Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Exploration of RNN Variants for Recognizing Daily Life Stress Levels by Using Multimodal Physiological Signals

Enduring stress can have negative impacts on human health and behavior. Widely used wearable devices are promising for assessing, monitoring and potentially alleviating high stress in daily life. Although numerous automatic stress recognition studies have been carried out in the laboratory environment with high accuracy, the performance of daily life studies is still far away from what the literature has in laboratory environments. Since the physiological signals obtained from these devices are time-series data, Recursive Neural Network (RNN) based classifiers promise better results than other machine learning methods. However, the performance of RNN-based classifiers has not been extensively evaluated (i.e., with several variants and different application techniques) for detecting daily life stress yet. They could be combined with CNN architectures, applied to raw data or handcrafted features. In this study, we created different RNN architecture variants and explored their performance for recognizing daily life stress to guide researchers in the field.
Calibrating Star Formation Rate Prescriptions at Different Scales (10 pc–1 kpc) in M31

We calibrate commonly used star formation rate (SFR) prescriptions using observations in five kiloparsec-sized fields in the nearby galaxy Andromeda (M31) at 10 pc spatial resolution. Our observations at different scales enable us to resolve the star-forming regions and to distinguish them from non-star-forming components. We use extinction-corrected Hα from optical integral field spectroscopy as our reference tracer and have verified its reliability via tests. It is used to calibrate monochromatic and hybrid (Hα+a×IR and far-UV+b×IR) SFR prescriptions, which use far-UV (GALEX), 22 μm (Wide-field Infrared Survey Explorer), and 24 μm (MIPS). Additionally, we evaluate other multiwavelength infrared tracers. Our results indicate that the SFR prescriptions do not change (in M31) with spatial scales or with subtraction of the diffuse component. For the calibration factors in the hybrid SFR prescriptions, we find a ≈ 0.2 and b ≈ 22 in M31, which are a factor of 5 higher than in the literature. As the fields in M31 exhibit high attenuation and low dust temperatures, lie at large galactocentric distances, and suffer from high galactic inclination compared to measurements in other galaxies, we propose that the fields probe a dust layer extended along the line of sight that is not directly spatially associated with star-forming regions. This (vertically) extended dust component increases the attenuation and alters the SFR prescriptions in M31 compared to literature measurements. We recommend that SFR prescriptions should be applied with caution at large galactocentric distances and in highly inclined galaxies, due to variations in the relative (vertical) distribution of dust and gas.
High Frequency of Antibiotic Resistance Genes (ARGs) in the Lerma River Basin, Mexico

The spread of beta-lactamase-producing bacteria is of great concern and the environment has been found to be a main source of contamination. Herein, it was proposed to determine the frequency of antimicrobial-resistant-Gram-negative bacteria throughout the Lerma River basin using phenotypic and molecular methods. Resistant bacteria were isolated with chromogenic media and antimicrobial susceptibility tests were used to characterize their resistance. ARGs for beta-lactams, aminoglycosides, and quinolones were detected by PCR. Species were identified by Sanger sequencing the 16S rRNA gene and the representative genomes of MDR strains were sequenced by NGS. A high variation in the number of isolates was observed in the 20 sampled sites, while observing a low diversity among the resistant bacteria. Of the 12 identified bacterial groups, C. freundii, E. coli, and S. marcescens were more predominant. A high frequency of resistance to beta-lactams, quinolones, and aminoglycosides was evidenced, where the blaCTX, qnrB, qnrS y, and aac(6′)lb-cr genes were the most prevalent. C. freundii showed the highest frequency of MDR strains. Whole genome sequencing revealed that S. marcescens and K. pneumoniae showed a high number of shared virulence and antimicrobial resistance genes, while E. coli showed the highest number of unique genes. The contamination of the Lerma River with MDR strains carrying various ARGs should raise awareness among environmental authorities to assess the risks and regulations regarding the optimal hygienic and sanitary conditions for this important river that supports economic activities in the different communities in Mexico.
Isolation and Genetic Characterization of a Bovine Coronavirus KBR-1 Strain from Calf Feces in South Korea

