Miracle-dz/UniFAR-FTUs huggingface dataset:
Socioeconomic variations in female fertility impairment: a study in a cohort of Portuguese mothers

Objectives This study aimed to assess the association of socioeconomic conditions with female fertility impairment among women who delivered a live birth. Design Cross-sectional analysis. Setting Population-based birth cohort (Generation XXI) assembled in 2005/2006 from five public maternity units in Porto Metropolitan Region, Northern Portugal. Participants 7472 women aged 18 or more with spontaneous conception and no male diagnosis of infertility were recruited and interviewed immediately after birth with structured questionnaires. Exposures of interest Maternal education, occupation and income were recorded as proxy indicators of social conditions. Outcome Impaired female fertility, defined as women who had unsuccessfully tried to conceive for over a year. Data analysis Multivariate logistic regression models were fitted to estimate the association between each socioeconomic indicator and impaired female fertility, stratified by previous pregnancy experience and adjusted for age, pregnancy planning and behavioural characteristics. Results Among primigravidae, 7.7% (95% CI 6.8% to 8.6%) presented impaired fertility and the prevalence was 9.6% (95% CI 8.7% to 10.6%) in multigravidae. In crude analysis, we found women with impaired fertility to be older, less educated, more likely to have planned the current pregnancy and to be overweight/obese; they had similar levels of income or occupation. In multivariate models, a significant independent association between educational level and female fertility impairment remained among primigravidae (OR (95% CI) vs ≤6 schooling years: 7–9: 0.85 (0.54 to 1.34); 10–12: 0.34 (0.21 to 0.54); >12: 0.24 (0.14 to 0.40), ptrend<0.001) but not in multigravidae. Conclusions This study shows that education might be important in understanding female fertility impairment, particularly among first-time pregnant women. It also points out that the association is not totally explained by other sociodemographic and lifestyle characteristics that have been previously found to be important to disclose this relation.
An Improved Algorithm for Learning to Perform Exception-Tolerant Abduction

Inference from an observed or hypothesized condition to a plausible cause or explanation for this condition is known as abduction. For many tasks, the acquisition of the necessary knowledge by machine learning has been widely found to be highly effective. However, the semantics of learned knowledge are weaker than the usual classical semantics, and this necessitates new formulations of many tasks. We focus on a recently introduced formulation of the abductive inference task that is thus adapted to the semantics of machine learning. A key problem is that we cannot expect that our causes or explanations will be perfect, and they must tolerate some error due to the world being more complicated than our formalization allows. This is a version of the qualification problem, and in machine learning, this is known as agnostic learning. In the work by Juba that introduced the task of learning to make abductive inferences, an algorithm is given for producing k-DNF explanations that tolerates such exceptions: if the best possible k-DNF explanation fails to justify the condition with probability ε, then the algorithm is promised to find a k-DNF explanation that fails to justify the condition with probability at most O(nkε), where n is the number of propositional attributes used to describe the domain. Here, we present an improved algorithm for this task. When the best k- DNF fails with probability ε, our algorithm finds a k-DNF that fails with probability at most O ̃(nk/2ε) (i.e., suppressing logarithmic factors in n and 1/ε). We also examine the empirical advantage of this new algorithm over the previous algorithm in two test domains, one of explaining conditions generated by a “noisy” k-DNF rule, and another of explaining conditions that are actually generated by a linear threshold rule.
Ipsative Comparisons of Index Scores for the Canadian WAIS-III.

L'Echelle d'Intelligence de Wechsler pour adultes - troisieme edition (WAIS-III; Wechsler, 1997a; Wechsler, 2001) fournit des indicateurs bases sur des facteurs et donne une analyse de niveau intermediaire entrele QI et les sous-tests individuels. Le present article presente des tableaux permettant de comparer tous les indices a l'indicateur moyen et d'identifier la signification statistique et la frequence relative des differences obtenues. La methode simultanee ou ipsative que nous presentons peut permettre d'eviter certains pieges inherents aux comparaisons multiples par paire, telles que la diminution de la possibilite d'interpretation et le risque exagere d'erreurs de type 1.
Face Recognition Based on Kernel Schur-Orthogonal Neighborhood Preserving Discriminant Embedding

In order to recognize faces more accurately, this paper proposes a new manifold learning algorithm named Kernel Schur-Orthogonal Neighborhood Preserving Discriminant Embedding (KSONPDE) which puts the vector orthogonal and kernel mapping into the Neighborhood Preserving Discriminant Embedding (NPDE). The algorithm extracts nonlinear information from face image by kernel method, mapping it into a high-dimensional space and finding optimal projection vector by schur-orthogonal when solving eigenvalues in order to extract the face features from the structure of nonlinear local area. The experiment on the ORL and Yale face database demonstrates effectiveness of the proposed method.
Fundamental Limits of CDF-Based Scheduling: Throughput, Fairness, and Feedback Overhead

