Miracle-dz/UniFAR-FTUs huggingface dataset:
A generalization of the Tarski-Seidenberg theorem, and some nondefinability results

This article points out some remarkable facts implicit in the results of Lojasiewicz [LI] and Gabrielov [Ga]. An important consequence of Tarski's work [T] on the elementary theory of the reals is a characterization of the sets which are elementarily definable from addition and multiplication on R. Allowing arbitrary reals as constants, this characterization consists of the identification of the definable sets with the semialgebraic sets. (A semialgebraic subset of R is by definition a finite union of sets of the form {x e R: p(x) = 0, qx(x) > 0, . ,qk(x) > 0} where p,q1,. ,qk are real polynomials. ) The fact that the system of semialgebraic sets is closed under definability is also known as the Tarski-Seidenberg theorem, and this property, together with the topological finiteness phenomena that go with it—triangulability of semialgebraic sets [L2, Gi], generic triviality of semialgebraic maps [Ha]—make the theory of semialgebraic sets a useful analytic-topological tool. Below we extend the system of semialgebraic sets in such a way that the Tarski-Seidenberg property, i.e., closure under definability, and the topological finiteness phenomena are preserved. The polynomial growth property of semialgebraic functions is also preserved. This extended system contains the arctangent function on R, the sine function on any bounded interval, the exponential function e on any bounded interval, but not the exponential function on all of R. (And it couldn't possibly contain the sine function on all of R without sacrificing the finiteness phenomena, and a lot more.) As a corollary we obtain that neither the exponential function on R, nor the set of integers, is definable from addition, multiplication, and the restrictions of the sine and exponential functions to bounded intervals. Questions of this type have puzzled logicians for a long time. (There still remain, of course, countless unsolved problems of this sort.) In a more positive spirit Tarski [T, p. 45] asked to extend his results so as to include, besides the algebraic operations on R, certain transcendental elementary functions like e; the theorem below is a partial answer. (More recently, Hovanskii [Ho, p. 562] and the author [VdDl, VdD2] asked similar questions, and in [VdD3] we
Single nucleotide variants in microRNA biosynthesis genes in Mexican individuals

Background: MicroRNAs (miRNAs) are important regulators in a variety of biological processes, and their dysregulation is associated with multiple human diseases. Single nucleotide variants (SNVs) in genes involved in the processing of microRNAs may alter miRNA regulation and could present high allele heterogeneity in populations from different ethnic groups. Thus, the aim of this study was to genotype 15 SNVs in eight genes involved in the miRNA processing pathway in Mexican individuals and compare their frequencies across 21 populations from five continental groups. Methods: Genomic DNA was obtained from 399 healthy Mexican individuals. SNVs in AGO2 (rs2293939 and rs4961280), DGCR8 (rs720012), DICER (rs3742330 and rs13078), DROSHA (rs10719 and rs6877842), GEMIN3 (rs197388 and rs197414), GEMIN4 (rs7813, rs2740349, and rs4968104), TNRC6B (rs9611280), and XP05 (rs11077 and rs34324334) were genotyped using TaqMan probes. The minor allele frequency of each SNV was compared to those reported in the 1,000 Genomes database using chi-squared. Sankey plot was created in the SankeyMATIC package to visualize the frequency range of each variant in the different countries analyzed. Results: In Mexican individuals, all 15 SNVs were found in Hardy-Weinberg equilibrium, with frequencies ranging from 0.04 to 0.45. The SNVs rs4961280, rs2740349, rs34324334, and rs720012 in Mexican individuals had the highest minor allele frequencies worldwide, whereas the minor allele frequencies of rs197388, rs10719, rs197414, and rs1107 were among the lowest in Mexican individuals. The variants had high allele heterogeneity among the sub-continental populations, ranging from monomorphic, as was the case for rs9611280 and rs34324334 in African groups, to >0.50, which was the case for variants rs11077 and rs10719 in most of the populations. Importantly, the variants rs197388, rs720012, and rs197414 had FST values > 0.18, indicating a directional selective process. Finally, the SNVs rs13078 and rs10719 significantly correlated with both latitude and longitude. Conclusion: These data indicate the presence of high allelic heterogeneity in the worldwide distribution of the frequency of SNVs located in components of the miRNA processing pathway, which could modify the genetic susceptibility associated with human diseases in populations with different ancestry.
Photophysics of Pt-porphyrin electrophosphorescent devices emitting in the near infrared