Bovine coronavirus (BCoV) causes severe diarrhea in neonatal calves, winter dysentery in adult cattle, and respiratory disease in feedlot cattle, resulting in economic losses. A total of 16/140 calf diarrheic feces samples collected in South Korea between 2017 and 2018 were positive for BCoV. Phylogenetic analysis of the complete spike and hemagglutinin/esterase genes revealed that the 16 Korean BCoV strains belonged to group GIIa along with Korean strains isolated after 2000, whereas Korean BCoV strains isolated before 2000 belonged to group GI. Mice and goats inoculated with an inactivated KBR-1 strain (isolated from this study) generated higher antibody titers (96 ± 13.49 and 73 ± 13.49, respectively) when mixed with the Montanide01 adjuvant than when mixed with the Carbopol or IMS1313 adjuvants. Viral antigens were detected in the large intestine, jejunum, and ileum of calves inoculated with inactivated KBR-1 vaccine (104.0 TCID50/mL) at 14 days of post-challenge (DPC). However, no viral antigens were detected in calves vaccinated with a higher dose of inactivated KBR-1 strain (106.0 TCID50/mL) at 14 DPC, and they had high antibody titers and stable diarrhea scores. Currently, the group GIIa is prevalent in cows in South Korea, and although further research is needed in the future, the recently isolated KBR-1 strain has potential value as a new vaccine candidate.
A Dynamic and Automated Access Control Management System for Social Networks

In recent years, online social networks (OSNs) have become an essential part of our social life. In OSNs, users can post resources to predefined groups of users, for example, family, friends, close friends. However, due to these predefined groups of users, few irrelevant users may get access to these published resources. Moreover, the users cannot configure privacy settings due to the lack of technical knowledge and the rigidity of the access control management system. To tackle these issues, we propose a text-based dynamic and fine-grain access control system for OSNs. Our proposed model uses a dynamic clustering algorithm to create user clusters based on the mutual interests of the users. After clustering, the proposed system creates automatic access rules based on the relationship between the users’ clusters and their resources. The proposed system will ensure fine-grained access control and automatic assignment of policies to the text-based resources. We have implemented our system to gauge the applicability, and the results are discussed in the experiments section.
Aircraft observations of gravity wave activity and turbulence in the tropical tropopause layer: prevalence, influence on cirrus and comparison with global-storm resolving models

The tropical tropopause layer (TTL) is a sea of vertical motions. Convectively-generated gravity waves create vertical winds on scales of a few to 1000s of kilometers as they propagate in a stable atmosphere. Turbulence from gravity wave breaking, radiatively-driven convection and Kelvin-Helmholtz instabilities stirs up the TTL on the kilometer scale. TTL cirrus, which moderate the water vapor concentration in the TTL and stratosphere, form in the cold phases of large-scale ( > 100 km) wave activity. It has been proposed in several modelling studies that small-scale ( < 100 km) vertical motions control the ice 5 crystal number concentration and the dehydration efficiency of TTL cirrus, but this has yet to be confirmed with observations. High-rate vertical winds measured by aircraft are a valuable and underutilized tool for constraining small-scale TTL vertical wind variability, examining its impacts on TTL cirrus, and evaluating atmospheric models. We use 20 Hz data from five National Aeronautics and Space Administration (NASA) campaigns to quantify small-scale vertical wind variability in the TTL, and to see how it varies with ice water content, distance from deep convective cores, and height in the TTL. 10 We find that 1 Hz vertical winds are well represented by a normal distribution with a standard deviation of 0.2–0.4 m s − 1 . We find that turbulence is enhanced within cirrus with high ice water content (IWC) ( > 10 − 3 g m − 3 ), which also have high ice crystal number concentrations. Consistent with a previous study, we find that turbulence is enhanced over the tropical West Pacific and within 500 km of convection, and is most common in the lower TTL (14-15.5 km) closer to deep convection, and in the upper TTL (15.5-17 km) further from brightness temperature thresholds for identifying deep convection. We are interested in deep convective cores as sources of gravity wave activity, moisture, and possible detrained ice in the TTL. Only one pixel in all four snapshots has a brightness temperature below 200 K (marked with a green star in the upper right plot), so that threshold is too strict. A threshold of 235 K (white contours) includes some outflow cirrus and remnants of deep convection, which are areas that are influenced by deep convection but that are less likely to generate gravity waves. We select an intermediate value of 210 K (pink points in Figure A2) as our threshold.
Framework to assess the quality of mHealth apps: a mixed-method international case study protocol

