Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-Dyslipidemic Properties of Saffron: Reduction in the Associated Risks of Atherosclerosis and Insulin Resistance

Background: Recently herbs considered as biological and safe agents to treat, control and prevent of many health problems such as obesity and its complications. Objectives: This study investigated protective effects of extracts from saffron stigma, petal, and their mixture on dyslipidemia, atherosclerosis, and insulin resistance in high-fat-fed obese rats. Methods: This experimental study was performed in animal house of Birjand University of Medical Sciences, Birjand, Iran. We used systematic random sampling to divide 56 adult male rats into 8 groups with 7 rats in each group that were fed a high-fat or standard diet for 10 weeks. Then, doses of saffron stigma, petal (40 and 80 mg/kg body weight, respectively), and their mixture (80 mg/kg body weight of both) were administered orally on a daily basis for three weeks. At the end of treatment priods, we examined all biochemical parameters. Data were analyzed by valid statistical analysis. Results: Saffron extracts markedly (P < 0.05) decreased the serum total cholesterol (TC, 90 ± 9.3), triglyceride (TG, 99 ± 10.5), and low-density lipoprotein cholesterol (LDL-C, 27 ± 1.1) in obese rats, while they increased the serum high-density lipoprotein cholesterol (HDL-C, 68 ± 2.2). The atherosclerosis-index (LDL/HDL, 0.39 ± 0.5), atherogenic index (TC/HDL, 1.32 ± 4.2), and the liver enzymes (ALT, AST, and ALP) were also reduced drastically (P < 0.05) after herbal treatments. Treatment with saffron extract significantly (P < 0.05) decreased the levels of leptin (0.26 ± 0.04), insulin (4.11 ± 0.1), resistin (11.1 ± 0.5) and homeostasis model assessment-estimated insulin resistance index (HOMA-IR, 20.0±1.4) and enhanced the levels of circulating adiponectin (108 ± 3), potentially indicating a cardio-protective influence. Antioxidant capacity (511.63 ± 19) increased after treatment with saffron extracts, but malondialdehyde levels (1.94 ± 0.1) decreased. Conclusions: This in vivo study demonstrated that saffron extracts, particularly the mixture of extracts from stigma and petal, ameliorated dyslipidemia in obese rats, leading to decreased atherosclerosis and insulin resistance. Our data suggested a potential therapeutic strategy against obesity and its related complications.
Judging Facial Expressions of Emotion: Effects of Gender

This study used reaction-time measures to examine how people respond to happy, neutral and angry emotional expressions in faces whose features vary in degree of femininity and masculinity. Prior researchers (Becker, Kenrick, Neuberg & Blackwell, 2007) proposed that the tendency to perceive happiness more quickly in women and anger more quickly in men is based primarily on differences in the physical structure of the face. We tested this theory and an alternative theory that gender-biased judgments of emotion are based less on the structure of the face, and more on the social cues used to distinguish gender. In addition, we examined whether neutral faces are perceived differently on women and men and proposed a social-learning framework to understand a current media-trend in social-evaluation called Resting Bitch Face . Facial features impacted judgments of emotion in women but not men. Also, a neutral expression with feminine features was more likely to be labeled as angry when the target was a woman than when it was a man. Results suggest that existing theories should be extended to include how feminine and masculine features interact with gender to form a more complex system of processing gender-biased judgments of emotion. Our findings indicate that a woman who deviates from the feminine norm may require more processing, and if she is not clearly displaying a pleasant expression she may be viewed as angry or hostile. More research is needed with neutral expressions to better understand why the same features are more likely to be labeled as angry when the target was a woman than when it was a man. Clearly, these findings suggest that we must include both physical features of the face and overall gender presentation of the target, in order to develop better theories of gender-biased judgments of emotional states.
Active SLAM for Mobile Robots With Area Coverage and Obstacle Avoidance

In this article, we present an active simultaneous localization and mapping (SLAM) framework for a mobile robot to obtain a collision-free trajectory with good performance in SLAM uncertainty reduction and in an area coverage task. Based on a model predictive control framework, these two tasks are solved by the introduction of a control switching mechanism. For SLAM uncertainty reduction, graph topology is used to approximate the original problem as a constrained nonlinear least squares problem. A convex half-space representation is applied to relax nonconvex spatial constraints that represent obstacle avoidance. Using convex relaxation, the problem is solved by a convex optimization method and a rounding procedure based on singular value decomposition. The area coverage task is addressed with a sequential quadratic programming method. A submap joining approach, called linear SLAM, is used to address the associated challenges of avoiding local minima, minimizing control switching, and potentially high computational cost. Finally, various simulations and experiments using an aerial robot are presented that verify the effectiveness of the proposed method, showing that our method produces a more accurate SLAM result and is more computationally efficient compared with multiple existing methods.
Programas educativos en pacientes con Enfermedad Pulmonar Obstructiva Crónica. Revisión integradora

