The review of Australia's asylum laws and policies: a case for strengthening Parliament's role in protecting rights through post-enactment scrutiny

The central contention of this article is that there is a need for greater involvement of legislators in overseeing a systematic and rights-based scrutiny of the impact of legislation and policy. The recent operation of Australia s asylum laws and policies, in particular provides an illustration of the reforms required. Challenges to the rights of non-citizens in Australia and other jurisdictions serve as a reminder of the extent of change required before rights are firmly entrenched in the processes of government. A useful step forward would be to enhance the role of legislators in setting the criteria and agenda for post-enactment scrutiny in light of issues raised during pre-legislative scrutiny.

Factors contributing to nurse job satisfaction in the acute hospital setting : a review of recent literature

Aim  To explore and discuss from recent literature the common factors contributing to nurse job satisfaction in the acute hospital setting. Background  Nursing dissatisfaction is linked to high rates of nurses leaving the profession, poor morale, poor patient outcomes and increased financial expenditure. Understanding factors that contribute to job satisfaction could increase nurse retention. Evaluation  A literature search from January 2004 to March 2009 was conducted using the keywords nursing, (dis)satisfaction, job (dis)satisfaction to identify factors contributing to satisfaction for nurses working in acute hospital settings. Key issues  This review identified 44 factors in three clusters (intra-, inter- and extra-personal). Job satisfaction for nurses in acute hospitals can be influenced by a combination of any or all of these factors. Important factors included coping strategies, autonomy, co-worker interaction, direct patient care, organizational policies, resource adequacy and educational opportunities. Conclusions  Research suggests that job satisfaction is a complex and multifactorial phenomenon. Collaboration between individual nurses, their managers and others is crucial to increase nursing satisfaction with their job. Implications for nursing management  Recognition and regular reviewing by nurse managers of factors that contribute to job satisfaction for nurses working in acute care areas is pivotal to the retention of valued staff.

Drug law enforcement : a review of the evaluation literature

This article describes the results of a systematic review of drug law enforcement evaluations. The authors describe the search procedures and document the results in five main categories: international/national interventions (e.g., interdiction and drug seizure), reactive/ directed interventions (e.g., crackdowns, raids, buy-busts, saturation patrol, etc.), proactive/ partnership interventions (e.g., third-party policing, problem-oriented policing, community policing, drug nuisance abatement, etc.), individualized interventions (e.g., arrest referral and diversion), or interventions that used a combination of reactive/directed and proactive/ partnership strategies. Results indicate that proactive interventions involving partnerships between the police and third parties and/or community entities appear to be more effective at reducing both drug and nondrug problems in drug problem places than are reactive/ directed approaches. But the general quality of research in drug law enforcement is poor, the range of interventions that have been evaluated is limited, and more high-quality research is needed across a greater variety of drug interventions.

Dietary modification for women after breast cancer treatment : a narrative review

Diet is thought to account for about 25% of cancers in developed countries. It is well documented that the risks associated with both the breast cancer itself and its treatments are important for women previously treated for breast cancer. Women are at risk of recurrence of the primary disease and prone to develop treatment-induced co-morbidities, some of which are thought to be modified by diet. With a view to making dietary recommendations for the breast cancer patients we encounter in our clinical nursing research, we mined the literature to scope the most current robust evidence concerning the role of the diet in protecting women against the recurrence of breast cancer and its potential to ameliorate some of the longer-term morbidities associated with the disease. We found that the evidence about the role of the diet in breast cancer recurrence is largely inconclusive. However, drawing on international guidelines enabled us to make three definitive recommendations. Women at risk of breast cancer recurrence, or who experience co-morbidities as a result of treatment, should limit their exposure to alcohol, moderate their nutritional intake so it does not contribute to postmenopausal weight gain, and should adhere to a balanced diet. Nursing education planned for breast cancer patients about dietary issues should ideally be individually tailored, based on a good understanding of the international recommendations and the evidence underpinning them

