Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of bias voltage dependent spectral response in Ga0.51In0.49P/Ga0.99In0.01As/Ge triple junction solar cell

Spectral response measurement plays great role in characterizing solar cell device because it directly reflects the efficiency by which the device converts the sunlight into an electrical current. Based on the spectral response results, the short circuit current of each subcell can be quantitatively determined. Although spectral response dependence on wavelength, i.e., the well-known external quantum efficiency (EQE), has been widely used in characterizing multijunction solar cell and has been well interpreted, detailed analysis of spectral response dependence on bias voltage (SR −Vbias) has not been reported so far. In this work, we have performed experimental and numerical studies on the SR −Vbias for Ga0.51In0. 49P/Ga0.99In0. 01As/Ge triple junction solar cell. Phenomenological description was given to clarify the mechanism of operation matching point variation in SR −Vbias measurements. The profile of SR−Vbias curve was explained in detail by solving the coupled two-diode current-voltage characteristic .
IMPACT OF DROUGHT STRESS ON CROP PRODUCTION AND ITS MANAGEMENT OPTIONS

In the development of agricultural crops, biotic and abiotic stresses result in considerable yield losses. One of the main obstacles to agricultural production and global food security is abiotic stress. Stress is a word that refers to several biotic and abiotic environmental factors that prevent crop plants from reaching their full genetic potential. Drought is one of the fundamental issues in the current climatic environment and is one of the most severe abiotic stresses in many areas of the world. Plants experience moisture stress when their evapotranspiration requirements are not met. Drought has a negative impact on plant development and other metabolic processes, making it one of the most significant abiotic stresses and factors restricting the successful production of plant products globally. Drought is caused by a lack of water as a result of erratic rainfall or inadequate irrigation, but it can also be hampered by other elements such as soil salinity, physical characteristics, and excessive air or soil temperatures. Insufficient water supply throughout a crop's life cycle, including precipitation and the capability of the soil to store moisture, limits the crop's potential to produce the highest possible genetic grain yield. The most significant stressor that has a significant impact on crop development and productivity is without a doubt drought. For better management, it is crucial to comprehend the physiological, biochemical, and ecological actions connected to these stresses. It is possible to generalize morphological, physiological, and biochemical responses to a broad range of plant responses to this stress. Due to physical damage, physiological disruptions, and biochemical alterations, inadequate water supplies and abnormal temperatures have a severe impact on crop growth and yields. Drought stress reduces the size of the leaves, stem extension, and root proliferation within the soil; it also disturbs plant water relations and reduces water-use efficiency, which in turn reduces the plant's ability to yield; as a result, breeding for drought resistance is a good approach. This approach combines conventional and molecular methods to develop a drought-tolerant variety. Breeding more drought-tolerant cultivars may be more successful when selection is based on a thorough testing strategy. Practical implications for treatments and management result from a greater understanding of how plants react to this stress. High demand for drought-tolerant types would seem to be a difficult issue for plant breeders, but difficulties are aggravated by the difficulty of crop yield on a genetic and physiological basis. Food security is seriously threatened by drought, which is the main reason for agricultural loss worldwide. Plant biotechnology is currently one of the most promising areas for creating crops that can generate large amounts of food in moisture environments.
Characterization of the formation of base sheet paper using spectral moments determined from time-series models

The fibre distribution in a sheet of paper, referred to as the formation, is largely the result of turbulence, a stochastic process. Continuous time-series models developed from discrete light-transmission profiles are used to characterize formation. The models are used to obtain explicit expressions for the spectral moments of the profiles. From the moments, estimates of two characteristic lengths of the fibre distribution can be obtained and are interpreted as the average and largest flock size. These lengths are used to develop an index for evaluating the formation of four samples of base sheet paper. The results of this characterization agree with other methods, but this technique has the advantage of providing a physical interpretation of the index.
Extraction d'informations liées au locuteur depuis un modèle acoustique personnalisé

). These services are based on models trained on a large amount of data to ensure their efficiency. The used data is collected via applications, from the users’ interactions. It often contains sensitive information, which can create significant privacy issues. In this context, new machine learning paradigms have been proposed, such as federated and distributed learning. Both of them allow the creation of personalized models from private data. Only the models are then shared, without exposing the data itself. An important question is whether the dissemination of these personalized acoustic models (AM) can also lead to the leakage of personal information. The results show that it is possible to recover speaker-related information based on the changes in weight of an AM induced by local adaptation on that speaker.
Can we simulate extreme heat waves in climate models using machine learning ?