The triplet annihilation dynamics of near infrared organic light-emitting devices are studied with peak electrophosphorescence at a wavelength of 772nm using a platinum-porphyrin derivative Pt(II)-tetraphenyltetrabenzoporphyrin as dopant. Both the photoluminescent decay transients of the thin films and the quantum efficiency versus current density characteristics of devices using tris(8-hydroxyquinoline) aluminum or 4,4′-bis(N-carbazolyl)biphenyl (CBP) as hosts are fitted by a model based on triplet-triplet annihilation. When the phosphor is codoped with Ir(III) bis(2-phenyl quinolyl-N,C2′) acetylacetonate in CBP, the quantum efficiency is enhanced, and the observed decrease of efficiency at high current densities is explained by field-induced charge pair dissociation. The external quantum efficiency has a maximum of (8.5±0.3)%, decreasing to (5.0±0.3)% at 1mA∕cm2.
Severe convection in the Mediterranean from microwave observations and a convection‐permitting model

This study investigates severe convection in the Mediterranean during the first Special Observation Period (SOP‐1; 5 September to 6 November 2012) of the Hydrological Cycle in the Mediterranean Experiment (HyMeX) with the objectives of providing novel information about severe convection on its vertical structure, spatio‐temporal variability as well as evaluating the ability of a convection‐permitting model to reproduce this variability. Two criteria, namely deep convection (DC) and convective overshooting (COV), are computed using the water vapour channels of the Microwave Humidity Sounder (MHS). Special attention is paid to the COV as it is associated with particularly severe weather. For the first time, the COV criterion was assessed in the Mediterranean, using two case‐studies conjointly observed by the airborne RASTA radar and MHS. COV is characterised by high ice water content (up to 2 g m−3) in the mid and upper troposphere (up to 12.5 km in the stratosphere). During the SOP‐1, DC and COV occurred about 0.1 and 0.03% of the total observation time, respectively. The Atlantic weather regimes appear to affect the temporal distribution of these convective events. Most of the DC and COV occurrences were found along the western coasts of Italy and Greece, mainly during the 10–15 October and 25 October–3 November episodes. These two episodes, for which severe meteorological events (e.g. tornadoes) were reported, are significant when compared with the 2002–2013 climatology (above the 75th percentile). Both criteria are also employed to assess the current ability of the Meso‐NH model to forecast severe convection using a model‐to‐satellite approach. The forecast DC and COV are found to be highly correlated in time with the observations, but are strongly underestimated. This suggests that the model missed a significant part of the most intense convective events and their associated hazards, and underlines the need for better characterisation of model uncertainties associated with severe convection.
Effects of Terahertz Radiation on the Aggregation of Alzheimer’s Aβ42 Peptide

The pathophysiology of Alzheimer’s disease is thought to be directly linked to the abnormal aggregation of β-amyloid (Aβ) in the nervous system as a common neurodegenerative disease. Consequently, researchers in many areas are actively looking for factors that affect Aβ aggregation. Numerous investigations have demonstrated that, in addition to chemical induction of Aβ aggregation, electromagnetic radiation may also affect Aβ aggregation. Terahertz waves are an emerging form of non-ionizing radiation that has the potential to affect the secondary bonding networks of biological systems, which in turn could affect the course of biochemical reactions by altering the conformation of biological macromolecules. As the primary radiation target in this investigation, the in vitro modeled Aβ42 aggregation system was examined using fluorescence spectrophotometry, supplemented by cellular simulations and transmission electron microscopy, to see how it responded to 3.1 THz radiation in various aggregation phases. The results demonstrated that in the nucleation aggregation stage, 3.1 THz electromagnetic waves promote Aβ42 monomer aggregation and that this promoting effect gradually diminishes with the exacerbation of the degree of aggregation. However, by the stage of oligomer aggregation into the original fiber, 3.1 THz electromagnetic waves exhibited an inhibitory effect. This leads us to the conclusion that terahertz radiation has an impact on the stability of the Aβ42 secondary structure, which in turn affects how Aβ42 molecules are recognized during the aggregation process and causes a seemingly aberrant biochemical response. Molecular dynamics simulation was employed to support the theory based on the aforementioned experimental observations and inferences.
Direct numerical simulations of three-dimensional bubbly flows

Direct numerical simulations of the motion of many buoyant bubbles are presented. The Navier–Stokes equation is solved by a front tracking/finite difference method that allows a fully deformable interface. The evolution of 91 nearly spherical bubbles at a void fraction of 6% is followed as the bubbles rise over 100 bubble diameters. While the individual bubble velocities fluctuate, the average motion reaches a statistical steady state with a rise Reynolds number of about 25.
Effective electromagnetic parameters of left-handed coplanar waveguide transmission lines

