Multiple Domain Models Research Articles

Evaluation of the multiple-deficit hypothesis among dyslexic Arabic-speaking children.

This study examined the multiple-deficit hypothesis among Arabic-speaking elementary school students. A total of 90 students, divided into three main groups based on their performance on an Arabic word-reading task: dyslexic (n = 30), regular age-matched (n = 30), and 3rd-grade regular students, who were matched to the dyslexic group in regard to their reading proficiency level (n = 30). Participants underwent a nine-domain Arabic reading experiment that measured accuracy and fluency to evaluate general reading proficiency. The performance of Arabic dyslexic students was significantly worse than age-matched controls, but similar to young matched controls based on the reading level of each cognitive task. Moreover, dyslexic students showed deficits in three or more cognitive functions, depending on severity. This study adds to the limited empirical research on the double-deficit hypothesis and its extension to the multiple-domain model among young Arabic students.

Adaptive multi-source domain collaborative fine-tuning for transfer learning.

Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning (AMCF) to address this issue. AMCF utilizes multiple source domain models for collaborative fine-tuning, thereby improving the feature extraction capability of model in the target task. Specifically, AMCF employs an adaptive multi-source domain layer selection strategy to customize appropriate layer fine-tuning schemes for the target task among multiple source domain models, aiming to extract more efficient features. Furthermore, a novel multi-source domain collaborative loss function is designed to facilitate the precise extraction of target data features by each source domain model. Simultaneously, it works towards minimizing the output difference among various source domain models, thereby enhancing the adaptability of the source domain model to the target data. In order to validate the effectiveness of AMCF, it is applied to seven public visual classification datasets commonly used in transfer learning, and compared with the most widely used single-source domain fine-tuning methods. Experimental results demonstrate that, in comparison with the existing fine-tuning methods, our method not only enhances the accuracy of feature extraction in the model but also provides precise layer fine-tuning schemes for the target task, thereby significantly improving the fine-tuning performance.

Disk anomaly detection for remote maintenance control system of natural gas pipeline based on multi-source domain transfer and incremental learning

Accurate disk anomaly detection is of great significance for maintaining the safe and stable operation of the remote control system of natural gas pipelines. Most disk transfer algorithms use only a single-source domain model for offline transfer learning, resulting in lower classifier performance. This paper proposes a disk online anomaly detection algorithm combining transfer learning and incremental learning. A transfer learning algorithm based on data distribution similarity and dynamic index weighted ensemble is proposed. According to multiple source domain models and target domain model negative data in hyperspace, k neighbour data of each target domain negative data is extracted, and then different test criteria are adopted for each sub-model according to the number of target domain positive data, and index reweighting is performed according to the model detection capability. An incremental learning algorithm based on online automatic annotation is proposed. The data popped up by the sliding window is automatically marked at the current disk state, and the update period of the prediction model is dynamically adjusted according to the false detection rate and the missed detection rate. By setting three different experiments for comparison, the effectiveness and advanced nature of the method proposed in this paper are illustrated.

Systematic literature review of domain-oriented specification techniques

Context:The popularity of domain-specific languages and model driven development has made the tacit use of domain knowledge in system development more tangible. Our vision is a development process where a (software) system specification is based on multiple domain models, and where the specification method is built from cognitive concepts, presumably derived from natural language. Goal:To realize this vision, we evaluate and reflect upon the existing literature in domain-oriented specification techniques. Method:We designed and conducted a systematic literature review on domain-oriented specification techniques. Results:We identified 53 primary studies, populated the classification framework for each study, and summarized our findings per classification aspect. We found many approaches for creating domain models or domain-specific languages. Observations include: (i) most methods are defined incompletely; (ii) none offers methodical support for the use of domain models or domain-specific languages to create other specifications; (iii) there are specification techniques to integrate models in general, but no study offers methodical support for multiple domain models. Conclusion:The results indicate which topics need further research and which can instead be reused to realize our vision on system development.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Predicting psychosis-spectrum diagnoses in adulthood from social behaviors and neighborhood contexts in childhood

