Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Poor Outcomes Among Individuals Seeking Care for Major Depressive Disorder

Objective To develop and validate algorithms to identify individuals with major depressive disorder (MDD) at elevated risk for suicidality or for an acute care event. Methods We conducted a retrospective cohort analysis among adults with MDD diagnosed between January 1, 2018 and February 28, 2019. Generalized estimating equation models were developed to predict emergency department (ED) visit, inpatient hospitalization, acute care visit (ED or inpatient), partial‐day hospitalization, and suicidality in the year following diagnosis. Outcomes (per 1000 patients per month, PkPPM) were categorized as all‐cause, psychiatric, or MDD‐specific and combined into composite measures. Predictors included demographics, medical and pharmacy utilization, social determinants of health, and comorbid diagnoses as well as features indicative of clinically relevant changes in psychiatric health. Models were trained on data from 1.7M individuals, with sensitivity, positive predictive value, and area‐under‐the‐curve (AUC) derived from a validation dataset of 0.7M. Results Event rates were 124.0 PkPPM (any outcome), 21.2 PkPPM (psychiatric utilization), and 7.6 PkPPM (suicidality). Among the composite models, the model predicting suicidality had the highest AUC (0.916) followed by any psychiatric acute care visit (0.891) and all‐cause ED visit (0.790). Event‐specific models all achieved an AUC >0.87, with the highest AUC noted for partial‐day hospitalization (AUC = 0.938). Select predictors of all three outcomes included younger age, Medicaid insurance, past psychiatric ED visits, past suicidal ideation, and alcohol use disorder diagnoses, among others. Conclusions Analytical models derived from clinically‐relevant features identify individuals with MDD at risk for poor outcomes and can be a practical tool for health care organizations to divert high‐risk populations into comprehensive care models.
A distribution‐matching method for parameter estimation and model selection in computational biology

The determination of the model parameters is a central challenge in computational modeling of biological systems. Typically, only a fraction of the parameters (such as kinetic rate constants) is experimentally measured, whereas the rest is fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The data, which can come from real‐time polymerase chain reaction (PCR), enzymatic reactions, flow cytometry, etc., tend to be very noisy, but the statistics of the noise can often be inferred by performing calibration procedures. In this paper, we show how the knowledge of the properties of the noise, expressed in terms of its cumulative distribution function, can be used to validate or invalidate the estimates provided by an upstream state observer (a particle filter) and to refine them in case they turn out not to be satisfactory. Furthermore, we show how the same tools can be used to discriminate among alternative models of the same biological process. We demonstrate these ideas on a simple gene expression model, and we show how the proposed method is able to handle estimation problems that cannot be effectively solved by classical techniques such as least‐squares estimation. Copyright © 2012 John Wiley & Sons, Ltd.
Magnetoresistance through a single nickel atom

The magnetoresistance (MR) of a nickel atomic contact has been measured using the break junction technique. When the contact is only between two atoms, the change of resistance with applied field reaches 40%. It is composed of a continuous bell-shaped curve on which discrete jumps are superimposed. The MR changes sign when the applied field is rotated, which we explain by a spin-orbit coupling change of orbital overlap between the Ni atoms forming the junction. Reproducible jumps in the MR curve are attributed to a field induced change of spin configuration within the few atoms composing the contact.
Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask

Water resources are an important component of ecosystem services. During long periods of cloudiness and precipitation, when a ground-based sample is not available, the water bodies are detected from satellite SAR (synthetic-aperture radar) data using threshold methods (e.g., Otsu and Kittler–Illingworth). However, such methods do not enable to obtain the correct threshold value for the backscattering coefficient (σ0) of relatively small water areas in the image. The paper proposes and substantiates a method for the mapping of the surface of water bodies, which makes it possible to correctly identify water bodies, even in “water”/“land” class imbalance situations. The method operates on a principle of maximum compliance of the resulting SAR water mask with a given reference water mask. Therefore, the method enables the exploration of the possibilities of searching and choosing the optimal parameters (polarization and speckle filtering), which provide the maximum quality of SAR water mask. The method was applied for mapping natural and industrial water bodies in the Pohjois-Pohjanmaa region (North Ostrobothnia), Finland, using Sentinel-1A and -1B ground range detected (GRD) data (ascending and descending orbits) in 2018–2021. Reference water masks were generated based on optical spectral indices derived from Sentinel-2A and -2B data. The polarization and speckle filtering parameters were chosen since they provide the most accurate σ0 threshold (on average for all observations above 0.9 according to the Intersection over Union criterion) and are resistant to random fluctuations. If a reference water mask is available, the proposed method is more accurate than the Otsu method. Without a reference mask, the σ0 threshold is calculated as an average of thresholds obtained from previous observations. In this case, the proposed method is as good in accuracy as the Otsu method. It is shown that the proposed method enables the identification of surface water bodies under significant class imbalance conditions, such as when the water surface covers only a fraction of a percent of the area under study.
eHealth Literacy and Patient Portal Use and Attitudes: Cross-sectional Observational Study