Introduction Healthcare professionals (HCPs) often recommend their patients to use a specific mHealth app as part of health promotion, disease prevention and patient self-management. There has been a significant growth in the number of HCPs downloading and using mobile health (mHealth) apps. Most mHealth apps that are available in app stores employ a ‘star rating’ system. This is based on user feedback on an app, but is highly subjective. Thus, the identification of quality mHealth apps which are deemed fit for purpose can be a difficult task for HCPs. Currently, there is no unified, validated standard guidelines for assessment of mHealth apps for patient safety, which can be used by HCPs. The Modified Enlight Suite (MES) is a quality assessment framework designed to provide a means for HCPs to evaluate mHealth apps before they are recommended to patients. MES was adapted from the original Enlight Suite for international use through a Delphi method, followed by preliminary validation process among a population predominantly consisting of medical students. This study aims to evaluate the applicability and validity of the MES, by HCPs, in low, middle and high income country settings. Methods and analysis MES will be evaluated through a mixed-method study, consisting of qualitative (focus group) and quantitative (survey instruments) research, in three target countries: Malaŵi (low income), South Africa (middle income) and Ireland (high income). The focus groups will be conducted through Microsoft Teams (Microsoft, Redmond, Washington, USA) and surveys will be conducted online using Qualtrics (Qualtrics International, Seattle, Washington, USA). Participants will be recruited through the help of national representatives in Malawi (Mzuzu University), South Africa (University of Fort Hare) and Ireland (University College Cork) by email invitation. Data analysis for the focus group will be by the means of thematic analysis. Data analysis for the survey will use descriptive statistics and use Cronbach alpha as an indicator of internal consistency of the MES. The construct validity of the mHealth app will be assessed by computing the confirmatory factor analysis using Amos. Ethics and dissemination The study has received ethical approval from the Social Research Ethics Committee (SREC) SREC/SOM/03092021/1 at University College Cork, Ireland, Malaŵi Research Ethics Committee (MREC), Malaŵi MZUNIREC/DOR/21/59 and Inter-Faculty Research Ethics Committee (IFREC) of University of Fort Hare (REC-2 70 710-028-RA). The results of the study will be disseminated through the internet, peer-reviewed journals and conference presentations.
Effective Field Theories for the LHC

In this thesis I study applications of effective field theories to understand aspects of QCD jets and their substructure at the Large Hadron Collider. In particular, I introduce an observable, $D_2$, which can be used for distinguishing boosted $W/Z/H$ bosons from the QCD background using information about the radiation pattern within the jet, and perform a precision calculation of this observable. To simplify calculations in the soft collinear effective theory, I also develop a helicity operator basis, which facilitates matching calculations to fixed order computations performed using spinor-helicity techniques, and demonstrate its utility by computing an observable relevant for studying the properties of the newly discovered Higgs boson.
Negative Magnetic Sorting Preserves the Functionality of Ex Vivo Cultivated Non-Adherent Human Monocytes