Since the pre-industrial era, the world has warmed by 1.1°C and the last IPCC report projects that it will keep warming if the emissions of greenhouse gasses continue to increase. One of the main and most societally relevant consequences of global warming is the changes in the distribution of meteo-climatic extreme events. Heat waves are among the most prominent of them, imposing terrible costs on societies and ecosystems [1]. Global warming shifts the distribution of temperatures towards higher values, therefore rendering extreme temperatures more likely – this is called the thermodynamic contribution. But global warming also modifies the general circulation of the atmosphere, which is the overall cause of extreme temperatures at the ground. This is called the dynamic contribution. Understanding the mechanisms by which global warming changes the dynamics leading to heat waves is however still an open question. The main difficulty associated with the study of heat waves, and extreme events in general, is their very nature: they are rare. Therefore, the data available to understand their dynamics is scarce. The solution to overcome this issue is to simulate the climate during very long periods of time in order to produce more extremes. However, this comes with a high computational cost. In recent years, methods have been developed to sample more effectively extreme events, using so-called rare event algorithms [2]. These methods usually rely on the parallel simulation of members and regular selection of the members which have the highest chance to reach extremes. Another approach consists of finding the optimal perturbations of the dynamics which would push the trajectory of the system towards extreme values [3]. Although this problem is mathematically well posed, it is very difficult to solve in practice for complex systems. The objective of the internship is to explore the use of machine learning techniques to
SUB-DOPPLER LASER SPECTROSCOPY OF H-2 AND D-2 IN THE RANGE 91-98 NM

The absorption spectra of hydrogen and deuterium have been measured in the range 91–98 nm. A narrowband extreme ultraviolet laser source is employed in combination with a molecular beam, allowing for sub-Doppler resolution and yielding linewidths of 0.23 cm−1. Transition frequencies of 291 lines of the Lyman and Werner bands are determined with an average absolute accuracy of 0.03 cm−1. The data are analyzed in a semi-empirical perturbation model, involving heterogeneous couplings between nearby lying levels of B1Σu+ and C1Πu states. This model is found to be accurate on the level of accuracy of the data obtained.
Parallel singularity-free design with actuation redundancy: A case study of three different types of 3-degree-of-freedom parallel mechanisms with redundant actuation

Typical parallel mechanisms suffer from parallel singularity due to kinematic coupling of multichains. This paper investigates how to remove parallel singularities by using redundant actuations. First, actuation wrenches and constraint wrenches forming the full direct kinematic Jacobian matrix are derived. After briefly addressing conditions for their constraint singularities, Grassmann–Cayley algebra is employed to identify parallel singularities. Then, employing Grassmann line geometry, the locations and the minimum number of redundant actuators are identified for the parallel mechanisms to have parallel singularity-free workspace. Three different types of 3-degree-of-freedom parallel mechanisms such as planar, spherical, and spatial parallel mechanisms are given as exemplary devices.
Reversible conductivity changes in discharge‐produced amorphous Si

A new reversible photoelectronic effect is reported for amorphous Si produced by glow discharge of SiH4. Long exposure to light decreases both the photoconductivity and the dark conductivity, the latter by nearly four orders of magnitude. Annealing above 150 °C reverses the process. A model involving optically induced changes in gap states is proposed. The results have strong implications for both the physical nature of the material and for its applications in thin‐film solar cells, as well as the reproducibility of measurements on discharge‐produced Si.
Envisioning translational hyperscanning: how applied neuroscience might improve family-centered care

Abstract From the very beginning of their life, human beings are immersed in a social and interactive environment that contributes to shaping their social and cognitive development under typical and at-risk conditions. In order to understand human development in its bidirectional relationship with the social environment, we need to develop a ‘complexity-sensitive’ approach in neuroscience. Recent advances have started to do so with the application of hyperscanning techniques which involve recording adult and child neural activity simultaneously and highlighting the presence of similar patterns of brain activity in the dyad. Numerous studies focused on typically developing children have been published in recent years with the application of this technique to different fields of developmental research. However, hyperscanning techniques could also be extremely beneficial and effective in studying development in atypical and clinical populations. Such application, namely translational hyperscanning, should foster the transition toward a two-brain translational neuroscience. In this paper, we envision how the application of hyperscanning to atypical and clinical child populations can inform family-centered care for children and their parents.
Analysis of material variation in the design of knitted sportswear compression stockings to escort thermo-physiological comfort using linear regression

Abstract Compression sportswear is specialized clothing chiefly designed for the athletes muscle fatigue reduction. Compression stockings are widely used in sportswear to support muscles and control excessive body vibrations. Compression/interface pressure between the wearer and stocking’s surface is considerable in compression sportswear efficacy. However, thermo-physiological comfort is also influential while preparing summer sportswear. The research investigates the effect of fibers type and the inlay yarns number of covering filaments on compression, air permeability, water vapor permeability, and thermal resistance of sportswear compression stockings. Three different fibers, sorbtek (channelized polyester), nylon 6, and micro Nylon (polyamide-6), have been used as main yarns. Also, three distinct levels of covering yarn number of filaments have been utilized. Data have been statistically analyzed using full factorial design of experiment and regression analysis to predict fiber type and the number of covering yarn filaments for achieving desired comfort properties without compromising compression characteristics.
Testing for the Presence of a Cure Fraction in Clustered Interval‐Censored Survival Data