Background Throughout the COVID-19 pandemic, patient portals have become more widely used tools of patient care delivery. However, not all individuals have equivalent access or ability to use patient portals. Objective The aim of this study is to evaluate the relationships between eHealth literacy (eHL) and patient portal awareness, use, and attitudes among hospitalized patients. Methods Inpatients completed patient portal surveys; eHL was assessed (eHealth Literacy Scale). Multivariable logistic regression analyses adjusted for age, self-reported race, gender, and educational attainment were completed with significance at P<. 006 (Bonferroni correction). Results Among 274 participants, most identified as Black (n=166, 61%) and female (n=140, 51%), mean age was 56.5 (SD 16.7) years, and 178 (65%) reported some college or higher educational attainment. One-quarter (n=79, 28%) had low eHL (mean 27, SD 9.5), which was associated with lower odds of portal access awareness (odds ratio 0.11, 95% CI 0.05-0.23; P<.001), having ever used portals (odds ratio 0.19, 95% CI 0.10-0.36; P<.001), less perceived usefulness of portals (odds ratio 0.20, 95% CI 0.10-0.38; P=.001), and lower likelihood of planning to use portals in the coming years (odds ratio 0.12, 95% CI 0.06-0.25; P<.001). As time through the COVID-19 pandemic passed, there was a trend toward increased perceived usefulness of patient portals (53% vs 62%, P=.08), but average eHL did not increase through time (P=.81). Conclusions Low eHL was associated with less awareness, use, and perceived usefulness of portals. Perceived usefulness of portals likely increased through the COVID-19 pandemic, but patients’ eHL did not. Interventions tailored for patients with low eHL could ensure greater equity in health care delivery through the COVID-19 pandemic.
Fitting the variance-gamma model to financial data

This paper has as its main theme the fitting in practice of the variance-gamma distribution, which allows for skewness, by moment methods. This fitting procedure allows for possible dependence of increments in log returns, while retaining their stationarity. It is intended as a step in a partial synthesis of some ideas of Madan, Carr and Chang (1998) and of Heyde (1999). Standard estimation and hypothesis-testing theory depends on a large sample of observations which are independently as well as identically distributed and consequently may give inappropriate conclusions in the presence of dependence.
On the birth of multiple limit cycles in nonlinear systems

One type of degenerate (or, singular) Hopf bifurcations determine the appearance of multiple limit cycles under system parameter perturbations. In the study of these degenerate Hopf bifurcations, computational formulas for the stability indexes (i.e., curvature coefficients) are essential. However, such formulas are very difficult to obtain, and hence are usually obtained by different approximation techniques. In this paper, by using engineering feedback system methodology and harmonic balance approximation techniques, these formulas are derived in the frequency domain. The results obtained in this paper can be used to study a nonlinear system within regions of one periodic solution, instead of directly dealing with multiple limit cycles. Thus, the complex multiplicity of nonlinear system dynamical behavior can be avoided.
Molecular Detection of Microsporidia in Rabbits (Oryctolagus cuniculus) in Tenerife, Canary Islands, Spain

Simple Summary Microsporidia are a group of fungal-related pathogens widely distributed in the environment, with some species having a negative impact on animals and public health. The European rabbit (Oryctolagus cuniculus) is considered the natural host of Encephalitozoon cuniculi, a microsporidian pathogen of mammals, including humans. The infection caused by E. cuniculi, encephalitozoonosis, ranges from asymptomatic to severe lesions in rabbits, with clinical signs involving the central nervous system, kidney, and eye being the most common. The majority of reported cases have been in domestic rabbits, while cases in wild rabbits are uncommon. Due to the lack of data on microsporidia in the Canary Islands, the aim of this work was to analyze the prevalence and identity of microsporidia in fecal samples from rabbits in Tenerife. Abstract Enterocytozoon bieneusi and Encephalitozoon spp. are microsporidia with zoonotic potential that have been identified in humans, as well as in a large group of wild and domestic animals. Several wildlife species have been studied as reservoirs of zoonotic microsporidia in mainland Spain, including the European rabbit (Oryctolagus cuniculus). Due to a lack of data on microsporidia infection in wildlife on the Canary Islands, the aim of this work was to analyze the prevalence and identify the species of microsporidia in rabbits in Tenerife. Between 2015 and 2017, a total of 50 fecal samples were collected from rabbits in eight municipalities of Tenerife, Canary Islands, Spain. Seven of the fifty samples (14%) were amplified using nested polymerase chain reaction (PCR) targeting the partial sequence of the 16S rRNA gene, the internal transcribed spacer (ITS) region, and the partial sequence of the 5.8S rRNA gene. Sanger sequencing reveals the presence of Encephalitozoon cuniculi genotype I in two samples (4%), and undescribed microsporidia species in five samples (10%). This study constitutes the first molecular detection and genotyping of E. cuniculi in rabbits in Spain.
Development of Chincho (Tagetes elliptica Sm.) Essential Oil Organogel Nanoparticles through Ionic Gelation and Process Optimization with Box–Behnken Design

