Miracle-dz/UniFAR-FTUs huggingface dataset:
Antibacterial and Biofilm Production Inhibition Activity of Thymus vulgaris L. Essential Oil against Salmonella spp. Isolates from Reptiles

Salmonellosis is an infectious disease affecting both animals and humans. Antimicrobial resistant (AMR) and biofilm-producing Salmonella spp. , frequently detected in reptiles (who can then act as asymptomatic carriers for warm-blooded animals), have developed resistance to biocides; this represents a warning for the emergence of biocide/antimicrobial cross-resistance. The aim of this study was to evaluate the efficacy of Thymus vulgaris L. essential oil (TEO) in inhibiting bacterial growth and biofilm production of Salmonella spp. , which had been isolated from wild reptiles housed in a Zoo in Italy. The resistance profile against different classes of antibiotics showed that all the isolates were susceptible to the tested antibiotics, despite the presence of several AMR genes. All the isolates were also tested with aqueous solutions of TEO at different dilutions (5% to 0.039%). Interestingly, TEO proved effective both in inhibiting bacterial growth at low dilutions, with MIC and MBC values ranging between 0.078% and 0.312%, and in inhibiting biofilm production, with values ranging from 0.039% to 0.156%. TEO demonstrated effective bioactivity against the biofilm producer Salmonella spp. , proving to be a valid disinfectant for the prevention of salmonellosis from reptiles, a possible source of infection for humans exposed to the reptiles’ environment.
Freeze-Drying Process for the Fabrication of Collagen-Based Sponges as Medical Devices in Biomedical Engineering

This paper presents a systematic review of a key sector of the much promising and rapidly evolving field of biomedical engineering, specifically on the fabrication of three-dimensional open, porous collagen-based medical devices, using the prominent freeze-drying process. Collagen and its derivatives are the most popular biopolymers in this field, as they constitute the main components of the extracellular matrix, and therefore exhibit desirable properties, such as biocompatibility and biodegradability, for in vivo applications. For this reason, freeze-dried collagen-based sponges with a wide variety of attributes can be produced and have already led to a wide range of successful commercial medical devices, chiefly for dental, orthopedic, hemostatic, and neuronal applications. However, collagen sponges display some vulnerabilities in other key properties, such as low mechanical strength and poor control of their internal architecture, and therefore many studies focus on the settlement of these defects, either by tampering with the steps of the freeze-drying process or by combining collagen with other additives. Furthermore, freeze drying is still considered a high-cost and time-consuming process that is often used in a non-optimized manner. By applying an interdisciplinary approach and combining advances in other technological fields, such as in statistical analysis, implementing the Design of Experiments, and Artificial Intelligence, the opportunity arises to further evolve this process in a sustainable and strategic manner, and optimize the resulting products as well as create new opportunities in this field.
Stabilized approximate inverse preconditioning based on A-orthogonalization process for parallel finite element analysis

Improved Stabilized Approximate Inverse (ISAINV) based on A-orthogonalization process is known as an effective preconditioning technique for the conjugate gradient (CG) method to solve highly ill-conditioned linear systems. This research aims to accelerate the convergence of the finite element analysis of shell structures by preserving the sparsity in the preconditioning matrix and by parallelizing the localized process of ISAINV preconditioning. In the numerical results, the proposed ISAINV preconditioner shows better convergence and faster computational time than the conventional preconditioning.
Association between Older Age and Psychiatric Symptoms in a Population of Hospitalized Patients with COVID-19