This paper presents a kind of wideband, low-loss left handed transmission line (LHTL). It is composed of a parallel meander inductance and a serial cutting capacitor based on the coplanar waveguide (CPW) transmission line (TL). A better method of parameter extraction is formulated based on a more accurate Nicolson–Ross–Weir (NRW) approach. According to medium theory, the complex effective parameters of permittivity and permeability have been extracted from transmission and reflection scattering matrix. Through extracting the β parameter of the CPW TL, the characteristics of LHTL have been verified. The effective bandwidth is about 100% from 11 GHz to 21 GHz with low loss.
Bias Reduced Semidefinite Relaxation Method for 3-D Moving Object Localization Using AOA

This paper addresses the localization of a constant velocity moving object in 3-D using angle-of-arrival (AOA) measurements. Compared with the maximum-likelihood estimator, pseudo-linear approach for AOA localization has a large amount of bias resulting from the model transformation to simplify the solution finding. This work aims at reducing the bias and developing bias-reduced semidefinite relaxation (SDR) methods for estimating the initial position and velocity of the object, in a batch or sequential manner. This is accomplished by first formulating a bias reduced constrained weighted least squares (BR-CWLS) problem from the transformed measurements, through introducing an auxiliary variable and adding a quadratic constraint. Such an intractable non-convex problem is tackled next by applying the SDR technique and relaxing it into a convex semidefinite program (SDP), which is shown to be capable of reaching the solution of the original BR-CWLS problem. For sequential estimation, we formulate a different BR-CWLS problem and utilize SDR for obtaining a sequential estimation method that updates the initial position and velocity estimates at each time step. We conduct the mean square error (MSE) and bias analyses for both estimation methods to assess their expected performance. Simulation results verify the ability of bias reduction and the good performance of the proposed methods.
What explains the increase in trade union density and female share of union members in the United Kingdom in 2017–2020?

Trade union density increased for three consecutive years in the United Kingdom between 2017 and 2020. This contrasts with a general decline in union membership since 1979. Since union density continued to fall amongst male employees in 2017–2020, the overall increase was entirely attributable to females. This paper explores the factors which explain why there was an increase in overall union density after a period of decline (for males and females) and why union density evolved so differently for males and females. Using decomposition methodologies, we find that the increase in union density in 2017–2020 was not due to a higher propensity to unionise within particular groups or across all employees. Instead, the principal driver of the overall rise in 2017–2020 was an increase in the proportion of employment in certain public sector organisations. The largest contributor to the difference across males and females was increases in the share of employment in more unionised occupations amongst female employees and decreases amongst male employees.
Indoor Pollutant Classification Modeling using Relevant Sensors under Thermodynamic Conditions with Multilayer Perceptron Hyperparameter Tuning

—Air pollutants that are generated from indoor sources such as cigarettes, cleaning products, air fresheners, etc. impact human health. These sources are usually safe but exposure beyond the recommended standards could be hazardous to health. Due to this fact, people started to use technology to monitor indoor air quality (IAQ) but have no capability of recognizing pollutant sources. This research is an improvement in building a classification model for recognizing pollutant sources using the multilayer perceptron. The current research model receives four data parameters under warm & humid and cool & dry conditions compared to nine parameters of the previous literature in detecting five pollutant sources. The classification model was optimized using GridSearchCV to obtain the best combination of hyperparameters while giving the best-fit model accuracy, loss, and computational time. The tuned classification model gives an accuracy of 98.9% and a loss function value of 0.0986 under the number of epochs equal to 50. In comparison with the previous research, the accuracy was 100% with the number of epochs equal to 1000. Computational time was greatly reduced at the same time giving the best-fit accuracy and loss function values without incurring the problem of overfitting.
All Phase Biorthogonal Transform and Its Application in MPEG-4 Video Compression

In this paper, we propose an efficient algorithm for encoding of MPEG-4 simple profile. In the proposed algorithm, I frame encoding adopts all phase biorthogonal transform (APBT); for P frames, each frame puts the reconstructed frame of previous coded one as the reference frame to perform motion estimation (ME) and motion compensation (MC), and then the encoding of residual frame uses discrete cosine transform (DCT). While the proposed algorithm applying on MPEG-4, the objective effect and visual quality of reconstructed video sequence are approximate to the one based on DCT at the same bit rates. But the advantage is that the APBT transform coefficients can be quantized uniformly. Therefore the computing time is shorted and hardware implementation becomes easier. The proposed algorithm uses low computational complexity to make it applicable to encoding of MPEG-4 simple profile.
Nonlinear flood routing with multilinear models

The category of multilinear models is analyzed that embraces several approaches developed in apparently independent references. Multilinear modeling is a means of describing a nonlinear system with a set of linear models. This is performed by distributing the inflow to the system (river reach) into inflows to submodels and treating these inflows with a family of different linear operators. The models considered are nearly as simple as linear models and almost as accurate as nonlinear hydrodynamical models.
Response of dissolved organic carbon dynamics to salinity in a Constructed Fen Peatland in the Athabasca Oil Sands region

