Miracle-dz/UniFAR-FTUs huggingface dataset:
Edwin Power and the birth of dressed atoms

This paper reviews the main results of a twenty year-long international collaborative effort led by the late E.A. Power on the physics of atoms dressed by the vacuum electromagnetic field. The presentation uses the historical, rather than the logical, order of development. This permits one to shed light on the influence of Power's personality and human qualities on the birth and evolution of the notion of the dressed atom, which is central to modern non-relativistic QED.
Attribute-Driven or Green-Driven: The Impact of Subjective and Objective Knowledge on Sustainable Tea Consumption

The market for green agricultural products has tremendous growth potential as the pressure on resources and the environment increases and the safety of agricultural products is garnering attention. The demand for green food (tea) is also rising as tea is among the top three beverages consumed worldwide. The study attempts to propose a model of the relationship between green food (tea) customers’ product knowledge, perceived product quality, trust, purchase intention, and purchase behaviour. In addition, we will provide an analysis of the role played by age, education, income, gender, etc. The study included 700 questionnaires on green food (tea) consumers that were collected through the Credemo questionnaire platform, and data analysis was carried out using the SmartPLS software to assess the model of product knowledge on green food (tea) consumption behaviour. The findings demonstrate that, concerning differences in age, education, income, and gender; product knowledge—including attribute knowledge and green knowledge—has a positive impact on perceived product quality and trust; perceived product quality has a positive impact on trust; perceived product quality and trust have a positive impact on purchase intention; and purchase intention has a positive impact on purchase behavior.
Closed-Loop Dynamic Blending Optimization Based on Variational Bayesian and its Application in Industry

There exist uncertainties in raw material components and distribution parameters in blending process. Considering the dynamic uncertainty of distribution parameters and the dynamic change of raw material inventory, a general closed-loop dynamic blending optimization (CDBO) is proposed. The proposed method consists of both data-driven system and model-based system. In the data-driven system, the feedback optimization problem is reconstructed into a linear regression problem, and the variational Bayesian is proposed to obtain the mean and variance of the distribution. Then, according to the incoming raw materials, the mean value of each distribution parameter is adjusted by expert rules. Last, in the model-based system, blending ratio is obtained by chance-constrained programming. To verify the effectiveness of CDBO, a detailed derivation process of variable Bayesian, expert rule and chance-constrained programming are established for zinc smelting process. Numerical studies and an industrial application for zinc smelting process are presented to demonstrate the advantages of the proposed method. Compared with the manual blending, the volatility index of chemical tests data is greatly reduced and the compliance rates of zinc and lead is increased by 6.7% and 3.3%, respectively.
Assessment of Tool Wear With Insufficient and Unbalanced Data Using Improved Conditional Generative Adversarial Net and High-Quality Optimization Algorithm

The reliability of manufacturing tooling is key for intelligent manufacturing process, which requires accurately online identification of abnormal tool condition. However, in practical applications, insufficient and unbalanced data bring great difficulty for reliable tool wear assessment. In this study, a combined improved conditional generative adversarial net with high-quality optimization algorithm (CGAN-HQOA) is proposed to generate tool data having higher similarity with real data. Assessment of tool wear condition utilizing this newly generated data within convolutional neural network is shown with increased accuracy. The generated data can maintain sample diversity while minimizing deviation from real sample characteristics with CGAN-HQOA. The effectiveness is investigated using unbalanced data under various scenarios, where the quality of generated data from the proposed model is compared to those from commonly used data generation algorithms, such as generative adversarial nets. Moreover, the robustness of the proposed method is investigated by using different cutting tools. Results demonstrate that with the proposed model, better quality data can be generated, and more accurate tool wear condition can be assessed using generated data. The findings will be beneficial in practical applications where only limited test data are available, whereas accurate and online tool wear can be evaluated with proposed method.
A consensus method for estimating physical activity levels in adults using accelerometry

