Miracle-dz/UniFAR-FTUs huggingface dataset:
The prevalence of 'alexithymic' characteristics in psychosomatic patients.

An attempt is made in this paper to investigate the prevalence among patients who suffer from various psychosomatic diseases, of a relative constriction in emotional functioning, poverty of fantasy life, and inability to find appropriate words to describe their emotions. For lack of a better term, I call these characteristics ‘alexithymic’. Twenty-five psychosomatic patients outnumbered 25 control patients by better than two to one as far as possession of these ‘alexithymic’ characteristics. The question is raised whether these defects are due to biological or developmental factors, and the conclusion is reached that patients with these characteristics may not be good candidates for dynamic psychotherapy.
Modeling Investor Optimism with Fuzzy Connectives

The present invention provides isolated and purified polypeptide components of the mammalian circadian clock, polynucleotides that encode those polypeptides, expression vectors containing those polynucleotides, host cells transformed with those expression vectors, a process of making the polypeptide components using those polynucleotides and vectors, and processes using those polypeptides and polynucleotides.
Nitrogen Fertilizer Management in Dryland Wheat Cropping Systems

Wheat is the most widely cultivated food crop in the world, which provides nutrition to most of the world population and is well adapted to a wide range of environmental conditions. Timely and efficient rates of nitrogen (N) application are vital for increasing wheat grain yield and protein content, and maintaining environmental sustainability. The goal of this study was to investigate the effect of using different rates and split application of N on the performance of spring wheat in dryland cropping systems. The experiment was conducted in three different locations in Montana and Idaho during two consecutive growing seasons. A split-plot experimental design was used with three at planting N fertilization application (0, 90 and 135 kg N ha−1) and two topdressing N fertilization strategies as treatments. A number of variables such as grain yield (GY), protein content (GP) in the grains and N uptake (NUP) were assessed. There was a significant effect of climate, N rate, and time application on the wheat performance. The results showed that at-planting N fertilizer application of 90 kg N ha−1 has significantly increased GY, GP and NUP. On the other hand, for these site-years, increasing at-planting N fertilizer rate to 135 kg N ha−1 did not further enhance wheat GY, GP and NUP values. For all six site-years, topdress N fertilizer applied at flowering did not improve wheat GY, GP and NUP compared to at-planting fertilizer alone. As the risk of yield loss is minimal with split N application, from these results we concluded the best treatment for study is treatments that had received 90 kg N ha−1 split as 45 kg N ha−1 at planting and 45 kg N ha−1 at flowering.
Induction of xenobiotic-metabolizing enzymes in hepatocytes by beta-naphthoflavone: Time-dependent changes in activities, protein and mRNA levels

Abstract In the present study, time-dependency of the induction effect of a selective inducer on the activity, protein and mRNA levels of cytochromes P450 1A1/2 (CYP1A1/2), NAD(P)H:quinone oxidoreductase 1 (NQO1) and glutathione S-transferases (GSTA), in primary culture of rat hepatocytes was tested and evaluated. To show the differences in responses of tested enzymes, the common aryl hydrocarbon receptor (AhR) ligand agonist, beta-naphthoflavone (BNF), was used. Induction of CYP1A1/2 by BNF was detected at all time intervals and at all levels (i.e., mRNA, protein, enzyme activity). Different responses of NQO1 and GSTA upon BNF treatment were observed. Our results demonstrate that the responses of different xenobiotic-metabolizing enzymes to the inducer vary in time and depend on the measured parameter. For these reasons, an induction study featuring only one-time interval treatment and/ or one parameter testing could produce misleading information.
Prevalence of Renal and Cervical Vertebral Anomalies in Patients with Isolated Microtia and/or Aural Atresia

