Miracle-dz/UniFAR-FTUs huggingface dataset:
Early childhood educators’ beliefs about mathematics education for children under three years of age

ABSTRACT Early childhood educators play a central role in fostering children’s mathematics learning; however, few studies have focused on educators’ beliefs about very young children’s capabilities in mathematics. Understanding these beliefs is critical for gaining insight into the mathematics education provided in birth to three settings. In light of this dearth of research, this article presents data from a study of Australian early childhood educators’ beliefs about mathematics education for children under three years of age. 506 educators responded to an online survey which generated both quantitative and qualitative data regarding educators’ beliefs. The findings indicate that this large sample of Australian educators seem to be positive and perceptive about mathematics. The educators demonstrate a good level of recognition of very early mathematical development, and the majority of respondents indicate a belief that children begin exploring a range of mathematical ideas before two years of age. The findings contribute to understandings of early childhood educators’ beliefs regarding mathematics education for children under three years of age and provide positive indications about the quality of mathematics education opportunities for very young children.
Compact Wide-Beam Circularly Polarized End-Fire Antenna Combining Ground Radiation Mode and Vertically Polarized Mode

A compact wide-beam endfire antenna with ground radiation mode and vertically polarized mode for circular polarization (CP) is presented in this article. The antenna is constructed based on a configuration of coupled-mode patch antenna (CMPA), which consists of two coupling cavities with a shorting wall, and can generate vertically polarized modes. The CP is realized through a pair of orthogonally polarized modes of horizontal and vertical polarizations. Meanwhile, rendering them acquires an equal amplitude and a 90° phase difference. The horizontally polarized radiation mode is realized through inserting a crossed ground slot. Offset probe feed is used to excite all the radiation modes and introduce an additional phase shift for the CP. A set of offset ground slots (OGSs) are then applied to perturb the electromagnetic coupling of the modes and adjust the phase and magnitude of the radiating electric fields (E-fields). Several effects are synthesized, which results in a wide-beam circularly polarized (CP) endfire radiation. Aiming to improve the radiation performances, a cavity-backed planar reflector was applied in the experiment. Simulated and measured results are detailed and analyzed, which demonstrate that the measured 3 dB axial ratio (AR < 3 dB) bandwidth (ARBW) ranges from 3.385 to 3.428 GHz (1.2%), and the peak gain can be up to 3.4 dBi if the planar reflector is added. The measured half-power beamwidth (HPBW) and 3 dB ARBW can be up to 88°/114° and 53°/112° at 3.4 GHz, respectively. The antenna size (excluding the reflector) is maintained at $0.49\lambda _{0}\times 0.25\lambda _{0}\times 0.017\lambda _{0}$ , where $\lambda _{0}$ is the wavelength in free space, performing a compact configuration.
IoT-Based COVID-19 Diagnosing and Monitoring Systems: A Survey

To date, the novel Coronavirus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning (ML) algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent IoT-based COVID-19 diagnosing, and monitoring systems have been proposed to tackle the pandemic. In this article we have analyzed a wide range of IoTs technologies which can be used in diagnosing and monitoring the infected individuals and hotspot areas. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.
Clinical Evaluation of Digital Therapeutics: Present and Future

Objectives Digital therapeutics (DTx) are software-based therapeutic interventions based on clinical evidence. Randomized clinical trials (RCTs) are often the source of clinical evidence, similar to conventional drugs or medical devices. However, novel approaches such as the use of real-world data or digital biomarkers are also utilized. This article aimed to review how DTx products have been clinically evaluated. Methods DTx products approved by the US Food and Drug Administration as of 2020 were reviewed and products with sufficient published information were selected. Pivotal clinical trials were analyzed according to the elements of the Consolidated Standards of Reporting Trials (CONSORT) guideline. Case reviews were presented for other clinical evaluation strategies, considering the small number of publications. Results Most approved DTx products used RCTs for clinical evaluations. Similar to conventional RCTs, parallel-group designs with statistical hypothesis testing were adopted. However, DTx trials were often not blinded due to practical issues and involved various comparator groups. In addition, DTx products could be readily evaluated in home-based settings and delivered through the internet. Other evaluation approaches included retrospective analyses using insurance claims data or usage data, which enabled long-term evaluations of effectiveness. Digital biomarkers obtained from real-time and continuous log data were also used to improve the objectiveness of endpoints. Conclusions RCTs accounted for the majority of DTx evaluations. The designs of DTx trials were comparable to those of drug or device trials, but blinding and comparator elements were often different. Furthermore, the use of real-world data and digital biomarkers are also being tried.
Electrically tunable spin polarization of chiral edge modes in a quantum anomalous Hall insulator

