Schema Model Research Articles

Pitch distribution, melodic contour or both? Modeling makam schema with multidimensional scaling and self-organizing maps

Experienced listeners internalize musical tonal knowledge via statistical learning of pitch distributions as a result of exposure to musical environment. Cross-cultural studies of music cognition offer new perspectives to investigate the acquisition of tonal schema. Makam music is a rich musical system characterized by modal structures defined by micro-tonal pitch sets, and melodic progression patterns (aka seyir features). Makam schema is possibly acquired by internalizing the seyir in addition to pitch features. In the current study, we examined whether an ideal model of makam schema is built with multidimensional scaling analysis and with self-organizing maps (SOMs). We were interested in whether statistical information about seyir features, in addition to pitch distributions, would form an acceptable makam schema model. We qualitatively analyzed topographical organizations in the models to understand whether they reflect complex relations between makams. Multidimensional scaling analyses did not produce an acceptable model for makam schema. The SOM trained with pitch distributions provided an adequate model for makam schema. However, the SOM trained with both pitch distributions and seyir features was better in capturing the complex relations between makams. Further behavioral research is necessary to understand whether melodic progression patterns are intrinsic features of the tonal knowledge of the experienced listeners of makam music.

The Cognitive Mechanism of the Number Systems

Number is everything and the number systems exist above the number itself. The paper studies the cognitive mechanism of number system from the aspects of embodiment, interactive feature and image schema models. As numbers, number system is also a cognitive tool with unique connotation and abundant extension.

Schema and Motor-Memory Consolidation

Recent research has demonstrated that memory-consolidation processes can be accelerated if newly learned information is consistent with preexisting knowledge. Until now, investigations of this fast integration of new information into memory have focused on the declarative and perceptual systems. We employed a unique manipulation of a motor-sequence-learning paradigm to examine the effect of experimentally acquired memory on the learning of new motor information. Results demonstrate that new information is rapidly integrated into memory when practice occurs in a framework that is compatible with the previously acquired memory. This framework consists of the ordinal representation of the motor sequence. This enhanced integration cannot be explained by differences in the explicit awareness of the sequence and is observed only if the previously acquired motor memory was consolidated overnight. Results are consistent with the schema model of memory consolidation and offer insights into how previous motor experience can accelerate learning and consolidation processes.

ROAD NETWORK COMPARISON AND MATCHING TECHNIQUES. A WORKFLOW PROPOSAL FOR THE INTEGRATION OF TRAFFIC MESSAGE CHANNEL AND OPEN SOURCE NETWORK DATASETS

Abstract. The rapid growth of methods and techniques to acquire geospatial data has led to a wide availability of overlapping geographic datasets with different characteristics. Road network data sources are today a significant number, with high differences in level of detail and modelling schemas, depending on the main purpose. In addition, continuous information about people and freight movement is today available also in real-time. This type of data is today exchanged between traffic operators using referencing standards as Traffic Message Channel. Integrating these heterogeneous databases, in order to build an added value product, is a serious task in geographical data management. The paper is focus on techniques to conflate the Traffic message Channel logical network on Open Source road network dataset, in order to allow the precise visualisation of traffic data also in real-time.A first step of the research was the quality assessment of available Open Source (OS) road network dataset, then, a specific procedure to conflate data was set up, using an iterative process in order to reduce at every step the number of possible matching features. A first application of the enhanced OTM dataset is shown for the city of Turin: real-time open data of traffic flows recorded by road network fixed sensors, made available by the metropolitan Traffic Operation Centre (5T) and based on the TMC location referencing, are matched on the OTM road network, allowing a detailed real-time visualisation of traffic state.