According to WHO, the aim of therapeutic education (TE) is help the patient to acquire the resources necessary to optimally manage your life with a chronic disease. The TE is part of treatment in secondary and tertiary prevention. The latest Cochrane review has shown positive results however, heterogeneity of populations, types of interventions and outcome measures makes it difficult to establish clear recommendations in this area. And what instruments are recommended to assess the therapeutic education in patients with COPD. It is therefore necessary a review with the aim of knowing the characteristics of clinical trials, the profile of COPD patient, the contents of ET and the variables studied in them. In this review are Identified 22 clinical trials with high methodological quality, Jadad scale> 3. Which showed that in clinical trials programs ET the most studied variables are the quality of life, hospital admissions and lung function variables, where ET decreases hospital admissions, promotes smoking cessation and improving knowledge of COPD. Whose interventions are group sessions with different educational materials. There being a rise in the number of publications in this area, there is a great variety of assessment tools and heterogeneity of interventions, to draw conclusions
Neural network approximated Bayesian inference of edge electron density profiles at JET

A neural network (NN) has been trained on the inference of the edge electron density profiles from measurements of the JET lithium beam emission spectroscopy (Li-BES) diagnostic. The novelty of the approach resides in the fact that the network has been trained to be a fast surrogate model of an existing Bayesian model of the diagnostic implemented within the Minerva framework. Previous work showed the very first application of this method to an x-ray imaging diagnostic at the W7-X experiment, and it was argued that the method was general enough that it may be applied to different physics systems. Here, we try to show that the claim made there is valid. What makes the approach general and versatile is the common definition of different models within the same framework. The network is tested on data measured during several different pulses and the predictions compared to the results obtained with the full model Bayesian inference. The NN analysis only requires tens of microseconds on a GPU compared to the tens of minutes long full inference. Finally, in relation to what was presented in the previous work, we demonstrate an improvement in the method of calculation of the network uncertainties, achieved by using a state-of-the-art deep learning technique based on a variational inference interpretation of the network training. The advantage of this calculation resides in the fact that it relies on fewer assumptions, and no extra computation time is required besides the conventional network evaluation time. This allows estimating the uncertainties also in real time applications.
ANTIDIABETIC EFFECT OF THYMUS SATUREIOIDES AQUEOUS EXTRACT IN STREPTOZOTOCIN-INDUCED DIABETIC RATS

Objective : The aerial parts of Thymus satureioides have been traditionally used to treat diabetes mellitus and its complications in Morocco. The aim of the present study was to investigate the antidiabetic and antihyperlipidemic effects of the aqueous extract of Thymus satureioides aerial parts in streptozotocin (STZ) induced diabetic rats. Methods: Experimental diabetes was induced in overnight fasted rats by intraperitoneal injection of streptozotocin (45 mg/kg). Diabetic rats were orally administered with aqueous extract of Thymus satureioides (500 mg/kg b.w.) for 28 d. Glibenclamide (2 mg/kg), a standard antidiabetic drug, was used as a positive control drug. Body weight and fasting blood glucose (FGB) were measured every week. Oral glucose tolerance, change in lipid parameters, urea, creatinine, aspartate aminotransferase (ASAT) and alanine aminotransferase (ALAT) levels of diabetic rats were evaluated at the end of the treatment. Results: Administration of Thymus satureioides aqueous extract to diabetic rats for 28 d reduced their fasting blood glucose levels significantly compared to the diabetic control rats. The extract improved body weight and glucose tolerance in diabetic rats. The antihyperlipidemic assessment of the extract revealed a decrease in plasma total cholesterol, triglycerides, LDL levels and an increase in HDL level in the plasma of treated diabetic rats. Furthermore, the biochemical liver and kidney functional tests have shown that serum biomarkers of liver and renal dysfunction were significantly reduced in treated diabetic rats. Conclusion: The present findings suggest that Thymus satureioides extract has both antidiabetic and antihyperlipidemic effects in experimental diabetic rats which can be beneficial in the management of diabetes and its complications.
Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting

Intelligent transportation systems are a key component in smart cities, and the estimation and prediction of the spatiotemporal traffic state is critical to capture the dynamics of traffic congestion, i.e., its generation, propagation and mitigation, in order to increase operational efficiency and improve livability within smart cities. And while spatiotemporal data related to traffic is becoming common place due to the wide availability of cheap sensors and the rapid deployment of IoT platforms, the data still suffer some challenges related to sparsity, incompleteness, and noise which makes the traffic analytics difficult. In this article, we investigate the problem of missing data or noisy information in the context of real-time monitoring and forecasting of traffic congestion for road networks in a city. The road network is represented as a directed graph in which nodes are junctions (intersections) and edges are road segments. We assume that the city has deployed high-fidelity sensors for speed reading in a subset of edges; and the objective is to infer the speed readings for the remaining edges in the network; and to estimate the missing values in the segments for which sensors have stopped generating data due to technical problems (e.g., battery, network, etc.). We propose a tensor representation for the series of road network snapshots, and develop a regularized factorization method to estimate the missing values, while learning the latent factors of the network. The regularizer, which incorporates spatial properties of the road network, improves the quality of the results. The learned factors, with a graph-based temporal dependency, are then used in an autoregressive algorithm to predict the future state of the road network with a large horizon. Extensive numerical experiments with real traffic data from the cities of Doha (Qatar) and Aarhus (Denmark) demonstrate that the proposed approach is appropriate for imputing the missing data and predicting the traffic state. It is accurate and efficient and can easily be applied to other traffic datasets.
The Participation of the Little Child Mother’s in the Programme of Adaptation to Dental Treatment as a Precondition of Its Effectiveness

Background. The original method of gradual adaptation (GAM) presented here was meant to influence the moth− er−child dyad. In social situations involving the little child (such as being seen by a doctor), direct contact with each person of the dyad takes place at a different level (it, so to speak, splits). A diagnostic process and medical proce− dures directly concern the child, whereas everything related to understanding the appointment situation is directed to the mother. Due to this split, some space for the mother−child emotional exchange is made between the child’s conscious level of comprehension and his/her readiness to undergo some medical procedures. Objectives. The purpose of our research was to evaluate the own gradual adaptation method as used for under three years old children, where particular attention was paid to the effect it had on their mothers’ attitude and behaviour. Material and Methods. The study, where three successive dental visits were videoed and then analysed by psy− chologists, involved 33 mother−child dyads. The mothers’ level of anxiety (DAS scale), their apprehension about their children (original scale) as well as their confidence in the dentist were measured as the indexes of the attitude towards dental treatment. Results. The statistically significant increase in the children’s co−operative and their mothers’ neutral attitude was observed. The occurrence of some important behavioural changes was also revealed by the comparison of the mothers’ anxiety, their apprehension about their children and their confidence in the dentist before and after the adaptation programme. Conclusion. The research results provided the evidence in support of the efficiency of the evaluated method. The mother is included in a diagnostic process, deepens her knowledge. More understanding makes her a thera− pist’s partner. This is how the results of therapeutic activity get a chance to consolidate (Dent. Med. Probl. 2009, 46, 2, 219–228).
A systematic literature review of product platform design under uncertainty

ABSTRACT With intense global competition, many enterprises adopt platform strategies to develop diverse products with a lower corresponding increase in cost and a shorter lead time. In the past, enormous efforts have been made in investigating product platforms. Recent researches and practices show that developing Uncertainty-Oriented Product Platform (UOPP) is a promising approach to improving the adaptability of enterprises to meet uncertain markets, customer requirements, technologies, policies, and regulations, etc., which includes flexible product platform, adaptable product platform, market-driven product platform, and sustainable product platform. This paper provides a comprehensive review in this field, including the connotation of uncertainty, the concepts of emerging UOPPs, the development process of UOPP, as well as the development technologies in each step. It also highlights the future research framework, opportunities and challenges. This literature review lays the foundation for future research of product platform design under uncertainty.
Real-Time Anomaly Detection of NoSQL Systems Based on Resource Usage Monitoring