Increased rates of anxiety and depression have been reported for older adults during the COVID-19 pandemic. However, little is known regarding the onset of mental health morbidity during the acute phase of the disease and the role of age as potential independent risk factor for psychiatric symptoms. The cross-sectional association between older age and psychiatric symptoms has been estimated in a sample of 130 patients hospitalized for COVID-19 during the first and second wave of the pandemic. Compared to younger patients, those who were 70 years of age or older resulted at a higher risk of psychiatric symptoms measured on the Brief Psychiatric Symptoms Rating Scale (BPRS) (adjusted (adj.) odds ratio (OR) 2.36, 95% confidence interval (CI) 1.05–5.30) and delirium (adj. OR 5.24, 95% CI 1.63–16.8)). No association was found between older age and depressive symptoms or anxiety. Age was associated with psychiatric symptoms independently of gender, marital status, history of psychiatric illness, severity of disease and cardiovascular morbidity. Older adults appear at high risk of developing psychiatric symptoms related to COVID-19 disease during hospital stay. Multidisciplinary preventive and therapeutic interventions should be implemented to reduce the risk of psychiatric morbidity and related adverse health care outcomes among older hospital inpatients with COVID-19.
Higher Overall Intakes Are the Defining Feature of Dietary Intakes in NAFLD and Compared to the General Population

We aimed to compare the dietary intakes of Australian patients with non-alcoholic fatty liver disease (NAFLD) to general Australian population intake data and determine whether the intake of any nutrient or food group was able to predict the degree of steatosis. Dietary data from fifty adult patients with NAFLD were compared to intake data from the Australian Health Survey for energy, macronutrients, fat sub-types, alcohol, iron, folate, sugar, fibre, sodium and caffeine. Linear regression models adjusting for potential confounders (age, sex, physical activity and body mass index) were used to examine predictive relationships between hepatic steatosis (quantified via magnetic resonance spectroscopy) and dietary components. The mean percentage differences between NAFLD and Australian usual intakes were significant for energy, protein, total fat, saturated fat, monounsaturated and polyunsaturated fats (all p < 0.001). The contribution of fat and protein to total energy intake was significantly higher in the NAFLD cohort (p < 0.05). No individual nutrients or food groups were strongly related to hepatic fat in the adjusted models. Higher overall consumption appears to be a major feature of dietary intake in NAFLD when compared to the general population. A whole-diet approach to NAFLD treatment and prevention is likely to be more effective than focusing on single food components.
Association between fatty liver disease and hearing impairment in Korean adults: a retrospective cross-sectional study

Background We hypothesized that fatty liver disease (FLD) is associated with a high prevalence of hearing loss (HL) owing to metabolic disturbances. This study aimed to evaluate the association between FLD and HL in a large sample of the Korean population. Methods We used a dataset of adults who underwent routine voluntary health checkups (n=21,316). Fatty liver index (FLI) was calculated using Bedogni’s equation. The patients were divided into two groups: the non-FLD (NFLD) group (n=18,518, FLI <60) and the FLD group (n=2,798, FLI ≥60). Hearing thresholds were measured using an automatic audiometer. The average hearing threshold (AHT) was calculated as the pure-tone average at four frequencies (0.5, 1, 2, and 3 kHz). HL was defined as an AHT of >40 dB. Results HL was observed in 1,370 (7.4%) and 238 patients (8.5%) in the NFLD and FLD groups, respectively (p=0.041). Compared with the NFLD group, the odds ratio for HL in the FLD group was 1.16 (p=0.040) and 1.46 (p<0.001) in univariate and multivariate logistic regression analyses, respectively. Linear regression analyses revealed that FLI was positively associated with AHT in both univariate and multivariate analyses. Analyses using a propensity score-matched cohort showed trends similar to those using the total cohort. Conclusion FLD and FLI were associated with poor hearing thresholds and HL. Therefore, active monitoring of hearing impairment in patients with FLD may be helpful for early diagnosis and treatment of HL in the general population.
Development and validation of a simple microwave-assisted digestion sample preparation technique for the estimation of aluminium and magnesium in a few pharmaceutical dosage forms by an inductively coupled-mass spectrometer (ICP-MS)