Research showing that risk for schizophrenia, bipolar disorder with psychosis, and other psychosis-spectrum diagnoses in adulthood is multidetermined has underscored the necessity of studying the additive and interactive factors in childhood that precede and predict future disorders. In this study, risk for the development of psychosis-spectrum disorders was examined in a 2-generation, 30-year prospective longitudinal study of 3,905 urban families against a sociocultural backdrop of changing economic and social conditions. Peer nominations of aggression, withdrawal, and likeability and national census information on neighborhood-level socioeconomic disadvantage in childhood, as well as changes in neighborhood socioeconomic conditions over the lifespan, were examined as predictors of diagnoses of schizophrenia, bipolar disorder, and other psychosis-spectrum disorders in adulthood relative to developing only nonpsychotic disorders or no psychiatric disorders. Individuals who were both highly aggressive and highly withdrawn were at greater risk for other psychosis-spectrum diagnoses when they experienced greater neighborhood disadvantage in childhood or worsening neighborhood conditions over maturation. Males who were highly aggressive but low on withdrawal were at greater risk for schizophrenia diagnoses. Childhood neighborhood disadvantage predicted both schizophrenia and bipolar diagnoses, regardless of childhood social behavior. Results provided strong support for multiple-domain models of psychopathology, and suggest that universal preventive interventions and social policies aimed at improving neighborhood conditions may be particularly important for decreasing the prevalence of psychosis-spectrum diagnoses in the future.

Transitional Complexity of Health Information System of Systems: Managing by the Engineering Systems Multiple-Domain Modeling Approach

A health information system is a system of systems that serves as a foundation of health information used for the evidence-based decision making of public health actions. Since practical solutions for addressing system transformation and its effect on system management have not been extensively discussed, this study examines an approach to model and analyze the transforming health information system of systems. The process and architecture in the case of the Cambodian malaria surveillance system were modeled using the engineering system multiple-domain matrix modeling framework. By using the attributes of the process, architecture, and the risk associated with the environment within the model, relative weights of the constituent systems were scored at each interval of time. The simulated confidence intervals of the absolute difference in the scores indicated that this approach yields the transformation of the system under investigation. By comparing the calculated scores with the results of the simulation test employing an agent-based modeling method, further insights into the process, the limitations, and advantages of the scoring approach were discussed. This approach provides the first step in analyzing the transitional conditions of constituent systems as well as the entire health information system, whereby informed decision making for optimizing continuous system management is facilitated.

Leveraging a design of experiments methodology to enhance impacts of modeling and simulations for engineered resilient systems

System engineers rely on a variety of models and simulations to help understand multiple perspectives in several domains throughout a system’s life-cycle. These domain models include operational simulations, life-cycle cost models, physics-based computational models, and many more. Currently, there is a technical gap with regard to our ability to untangle system design drivers within system life-cycle domains. This article provides a procedural workflow that addresses this technical gap by leveraging the methods of experimental design in order to clearly identify tradable variables and narrow the search for viable system variants. Our purpose is to illuminate trade decisions across several different viewpoints by integrating metamodels that approximate the behavior of multiple domain models; a metamodel is a statistical function that acts as a surrogate to a model. Model inputs often represent value properties that define a system alternative configuration or environmental conditions that represent uncertain factors within the system boundary. Model outputs represent measures of performance or effectiveness that allow us to compare alternatives and understand the tradeoffs among several objectives. In order to illuminate the tradeoffs that exist in a complex system design problem we propose an approach that approximates model input and output behavior using the functional form of statistical metamodels. After performing an experimental design, we can fit a metamodel with a functional form known as a response surface. We utilize contour profilers that show horizontal cross sections of multiple response surfaces to visualize where key trade decisions exist. Our research supports the tradespace analytics pillar for the development of the engineered resilient system (ERS) architecture. The article concludes with instructions on how to perform simulation experiments to construct a dynamic dashboard that illuminates system tradeoffs.

Paleotemperatures at the lunar surfaces from open system behavior of cosmogenic 38Ar and radiogenic 40Ar

