Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and analysis of a high-gain dual-input single-output DC-DC converter for hybrid energy systems applications

The high gain converters are one among the proven topologies that are found attractive for industrial and commercial applications. They deal with high power ratings like automobile headlamps, energy backup systems, and fuel cell electric vehicles (FCEV). In literature, most of the topologies were used a single duty ratio. This may not be reliable for high-duty cycle operation because to improve the voltage gain, the components will be increases. This paper proposes the time-sharing concept based on the non-isolated high step-up dc-dc converter. With the parallel-charged and series-discharged inductors, the proposed converter can produce high output voltage gains. The effectiveness of the proposed circuit configuration is verified in MATLAB/Simulink and the results are corroborated by theoretical analysis.
Long-Term Results after Bilateral Sacrospinous Colposuspension: A Prospective Study

The loss of apical support is usually present in patients with pelvic organ prolapse. An effective correction for the vaginal apex may be an essential part of a durable repair for these women. Apical suspension of the sacrospinous ligament is likely one of the best treatments by the vaginal route. We proposed the evaluation of the functional and anatomical long-term results of an ultralight and macroporous sling. In this prospective study, bilateral sacrospinous colposuspension was performed in 32 patients with a specific mesh. Functional assessment with several validated quality of life questionnaires and pelvic examination was performed at 1, 6, 12, and 24 months after surgery. Pelvic examination using the POP-Q classification showed a very good efficacy of the BSC mesh with only three prolapse recurrences at 24 months after surgery. All the following QoL scores were significantly improved by two years: PFIQ-7 (p < 0.0001), PFDI-20 (p < 0.0001), and SF-12 (p < 0.0001). No improvement was achieved by the PISQ12 questionnaire. This vaginal minimally invasive procedure is effective, quick, reproducible, and easy. It may be a relevant option for a vaginal vault or cervical or uterine prolapse.
Harnessing Nature’s Ingenuity: A Comprehensive Exploration of Nanocellulose from Production to Cutting-Edge Applications in Engineering and Sciences

Primary material supply is the heart of engineering and sciences. The depletion of natural resources and an increase in the human population by a billion in 13 to 15 years pose a critical concern regarding the sustainability of these materials; therefore, functionalizing renewable materials, such as nanocellulose, by possibly exploiting their properties for various practical applications, has been undertaken worldwide. Nanocellulose has emerged as a dominant green natural material with attractive and tailorable physicochemical properties, is renewable and sustainable, and shows biocompatibility and tunable surface properties. Nanocellulose is derived from cellulose, the most abundant polymer in nature with the remarkable properties of nanomaterials. This article provides a comprehensive overview of the methods used for nanocellulose preparation, structure–property and structure–property correlations, and the application of nanocellulose and its nanocomposite materials. This article differentiates the classification of nanocellulose, provides a brief account of the production methods that have been developed for isolating nanocellulose, highlights a range of unique properties of nanocellulose that have been extracted from different kinds of experiments and studies, and elaborates on nanocellulose potential applications in various areas. The present review is anticipated to provide the readers with the progress and knowledge related to nanocellulose. Pushing the boundaries of nanocellulose further into cutting-edge applications will be of particular interest in the future, especially as cost-effective commercial sources of nanocellulose continue to emerge.
Adaptive and iterative learning control to simultaneously control end-effector force and direction by normal vectors learning

Abstract It is very challenging for robots to perform grinding and polishing tasks on surfaces with unknown geometry. Most existing methods solve this problem by modeling the relationship between the force sensing information and surface normal vectors by analyzing the forces on special end tools such as spherical tools and cylindrical tools and simplified friction model. In this paper, we propose a normal vectors learning method to simultaneously control end-effector force and direction on unknown surfaces. First, the relation that mapping the force sensing information to the surface normal vectors is learned from the demonstrated data on the known plane using locally weighted regression. Next, the learned relation is used to estimate surface normal vectors on the unknown surface. To improve the force control precision on the unknown geometry surface, the adaptive force control is developed. To improve the direction control precision due to friction, the iterative learning control is developed. The proposed method is verified by comparative simulations and experiments using the Franka robot. Results show that the end-effector can be controlled perpendicular to the surface with a certain force.
3D Kinematic Modeling and Evaluation of Rough-Terrain Locomotion Modes for an ExoMars-like Mobility Subsystem

