University Students Depression Inventory : measurement model and psychometric properties

Depression is a serious condition that impacts the academic success and emotional well-being of the university students globally. Keeping in view the debilitating nature of this condition, the present study examined the stability of the factor structure and psychometric properties of the University Student Depression Inventory (USDI; Khawaja and Bryden, 2006). There is a need to translate and validate the scale for Persian speaking students, who live in Iran, its neighboring countries and in many other Western countries. The scale was translated into the Persian language and was used as part of a battery consisting of the scales measuring suicide, depression, stress, happiness and academic achievement. The battery was administered to 359 undergraduate students, and an additional 150 students who had been referred to the mental health center of the University of Tehran as clinical sample. Confirmatory factor analysis upheld the original three-factor structure. The results exhibited internal consistency, test-retest reliability, convergent, and divergent validity, and discriminant validity. There were gender differences and male had higher mean scores on Lethargy, Cognitive\emotion, and Academic motivation subscales than female students. Findings supported the Persian version of the USDI for cross-cultural use as a valid and reliable measure in the diagnosis of depression.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

An Asian perspective of occupational stress coping model : a case study of Sri Lankan employees

Sri Lanka has one of the highest rates of natural disasters and violent conflicts in the world. Yet there is a lack of research on its unique socio-cultural characteristics that determine an individual's cognitive and behavioural responses to distressing encounters. This study extends Goh, Sawang and Oei's (2010) revised transactional model to examine the cognitive and behavioural processes of occupational stress experience in the collectivistic society of Sri Lanka. A time series survey was used to measure the participant's stress-coping process. Using the revised transactional model and path analysis, a unique Sri Lankan model is identified that provides theoretical insights on the revised transactional model, and sheds light on socio-cultural dimensions of occupational stress and coping, thus equipping practitioners with a sound theoretical basis for the development of stress management programs in the workplace.

Demand-side response model to avoid spike of electricity price

The aim of this work is to develop a demand-side-response model, which assists electricity consumers exposed to the market price to independently and proactively manage air-conditioning peak electricity demand. The main contribution of this research is to show how consumers can optimize the energy cost caused by the air conditioning load considering to several cases e.g. normal price, spike price, and the probability of a price spike case. This model also investigated how air-conditioning applies a pre-cooling method when there is a substantial risk of a price spike. The results indicate the potential of the scheme to achieve financial benefits for consumers and target the best economic performance for electrical generation distribution and transmission. The model was tested with Queensland electricity market data from the Australian Energy Market Operator and Brisbane temperature data from the Bureau of Statistics regarding hot days from 2011 to 2012.

Export readiness index : construction and analysis

The internationalisation process of firms has attracted much research interest since the 1970s. It is noted, however, that a significant research gap exists in studies with a primary focus on the pre-internationalisation behaviour of firms. This paper proposes the incorporation of a pre-internationalisation phase into the traditional Uppsala model of firm internationalisation to address the issue of export readiness. Through extensive literature review, the concepts fundamental to the ability of an Uppsala type firm to begin internationalisation through an export entry mode are identified: exposure to stimuli factors, attitudinal commitment of decision makers towards exporting, the firm’s resource capabilities, as well as the moderating effect of lateral rigidity. The concept of export readiness is operationalised in this study through the construction of an export readiness index (ERI) using exploratory and confirmatory factor analysis. The index is then applied to some representative cases and tested using logistic regression to establish its validity as a diagnostic tool. The proposed ERI presents not only a more practical approach towards analysing firms’ export readiness but has also major public policy implications as a possible tool for government export promotion agencies.

Understanding the pre-internationalization phase decision heuristic of Australian SMEs

Since the 1970s, the Uppsala stages model has been one of the dominant explanations of firm internationalization. The model's focus on internationalization as a firm's gradual and incremental process of increasing international involvement has attracted much debate, with one criticism being that it is unclear in explaining how the internationalization process first originates within a firm. In this paper, the Uppsala model is extended through the incorporation of a pre-internationalization phase to explore the antecedents of firm internationalization. Adopting the Uppsala model's theoretical underpinnings, this paper develops and operationalizes a pre-internationalization phase decision heuristic in the form of an ‘export readiness index'. Four constructs are proposed that drive and inhibit export commencement decision-making during a firm's preinternationalization phase: export stimuli, attitudinal/psychological commitment, resources and lateral rigidity. Through a survey of Australian exporting and non-exporting small-medium sized enterprises (SMEs), the Export Readiness Index (ERI) is developed through factor analysis and tested using logistic regression. Results of the study and their potential implications are discussed.

Defining expected cost for the air conditioning to avoid a price spike of electricity market under DSR model

This work presents a demand side response model (DSR) which assists small electricity consumers, through an aggregator, exposed to the market price to proactively mitigate price and peak impact on the electrical system. The proposed model allows consumers to manage air-conditioning when as a function of possible price spikes. The main contribution of this research is to demonstrate how consumers can minimise the total expected cost by optimising air-conditioning to account for occurrences of a price spike in the electricity market. This model investigates how pre-cooling method can be used to minimise energy costs when there is a substantial risk of an electricity price spike. The model was tested with Queensland electricity market data from the Australian Energy Market Operator and Brisbane temperature data from the Bureau of Statistics during hot days on weekdays in the period 2011 to 2012.

