Miracle-dz/UniFAR-FTUs huggingface dataset:
Transmission loss allocation in power systems using artificial neural network

Cost allocation and determining the part of the operators in a power system from the total costs are among the most important issues appeared along with the restructuring in the power industry. One of these imposed costs in the utilization domain is the cost of the power losses which should be fairly distributed between the participants in the electric power market. In this paper, using the load flow calculations, the part of each bus in the power losses is determined by the Z-Bus method. These results are compared with those obtained from the load flow calculations using the artificial neural network. It is shown that the artificial neural network is an efficient tool for power loss allocation in the large and complicated power systems which may have a nonlinear nature. The proposed method is then applied to two test bench systems, the IEEE 5-bus and 30-bus test benches, and the results from two approaches are compared and the differences in term of error are reported. A real case study including a 400kV transmission system is also studied and the annual peak-load power loss allocation assuming the peak hour in a month is determined and the related errors are computed.
Building Handwriting Recognizers by Leveraging Skeletons of Both Offline and Online Samples

We present an approach to leveraging both offline and online handwriting samples to build a single recognizer for recognizing both offline and online handwritings. Given a training set of offline handwriting samples and another set of online handwriting samples, a skeleton is derived first from each offline handwriting sample via vectorization. Then both the skeleton samples and online handwriting samples are normalized and rendered by using the same method to generate a combined training set of skeleton images. Finally a handwriting recognizer based on Deep Bidirectional Long Short-Term Memory (DBLSTM) and Hidden Markov Model (HMM) is built from the skeleton images. In recognition, a preprocessing step consistent with that in training is applied to an unknown offline or online handwriting sample to derive a skeleton image, which is recognized by the hybrid DBLSTM-HMM handwriting recognition system accordingly. We have built such a recognizer by using IAM benchmark databases of offline and online English handwritings plus an internal online handwriting corpus, which outperforms the recognizers built from either offline or online handwriting samples only.
A scalable machine-learning approach for semi-structured named entity recognition

Named entity recognition studies the problem of locating and classifying parts of free text into a set of predefined categories. Although extensive research has focused on the detection of person, location and organization entities, there are many other entities of interest, including phone numbers, dates, times and currencies (to name a few examples). We refer to these types of entities as "semi-structured named entities", since they usually follow certain syntactic formats according to some conventions, although their structure is typically not well-defined. Regular expression solutions require significant amount of manual effort and supervised machine learning approaches rely on large sets of labeled training data. Therefore, these approaches do not scale when we need to support many semi-structured entity types in many languages and regions. In this paper, we study this problem and propose a novel three-level bootstrapping framework for the detection of semi-structured entities. We describe the proposed techniques for phone, date and time entities, and perform extensive evaluations on English, German, Polish, Swedish and Turkish documents. Despite the minimal input from the user, our approach can achieve 95% precision and 84% recall for phone entities, and 94% precision and 81% recall for date and time entities, on average. We also discuss implementation details and report run time performance results, which show significant improvements over regular expression based solutions.
Contextual modeling content-based approaches for new-item recommendation

The new-item cold-start problem is a well-known limitation of context-free and context-aware Collaborative Filtering (CF) prediction models. In such situations, only Content-based (CB) approaches can produce meaningful recommendations. In this paper, we propose three Context-Aware Content-Based (CACB) models that extend a linear CB prediction model with context-awareness by including additional parameters that represent the influence of context with respect to the users' interests and rating behaviour. The precision of the proposed models has been evaluated using a contextually-tagged rating data set for journey plans in the city of Barcelona (Spain), which has a high number of new items. We demonstrate that, in this data set, the most sophisticated CACB model, which exploits the contextual information at different granularities and also the distributional similarities between contextual conditions during user modeling, significantly outperforms a context-free CB model as well as a state-of-the-art context-aware approach.
Map merging using cycle consistency check and RANSAC-based spanning tree selection

This paper proposes a method of map merging using cycle consistency check and spanning tree selection by RANSAC. Key issues in map merging are the transformation of the coordinate frame of each submap to a global coordinate frame and the rejection of outliers (false arcs) in a pose graph. The proposed method reduces outliers using cycle consistency constraints. To cope with the exponential increase of cycles, cycle consistency check is applied iteratively with a limited number of chordless cycles. Then, spanning trees having only inlier arcs are selected by RANSAC to find the transformation of each submap to a global coordinate frame. Experiments using public datasets show that the proposed method successfully obtained good spanning trees at high outlier ratio.
Higher Order Spike Synchrony in Prefrontal Cortex during Visual Memory

