A study of Chinese private passenger cars based on BP neural network

This study employs BP neural network to simulate the development of Chinese private passenger cars. Considering the uncertain and complex environment for the development of private passenger cars, indicators of economy, population, price, infrastructure, income, energy and some other fields which have major impacts on it are selected at first. The network is proved to be operable to simulate the progress of chinese private passenger cars after modeling, training and generalization test. Based on the BP neural network model, sensitivity analysis of each indicator is carried on and shows that the sensitivity coefficients of fuel price change suddenly. This special phenomenon reveals that the development of Chinese private passenger cars may be seriously affected by the recent high fuel price. This finding is also consistent with facts and figures

A longitudinal study comparing bereavement phenomena in recently bereaved spouses, adult children and parents

Objective: The study investigated previous research findings and clinical impressions which indicated that the intensity of grief for parents who had lost a child was likely to be higher than that for widows/widowers, who in turn were likely to have more intense reactions than adult children losing a parent. Method: In order to compare the intensities of the bereavement reactions among representative community samples of bereaved spouses (n = 44), adult children (n = 40) and parents (n = 36), and to follow the course of such phenomena, a detailed Bereavement Questionnaire was administered at four time points over a 13-month period following the loss. Results: Measures based on items central to the construct of bereavement showed significant time and group differences in accordance with the proposed hypothesis. More global items associated with the construct of resolution showed a significant time effect, but without significant group differences. Conclusions: Evidence from this study supports the hypothesis that in non-clinical, community-based populations the frequency with which core bereavement phenomena are experienced is in the order: bereaved parents bereaved spouses bereaved adult children.

Stem cell regulatory gene expression in human adult dental pulp and periodontal ligament cells undergoing odontogenic/osteogenic differentiation

Introduction During development and regeneration, odontogenesis and osteogenesis are initiated by a cascade of signals driven by several master regulatory genes. Methods In this study, we investigated the differential expression of 84 stem cell–related genes in dental pulp cells (DPCs) and periodontal ligament cells (PDLCs) undergoing odontogenic/osteogenic differentiation. Results Our results showed that, although there was considerable overlap, certain genes had more differential expression in PDLCs than in DPCs. CCND2, DLL1, and MME were the major upregulated genes in both PDLCs and DPCs, whereas KRT15 was the only gene significantly downregulated in PDLCs and DPCs in both odontogenic and osteogenic differentiation. Interestingly, a large number of regulatory genes in odontogenic and osteogenic differentiation interact or crosstalk via Notch, Wnt, transforming growth factor β (TGF-β)/bone morphogenic protein (BMP), and cadherin signaling pathways, such as the regulation of APC, DLL1, CCND2, BMP2, and CDH1. Using a rat dental pulp and periodontal defect model, the expression and distribution of both BMP2 and CDH1 have been verified for their spatial localization in dental pulp and periodontal tissue regeneration. Conclusions This study has generated an overview of stem cell–related gene expression in DPCs and PDLCs during odontogenic/osteogenic differentiation and revealed that these genes may interact through the Notch, Wnt, TGF-β/BMP, and cadherin signalling pathways to play a crucial role in determining the fate of dental derived cell and dental tissue regeneration. These findings provided a new insight into the molecular mechanisms of the dental tissue mineralization and regeneration

The potential for a web based intervention to improve young adult passenger safety

Young drivers aged 17-24 are consistently overrepresented in motor vehicle crashes. Research has shown that a young driver’s crash risk increases when carrying similarly aged passengers, with fatal crash risk increasing two to three fold with two or more passengers. Recent growth in access to and use of the internet has led to a corresponding increase in the number of web based behaviour change interventions. An increasing body of literature describes the evaluation of web based programs targeting risk behaviours and health issues. Evaluations have shown promise for such strategies with evidence for positive changes in knowledge, attitudes and behaviour. The growing popularity of web based programs is due in part to their wide accessibility, ability for personalised tailoring of intervention messages, and self-direction and pacing of online content. Young people are also highly receptive to the internet and the interactive elements of online programs are particularly attractive. The current study was designed to assess the feasibility for a web based intervention to increase the use of personal and peer protective strategies among young adult passengers. An extensive review was conducted on the development and evaluation of web based programs. Year 12 students were also surveyed about their use of the internet in general and for health and road safety information. All students reported internet access at home or at school, and 74% had searched for road safety information. Additional findings have shown promise for the development of a web based passenger safety program for young adults. Design and methodological issues will be discussed.

Towards a cognitive-behavioural intervention for adult grief reactions

Published accounts of behavioural interventions for grief have relied on exposure and habituation to grief cues as the primary strategy. Such an approach is excessively narrow, since it does not adequately confront the challenges that are posed by a bereavement. Many people cope with a bereavement by themselves, and for those, intervention may well be counterproductive. A cognitive-behavioural intervention, following models for depression/anxiety, can assist vulnerable individuals obtain a more rapid or complete adjustment. The proposed approach differs from dynamic treatments by placing less emphasis on defensive behavior, insight, and interpretation and more emphasis on training of coping skills.