In this paper, we investigate fundamental performance limits of cumulative distribution function (CDF)-based scheduling (CS) in downlink cellular networks. CS is known as an efficient scheduling method that can assign different time fractions for users or, equivalently, satisfy different channel access ratio (CAR) requirements of users while exploiting multiuser diversity. We first mathematically analyze the throughput characteristics of CS in arbitrary fading statistics and data rate functions. It is shown that the throughput gain of CS increases as the CAR of a user decreases or the number of users in a cell increases. For Nakagami-m fading channels, we obtain the average throughput in closed form and investigate the effects of the average signal-to-noise ratio, the shape parameter m, and the CAR on the throughput performance. In addition, we propose a threshold-based opportunistic feedback technique in order to reduce feedback overhead while satisfying the CAR requirements of users. We prove that the average feedback overhead of the proposed technique is upper-bounded by -lnp, where p is the probability that no user satisfies the threshold condition in a cell. Finally, we adopt a novel fairness criterion, called qualitative fairness, which considers not only the quantity of the allocated resources to users, but also the quality of the resources. It is observed that CS provides a better qualitative fairness than other scheduling algorithms designed for controlling CARs of users.
On constructing a knowledge management pyramid model

In this knowledge-based economy, knowledge has become an important asset to an organization and, consequently, knowledge management has emerged, as issue managers have to deal with Peter F. Drucker (1993) said that knowledge would be the only competitive resource for companies in the future. This is an era in which knowledge plays a dominant role in our daily business lives. Intellectual capital has gradually taken the place of traditional tangible capital in many business organizations. However, practitioners are often puzzled about where they stand, how well they are doing, and what they should do next. This paper aims to construct a knowledge management maturity model as a means to help answer these and related questions. There are three components in the proposed model: maturity levels, knowledge management processes, and knowledge management capabilities or enabling infrastructures. The maturity levels are based on the capability maturity model (CMM) of SEI. This model divides the knowledge management process into four main sub-processes: knowledge creation, knowledge storage, knowledge sharing, and knowledge application, emphasizing the need for continuous process improvement. Furthermore, knowledge management enablers are integrated into KMMM to describe how it supports the practices at each maturity level. The applicability of this model is evaluated through case studies, which also serve to illustrate how knowledge management practices among organizations are compared, and what maturity paths organizations can follow. Three banks were taken for cases study through in depth interview with their senior managements. By analyzing data gathered through the process described above, the conclusion of this study is induced.
Direct Torque Control drived by matrix converter based on space vector modulated

Direct Torque Control (DTC) is becoming one of the most popular control methods for induction motors. This technique was introduced in the mid 1980's for voltage fed PWM inverter drives. Matrix converter (MC) has been studied widely for its advantages over the traditional converters. It is gradually employed for applications with its maturity in theoretical studies. A novel control strategy is put forward in this paper, which properly combines the space vector modulation (SVM) of MC with the DTC of induction machine through PI regulation of flux and torque. It improves the control performance of system, especially for the torque at low-speed. Conventional principles of DTC and SVM of MC were described. Then the combination of the two was given in detail. Finally, the simulation research was carried out based on MATLAB. Simulation results demonstrating the effectiveness of the torque control response and the input power factor correction are presented.
Fixation of Displaced Medial Epicondyle Fractures in Adolescents

Background: Open reduction and internal fixation of displaced medial epicondyle fracture dislocations in adolescents is recommended for incarceration of the epicondyle in the joint and for athletes who need a stable elbow for their sport. A screw placed across the epicondyle into the medial column avoiding the olecranon fossa is a common fixation method. One author has recommended adding a metal washer to the screw fixation because of the perceived risk of epicondyle fragmentation or penetration when using a screw alone. The purpose of this study was to determine whether the use of a screw and washer for the fixation of pediatric medial epicondyle fractures results in less fragmentation of the epicondyle at the time of surgery and more complaints of hardware prominence leading to a second surgery to remove a deep implant. Methods: A retrospective review was performed of patients treated surgically for displaced medial epicondyle fracture dislocations between 2008 and 2014. Results: Sixteen patients with a total of 17 fracture dislocations were included in the study. The average follow-up was 11.5 months. Twelve fractures were treated with a screw and washer and 5 fractures were treated with a screw alone. All fractures healed. No fracture treated with a screw alone resulted in fragmentation or penetration of the epicondyle fragment. Seven of 12 patients treated with a screw and washer requested deep metal removal due to prominence and irritation at the medial epicondyle. No patient treated with a screw alone requested metal removal (58% vs. 0%; P=0.04). Conclusions: One author suggested that adding a washer to the screw for fixation of medial epicondyle fractures improved the ability to safely compress the fragment. However, the results of the present study report no case of fragmentation or penetration of the epicondyle when a washer was not used. In addition, the use of a screw and washer significantly increased the likelihood of a second surgery for removal of prominent hardware. Level of Evidence: Level IV.
Crystal Structure and Autoactivation Pathway of the Precursor Form of Human Tripeptidyl-peptidase 1, the Enzyme Deficient in Late Infantile Ceroid Lipofuscinosis