In northern Alberta, oil sands mining disturbs the boreal landscape, and reclamation to an ‘equivalent land capability’ is required. Industry is testing peatland construction as part of landscape reclamation. To determine if constructed peatlands can be self‐sustaining, an understanding of the cycling of solutes in pore water and their interactions with dissolved organic carbon (DOC) is needed since DOC can represent an important carbon loss from peatlands. DOC is of interest due to its biotic origin and use by the microbial community and impact on carbon budgets. Additionally, salinity as a control on DOC quantity and quality may be important in oil sands reclaimed systems due to the likelihood of elevated sodium (Na+) from saline groundwater input derived from tailings used to construct catchments, and natural sources. For this research, DOC concentration and quality, and Na+ concentration were measured in the rooting zone (10 and 30 cm depth) of Nikanotee Fen to evaluate the role of Na+ in DOC dynamics. DOC concentration and quality suggested that DOC in the fen was largely sourced from vegetation inputs, with quality also suggesting increases in vegetation inputs between years. Elevated Na+ at 30 cm below ground surface corresponded with high concentrations of labile DOC. At 10 cm below ground surface, sampling location and temperature were the best predictors of DOC concentration and quality. With expected increases in Na+, increased production of mobile and microbially active DOC may lead to higher rates of carbon export.
The Role of Patient-Derived Organoids in Triple-Negative Breast Cancer Drug Screening

Triple-negative breast cancer (TNBC) is one of the most aggressive breast cancer subtypes, with a grave prognosis and few effective treatment options. Organoids represent revolutionary three-dimensional cell culture models, derived from stem or differentiated cells and preserving the capacity to differentiate into the cell types of their tissue of origin. The current review aims at studying the potential of patient-derived TNBC organoids for drug sensitivity testing as well as highlighting the advantages of the organoid technology in terms of drug screening. In order to identify relevant studies, a literature review was conducted using the MEDLINE and LIVIVO databases. The search terms “organoid” and “triple-negative breast cancer” were employed, and we were able to identify 25 studies published between 2018 and 2022. The current manuscript represents the first comprehensive review of the literature focusing on the use of patient-derived organoids for drug sensitivity testing in TNBC. Patient-derived organoids are excellent in vitro study models capable of promoting personalized TNBC therapy by reflecting the treatment responses of the corresponding patients and exhibiting high predictive value in the context of patient survival evaluation.
ACTA: Autonomy and Coordination Task Assignment in Spatial Crowdsourcing Platforms

Spatial platforms have become increasingly important in people's daily lives. Task assignment is a critical problem in these platforms that matches real-time orders to suitable workers. Most studies only focus on independent platforms that are in a competitive relationship. Recently, an emerging service model was proposed, where orders are shared with multiple similar platforms. It aims to solve the imbalance between supply and demand through cooperation. However, it faces the following main challenges: 1) Coordinating independent platforms fairly based on the limited information; 2) Building a task assignment process with personalized algorithms. In this paper, we study real applications and define the Autonomy and Coordination Task Assignment problem (ACTA) to maximize the global revenue and fairness. We propose a framework to solve ACTA that consists of public order sending, local matching, global conflict adjustment and results notification. The framework uses mid-products and public data to train a revenue estimation model to coordinate participants. We further propose dynamic weight task assignment algorithms to guarantee fairness. Through the experiments, we prove that the platforms can obtain higher revenue, which shows the effectiveness and efficiency of our work.
Do Spinal Needle Sizes Affect the Development of Traumatic CSF in Neonatal LP Procedures?

Lumbar puncture (LP) is widely employed to evaluate infectious, neurological and metabolic diseases in the newborn. Neonatal LP is a difficult procedure with 45–54% success rates. Although there are studies examining traumatic LP failure, studies on the effects of needle sizes are limited. This study was intended to investigate the effect of needle sizes on LP traumatization. Term and premature babies who underwent LP in the neonatal intensive care unit between 30 November 2017 and 30 July 2019 were included in the study by retrospective file scanning. LP was performed by a pediatric or neonatal specialist using a 22 Gauge pen (G) or 25 G pen spinal needle in all cases, with all patients being placed in the lateral decubitus position. The primary outcome was to evaluate the effect of needle sizes used in LP on traumatization. The secondary outcome was to evaluate traumatization rates and complications. A statistically significant difference was determined in the rate of traumatized LP and desaturation development between needle sizes and CSF microscopic findings (p = 0.031, p = 0.005, and p = 0.006, respectively). The study data show that 25 G pen-tip spinal needles cause less traumatic LP in neonates than 22 G pen-tip spinal needles.
AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers’ physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%.