ABSTRACT Identifying the best analytical approach for capturing moderate-to-vigorous physical activity (MVPA) using accelerometry is complex but inconsistent approaches employed in research and surveillance limits comparability. We illustrate the use of a consensus method that pools estimates from multiple approaches for characterising MVPA using accelerometry. Participants (n = 30) wore an accelerometer on their right hip during two laboratory visits. Ten individual classification methods estimated minutes of MVPA, including cut-point, two-regression, and machine learning approaches, using open-source count and raw inputs and several epoch lengths. Results were averaged to derive the consensus estimate. Mean MVPA ranged from 33.9–50.4 min across individual methods, but only one (38.9 min) was statistically equivalent to the criterion of direct observation (38.2 min). The consensus estimate (39.2 min) was equivalent to the criterion (even after removal of the one individual method that was equivalent to the criterion), had a smaller mean absolute error (4.2 min) compared to individual methods (4.9–12.3 min), and enabled the estimation of participant-level variance (mean standard deviation: 7.7 min). The consensus method allows for addition/removal of methods depending on data availability or field progression and may improve accuracy and comparability of device-based MVPA estimates while limiting variability due to convergence between estimates.
Singular Poisson tensors

The Hamiltonian structures discovered by Morrison and Greene for various fluid equations were obtained by guessing a Hamiltonian and a suitable Poisson bracket formula, expressed in terms of noncanonical (but physical) coordinates. In general, such a procedure for obtaining a Hamiltonian system does not produce a Hamiltonian phase space in the usual sense (a symplectic manifold), but rather a family of symplectic manifolds. To state the matter in terms of a system with a finite number of degrees of freedom, the family of symplectic manifolds is parametrized by a set of Casimir functions, which are characterized by having vanishing Poisson brackets with all other functions. The number of independent Casimir functions is the corank of the Poisson tensor Jij, the components of which are the Poisson brackets of the coordinates among themselves. Thus, these Casimir functions exist only when the Poisson tensor is singular.
Sexualized Images on Social Media and Adolescent Girls’ Mental Health: Qualitative Insights from Parents, School Support Service Staff and Youth Mental Health Service Providers

This research explored adults’ perceptions of how sexualized images typically found on social media might influence adolescent girls’ mental health, what support girls might need should they experience mental health difficulties, and how such difficulties could be prevented or reduced. Qualitative data were collected using semi-structured in-depth interviews with parents of adolescent girls (n = 11) and those who provide support to them: school support service staff (n = 7) and youth mental health service providers (n = 10) located in Perth, Western Australia. All three participant groups perceived sexualized images typically found on social media as exacerbating poor mental health among adolescent girls. Two interrelated themes, emerged with participants describing the ‘potential for comparison’ and ‘pressure to conform’ they believed girls encounter on social media that influences their mental health. Participants also explained how they perceived ‘counteracting negative influences’ related to sexualized images on social media could prevent or reduce the potential for mental health harms among girls, and the importance of adults and services ‘keeping up to date’ and being ‘approachable and trustworthy’ when describing the support they believed girls might need. The findings of this study have important implications for the development of health promotion programs focused on social media use and mental health among adolescent girls.
Looking for high-mass young stellar objects: H2O and OH masers in ammonia cores

