Sequential Process Model Research Articles

A Novel Filtered Based Grid partitioning multiple reducers skyline computation using Hadoop framework

Due to the exponential growth of multi-dimensional skyline objects, the computational memory and efficiency of the traditional skyline measures also increases on large spatial datasets. Skyline query computation has attracted a major research problem recently, due to its exponential time and space complexities over imbalanced datasets. A large number of sequential skyline query processing models have been implemented to evaluate the spatial pattern discovery on limited datasets. It is practically expensive and time consuming process to predict various spatial patterns from a large spatial candidate sets. Another major limitation with the sequential spatial pattern mining models is that a large number of spatial candidate sets are generated with duplicate event sets. In the proposed model, a novel parallel skyline processing using MapReduce framework is implemented on large spatial uncertain datasets. In this model, a filtered based k-nearest neighbor approach is used to eliminate the sparsity or empty patterns using the hadoop framework. Experimental results proved that the proposed model has high computational efficiency in terms of time and candidate sets are concerned.

Developing a Sequential Process Model of Coaching

Abstract. Coaching research has to keep pace with the rapidly developing coaching practice. In order to strengthen theoretical and empirical knowledge, it is necessary to examine the efficacy of coaching, but even more important to develop a model of the underlying processes. By gaining a deeper insight into the coaching process and its causal mechanisms, knowledge can be generated that will enhance the efficiency of coaching in the future. In this paper, a new process model is developed, which draws on insights and methodological tools from psychotherapy research that are then applied to the coaching process. This model expands on existing process models by making new assumptions about concrete independent and intervening variables that influence coaching outcomes, and about the specific causal paths linking these variables. Special emphasis is placed on crucial variables that can play an important role in the improvement of coaching processes and in the prevention of negative coaching effects. In the second part of the paper, research on coaching is related to and integrated into the model. Finally, possible limitations of the model and present recommendations for future research are discussed.

A processing schema for children in the auditory equiprobable Go/NoGo task: ERP components and behaviour

A sequential processing model for adults in the auditory equiprobable Go/NoGo task has been developed in recent years. This used temporal principal components analysis (PCA) to decompose Go/NoGo event related potential (ERP) data into components that mark stages of perceptual and cognitive processing. The model has been found useful in frameworking several studies in young and older adults, and in children. Recently, it has been demonstrated that the common PCA approach of decomposing Go and NoGo ERP data together results in misallocation of variance between the conditions, distorting the timing, topography, and amplitudes of the resultant components in each condition. The present study thus reanalyses data from a child study, conducting separate PCAs on the data from each condition. Multiple regression was then used to seek links with behavioural measures from the task. In addition to confirming the previous NoGo N2b/inhibitory processing link, novel NoGo Negative Slow Wave/error evaluation and Go N1-1/RT variability links were obtained. Based on these outcomes, the recommended separate application of PCAs to Go and NoGo data was confirmed. The present data were used to develop a child-specific sequential processing schema for this paradigm, suggesting earlier separation of the Go and NoGo processing chains, and the need to include an additional inhibition and evaluation stage. The child schema should be useful in future studies involving this and other two-choice reaction tasks.

Constructing a process model for low-carbon supply chain cooperation practices based on the DEMATEL and the NK model

PurposeThis paper aims to introduce a joint DEMATEL and NK methodology to develop a process model for introducing and implementing relational supply chain practices for low-carbon supply chains. Using this process model as a guide, insights into specific practices and how to implement these relational practices to achieve competitive advantage across organizations are introduced.Design/methodology/approachLow-carbon cooperation practices framework based on the relational view is developed. A methodology based on DEMATEL and the NK model is used to construct a sequential process model for introducing and implementing these relational practices. Empirical data from three manufacturing organizations in China are utilized to validate the model.FindingsInitial results provide a sequence of relational practices for guiding those organizations and their suppliers for healthy and low-carbon development. Interdependencies between relational practices are analyzed and evaluated from four aspects. Insights into the broader application of the methodology and initial results from both a research and managerial perspective are presented, especially with consideration of the China, an emerging economy, context.Research limitations/implicationsThe methodology remains relatively abstract in nature, yet the tool can provide very useful interpretations and information for both researchers and practitioners.Practical implicationsThis paper stipulates that in addition to internal operational practices, the relational practices between buyer and supplier may be equally important to achieve a low-carbon outcome, especially in supply chain setting. This paper also shows that not only the relational practice itself but also the implementation sequence of the relational practices can relate to performance. According to the authors’ initial results, organizations in this study should first develop product development cooperation, then exchange carbon knowledge and implement effective governance and last build a trust relationship with its suppliers for low-carbon cooperation.Originality/valueThis is one of the few approaches that directly evaluates and identifies the interdependencies among relational practices and to construct a process model for introducing and implementing low-carbon supply chain cooperation. It is also the first time that the NK model has been integrated with DEMATEL. Focusing on Chinese supply chain carbon emissions concerns is also a unique perspective.