Parameters and the design of the Language Faculty

AbstractFollowingBerwick and Chomsky (2011), parameters are degrees of freedom open at the externalization (EXT) of syntactico-semantic structures (SEM) by sensorimotor systems (PHON) (Section 1). Within this framework, inSection 2I focus on a case study concerning Northern Italian subject clitics, also raising the well-known question how to reconcile observable microvariation with the desideratum of a reduced number of (macro)parameters.Sections 3reviews recent relevant models of parameterization, the Rethinking Comparative Syntax model (ReCoS,Biberauer et al. 2014) and the Parameters & Schemata model (Longobardi 2005,2017).Sections 4–5return to the case study, taking the reductionist view that parameters may be just categorial cuts, such as the 1/2P vs 3P split, interacting with externalization and other general principles of grammar.

Multivariate modeling of engineered nanomaterial features associated with developmental toxicity

Despite the increasing prevalence of engineered nanomaterials (ENMs) in consumer products, their toxicity profiles remain to be elucidated. ENM physicochemical characteristics (PCC) are known to influence ENM behavior, however the mechanisms of these effects have not been quantified. Further confounding the question of how the PCC influence behavior is the inclusion of structural and molecular descriptors in modeling schema that minimize the effects of PCC on the toxicological endpoints. In this work, we analyze ENM physico-chemical measurements that have not previously been studied within a developmental toxicity framework using an embryonic zebrafish model. In testing a panel of diverse ENMs to build a consensus model, we found nonlinear relationships between any singular PCC and bioactivity. By using a machine learning (ML) method to characterize the information content of combinatorial PCC sets, we found that concentration, surface area, shape, and polydispersity can accurately capture the developmental toxicity profile of ENMs with consideration to whole-organism effects.

Resistance through the Language of Palestinian Poets

This study analyzed the language of resistance in the poems of three Palestinian poets, namely, Samih Al-Qasim, Mahmoud Darwish, and Tawfiq Zayyad. The analysis incorporated Van Dijk's (1997) approach to Political Discourse Analysis (PDA). The elicited linguistic features from PDA were analyzed against Van Dijk's (1993, 2005) Ideological Square Model and Johnson's (1987) Containment Schema. From the perspective of Van Dijk's (1993, 2005) ideological square model, the study found that the poets' expressions of resistance can be presented under the positive self-representation and negative other-representation with varying referents (i.e. Palestine, Palestinians, or Israelis). The analysis from Johnson's (1987) containment schema showed that the poets' political position has an influential role in their expressions of resistance and their views of themselves in relation to Palestine, Palestinians, and Israelis.

Dashboarding the maternal and child health profiles for health supporting system

Various types of health data are routinely collected and published as national health profiles. However, these vast amounts of data are not always used effectively since they are presented in raw tabular data sets, fragmented, and are not well-equipped with the statistical data inferences and the trend of changes which are essential to explore some unknown previous information. In this paper, we propose how to manage the national open health data sets as a dashboard application to support the decision making for maternal and child health-care services in Indonesia. The research was conducted in four steps. First, we described what maternal and child health parameters are involved in relation to the availability of open and accessible data set. Second, we designed the dashboard application based on Kimball’s four step dimensional modelling. Third, we implemented the schema modelling as data warehouse by executing ETL (extract, transform and load) and OLAP (On Line Analytical Processing) cubes. Fourth, we analyzed the results with the business intelligence approach. We explored the open data sets in four consecutive years (2014-2017) and determined seven types of business process, i.e. analysis of health profile on neonates, infants, and children, analysis of maternal health profile before, during and after pregnancy period, and analysis of health supporting factors. By applying data warehouse and business intelligence, our proposed system has the ability to integrate and extract a number of tabular and fragmented data sets into an easy and quickly inferred information about the current situation of maternal and children health care services.