Context. The earliest stages of high-mass star formation have yet to be characterised well, because high-angular resolution observations are required to infer the properties of the molecular gas hosting the newly formed stars. Aims. We search for high-mass molecular cores in a large sample of 15 high-mass star-forming regions that are observed at high-angular resolution, extending a pilot survey based on a smaller number of objects. Methods. The sample was chosen from surveys of H 2 O and OH masers to favour the earliest phases of high-mass star formation. Each source was first observed with the 32-m single-dish Medicina antenna in the (1,1) and (2, 2) inversion transitions at 1.3 cm of ammonia, which is an excellent tracer of dense gas. High-resolution maps in the NH 3 (2, 2) and (3, 3) lines and the 1.3 cm continuum were obtained successively with the VLA interferometer. Results. We detect continuum emission in almost all the observed star-forming regions, which corresponds to extended and UCHII regions created by young stellar objects with typical luminosities of ~10 4 L ⊙ . However, only in three cases do we find a projected overlap between HII regions and H 2 O and OH maser spots. On the other hand, the VLA images detect eight ammonia cores closely associated with the maser sources. The ammonia cores have sizes of ~10 4 AU, and high masses (up to 10 4 M ⊙ ), and are very dense (from ~10 6 to a few x10 9 cm -3 ). The typical relative NH 3 abundance is ≤10 -7 , in agreement with previous measurements in high-mass star-forming regions. Conclusions. The statistical analysis of the distribution between H 2 O and OH masers, NH 3 cores, and HII regions confirms that the earliest stages of high-mass star formation are characterised by high-density molecular cores with temperatures of on average ≥30 K, either without a detectable ionised region or associated with a hypercompact HII region.
Characterization of Human-Induced Neural Stem Cells and Derivatives following Transplantation into the Central Nervous System of a Nonhuman Primate and Rats

Neural stem cells (NSCs) and derivatives are potential cellular sources to treat neurological diseases. In the current study, we reprogrammed human peripheral blood mononuclear cells into induced NSCs (iNSCs) and inserted GFP gene into the AAVS1 site for graft tracing. Targeted integration of GFP does not affect the proliferation and differentiation capacity of iNSCs. iNSC-GFP can be further differentiated into dopaminergic precursors (DAPs) and motor neuron precursors (MNPs), respectively. iNSCs were engrafted into the motor cortex and iNSC-DAPs into the striatum and substantia nigra (SN) of a nonhuman primate, respectively. The surviving iNSCs could respond to the microenvironment of the cortex and spontaneously differentiate into mature neurons that extended neurites. iNSC-DAPs survived well and matured into DA neurons following transplantation into the striatum and SN. iNSC-MNPs could also survive and turn into motor neurons after being engrafted into the spinal cord of rats. The results suggest that iNSCs and derivatives have a potential to be used for the treatment of neurological diseases.
Toward intelligent resource management in dynamic Fog Computing‐based Internet of Things environment with Deep Reinforcement Learning: A survey

Fog computing has already started to gain a lot of momentum in the industry for its ability to turn scattered computing resources into a large‐scale, virtualized, and elastic computing environment. Resource management (RM) is one of the key challenges in fog computing which is also related to the success of fog computing. Deep learning has been applied to the fog computing field for some time, and it is widely used in large‐scale network RM. Reinforcement learning (RL) is a type of machine learning algorithms, and it can be used to learn and make decisions based on reward signals that are obtained from interactions with the environment. We examine current research in this area, comparing RL and deep reinforcement learning (DRL) approaches with traditional algorithmic methods such as graph theory, heuristics, and greedy for managing resources in fog computing environments (published between 2013 and 2022) illustrating how RL and DRL algorithms can be more effective than conventional techniques. Various algorithms based on DRL has been shown to be applicable to RM problem and proved that it has a lot of potential in fog computing. A new microservice model based on the DRL framework is proposed to achieve the goal of efficient fog computing RM. The positive impact of this work is that it can successfully provide a resource manager to efficiently schedule resources and maximize the overall performance.
Masses, radii, and magnetic fields of pulsating X-ray sources : is the "standard" model self-consistent ?