Clustered interval‐censored survival data are often encountered in clinical and epidemiological studies due to geographic exposures and periodic visits of patients. When a nonnegligible cured proportion exists in the population, several authors in recent years have proposed to use mixture cure models incorporating random effects or frailties to analyze such complex data. However, the implementation of the mixture cure modeling approaches may be cumbersome. Interest then lies in determining whether or not it is necessary to adjust the cured proportion prior to the mixture cure analysis. This paper mainly focuses on the development of a score for testing the presence of cured subjects in clustered and interval‐censored survival data. Through simulation, we evaluate the sampling distribution and power behaviour of the score test. A bootstrap approach is further developed, leading to more accurate significance levels and greater power in small sample situations. We illustrate applications of the test using data sets from a smoking cessation study and a retrospective study of early breast cancer patients.
Prediction of movie success based on machine learning and twitter sentiment analysis using internet movie database data

Nowadays, predicting the success of a new movie is a crucial task. In this work, the hybrid approach considers the movie features as well as sentiment expressed in the movie review to predict the success rate of a movie. Multiple movie features such as title, director, star cast, and writer. Are considered for prediction. The related raw data is collected from the internet movie database (IMDb) website and after pre-processing, the collected data is used to generate the supervised machine learning model. Different supervised learning models are compared and the one with the best results is used further. The mean squared error, root mean squared error and r2 score of the models generated are comparable with existing models. Further, sentiment analysis of the movie-related tweets is performed. The accuracy of best sentiment analysis model is 88.47%. Finally, the two models are combined to give the success prediction rating of new movies and the results of the hybrid model are encouraging. The proposed model may be used to find the top-rated movies of a particular calendar year.
Real-Time Frequency Stabilization of Standalone PV System with SMC

This paper aims to obtain suitable control for the Standalone Photo Voltaic (SAPV) system to meet consumer demands in weather conditions. A SAPV system is developed with a solar array with Maximum Power Point Tracking (MPPT), a Boost converter, and an inverter. This system uses controllers for maximum power output tracking, constant DC output of the Boost converter, and constant output voltage and frequency with Pulse Width Modulation (PWM) control mode. MPPT, boost converter, and inverter controller parameters are designed and tuned, respectively. Proportional gain (Kp) and Integral gain (Ki) values and the Sliding Mode Method (SMC) tune the controller parameters. The innovative aspect of this work is to propose a standalone PV system with controllers based only on the sliding mode control approach. Moreover, the current controller provides an output current of high quality with a THD of 1.6 %. Effectiveness and robustness of the proposed scheme modeled and simulated under OPAL-RT real-time Software in Loop (SIL) platform with MATLAB Simulink under fast variations of irradiance and temperature. Various analyses have been carried out for examining the proposed Sliding Mode Controller-based standalone photovoltaic system.
Analysis of antibiotic resistance genes in pig feces during the weaning transition using whole metagenome shotgun sequencing

Abstract Antibiotics have been used in livestock production for not only treatment but also for increasing the effectiveness of animal feed, aiding animal growth, and preventing infectious diseases at the time when immunity is lowered due to stress. South Korea and the EU are among the countries that have prohibited the use of antibiotics for growth promotion in order to prevent indiscriminate use of antibiotics, as previous studies have shown that it may lead to increase in cases of antibiotic-resistant bacteria. Therefore, this study evaluated the number of antibiotic resistance genes in piglets staging from pre-weaning to weaning. Fecal samples were collected from 8 piglets just prior to weaning (21 d of age) and again one week after weaning (28 d of age). Total DNA was extracted from the 200 mg of feces collected from the 8 piglets. Whole metagenome shotgun sequencing was carried out using the Illumina Hi-Seq 2000 platform and raw sequence data were imported to Metagenomics Rapid Annotation using Subsystem Technology (MG-RAST) pipeline for microbial functional analysis. The results of this study did not show an increase in antibiotic-resistant bacteria although confirmed an increase in antibiotic-resistant genes as the consequence of changes in diet and environment during the experiment.
Monte Carlo simulation of charged species kinetics in weakly ionized gases