Geographical topic learning for social images with a deep neural network

The use of geographical tagging in social-media images is becoming a part of image metadata and a great interest for geographical information science. It is well recognized that geographical topic learning is crucial for geographical annotation. Existing methods usually exploit geographical characteristics using image preprocessing, pixel-based classification, and feature recognition. How to effectively exploit the high-level semantic feature and underlying correlation among different types of contents is a crucial task for geographical topic learning. Deep learning (DL) has recently demonstrated robust capabilities for image tagging and has been introduced into geoscience. It extracts high-level features computed from a whole image component, where the cluttered background may dominate spatial features in the deep representation. Therefore, a method of spatial-attentional DL for geographical topic learning is provided and we can regard it as a special case of DL combined with various deep networks and tuning tricks. Results demonstrated that the method is discriminative for different types of geographical topic learning. In addition, it outperforms other sequential processing models in a tagging task for a geographical image dataset.

Transforming core emotional pain in a course of emotion-focused therapy for depression: A case study

Objective: To investigate the pattern of change in emotional states over a course of emotion-focused therapy using the model of sequential emotional processing as an initial framework for analysis. Method: This was a single case study observational design examining 15 sessions of therapy with one client. A qualitative analysis of moment-to-moment shifts in client emotional events was conducted. This conceptualised the interplay between experienced emotions using the sequential emotional processing model as an interpretative framework. The analysis was triangulated by using existing observer-based rating scales and reliability assessed with an independent rater. Results: The sequential emotional processing model was found to be an effective means to explain the sequence of expressed emotional events, although some emotional events and emotion scheme change processes pertaining to this particular case required additional explanation than provided in the original model descriptions. Conclusions: Observed nuances in this specific case included highlighting triggers to emotional experience and avoidance processes fuelled by anticipatory fear. The observations included a process of change through accessing core feelings of shame, fear, and loneliness and their transformation through the generation of self-compassion and assertive anger. Implications for practice are discussed in terms of case conceptualisation and therapeutic strategy.

Mindfulness and emotional regulation as sequential mediators in the relationship between attachment security and depression

Depression is a significant global health issue that has previously been associated with negative early care experiences and insecure attachment styles. This has led to much interest in identifying variables that may interrupt this relationship and prevent detrimental personal, social and economic outcomes. Recent research has indicated associations between the two seemingly distinct constructs of secure attachment and mindfulness, with similar positive outcomes. One hundred and forty eight participants completed an online survey exploring a possible sequential cognitive processing model, which predicted that higher levels of mindfulness and then emotional regulation would mediate the relationship between attachment and depression. Full mediation was found in regards to secure, preoccupied and dismissive attachment, whereas partial mediation was identified in the case of fearful attachment. The results support the possibility of an alternative cognitive processing pathway that may interrupt the association between negative early care experiences and concomitant negative mental health outcomes. Further exploration of this relationship is indicated.

DSP-CC-: I/O Efficient Parallel Computation of Connected Components in Billion-Scale Networks

Computing connected components is a core operation on graph data. Since billion-scale graphs cannot be resident in memory of a single server, several approaches based on distributed machines have recently been proposed. The representative methods are $\mathsf{Hash\hbox{-}To\hbox{-}Min}$ and $\mathsf{PowerGraph}$ . $\mathsf{Hash\hbox{-}To\hbox{-}Min}$ is the state-of-the art disk-based distributed method which minimizes the number of MapReduce rounds. $\mathsf{PowerGraph}$ is the-state-of-the-art in-memory distributed system, which is typically faster than the disk-based distributed one, however, requires a lot of machines for handling billion-scale graphs. In this paper, we propose an I/O efficient parallel algorithm for billion-scale graphs in a single PC. We first propose the Disk-based Sequential access-oriented Parallel processing (DSP) model that exploits sequential disk access in terms of disk I/Os and parallel processing in terms of computation. We then propose an ultra-fast disk-based parallel algorithm for computing connected components, $\mathsf{DSP\hbox{-}CC}$ , which largely improves the performance through sequential disk scan and page-level cache-conscious parallel processing . Extensive experimental results show that $\mathsf{DSP\hbox{-}CC}$ 1) computes connected components in billion-scale graphs using the limited memory size whereas in-memory algorithms can only support medium-sized graphs with the same memory size, and 2) significantly outperforms all distributed competitors as well as a representative disk-based parallel method.