A Brief Version of the Leahy Emotional Schema Scale: a Validation Study

The Leahy Emotional Schema Scale II (LESS II) was developed based on the emotional schema model, a meta-experiential model targeting emotion and emotion regulation (Leahy 2012). This model is important for identifying individuals’ negative beliefs about emotions as well as for dealing with noncompliance, resistance, and drop-outs in cognitive behavioral therapy. The primary aim of the current study was to explore the factor structures of the previously validated LESS II, and investigate its reliability and validity within a Korean sample. The secondary aim of this study was to examine how emotional schema would be associated with the emotional distress and behavioral problems of Korean college students. A total of 1478 college students participated in the current study and were administered questionnaires including the Leahy Emotional Schema Scale II, Counseling Center Assessment of Psychological Symptoms (CCAPS), and College Adjustments Inventory-Short Form (CAIS). A series of statistical analyses resulted in the deletion of several items and emotional schema dimensions from the LESS II, owing to methodological and cultural reasons. The final results showed that the two-factor model with 10 items was the model with the better fit than was the original 14-factor structure model with 28 items. The total score of the 10-item LESS II showed great convergent validity, with moderate to strong positive correlations with CCAPS subscales. Furthermore, the 10-item LESS II demonstrated high internal consistency and shared approximately 87.05% of variance with the original 28-item LESS II. The findings of the current study provide strong support for the clinical and research application of the 10-item LESS II.

Analysis of cognitive disorders with early maladaptive schemas in irritable bowel syndrome

Introduction: The relationship between irritable bowel syndrome (IBS) and stress, depression and anxiety; negative effects on quality of life have been shown in studies so far. In this study, we aimed to investigate cognitive personality disorders through schema model. Methods: Our study was planed as a case-control study in patients with IBS who were admitted to the internal medicine outpatient clinic of our hospital. All participants completed the Young Schema Questionnaire-Short Form (YSQ-SF3), Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI). Results: Case-control study including patients with IBS and 50 healthy individuals without any chronic disease. According to the Young schema model, IBS patients had higher pessimism, dependence / nesting and insecurity / social isolation schemas than control group. . In addition, IBS patients were more depressive than the control group (12% to 2%; p

Non-Linear Input Variable Selection Approach Integrated With Non-Tuned Data Intelligence Model for Streamflow Pattern Simulation

Streamflow modeling is considered as an essential component for water resources planning and management. There are numerous challenges related to streamflow prediction that are facing water resources engineers. These challenges due to the complex processes associated with several natural variables such as non-stationarity, non-linearity, and randomness. In this study, a new model is proposed to predict long-term streamflow. Several lags that cover several years are abstracted using the potential of Extreme Gradient Boosting (XGB) then after the selected inputs variables are imposed into the predictive model (i.e., Extreme Learning Machine (ELM)). The proposed model is compared with the stand-alone schema in which the optimum lags of the variables are supplied into the XGB and ELM models. Hydrological variables including rainfall, temperature and evapotranspiration are used to build the model and predict the streamflow at Goksu-Himmeti basin in Turkey. The results showed that XGB model performed an excellent result in which can be used for predicting the streamflow pattern. Also, it is clear from the attained results that the accuracy of the streamflow prediction using XGB technique could be improved when the high number of lags was used. However, the implementation of the XGB is tree-based technique in which several issues could be raised such as overfitting problem. The proposed schema XGBELM in which XGB approach is selected the correlated inputs and ranking them according to their importance; then after, the selected inputs are supplied into the ELM model for the prediction process. The XGBELM model outperformed the stand-alone schema of both XGB and ELM models and the high-lagged schema of the XGB. It is important to indicate that the XGBELM model found to improve the prediction ability with minimum variables number.

A Model-Driven Approach to Generate Schemas for Object-Document Mappers

Many actual NoSQL systems are schemaless, that is, the structure of the data is not defined beforehand in any schema, but it is implicit in the data itself. This characteristic is very convenient when the data structure suffers frequent changes. However, the agility and flexibility achieved is at the cost of losing some important benefits, such as 1) assuring that the data stored and retrieved fits the database schema; 2) some database utilities require to know the schema, and; 3) schema visualization helps developers to write better code. In previous work, we proposed a model-based reverse engineering approach to infer schema models from NoSQL data. Model-driven engineering (MDE) techniques can be used to take advantage of extracted models with different purposes, such as schema visualization or automatic code generation. Here, in this paper, we present an MDE solution to automate the usage of Object-NoSQL mappers when the database already exists. We will focus on mappers that are available for document systems (Object-Document mappers, ODMs), but the proposed approach is mapper-independent. These mappers are emerging to provide similar functionality to Object-Relational mappers: they are in charge of the mapping of objects into NoSQL data (documents in the case of ODMs) for object-oriented applications. We show how schemas and other artifacts (e.g. validators and indexes) for ODMs can be automatically generated from inferred schemas. The solution consists of a two-step model transformation chain, where an intermediate model is generated to ease the code generation. We have applied our approach for two popular ODMs: Mongoose and Morphia and validated it with the StackOverflow dataset.