In the quantum anomalous Hall effect, chiral edge modes are expected to conduct spin polarized current without dissipation and thus hold great promise for future electronics and spintronics with low energy consumption. However, spin polarization of chiral edge modes has never been established in experiments. In this work, we theoretically study spin polarization of chiral edge modes in the quantum anomalous Hall effect, based on both the effective model and more realistic tight-binding model constructed from the first principles calculations. We find that spin polarization can be manipulated by tuning either a local gate voltage or the Fermi energy. We also propose to extract spin information of chiral edge modes by contacting the quantum anomalous Hall insulator to a ferromagnetic (FM) lead. The establishment of spin polarization of chiral edge modes, as well as the manipulation and detection in a fully electrical manner, will pave the way to the applications of the quantum anomalous Hall effect in spintronics.
Overview of Wind Parameters Sensing Methods and Framework of a Novel MCSPV Recombination Sensing Method for Wind Turbines

The paper presents an overview of the traditional methods to obtain wind parameters such as wind speed, wind direction and air density. After analyzing wind turbines’ arrangements and communication characteristics and the correlation of operation data between wind turbines, the paper proposes a novel recombination-sensing method route of “measuring–correlating–sharing–predicting–verifying” (MCSPV) and explores its feasibility. The analysis undertaken in the paper shows that the wind speed and wind direction instrument fixed on the wind turbine nacelle is simple and economical. However, it performs in-process measurement, which restricts the control optimization of wind turbines. The light detection and ranging (LIDAR) technology which is accurate and fast, ensures an early and super short-time sensing of wind speed and wind direction but it is costly. The wind parameter predictive perception method can predict wind speed and wind power at multiple time scales statistically, but it has limited significance for the control of the action of wind turbines. None of the traditional wind parameter-sensing methods have ever succeeded in air density sensing. The MCSPV recombination sensing method is feasible, both theoretically and in engineering, for realizing the efficient and accurate sensing and obtaining of such parameters as wind speed, wind direction and air density aimed at the control of wind turbines.
Behavior-Based Anomaly Detection in Log Data of Physical Access Control Systems

Behavior-based anomaly detection (AD) approaches for enterprise-IT security are not easily applicable to other domains, such as embedded devices and IoT nodes in cyber-physical systems. AD approaches are usually highly optimized for specific purposes, tightly bound to domain-specific technologies and rely on a specific syntax of investigated data. Data from cyber-physical systems is however highly diverse, often poorly documented and not easily ingested for automated analysis. AECID provides an anomaly detection approach, that monitors unstructured textual event data (i.e., log data), and implements self-learning for autonomous operation. A parser generator establishes a model of normal system behavior on top of observed events, which then can be leveraged to detect anomalies as deviations from that baseline. The unsupervised anomaly detection approaches of AECID apply machine learning techniques to perform sequence analysis, correlation analysis and statistical tests of events represented in log data. This paper discusses AECID's applicability in a building security system use case. A proof of concept demonstrates the effective detection of anomalies in log data of a building access control system stemming from card misuse, including stolen access cards and cloned cards.
A Dynamic Model for Prediction of Hysteresis and Loss Property of Soft Material Under Multiple Harmonic Magnetizations

This article presents an improved algorithm for identification of the excess loss statistical parameter to get more accurate prediction of multiple harmonic magnetic hysteresis and loss properties of grain-oriented silicon steel sheets. Magnetic hysteresis characteristics of a specimen are measured on a magnetic measurement platform. Static hysteresis property can be simulated accurately by the Preisach model. The excess loss field expression under multiple harmonic magnetizations is constructed based on an extraction algorithm for the statistical parameters of excess loss. According to the equivalence of loss separation and field separation, a new dynamic model is established. In the case of different multiple harmonic magnetizations, the magnetic hysteresis characteristics obtained by predictions are compared with the test results. Also, the influences of peaked flux density, harmonic order, and harmonic content upon the total iron loss are investigated.
Binge-Watching and Mental Health Problems: A Systematic Review and Meta-Analysis