One of the main challenges of planetary rover missions is the traversability of rough terrain areas, steep slopes and very soft soil fields. In the past, rovers repeatedly struggled while driving especially in these soft soils, with the Spirit rover even having to be abandoned. With ESA's upcoming ExoMars mission, a rover with an articulated suspension will land on Mars in 2021 that offers additional degrees of freedom through its actuated deployment joints. A full-body kinematic model allows to take full advantage of such a locomotion system. In this thesis, such a model was derived for EXM-BB2, a mobility subsystem prototype for the ExoMars rover. The model allows to specify desired body velocities and additional constraints on joint rates to achieve desired motions and includes a slip formulation on wheel level. Additionally, kinematic redundancies can be exploited by the introduction of secondary sub-tasks through null-space projection. Based on this model, two locomotion modes were implemented for increased traversability performance in rough terrain: Balancing, a mode for body pose adjustment that allows to increase the rover's tip-over stability and decrease its body tilt, and Wheel Walking, a mode for increasing gradeability on slopes and traction in soft soils. The effectiveness of the kinematic model and the locomotion modes were shown in a series of tests in the Planetary Exploration Laboratory at DLR. Balancing was able to reduce the rover body pitch and yaw angle by 5 degrees in uneven terrain and decrease side-slip while driving sideways along a slope by more than 50 percent. Wheel Walking managed to outperform Normal Driving (which only actuates the rover wheels) significantly in terms of traction on a slope. Using Wheel Walking, the rover managed to traverse a 30 degree slope with 70 percent of slip while Normal Driving failed at 21 degrees. Additionally, using Wheel Walking, energy consumption for slope traversal could be reduced by 50 percent.
Direct adaptive control of parabolic systems: algorithm synthesis, and convergence and stability analysis

This paper presents a model reference adaptive control of a class of distributed parameter systems described by linear, n-dimensional, parabolic partial differential equations. Unknown parameters appearing in the system equation are either constant or spatially-varying. Distributed sensing and actuation are assumed to be available. Adaptation laws are obtained by the Lyapunov redesign method. It is shown that the concept of persistency of excitation, which guarantees the parameter error convergence to zero in finite dimensional adaptive systems, in infinite dimensional adaptive systems should be investigated in relation to time variable, spatial variable, and also boundary conditions. Unlike finite dimensional case, in infinite dimensional adaptive systems even a constant input is shown to be persistently exciting in the sense that it guarantees the convergence of parameter errors to zero. Averaging theorems for two-time scale systems which involve a finite dimensional slow system and an infinite dimensional fast system are developed. The exponential stability of the adaptive system, which is critical in finite dimensional adaptive control in terms of tolerating disturbances and unmodeled dynamics, is shown by applying averaging. A numerical example which demonstrates an averaged system, and computer simulations are provided. <<ETX>>
Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches

ABSTRACT The main objective of this study is to combine remote-sensing and artificial intelligence (AI) approaches to estimate surface soil moisture (SM) at 100 m spatial and daily temporal resolution. The two main variables used in the Triangle method, that is, land-surface temperature (LST) and vegetation cover, were downscaled and calculated at 100 m spatial resolution. LSTs were downscaled applying the Wavelet-Artificial Intelligence Fusion Approach (WAIFA) on Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imageries. Vegetation fractions were also estimated at 100 m spatial resolution using linear spectral un-mixing and Wavelet–AI models. Vegetation indices (VIs) were replaced with the vegetation fractions obtained from sub-pixel classification in the Ts–VI triangle space. The downscaled data were then used for calculating the evaporative fraction (EF), temperature-vegetation-dryness index (TVDI), vegetation temperature condition index (VTCI), and temperature-vegetation index (TVX) at 100 m spatial resolution. Thereafter, surface SM modelling was performed using a combination of Particle Swarm Optimization with Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Support Vector Regression (PSO-SVR) modelling approaches. Results showed that the best input data set to estimate SM includes EF, TVDI, Ts, Fvegetation, Fsoil, temperature (T), precipitation at time t (Pt, Pt – 1, Pt – 2), and irrigation (I). It was also confirmed that PSO-SVR outperformed the PSO-ANFIS modelling approach and could estimate SM with a coefficient of determination (R2) of 0.93 and a root mean square error (RMSE) of 1.29 at 100 spatial resolution. Range of error was limited between −2.64% and 2.8%. It was also shown that the method proposed by Tang et al., (2010) improved the final SM estimations.
The effects of online game addiction on reduced academic achievement motivation among Chinese college students: the mediating role of learning engagement

Introduction The present study aimed to examine the effects of online game addiction on reduced academic achievement motivation, and the mediating role of learning engagement among Chinese college students to investigate the relationships between the three variables. Methods The study used convenience sampling to recruit Chinese university students to participate voluntarily. A total of 443 valid questionnaires were collected through the Questionnaire Star application. The average age of the participants was 18.77 years old, with 157 males and 286 females. Statistical analysis was conducted using SPSS and AMOS. Results (1) Chinese college students’ online game addiction negatively affected their behavioral, emotional, and cognitive engagement (the three dimensions of learning engagement); (2) behavioral, emotional, and cognitive engagement negatively affected their reduced academic achievement motivation; (3) learning engagement mediated the relationship between online game addiction and reduced academic achievement motivation.
Adoption of Integrated Pest Management Practices Among Oklahoma Greenhouse Producers: A Case Study for Experiential Learning

An instruction- and experience-based workshop was conducted for Oklahoma greenhouse producers to teach principles of integrated pest management (IPM) for common arthropod pests of greenhouse crops. Workshop effectiveness was evaluated using a pre- and post-test instrument to measure changes in knowledge and attitudes about IPM and current use and intention to adopt IPM practices. Findings indicate the objectives were accomplished as participants reported significant increases in IPM knowledge, a marked acceptance of IPM practices, and a willingness to adopt IPM. Extension workshops that combine classroom instruction and hands-on demonstration can be used effectively to teach relevant concepts to stakeholders.
"Pretty boys do not cry": The role of facial expressions in facial attractiveness rating

Facial attractiveness is the concept that has been widely explored in previous studies. The findings suggest that some of the factors that affect aesthetical judgment of faces include symmetry, averageness, and facial expressions. The role of facial expressions is not fully established, but it seems that faces with the facial expression of happiness are rated higher (at least female faces). The aim of our study is to explore whether the presence of facial expressions has a different effect on the aesthetic judgment of female and male faces. Unlike previous studies, in which this was not explicitly controlled, we only considered facial expressions that were correctly recognized at 90% or more. A total of 61 respondents participated in this study. They evaluated female and male faces with the expressions of happiness, anger, sadness, or neutral on several scales: Beautiful, Pleasant, Attractive, and Harmonious. Overall, female faces were rated as more attractive, beautiful, and pleasant, but not harmonious. In addition, faces with the expressions of anger and sadness were rated lower on each scale compared to neutral and happy faces, and sad male faces were rated lower compared to sad female faces. One of the possible explanations for such a result could be the role of the social context in which the society discourages the display of certain emotions, particularly for men. Sad men are seen as weak and weak man are not attractive (because this does not fit into their gender role).
Model selection consistency of U-statistics with convex loss and weighted lasso penalty