Developing HL7 CDA-Based Data Warehouse for the Use of Electronic Health Record Data for Secondary Purposes

Background The growing availability of clinical and administrative data collected in electronic health records (EHRs) have led researchers and policy makers to implement data warehouses to improve the reuse of EHR data for secondary purposes. This approach can take advantages from a unique source of information that collects data from providers across multiple organizations. Moreover, the development of a data warehouse benefits from the standards adopted to exchange data provided by heterogeneous systems. Objective This article aims to design and implement a conceptual framework that semiautomatically extracts information collected in Health Level 7 Clinical Document Architecture (CDA) documents stored in an EHR and transforms them to be loaded in a target data warehouse. Results The solution adopted in this article supports the integration of the EHR as an operational data store in a data warehouse infrastructure. Moreover, data structure of EHR clinical documents and the data warehouse modeling schemas are analyzed to define a semiautomatic framework that maps the primitives of the CDA with the concepts of the dimensional model. The case study successfully tests this approach. Conclusion The proposed solution guarantees data quality using structured documents already integrated in a large-scale infrastructure, with a timely updated information flow. It ensures data integrity and consistency and has the advantage to be based on a sample size that covers a broad target population. Moreover, the use of CDAs simplifies the definition of extract, transform, and load tools through the adoption of a conceptual framework that load the information stored in the CDA in the data warehouse.

The effects of Toxic Early Childhood Experiences on Depression according to Young Schema Model: A Scoping Review.

Young Schema Model (YSM) emphasizes Toxic Early Childhood Experiences (TECE) as vulnerability factors to depression. However, the lack of consensus on how to define and measure TECE is likely to have led to inconsistent findings in current literature. This review maps supported and unsupported pathways to depression and measures used. A scoping review was conducted in accordance with the Joanna Briggs Institute protocol to identify primary research studies that examined developmental and maintenance pathways to depression within the YSM framework. 2463 articles were identified with 49 primary research studies selected for inclusion. As a subset of the overall review, this paper focuses on the 14 studies that examined effects of TECE on depression in context of YSM. The studies used 11 different measures to examine the role of TECE in depression. Effects of maltreatment (i.e., physical abuse, emotional abuse, emotional neglect,) and perceived parenting (i.e., low care, high rejection, high overprotection and/or control, maladaptive parenting styles) on depression are well supported. Effects of sexual abuse and physical neglect on depression are currently inconclusive. In conclusion, findings revealed the inconsistent definition and measurement of TECE (with 11 different measures) in current literature. Findings support the effects of physical abuse, emotional abuse, emotional neglect, low care, high rejection, high overprotection and/or control, and maladaptive parenting styles on depression. Limitations of existing studies include researchers' inconsistent definition and measurement of TECE, under-examined TECE sub-constructs, possible moderating relationships, predominant cross-sectional design, and homogeneous sampling. Limitations of the scoping review include its small number of studies and the absence of evaluation of the measures used. Nevertheless, this review represents the first step in the systematic examination of the empirical basis of YSM and is an important contribution to depression treatment and innovation.