Objective The objective of this study was to determine whether patients with isolated microtia or aural atresia have an increased prevalence of renal or cervical vertebral anomalies. Design The study design was a retrospective medical record review. Setting The setting was the following four distinct institutions: an urban tertiary care children's hospital, two urban academic medical centers, and a staff-model health maintenance organization. Participants Patients diagnosed with microtia, aural atresia, or oculoauriculovertebral spectrum were identified. Patients with facial asymmetry, craniofacial microsomia, and other craniofacial abnormalities or syndromes were excluded. Main Outcome Measures Main outcome measures were the number of patients with isolated microtia or aural atresia who underwent a renal ultrasound or cervical spine X-ray, the results of those studies, and further evaluation or treatment for any abnormalities found. Statistical Analysis A binomial analysis using a one-sided 95% confidence level was performed. Results A total of 514 patients with isolated microtia and/or aural atresia were identified. Of these patients, 145 (28%) had undergone a renal ultrasound and 81 (16%) had undergone cervical spine X-rays. A total of 3 patients (2%) had minimal renal pelviectasis, all of which had resolved on repeat ultrasound and required no treatment. There were no structural renal abnormalities identified, and there were no cervical spine abnormalities identified. Conclusions The data suggest that there is no increased prevalence of structural renal or cervical vertebral anomalies in patients with isolated microtia and/or aural atresia. Therefore, these patients do not require routine screening renal ultrasound or cervical spine X-rays.
Critical Analysis on Algorithm Visualization Study

This paper reports on an ongoing study, which intends to propose a principle of interactive algorithm visualization on hybrid mobile application (INAVOHMA) that is created in order to help IT students learn data structure and algorithm (DSA) subject. Totally, 8 existing AV guidelines and models were reviewed comprehensively with the main purposes (1) to determine the research gaps in proposing principles of INAVOHMA and (ii) to identify their common components. Through a systematic and critical analysis, this study discovers there is still lack of inclusive principles or guidelines of AV that focused on mobile platform, mostly for desktop or website platform. Only, two guidelines draw attention to mobile platform, yet the focus of them just for sorting algorithm only. It is noted that this is the research gap that should be the focal point for further study.
Masked Conditional Neural Networks for Automatic Sound Events Recognition

Deep neural network architectures designed for application domains other than sound, especially image recognition, may not optimally harness the time-frequency representation when adapted to the sound recognition problem. In this work, we explore the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) for multi-dimensional temporal signal recognition. The CLNN considers the inter-frame relationship and the MCLNN enforces a systematic sparseness over the network’s links to enable learning in frequency bands rather than bins allowing the network to be frequency shift invariant mimicking a filterbank. The mask also allows considering several combinations of features concurrently, which is usually handcrafted through exhaustive manual search. We applied the MCLNN to the environmental sound recognition problem using the ESC-10 and ESC-50 datasets. MCLNN achieved competitive performance, using 12% of the parameters and without augmentation, compared to state-of-the-art Convolutional Neural Networks.
Menopause and cardiovascular risk

Menopause is not a disease, but a physiologic phase of a woman's life, due to the changes of their hormonal status. Fastidious symptoms may be associated with changes in the metabolism together with new cardiovascular risk factors, particularly aggressive for the female cardiovascular system, unprepared because of the protection due to the fertile period. Changes of the lipid profile, obesity, hypertension, glucose intolerance and diabetes mellitus may intervene as severe risk factors. Cardiovascular disease represents therefore the most frequent cause of mortality and morbidity also in the female gender more than cancer either in the United States as in Europe. The risks related to post-menopause are mainly due to the abrupt interruption of estrogen, which has indirect protective effects on lipid, glycidic metabolism and direct effects on vessel function. They have, in fact, vasodilator action due to nitric oxide release, calcium-antagonist like action and an antiproliferative effect on smooth muscle cells. Post-menopause is also frequently associated with hypertension, the most frequent related factor to coronary artery disease. Hypertension is due to increased body mass index, with insulin-resistance, sodium retention, increased blood viscosity and estrogen deficiency with increased smooth muscle cell proliferation which determines an increase in systemic vascular resistance. Age and estrogen deficiency are together the most important cause of cardiovascular risk in post-menopause. The discovery of alpha and recently beta estrogen receptors on coronary female vessels unaffected by atherosclerosis either during pre and post-menopause phase are possible key of interpretation of pathophysiology of coronary artery disease in women, with important therapeutic consequence.
Hierarchical Clustering- An Efficient Technique of Data mining for Handling Voluminous Data