Rationale: A simple, sensitive, reliable, validated, inductively coupled plasma mass spectrometric method for the determination of aluminium and magnesium using a simple common microwave-assisted digestion sample preparation technique for a few commonly used formulations was developed and validated according to International Conference on Harmonization Q3D and the United States Pharmacopeia general chapter <232> and <233>. The following pharmaceutical dosage forms were considered for estimation of aluminium and magnesium: Alumina, magnesia simethicone oral suspension, Alumina, magnesia simethicone chewable tablets, alumina and magnesia oral suspension, alumina and magnesium carbonate oral suspension. Methods: The methodology included optimizing a common microwave assisted digestion method, selecting the isotopes, choosing the measurement technique, and designating internal standards. The finalized microwave assisted procedure was a two-step program where in the first step the samples were ramped for 10 min to a temperature of 180 °C and hold for 5 min followed by ramping for 10 min to a temperature of 200 °C and hold for 10 min. Magnesium (24Mg) and aluminium (27Al) isotopes were finalized, internal standard assigned for both the isotopes was yttrium (89Y) with Helium (kinetic energy discrimination-KED) as the measuring mode. System suitability was run before initiating analysis to ensure that system performance was consistent. Results: Analytical validation parameters like specificity, linearity (from 25% to 200% of sample concentration), the detection limit and the limit of quantification were established. For all these dosage forms, the method's precision was demonstrated by analyzing the percentage relative standard deviation for six injections. Accuracy was established from 50% to 150% of instrument working concentration (J-levels) for aluminium and magnesium for all the formulations and was found to be within the range of 90–120%. Conclusion: This common analysis method, along with the common microwave-digestion technique applies to numerous types of matrices for a finished dosage form with aluminium and magnesium.
Plane fronted electromagnetic waves and an asymptotic limit of Lienard–Wiechert fields

Colliding or noncolliding plane fronted electromagnetic or gravitational waves are the asymptotic limit of Robinson--Trautman spherical electromagnetic or gravitational waves. Noncolliding plane fronted waves contain no information about their sources whereas colliding waves contain information about possibly the motion of their sources. As a first step to investigate the latter phenomenon we construct an asymptotic limit of Li\'enard--Wiechert electromagnetic fields in the context of Minkowskian space--time. This has the advantage that the source is well known and the calculations can be carried out in full detail. The final result is an algebraically general Maxwell field which consists of colliding plane fronted waves in a subregion of Minkowskian space--time and an interesting byproduct is a novel perspective on a Maxwell field originally discovered by Bateman.
High-Dimensional Canonical Correlation Analysis

This paper studies high-dimensional canonical correlation analysis (CCA) with an emphasis on the vectors that define canonical variables. The paper shows that when two dimensions of data grow to infinity jointly and proportionally, the classical CCA procedure for estimating those vectors fails to deliver a consistent estimate. This provides the first result on the impossibility of identification of canonical variables in the CCA procedure when all dimensions are large. As a countermeasure, the paper derives the magnitude of the estimation error, which can be used in practice to assess the precision of CCA estimates. Applications of the results to cyclical vs. non-cyclical stocks and to a limestone grassland data set are provided.
Can Twitter be used to Acquire Reliable Alerts against Novel Cyber Attacks?

Time-relevant and accurate threat information from public domains are essential for cyber security. In a constantly evolving threat landscape, such information assists security researchers in thwarting attack strategies. In this work, we collect and analyze threat-related information from Twitter to extract intelligence for proactive security. We first use a convolutional neural network to classify the tweets as containing or not valuable threat indicators. In particular, to gather threat intelligence from social media, the proposed approach collects pertinent Indicators of Compromise (IoCs) from tweets, such as IP addresses, URLs, File hashes, domain addresses, and CVE IDs. Then, we analyze the IoCs to confirm whether they are reliable and valuable for threat intelligence using performance indicators, such as correctness, timeliness, and overlap. We also evaluate how fast Twitter shares IoCs compared to existing threat intelligence services. Furthermore, through machine learning models, we classify Twitter accounts as either automated or human-operated and delve into the role of bot accounts in disseminating cyber threat information on social media. Our results demonstrate that Twitter is growing into a powerful platform for gathering precise and pertinent malware IoCs and a reliable source for mining threat intelligence.
Latent SDEs on Homogeneous Spaces