Precise temporal synchrony of spike firing has been postulated as an important neuronal mechanism for signal integration and the induction of plasticity in neocortex. As prefrontal cortex plays an important role in organizing memory and executive functions, the convergence of multiple visual pathways onto PFC predicts that neurons should preferentially synchronize their spiking when stimulus information is processed. Furthermore, synchronous spike firing should intensify if memory processes require the induction of neuronal plasticity, even if this is only for short-term. Here we show with multiple simultaneously recorded units in ventral prefrontal cortex that neurons participate in 3 ms precise synchronous discharges distributed across multiple sites separated by at least 500 μm. The frequency of synchronous firing is modulated by behavioral performance and is specific for the memorized visual stimuli. In particular, during the memory period in which activity is not stimulus driven, larger groups of up to seven sites exhibit performance dependent modulation of their spike synchronization.
Optical Mouse Array Position Calibration for Mobile Robot Velocity Estimation

This paper presents the position calibration of a polygonal array of optical mice for improved mobile robot velocity estimation using optical mice. First, the least squares velocity estimate of an omnidirectional mobile robot is obtained as the simple average of the velocity measurements from optical mice. Second, the sensitivity of the least squares velocity estimation to an imprecisely installed optical mouse array is analyzed. Third, with the aid of other accurate sensors, for example, wheel encoders, a simple but effective calibration for imperfect optical mouse array position is proposed. Finally, the position calibration of a regular triangular optical mouse array is given to demonstrate the effectiveness of the proposed method.
Supporting Queries and Analyses of Large-Scale Social Media Data with Customizable and Scalable Indexing Techniques over NoSQL Databases

Social media data analysis demonstrates two special characteristics in Big Data processing. First, most analyses focus on data subsets related to specific social events or activities instead of the whole dataset. Second, analysis workflows consist of multiple stages, and algorithms applied in each stage may use different computation and communication patterns depending on processing frameworks. This paper presents our efforts in supporting the data storage and processing requirements for such characteristics. To achieve efficient queries about target data subsets, we propose a general customizable and scalable indexing framework that can be built over distributed NoSQL databases. This framework allows users to define suitable customized index structures for their query patterns against social media data, and supports scalable indexing of both historical and streaming data. We implement this framework on HBase, and name it IndexedHBase. Starting from IndexedHBase, we build a distributed analysis stack based on YARN to support analysis algorithms using different processing frameworks, such as Hadoop MapReduce, Harp, and Giraph. This analysis stack is used to host the Truthy social media data observatory, and we have applied the customized index structures in supporting both query evaluation and sophisticated analysis algorithms. Performance tests show that our solutions outperform implementations using both direct raw data scans and current indexing mechanisms in existing NoSQL databases.
Self-Efficacy, Stress, and Parental Adaptation: Applications to the Care of Childbearing Families

Successful adaptation to parenthood may require that parents believe they have the ability to succeed at this challenging task Relationships of selficacy, stress, and parental adaptation were assessed during the early transition to parenthood. Mothers exceeded fathers in self-efficacy, confidence, satisfaction, and support. For mothers, stress and self-efficacy were inversely related, and self-efficacy postpartum was positively associated with partner relationship, satisfaction, confidence, and support. Forfathers, self-efficacyand stress were not related, and self-efficacy at 4 months was associated with confidence and life situation. Self-efficacy and stress as predictor variables modestly explained adaptation to parenthood. Gender predicted confidence in parenting, and the interaction of gender and self-efficacy predicted support for parenting. Strategiesforassessingandenhancingparentingself-efficacyand implicationsforfuture research are discussed.
Postgraduate student teachers’ developing conceptions of the place of theory in learning to teach: ‘more important to me now than when I started’

Integrating theoretical knowledge within teacher education has often been portrayed as difficult, with previous studies reporting student teachers’ ambivalence, or even scepticism, about the value of research findings and theory to classroom practice. Moreover, the nature of teachers’ professional knowledge is itself uncertain and highly complex. This paper reports on the developing conceptions held by a group of postgraduate student teachers about the relationship of theory to classroom practice in learning to teach. The data are drawn from a small-scale longitudinal case study. They capture participants’ preconceptions about theory before beginning training and subsequent developments through the course and into the first teaching post. The research finds these students to be far from naïve at the outset, entering training open to a range of forms of learning, with a positive view of the potential contribution of theory to practice. Alongside a growing appreciation of the complex, situated and contested nature of theory, the data suggest that theory comes to be increasingly valued over time. As newly qualified teachers, the participants not only see theory as integral to their practice, but recognise the important, largely unanticipated, role of the university in this process. As a result of these insights, potential considerations for course design are offered, at a time when teacher education in many countries is becoming more school-based and new forms of partnership are being developed.
Effects of CO2 laser irradiation on matrix-rich biofilm development formation–an in vitro study

