Career Adapt-Abilities Scale—Iran Form: Psychometric properties and relationships with career satisfaction and entrepreneurial intentions

This study examined the psychometric properties of a Persian translation of the Career Adapt-Abilities Scale (CAAS—Iran Form) and its relationships with career satisfaction, business opportunity identification, and entrepreneurial intentions. It was hypothesized that career adaptability relates positively to these three outcomes, even when controlling for demographic and employment characteristics. Data were provided by 204 workers from Iran. Results showed that the overall CAAS score and sub-dimension scores (concern, control, curiosity, and confidence) were highly reliable. Moreover, confirmatory factor analyses indicated that the CAAS—Iran Form measures four distinct dimensions that can be combined into a higher-order career adaptability factor. Findings also demonstrated criterion-related validity of the scale with regard to career satisfaction and entrepreneurial intentions. In contrast, overall career adaptability was not significantly related to opportunity identification, while concern related positively, and control related negatively to opportunity identification. Overall, the CAAS—Iran Form has very good psychometric properties and predicts important career outcomes, suggesting that it can be used for career counseling and future research with Persian-speaking workers.

Crime Justice and Social Democracy: Proceedings of the 3rd International Conference, 9-10 July 2015

We are pleased to present these selected papers from the proceedings of the 3rd Crime, Justice and Social Democracy International Conference, held in July 2015 in Brisbane, Australia. Over 350 delegates attended the conference from 19 countries. The papers collected here reflect the diversity of topics and themes that were explored over three days. The Crime, Justice and Social Democracy International Conference aims to strengthen the intellectual and policy debates concerning links between justice, social democracy, and the reduction of harm and crime, through building more just and inclusive societies and proposing innovative justice responses. In 2015, attendees discussed these issues as they related to ideas of green criminology; indigenous justice; gender, sex and justice; punishment and society; and the emerging notion of ‘Southern criminology’. The need to build global connections to address these challenges is more evident than ever and the conference and these proceedings reflect a growing attention to interdisciplinary, novel, and interconnected responses to contemporary global challenges. Authors in these conference proceedings engaged with issues of online fraud, queer criminology and law, Indigenous incarceration, youth justice, incarceration in Brazil, and policing in Victoria, Australia, among others. The topics explored speak to the themes of the conference and demonstrate the range of challenges facing researchers of crime, harm, social democracy and social justice and the spaces of possibility that such research opens. Our thanks to the conference convenor, Dr Kelly Richards, for organising such a successful conference, and to all those presenters who subsequently submitted such excellent papers for review here. We would also particularly like to thank Jess Rodgers for their tireless editorial assistance, as well as the panel of international scholars who participated in the review process, often within tight timelines.

On the Todaro paradox

This paper presents a version of the Harris-Todaro model in which the rural labour market is characterised by monopsonistic behaviour. It is shown that the ‘Todaro paradox’, i.e. that the creation of jobs in the urban sector actually increases urban unemployment, does not hold if the urban employed outnumber the urban unemployed. The latter is the rule in all LDCs.

Defects in the IFT-B component IFT172 cause Jeune and Mainzer-Saldino syndromes in humans

Intraflagellar transport (IFT) depends on two evolutionarily conserved modules, subcomplexes A (IFT-A) and B (IFT-B), to drive ciliary assembly and maintenance. All six IFT-A components and their motor protein, DYNC2H1, have been linked to human skeletal ciliopathies, including asphyxiating thoracic dystrophy (ATD; also known as Jeune syndrome), Sensenbrenner syndrome, and Mainzer-Saldino syndrome (MZSDS). Conversely, the 14 subunits in the IFT-B module, with the exception of IFT80, have unknown roles in human disease. To identify additional IFT-B components defective in ciliopathies, we independently performed different mutation analyses: candidate-based sequencing of all IFT-B-encoding genes in 1,467 individuals with a nephronophthisis-related ciliopathy or whole-exome resequencing in 63 individuals with ATD. We thereby detected biallelic mutations in the IFT-B-encoding gene IFT172 in 12 families. All affected individuals displayed abnormalities of the thorax and/or long bones, as well as renal, hepatic, or retinal involvement, consistent with the diagnosis of ATD or MZSDS. Additionally, cerebellar aplasia or hypoplasia characteristic of Joubert syndrome was present in 2 out of 12 families. Fibroblasts from affected individuals showed disturbed ciliary composition, suggesting alteration of ciliary transport and signaling. Knockdown of ift172 in zebrafish recapitulated the human phenotype and demonstrated a genetic interaction between ift172 and ift80. In summary, we have identified defects in IFT172 as a cause of complex ATD and MZSDS. Our findings link the group of skeletal ciliopathies to an additional IFT-B component, IFT172, similar to what has been shown for IFT-A.

Automated feature engineering for HTTP tunnel detection

Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.