Seeing red: How perceptions of social status and worth influence hostile attributions and endorsement of aggression.

Within social hierarchies, low social status is associated with increased vigilance, hostile expectations, and reactive aggression. We propose that societal devaluation is common across many low social status groups and produces a sense of threatened social worth. Threatened social worth may lead those of low status to be more vigilant towards social threats, thereby increasing the likelihood of hostile attributions and endorsement of aggression. Integrating theory on belongingness, social rejection, and stigma compensation, two studies test a sequential process model demonstrating that threatened social worth mediates the relationship between status, hostile attributions, and endorsement of aggression. Employing a relative status manipulation, Study 2 reveals a causal effect of status and highlights the importance of perceptions of low social status on threatened social worth. These data demonstrate the role of social worth in explaining the link between status and hostility and have implications for research in the social, health, and developmental domains.

Extending Theory on Job Stress

This article proposes that the variation in perceived job stress by individuals is explained by the interaction between the situational “Other 3” dimensions (locus of control, self-monitoring, and self-efficacy) and the “Big 5” personality traits. Situational stressors primarily impact the “Other 3” dimensions in the general model. The “Other 3” dimensions mediate the stressor–job stress relationship, while the “Big 5” dimensions moderate the “Other 3”–job stress relationship. In the sequential process model, the “Other 3” dimensions and job stress are cross-lagged related to define subsequent variations in the “Other 3” dimensions of personality and job stress. This perspective is consistent with the interactional perspective, wherein personality mediates the relationship of the individual with the environment. We offer several research propositions and conclude with implications for research and practice.

Sequential process convolution Gaussian process models via particle learning

Sequential process convolution Gaussian process models via particle learning

An Agile Approach towards eGovernment Solution Procurement and Implementation: An Experience from Sri Lanka

The global IT industry and software engineering academia have over the years, fine-tuned many aspects of iterative software development process models. As a result, almost all large scale enterprise systems are now quite successfully built using agile, iterative software engineering processes that features small phases with significant feedback loops. Many global enterprises have further enhanced these successes with good return-on-investment by utilizing the specialized and optimized software development services provided by outsourced and often offshore-based providers. In contrast, many large-scale eGovernment solution implementations from around the world are procured and implemented according to a sequential software development process model, commonly known as the Waterfall Model. This phenomenon is primarily due to the nature of eGovernment solution procurement models. Nearly all such eGovernment procurement models dictate that comprehensive requirements have to be detailed out up-front. A solutions provider (vendor) is then procured by following a stringent process and is tasked with the implementation of the entire solution. This methodology is particularly prevalent in eGovernment solution implementation scenarios in developing countries, and unfortunately, such solutions tend to display a high rate of failure. This paper discusses an approach that leverages best practices in enterprise software engineering in order to improve the success and development speed of eGovernment solutions. This approach was successfully employed in a large-scale eGovernment initiative in Sri Lanka.

Relational aggression, victimization, and adjustment during middle childhood

A secondary analysis of the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development was conducted to test the mechanisms by which relational aggression in third grade was associated both directly and indirectly with relational victimization in sixth grade. A large sample (N = 1,035; 522 girls; M = 8.3 years old; SD = 0.23) and multiple informants (teacher, child, and parent report) and methods were used to test several theoretically driven hypotheses. Our path analysis model suggested evidence for both direct and indirect pathways consistent with the sequential social process model of peer harassment. Relational aggression was significantly associated with future relational victimization even after controlling for physical aggression and gender. Loneliness mediated the direct association between relational aggression and peer victimization. A second model testing the reverse direction of effect revealed that relational victimization in third grade predicted relational aggression in sixth grade and was associated with loneliness and depressive symptoms in fifth grade, but there was no evidence for any of the indirect pathways.

