eating Programme for Dutch people with type 2 diabetes

Abstract The salutogenic model of health (SMH) is a health-promoting theory that focuses on resources, strengths, and coping capacities in everyday life as a way to improve health and wellbeing. The SMH was applied to develop a programme for enabling healthy eating practices among people with type 2 diabetes mellitus (T2DM): the SALUD programme. This study aims to gain insight in how participants with T2DM experienced the content and meaning of the SALUD programme. Three focus groups with participants (six to eight participants/group) that finished the SALUD programme were conducted. The focus groups were video-recorded, transcribed ad verbatim and thematically analysed. The SALUD programme was perceived by the participants as a positive, meaningful learning experience. Key factors why the participants described the programme as positive were that they felt a sense of social-belonging and (emotionally) safe (theme 1). The SALUD programme’s positive encouraging approach presented by the coach invited participants to join an active learning process (theme 2). Performing trial-and-error experiments and weekly reflecting on goals is what characterized active learning. The meaningfulness of the learning process (theme 3) was derived from (i) positive self-confrontation; (ii) exploring mind-body connections and perceiving improved health and (iii) noticing positive changes in their social environment (theme 3). To conclude, the study uncovered that the SALUD programme incited a positive, meaningful learning process for healthy eating and the contextual factors important in this. Potential leads to further improve the SALUD programme are discussed in order to maximize the chance of inciting an inclusive, active learning process.
Improved bounds for five-term arithmetic progressions

Let $r_5(N)$ be the largest cardinality of a set in $\{1,\ldots,N\}$ which does not contain $5$ elements in arithmetic progression. Then there exists a constant $c\in (0,1)$ such that \[r_5(N)\ll \frac{N}{\exp((\log\log N)^{c})}. \] Our work is a consequence of recent improved bounds on the $U^4$-inverse theorem of the first author and the fact that $3$-step nilsequences may be approximated by locally cubic functions on shifted Bohr sets. This combined with the density increment strategy of Heath-Brown and Szemer{\'e}di, codified by Green and Tao, gives the desired result.
The Conditioning Bias in Binary Decision Trees and Random Forests and Its Elimination

Decision tree and random forest classification and regression are some of the most widely used in machine learning approaches. Binary decision tree implementations commonly use conditioning in the form 'feature $\leq$ (or $<$) threshold', with the threshold being the midpoint between two observed feature values. In this paper, we investigate the bias introduced by the choice of conditioning operator (an intrinsic property of implementations) in the presence of features with lattice characteristics. We propose techniques to eliminate this bias, requiring an additional prediction with decision trees and incurring no cost for random forests. Using 20 classification and 20 regression datasets, we demonstrate that the bias can lead to statistically significant differences in terms of AUC and $r^2$ scores. The proposed techniques successfully mitigate the bias, compared to the worst-case scenario, statistically significant improvements of up to 0.1-0.2 percentage points of AUC and $r^2$ scores were achieved and the improvement of 1.5 percentage points of $r^2$ score was measured in the most sensitive case of random forest regression. The implementation of the study is available on GitHub at the following repository: \url{https://github.com/gykovacs/conditioning_bias}.
Topic Shifts as a Proxy for Assessing Politicization in Social Media

Politicization is a social phenomenon studied by political science characterized by the extent to which ideas and facts are given a political tone. A range of topics, such as climate change, religion and vaccines has been subject to increasing politicization in the media and social media platforms. In this work, we propose a computational method for assessing politicization in online conversations based on topic shifts, i.e., the degree to which people switch topics in online conversations. The intuition is that topic shifts from a non-political topic to politics are a direct measure of politicization -- making something political, and that the more people switch conversations to politics, the more they perceive politics as playing a vital role in their daily lives. A fundamental challenge that must be addressed when one studies politicization in social media is that, a priori, any topic may be politicized. Hence, any keyword-based method or even machine learning approaches that rely on topic labels to classify topics are expensive to run and potentially ineffective. Instead, we learn from a seed of political keywords and use Positive-Unlabeled (PU) Learning to detect political comments in reaction to non-political news articles posted on Twitter, YouTube, and TikTok during the 2022 Brazilian presidential elections. Our findings indicate that all platforms show evidence of politicization as discussion around topics adjacent to politics such as economy, crime and drugs tend to shift to politics. Even the least politicized topics had the rate in which their topics shift to politics increased in the lead up to the elections and after other political events in Brazil -- an evidence of politicization.
Soft-photon spectra and the LBK theorem

The study of next-to-leading-power (NLP) corrections in soft emissions continues to attract interest both in QCD and in QED. Soft-photon spectra in particular provide a clean case-study for the experimental verification of the Low-Burnett-Kroll (LBK) theorem. In this paper we study the consistency of the LBK theorem in the context of an ambiguity arising from momentum-conservation constraints in the computation of non-radiative amplitudes. We clarify that this ambiguity leads to various possible formulations of the LBK theorem, which are all equivalent up to power-suppressed effects (i.e. beyond the formal accuracy of the LBK theorem). We also propose a new formulation of the LBK theorem with a modified shifted kinematics which facilitates the numerical computation of non-radiative amplitudes with publicly available tools. Furthermore, we present numerical results for soft-photon spectra in the associated production of a muon pair with a photon, both in $e^+e^-$ annihilation and proton-proton collisions.
Detection of Model-based Planted Pseudo-cliques in Random Dot Product Graphs by the Adjacency Spectral Embedding and the Graph Encoder Embedding