Late infantile neuronal ceroid lipofuscinosis is a fatal childhood neurological disorder caused by a deficiency in the lysosomal protease tripeptidyl-peptidase 1 (TPP1). TPP1 represents the only known mammalian member of the S53 family of serine proteases, a group characterized by a subtilisin-like fold, a Ser-Glu-Asp catalytic triad, and an acidic pH optimum. TPP1 is synthesized as an inactive proenzyme (pro-TPP1) that is proteolytically processed into the active enzyme after exposure to low pH in vitro or targeting to the lysosome in vivo. In this study, we describe an endoglycosidase H-deglycosylated form of TPP1 containing four Asn-linked N-acetylglucosamines that is indistinguishable from fully glycosylated TPP1 in terms of autocatalytic processing of the proform and enzymatic properties of the mature protease. The crystal structure of deglycosylated pro-TPP1 was determined at 1.85 Å resolution. A large 151-residue C-shaped prodomain makes extensive contacts as it wraps around the surface of the catalytic domain with the two domains connected by a 24-residue flexible linker that passes through the substrate-binding groove. The proenzyme structure reveals suboptimal catalytic triad geometry with its propiece linker partially blocking the substrate-binding site, which together serve to prevent premature activation of the protease. Finally, we have identified numerous processing intermediates and propose a structural model that explains the pathway for TPP1 activation in vitro. These data provide new insights into TPP1 function and represent a valuable resource for constructing improved TPP1 variants for treatment of late infantile neuronal ceroid lipofuscinosis.
Intensive versus regular speech therapy in global aphasia: A controlled study

Abstract A controlled study of the comparative efficacy of intensive (daily) versus regular (three treatments weekly) speech therapy treatment in a group of global aphasics of vascular origin is reported. Application of psychometric single-case analysis showed that the number of patients reaching a significant improvement in all language modalities was greater in the intensive therapy group.
Comparison between Artificial Neural Network and neuro-fuzzy for gold price prediction

This article presents a comparison of Artificial Neural Network (ANN) and Adaptive Neural Fuzzy Inference System (ANFIS) for predicting a real system, gold price. Also, we compared a new hybrid model which is a weighted average of the ANN and ANFIS model. The main objective is to predict the gold price in the Forex market. We used two prediction machine models in ANN, a model which feeds back the network output as input and another model that does not do it. Our results show that the performance error of the former model is more than the latter, and also the performance of ANFIS is better than both models of ANN. To evaluate the methods three performance measurements are used: Root Mean Squared Error (RMSE), percentage error and Mean Tendency Error (MTE) which is proposed in this study. The strength point of our method is the prediction machine model that is one of the most powerful prediction machine models of ANN. At last, a Wavelet denoising algorithm is applied to the data, but due to the chaotic structure of the gold price, it impairs data and causes to reduce the performance of prediction result.
Quantitative immunohematologic studies of hemagflutination. II. Assay of the agglutinogen A.

Summary The log-probit hemagglutinins assay has been modified and applied to a study of human group A and AB red cells reacted with homologous stimulated group B sera. Qualitative differences among the A antigens were shown by the characteristic slopes produced by each subgroup. The decreasing progression in slope of the A cells was of the order: A 1 > A 1 B > A 2 > A int B > A 2 B > A 3 . The findings of differences in the mode of reaction of A and AB cells along with that previously reported for the B and A 1 B cells lends support to the theory of Morgan and Watkins that the substance produced by the gene in the heterozygote differs from that of either gene in the homozygous condition. Significant quantitative differences in the content of agglutinogen were found among the cells of A 1 individuals with stimulated group B sera. Three percentage grades of A 1 have been recognized which are not related to genotype.
Intelligent Sensor Fusioln for the 1997 AAAI Mobile obot Competition