Today, the emergence of the industry revolution systems such as Industry 4.0, Internet of Things, and big data frameworks poses new challenges in terms of storage and processing of real-time data. As systems scale in humongous sizes, a crucial task is to administer the variety of different subsystems and applications to ensure high performance. This is directly related with the identification and elimination of system failures and errors, while the system runs. In particular, database systems may experience abnormalities related with decreased throughput or increased resource usage, that in turn affects system performance. In this article, we focus on not only SQL (NoSQL) database systems that are ideal for storing sensor data in the concept of Industry 4.0. This typically includes a variety of applications and workloads that are difficult to online monitor, thus making anomaly detection a challenging task. Creating a robust platform to serve such infrastructures with minimum hardware or software failures is a key challenge. In this article, we propose RADAR, an anomaly detection system that works on real time. RADAR is a data-driven decision-making system for NoSQL systems, by providing process information extraction during resource monitoring and by associating resource usage with the top processes, to identify anomalous cases. In this article, we focus on anomalies such as hardware failures or software bugs that could lead to abnormal application runs, without necessarily stopping system functionality, e.g., due to a system crash, but by affecting its performance, e.g., decreased database system throughput. Although different patterns may occur through time, we focus on periodic running workloads (e.g., monitoring daily usage) that are very common for NoSQL systems, and Internet of Things scenarios where data streams are forwarded to the Cloud for storage and processing. We apply various machine learning algorithms such as autoregressive integrated moving average (ARIMA), seasonal ARIMA, and long–short-term memory recurrent neural networks. We experimentally analyze our solution to demonstrate the benefits of supporting online erroneous state identification and characterization for modern applications.
Coping and laughing in the face of broken promises: implications for creative behavior

The purpose of this paper is to consider how employees’ perceptions of psychological contract breach, due to their sense that their organization has not kept its promises, might diminish their creative behavior. Yet access to two critical personal resources – emotion regulation and humor skills – might buffer this negative relationship. ,Survey data were collected from employees in a large organization in the automobile sector. ,Employees’ beliefs that their employer has not come through on its promises diminishes their engagement in creative activities. The effect is weaker among employees who can more easily control their emotions and who use humor in difficult situations. ,For organizations, the results show that the frustrations that come with a sense of broken promises can be contained more easily to the extent that their employee bases can rely on pertinent personal resources. ,This investigation provides a more comprehensive understanding of when perceived contract breach steers employees away from productive work activities, in the form of creativity. This damaging effect is less prominent when employees possess skills that enable them to control negative emotions or can use humor to cope with workplace adversity.
Recognizable Patterns of Human Malformation. Genetic, Embryologic and Clinical Aspects

Find the secret to improve the quality of life by reading this recognizable patterns of human malformation genetic embryologic and clinical aspects. This is a kind of book that you need now. Besides, it can be your favorite book to read after having this book. Do you ask why? Well, this is a book that has different characteristic with others. You may not need to know who the author is, how well-known the work is. As wise word, never judge the words from who speaks, but make the words as your good value to your life.
From the Frontlines: Perceptions of Paraprofessionals’ Roles and Responsibilities

The paraeducator workforce as well as the breadth of their responsibilities to serve students in special education has increased considerably in public schools. Unfortunately, research to identify the most effective methods for training paraeducators has not kept pace. Addressing this dynamic, through an implementation science framework, requires a better understanding of contextual factors. In this study, focus groups (FGs) with paraeducators and teachers were conducted to examine (a) responsibilities, training needs, and related issues of paraprofessionals; (b) training needs and related issues of teachers as supervisors of paraeducators; and (c) factors that support or impede special educators’ self-efficacy and their perception of competence of their colleagues. Four FGs, homogeneous based on role and assignment (mild vs. moderate-to-severe disabilities), were conducted utilizing a semistructured interview protocol. Qualitative content analysis revealed important challenges and training-related deficiencies experienced by both teachers and paraeducators, as well as suggestions for addressing these issues.
Housing prices and household savings: evidence from urban China

Abstract Based on precautionary saving motives, this research develops a three-period life-cycle model to manifest the impact of housing prices on household savings in urban China. The theoretical model illustrates that the expected appreciation of housing prices at a household’s middle age leads to the increase in household savings at a household’s young age. Second, household savings at a household’s young age are positively associated with both expected educational and medical expenditures in a household’s middle age and pension expenditures at a household’s old age. Third, the expected housing prices crowd out educational and medical expenditures at a household’s middle age. With the panel data sets of China’s 31 provinces during 1996–2016, results suggest that the expected housing prices significantly interact with the current household savings. However, the influence of the expected housing prices on the current household savings is greater than that of the current household savings on the expected housing prices. Third, the expected expenditures of education, medical care and pension fuel up the current household savings. Meanwhile, the housing prices crowd out the expenditures of education, medical care and pension. Finally, data of the Urban Household Survey (UHS) over the period 2002–2007 show that the household head age has an effect of reverse U-shape on household savings. Accordingly, to prevent a housing bubble and promote household consumption, policy makers should curb housing price inflation by enacting appropriate countercyclical housing policies.