In this tutorial work, the Monte Carlo method to simulate the kinetics of charged particles in weakly ionized gases is explained. The method is collocated in the wider context of the general Monte Carlo and propagation methods. Besides providing the necessary background to develop basic models for the linear and the non-linear transport case, some more advanced topics are treated, e.g. the inclusion of the thermal distribution of neutral particles and the differential weighting technique.
Association between risk disclosure and (in) existence of corporate reputation of publicly traded companies in Brazil

Purpose - To analyze the possible association between non-financial risk disclosure and the (in) existence of reputation in the most prominent Brazilian publicly traded companies considering the legitimacy theory. Design/methodology/approach - The sample assembles 118 corporations listed on B3 and 345 reference forms from 2006 to 2018. In the analysis of corporate reputation, the presence or absence on ISE and, or ICO2 from B3 was considered. For non-financial risk disclosure, a factor checklist containing 31 risks was taken, categorized under operational, damage, integrity, and strategic risks, from the analysis of Reference Forms. A tree regression, a multiple correspondence analysis, and a T-test of the difference between means were used for data analysis. Findings - There is scientific evidence that the companies with reputations demonstrate non-financial risk disclosure superior to the others. This behavior is confirmed in the subcategories of integrity and strategic risks. Moreover, risk factors in the subcategory of integrity risks were crucial to non-financial risk disclosure. Originality/value - This research paves the way to deepen the early debate surrounding risk disclosure and corporate reputation, especially regarding the quality of non-financial risk disclosure.
Peran grit dan self efficacy terhadap work engagement pada tenaga kesehatan rumah sakit

This study aims to determine the role of grit and self-efficacy on work engagement for health workers at type C hospitals in Depok City. The data analysis used Structural Equation Modeling (SEM), namely Partial Least Square (PLS). The sample size in this study was 200 respondents. The results showed that grit positively and significantly affects work engagement. Grit has a positive and significant effect on self-efficacy. Self-efficacy has a positive and significant effect on work engagement. Self-efficacy mediates the relationship between grit which has a positive and significant effect on work engagement. Based on the Variance Accounted For (VAF) calculation, self-efficacy mediates partially. With self-efficacy, grit will affect increasing work engagement. This research can provide literature about the role of grit and self-efficacy on the work engagement of nurses at Type C Hospital. This research is helpful for hospitals in developing strategies to increase nurse work engagement.
Adverse childhood experiences and neurodevelopmental disorders among youth with and without prenatal substance exposure: A longitudinal matched register-based cohort study

Background: Previous research has shown an association between adverse childhood experiences (ACEs) and secondary mental health problems in youth with prenatal substance exposure (PSE), but the association between ACEs and neurodevelopmental disorders is less clear. Methods: This longitudinal register-based cohort study investigated relationships between health at birth, ACEs (out-of-home care (OHC) and maternal adversities), and neurodevelopmental disorders among youth with PSE (alcohol/drugs, n = 615) and matched unexposed controls (n = 1787). Hospital medical records and register data were merged and analysed using Cox regression models. Results: Conduct and emotional disorders (International Statistical Classification of Diseases and Related Health Problems ICD-10, F90–F94) were more common among the exposed than the controls but only when the exposed and controls with no OHC were compared. The difference appeared in hyperkinetic disorders (ADHD, F90), mixed disorders of conduct and emotions (F92) and emotional disorders with onset specific to childhood (F93). Among the exposed and controls with OHC, over 40% had received an F90–F94 diagnosis. Regarding specific developmental disorders (F80–F83, e.g., impairments in speech and language and scholastic skills) the moderate difference between the exposed and controls attenuated after adjustment for OHC. Again, the rates were highest among the exposed with OHC and the controls with OHC. OHC and maternal risks were interrelated and, together with male sex and low birth weight, were associated with neurodevelopmental disorders both among the exposed and controls and decreased the difference between them. Conclusions: A strong association between ACEs and neurodevelopmental disorders was found. Brain research is needed to examine whether ACEs worsen neurodevelopmental outcomes caused by PSE.
Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment. Part III: Semi-Prognostic Test of the Arakawa-Schubert Cumulus Parameterization

Abstract The verification of the Arakawa and Schubert (1974) cumulus parameterization is continued using a semi-prognostic approach. Observed data from Phase III of GATE are used to provide estimates of the large-scale forcing of a cumulus ensemble at each observation time. Instantaneous values of the precipitation and the warming and drying due to cumulus convection are calculated using the parameterization. The results show that the calculated precipitation agrees very well with estimates from the observed large-scale moisture budget and from radar observations. The calculated vertical profiles of cumulus warming and drying also are quite similar to the observed. It is shown that the closure assumption adopted in the, parameterization (the cloud-work function quasi-equilibrium) results in errors of generally <10% in the calculated precipitation. The sensitivity of the parameterization to some assumptions of the cloud ensemble model and the solution method for the cloud-base mass flux is investigated.