Background: Binge-watching, the viewing of online videos or streamed content, may be associated with different types of mental health problems. The present study aimed to investigate the associations between binge-watching and five mental health concerns including depression, loneliness, sleep problems, anxiety, and stress. Methods: Academic databases of PubMed, Scopus, Web of Science, ProQuest, PsycINFO, and Psych Articles were systematically searched through February of 2022. The Newcastle–Ottawa Scale was used to assess the methodological quality. A meta-analysis was performed on Fisher’s z values as effect sizes, using a random effect model. Publication bias, small study effect, and moderators in this association were assessed. Results: Binge-watching was significantly associated with the five types of mental health concerns with the most robust correlations found with stress (0.32) and anxiety (0.25). Stronger associations between binge-watching and two types of mental health problems (depression and sleep problems) were found during the COVID-19 pandemic than before the pandemic. Moreover, stronger associations between binge-watching and two types of mental health problems (stress and sleep problems) were found in developing countries than in developed countries. Conclusions: The associations between binge-watching and mental health concerns were significant and positive. Programs and interventions to reduce binge-watching should be considered and tested.
Biocontrol of Aflatoxin through Biodegradation
by Using Environment Friendly Microbes

Aflatoxins are secondary metabolites of fungal species usually produced in tough environmental conditions. The interruption of fatty acid formation results in aflatoxins production. It is equally important in food and feed due to its health complication for animals and human beings. Biodegradation through environment friendly microbes is an acceptable strategy for the control of mycotoxins. A study has been designed to evaluate the environment friendly bacterial species for the biodegradation of aflatoxins. Specific double strength broth media (BHI, TSB, MRS and SDB) were mixed with standard Aflatoxins B1 to obtained 30 ppb final concertation. Different microbial species ( Rhodococcus erythropolis , Bacillus licheniformis , Lactobacillus Pentosus , Lactobacillus casei , Aspergillus niger and Saccharomyces crevice ) were added to the mixture containing aflatoxins and incubated for 48-72 h. The concentration of the aflatoxins in the mixture was confirmed with Agra strip, Agra quant and HPLC at initial and final time of incubation. Results showed that Rhodococcus erythropolis and Bacillus licheniformis can degrade or alter the structure of aflatoxins to the undetectable level of the Agra strip (cutoff value less than 4 ppb) and Agra quant (cutoff value less than 2 ppb), these results were double confirmed with the of HPLC, which showed the reduction of aflatoxins to 0.57 and 0.95 ppb by Rhodococcus erythropolis and Bacillus licheniformis respectively. These results conforming the biodegradation of aflatoxins B1 used in the study. The organisms used in the study are environment friendly and can have the potential to decrease aflatoxins to the acceptable consumption level. Uses of these microbes for biodegradation will have positive impacts over the biocontrol strategies for mycotoxin.
A demonstrator for a flexible active microelectrode array with high electrode number

Abstract The integration of dies is a possibility to reduce the number of conducting tracks within electrical active implants. For passive microelectrode arrays the number of conducting tracks limits the number of electrodes. By embedding an array of small dies (250 μm edge length) employed to amplify and multiplex the signals of 25 electrodes each into a flexible foil we create a flexible active microelectrode array with more than 1000 electrodes. A fabrication process was developed containing a transfer process for the dies as well as an embedding procedure. Here a non-functional Dummy-System is presented as a demonstrator proving the feasibility of the proposed microelectrode array.
Usando Criptografia de Limiar para Tolerar Clientes Maliciosos em Memória Compartilhada Dinâmica

Sistemas de quóruns Bizantinos são ferramentas usadas na implementação consistente e confiável de sistemas de armazenamento de dados em presença de falhas arbitrárias. Vários protocolos para implementação destes sistemas foram propostos para ambientes estáticos e, mais recentemente, também surgiram propostas de protocolos para ambientes dinâmicos. Um dos desafios na implementação desses sistemas em ambientes dinâmicos está na reconfiguração do conjunto de servidores devido a entradas e saídas arbitrárias de processos. Este trabalho vai além e apresenta protocolos que toleram a presença de clientes maliciosos em sistemas de quóruns Bizantinos dinâmicos, através do emprego de um mecanismo de criptografia de limiar que fornece a flexibilidade suficiente para operação nestes ambientes. Este mecanismo é utilizado para controlar as ações que clientes maliciosos podem executar contra o sistema. Além disso, todos os protocolos apresentados são para sistemas assíncronos, não necessitando de nenhuma premissa temporal sobre o comportamento do sistema.
Silicon Microring Modulator for High SFDR Analog Links in Monolithic 45nm CMOS

We characterize the linearity of a lateral junction microring modulator in a monolithic 45 nm CMOS platform versus modulator bias, optical wavelength, and input power, and achieve peak third-order SFDR of 96.4dB · ${\text{H}}{{\text{z}}^{\frac{2}{3}}}$ at 0dBm laser power.
The Journey of Human Transthyretin: Synthesis, Structure Stability, and Catabolism