Simple Summary Immune cells are of increasing interest for cellular therapeutic products to treat inflammation-related diseases and cancer. However, the isolation method and the culture conditions influence the cells’ functionality. In contrast to dendritic cells and macrophages, the effects of the isolation method on the functionality of non-adherent peripheral monocytes have not yet been investigated. Hence, the present study examines the impact of the isolation method on cell viability, growth, metabolism, inflammation-induced cytokine response, migratory capacity, and adherence of non-adherent human peripheral monocytes. Monocytes were isolated by magnetic sorting using either positive or negative selection and cultured plates, which prevented the adhesion of the monocytes. The purity and yield of monocytes were higher after positive selection, confirming previous findings. However, the adherence and migratory capacity, cytokine response, and metabolic activity were decreased compared to negatively selected monocytes. The impaired functionality, combined with cell shrinking, thus, indicates the start of cell viability loss. Negatively selected non-adherent monocytes showed no impairment in functionality, and the viability remained high. In conclusion, this approach is better suited for conducting ex vivo modifications of monocytes prior to the intended experimental setup or therapeutic application. Abstract Background: Monocyte-derived macrophages or dendritic cells are of increasing interest for cellular therapeutic products to treat inflammation-related diseases and cancer. However, the isolation method and the culture conditions applied influence the functionality of cells. For some approaches, the adhesion-induced differentiation into macrophages must be prevented to maintain functions attributed to circulating monocytes. The effects of the isolation method on the functionality of non-adherent peripheral monocytes have not yet been investigated. Methods: The present study examines the impact of the isolation method on cell viability, growth, metabolism, inflammation-induced cytokine response, migratory capacity, and adherence of non-adherent human peripheral monocytes. The monocytes were isolated by magnetic sorting using either positive or negative selection and cultured in cell-repellent plates. Results: The purity and yield of monocytes were higher after positive selection. However, the adherence and migratory capacity, cytokine response, and metabolic activity were decreased compared to negatively selected monocytes. The impaired functionality presented in combination with cell shrinking, thus, indicates the start of cell viability loss. Negatively selected non-adherent monocytes showed no impairment in functionality, and the viability remained high. In conclusion, this approach is better suited for conducting ex vivo modifications of monocytes prior to the intended experimental setup or therapeutic application.
The atmospheric composition of Mars: ISM and ground-based observational data.

Monitoring the abundances of Martian minor constituents and searching for local variations can provide valuable information about the aeronomy of the Martian atmosphere. Recent results from the PHOBOS ISM experiment suggest possible local variations of the carbon monoxide (CO) abundance. Additional constraints have been given by ground-based observations of CO in the infrared (IR) and millimeter range, a search for minor species in the millimeter range, and the millimeter detection of deuterated water (HDO). From these data, information on the thermal profile and the vertical distribution low CO and H 2 O has been retrieved
T h eo ry o f q u asip article sp e c tr a for Fe, C o, and Ni: bulk and surface

The correlated quasiparticle band structure of iron, cobalt, and nickel is investigated within the dynamical mean-field theory formalism using the recently developed full-potential linear-muffin-tin-orbital-based local-density approximation plus dynamical mean-field theory (LDA+DMFT) code. Detailed analysis of the calculated electron self-energy, density of states, and the spectral density is presented for these metals. It has been found that all these elements show strong correlation effects for majority-spin electrons, such as strong damping of quasiparticles and formation of a satellite state below the bottom of d bands. In particular, our work clearly predicts the existence of a photoemission satellite for bcc iron. The LDA+DMFT data for fcc nickel and cobalt (111) surfaces and bcc iron (001) surface are also presented. The electron self-energy is found to depend strongly on the number of nearest neighbors, and it practically reaches the bulk value already in the second layer from the surface. The dependence of correlation effects on the dimensionality of the problem is also discussed.
Experimental Demonstration of Dynamical Input Isolation in Nonadiabatically Modulated Photonic Cavities