The Colorado School of Mines (CSM) will field a team of undergraduates for two events in the 1997 AAAI Mobile Robot Competition: Life on Mars and HOTS D’oeuvres. The objectives of the team are (1) to gain experience with implementing behaviors under a hybrid deliberative/reactive style of architecture and (2) to transfer research being conducted at CSM in intelligent sensor fusion to new applications. The students are preparing the entries as part of the class projects for the MACS415: Introduction to AI Robotics and Computer Vision and MACS370: Field Session courses. The team is using two different robots, both running subsets of the Sensor Fusion Effects (SFX) architecture implemented in C++. The term intelligent sensor fusion denotes a broad characterization of sensor fusion, dealing with how observations from one or more logical sensors can be combined reliably into a coherent percept. Two components of our research in intelligent sensor fusion are being applied to the competition. The first component addresses how to combine the observations from multiple sensors into a percept and generate a measure of belief. This is a key element of the sensing strategy for the Hors D’oeuvres event, where evidence from simple features (audio, motion, and flesh colored regions) are combined to produce a measure of belief in the presence of a person. Our approach is an adaptation of (Goodridge 1996) for a Dempster-Shafer evidential scheme. The second component deals with combining sensor observations over time. This is an important aspect of our approach to the Find Life on Mars event, since a suspected Martian may disappear due to a temporary occlusion, because it has attempted to hide, or the sensor reported a faulty observation. In order to successfully balance resources (energy, time) with the goals (find Martians), the robot must persist in its belief that it saw a Martian for some period of time in order to continue searching, but it must eventually give up if it cannot reacquire the target. The problem is exacerbated by the lack of explicit a priori models of the Martian to guide
Segmentation of multibeam acoustic imagery in the exploration of the deep sea-bottom

The new generation of low-frequency echosounders, primarily used for bathymetric purposes, are also able to record acoustic images of the sea floor. Reflected energy, as a function of the incidence angle, is known to be strongly dependent on seabed type, and therefore stands as a potential tool in sea floor characterization. On the other hand, acoustic images of the reflected energy (mosaics), illustrate the variability of the acoustic interface and are invaluable for sea floor cartography. We describe a method of semi-automatic mosaic interpretation where the two different aspects are considered simultaneously. This is achieved by supervised segmentation using a Markov Random Field model where the neighbourhood system and energies have been carefully studied in order to comply to a priori knowledge. We present results obtained with this method, enhancing the possibility of using such a technique for low-frequency echosounders.
Numerical Analysis of Singular Weighted Integrals

{Zusammenfassung Numerical Analysis of Singular Weighted Integrals. In this article we investigate the numerical aspects of integrals of the form b Z a f(x)(x)dx (1) where f is an unobjectionable function and is singular, i.e. is oscillating with high frequency, is discontinuous or unbounded. Suitable integration algorithms are presented. Numerik singul ar gewichteter Integrale. In diesem Artikel wird die Numerik von Integralen der Form (1) untersucht, wobei f eine numerisch unbedenkliche Funktion und eine singull are Funktion ist, d.h. oszilliert mit hoher Frequenz, ist unstetig oder unbeschrr ankt. Geeignete Integrationsalgorithmen werden entwickelt.
Screening Suitable Reference Genes for Normalization in Reverse Transcription Quantitative Real-Time PCR Analysis in Melon

Melon (Cucumis melo. L) is not only an economically important cucurbitaceous crop but also an attractive model for studying many biological characteristics. Screening appropriate reference genes is essential to reverse transcription quantitative real-time PCR (RT-qPCR), which is key to many studies involving gene expression analysis. In this study, 14 candidate reference genes were selected, and the variations in their expression in roots and leaves of plants subjected to biotic stress, abiotic stress, and plant growth regulator treatment were assessed by RT-qPCR. The stability of the expression of the selected genes was determined and ranked using geNorm and NormFinder. geNorm identified the two most stable genes for each set of conditions: CmADP and CmUBIep across all samples, CmUBIep and CmRPL in roots, CmRAN and CmACT in leaves, CmADP and CmRPL under abiotic stress conditions, CmTUA and CmACT under biotic stress conditions, and CmRAN and CmACT under plant growth regulator treatments. NormFinder determined CmRPL to be the best reference gene in roots and under biotic stress conditions and CmADP under the other experimental conditions. CmUBC2 and CmPP2A were not found to be suitable under many experimental conditions. The catalase family genes CmCAT1, CmCAT2, and CmCAT3 were identified in melon genome and used as target genes to validate the reliability of identified reference genes. The catalase family genes showed the most upregulation 3 days after inoculation with Fusarium wilt in roots, after which they were downregulated. Their levels of expression were significantly overestimated when the unsuitable reference gene was used for normalization. These results not only provide guidelines for the selection of reference genes for gene expression analyses in melons but may also provide valuable information for studying the functions of catalase family genes in stress responses.
Self-rectifying resistive memory based on Au nanocrystal-embedded zirconium oxide for crossbar array application

The self-rectifying resistive switching behavior is investigated in the Au/ZrO2: nc-Au/n+ Si sandwich structure with the Au nanocrystals embedded ZrO2 films (ZrO2: nc-Au) fabricated by e-beam evaporation. The rectification ratio is obtained to be 7×102 under ±0.5 V at low-resistance state (LRS), which can alleviate the cross talk effect in crossbar structure arrays without additional switching elements. The different current conduction mechanisms at LRS are studied. A model is proposed to explain this self-rectifying resistive switching behavior.