The simultaneous diffusion of both cosmogenic 38Ar and radiogenic 40Ar from solid phases is controlled by the thermal conditions of rocks while residing near planetary surfaces. Combined observations of 38Ar/37Ar and 40Ar/39Ar ratios during stepwise degassing analyses of neutron-irradiated Apollo samples can distinguish between diffusive loss of Ar due to solar heating of the rocks and that associated with elevated temperatures during or following impact events; the data provide quantitative constraints on the durations and temperatures of each process. From sequentially degassed 38Ar/37Ar ratios can be calculated a spectrum of apparent 38Ar exposure ages versus the cumulative release fraction of 37Ar, which is particularly sensitive to conditions at the lunar surface typically over ∼106–108 year timescales. Due to variable proportions of K- and Ca-bearing glass, plagioclase and pyroxene, with variability in the grain sizes of these phases, each sample will have distinct sensitivity to, and therefore different resolving power on, past near-surface thermal conditions. We present the underlying assumptions, and the analytical and numerical methods used to quantify the Ar diffusion kinetics in multi-phase whole-rock analyses that provide these constraints.For Apollo 15 samples 15016, 15556, and 15596 we find apparent 40Ar/39Ar plateau ages between 3.21 and 3.28Ga and evidence for diffusive loss of radiogenic 40Ar primarily from K-bearing glass. From 38Ar/37Ar spectra normalized to the apparent Ca/K ratios, we also find evidence of diffusive loss of cosmogenic 38Ar that requires elevated temperatures either during or after surface exposure. Using 39Ar and 37Ar, we construct multiple-phase–multiple diffusion domain (MP-MDD) models to quantify the diffusion kinetics of Ar from a range of macroscopic grain sizes of each phase. While diffusive loss of 40Ar can be explained by brief reheating conditions after crystallization (e.g., during an impact event), we find that both the radiogenic 40Ar and cosmogenic 38Ar abundances can be explained by internally consistent thermal conditions expected for solar heating of the rocks at the lunar surface. These conditions correspond to effective diffusion temperatures (EDT, i.e., the temperature corresponding to the mean diffusivity over temperature variability) between 65 and 81°C, with an error-weighted mean of 77.0±1.3°C, despite differences in diffusion kinetics and large differences in surface exposure duration between the three samples (∼56–621Ma). This EDT corresponds to a maximum daytime temperature of ∼96°C, which is in excellent agreement direct measurements of temperature at the Apollo 15 landing site. The open system behavior in both a radiogenic and a cosmogenic nuclide provides tests for internal consistency in best-fitting solutions of time-integrated thermal conditions of rocks collected from planetary surfaces. This thermal information is important for the study of habitable conditions at planetary surfaces, and has implications for Ar-based geochronology and paleomagnetic observations applied to planetary materials.

Query-driven soft traceability links for models

Model repositories play a central role in the model driven development of complex software-intensive systems by offering means to persist and manipulate models obtained from heterogeneous languages and tools. Complex models can be assembled by interconnecting model fragments by hard links, i.e., regular references, where the target end points to external resources using storage-specific identifiers. This approach, in certain application scenarios, may prove to be a too rigid and error prone way of interlinking models. As a flexible alternative, we propose to combine derived features with advanced incremental model queries as means for soft interlinking of model elements residing in different model resources. These soft links can be calculated on-demand with graceful handling for temporarily unresolved references. In the background, the links are maintained efficiently and flexibly by using incremental model query evaluation. The approach is applicable to modeling environments or even property graphs for representing query results as first-class relations, which also allows the chaining of soft links that is useful for modular applications. The approach is evaluated using the Eclipse Modeling Framework (EMF) and EMF-IncQuery in two complex industrial case studies. The first case study is motivated by a knowledge management project from the financial domain, involving a complex interlinked structure of concept and business process models. The second case study is set in the avionics domain with strict traceability requirements enforced by certification standards (DO-178b). It consists of multiple domain models describing the allocation scenario of software functions to hardware components.

Soil Macropore Structures and their Effect on Preferential Flow

Recent researches on soil macropore and preferential flow were reviewed in this paper. Definition of macropore, causes of preferential flow, observation technologies, and preferential simulated models were introduced. Soil macropore investigated methods include dye tracing, sectioning, CT scan, tension infiltration curve, and markers tracing. Preferential flow models include two domain model and multiple domain model, two phase model and hybrid model, etc.

Hysteresis and vertical anisotropy of magnetoresistance in La0.67A0.33MnOz (A=Ca, Sr) polycrystalline films deposited on amorphous quartz substrates

Polycrystalline La0.67A0.33MnOz (A=Ca, Sr) thin films with [202] preferred orientation were synthesized on amorphous quartz substrates by means of metal organic deposition (MOD) technology. The Curie temperature (TC) and metal−insulator transition temperature (Tp) are 172 K, 247 K and 227 K, 335 K for La0.67Ca0.33MnOz (LCMO) film and La0.67Sr0.33MnOz (LSMO) film, respectively. The hysteresis and vertical anisotropy behaviors of MR are exhibited by the films below TC, which strongly relies on the magnetization process. The magnitude of these magnetotransport properties would decline with increasing temperature. The in-plane hysteric MR behaviors could be explained by multiple-domain model. For vertical anisotropy of MR, demagnetization effect plays an important role. Especially, compared to LCMO film, LSMO film still presents the above hysteretic MR effect and vertical anisotropic MR at RT due to its higher TC.