The objective of data mining is to take out information from large amounts of data and convert it into form that can be used further. It comes with several functionalities, among which Clustering is worked upon in this paper. Clustering is basically an unsupervised learning where the categories in which the data to put is not known priorly. It is a process where we group set of abstract objects into similar objects such that objects in one cluster are highly similar in comparison to each and dissimilar to objects in other clusters. Clustering can be done by different number of methods such as-partitioning based methods, methods based on hierarchy, density based ,grid based ,model based methods and constraint based clustering. In this survey paper review of clustering and its different techniques is done with special focus on Hierarchical clustering. A number of hierarchical clustering methods that have recently been developed are described here, with a goal to provide useful references to fundamental concepts accessible to the broad community of clustering practitioners.
Addressing the Issue of Routing Unfairness in Opportunistic Backhaul Networks for Collecting Sensed Data

Widely deploying sensors in the environment and embedding them in physical objects is a crucial step towards realizing smart and sustainable cities. To cope with rising resource demands and limited budgets, opportunistic networks (OppNets) offer a scalable backhaul option for collecting delay-tolerant data from sensors to gateways in order to enable efficient urban operations and services. While pervasive devices such as smartphones and tablets contribute significantly to the scalability of OppNets, closely following human movement patterns and social structure introduces network characteristics that pose routing challenges. Our study on the impact of these characteristics reveals that existing routing protocols subject a key set of devices to higher resource consumption, to which their users may respond by withdrawing participation. Unfortunately, existing solutions addressing this unfairness do not guarantee achievable throughput since they are not specifically designed for sensed data collection scenarios. Based on concepts derived from the study, we suggest design guidelines for adapting applicable routing protocols to sensed data collection scenarios. We also follow our design guidelines to propose the Fair Locality Aware Routing (FLARoute) technique. Evaluating FLARoute within an existing routing protocol confirms improved fairness and throughput under conditions that compromise the performance of existing solutions.
Peripartum cardiomyopathy: a review

Peripartum cardiomyopathy (PPCM) is a form of dilated cardiomyopathy of unclear etiology affecting women without preexisting heart disease during the last month of pregnancy or during the first 5 months postpartum. Its incidence shows marked geographic and ethnic variation, being most common in Africa and among women of African descent. Most women present in the first month postpartum with typical heart failure symptoms such as dyspnea, lower extremity edema, and fatigue. These symptoms are often initially erroneously diagnosed as part of the normal puerperal process. Diagnosis can be aided by the finding of a significantly elevated serum brain natriuretic peptide. The etiology of PPCM is unclear; however, recent research suggests abnormal prolactin metabolism is seminal in its development, and prolactin antagonism with bromocriptine shows promise as a novel treatment for PPCM.
Modeling of Fretting Wear Based on Cumulative Plastic Strain and Saturated Shear Stress

Fretting fatigue has grasped strong interest in last decades, some quantitative methods for the evaluation of fretting fatigue were developed. However, only very few studies have been reported on fretting wear, especially on its mechanical model and evaluation method. In this study, cumulative plastic strain is analyzed by FEM. To obtain accurate plastic strain, the shape change due to the plastic deformation has been taken into account. It is found that the cumulative plastic strain will be saturated after several hundred cycles at the initial fretting stage. Considering that fretting wear is very small during this early stage, as it can be observed from the fretting test, the wear at the contact interface before the saturation of cumulative plastic strain can be neglected. Since the saturated cumulative plastic deformation represents the stable deformation of the contact interface, it is proposed that the fretting wear can be characterized by the saturated cumulative plastic strain and accumulative shear stress. With this method, the wear profile of the specimen is predicted. By comparing FEM results with the experimental results of fretting wear, the proposed wear formulation is validated.
Soldadura TIG de espumas de aluminio. Análisis de los parámetros operacionales de espumado(

