Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduced Birth Weight and Exposure to Per- and Polyfluoroalkyl Substances: A Review of Possible Underlying Mechanisms Using the AOP-HelpFinder

Prenatal exposure to per- and polyfluorinated substances (PFAS) may impair fetal growth. Our knowledge of the underlying mechanisms is incomplete. We used the Adverse Outcome Pathway (AOP)-helpFinder tool to search PubMed for studies published until March 2021 that examined PFAS exposure in relation to birth weight, oxidative stress, hormones/hormone receptors, or growth signaling pathways. Of these 1880 articles, 106 experimental studies remained after abstract screening. One clear finding is that PFAS are associated with oxidative stress in in vivo animal studies and in vitro studies. It appears that PFAS-induced reactive-oxygen species (ROS) generation triggers increased peroxisome proliferator-activated receptor (PPAR)γ expression and activation of growth signaling pathways, leading to hyperdifferentiation of pre-adipocytes. Fewer proliferating pre-adipocytes result in lower adipose tissue weight and in this way may reduce birth weight. PFAS may also impair fetal growth through endocrine effects. Estrogenic effects have been noted in in vivo and in vitro studies. Overall, data suggest thyroid-damaging effects of PFAS affecting thyroid hormones, thyroid hormone gene expression, and histology that are associated in animal studies with decreased body and organ weight. The effects of PFAS on the complex relationships between oxidative stress, endocrine system function, adipogenesis, and fetal growth should be further explored.
Fire Extinguishing Performance of Chemically Bonded Struvite Ceramic Powder with High Heat-Absorbing and Flame Retardant Properties

Struvite is a chemically bonded ceramic product in the pipeline of a sewage treatment plant. In order to explore the fire extinguishing potential of struvite, a new type of struvite ultrafine dry powder with excellent performance was prepared by a simple process, and its fire extinguishing performance and mechanism were analyzed in depth. Under the same process conditions, the refinement degree (D50 = 5.132 μm) and the specific surface area (BET = 25.72 m2/g) of ultrafine struvite were larger than those of NH4H2PO4 (D50 = 8.961 μm, BET = 13.64 m2/g), making struvite more suitable for fire extinguishing. Besides, the pyrolysis process of struvite was relatively concentrated and absorbed more heat in a short time. Its heat absorption (458.4 J/mg) was higher than that of NH4H2PO4 (156.4 J/mg). Water, ammonia, and PO· were released during the pyrolysis of struvite, which effectively reduced fire temperature, diluted oxygen concentrations and captured free radicals. At the same time, the final products were magnesium orthophosphate and magnesium pyrophosphate, which formed a dense flame-retardant ceramic layer with good thermal insulation and environmental protection functions. In these cases, the fire extinguishing mechanism of struvite was determined to have three stages: the cooling effect, the asphyxiation effect, and the chemical effect. Correspondingly, the fire extinguishing time of struvite was three seconds faster than that of ammonium phosphate under 0.2 MPa based on the local oil basin test.
Performance assessment of RBFMOpt, NSGA2, and MHACO on the thermal and energy optimization of an office building

Simulation-based Optimization processes (SBO) can be valuable methods in searching for efficient buildings. This study evaluates the performance of the multi-objective algorithms RBFMOpt, NSGA2, and MHACO facing the same SBO problem. The goal is to maximize thermal comfort while minimizing the energy consumption with HVAC systems for a typical Brazilian office building. We proposed a scoring method based on four algorithms’ performance metrics: hypervolume, variability, IGD+, and coverage. We also applied a Kruskal–Wallis test to determine whether the SBO process needs multiple runs to obtain the average performance of each algorithm. The results show that RBFMOpt presents the best performance, reaching a higher score, especially in situations with low budgets for the simulation and optimization process. The results also pointed out that the number of cycles for RBFMOpt impacts directly the quality of solutions, and a higher number of cycles provided better results.
Multi-Exposure Image Fusion using Convolutional Neural Network (CNN)

