Miracle-dz/UniFAR-FTUs huggingface dataset:
Description of global EGAM in the maximum of local frequency during current ramp-up discharges in DIII-D

Energetic-particle-induced geodesic acoustic modes, EGAMs (Fu, Phys. Rev. Let., vol. 101, 2008, pp. 185002), driven by neutral beam injection (NBI), have been observed in many DIII-D tokamak experiments (Nazikian et al., Phys. Rev. Lett., vol. 101, 2008, pp. 185001). This mechanism has been theoretically investigated in (Qiu et al., Plasma Phys. Control. Fusion, vol. 52, 2010, pp. 095003), using a sharp energetic particle distribution function, and in (Qu et al., Plasma Phys. Control. Fusion, vol. 59, 2017, pp. 055018), where the dispersion relation and eigenmode behaviour were obtained for the situation of early beam scenario, that is, for times smaller than the beam slowing down time. In this work, we extend these studies determining the eigenmode for beyond the slowing down time, in a scenario with reverse safety factor $q$ profile, where a small concentration of energetic ions can produce an off-axis maximum in the GAM dispersion relation. The characteristics of EGAM are analytically studied with the drift kinetic equation together with the MHD code NOVA. The toroidal energetic ion transit frequency, coupled with the GAM frequency, produces the maximum in the dispersion relation where the eigenmode can be found. The quantitative correspondence of experimental results with the predictions of the proposed model is analysed.
Prevalence of pica and rumination behaviours in adults and associations with eating disorder and general psychopathology: findings form a population-based study

Abstract Aims Pica and rumination disorder are known as feeding disorder diagnoses in childhood, but little is known about their occurrence in adulthood. This study aimed to assess prevalence rates of one-time and recurrent pica and rumination behaviours (PB and RB) in adults, including sociodemographic subgroups, and to examine associations with other eating disorder and general psychopathology. Methods The representative population sample (N = 2403) completed measures on PB and RB, symptoms of avoidant/restrictive food intake disorder (ARFID), body image and symptoms of depression and anxiety. Results Any PB and RB were reported in 5.33 and 5.49%, respectively, while recurrent PB or RB occurred in 1.08 and 0.71%, respectively. Co-occurrence was high, with 35.29% of recurrent PB in RB, and 23.08% vice versa. Prevalence rates of recurrent PB or RB did not differ by gender, weight status, educational or migration history from those without recurrent behaviours. Adults with v. without recurrent PB and RB showed more symptoms of ARFID, general eating disorders depression and anxiety, and behavioural symptoms of eating disorders (with the exception of compensatory behaviours in recurrent PB), and less positive body image. However, there were no differences regarding age and body mass index. Conclusions Our findings highlight the clinical significance of PB and RB in adults regarding both prevalence and associations with other psychopathological symptoms. In particular, associations with body image need to be investigated further, as in contrast to other eating disorders, body image disturbance is not yet represented in the diagnostic criteria for pica and rumination disorder. In sum, the findings highlight the need for clinical attention for these disorders and related behaviours in adults.
Ocular Abnormalities and Systemic Disease in Down Syndrome

Introduction: Ocular problems as refractive errors, strabismus, accommodation, and cataract are well known in children with Down syndrome (DS). However, there is little information on the possible correlation of eye problems with systemic diseases such as heart defect (with or without surgery), hypotony, hypothyroidism, hearing loss, and others.Methods: Ophthalmic problems versus certain systemic diseases were studied in 65 children with DS, aged 2 months to 13 years, referred to the University Eye Hospital Ljubljana, Slovenia from 2008 to 2010. Standard ophthalmic examination methods were used, and physical data were taken from pediatric records.Results: Ocular findings included nystagmus (29.2%), esotropia (26.1%), epiphora (21.5%), Brushfield spots (16.9%), lens opacities (12.3%), abnormalities of the retinal vessels, foveal hypoplasia, or retinal pigment epithelium hyperplasia (32.2%), and optic disc pallor (7.6%). Hyperopia (36.9%) was the most frequent refractive error in the group, followed by asti.
Re-visiting the coupling between mutants and real faults with Defects4J 2.0

