Miracle-dz/UniFAR-FTUs huggingface dataset:
Late B-type stars: rotation and the incidence of HgMn stars.

High-dispersion spectrograms are used to derive the distribution of rotational velocities for a sample of 256 late B-type stars. Approximately half the stars have sharp lines (upsilon sini<100 km s/sup -1/), and a statistical treatment of the data shows that the distribution of true rotational velocities psi (upsilon) reaches a maximum in the velocity interval 300.1 is marginally higher among the HgMn stars (31%) than among normal B-type stars (19%) in the same temperature range, probably because slow rotation, which is characteristic of such binaries, favors the appearance of HgMn anomalies. However, the majority of HgMn stars are single, and their slow rotation cannot be attributed to tidal interactions. The HgMn stars are single, and their slow rotation cannot be attributed to tidal interactions. The HgMn stars are found throughout the main-sequence band from the zero- to the terminal age main sequence. The present observations show that HgMn stars are not distinguished from normal ones by their age, duplicity, rotation, or any combination of these characteristics.
Brain-Computer Interface of Emotion and Motor Imagery Using 2D Convolutional Neural Network — Recurrent Neural Network

Brain-Computer Interface (BCI) allows human communication without gestures or muscle movements with external devices. BCI works by translating commands from the brain into actions through intermediate devices such as Electroencephalogram (EEG). Usually, EEG captures when imagining movement, which is called motor imagery. Some BCIs consider other variables of the EEG signal to execute commands, such as emotion or attention. Each variable has different characteristics and is recorded from different channels, so it is necessary to use multi networks. The EEG signal comes from multiple channels. Therefore, it needs to be connected between channels besides time series. This study proposed the identification of BCI using multiple networks. Each network is identified using a two-dimension Convolutional Neural Network (2D CNN) - Recurrent Neural Network (RNN). The EEG signal is pre-filtered using a Wavelet containing Motor Imagery and two emotion states: happy and neutral. The results showed that using multi networks increased the accuracy by 93.55% compared to 83.87% for using a single network. The difference in accuracy is more significant than the use of 2DCNN-RNN, which increased by 3% against RNN alone and only 1.5% against 1DCNN-RNN. Experiments show that the LSTM memory performed almost the same as the GRU. The experiment also tested the learning parameters, which showed a small learning rate of 0.000350 providing high accuracy and short transient time.
The Dynamics of Coastally Trapped Mesoscale Ridges in the Lower Atmosphere

Abstract The dynamics of coastally trapped ridges that propagate in the marine layers of western North America and southeastern Australia is examined. A nonlinear semigeostrophic theory shows that the coastal ridges develop initially as an alongshore intrusion of denser marine air that is driven by the synoptic-scale pressure gradient. Nonlinear Kelvin waves evolve with the intruding flow on a slower time scale governed by the dynamic parameters. If dispersive effects balance the nonlinearities, then these waves evolve into solitary form. Otherwise, the nonlinear waves steepen so that the leading edge of the ridge eventually propagates as a shock.The theory is applied to two ridging events in California and one in southeastern Australia. In each case, good agreement is found between theory and observations of the evolution times and propagation speeds of the coastal ridges. The theory also explains the observed behavior of the events at prominent convex bends and gaps in the coastal topography.
Planetary health and sustainability teaching in UK medical education: A review of medical school curricula

Abstract Background The doctors of the future need to be empowered to deliver healthcare sustainably while protecting their patients’ health in the context of a degrading environment. This study aimed to objectively review the extent and nature of the teaching of planetary health and sustainability topics in UK medical education. Methods A multi-centre national review of the timetabled teaching sessions in medical courses in the UK during the academic year 2020/2021 against the General Medical Council’s adopted ‘Educating for Sustainable Healthcare – Priority Learning Outcomes’. Medical students were recruited and reviewed the entirety of their own institution’s online teaching materials associated with core teaching sessions using a standardised data collection tool. Learning outcome coverage and estimated teaching time were calculated and used to rank participating medical schools. Results 45% of eligible UK medical schools were included in the study. The extent of teaching varied considerably amongst courses. Mean coverage of the 13 learning outcomes was 9.9 (SD:2.5) with a mean estimated teaching time of 140 min (SD:139). Courses with dedicated planetary health and sustainability sessions ranked best. Conclusion There is large disparity in the education that medical students receive on these topics. Teaching may not adequately prioritise sustainability or reflect advances in planetary health knowledge. Practice points Medical education on planetary health and sustainability topics varies widely amongst UK medical schools. UK medical education does not necessarily reflect recent advances in planetary health knowledge. Greater educational focus is required on sustainability in healthcare. Centrally mandated teaching on these topics may improve disparity in education. This study’s methodology provides a possible approach for future curriculum evaluations.
A colour harmony model for two-colour combinations