Because calculating the amount of light in a photograph has been the topic of so much research, multiexposure photos frequently have a smeared appearance. The fundamental purpose of these investigations is to identify the patch-smart solution for illumination estimation with human intervention using the CNN method, which can be utilized to distinguish between distinct time periods in multi-exposure fusion photos. Common picture fusion segmentation techniques, such as the seeded watershed algorithm, are time-consuming and error-prone. In addition, parameter adjustment is necessary when employing such algorithms, therefore a competent consumer is desirable. This research intends to address the critical issues connected with patch-wise illumination estimation in multi-source photo fusion. Using convolutional neural networks, multiple-publicity image fusion snapshots will be created. The neural network can be trained with visual data that has been manually tagged, and its performance can be measured with summative metrics.
Fluctuations in quantum one-dimensional thermostatted systems with off-diagonal disorder

We analyze a one-dimensional quantum model with off-diagonal disorder, consisting of a sequence of potential energy barriers whose width is a random variable, either uniformly or ‘half-normally’ distributed. We investigate how the disorder and the energy distribution (due to a thermostat at room temperature) affect the resulting value of the transmission coefficient, and discuss the structure of the fluctuations of such a coefficient at different length scales and the onset of different size-dependent regimes. Our analysis suggests an efficient way to detect tunneling resonances in a regime of off-diagonal disorder.
Investigating Drivers Stimulating Demand for Green Renovation of Existing Buildings and Systems

The main purpose of the research is to investigate drivers that motivate homeowners, investors, government institutions etc., to undertake green renovation. Sustainable upgrade actions have been slow although new smart technologies such as solar panels, e-glazing, insulation systems, cogeneration etc., are developed or upgraded every year. At such a slow pace, the existing building stock presents a challenge as drivers are not rigorously identified and applied. A survey questionnaire was designed to examine all the drivers that encourage energy renovation. Extensive review of the literature provided a theoretical framework that supported the study. The survey was administered to energy consultants, architects, quantity surveyors, facility managers and engineers with sufficient professional experience. The data was analysed using means, T-test analysis and Mann–Whitney U test. The results establish a relationship between drivers and upgrade of existing buildings and systems. The findings identified a strong level of agreement among the respondents on the drivers of green renovation. Incentive and support systems, penalties for noncompliance, high energy bills, energy conservation and policy and regulations, awareness etc., are some of the motivating factors that drive energy management retrofit.
Bisimulation equivalence of DAE systems

In this paper the notion of bisimulation relation for linear input-state-output systems is extended to general linear differential-algebraic (DAE) systems. Geometric control theory is used to derive a linear-algebraic characterization of bisimulation relations, and an algorithm for computing the maximal bisimulation relation between two linear DAE systems. The general definition is specialized to the case where the matrix pencil $sE - A$ is regular. Furthermore, by developing a one-sided version of bisimulation, characterizations of simulation and abstraction are obtained.
X‐ray polarimetry as a new tool to discriminate reflection from absorption scenarios – predictions for MCG‐6‐30‐15

We present modelling of X-ray polarization spectra emerging from the two competing scenarios that are proposed to explain the broad Fe Kα line in the Seyfert 1 galaxy MCG-6-30-15. The polarization signature of complex absorption is studied for a partial covering scenario using a clumpy wind and compared to a reflection model based on the lamppost geometry. The shape of the polarization percentage and angle as a function of photon energy are found to be distinctly different between the reflection and the absorption cases. Relativistic reflection produces significantly stronger polarization in the 1–10 keV energy band than absorption. The spectrum of the polarization angle adds additional constraints: in the absorption case it shows a constant shape, whereas the relativistic reflection scenario typically leads to a smooth rotation of the polarization angle with photon energy. Based on this work, we conclude that a soft X-ray polarimeter onboard a small X-ray satellite may already discriminate between the absorption and the reflection scenarios. A promising opportunity may arise with the X-ray Imaging Polarimetry Explorer mission, which has been proposed to the European Space Agency in response to a small-size (S-class) mission call due for launch in 2017.
Herbal Fennel Essential Oil Nanogel: Formulation, Characterization and Antibacterial Activity against Staphylococcus aureus