Mutation analysis is a well-known testing criterion that involves seeding changes in the system under test, i.e. creating mutants, to simulate faults, and measuring the capacity of the test suite to detect these changes. The question of whether real faults are coupled with the mutants is central, as it determines whether tests that detect the mutants will also detect faults that actually occur in code, making the mutants reasonable test requirements. Prior work has explored this question, notably using the Defects4J dataset in Java. As the dataset and the mutation tools used in these prior works have evolved, this work re-visits this question using the newest available versions in order to strengthen and extend prior results. In this work we use 337 real faults from 15 different projects in the Defects4J 2.0. 0 dataset, 2,828 test suites, and two well-known Java mutation testing tools (Major and Pitest) to explore (i) to what extent real faults are coupled with mutants, (ii) how both tools compare in terms of producing mutants coupled with faults, (iii) the characteristics of the mutants that are coupled with real faults, and (iv) the characteristics of faults not coupled with the mutants. Most (80.7%) of the faults used were coupled with at least one mutant created by Pitest or Major, most often with mutants created by both tools. All operators used produced a low (<4%) proportion of coupled mutants, although some operators are exclusively coupled to more faults, i.e. coupled to faults where no other operator produces coupled mutants. Finally, faults not coupled with any mutants usually had small fix patches, and although the code related to these faults was mostly affected by the mutation operators used the mutants produces were still not coupled. Results confirm previous findings showing that the coupling effect mostly holds but that additional mutation operators are needed to capture all faults.
Si-MEMS gyro by Safran: Towards the navigation grade

Prolonging the pioneering works initiated in the 60’s [1], [2] and bringing radical innovations such as planar electrodes, Safran Electronics & Defense has successfully achieved a major technological breakthrough by developing and industrializing a navigation-grade Inertial Navigation System (INS) technology based on HRG Crystal™. Further optimization of the Cost Size Weight And Power (CSWAP) implies the development of a navigation grade MEMS gyroscope. Safran decided to take up the challenge. In this paper, we introduce a design evolution which has brought great performance results. While keeping an axisymmetric design and a fully balanced gyro mode [3], the innovation relies on electrostatic transducers now located between the vibrating masses. In the first part of this paper, we describe the key design rules and the innovation brought on electrostatic transducers location. The second part deals with the technological implementation of this new design on a silicon MEMS die. Finally, test results are presented with a special focus on performances under external vibrations.
An Industrial Case-Study on GUI Testing With RPA

Automating regression tests can bring many benefits, such as increased testing frequency and improved bug-finding capabilities, leading to an overall positive impact on software quality. However, transitioning manual testing into automated testing is not possible without effort. In this work, we attempted to transition a manual test suite into an automated graphical user interface-based regression test suite using a Robotic Process Automation framework. The study reports on the efforts needed to implement the test suite and test case maintenance efforts. Furthermore, the study reports on bug-finding capabilities and discusses the feasibility of applying Robotic Process Automation for automated graphical user interface-based regression testing more broadly. Due to challenges related to current testing practices within the organization, only a small subset of the manual test cases could be successfully transitioned. The results indicate that the implementation and maintenance efforts patterns are similar to those of previous studies from the literature - even similar benefits and challenges could be observed. These findings suggest that Robotic Process Automation is feasible for graphical user interface-based regression testing, but further (long-term) investigations are needed in this area.
Ultra-fast Characterization of Detection Electrode Errors under Whole-angle Mode in 10 Seconds

This paper describes the ultrafast characterization method of detection electrode errors under the whole-angle mode. The angle estimation errors, as a direct effect of detection electrode error, are investigated through experiments and theoretical analysis under different detection gain and angle errors. Besides, the consistency of experimental and simulation results proves that the sin and cos components of angle estimation error can be used to characterize detection gain and angle errors respectively. The ultrafast characterization method is proposed based on the analysis of angle estimation error to obtain the compensation parameters of detection electrode errors. After the compensation of detection electrode errors, the angle estimation error has decreased about 200 times to the noise level.
Designing A Secure Smart Healthcare System with Blockchain: A Review

Internet of Things (IoT) is the new development of Information Technology which connects the devices with Internet to make them smart. IoT has a variety of applications viz., smart healthcare, smart transport, smart agriculture, and smart city. Healthcare IoT facilitates correspondence between doctors and patients. The patients can be analysed remotely in critical situations with help of smart wearable devices. Healthcare records are very sensitive and any violation can cause unwanted severe consequences. Therefore, patients' data must be highly secured. Blockchain helps to maintain the high-level security in the healthcare IoT. Blockchain also provides immutability and confidentiality. This paper presents a brief survey on security in healthcare IoT with blockchain. We found that security can be enhanced in healthcare framework using blockchain.
Emotion Recognition from Facial Images using Hybrid Deep Learning Models