Modern restful api dls and frameworks for restful web services api schema modeling, documenting, visualizing

The given paper presents an overview of modern RESTful API description languages (belongs to interface description languages set) - OpenAPI, RAML, WADL, Slate - designed to provide a structured description of a RESTful web APIs (that is useful both to a human and for automated machine processing), with related RESTful web API modeling frameworks. We propose an example of the schema model of web API of the service for pre-trained distributional semantic models (word embeddings) processing. This service is a part of the Personal Research Information System services ecosystem - the Research and Development Workstation Environment class system for supporting research in the field of ontology engineering: the automated building of applied ontology in an arbitrary domain area as a main feature; scientific and technical creativity: the automated preparation of application documents for patenting inventions in Ukraine. It also presents a quick look at the relationship of Service-Oriented Architecture and Web services as well as REST fundamentals and RESTful web services; RESTful API creation process.

Early Maladaptive Schemas and Cognitive-Behavioral Aspect of Anger: Schema Model Perspective

Anger in Novaco model (in: Monahan, Steadman (eds) Violence and mental disorder: developments in risk assessment, The University of Chicago Press, Chicago, 1994) follows in cognitive, arousal and behavioral dimensions. Considerable research (Anderson and Bushman in Annu Rev Psychol 53(1):27–51, 2002; Dozois et al. in Early maladaptive schemas, styles of humor and aggression, 2013; Huesmann, in: Geen, Donnerstein (eds) Human aggression, Elsevier, Amsterdam, pp 73–109, 1998) has documented that one’s underlying belief system may influence dispositions toward anger and aggression. The aggressive individuals tend to hold several kinds of elaborate and readily accessible aggression-related cognitions (Gilbert et al. in Crim Justice Behav 40(2):119–138. https://doi.org/10.1177/0093854812467943 , 2013). Recognizing the schemas or core beliefs that are associated with anger and aggression would facilitate the therapy for patients with anger and aggression. This study examined the relationships between early maladaptive schemas (EMSs), anger, and aggression among 86 adult individuals in Anger therapy group (n = 24), Control group (n = 29) and Outpatients group (n = 33). The results indicated that the mistrust/abuse schema among five schemas in disconnection/rejection domain, and the entitlement/grandiosity schema among two schemas in impaired limits domain were the strongest predictors of anger and aggression. The next schemas that were associated or predicted anger and aggression and might be considered in therapy are insufficient self-control, abandonment, emotional deprivation, unrelenting standards, approval seeking, subjugation, self-sacrifice, and punitiveness. A comprehensive assessment, interpretation, and intervention for these EMSs, their interaction, and the coping styles would be helpful in anger and aggression therapies.

Metacognition and Emotional Schemas: Effects on Depression and Anxiety

The cognitive attentional syndrome (CAS) is characterized by threat monitoring, repetitive thinking, limitation in cognitive resources, unhelpful control strategies, and continued focus on the content of thinking (Wells 2000, 2002, 2006; Wells & Matthews in Cognition & Emotion, 8(3), 279-295, 1994). An alternative model of worry—the avoidance theory—proposes that worry is reinforced by the temporary decrease in arousal during the activation of “cognitive” or abstract worry (Borkovec et al. 2004; Borkovec and Inz in Behaviour Research and Therapy, 28(2), 153-158, 1990). The current study proposed that metacognitive processes are more likely to be activated if individuals have negative beliefs about emotional experience. Four hundred twenty-five participants completed the Metacognition Questionnaire (MCQ), Leahy Emotional Schema Scale (Leahy in Cognitive and Behavioral Practice, 9(3), 177-190, 2002), BDI-II, and the BAI. Negative beliefs about emotions were significantly correlated with each of the five metacognitive factors and with both depression (BDI-II) and anxiety (BAI). Almost all of the 14 emotional schema dimensions were related to the five metacognitive factors of worry. Mediational analyses suggested that uncontrollability/danger of worry partially mediated the relationship between negative beliefs about emotion and symptoms of anxiety. Similarly, uncontrollability/danger of worry and cognitive competence/confidence partially mediated the relationship between negative beliefs about emotion and symptoms of depression. These findings support metacognitive, avoidance, and emotional schema models by indicating that both metacognitive processes and beliefs about emotion separately contribute to depression and anxiety and that worry appears to be a strategy that is utilized to cope with negative beliefs about emotion.