The aim of this work was to obtain chitosan nanoparticles (<1000 nm) with chincho (Tagetes elliptica Sp.) essential oil (CEO-CSNPs) using the ionic gelation method. A Box–Behnken design (BBD) was applied, using chitosan solution (CS) pH (4.0, 4.4, 4.8); the mass ratio of CS/CEO (1:0.7, 1:0.85, 1:1.0) and the mass ratio of CS/CS-tripolyphosphate (1:0.46, 1:0.58, 1:0.7) as independent variables. The formulation-dependent variables, encapsulation efficiency (EE) and loading capacity (LC) of the CEO-CSNPs were evaluated. BBD determined that optimal conditions for CEO-CSNPs were pH: 4.4, CS/CEO mass ratio 1:0.7 and CS/TPP mass ratio 1:0.46. Once the optimization was defined, particle size (PS), zeta potential (ZP), polydispersity index (PDI), CEO-CSNPs morphological studies, in vitro CEO release, and antibacterial activity were determined. The CEO-CSNPs showed an EE of 52.64% and a LC of 11.56%, with a diameter of 458.5 nm, with a ZP of 23.30mV, and a PDI of 0.418. The SEM studies showed that the nanoparticles were rounded and had uniform shapes. In addition, CEO-CSNPs showed a minimum inhibitory concentration against Staphylococcus aureus, Salmonella infantis and Escherichia coli of 5.29, 10.57 and 10.57 µg/mL, respectively. These results could be very useful for the stabilization of chincho essential oil for food industry purposes. However, several studies about the release, as well as interaction with food matrices, will be necessary.
Drought disrupts year-round breeding readiness in a tropical songbird

Climate change is expected to increase the prevalence and severity of extreme weather events like drought. For many opportunistically breeding tropical bird species, precipitation serves as a primary cue for onset of breeding, and sustained drought can have major impacts on reproductive fitness. The physiological effects of drought are poorly understood, but understanding physiological responses can help resolve the mechanisms underlying effects of drought on population demography. We used physiological data collected on a year-round breeding New Guinea endemic passerine, the White-shouldered Fairywren ( Malurus alboscapulatus ), during years with persistent rainfall and during the 2015–2016 El Niño drought event to assess patterns of breeding readiness, body condition, and molt. Many males lost their cloacal protuberances, a sperm storage organ they typically maintain year-round, during the drought period. Body molt, which also occurs year-round in this species, was higher in males during the drought period. Using a sliding-window statistical model approach, we identified a critical precipitation window of 0–40 days prior to capture for cloacal protuberance volume and a window of 8–53 days for molt, with lower precipitation associated with smaller cloacal protuberances and greater molt. Plasma androgens were predictive of variation in male cloacal protuberance volume and molt, thus potentially mediating transitions between life-history stages in response to environmental conditions. Female molt, androgens, and male mass varied with photoperiod, with longer but decreasing day lengths characteristic of the austral summer associated with increasing molt and decreasing androgens. Collectively, our results indicate the potential for photoperiod to cue some life-history stages, whereas reduced rainfall impacts male but apparently not female reproductive physiology and readiness to breed. Improving our understanding of environmental regulation of life-history transitions in tropical taxa is essential for identifying which taxa are most at risk under a changing climate
Offline User Authentication Ensuring Non-Repudiation and Anonymity

User authentication is the key to ensuring that only authorized users can deal with specific affairs and access services. Applications or systems possessing different properties or requirements need different authentication schemes. For example, some institutions or companies need executives to manage or inspect their corresponding departments while the inspected department should not know who the executives are but only can verify their legitimacy. This paper designs a non-repudiation and anonymity-ensured user authentication system to meet the mentioned special requirements. We also propose a user authentication scheme to ensure that the designed system can work as claimed. In the system, a department is equipped with an authentication device, namely the department authentication device, to authenticate an executive while the executive’s identity is not revealed to the department and only the department’s authentication device can identify the executive for non-repudiation. An executive is equipped with an authentication device to have himself/herself authenticated by the department’s authentication device. Moreover, authentication data stored in an executive’s authentication device does not need to be updated even when management personnel changes are made.
A family of simultaneous methods for the determination of polynomial complex zeros