In this paper, we explore the capability of both the Adjacency Spectral Embedding (ASE) and the Graph Encoder Embedding (GEE) for capturing an embedded pseudo-clique structure in the random dot product graph setting. In both theory and experiments, we demonstrate that this pairing of model and methods can yield worse results than the best existing spectral clique detection methods, demonstrating at once the methods' potential inability to capture even modestly sized pseudo-cliques and the methods' robustness to the model contamination giving rise to the pseudo-clique structure. To further enrich our analysis, we also consider the Variational Graph Auto-Encoder (VGAE) model in our simulation and real data experiments.
DiffTune-MPC: Closed-Loop Learning for Model Predictive Control

Model predictive control (MPC) has been applied to many platforms in robotics and autonomous systems for its capability to predict a system's future behavior while incorporating constraints that a system may have. To enhance the performance of a system with an MPC controller, one can manually tune the MPC's cost function. However, it can be challenging due to the possibly high dimension of the parameter space as well as the potential difference between the open-loop cost function in MPC and the overall closed-loop performance metric function. This paper presents DiffTune-MPC, a novel learning method, to learn the cost function of an MPC in a closed-loop manner. The proposed framework is compatible with the scenario where the time interval for performance evaluation and MPC's planning horizon have different lengths. We show the auxiliary problem whose solution admits the analytical gradients of MPC and discuss its variations in different MPC settings. Simulation results demonstrate the capability of DiffTune-MPC and illustrate the influence of constraints (from actuation limits) on learning.
Retrieval-Augmented Generation for Large Language Models: A Survey

Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases. This enhances the accuracy and credibility of the generation, particularly for knowledge-intensive tasks, and allows for continuous knowledge updates and integration of domain-specific information. RAG synergistically merges LLMs' intrinsic knowledge with the vast, dynamic repositories of external databases. This comprehensive review paper offers a detailed examination of the progression of RAG paradigms, encompassing the Naive RAG, the Advanced RAG, and the Modular RAG. It meticulously scrutinizes the tripartite foundation of RAG frameworks, which includes the retrieval, the generation and the augmentation techniques. The paper highlights the state-of-the-art technologies embedded in each of these critical components, providing a profound understanding of the advancements in RAG systems. Furthermore, this paper introduces up-to-date evaluation framework and benchmark. At the end, this article delineates the challenges currently faced and points out prospective avenues for research and development.
Anomalous relaxation and hyperuniform fluctuations in center-of-mass conserving systems with broken time-reversal symmetry

We study a paradigmatic model of absorbing-phase transition - the Oslo model - on a one-dimensional ring of $L$ sites with a fixed global density $\bar{\rho}$; notably, microscopic dynamics conserve both mass and \textit{center of mass (CoM), but lacks time-reversal symmetry}. Despite having highly constrained dynamics due to CoM conservation, the system exhibits diffusive relaxation away from criticality and superdiffusive relaxation near criticality. Furthermore, the CoM conservation severely restricts particle movement, rendering the mobility to vanish exactly. Indeed the temporal growth of current fluctuation is qualitatively different from that observed in diffusive systems with a single conservation law. Away from criticality, steady-state fluctuation $\langle \mathcal{Q}_i^2(T,\Delta) \rangle$ of current $\mathcal{Q}_i$ across $i$th bond up to time $T$ \textit{saturates} as $\langle \mathcal{Q}_i^2 \rangle \simeq \Sigma_Q^2(\Delta) - {\rm const. } T^{-1/2}$; near criticality, it grows subdiffusively as $\langle \mathcal{Q}_i^2 \rangle \sim T^\alpha$, with $0<\alpha<1/2$, and eventually \textit{saturates} to $\Sigma_Q^2(\Delta)$. The asymptotic current fluctuation $\Sigma_Q^2(\Delta)$ is a \textit{nonmonotonic} function of $\Delta$: It diverges as $\Sigma_Q^2(\Delta) \sim \Delta^2$ for $\Delta \gg \rho_c$ and $\Sigma_Q^2(\Delta) \sim \Delta^{-\delta}$, with $\delta>0$, for $\Delta \to 0^+$. By using a mass-conservation principle, we exactly determine the exponents $\delta = 2(1-1/\nu_\perp)/\nu_\perp$ and $\alpha = \delta/z \nu_\perp$ via the correlation-length and dynamic exponents, $\nu_\perp$ and $z$, respectively. Finally, we show that, in the steady state, the self-diffusion coefficient $\mathcal{D}_s(\bar{\rho})$ of tagged particles is connected to activity by $\mathcal{D}_s(\bar{\rho}) = a(\bar{\rho}) / \bar{\rho}$.