Modulated optical cavities have been proposed and demonstrated for applications in communications, laser frequency stabilization, microwave-to-optical conversion and frequency comb generation. However, most studies are restricted to the adiabatic regime, where either the maximum excursion of the modulation or the modulation frequency itself is below the linewidth of the cavity. Here, using a fiber ring resonator with an embedded electro-optic phase modulator, we investigate the nonadiabatic regime. By strongly driving the modulator at frequencies that are significantly smaller than the free-spectral range of the ring resonator, but well beyond the linewidth of the resonator, we experimentally observe counterintuitive behavior predicted in a recent theoretical study by Minkov et al. [APL Photonics 2, 076101 (2017)], such as the complete suppression of drop-port transmission even when the input laser wavelength is on resonance with the optical cavity. This can be understood as dynamical isolation of the cavity from the input light. We also show qualitative differences in the steady-state responses of the system between the adiabatic and nonadiabatic limits. Our experiments probe a seldom explored regime of operation that is promising for applications in integrated photonic systems with current state-of-the-art technology.
Etude de noyaux de semigroupe pour objets structurés dans le cadre de l'apprentissage statistique

Kernel methods refer to a new family of data analysis tools which may be used in standardized learning contexts, such as classification or regression. Such tools are grounded on an \textit{a priori} similarity measure between the objects to be handled, which have been named ``kernels'' in the statistical learning and functional analysis literature. The simplicity of kernel methods comes from the fact that, given a learning task, such methods only require the definition of a kernel to compare the objects to yield practical results. The problem of selecting the right kernel for a task is nonetheless tricky, notably when the objects have complex structures. We propose in this work various families of generic kernels for composite objects, such as strings, graphs or images. The kernels that we obtain are tailored to compare clouds of points, histograms or more generally positive measures. Our approach is mainly motivated by algebraic considerations on the sets of interests, which is why we make frequent use of the theory of harmonic functions on semigroups in this work. The theoretical justification for such kernels is further grounded on the use of reproducing kernel Hilbert spaces, in which the measures are embedded, along with elements of convex analysis and descriptors of the measures used in statistics and information theory, such as variance and entropy. By mapping any structured object to a cloud of components, \eg taking a string and turning it into a cloud or a histogram of substrings, we apply these kernels on composite objects coupled with discriminative methods, such as the support vector machine, to address classification problems encountered in bioinformatics or image analysis. We extend this framework in the end of the thesis to propose a different viewpoint where objects are no longer seen as clouds of points but rather as nested clouds, where each cloud is labelled according to a set of events endowed with a hierarchy. We show how to benefit from such a description to apply a multiresolution comparison scheme between the objects.
Modelling the effects of pecan nut [Carya illinoinensis (Wangenh.) K.Koch], roselle (Hibiscus sabdariffa L.), and salt on the quality characteristics of beef patties

The meat industry has become increasingly interested in developing products containing new ingredients oriented to consumers’ health. Health benefits are gained from frequent consumption of pecan nut and roselle, which can therefore be used as ingredients in meat product formulations. However, incorporating novel ingredients or reducing the content of traditional ingredients might affect meat product quality, thus needs to be evaluated and optimised for the development of functional foods. The objective of the present work was to assess how pecan nut (0 to 10%), roselle (0 to 2%), and salt (0 to 2%) affect the physicochemical properties, antioxidant, and sensory characteristics of beef patties, and use response surface methodology to optimise the content of these ingredients in a meat product formulation. Regression models were developed to predict quality properties. All models were significant (p < 0.05) with an R2 > 0.85 and a nonsignificant lack of fit (p > 0.05), thus indicating that these models could adequately predict response variables. The optimised formulation was 7.97% pecan nut, 1.59% roselle, and 1.08% salt. The predicted physicochemical properties were L* = 42.88, a* = 12.29, b* = 8.51, pH = 5.10, and cooking loss = 24.66%; the antioxidant properties were DPPH = 0.53 mg TE/g fp, ABTS = 0.65 mg TE/g fp, and total phenolic content = 0.46 mg GA/g fp; and the sensory properties were flavour = 7.03, tenderness = 6.98, and juiciness = 7.01. Pecan nut and roselle are promising natural ingredients that can be used to prepare low-salt beef patties.