With the advancement of robots and their assimilation in daily chores, it has become essential to maintain an effective mode of communication between them and humans, that in turn requires development of highly intelligent systems so that robot can sense and adapt the behavior accordingly. The recognition of actual human emotion and its exact interpretation by the machines poses a great challenge to computer vision community, and in quest to improve the adaptivity of machines, a variety of deep learning convolutional neural network (CNN) algorithms have been proposed to serve the purpose. But as Facial Expression Recognition’s (FERs) have found their applications in a number of society verticals like health, education, marketing, gaming, surveillance etc. The single algorithm to provide perfect recognition in all the scenarios has never been established so far; however, the research is still in progress to develop the substitutes or new models to improve the recognition process. In this paper, deep learning algorithms have been utilized for classifying the facial expression of the humans.Two benchmark datasets of facial expression images have been used. The proffered method investigated the effectiveness of DCNN with the help of multiple models. EfficientNetB0, DenseNet169 and a combined model of EfficientNetB0+VGG16 have been proposed to be used in our work.With the hybrid model, we have achieved recognition accuracy of 90.6% and 95.6% on FER2013 and JAFFE datasets respectively. The recognition rates achieved are competitive as compared to previous reported results in literature on the two datasets.
Synchronous Inference for Multilingual Neural Machine Translation

Multilingual neural machine translation allows a single model to translate between multiple language pairs, which greatly reduces the cost of model training and receives much attention recently. Previous studies mainly focus on training stage optimization and improve positive knowledge transfer among languages with different levels of parameter sharing, but ignore the multilingual knowledge transfer during inference although the translation in one language may help the generation of other languages. This work enhances knowledge sharing among multiple target languages in the inference phase. To achieve this, we propose a synchronous inference method that can simultaneously generate translations in multiple languages. During generation, the model that predicts the next word of each language not only based on source sentence and previously predicted segments, but also based on predicted words of other target languages. To maximize the inference stage knowledge sharing, we design a cross-lingual attention module which allows the model to dynamically selects the most relevant information from multiple target languages. The synchronous inference model requires multi-way parallel training data which is scarce. We therefore propose to adopt multi-task learning to incorporate large-scale bilingual data. We evaluate our method on three multilingual translation datasets and prove that the proposed method significantly improve the translation quality and the decoding efficiency compared to strong bilingual and multilingual baselines.
Effect of Metallization on Fused Silica Dual-Shell Gyroscopes

This paper reports the effect of metallization on performance of Fused Silica (FS) Micro-machined Hemispherical Resonator Gyroscopes (micro-HRGs). For capacitive detection of motion, a conductive coating of the sensing element is required, which results in a reduction of the mechanical quality factors as well as an introduction of the electrical resistance. We experimentally demonstrated the impact of metal coating on both the quality factor and the electrical resistance using a developed in-house FS Dual-Shell Gyroscope (DSG), a version of micro-HRG. We derived an electromechanical model, analytically predicted behavior, and experimentally confirmed the effect of electrical resistance on the motion detection sensitivity and the overall noise performance of the DSG. For the device under test, we identified an optimal thickness of Cr coating to achieve a low resistance for optimal noise performance with a minimal loss of the quality factor.
Development of model output statistics based on the least absolute shrinkage and selection operator regression for forecasting next‐day maximum temperature in South Korea

Regression models for model output statistics (MOS) based on least absolute shrinkage and selection operator methods were developed to forecast next‐day maximum surface air temperature (TMAX) during the warm season in South Korea. The forecast fields from the operational numerical weather prediction (NWP) system of the Korean Meteorological Administration for global and local forecasts and the observed TMAX data in 225 observation stations were used as input variables for the MOS. The training period was July and August (JA) from 2015 to 2018, and the regression models were tested using data from JA 2019. As a result of hindcasting for the test period, the MOS models performed significantly better for next‐day TMAX forecasting over South Korea than the numerical models during JA 2019. The mean TMAX errors were reduced by over 1°C in MOSs compared to those in the numerical models. However, the TMAX forecast performance was generally lower in the higher‐resolution NWP Local Data Assimilation and Prediction System (LDAPS)‐based MOS (LMOS) than in the lower‐resolution NWP Global Data Assimilation and Prediction System (GDAPS)‐based MOS. This pattern was dominant when LDAPS simulated the TMAX more accurately than average. In particular, the random TMAX error of LDAPS was larger than that of GDAPS during the training period, and a positive random error of TMAX was magnified in LMOS. Because the other predictors forecasted from LDAPS can be associated with lower TMAX forecast performance of LMOS, in addition to TMAX effects as a predictor, a new MOS was developed using both LDAPS and GDAPS outputs. The forecast accuracy was improved by up to 0.3°C when the forecast fields from the GDAPS substituted several LMOS predictors, even though TMAX was the primary predictor for LMOS.
The effects of gestational stress and SSRI antidepressant treatment on structural plasticity in the postpartum brain - a translational model for postpartum depression