In this work the influence of main parameters that take part during TIG welding process are analyzed. Some of these parameters belong to the welding process, as for example the welding speed, intensity and voltage while others are from the material and tooling features, as for example foaming material and tooling design. The result of this work shows a strong dependence on these parameters of the TIG welding process for metallic foams.
Some Reflections on Legibility of the Cityscape

The legibility of the cityscape is of great importance in order to make the city more attractive for tourists. So I spared no efforts to do researches on this subject through field investigation and case study so as to find ways to make the cityscape easier to identify because its legibility is essential not only to the tourists, but also to the improvement of the city environment. Through studies I found that we could make full use of the streets, terrains as well as its historical characters to make the cityscape more distinctive and legible for tourists. And this is also one of the ways to make the tourism industry of the city more prosperous.
An Algorithm for Face Recognition based on Isolated Image Points with Neural Network

On the last years, face recognition has become a popular area of research in computer vision and one of the most successful applications of image analysis and understanding. Face recognition is of two types. One is pixel-based and the other is feature-based. Pixel-based techniques use principal component analysis (PCA) for face recognition, whereas feature-base techniques extract the facial features and use them to classify faces. Feature-based technique has been used in this work. Feature-based techniques extract the features of the face and use it for recognition. The recognition system should be robust enough to classify the face. Therefore, the training set should contain all the orientations of the face. In this work, the front view has only been taken in to consideration assuming that each person should stand in front of camera. The first step of the proposed algorithm is to resolve the image to Red, Green and Blue bands, then deal with each image as a gray scale one which is represented as a 2-D matrix. The second step is to detect isolated image points using simple method and alternative method. The third step is to extract features from each band. Finally, extracted features should be trained by neural network structure. Ten images have been tested by the proposed algorithm and the result of accuracy rate was 100%.
Look-ahead SLP: auto-vectorization in the presence of commutative operations

Auto-vectorizing compilers automatically generate vector (SIMD) instructions out of scalar code. The state-of-the-art algorithm for straight-line code vectorization is Superword-Level Parallelism (SLP). In this work we identify a major limitation at the core of the SLP algorithm, in the performance-critical step of collecting the vectorization candidate instructions that form the SLP-graph data structure. SLP lacks global knowledge when building its vectorization graph, which negatively affects its local decisions when it encounters commutative instructions. We propose LSLP, an improved algorithm that can plug-in to existing SLP implementations, and can effectively vectorize code with arbitrarily long chains of commutative operations. LSLP relies on short-depth look-ahead for better-informed local decisions. Our evaluation on a real machine shows that LSLP can significantly improve the performance of real-world code with little compilation-time overhead.
High-Accuracy Positioning in Urban Environments Using Single-Frequency Multi-GNSS RTK/MEMS-IMU Integration