Antimicrobial resistance (AMR) is one of the greatest threats to humanity in the world. Antibiotic-resistant bacteria spread easily in communities and hospitals. Staphylococcus aureus (S. aureus) is a serious human infectious agent with threatening broad-spectrum resistance to many commonly used antibiotics. To prevent the spread of pathogenic microorganisms, alternative strategies based on nature have been developed. Essential oils (EOs) are derived from numerous plant parts and have been described as antibacterial agents against S. aureus. Fennel essential oils were selected as antibacterial agents encapsulated in nanoparticles of polylactic acid and glycolic acid (PLGA). The optimum size of the formulation after loading with the active ingredient was 123.19 ± 6.1595 nm with a zeta potential of 0.051 ± 0.002 (23 ± 1.15 mV). The results of the encapsulation efficiency analysis showed high encapsulation of EOs, i.e., 66.4 ± 3.127. To obtain promising carrier materials for the delivery of fennel EOs, they were incorporated in the form of nanogels. The newly developed fennel oils in PLGANPs nanogels have good drug release and MIC against S. aureus. These results indicate the potential of this novel delivery system for antimicrobial therapy.
Dalitz plot analysis of theD+→K−π+π+decay

Using 281 pb^{-1} of data recorded by the CLEO-c detector in e^+e^- collisions at the psi(3770), corresponding to 0.78 million D^+D^- pairs, we investigate the substructure of the decay D^+ to pi^- pi^+ pi^+ using the Dalitz plot technique. We find that our data are consistent with the following intermediate states: rho(770)pi^+, f_2(1270)pi^+, f_0(1370)pi^+, f_0(1500)pi^+, f_0(980) pi^+, and sigma pi^+. We confirm large S wave contributions at low pi pi mass. We set upper limits on contributions of other possible intermediate states. We consider three models of the pi pi S wave and find that all of them adequately describe our data.
An Internet of Things Botnet Detection Model Using Regression Analysis and Linear Discrimination Analysis

The Internet of Things (IoT) has become a hot area of research in recent years due to the significant advancements in the semiconductor industry, wireless communication technologies, and the realization of its ability in numerous applications such as smart homes, health care, control systems, and military. Furthermore, IoT devices inefficient security has led to an increase cybersecurity risks such as IoT botnets, which have become a serious threat. To counter this threat there is a need to develop a model for detecting IoT botnets. This paper's contribution is to formulate the IoT botnet detection problem and introduce multiple linear regression (MLR) for modelling IoT botnet features with discriminating capability and alleviating the challenges of IoT detection. In addition, a linear discrimination analysis (LDA) model for distinguishing between normal activities and IoT botnets was developed. Network-based detection of IoT (N-BaIoT) dataset was used to evaluate the performance of the proposed IoT botnet detection model in terms of accuracy, precision, and detection rate. Experimental results revealed that the proposed IoT botnet detection model provides a relevant feature subset and preserves high accuracy when compared with state-of-the-art and baseline methods, particularly LDA.
Highly Durable Antibacterial Properties of Cellulosic Fabric via β-Cyclodextrin/Essential Oils Inclusion Complex

Essential oils (EOS), which naturally come from plants, have significant antibacterial properties against a variety of pathogens, but their high volatility and poor water solubility severely restrict their use in the textile industry. In this study, an inclusion complex based on β-cyclodextrin (β-CD)/EOS was prepared by two different simple methods: pad dry cure (method 1) and pad batch (method 2). A glyoxal crosslinking agent was used for the fixation of the inclusion complexes on the surface of the fabric. Lavender, rosemary, salvia, and lemon essential oils were applied. The structure of the β-CD/EOs inclusion complex was investigated using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and weight gain (%), which indicated that the β-CD/EOs were successfully deposited on cellulose-based fabric. The results demonstrated that β-CD enhanced the oils’ scent stability, with the advantage of exhibiting no major change in the tensile strength or permeability of cotton. Lavender oil had the highest stability scent with a value of 3.25, even after 30 days of evaluation. The antibacterial activity showed that EOS/βCD-impregnated fabrics for method 1 had an inhibition zone ranging from 33 to 23 mm, while the inhibition zone for method 2 ranged from 39 mm to 29 mm, indicating that our treatment was able to control the growth of bacteria, even after five washing cycles. This study confirmed that the EOs/βCD inclusion-complex-deposited cotton fabric might hold further promise for medical and hospital use.
Scaling Algorithms for Unbalanced Transport Problems