Postpartum depression (PPD) is a common complication following childbirth experienced by one in every five new mothers. Although the neural basis of PPD remains unknown previous research in rats has shown that gestational stress, a risk factor for PPD, induces depressive-like behavior during the postpartum period. Moreover, the effect of gestational stress on postpartum mood is accompanied by structural modifications within the nucleus accumbens (NAc) and the medial prefrontal cortex (mPFC) – limbic regions that have been linked to PPD. Mothers diagnosed with PPD are often prescribed selective serotonin reuptake inhibitor (SSRI) antidepressant medications and yet little is known about their effects in models of PPD. Thus, here we investigated whether postpartum administration of Citalopram, an SSRI commonly used to treat PPD, would ameliorate the behavioral and morphological consequences of gestational stress. In addition, we examined the effects of gestational stress and postpartum administration of Citalopram on structural plasticity within the basolateral amygdala (BLA) which together with the mPFC and NAc forms a circuit that is sensitive to stress and is involved in mood regulation. Our results show that postpartum rats treated with Citalopram do not exhibit gestational stress-induced depressive-like behavior in the forced swim test. In addition, Citalopram was effective in reversing gestational stress-induced structural alterations in the postpartum NAc shell and mPFC. We also found that gestational stress increased spine density within the postpartum BLA, an effect which was not reversed by Citalopram treatment. Overall, these data highlight the usefulness of gestational stress as a valid and informative translational model for PPD. Furthermore, they suggest that structural alterations in the mPFC-NAc pathway may underlie stress-induced depressive-like behavior during the postpartum period and provide much needed information on how SSRIs may act in the maternal brain to treat PPD.
Dissecting the mechanisms of environment sensitivity of smart probes for quantitative assessment of membrane properties

The plasma membrane serves as a crucial platform for a multitude of cellular processes. Its collective biophysical properties are largely determined by the structural diversity of the different lipid species it accommodates. Therefore, a detailed investigation of biophysical properties of the plasma membrane is of utmost importance for a comprehensive understanding of biological processes occurring therein. During the past two decades several environment-sensitive probes have been popular tools to investigate membrane properties. Although these probes are assumed to report on membrane order in similar ways, their individual mechanisms remain to be elucidated. In this study, using model membrane systems, we studied the probes Pro12A, NR12S and NR12A in depth and examined their sensitivity to parameters with potential biological implications, such as the degree of lipid saturation, double bond position and configuration, phospholipid headgroup and cholesterol content. Applying spectral imaging together with atomistic molecular dynamics simulations and time-dependent fluorescent shift analyses, we unravelled individual sensitivities of these probes to different biophysical properties, their distinct localizations and specific relaxation processes in membranes. Overall, Pro12A, NR12S and NR12A serve together as a toolbox with a wide range of applications allowing to select the most appropriate probe for each specific research question.
Developmental cell fate choice employs two distinct cis regulatory strategies

In many developing tissues the patterns of gene expression that assign cell fate are organised by secreted signals functioning in a graded manner over multiple cell diameters. Cis Regulatory Elements (CREs) interpret these graded inputs to control gene expression. How this is accomplished remains poorly understood. In the neural tube, a gradient of the morphogen Sonic hedgehog allocates neural progenitor identity. Here, we uncover two distinct ways in which CREs translate graded Shh signaling into differential gene expression. In the majority of ventral neural progenitors a common set of CREs are used to control gene activity. These CREs integrate cell type specific inputs to control gene expression. By contrast, the most ventral progenitors use a unique set of CREs. These are established by the pioneer factor FOXA2, paralleling the role of FOXA2 in endoderm. Moreover, FOXA2 binds a subset of the same sites in neural and endoderm cells. Together the data identify distinct cis regulatory strategies for the interpretation of morphogen signaling and raise the possibility of an evolutionarily conserved role for FOXA2-mediated regulatory strategy across tissues.
A Multi-scale, Multiomic Atlas of Human Normal and Follicular Lymphoma Lymph Nodes

Reference atlases, molecular and spatial maps of mammalian tissues, are critical resources for discovery efforts and translational research. Their utility is dependent on operationalizing the resulting data by identifying cell types, histological patterns, and predictive biomarkers underlying health and disease. The human lymph node (LN) offers a compelling use case because of its importance in immunity, structural and cellular diversity, and neoplastic involvement. One hematological malignancy, follicular lymphoma (FL), evolves from developmentally blocked germinal center B cells residing in and trafficking through these tissues. To promote survival and immune escape, tumor B cells undergo significant genetic changes and extensively remodel the lymphoid microenvironment. Here, we present an integrated portrait of healthy and FL LNs using multiple genomic and advanced imaging technologies. By leveraging the strengths of each platform, we identified several tumor-specific features and microenvironmental patterns enriched in individuals who experience early relapse, the most high-risk of FL patients.