Corporate governance, accounting and finance: a review

We review accounting and finance research on corporate governance (CG). In the course of our review, we focus on a particularly vexing issue, namely endogeneity in the relationships between CG and other matters of concern to accounting and finance scholars, and suggest ways to deal with it. Given the advent of large commercial CG databases, we also stress the importance of how CG is measured and in particular, the construction of CG indices, which should be sensitive to local institutional arrangements, and the need to capture both internal and external aspects of governance. The ‘stickiness’ of CG characteristics provides an additional challenge to CG scholars. Better theory is required, for example, to explain whether various CG practices substitute for each other or are complements. While a multidisciplinary approach to developing better theory is never without its difficulties, it could enrich the current body of knowledge in CG. Despite the vastness of the existing CG literature, these issues do suggest a number of avenues for future research.

Ambient temperature and morbidity: A review of epidemiological evidence

OBJECTIVE: This paper reviews the epidemiological evidence on the relationship between ambient temperature and morbidity. It assesses the methodological issues in previous studies, and proposes future research directions. DATA SOURCES AND DATA EXTRACTION: We searched the PubMed database for epidemiological studies on ambient temperature and morbidity of non-communicable diseases published in refereed English journals prior to June 2010. 40 relevant studies were identified. Of these, 24 examined the relationship between ambient temperature and morbidity, 15 investigated the short-term effects of heatwave on morbidity, and 1 assessed both temperature and heatwave effects. DATA SYNTHESIS: Descriptive and time-series studies were the two main research designs used to investigate the temperature–morbidity relationship. Measurements of temperature exposure and health outcomes used in these studies differed widely. The majority of studies reported a significant relationship between ambient temperature and total or cause-specific morbidities. However, there were some inconsistencies in the direction and magnitude of non-linear lag effects. The lag effect of hot temperature on morbidity was shorter (several days) compared to that of cold temperature (up to a few weeks). The temperature–morbidity relationship may be confounded and/or modified by socio-demographic factors and air pollution. CONCLUSIONS: There is a significant short-term effect of ambient temperature on total and cause-specific morbidities. However, further research is needed to determine an appropriate temperature measure, consider a diverse range of morbidities, and to use consistent methodology to make different studies more comparable.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

Why adopt now? Multiple case studies and survey studies comparing small, medium and large firms

While a number of factors have been highlighted in the innovation adoption literature, little is known about whether different factors are related to innovation adoption in differently-sized firms. We used preliminary case studies of small, medium and large firms to ground our hypotheses, which were then tested using a survey of 94 firms. We found that external stakeholder pressure and non-financial readiness were related to innovation adoption in SMEs; but that for large firms, adoption was related to the opportunity to innovate. It may be that the difficulties of adopting innovations, including both the financial cost and the effort involved, are too great for SMEs to overcome unless there is either a compelling need (external pressure) or enough in-house capability (non-financial readiness). This suggests that SMEs are more likely to have innovation “pushed” onto them while large firms are more likely to “pull” innovations when they have the opportunity.

Current AI in games : a review

As the graphics race subsides and gamers grow weary of predictable and deterministic game characters, game developers must put aside their “old faithful” finite state machines and look to more advanced techniques that give the users the gaming experience they crave. The next industry breakthrough will be with characters that behave realistically and that can learn and adapt, rather than more polygons, higher resolution textures and more frames-per-second. This paper explores the various artificial intelligence techniques that are currently being used by game developers, as well as techniques that are new to the industry. The techniques covered in this paper are finite state machines, scripting, agents, flocking, fuzzy logic and fuzzy state machines decision trees, neural networks, genetic algorithms and extensible AI. This paper introduces each of these technique, explains how they can be applied to games and how commercial games are currently making use of them. Finally, the effectiveness of these techniques and their future role in the industry are evaluated.

Data preprocessing for anomaly based network intrusion detection : a review

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches. Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.