Systematic variations of argon diffusion in feldspars and implications for thermochronometry

Coupled information about the time-dependent production and temperature-dependent diffusion of radiogenic argon in feldspars can be used to constrain the thermal evolution attending a host of Earth and planetary processes. To better assess the accuracy of thermal models, an understanding of the mechanisms and pathways by which argon diffuses in feldspars is desirable. Here we present step-heating Ar diffusion experiments conducted on feldspars with diverse compositions, structural states, and microstructural characteristics. The experiments reveal systematic variations in diffusive behavior that appear closely related to these variables, with apparent closure temperatures for 0.1–1mm grains of ∼200–400°C (assuming a 10°C/Ma cooling rate). Given such variability, there is no broadly applicable set of diffusion parameters that can be utilized in feldspar thermal modeling; sample-specific data are required. Diffusion experiments conducted on oriented cleavage flakes do not reveal directionally-dependent diffusive anisotropy to within the resolution limits of our approach (approximately a factor of 2). Additional experiments aimed at constraining the physical significance of the diffusion domain are presented and indicate that unaltered feldspar crystals with or without coherent exsolution lamellae diffuse at the grain-scale, whereas feldspars containing hydrothermal alteration and/or incoherent sub-grain intergrowths do not. Arrhenius plots for argon diffusion in plagioclase and alkali feldspars appear to reflect a confluence of intrinsic diffusion kinetics and structural transitions that occur during incremental heating experiments. These structural transitions, along with sub-grain domain size variations, cause deviations from linearity (i.e., upward and downward curvature) on Arrhenius plots. An atomistic model for Arrhenius behavior is proposed that incorporates the variable lattice deformations of different feldspars in response to heating and compression. The resulting implications for accurately extrapolating laboratory-derived diffusion parameters to natural settings and over geologic time are discussed. We find that considerable inaccuracies may exist in published thermal histories obtained using multiple diffusion domain (MDD) models fit to Arrhenius plots for exsolved alkali feldspar, where the inferred Ar partial retention zones may be spuriously hot.

A Test of an Adapted Multiple Domain Model in Predicting Sexual Behaviors Among Unmarried Young Adults in India

Theory-based, scientific research examining sexual behaviors of young adults is sparse in India, even though pre-marital sex among unmarried young people has been rising in recent years. At the same time, young people aged 15 to 24 are disproportionately affected by HIV/AIDS. This has been attributed in part to rising pre-marital sexual behaviors, coupled with a lack of sex education. The objective of this study was to advance an understanding of the determinants of sexual behavior among unmarried young adults in northern India. An adaptation of a comprehensive model of health behavior, the Multiple Domain Model, was employed to study the effects of environmental/cultural influences (parental and media), structural determinants (sex, socioeconomic status, age, caste, and place of residence), personality factors (sensation-seeking and impulsive decision making), gender role identity, psychosocial variables (attitudes, norms, and self-efficacy), contextual influences (relationship status and alcohol/drug use) and preparatory behaviors (frequency of being in sexual situations) on adolescents' sexual behaviors. Results of path analysis indicated that key predictors of ever having had vaginal sex included preparatory behaviors, masculine gender role identity, attitudes toward having sex and peer norms regarding sex. Implications of these findings for future research and intervention are discussed.

Adapting and applying a multiple domain model of condom use to Chinese college students

This study adapts a multiple domain model (MDM) to explain condom use among a sample of sexually active Chinese college students. A cross-sectional survey was conducted and structural equation modeling was used to test the proposed model. Preparatory behaviors, theory of reasoned action (TRA)/theory of planned behavior variables, impulsivity, length of relationship, and alcohol use were significant direct predictors of condom use. The results suggest that MDM can provide a better understanding of heterosexual condom use among Chinese youth, and help in the design of HIV-preventive and safer sex interventions in China.

Harsh Parenting and Child Externalizing Behavior: Skin Conductance Level Reactivity as a Moderator

Skin conductance level reactivity (SCLR) was examined as a moderator of the association between harsh parenting and child externalizing behavior. Participants were 251 boys and girls (8-9 years). Mothers and fathers provided reports of harsh parenting and their children's externalizing behavior; children also provided reports of harsh parenting. SCLR was assessed in response to a socioemotional stress task and a problem-solving challenge task. Regression analyses revealed that the association between harsh parenting and externalizing behavior was stronger among children with lower SCLR, as compared to children with higher SCLR. SCLR may be a more robust moderator among boys compared to girls. Results are discussed with regard to theories on antisocial behavior and multiple-domain models of child development.

Longitudinal Test of a Multiple Domain Model of Adolescent Condom Use

A comprehensive multiple domain model (MDM) to understand condom use in adolescents was proposed and tested using structural equation modeling of data at three time points. The proposed model integrates social psychological theory, demographic and personality factors, social environment, and situational/contextual variables. Adolescents who were sexually active at time 2 (6 months after baseline) and time 3 (1 year after time 2) and completed surveys at all three time points were included in the analyses (N = 511). An iterative process of model testing resulted in a structural equation model that provided a good fit to the data (CFI = .92, RMSEA = .04). Models comparing gender and race as moderators also were calculated and supported the generalizability of the model. Results provide support for an MDM that goes beyond traditional social psychological models for a broader understanding of condom use in adolescents. Implications for further theory testing and safer sexual interventions are discussed.