We present prototype calculations for Her X-1 and 4U 0115+63 which are designed to test the ''standard'' model of a pulsating X-ray source for self-consistency. We construct ''semiempirical'' mass-radius (M-R/sub s/) relations for these two X-ray pulsars based on observational data (luminosities pulse periods, spin-up rates, cylotron spectral features) and their theoretical interpretation in terms of the MHD accretion torque theory of Ghosh and Lamb. These semiemperical M-R/sub s/, relations depend only on physical processes occurring exterior to an accreting neutron star. We compare them to theoretical M-R/sub s/ relations derived by integrating the hydrostatic equilibrium (Oppenheimer-Volkoff) equation for representative equations of state which might characterize the condensed matter in a neutron star's interior.
Predicting Long-Term Recovery of Consciousness in Prolonged Disorders of Consciousness Based on Coma Recovery Scale-Revised Subscores: Validation of a Machine Learning-Based Prognostic Index

Prognosis of prolonged Disorders of Consciousness (pDoC) is influenced by patients’ clinical diagnosis and Coma Recovery Scale-Revised (CRS-R) total score. We compared the prognostic accuracy of a novel Consciousness Domain Index (CDI) with that of clinical diagnosis and CRS-R total score, for recovery of full consciousness at 6-, 12-, and 24-months post-injury. The CDI was obtained by a combination of the six CRS-R subscales via an unsupervised machine learning technique. We retrospectively analyzed data on 143 patients with pDoC (75 in Minimally Conscious State; 102 males; median age = 53 years; IQR = 35; time post-injury = 1–3 months) due to different etiologies enrolled in an International Brain Injury Association Disorders of Consciousness Special Interest Group (IBIA DoC-SIG) multicenter longitudinal study. Univariate and multivariate analyses were utilized to assess the association between outcomes and the CDI, compared to clinical diagnosis and CRS-R. The CDI, the clinical diagnosis, and the CRS-R total score were significantly associated with a good outcome at 6, 12 and 24 months. The CDI showed the highest univariate prediction accuracy and sensitivity, and regression models including the CDI provided the highest values of explained variance. A combined scoring system of the CRS-R subscales by unsupervised machine learning may improve clinical ability to predict recovery of consciousness in patients with pDoC.
Integrating the dyadic Green's function near sources

Formulas are derived which allow the dyadic Green's function to be integrated for well-behaved currents in the source region. The result is that the electric field due to a current distribution local to an observer can be expressed as a function of the current and its spatial derivatives at the point of observation plus a nonsingular integral over a surface containing the local currents. Although a spherical principal volume is used to derive the theory, the field due to this principal volume is exactly canceled by other terms. The exact form for pulse currents is derived. The theory is extended to nonpulse currents in an appendix. >
Different sleep patterns during the COVID-19 pandemic: association with mood, exercise and light exposure

ABSTRACT The aim was to evaluate the sleep-wake cycle pattern, mood, perceived stress and some behaviors, such as physical exercise and exposure to natural light of college students during the COVID-19 pandemic. This is a cross-sectional study conducted between June and August 2020 using an electronic form provided by Google. The sample consisted of undergraduate students aged between 18 and 30 years old and residents of the northeast region of Brazil. The students generally had sleepiness and poor sleep quality, high levels of anxiety, mild to moderate depressive symptoms and moderate perceived stress. Some of these aspects were worse in women. The college students showed three sleep patterns: one group had good sleep quality without excessive daytime sleepiness; another group had poor sleep quality, but no excessive daytime sleepiness; and a third group had high daytime sleepiness, and less expressive sleep quality impairment. Greater exposure to sunlight and practicing physical exercise predominated in individuals with better sleep quality, suggesting that they are protective factors. In addition, excessive daytime sleepiness and poor sleep quality were separately associated with higher anxiety, depression and stress perception levels, proving to be important aspects for care in order to favor mental health during the pandemic. In conclusion, it is suggested that the COVID-19 pandemic affected the sleep of college students in a heterogeneous way. The differentiated sleep patterns are associated with exposure to natural light and exercising.
A Bayesian Mixture Cure Rate Model for Estimating Short-Term and Long-Term Recidivism