This article introduces a new class of fast algorithms to approx-imate variational problems involving unbalanced optimal transport. While classical optimal transport considers only normalized probability distributions, it is important for many applications to be able to compute some sort of re-laxed transportation between arbitrary positive measures. A generic class of such “unbalanced” optimal transport problems has been recently proposed by several authors. In this paper, we show how to extend the, now classical, entropic regularization scheme to these unbalanced problems. This gives rise to fast, highly parallelizable algorithms that operate by performing only diagonal scaling (i.e. pointwise multiplications) of the transportation couplings. They are generalizations of the celebrated Sinkhorn algorithm. We show how these methods can be used to solve unbalanced transport, unbalanced gradient flows, and to compute unbalanced barycenters. We showcase applications to 2-D shape modification, color transfer, and growth models.
Unification of Dark Matter and Dark Energy in a Modified Entropic Force Model

In Verlinde's entropic force scenario of gravity, Newton's laws and Einstein equations can be obtained from the first principles and general assumptions. However, the equipartition law of energy is invalid at very low temperatures. We show clearly that the threshold of the equipartition law of energy is related with horizon of the universe. Thus, a one-dimensional Debye (ODD) model in the direction of radius of the modified entropic force (MEF) may be suitable in description of the accelerated expanding universe. We present a Friedmann cosmic dynamical model in the ODD-MEF framework. We examine carefully constraints on the ODD-MEF model from the Union2 compilation of the Supernova Cosmology Project (SCP) collaboration, the data from the observation of the large-scale structure (LSS) and the cosmic microwave background (CMB), i.e. SNe Ia+LSS+CMB. The combined numerical analysis gives the best-fit value of the model parameters zeta similar or equal to 10(-9) and Omega(m0) = 0.224, with chi(2)(min) = 591.156. The corresponding age of the universe agrees with the result of D. Spergel et al. [J.M. Bardeen, B. Carter, and S.W. Hawking, Commun. Math. Phys. 31 (1973) 161] at 95% confidence level. The numerical result also yields an accelerated expanding universe without invoking any kind of dark energy. Taking zeta( 2 pi omega(D)/H-0) as a running parameter associated with the structure scale r, we obtain a possible unified scenario of the asymptotic flatness of the radial velocity dispersion of spiral galaxies, the accelerated expanding universe and the Pioneer 10/11 anomaly in the entropic force framework of Verlinde.
Derivation of renormalized Gibbs measures from equilibrium many-body quantum Bose gases

We review our recent result on the rigorous derivation of the renormalized Gibbs measure from the many-body Gibbs state in 1D and 2D. The many-body renormalization is accomplished by simply tuning the chemical potential in the grand-canonical ensemble, which is analogous to the Wick ordering in the classical field theory.
Flat Field Coupler with Reduced Ferrite for Wireless Power Transfer System

The stable mutual inductance and coefficient of coupling with lateral variation in Wireless power transfer (WPT) system is major concern. Offset between coupled coils degrade the system power transfer capability and output voltage. The high anti-offset tolerant coil designs are favorable to make stable and efficient power transfer under offset conditions. This paper presents a transmitter coil structure having flat field distribution with reduced ferrite requirement for WPT systems. Ansys-Maxwell is used in the process of developing the 3D model of the proposed coil structure. The field density plot for the proposed coupler is found to be uniform between −50 mm to +50 mm. In addition, the performance of proposed coupler for coupling coefficient (k) and Mutual inductance (M) for different offset conditions are also presented. The mutual inductance and coefficient of coupling are found to be uniform up-to 30 mm.
Analysis on Tweets Towards COVID-19 Pandemic: An Application of Text-Based Depression Detection