Background A carbon dioxide (CO2) laser has been used to morphologically and chemically modify the dental enamel surface as well as to make it more resistant to demineralization. Despite a variety of experiments demonstrating the inhibitory effect of a CO2 laser in reduce enamel demineralization, little is known about the effect of surface irradiated on bacterial growth. Thus, this in vitro study was preformed to evaluate the biofilm formation on enamel previously irradiated with a CO2 laser (λ = 10.6 µM). Methods For this in vitro study, 96 specimens of bovine enamel were employed, which were divided into two groups (n = 48): 1) Control-non-irradiated surface and 2) Irradiated enamel surface. Biofilms were grown on the enamel specimens by one, three and five days under intermittent cariogenic condition in the irradiated and non-irradiated surface. In each assessment time, the biofilm were evaluated by dry weigh, counting the number of viable colonies and, in fifth day, were evaluated by polysaccharides analysis, quantitative real time Polymerase Chain Reaction (PCR) as well as by contact angle. In addition, the morphology of biofilms was characterized by fluorescence microscopy and field emission scanning electron microscopy (FESEM). Initially, the assumptions of equal variances and normal distribution of errors were conferred and the results are analyzed statistically by t-test and Mann Whitney test. Results The mean of log CFU/mL obtained for the one-day biofilm evaluation showed that there is statistical difference between the experimental groups. When biofilms were exposed to the CO2 laser, CFU/mL and CFU/dry weight in three day was reduced significantly compared with control group. The difference in the genes expression (Glucosyltransferases (gtfB) and Glucan-binding protein (gbpB)) and polysaccharides was not statically significant. Contact angle was increased relative to control when the surface was irradiated with the CO2 laser. Similar morphology was also visible with both treatments; however, the irradiated group revealed evidence of melting and fusion in the specimens. Conclusion In conclusion, CO2 laser irradiation modifies the energy surface and disrupts the initial biofilm formation.
An examination of the roles played by early adolescent children in interactions with their local food environment.

OBJECTIVES The purpose of this study was to examine how pre- and early adolescent (10-14 years old) children from a wide range of neighbourhoods interact with their local food environment (FE), with a focus on the foods and food sources they access and their locations. Children in this age group are developing independence and mobility within (and beyond) their home neighbourhoods but are somewhat geographically bound as they cannot yet drive. METHODS This research consists of qualitative interviews with 31 children (15 males, 16 females) aged 10-14 years living in socio-economically diverse neighbourhoods across Saskatoon, SK. A thematic analysis was conducted. RESULTS Children's descriptions of what constitutes their neighbourhood FE were varied, ranging from a couple of city blocks to several kilometres from home. Children were familiar with the types of establishment that sell food within their perceived neighbourhood. When children purchased their own food they most frequently cited buying snacks such as ice cream, candy and slushes, and the majority of these purchases were made in convenience stores, gas stations and grocery stores. Few children reported frequenting fast-food or other restaurants without adults, and when they did it was usually to buy snacks such as French fries and ice cream rather than meals. CONCLUSIONS Our results highlight the importance of interventions targeted to this age group, in which personal food choices were reported almost exclusively as being energy- but not nutrient-dense snack foods.
Efficient estimation of multidimensional regression model with multilayer perceptron

This work concerns estimation of multidimensional nonlinear regression models using multilayer perceptron (MLP). The main problem with such model is that we have to know the covariance matrix of the noise to get optimal estimator. however we show that, if we choose as cost function the logarithm of the determinant of the empirical error covariance matrix, we get an asymptotically optimal estimator.
An Optimal Power Flow Based Algorithm for Coordinated Secondary Voltage Control

Misbalance between reactive power production and consumption in power system causes the variation of node voltage outside the proposed limit. It is a main task for system operators to maintain the voltage profile against any unexpected disturbance. In this paper a practical coordinated secondary voltage control (CSVC) scheme was present, which was based on the optimal power flow algorithm. Additional measurements were used in order to offset the effects of the loads variation among neighboring areas. By taking the changing tendency of reactive power into consideration, a discrete optimal control model was proposed. An optimal power flow calculation was used to get the control law in each control step with the consideration of reactive power changing tendency. The simulation results as examples on the large power system were presented to illustrate the validity of the control scheme for coordinated secondary voltage control.
Renewing Class Analysis in Studies of the Workplace: A Comparison of Working-class and Middle-class Women's Aspirations and Identities