Mixture cure rate models have been developed to analyze failure time data where a proportion never fails. For such data, standard survival models are usually not appropriate because they do not account for the possibility of non-failure. In this context, mixture cure rate models assume that the studied population is a mixture of susceptible subjects who may experience the event of interest and non-susceptible subjects that will never experience it. More specifically, mixture cure rate models are a class of survival time models in which the probability of an eventual failure is less than one and both the probability of eventual failure and the timing of failure depend (separately) on certain individual characteristics. In this paper, we propose a Bayesian approach to estimate parametric mixture cure rate models with covariates. The probability of eventual failure is estimated using a binary regression model, and the timing of failure is determined using a Weibull distribution. Inference for these models is attained using Markov Chain Monte Carlo methods under the proposed Bayesian framework. Finally, we illustrate the method using data on the return-to-prison time for a sample of prison releases of men convicted of sexual crimes against women in England and Wales and we use mixture cure rate models to investigate the risk factors for long-term and short-term survival of recidivism.
A Bayesian Model for Markov Chains via Jeffrey's Prior

Abstract This work deals with the problem of Bayesian estimation of the transition probabilities associated with multistate Markov chain. The model is based on the Jeffreys' noninformative prior. The Bayesian estimator is approximated by means of MCMC techniques. A numerical study by simulation is done in order to compare the Bayesian estimator with the maximum likelihood estimator.
Dermatology Patients’ Knowledge of Sunscreen Guidelines at a University Hospital in Saudi Arabia

Objective The goal of this study is to assess the knowledge and attitude of dermatology patients regarding sunscreen use as recommended by the American Academy of Dermatology. Methods This was an observational cross-sectional study at a university hospital in Riyadh. Participants were asked to complete a questionnaire to measure their knowledge and attitude regarding sunscreen use. Results were analyzed using a chi-square test. Results Most participants heard about sunscreen (93.4%) and had indicated previous sunscreen use (72%). Sunscreen use was higher among women and participants with a postgraduate education. Sunscreen knowledge was higher among women, participants with non-photosensitive disorders, and participants with Fitzpatrick I, II, and III skin types. A total of 80% knew the worst time for sun exposure, but only 20.5% of the participants knew that sunscreen should be applied daily throughout the year, and 13.5% knew the correct minimum recommended SPF. A total of 34% were aware of the minimum time allowed for sunscreen application before sun exposure, and 20% were aware of the longest period allowed between reapplications. Only 4.5% knew the proper amount of sunscreen to cover the entire body. Conclusion Our study revealed that most participants had heard of sunscreen, however their knowledge of the guidelines for sunscreen use was poor. Only 33% of the participants had received a recommendation for using sunscreen from their general practitioner or dermatologist. More comprehensive and directed efforts need to be made in counselling and educating these patients on proper sunscreen practice.
Stochastic Volatility Modeling of Daily Streamflow Time Series

Under the changing climate, the natural characteristics of hydrological processes are assumed to have been intensified, and the volatility of these processes to have increased significantly. However, the applicability of traditional time series analysis methods and the commonly used Gaussian GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) to streamflow modeling has been limited. Although SV‐type (stochastic volatility) models have appeared as competitive alternatives to GARCH‐type models owing to their flexibility in modeling volatilities, they have not been applied in hydrological studies. Therefore, this study assesses the applicability of SV models to streamflow modeling using daily streamflow series from 21 hydrological stations in the Yellow River basin. Considering the influence of different types of GARCH and residual distributions, 7 hybrid models with variance fluctuation (fractional autoregressive integrated moving average‐GARCH) based on 9 distributions are compared to determine the optimal model for each station. Then 4 SV‐type models are introduced and compared with the results of the optimal GARCH‐type model to verify their applicability. The results show that: (a) the Gaussian distribution is not applicable in both GARCH‐type and SV‐type models for modeling daily streamflow; (b) although the GARCH‐type models are shown to describe the volatility of streamflow processes and improve the modeling performance, large residuals have been observed in the results of the analysis during the peak flow period; and (c) SV‐type models can better describe the streamflow series with time‐varying variance and accurately capture the occurrence of peak streamflow. The findings of this study offer practical and promising time series analysis methods for daily streamflow modeling.