An empirical study of AI-based multiple domain models for selecting attributes that drive company wealth

This paper explores what attributes drive company wealth creation in the Miscellaneous Industrials sector of the Australian Stock Market. We examine traditional and artificial intelligent (AI) feature selection techniques, to select attributes that drive company wealth and observe if a multiple domain model outperforms single domain models with regards to predicting company wealth. Using a large number of calculated attributes, our empirical findings suggest that a multiple domain model was most effective. We find that Market Capitalisation, Debt Asset Ratio, ROI and Beta Value attributes are the main contributors to following the directional change in the shareholder value. This was using a J4.8 and GRNN multiple domain model.

On single and multiple models of protein families for the detection of remote sequence relationships

BackgroundThe detection of relationships between a protein sequence of unknown function and a sequence whose function has been characterised enables the transfer of functional annotation. However in many cases these relationships can not be identified easily from direct comparison of the two sequences. Methods which compare sequence profiles have been shown to improve the detection of these remote sequence relationships. However, the best method for building a profile of a known set of sequences has not been established. Here we examine how the type of profile built affects its performance, both in detecting remote homologs and in the resulting alignment accuracy. In particular, we consider whether it is better to model a protein superfamily using a single structure-based alignment that is representative of all known cases of the superfamily, or to use multiple sequence-based profiles each representing an individual member of the superfamily.ResultsUsing profile-profile methods for remote homolog detection we benchmark the performance of single structure-based superfamily models and multiple domain models. On average, over all superfamilies, using a truncated receiver operator characteristic (ROC5) we find that multiple domain models outperform single superfamily models, except at low error rates where the two models behave in a similar way. However there is a wide range of performance depending on the superfamily. For 12% of all superfamilies the ROC5 value for superfamily models is greater than 0.2 above the domain models and for 10% of superfamilies the domain models show a similar improvement in performance over the superfamily models.ConclusionUsing a sensitive profile-profile method we have investigated the performance of single structure-based models and multiple sequence models (domain models) in detecting remote superfamily members. We find that overall, multiple models perform better in recognition although single structure-based models display better alignment accuracy.

Patterns of risk and trajectories of preschool problem behaviors: A person-oriented analysis of attachment in context

A small proportion of children exhibit extreme and persistent conduct problems through childhood. The present study employed the multiple-domain model of Greenberg and colleagues as the framework for person-oriented analyses examining whether parent-child attachment combines with parenting, family ecology, and child characteristics in particular configurations of risk that are linked to this problematic developmental pathway. Using prospective data from a community sample of adolescent mothers and their children, latent variable growth mixture modeling identified a normative trajectory with declining problem behaviors during the preschool period. Consistent with research on early-starter pathways, a distinct group of children featured a higher intercept and a positive slope, indicating an escalation in disruptive behaviors. Attachment security played a role in defining specific risk profiles associated with the probability of exhibiting this problem trajectory. Given particular patterns of risk exposure, secure attachment served a protective function. Avoidant, but not disorganized, attachment was associated with significantly higher likelihood of the disruptive problem trajectory. The results also indicated the general accumulation of risk was detrimental, but the particular configuration of risk made a difference. Overall, the findings suggest early attachment operates in conjunction with personal and contextual risk to distinguish the development of later problem behaviors.

Modeling and Testing Change: An Introduction to the Latent Growth Curve Model

The purposes of this article are threefold: (a) to outline the basic concepts associated with latent growth curve (LGC) modeling; (b) to demonstrate the modeling and testing of LGC models based on three relatively simple, albeit increasingly complex, examples; and (c) to illustrate the modeling mechanism used in testing for the tenability of key statistical assumptions associated with LGC modeling. Based on 3-wave data comprising an original sample of 601 adolescents (Grades 8, 9, and 10) and using a multiple-sample approach that takes into account missing data resulting from time-related attrition, we "walk" the reader through the various stages of the model specification and testing processes. Based on self-rating scores of perceived ability as the outcome variable, we begin with a single-domain LGC model of perceived math ability and then follow with a more complex multiple-domain model that includes perceived ability in math, language, and science. Our final application extends the multiple-domain model to include the predictor variable of gender. We conclude by summarizing several advantages of LGC modeling over the more traditional methods used in the measurement of change.