ABSTRACT In the paper we consider minimisation of U-statistics with the weighted Lasso penalty and investigate their asymptotic properties in model selection and estimation. We prove that the use of appropriate weights in the penalty leads to the procedure that behaves like the oracle that knows the true model in advance, i.e. it is model selection consistent and estimates nonzero parameters with the standard rate. For the unweighted Lasso penalty, we obtain sufficient and necessary conditions for model selection consistency of estimators. The obtained results strongly based on the convexity of the loss function that is the main assumption of the paper. Our theorems can be applied to the ranking problem as well as generalised regression models. Thus, using U-statistics we can study more complex models (better describing real problems) than usually investigated linear or generalised linear models.
Contrastive Learning Based on Category Matching for Domain Adaptation in Hyperspectral Image Classification

Cross-scene hyperspectral image classification (HSIC) is a challenging topic in remote sensing, especially when there are no labels in the target domain. Domain adaptation (DA) techniques for cross-scene HSIC aim to label a target domain by associating it with a labeled source domain. Most existing DA methods learn domain-invariant features by reducing feature distance across domains. Recently, contrastive learning has shown excellent performance in computer vision (CV) tasks, but there is little or no research on the performance of cross-scene HSIC. Considering that its idea is similar to reducing feature distance, this article attempts to explore whether contrastive learning can achieve cross-scene HSIC. In this work, an instance-to-instance contrastive learning framework based on category matching (CLCM) is designed. The main idea is to take the category information as the premise in the feature space, regard the source sample as an anchor, and find its positive and negative matching samples across domains. The instance-level discriminative feature embeddings are learned through positive matching pairs attracting each other and negative matching pairs repelling each other. Among them, the target label is a pseudo-label. To further improve the quality of contrastive learning, it is considered to focus on extracting the spectral–spatial features of hyperspectral images (HSIs) to more accurately represent semantic information. Simultaneously, high-confidence target samples are screened to update the network. Three DA tasks confirm the effectiveness and feature discriminativeness of CLCM, while also providing new ideas for cross-scene image classification.
ALMA Study of the Lensed Galaxy SDP.81

In this paper we present high resolution data from Atacama Large Millimeter /sub-millimeter Array (ALMA) of CO (5-4) and CO (8-7) rotational transitions toward the gravitational lensed galaxy (SDP.81). We calculate the molecular gas mass in the clumpy region which is around 1.5´109 Mʘ. We overly the CO(8- 7) and CO(5- 4) intensity distributions to calculate the brightness temperature ratio R85 at various region. The CO line ratio was in the range between 0.39 and 0.75 with an average value about 0.48. This line ratio suggests that most sub-millimeter galaxies, SMGs, include a significant proportion of extended, moderate-density, cool gas.
Eigenvector space of narrow-line Seyfert 1 galaxies

We have performed an analysis of the correlation space of nar rowand broad-line Seyfert 1 galaxies, in order to identify main drivers of their intriguing em ission-line and continuum properties. In particular, we paid attention to the density of the narrow -line region. A principal component analysis then shows that the density is a key ingredient of th e Eigenvector 1 space of our sample, as important as the Eddington ratio. Our finding implies a clo se link between the properties of the central engine and the host galaxy.
‘I get headspace here… you forget everything when in open water’: motives for participation and perceived benefits derived from open water swimming: a rapid ethnographic study