We consider the problem of variational Bayesian inference in a latent variable model where a (possibly complex) observed stochastic process is governed by the solution of a latent stochastic differential equation (SDE). Motivated by the challenges that arise when trying to learn an (almost arbitrary) latent neural SDE from data, such as efficient gradient computation, we take a step back and study a specific subclass instead. In our case, the SDE evolves on a homogeneous latent space and is induced by stochastic dynamics of the corresponding (matrix) Lie group. In learning problems, SDEs on the unit n-sphere are arguably the most relevant incarnation of this setup. Notably, for variational inference, the sphere not only facilitates using a truly uninformative prior, but we also obtain a particularly simple and intuitive expression for the Kullback-Leibler divergence between the approximate posterior and prior process in the evidence lower bound. Experiments demonstrate that a latent SDE of the proposed type can be learned efficiently by means of an existing one-step geometric Euler-Maruyama scheme. Despite restricting ourselves to a less rich class of SDEs, we achieve competitive or even state-of-the-art results on various time series interpolation/classification problems.
Blockwise Feature Interaction in Recommendation Systems

Feature interactions can play a crucial role in recommendation systems as they capture complex relationships between user preferences and item characteristics. Existing methods such as Deep&Cross Network (DCNv2) may suffer from high computational requirements due to their cross-layer operations. In this paper, we propose a novel approach called blockwise feature interaction (BFI) to help alleviate this issue. By partitioning the feature interaction process into smaller blocks, we can significantly reduce both the memory footprint and the computational burden. Four variants (denoted by P, Q, T, S, respectively) of BFI have been developed and empirically compared. Our experimental results demonstrate that the proposed algorithms achieves close accuracy compared to the standard DCNv2, while greatly reducing the computational overhead and the number of parameters. This paper contributes to the development of efficient recommendation systems by providing a practical solution for improving feature interaction efficiency.
Fast Kernel Feature Extraction Method and Its Application

For a large data set,there is a problem which a kernel matrix K is not easily to be computed very mach for Kernel Principal Component Analysis(KPCA). A fast principal component analysis method is proposed to solve the computation problem for kernel matrix K based on a multi-block feature vector selection.A sample set is extracted by the multi-block feature vector selection.A KPCA model is build up by the sample.The proposed method is applied to a chemical process.Experimental result shows that the proposes method is almost same compared to the KPCA based on the all sample,but,less time are spent on modeling and extracting feature for the proposed method.
Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery

Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management. However, generating accurate LC maps is a complex and time-consuming task that requires the expertise of multiple annotators and regular updates to account for environmental changes. In this work, we introduce SPADA, a framework for fuel map delineation that addresses the challenges associated with LC segmentation using sparse annotations and domain adaptation techniques for semantic segmentation. Performance evaluations using reliable ground truths, such as LUCAS and Urban Atlas, demonstrate the technique’s effectiveness. SPADA outperforms state-of-the-art semantic segmentation approaches as well as third-party products, achieving a mean Intersection over Union (IoU) score of 42.86 and an F1 score of 67.93 on Urban Atlas and LUCAS, respectively.
Estimation of Burger model parameters using inverse formulation

Abstract This paper reports the development of a generalized inverse analysis formulation for the parameter estimation of four-parameter Burger model. The analysis is carried out by defining the problem as a mathematical programming formulation in terms of identification of the design vector, the objective function and the design constraints. Thereafter, the formulated constrained nonlinear multivariable problem is solved with the aid of fmincon: an in-built constrained optimization solver module available in MatLab. In order to gain experience, a synthetic case-study is considered wherein key issues such as the determination and setting up of variable bounds, global optimality of the solution and minimum number of data-points required for prediction of parameters is addressed. The results reveal that the developed technique is quite efficient in predicting the model parameters. The best result is obtained when the design variables are maintaining a lower bound while providing a full slack on the upper bound. Global optimality of the solution is achieved using the developed technique. A minimum of 4-5 randomly selected data-points are required to achieve the optimal solution. The developed technique has also been tested for real-time field and laboratory investigations with encouraging results.
Next Steps for Human-Centered Generative AI: A Technical Perspective

Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI). We contribute a comprehensive research agenda that lays out future directions of Generative AI spanning three levels: aligning with human values; assimilating human intents; and augmenting human abilities. By identifying these next-steps, we intend to draw interdisciplinary research teams to pursue a coherent set of emergent ideas in HGAI, focusing on their interested topics while maintaining a coherent big picture of the future work landscape.
Inhomogeneous condensation in the Gross-Neveu model in noninteger spatial dimensions 
1≤d<</mml:mo>3

The Gross-Neveu model in the $N \to \infty$ approximation in $d=1$ spatial dimensions exhibits a chiral inhomogeneous phase (IP), where the chiral condensate has a spatial dependence that spontaneously breaks translational invariance and the $\mathbb{Z}_2$ chiral symmetry. This phase is absent in $d=2$, while in $d=3$ its existence and extent strongly depends on the regularization and the value of the finite regulator. This work connects these three results smoothly by extending the analysis to non-integer spatial dimensions $1 \leq d<3$, where the model is fully renormalizable. To this end, we adapt the stability analysis, which probes the stability of the homogeneous ground state under inhomogeneous perturbations, to non-integer spatial dimensions. We find that the IP is present for all $d<2$ and vanishes exactly at $d=2$. Moreover, we find no instability towards an IP for $2\leq d<3$, which suggests that the IP in $d=3$ is solely generated by the presence of a regulator.
Receptivity of compressible boundary layers over porous surfaces

Supersonic pre-transitional boundary layers flowing over porous flat and concave surfaces are studied using numerical and asymptotic methods. The porous wall is composed of thin equally-spaced cylindrical microcavities. The flow is perturbed by small-amplitude, free-stream vortical disturbances of the convected gust type. From the proximity of the leading edge, these external agents generate the compressible Klebanoff modes, i.e. low-frequency disturbances of the kinematic and thermal kind that grow algebraically downstream. For Klebanoff modes with a spanwise wavelength comparable with the boundary-layer thickness, the porous surface has a negligible effect on their growth. When the spanwise wavelength is instead larger than the boundary-layer thickness, these disturbances are effectively attenuated by the porous surface. For a specified set of frequency and wavelengths, the Klebanoff modes evolve into oblique Tollmien-Schlichting waves through a leading-edge-adjustment receptivity mechanism. The wavenumber of these waves is only slightly modified over the porous surface, while the growth rate increases, thus confirming previous experimental results. An asymptotic analysis based on the triple-deck theory confirms these numerical findings. When the wall is concave, the amplitude of the Klebanoff modes is enhanced by the wall curvature and is attenuated by the wall porosity during the initial development.
Bayesian inference for OPC modeling

The use of optical proximity correction (OPC) demands increasingly accurate models of the photolithographic process. Model building and inference techniques in the data science community have seen great strides in the past two decades which make better use of available information. This paper aims to demonstrate the predictive power of Bayesian inference as a method for parameter selection in lithographic models by quantifying the uncertainty associated with model inputs and wafer data. Specifically, the method combines the model builder's prior information about each modelling assumption with the maximization of each observation's likelihood as a Student's t-distributed random variable. Through the use of a Markov chain Monte Carlo (MCMC) algorithm, a model's parameter space is explored to find the most credible parameter values. During parameter exploration, the parameters' posterior distributions are generated by applying Bayes' rule, using a likelihood function and the a priori knowledge supplied. The MCMC algorithm used, an affine invariant ensemble sampler (AIES), is implemented by initializing many walkers which semiindependently explore the space. The convergence of these walkers to global maxima of the likelihood volume determine the parameter values' highest density intervals (HDI) to reveal champion models. We show that this method of parameter selection provides insights into the data that traditional methods do not and outline continued experiments to vet the method.