Optimization of an integrated model for automatic reduction and expansion of long queries

A long query provides more useful hints for searching relevant documents, but it is likely to introduce noise which affects retrieval performance. In order to smooth such adverse effect, it is important to reduce noisy terms, introduce and boost additional relevant terms. This paper presents a comprehensive framework, called Aspect Hidden Markov Model (AHMM), which integrates query reduction and expansion, for retrieval with long queries. It optimizes the probability distribution of query terms by utilizing intra-query term dependencies as well as the relationships between query terms and words observed in relevance feedback documents. Empirical evaluation on three large-scale TREC collections demonstrates that our approach, which is automatic, achieves salient improvements over various strong baselines, and also reaches a comparable performance to a state of the art method based on user’s interactive query term reduction and expansion.

An additive model for spatio-temporal smoothing of cancer mortality rates

In this paper, a Bayesian hierarchical model is used to anaylze the female breast cancer mortality rates for the State of Missouri from 1969 through 2001. The logit transformations of the mortality rates are assumed to be linear over the time with additive spatial and age effects as intercepts and slopes. Objective priors of the hierarchical model are explored. The Bayesian estimates are quite robustness in terms change of the hyperparamaters. The spatial correlations are appeared in both intercepts and slopes.

A stochastic neighborhood conditional autoregressive model for spatial data

A spatial process observed over a lattice or a set of irregular regions is usually modeled using a conditionally autoregressive (CAR) model. The neighborhoods within a CAR model are generally formed deterministically using the inter-distances or boundaries between the regions. An extension of CAR model is proposed in this article where the selection of the neighborhood depends on unknown parameter(s). This extension is called a Stochastic Neighborhood CAR (SNCAR) model. The resulting model shows flexibility in accurately estimating covariance structures for data generated from a variety of spatial covariance models. Specific examples are illustrated using data generated from some common spatial covariance functions as well as real data concerning radioactive contamination of the soil in Switzerland after the Chernobyl accident.

A multidimensional model of client success when engaging external consultants

Too often the relationship between client and external consultants is perceived as one of protagonist versus antogonist. Stories on dramatic, failed consultancies abound, as do related anecdotal quips. A contributing factor to many "apparently" failed consultancies is a poor appreciation by both the client and consultant of the client's true goals for the project and how to assess progress toward these goals. This paper presents and analyses a measurement model for assessing client success when engaging an external consultant. Three main areas of assessment are identified: (1) the consultant;s recommendations, (2) client learning, and (3) consultant performance. Engagement success is emperically measured along these dimensions through a series of case studies and a subsequent survey of clients and consultants involved in 85 computer-based information system selection projects. Validation fo the model constructs suggests the existence of six distinct and individually important dimensions of engagement success. both clients and consultants are encouraged to attend to these dimensions in pre-engagement proposal and selection processes, and post-engagement evaluation of outcomes.

Advancing practice in critical care : a model of knowledge integration

Aim The aim of this paper was to discuss the potential development of a conceptual model of knowledge integration pertinent to critical care nursing practice. A review of the literature identified that reflective practice appeared to be at the forefront of professional development. Background It could be argued that advancing practice in critical care has been superseded by the advanced practice agenda. Some would suggest that advancing practice is focused on the core attributes of an individual’s practice, which then leads onto advanced practice status. However, advancing practice is more of a process than identifiable skills and as such is often negated when viewing the development of practitioners to the advanced practice level. For example, practice development initiatives can be seen as advancing practice for the masses, which ensures that practitioners are following the same level and practice of care. The question here is, are they developing individually? Relevance to clinical practice What this paper presents is that reflection may not be best suited to advancing practice if the individual practitioner does not have a sound knowledge base both theoretically and experientially. The knowledge integration model presented in this study uses multiple learning strategies that are focused in practice to develop practice, e.g. the use of work-based learning and clinical supervision. To demonstrate the models application, an exemplar of an issue from practice shows its relevance from a practical perspective. Conclusions In conclusion, further knowledge acquisition and its relationship with previously held theory and experience will enable individual practitioners to advance their own practice as well as being a resource for others.

Approximate Bayesian computation using auxiliary model based estimates

We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms using indirect infer- ence. We embed this approach within a sequential Monte Carlo algorithm that is completely adaptive. This methodological development was motivated by an application involving data on macroparasite population evolution modelled with a trivariate Markov process. The main objective of the analysis is to compare inferences on the Markov process when considering two di®erent indirect mod- els. The two indirect models are based on a Beta-Binomial model and a three component mixture of Binomials, with the former providing a better ¯t to the observed data.

A sequential Monte Carlo framework for adaptive Bayesian model discrimination designs using mutual information

In this paper we present a unified sequential Monte Carlo (SMC) framework for performing sequential experimental design for discriminating between a set of models. The model discrimination utility that we advocate is fully Bayesian and based upon the mutual information. SMC provides a convenient way to estimate the mutual information. Our experience suggests that the approach works well on either a set of discrete or continuous models and outperforms other model discrimination approaches.

Impact of passenger group dynamics on an airport evacuation process using an agent-based model

The safety of passengers is a major concern to airports. In the event of crises, having an effective and efficient evacuation process in place can significantly aid in enhancing passenger safety. Hence, it is necessary for airport operators to have an in-depth understanding of the evacuation process of their airport terminal. Although evacuation models have been used in studying pedestrian behaviour for decades, little research has been done in considering the evacuees’ group dynamics and the complexity of the environment. In this paper, an agent-based model is presented to simulate passenger evacuation process. Different exits were allocated to passengers based on their location and security level. The simulation results show that the evacuation time can be influenced by passenger group dynamics. This model also provides a convenient way to design airport evacuation strategy and examine its efficiency. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.