This study investigates harmony in two-colour combinations in order to develop a quantitative model. A total of 1431 colour pairs were used as stimuli in a psychophysical experiment for the visual assessment of harmony. These colour pairs were generated using 54 colours selected systematically from CIELAB colour space. During the experiment, observers were presented with colour pairs displayed individually against a medium gray background on a cathode ray tube monitor in a darkened room. Colour harmony was assessed for each colour pair using a 10-category scale ranging from “extremely harmonious” to “extremely disharmonious. ” The experimental results showed a general pattern of two-colour harmony, from which a quantitative model was developed and principles for creating harmony were derived. This model was tested using an independent psychophysical data set and the results showed satisfactory performance for model prediction. The study also discusses critical issues including the definition of colour harmony, the relationship between harmony and pleasantness, and the relationship between harmony and order in colour. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 191–204, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20208
Comparative Analysis of Machine Learning Algorithms for Disease Detection in Apple Leaves

Leaves serve as unique indicators to distinguish the diseased leaves because the image information of the leaf changes when it is suffering from some disease. To detect these diseases, we need to recognize the patterns formed by the diseases in the leaves. Generally, plants are observed with a naked eye by either experts or farmers to detect and identify the plants. But this method can be expensive and time processing; therefore, it is essential to automate crop disease diagnosis in regions with few experts. This work revolves around an approach to developing a plant disease detection model based on apple leaves. The proposed methodology uses the following three feature extraction techniques: Hu Moments, Haralick Texture, and Color Histogram. The research work provides a comparative analysis of machine learning models for detecting diseases in apple leaves, namely: Black Rot, Cedar Apple Rust, and Apple Scab. The model is evaluated on a subset of the “Plant Village Dataset” dealing with apple leaves. Out of all the machine learning models fitted, Random Forest has obtained the highest test accuracy of 98.125 percent.
4. Comprehensive Medical Evaluation and Assessment of Comorbidities: Standards of Care in Diabetes-2023.

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes. org/SOC.
Improvement of Visible Photodetection of Chemical Vapor Deposition-Grown MoS2 Devices via Graphene/Au Contacts

Two-dimensional (2D) molybdenum disulfide (MoS2) is a promising material for constructing high-performance visible photosensor arrays because of its high mobility and scale-up process. These distinct properties enable the construction of practical optoelectrical sensor arrays. However, contact engineering for MoS2 films is not still optimized. In this work, we inserted a graphene interlayer between the MoS2 films and Au contacts (graphene/Au) via the wet-transfer method to boost the device performance. Using graphene/Au contacts, outstanding electrical properties, namely field-effect mobility of 12.06 cm2/V∙s, on/off current ratio of 1.0 × 107, and responsivity of 610 A/W under illumination at 640 nm, were achieved. These favorable results were from the Fermi-level depinning effect induced by the graphene interlayer. Our results may help to construct large-area photonic sensor arrays based on 2D materials.
Chondrocyte De-Differentiation: Biophysical Cues to Nuclear Alterations

Autologous chondrocyte implantation (ACI) is a cell therapy to repair cartilage defects. In ACI a biopsy is taken from a non-load bearing area of the knee and expanded in-vitro. The expansion process provides the benefit of generating a large number of cells required for implantation; however, during the expansion these cells de-differentiate and lose their chondrocyte phenotype. In this review we focus on examining the de-differentiation phenotype from a mechanobiology and biophysical perspective, highlighting some of the nuclear mechanics and chromatin changes in chondrocytes seen during the expansion process and how this relates to the gene expression profile. We propose that manipulating chondrocyte nuclear architecture and chromatin organization will highlight mechanisms that will help to preserve the chondrocyte phenotype.
Multi-Target Parameter Estimation of the FMCW-MIMO Radar Based on the Pseudo-Noise Resampling Method

Subspace methods are widely used in FMCW-MIMO radars for target parameter estimations. However, the performances of the existing algorithms degrade rapidly in non-ideal situations. For example, a small number of snapshots may result in the distortion of the covariance matrix estimation and a low signal-to-noise ratio (SNR) can lead to subspace leakage problems, which affects the parameter estimation accuracy. In this paper, a joint DOA–range estimation algorithm is proposed to solve the above issues. Firstly, the improved unitary root-MUSIC algorithm is applied to reduce the influence of non-ideal terms in building the covariance matrix. Subsequently, the least squares method is employed to process the data and obtain paired range estimation. However, in a small number of snapshots and low SNR scenarios, even if the impact of non-ideal terms is reduced, there will still be cases where the estimators sometimes deviate from the true target. The estimators that deviate greatly from targets are regarded as outliers. Therefore, threshold detection is applied to determine whether outliers exist. After that, a pseudo-noise resampling (PR) technology is proposed to form a new data observation matrix, which further alleviates the error of the estimators. The proposed method overcomes performance degradation in a small number of snapshots or low SNRs simultaneously. Theoretical analyses and simulation results demonstrate the effectiveness and superiority.
RF system for electron and positron orbit separation at VEPP-4M

