Search results

The purpose of this paper is to investigate the relationship between child sexual abuse and sexual risk behaviours as well as its potential mediators.

This cross-sectional study used data from a cross-sectional study from 12,800 women between 15 and 49 years of age included in the 2008 Nigerian Demographic and Health Survey. Structural equation modelling (SEM) was applied to assess the association between childhood sexual abuse (CSA) and sexual risk behaviours.

The authors found that CSA was directly associated with sexual risk behaviours. In addition, the association between CSA and sexual risk behaviour was also partially mediated by alcohol and cigarette use.

The results show that being abused in childhood is important for the subsequent development of sexual risk behaviours in adulthood and the association is mediated by alcohol and cigarette use.

The results may be helpful for policy makers and health care planners in designing cultural sensitive public health intervention that will reduce the burden of CSA, its long-term effects (sexual risk behaviours) and intervening mediators that increase the risks.

These findings suggest that to reduce sexual risks, interventions to address sexual abuse needs to include other social problems (smoking, alcohol) that victims result to when faced with trauma.

The current study is the only one so far in sub-Saharan Africa to have explored the relation between CSA and sexual risk behaviours using SEM.

This study aims to examine the relationship between having a learning organization (LO) and organizational citizenship behavior (OCB), and the moderating role of team-oriented culture on this relationship.

Using 516 respondents from diverse industries in West Malaysia, the authors tested the psychometric properties of the three variables.

LO positively influenced OCB. Team-oriented culture moderated the relationship between having a LO and OCB.

This study helps explain how to achieve a LO in a context of high power distance and collectivist culture such as Malaysia. This study also highlights the importance of the anticipated synergistic effects of a LO and team-oriented culture in promoting OCB.

This study aims to test the impact of digital skills on the entrepreneurial intentions of last-year undergraduate students in Jordanian universities, especially after the Coronavirus disease pandemic and the digital transformation in education and business patterns. In addition, it aims to assess the role of entrepreneurial alertness as a mediator and entrepreneurship education as a moderator in the relationship between the independent and dependent variables.

The quantitative study used a questionnaire distributed to 401 students from different Jordanian universities. The data was collected over 2 months and two structural equation models were developed using AMOS 25 to examine the relationship.

A significant negative relationship was found between digital skills and entrepreneurial intentions of last-year undergraduate students in Jordanian universities post-coronavirus disease 2019 (COVID-19) pandemic. A fully mediating role of the alertness variable has appeared in addition to a significant moderating role of entrepreneurship education.

This is the first study that attempts to investigate the impact of digital skills on students' entrepreneurial intentions in Jordan after the COVID-19 pandemic, In addition, it is one of the few studies that assess the mediator's and moderator's effects on the same conditions. Finally, the study provided a review of the definitions and models used as part of the contribution to upcoming reviews.

The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China’s table grape.

The approaches follows the framework of “decomposition and ensemble,” using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems.

The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China’ grape price forecasting in terms of its statistical measures and prediction performance.

This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well.

The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.

The purpose of this study is to examine how the recent recession affected Iowa's gaming industry by analyzing gaming volumes before and through the recession.

This study used autoregressive integrated moving average (ARIMA) with intervention analysis to examine Iowa statewide aggregated monthly slot coin-in, table drop, and admission from December 2001 through June 2012.

The results of analyses show that: slot coin-in was not affected by the recession; table drop was slightly affected, but started to recover in late 2010; and monthly admission was not affected by the recession, and showed a significant increase after the recession. The results also indicate that the decrease in table drop in Iowa casinos represented only a very small amount of state gaming revenue in 2008. Therefore, the findings of this study suggest that Iowa's gaming volume was not significantly affected by the recent recession. In other words, Iowa's gaming industry is still recession-proof.