The Research on Malfunction Diagnosis of Thermal Power System of Steam Turbine Based on Structure Theory

The improved diagnostic technique based on the structure theory is applied to a 600MW coal fired power plant. Two typical malfunctions which only one malfunction occurs and multiple malfunctions occur simultaneously are researched by the malfunction simulation. The induced malfunctions could be separated by the sequential process model. The results show the modified diagnostic model based on thermo-economics structural theory is correct and practical.

Game Real Time Action RPG Online Berbasis Flash: Alternatif Bermain Game dengan Spesifikasi Perangkat Keras Minimal

Various games are available for a variety of community as one means of entertainment which is getting more popular these days. However, sometimes several games can not be played on computer due to insufficient hardware specifications support. This situation developed the making of ChainKiller Game, a Flash based real time action RPG online game to provide an alternative as well as interactive and interesting game with a minimum hardware specification. The sequential linear process model is used to develop the overall game. Besides, a literature study was implemented to enrich the insight to develop the interesting mini game. The real-time action RPG online game which is based on Flash can be easyly used with minimum hardware specification.

Connections among Decision Field Theory models of cognition

Sequential sampling process models provide promising approaches to characterizing human judgment and decision making. In this paper, we study one class of sequential sampling process models referred to as Decision Field Theory (DFT). These models not only provide qualitative explanations of empirical findings, but also provide quantitative predictions of real time performance. We analyze major DFT models in the literature, and demonstrate that models which are seemingly similar can behave very differently, depending upon the cognitive basis for model parameters. Based on this analysis, we propose a mathematical framework which attempts to unify DFT models.

Neural network structure for spatio-temporal long-term memory.

This paper proposes a neural network structure for spatio-temporal learning and recognition inspired by the long-term memory (LTM) model of the human cortex. Our structure is able to process real-valued and multidimensional sequences. This capability is attained by addressing three critical problems in sequential learning, namely the error tolerance, the significance of sequence elements and memory forgetting. We demonstrate the potential of the framework with a series of synthetic simulations and the Australian sign language (ASL) dataset. Results show that our LTM model is robust to different types of distortions. Second, our LTM model outperforms other sequential processing models in a classification task for the ASL dataset.

Simulation of Effect of Solder Balls Layout on Warpage and Interfacial Stresses of FBGA

The finite element method is used to construct the sequential processing model of the package. The model is used to simulate the warpage and interfacial stresses of FBGA (Fine-pitch Ball Grid Array) package due to the coefficient of thermal expansion mismatch between layered materials of package during assembly processes. The processing model uses the function of element birth and death to simulate the processing step of package model, considering the sequentially accumulated warpage and stresses during the processing steps. The induced package stresses can cause the delamination between die/die attach interface. In order to verify the simulation results, the simulated package warpage of a standard package is compared with the measurement of package warpage of the package. In this study, the effect of solder balls layout on the warpage and interfacial stresses at die/die attach interface is discussed by simulation results.

A Sequential Point Process Model and Bayesian Inference for Spatial Point Patterns with Linear Structures

Abstract. We introduce a flexible spatial point process model for spatial point patterns exhibiting linear structures, without incorporating a latent line process. The model is given by an underlying sequential point process model. Under this model, the points can be of one of three types: a ‘background point’ an ‘independent cluster point’ or a ‘dependent cluster point’. The background and independent cluster points are thought to exhibit ‘complete spatial randomness’, whereas the dependent cluster points are likely to occur close to previous cluster points. We demonstrate the flexibility of the model for producing point patterns with linear structures and propose to use the model as the likelihood in a Bayesian setting when analysing a spatial point pattern exhibiting linear structures. We illustrate this methodology by analysing two spatial point pattern datasets (locations of bronze age graves in Denmark and locations of mountain tops in Spain).

Sistem Pelayanan Jasa dan Penjualan Produk Percetakan Berbasis Web pada Proposter Indonesia

Various constraints related to the availability of up-to-date information services and product sales happen to ProPoster Indonesia, a business of digital printing and display services system run since 2002. ProPoster Indonesia needs to offer an easy access to services and online service sales of printing products to its customers and plans to expand its business services throughout Indonesia. These conditions initiate the development of web-based service and products sales system for ProPoster Indonesia. The main objectives are to overcome the difficulties in obtaining the latest information related to services and products and facilitate promotional printing services to all parts of Indonesia. The sequential linear process model is used to develop the overall system while literature study is done to enrich the insight to develop a better system. The user friendly system makes easy for customers to get the latest information that ultimately facilitate the service process and product sales.