Fractional weightage based objective function to a hybrid optimization algorithm for model transformation

Model transformation (MT) contributes a major role in the model-driven engineering (MDE), which are used to transfer the models among various languages to refactor and simulate the models or to obtain the useful codes from the models. Thus, the need for MT in MDE is very important, but the practical methods are not suitable for the detection of errors in transformations. This paper proposes an advanced algorithm, called fractional whale optimization integrated adaptive dragonfly (F-WOADF) algorithm to perform the MT from the class diagram (CLD) to relational schema (RS) model. The proposed algorithm modifies the adaptive dragonfly (ADF) algorithm with the concept of whale optimization algorithm (WOA) using the fractional theory. The UML CLD is transformed into the RS model using the optimal blocks that are selected with the use of the proposed algorithm. The performance of the F-WOADF method is evaluated using automatic correctness (AC), and fitness measure. The proposed method produces the maximum AC of 0.8583 and the maximum fitness measure of 0.8984 that indicates the effectiveness of the proposed method.

Polish adaptation of the Young Schema Questionnaire 3 Short Form (YSQ-S3-PL).

The aim of the study was to prepare the Polish adaptation of the Young Schema Questionnaire S3-PL. The scale is a self-assessment tool designed to measure 18 early maladaptive schemas. The sample consists of 1,529 adults (927 women and 585 men), aged 18-85 years (mean age was 32 years), from non-clinical groups. After language validation the internal consistency was assessed. The Cronbach's alpha ranged from 0.62 (Entitlement/grandiosity) to 0.81 (Failure), and it was 0.96 for total score. The best solution obtained in exploratory factor analysis was an eight-factor model, instead of the assumed 18-factor structure. Confirmatory factor analysis also did not fully supported Young's theoretical model. From all the tested models, bi-factor model (i.e., one generic factor and correlated specific factors - schemas) fitted the data best. In accordance with this model schema variance is explained concurrently by generic and specific factors; generic factor explains most of the Defectiveness variance, while only slightly - Self-sacrifice variance. Convergent validity analysis confirmes positive medium correlations with scales measuring psychopathology. Similarly, negative correlations with self-efficacy and optimism indicate good divergent validity. The psychometric characteristics of the Polish adaptation of the YSQ-S3PL is similar to those reported for other language versions. The results allow to recommend the method for scientific research. However, using it in therapeutic practice needs caution - especially in the case of clinical diagnosis. Further analyses are necessary to assess criterion validity and discriminative power in clinical settings.

The transdiagnostic model of worry: The mediating role of experiential avoidance

The aim of this study was to examine a transdiagnostic risk factor for predicting pathological worry among non-clinical population. The prediction model of worry was examined according to the propositions of the four cognitive models of generalized anxiety disorder; including intolerance of uncertainty model, metacognitive model, model of emotional schemas and acceptance-based model. Participants were 557 students whom were studied at University of Tehran in Iran. Data were gathered by Penn State Worry Questionnaire (PSWQ), Metacognitions Questionnaire-30 (MCQ-30), Leahy Emotional Schema Scale (LESS), Intolerance of Uncertainty Scale (IUS), and Acceptance and Action Questionnaire–II (AAQ-II). Structural equation modeling confirmed the proposed model and indicated that intolerance of uncertainty, metacognitive beliefs and emotional schemas predicted worry both directly and indirectly through experiential avoidance (χ2/df = 1.19, CFI = 0.98, NFI = 0.96, GFI = 0.97, SRMR = 0.03, RMSEA = 0.05). These risk factors had reciprocal relationships and experiential avoidance explained the commonalities between them as a linking mechanism. The variables in the model accounted for 74% of the variance in participants' worry. According to the examined model, individual differences in the ways people use to control their internal experiences and the process of inability to accept these experiences, act like vulnerability and maintenance mechanisms in explaining worry. The findings of this study support the rationale of recent psychotherapies for generalized anxiety disorders that emphasizes both the challenging cognitions and facilitating the acceptance of internal experiences.