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Predicting project performance through neural networks

Successful project delivery of construction projects depends on many factors. With regard to the construction of a facility, selecting a competent contractor for the job is paramount. As such, various approaches have been advanced to facilitate tender award decisions. Essentially, this type of decision involves the prediction of a bidderÕs performance based on information available at the tender stage. A neural network based prediction model was developed and presented in this paper. Project data for the study were obtained from the Hong Kong Housing Department. Information from the tender reports was used as input variables and performance records of the successful bidder during construction were used as output variables. It was found that the networks for the prediction of performance scores for Works gave the highest hit rate. In addition, the two most sensitive input variables toward such prediction are ‘‘Difference between Estimate’’ and ‘‘Difference between the next closest bid’’. Both input variables are price related, thus suggesting the importance of tender sufficiency for the assurance of quality production.

Empirically validated interventions for adult disorders

The movement toward evidence-based practice in psychology and medicine should offer few problems in cognitive-behavior therapies because it is consistent with the principles by which they have been developed and disseminated. However, the criteria for assessing empirical status, including the heavy emphasis on manualized treatments, need close examination. A possible outcome of the evidence-based movement would be to focus on the application of manualized treatments in both training and clinical practice; problems with that approach are discussed. Commitment to evidence-based treatment should also include comparisons between psychological and pharmacological interventions, so that rational health care decisions can be made. Psychologists should not be afraid of following the evidence, even when it supports treatments that are not cognitive-behavioral in stated orientation. Such results should be taken as an opportunity for theoretical development and new empirical inquiry rather than be a cause for concern.

The psychological management of adult grief reactions

Despite the severe challenges which are posed by the loss of a close friend or relative, bereavement has a relatively benign outcome in most cases. While the majority of patients cope with bereavement, a significant minority develop problems. A behavioural approach may help the bereaved avoid adverse grief reactions.

Girl in the Shadows and resilience and coping strategies in contemporary young adult fiction

The novel manuscript Girl in the Shadows tells the story of two teenage girls whose friendship, safety and sanity are pushed to the limits when an unexplained phenomenon invades their lives. Sixteen-year-old Tash has everything a teenage girl could want: good looks, brains and freedom from her busy parents. But when she looks into her mirror, a stranger’s face stares back at her. Her best friend Mal believes it’s an evil spirit and enters the world of the supernatural to find answers. But spell books and ouija boards cannot fix a problem that comes from deep within the soul. It will take a journey to the edge of madness for Tash to face the truth inside her heart and see the evil that lurks in her home. And Mal’s love and courage to pull her back into life. The exegesis examines resilience and coping strategies in adolescence, in particular, the relationship of trauma to brain development in children and teenagers. It draws on recent discoveries in neuroscience and psychology to provide a framework to examine the role of coping strategies in building resilience. Within this broader context, it analyses two works of contemporary young adult fiction, Freaky Green Eyes by Joyce Carol Oates and Sonya Hartnett’s Surrender, their use of the split persona as a coping mechanism within young adult fiction and the potential of young adult literature as a tool to help build resilience in teen readers.

A fuzzy neural network approach for contractor pre-qualification

Nonlinearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which cause the process more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through the FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractor’s ranking orders, the model efficiency (R2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The FNN is a practical approach for modelling contractor prequalification.

Decision support system for contractor pre-qualification : artificial neural network model

The selection criteria for contractor pre-qualification are characterized by the co-existence of both quantitative and qualitative data. The qualitative data is non-linear, uncertain and imprecise. An ideal decision support system for contractor pre-qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre-qualification criteria (variables) were identified for the model. One hundred and twelve real pre-qualification cases were collected from civil engineering projects in Hong Kong, and eighty-eight hypothetical pre-qualification cases were also generated according to the “If-then” rules used by professionals in the pre-qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre-qualification case consisted of input ratings for candidate contractors’ attributes and their corresponding pre-qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross-validation was applied to estimate the generalization errors based on the “re-sampling” of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors’ attributes and their corresponding pre-qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre-qualification task.

Cognitive behavioral interviewing for adult disorders : a practical handbook

This is a guidebook for clinicians on how to conduct assessment interviews with patients presenting with common psychological disorders. The orientation is behavioural and cognitive; so the book has wide applicability, as most clinicians explicitly or implicitly accept this combination of models as a useful basis for assessing and treating these problems. The problem areas covered are: fear and anxiety problems; depression, obesity; interpersonal problems; sexual dysfunction; insomnia; headache; and substance abuse.

Generating rules with predicates, terms and variables from the pruned neural networks

Artificial neural networks (ANN) have demonstrated good predictive performance in a wide range of applications. They are, however, not considered sufficient for knowledge representation because of their inability to represent the reasoning process succinctly. This paper proposes a novel methodology Gyan that represents the knowledge of a trained network in the form of restricted first-order predicate rules. The empirical results demonstrate that an equivalent symbolic interpretation in the form of rules with predicates, terms and variables can be derived describing the overall behaviour of the trained ANN with improved comprehensibility while maintaining the accuracy and fidelity of the propositional rules.