Current economic conditions suggest that the threat of a double-dip recession is quite real. The findings of this study are expected to help casino managers in Iowa understand how non-destination casinos behaved differently through the recession and strategically plan for a possible future economic downturn. In fact, the significant increase of monthly admission during the last recession implies that the Iowa gaming industry has actually benefited from the recession by accommodating more patrons. Therefore, to capitalize on the next recession, Iowa's casino operators should consider reducing the number of table games and increasing the number of slot machines to accommodate more slot players and reduce operating costs.

Most existing gaming-related research focuses on gaming destinations such as Las Vegas and Atlantic City. No known study on gaming volume in non-destination gaming markets has been identified. By examining Iowa's gaming volume through the recession, this study provides initial empirical evidence of the impact of recession on non-destination gaming markets.

This chapter provided systematic and comprehensive analysis on trawl fisheries management and conservation measures in the Straits of Malacca. Detailed analysis is conducted on Malaysian fishery management framework particularly domestic country's trawl fishery status, legal structure, input-control strategies, ecosystem protection plan, pollution, law enforcement, and complementary measures that designed to reduce and prevent overfishing in the exclusive economic zone (EEZ) of Malacca Straits. Gaps and challenges found in existing trawl fisheries literature are presented followed by recommendations for improvement in the management and conservation of trawl fisheries.

This paper investigates twenty financial ratios to develop a local financial failures prediction model. The study covers the period of 1993‐2001. We used mean and comparison of difference to the data set of fiveyears before the failures to identify the most superlative ratios. From these ratios, we developed two prediction models by using a logistic regression. The results indicae that these models are excellent in predicting financial failures a year before failure. Both models are able to predict financial failure two years before the failures with more than 90 per cent accuracy rate. It is hoped that this study, which is conducted using a recent data can contribute towards existing literatures on corporate failure prediction.

Additive manufacturing (AM) has been increasingly used in various applications in recent years. However, it is still challenge when it comes to selecting a suitable AM process. This is because the outcome may vary due to not only different materials and printers but also different parameters and post-processes. This paper aims to develop an efficient method to help users understand trade-offs and make right decisions.

A hybrid method is proposed to help users select appropriate options from a large-scale and discrete option space in an interactive way. First, the design-by-shopping approach is applied to allow users exploring and refining the option space. The analytical hierarchical process method is then used to capture customers’ preferences. After analyzing the results of different normalization methods, a modified Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach is proposed to rank solutions and provide suggestions.

The usefulness of proposed method is illustrated in a case study. The results show that it can help customers understand performance distributions and find most suitable options accurately. The ranking of the modified TOPSIS method is more reasonable.

Due to the complexity of AM technologies, the process selection is considered at the parameter level. A new system framework is proposed for decision support. The TOPSIS method is modified to achieve a stable performance.

Optimization under a deterministic approach generally leads to a final design in which the performance may degrade significantly and/or constraints can be violated because of perturbations arising from uncertainties. The purpose of this paper is to obtain a better strategy that would obtain an optimum design which is less sensitive to changes in uncertain parameters. The process of finding these optima is referred to as robust design optimization (RDO), in which improvement of the performance and reduction of its variability are sought, while maintaining the feasibility of the solution. This overall process is very time consuming, requiring a robust tool to conduct this optimum search efficiently.

In this paper, the authors propose an integrated tool to efficiently obtain RDO solutions. The tool encompasses suitable multiobjective optimization (MO) techniques (encompassing: Normal-Boundary Intersection, Normalized Normal-Constraint, weighted sum method and min-max methods), a surrogate model using reduced order method for cheap function evaluations and adequate procedure for uncertainties quantification (Probabilistic Collocation Method).

To illustrate the application of the proposed tool, 2D structural problems are considered. The integrated tool prove to be very effective reducing the computational time by up to five orders of magnitude, when compared to the solutions obtained via classical standard approaches.

The proposed combination of methodologies described in the paper, leads to a very powerful tool for structural optimum designs, considering uncertainty parameters, that can be extended to deal with other class of applications.