The method for RF orbit separation of electron and positron beams in the VEPP-4M collider is developed and demonstrated. This system is designed to replace the conventional electrostatic orbit separator in the prospective CPT test experiment based on comparison of the spin precession frequencies of electron and positron. The electrostatic separator may yield an unacceptable systematic error of 10−6 in the experiment against the required one of better than 10−8 which can be provided with the RF system.
Identification of an Individualized Prognostic Biomarker for Serous Ovarian Cancer: A Qualitative Model

Serous ovarian cancer is the most common type of ovarian epithelial cancer and usually has a poor prognosis. The objective of this study was to construct an individualized prognostic model for predicting overall survival in serous ovarian cancer. Based on the relative expression orderings (Ea > Eb/Ea ≤ Eb) of gene pairs closely associated with serous ovarian prognosis, we tried constructing a potential individualized qualitative biomarker by the greedy algorithm and evaluated the performance in independent validation datasets. We constructed a prognostic biomarker consisting of 20 gene pairs (SOV-P20). The overall survival between high- and low-risk groups stratified by SOV-P20 was statistically significantly different in the training and independent validation datasets from other platforms (p < 0.05, Wilcoxon test). The average area under the curve (AUC) values of the training and three validation datasets were 0.756, 0.590, 0.630, and 0.680, respectively. The distribution of most immune cells between high- and low-risk groups was quite different (p < 0.001, Wilcoxon test). The low-risk patients tended to show significantly better tumor response to chemotherapy than the high-risk patients (p < 0.05, Fisher’s exact test). SOV-P20 achieved the highest mean index of concordance (C-index) (0.624) compared with the other seven existing prognostic signatures (ranging from 0.511 to 0.619). SOV-P20 is a promising prognostic biomarker for serous ovarian cancer, which will be applicable for clinical predictive risk assessment.
Localization of Ultra Wide-Band Enabled Devices

Localization is the technique used to determine precise location of one object with respect to another object. Effective and efficient indoor localization solution will enable advancement in the fields of Automotive, Communication Infrastructure, Industrial, Mobile, Smart City and Smart Home which will all aide in the easy way of leading human life. Numerous innovations in techniques and technologies have happened in this direction but to achieve precise and reliable solution for the indoor localization problem is still a challenge. Ultra Wide-Band (UWB) is one such wireless technology that makes use of pulse radio signals to estimate accurate distance and angle between the involved entities. UWB is a pulse radio technology that follows the IEEE 802.15. 4 that is Low-Rate Wireless Personal Area Network (LR-WPAN) standard. It can determine the relative distance and angle of an object in line of sight up till 200mtrs using the Time of Flight (TOF) technique. Apart from using the short radio pulses for establishing wireless connectivity UWB is also advantageous because of its low power spectral density, high operational range, use of wide channels for data transmission and high data transmission rates. UWB also supports secure transmission of data by using the concept of secure sessions defined by FiRa Consortium (Fine Ranging Consortium) applets. All these reasons make UWB a reliable, effective, and secure solution for the problem of indoor localization.
Multi-Class Support Vector Machine for Arabica Coffee Bean Roasting Grade Classification

The presence of computer technology based on vision machines has resolved numerous issues in various industries, most notably agriculture-related to coffee plants' development. Before being consumed, coffee beans must pass a roasting process to achieve a desirable flavor. There are three roasting levels: light, medium, and dark. This work proposed a method for classifying Arabica coffee bean roasting grades. Two primary procedures necessitated feature extraction and classification. Following the extraction of statistical data, classification was performed using the support vector machine (SVM) method. Four kernels of linear, polynomial, radial basis, and sigmoid were used in this classifier. Cross-validation was used to evaluate the proposed method's performance with k-fold values of 3, 5, and 10. The evaluation result indicates that the polynomial kernel implementation obtained a maximum accuracy of 100% using k-fold values of 5 and 10.
Oral edaravone demonstrated a favorable safety profile in patients with amyotrophic lateral sclerosis after 48 weeks of treatment