The integration of Global Positioning System (GPS) real-time kinematics (RTK) and an inertial navigation system (INS) has been widely used in many applications, such as mobile mapping and autonomous vehicle control. Such applications require high-accuracy position information. However, continuous and reliable high-accuracy positioning is still challenging for GPS/INS integration in urban environments because of the limited satellite visibility, increasing multipath, and frequent signal blockages. Recently, with the rapid deployment of multi-constellation Global Navigation Satellite System (multi-GNSS) and the great advances in low-cost micro-electro-mechanical-system (MEMS) inertial measurement units (IMUs), it is expected that the positioning performance could be improved significantly. In this contribution, the tightly-coupled single-frequency multi-GNSS RTK/MEMS-IMU integration is developed to provide precise and continuous positioning solutions in urban environments. The innovation-based outlier-resistant ambiguity resolution (AR) and Kalman filtering strategy are proposed specifically for the integrated system to resist the measurement outliers or poor-quality observations. A field vehicular experiment was conducted in Wuhan City to evaluate the performance of the proposed algorithm. Results indicate that it is feasible for the proposed algorithm to obtain high-accuracy positioning solutions in the presence of measurement outliers. Moreover, the tightly-coupled single-frequency multi-GNSS RTK/MEMS-IMU integration even outperforms the dual-frequency multi-GNSS RTK in terms of AR and positioning performance for short baselines in urban environments.
Qingxuan Jiangya Decoction Mitigates Renal Interstitial Fibrosis in Spontaneously Hypertensive Rats by Regulating Transforming Growth Factor-β1/Smad Signaling Pathway

Qingxuan Jiangya Decoction (QXJYD) is a traditional Chinese medicine commonly used in the clinical treatment of hypertension. Earlier studies had shown that QXJYD could inhibit the elevation of blood pressure in spontaneously hypertensive rats (SHRs) and prevent remodeling of arterial vessels. This study examines the therapeutic efficacy of QXJYD against elevated blood pressure using the SHR model, as well as the mechanisms behind its antihypertensive activity and protection against renal fibrosis. The results showed that QXJYD significantly attenuated the increase in blood pressure in SHRs and mitigated the development of renal interstitial fibrosis. In addition, QXJYD also robustly decreased the excess accumulation of extracellular matrix and attenuated the elevated expression of MMPs. The antihypertensive effects and renal protection of QXJYD were determined to be strongly associated with inhibition of TGF-β1/Smad signaling pathway.
Tuning the microstructure and biodegradation of three-phase scaffolds for bone regeneration made of PCL, Zein, and HA

The aim of this study has been the design of novel multi-phase porous scaffolds with bi-modal pore size distributions and controlled biodegradation rate for bone tissue engineering (bTE), via a gas foaming—leaching approach. Poly(ε-caprolactone) (PCL) has been melt mixed with thermoplastic zein (TZ) and hydroxyapatite particle, to prepare multi-phase PCL—TZ and PCL—TZ—HA composites suitable to be further processed for the fabrication of 3D porous scaffolds. To this aim, these systems have been gas foamed by using CO2 as blowing agent and, subsequently, soaked in H2O to leach out the plasticizer from the TZ. This combined process allows the formation of an interpenetrated micro- and macro-porosity network within the samples. The effect of the different formulations on the micro-structural properties and in vitro biodegradation of the scaffolds has been investigated, and the results correlated to the mechanisms involved in the formation of the bi-modal pore structure. Results demonstrated that the multi-phase nature of the biomaterials prepared as well as their composition significantly affect the micro-structural properties and biodegradation rate of the scaffolds. The optimal selection of the processing conditions may allow for the design of multi-phase 3D porous scaffolds suitable for bTE.
Advanced tutorial: Input uncertainty and robust analysis in stochastic simulation

Input uncertainty refers to errors caused by a lack of complete knowledge about the probability distributions used to generate input variates in stochastic simulation. The quantification of input uncertainty is one of the central topics of interest and has been studied over the years among the simulation community. This tutorial overviews some methodological developments in two parts. The first part discusses major established statistical methods, while the second part discusses some recent results from a robust-optimization-based viewpoint and their comparisons to the established methods.
Study of Underground Oil-Gas Pipeline Corrosion Pits Estimation Based on MFL Inspection Method

The paper presents a new approach for detection of oil-gas pipeline corrosion pit defects in which pipeline magnetic flux leakage (MFL) detection system is adopted. To test the new approach, some artificial pits are fabricated on the surface of oil-gas pipes to simulate different pits happening in practice. The interference eliminating method is introduced for compensation of defected MFL field and helping map extraction from the background. Both qualitative and quantitative methods of identifying erosion pit defects are described in the paper.