A renewed class analysis has shown the importance of culture, emotions and identity in conceptualizing and understanding how class is lived. However, proponents of the new sociology of class rarely explore these issues in an occupational setting. This article argues that the insights developed in the new cultural approaches to class can be used fruitfully to analyse contemporary experiences of work. Using a comparative study of women working in working-class and middle-class occupations, the article illustrates the implicit and emotional dimensions of the classed experience of work through a study of the women's aspirations and their class identities. Rather than equating class with economic resources and constraints, the article shows how class `thinking and feelings' (Reay, 2005) also shape the experiences of work.
Schema-Focused Therapy for Major Depressive Disorder and Personality Disorder

This is a single case study of a college age male with major depressive disorder and personality disorder not otherwise specified (NOS). The patient endorsed feelings of sadness, worthlessness, inattention, isolation, and suicidal and homicidal ideation. In addition, the patient endorsed inflexible and pervasive behavioral patterns such as paranoia, isolation, and narcissism. Treatment consisted of schema therapy, with an emphasis on the therapeutic relationship (limited reparenting). The patient completed Young’s Schema Questionnaire (YSQ) prior to initiating treatment and again following treatment. Schemas endorsed at moderate to high levels include Failure, Enmeshment, Abandonment, Emotional Deprivation, Punitiveness, Negativity/Pessimism, and Social Isolation. Following 68 weeks of schema therapy, the patient endorsed all 18 schemas at a “low” level. In addition, whether these findings demonstrated clinically significant change was examined. Results indicate that four out of seven of the patient’s endorsed schemas demonstrated clinically significant change (Emotional Deprivation, Punitiveness, Negativity/Pessimism, and Social Isolation).
The tactical approach to teaching games from teaching, learning and mentoring perspectives

This study examined the teaching of games via a tactical approach (also known as teaching games for understanding) from the theoretical perspective of social constructivism. A pilot study (of student teachers (STs) and pupils) informed the ‘main’ study which included collecting data on 49 STs, 58 cooperating teachers (CTs) and 1177 pupils. ST data were collected through videotaping (lessons), interviews and questionnaires; CT data through interviews and questionnaires; and pupil data through questionnaires. Data analysis techniques included systematic coding of teaching behaviours through computer software, using constant comparison to code open-ended question responses, and computing frequencies of Likert-scale question responses. T-tests were used to compare pre- and post-CT workshop data. Findings suggest that STs’ teaching of the tactical approach was in line with a social constructivist perspective, in part because lessons were student-centred. Overall student activity was 53% of all lessons taught, and STs facilitated pupil learning through several question and answer scenarios. Pupils wrote that they enjoyed playing games in what amounted to ‘authentic’ settings. Their responses also suggest that learning took place through ‘legitimate peripheral participation in communities of practice’ and this facilitated pupil movement through the ‘zone of proximal development’. There were indications that pupil experiences were mediated by ST competence. CTs reported that an in-service workshop on the tactical approach and mentoring was beneficial and subsequent pairings with STs elicited positive feelings from both parties.
The efficacy of endoscopic therapy for pancreas divisum: a meta-analysis

Background The purpose of this study was to perform a meta-analysis assessing the efficacy and predictors of success of endoscopic therapy in the management of patients with pancreas divisum. Methods An electronic database search (PubMed and ScienceDirect) was performed for relevant studies. Studies were selected based on predefined criteria and data were extracted on patient population, follow up, endotherapy methods, success rates and complication rates. A random-effect model was used to pool the effect size across studies. Heterogeneity testing and publication bias assessment were performed. Multivariate regression analysis was performed to identify predictors of successful endoscopic therapy. Results Of 381 articles reviewed, 23 studies with 874 patients met the inclusion criteria. All were case series with suboptimal quality. Endoscopic therapy included minor papilla sphincterotomy, minor papilla sphincteroplasty and dorsal duct stenting. Mean follow-up duration was 37 months. The rate of “improvement” as defined by authors after endoscopic therapy varied significantly across studies, ranging from 31-96%: 589/874 patients were reported to have improved, corresponding to a pooled efficacy rate of 67.5% (95% confidence interval [CI] 0.610-0.734; P=0.0001). The pooled rate of pancreatitis after endoscopic retrograde cholangiopancreatography was 10.1% (95%CI 0.084-0.124; 2-sided P=0.0001). On subgroup analysis, patients with recurrent acute pancreatitis had better endoscopic outcomes (pooled efficacy rate 76%, 95%CI 0.712-0.803, P=0.0001). Dorsal duct stenting and longer follow up were the only parameters predictive of successful endotherapy. Significant heterogeneity was observed within and across studies. Conclusions Endoscopic efficacy in pancreas divisum is estimated at 67.5%. Available studies are of poor quality with significant heterogeneity. Comparative studies with rigorous methodology are needed.