Using the development of a rational function by elementary fractions, a family of methods for the simultaneous determination of polynomial complex zeros is derived. All the methods of the family are cubically convergent for simple zeros. The known simultaneous procedures of the third order are included. The presented class of iteration functions is suitable for the parallel inclusion of polynomial complex zeros by circular regions. The family of methods, defined in complex circular arithmetic, gives a new interval method with cubic convergence. Numerical example is given.
Performance Evaluation of Predictive Machine Learning Models for Diabetic Disease Using Python

The discovery of knowledge from medical database is always beneficial as well as challenging task for diagnosis. For example, patients having high blood glucose are required to diagnose as they fall within a group of Diabetes mellitus. Prediction of diabetes mellitus is an essential research in the domain of medical industry. With the advent of artificial intelligence and machine learning this type of prediction removes the hurdles faced in data mining used for similar task. In case of data mining, extraction of knowledge from information stored in database takes place and an understandable description of patterns is achieved. A large number of researches have been already taken place to predict diabetes using traditional machine learning algorithm such as artificial neural network, Naïve Bayes theorem, decision tree, etc. However, determination of diabetes with a certain degree of confidence is required from the accuracy or any other performance measures point of view. In this context, this research work presents machine learning models such as decision tree, support vector machine, random forest, k-nearest neighbours and Naïve-Bayes as classifier to classify whether a patient is diabetic or prone to diabetic. Performance measures of these algorithms have been carried out in terms of accuracy score. Dataset for training and testing the algorithms mentioned is retrieved from Pima Indian Database. On the basis of their comparative evaluation, most important feature with respect to identification of diabetic is extracted. A complete python code has been developed for this research work.
Sensorless linear induction motor speed tracking using fuzzy observers

In this study, the authors develop a sensorless speed controller for linear induction motors (LIMs) based on a fuzzy observer. The authors use a fifth-order non-linear LIM dynamical model with end-effects approximated by Taylor series. Then, the authors represent the LIM by a T-S fuzzy model and construct a fuzzy observer to estimate the immeasurable mover speed and secondary flux states. By computationally solving a set of linear matrix inequalities to obtain the observer gains, the authors achieve exponential convergence of estimation errors when fuzzy membership functions fit a Lipshitz-like property. Based on the fuzzy observer, the control law synthesis is carried out by using a virtual desired variable concept where a skew-symmetric property is utilised. The exponential convergence for both estimation error and tracking error is ensured by the controller design. Finally, speed tracking experiments are carried out to verify the theoretical results and show satisfactory performance.
Development of Roughness in Crack Propagation

We report on the statistical analysis of experimental measurements of the crack roughness for a fracture initiated from a straight notch in a granite sample. The crack surface is shown to develop a self-affine character over a correlation length ξ which depends on the distance to the notch y as a power law ξ ∝ yα characterized by a dynamic exponent 1/α estimated to be 1.2 ± 0.15. The self-affine roughness exponent below the correlation length is found to be 0.80 ± 0.05. The scaling is obtained from two maps of the surface, consisting each in more than 105 data points.
Control of CLLC Resonant Converter in Grid Connected Electric Bus charging station

Battery Energy Storage System(BESS) is essential for a resilient microgrid. Electric vehicle charging stations operating in vehicle to grid(V2G) can act as a portable power source in the microgrid. To regulate the power flow for both BESS and electric vehicle charging stations, a bi-directional converter is necessary. This paper presents a test case where the BESS system and electric bus(EB) charging station are connected in parallel with the utility grid and loads. The Capacitor- Inductor-Capacitor (CLLC) converter is utilized as a bi-directional DC-DC converter. The CLLC converter is widely used due to its simple symmetrical design and ability to achieve soft switching. A novel dual hysteresis band logic along with variable frequency control method has been implemented for the CLLC converter. The DC link voltage, battery current, ZVS, and ZCS operations have all been examined in MATLAB/SIMULINK environment with an appropriate controller. Findings show that the BESS and EB charging station works satisfactorily in both forward and reverse modes.
Frequency divided group beamforming with sparse space‐frequency code for above 6 GHz URLLC systems

In this study, we propose a limited feedback‐based frequency divided group beamforming with sparse space‐frequency transmit diversity coded orthogonal frequency division multiplexing (OFDM) system for ultrareliable low latency communication (URLLC) scenario. The proposed scheme has several advantages over the traditional hybrid beamforming approach, including not requiring downlink channel state information for baseband precoding, supporting distributed multipoint transmission structures for diversity, and reducing beam sweeping latency with little uplink overhead. These are all positive aspects of physical layer characteristics intended for URLLC. It is suggested in the system to manage the multipoint transmission structure realized by distributed panels using a power allocation method based on cooperative game theory. Link‐level simulations demonstrate that the proposed scheme offers reliability by achieving both higher diversity order and array gain in a non‐line‐of‐sight channel of selectivity and limited spatial scattering.