An intravenous (IV) formulation of edaravone has been shown to slow the rate of physical functional decline in amyotrophic lateral sclerosis (ALS). An oral suspension formulation of edaravone was recently approved by the United States Food and Drug Administration for use in patients with ALS. This study assessed the safety and tolerability of oral edaravone.
The Effect of Insulin Administration on Medication Adherence in Type 2 Diabetes Mellitus Patients with Neurological Complications

Background: Medication adherence is essential to achieving controlled blood sugar in diabetic patients. Insulin generally provides better glycemic control but is considered painful and requires special techniques. Insulin administration in patients with neurological complications requires particular consideration because these complications can cause physical and cognitive barriers. Objective: This study analyses the effect of insulin administration on medication adherence in diabetic patients with neurological complications and the influence of various confounding variables (baseline characteristics, medical and medication history). Methods: This observational study was conducted with a cross-sectional design at a government hospital in East Jakarta from September 2021 to January 2022. The sample was type 2 diabetes mellitus patients with neurological complications who received antidiabetics for at least six months. The neurological complications include central nervous disorders (such as stroke) and peripheral nervous disorders (such as neuropathy). The independent variable was insulin administration, while the dependent variable was adherence, measured using subjective methods [Adherence to Refills and Medications Scale (ARMS)] and objective methods (Medication Refill Adherence (MRA) and HbA1c]. Results: Of 175 respondents, based on the three methods (MRA, ARMS, HbA1c), 13 respondents (7.4%) were adherent, namely one respondent (1.8%) in the insulin group and 12 respondents (10.1%) in the non-insulin group. Insulin administration affects adherence to antidiabetics by 0.123 times (95% CI: 0.015 - 1.024), or patients who use insulin have 87.7% lower adherence controlled by antidiabetic changes and the total number of medicines used. Conclusion: Insulin administration significantly affects medication adherence in diabetes mellitus patients with neurological complications.
Willingness to Pay (WTP) for COVID-19 Vaccine Booster Dose and Its Determinants in Indonesia

Willingness to pay (WTP) for booster doses of coronavirus disease 2019 (COVID-19) vaccines is an under studied research topic. Therefore, the current study aimed to investigate the WTP for the booster doses of COVID-19 vaccines and its predictors in Indonesia using an online survey distributed all over the provinces of this low-middle-income country. The WTP was evaluated using a basic dichotomous contingent valuation approach, and its associated determinants were evaluated using a linear regression model. Out of 2935 responders, 66.2% (1942/2935) were willing to pay for a booster dose of the COVID-19 vaccine. The majority of respondents (63.5%) were willing to pay within a price range of 100,000–500,000 Indonesian rupiah (IDR), i.e., USD 6.71–33.57. Being older than 40 years, having a higher educational level, having a higher income, knowing and understanding that booster doses were important, and having a vaccine status that is certified halal (permissible in Islamic law), were all associated with a higher WTP for the booster dose of COVID-19 vaccines. The study findings imply that the WTP for a booster dose of COVID-19 vaccination in Indonesia is lower compared to acceptance of vaccines provided free of charge. This WTP data can be utilized to develop a pricing scheme for the booster doses of COVID-19 vaccination in the country with potential benefits in other low-income countries. The government may be required to provide subsidies for the herd immunity vaccination process to proceed as anticipated. Furthermore, the public community must be educated on the importance of vaccination as well as the fact that the COVID-19 epidemic is far from being over.
Supercritical self-interference fluorescence microscopy for full-field membrane imaging

We present a new technique based on the self-interference of Supercritical Angle Fluorescence (SAF) emission in order to perform full-field cell membrane imaging. We show that our Point Spread Function (PSF) engineering technique allows us to obtain a 100 nm axial sectioning while conserving the original lateral resolution of the microscope. The images are acquired using an optical module that can be connected to any fluorescent microscope to simultaneously monitor in real time both the cell membrane and in-depth phenomena.
Analog/Mixed-Signal Classification for Voice Activity Detection

This paper presents a novel analog/mixed-signal classifier for voice activity detection (VAD) in 65-nm CMOS technology. We propose several new circuit blocks, such as a current copier based mixed-signal multiplier, a scalable current mode max-pooling circuit, and an analog rectified linear unit (ReLU) circuit, which we used to implement a convolutional neural network (CNN) based machine learning (ML) model. The weights of this model were computed via offline training using open source datasets. The trained VAD was tested on multiple audio files by carrying out system-level simulations in MATLAB and transistor-level simulations in Cadence. Both MATLAB and Cadence simulations produce identical outputs, and we observe a classification accuracy of 90–95%.