Transthyretin (TTR) is a homotetrameric protein mainly synthesised by the liver and the choroid plexus whose function is to carry the thyroid hormone thyroxine and the retinol-binding protein bound to retinol in plasma and cerebrospinal fluid. When the stability of the tetrameric structure is lost, it breaks down, paving the way for the aggregation of TTR monomers into insoluble fibrils leading to transthyretin (ATTR) amyloidosis, a progressive disorder mainly affecting the heart and nervous system. Several TTR gene mutations have been characterised as destabilisers of TTR structure and are associated with hereditary forms of ATTR amyloidosis. The reason why also the wild-type TTR is intrinsically amyloidogenic in some subjects is largely unknown. The aim of the review is to give an overview of the TTR biological life cycle which is largely unknown. For this purpose, the current knowledge on TTR physiological metabolism, from its synthesis to its catabolism, is described. Furthermore, a large section of the review is dedicated to examining in depth the role of mutations and physiological ligands on the stability of TTR tetramers.
Aloe-Emodin Suppresses Oxidative Stress and Inflammation via a PI3K-Dependent Mechanism in a Murine Model of Sepsis

Background This study was designed to assess the impact of aloe-emodin (AE) on oxidative stress and inflammation in a murine model of LPS-induced sepsis. In addition, the mechanistic basis for anti-inflammatory and antioxidant activity was assessed. Methods Male ICR mice received an intraperitoneal injection of LPS (10 mg/kg), and the preventive properties of AE (80 or 150 mg/kg) on these mice were assessed by monitoring spleen index, and levels of inflammatory and oxidative stress-related factors. Peripheral blood TNF-α and IL-6 levels were assessed via ELISA kits, while changes in hepatic SOD and GSH-Px levels were assessed using appropriate biochemical kits. Splenic PI3K, AKT, and mTOR levels were assessed via qPCR and western blotting. Results Relative to animals in the LPS model group, those in the AE treatment groups exhibited reduced spleen index, decreased inflammatory cytokine levels, and improved SOD and GSH-Px activity in liver tissues. Splenic PI3K, Akt, and mTOR levels were also reduced in response to AE treatment. Conclusions These findings indicated that AE can alleviate sepsis-related tissue damage, inflammation, and oxidative stress, at least in part by suppressing the PI3K/Akt/mTOR signaling pathway. These results offer a clinical basis for the use of AE to treat sepsis and associated diseases.
Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators?

This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories of technical analysis: Price, Volume, and Turnover. Various error metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root-Mean-Squared-Error (RMSE) have been used to check the performance of the models. Results show that the XGBoost algorithm performs best among the four ensemble models. The mean of absolute error and the root-mean-square -error vary around 3–5%. The feature importance plots generated by the models depict the importance of the variables in predicting the output. The proposed machine learning models help traders, investors, as well as portfolio managers, better predict the stock market trends and, in turn, the returns, particularly in banking stocks minimizing their sole dependency on macroeconomic factors. The techniques further assist the market participants in pre-empting any price-volume action across stocks irrespective of their size, liquidity, or past turnover. Finally, the techniques are incredibly robust and display a strong capability in predicting trend forecasts, particularly with any large deviations.
Total Pain and Illness Acceptance in Pelvic Cancer Patients: Exploring Self-Efficacy and Stress in a Moderated Mediation Model

Cancer patients experience pain not only in its physical dimension, but also in a broader context that includes psychological, social, and spiritual aspects due to a higher level of anxiety and stress. The present prospective, longitudinal study examined the relationship between total pain and illness acceptance among pelvic cancer patients, taking into consideration the moderated mediation effects of self-efficacy and stress. The study involved a sample of pelvic cancer patients receiving radiotherapy treatment. Assessments were completed at T1 (before radiotherapy), T2 (after 3–4 weeks), and T3 (after radiotherapy) to assess the psychosocial dynamics of illness acceptance (N = 267). The more physical, psychological, social, and spiritual pain symptoms the patients experienced, the less they accepted negative health conditions and the effects of their illness. Stress moderated the indirect effect between total pain dimensions and illness acceptance through self-efficacy, but it did not moderate the relationship between total pain and illness acceptance. The relationships between total pain dimensions and illness acceptance thus depend on both the mediating effect of self-efficacy and the moderating effect of stress. This highlights the need to control one’s motivation and behavior and manage emotional strain or tension.