Early depression detection is crucial for both people at risk and the whole society. After the COVID-19 pandemic, the depression level is expected to get higher. Social media is an affluent source of users' opinions and feelings that can be used to detect depression. Depression detection from users' tweets is a challenging task that many researchers have tried to tackle recently as it depends on the whole tweet context. This work introduces a machine learning-based approach for depression detection from tweets. We first obtained a tweets depression detection dataset and use it to train different machine learning and deep learning models for depression detection. We trained some classical machine learning models, then we also fine-tuned the state-of-the-art transformer-based pre-trained language models like BERT, RoBERTa, MobileBERT, and DistilBERT. Our best model was RoBERTa gaining a 78.85 percent F1-score on the test set. This model has then been used to pseudo-label two different datasets of about 4.35 million tweets from about 1 million Twitter users related to COVID-19 and vaccination to gain more deep insights on COVID-19 effect on the depression level of social media users. We show that depression level got doubled from four percent to eight percent from the beginning to the end of March 2020. Depression also increased after nearly two years to reach 15 percent in December 2021. The boosting of the level of depression should be taken into consideration by mental health institutions.
Does Domestication Affect Structural and Functional Leaf Epidermal Traits? A Comparison between Wild and Cultivated Mexican Chili Peppers (Capsicum annuum)

During domestication, lineages diverge phenotypically and genetically from wild relatives, particularly in preferred traits. In addition to evolutionary divergence in selected traits, other fitness-related traits that are unselected may change in concert. For instance, the selection of chili pepper fruits was not intended to change the structure and function of the leaf epidermis. Leaf stomata and trichome densities play a prominent role in regulating stomatal conductance and resistance to herbivores. Here, we assessed whether domestication affected leaf epidermis structure and function in Capsicum annuum. To do this, we compared leaf stomata and trichome densities in six cultivated varieties of Mexican Capsicum annuum and their wild relative. We measured stomatal conductance and resistance to herbivores. Resistance to (defense against) herbivores was measured as variation in the herbivory rate and larvae mortality of Spodoptera frugiperda fed with leaves of wild and cultivated plants. As expected, the different varieties displayed low divergence in stomatal density and conductance. Leaf trichome density was higher in the wild relative, but variation was not correlated with the herbivory rate. In contrast, a higher mortality rate of S. frugiperda larvae was recorded when fed with the wild relative and two varieties than larvae fed with four other varieties. Overall, although domestication did not aim at resistance to herbivores, this evolutionary process produced concerted changes in defensive traits.
An efficient multigrid solver for 3D biharmonic equation with a discretization by 25-point difference scheme.

In this paper, we propose an efficient extrapolation cascadic multigrid (EXCMG) method combined with 25-point difference approximation to solve the three-dimensional biharmonic equation. First, through applying Richardson extrapolation and quadratic interpolation on numerical solutions on current and previous grids, a third-order approximation to the finite difference solution can be obtained and used as the iterative initial guess on the next finer grid. Then we adopt the bi-conjugate gradient (Bi-CG) method to solve the large linear system resulting from the 25-point difference approximation. In addition, an extrapolation method based on midpoint extrapolation formula is used to achieve higher-order accuracy on the entire finest grid. Finally, some numerical experiments are performed to show that the EXCMG method is an efficient solver for the 3D biharmonic equation.
Random point sets and their diffraction

The diffraction of various random subsets of the integer lattice ℤ d , such as the coin tossing and related systems, are well understood. Here, we go one important step beyond and consider random point sets in ℝ d . We present several systems with an effective stochastic interaction that still allow for explicit calculations of the autocorrelation and the diffraction measure. We concentrate on one-dimensional examples for illustrative purposes, and briefly indicate possible generalisations to higher dimensions. In particular, we discuss the stationary Poisson process in ℝ d and the renewal process on the line. The latter permits a unified approach to a rather large class of one-dimensional structures, including random tilings. Moreover, we present some stationary point processes that are derived from the classical random matrix ensembles as introduced in the pioneering work of Dyson and Ginibre. Their reconsideration from the diffraction point of view improves the intuition on systems with randomness and mixed spectra.