Abstract Interest in open water swimming (OWS) has experienced a surge in participation in recent years, with over four million people in the UK reportedly taking to the water in a variety of settings. This study investigated the motives behind engagement in the sport as well as perceived health and wellbeing benefits. A rapid ethnographic methodology was employed, in which a field researcher, who kept a reflective diary, also conducted semi-structured interviews with 17 swimmers (n = 11 female, n = 6 male, mean age 52.7 years). Five main themes were identified: motives for engagement/continued participation; the uniqueness of OWS experiences; perceived physical and psychological health benefits; blue and green exercise comparisons; and the utility of social interactions. Core motives focused upon nature connectedness; health outcomes; training for performance; cold-water experience; and self-actualisation. The perceived physical and psycho-social benefits reported by participants appeared to be mediated by numerous influences, including: the restorative and stress reducing natural environments; social interactions; personal challenge; and the ‘highs’ of cold-water immersion - and moderated by factors including access issues; extent of social support/networks; perceptions of safety; and levels of competency. Participants also highlighted substantive differences in benefits derived from OWS compared to green exercise alternatives. The study provides useful insights for relevant health practitioners that might inform potential social prescribing initiatives. Facility operators, who manage open water venues such as the one featured in this study, and swimming coaches at these venues, can also better understand how they can tailor services more appropriately to meet participants’ needs, thus enhancing the overall quality of the OWS ‘offer’ and infrastructure.
Effects of Atlantic butterfish ( Peprilus triacanthus ) in diets of Common Terns ( Sterna hirundo ): a case study of climate change effects in the Gulf of Maine

Climate change and associated shifts in marine prey communities can alter food availability for foraging seabirds. This issue is illustrated in the Gulf of Maine by the northward shift of Atlantic butterfish ( Peprilus triacanthus ; hereafter butterfish) and their subsequent increase in seabird diets. Here, we examine effects of butterfish in diets of the Common Tern ( Sterna hirundo ), a threatened species in this region. Our objectives were to evaluate butterfish suitability for tern chick diet through observing handling time and feeding success, to examine effects of butterfish on tern chick growth, and to explore signs of adaptive foraging in adults. The diet and growth of Common Tern chicks were studied for three breeding seasons on Seavey Island, New Hampshire, USA in the Gulf of Maine. Prey items were identified during feedings and were grouped into five prey categories: butterfish, herring, hake, other fish, and invertebrates. Chicks were weighed to calculate growth rate, which was examined as a response to diet. Across prey categories, butterfish were handled by chicks for the greatest amount of time (P < 0.001) but were swallowed the least (P < 0.001). Furthermore, chick growth rates were negatively associated with proportion of butterfish in the diet (P < 0.001). There was significant variation in chick diet across different nests in two of the three years studied (P < 0.05 in 2018 and 2019), and some breeding pairs were never observed provisioning butterfish to their young. Although the mechanism underlying individual specialization is not known, chicks that are fed fewer butterfish are at an advantage. The provisioning (or not) of unsuitable prey is particularly important because seawater warming in the Gulf of Maine is expected to increase, and butterfish are likely to become even more prevalent in seabird diets.
Factors affecting self-esteem and disease acceptance in patients from infertile couples

Introduction Infertility has been diagnosed in millions of people around the world and is described as a complex medical, mental, and social problem that affects many aspects of life. The aim of the study was to extract the determining factors and the level of self-esteem and the degree of acceptance of the disease in infertile patients and to find differences between women and men in this aspect. Methods A total 456 patients (235 women and 221 men) from infertile couples participated in a cross-sectional study. To collect data a Personal Information Form (PIF), Rosenberg Self-Esteem Scale (SES), and Acceptance of Illness Scale (AIS) were used. Results The overall self-esteem score for the whole sample was 30.50 (15 ± 30) points and for acceptance of the disease 32.4 (8 ± 40) points. In the study group, men obtained a slightly higher level of self-esteem than women (31.00 vs. 30.04 points). Additionally, men had a higher level of acceptance of the disease (33.12 vs. 31.80) than women. Socio-demographic factors such as age and level of education had impact on scores SES and AIS. Clinical factors did not determine the results of SES and AIS, both in the overall sample and in the female and male groups. Conclusion Self-esteem in patients from infertile couples increases with age and level of education. There are also significant differences between women and men, i.e., positive correlations between the level of education and self-esteem in men and the degree of acceptance of the disease in women.