Subacute effects of ecstasy on mood : an exploration of associated risk factors

Ecstasy use may result in lowered mood, anxiety or aggression in the days following use. Yet, few studies have investigated what factors increase the risk of experiencing such symptoms. Ecstasy users (at least once in the last 12 months) who subsequently took ecstasy (n=35) over the next week, were compared on measures of mood, sleep, stress and drug use, with those who abstained (n=21) that week. Measures were administered the week prior to ecstasy use and 1 and 3 days following use, or the equivalent day for abstainers. Mood symptoms were assessed using the Kessler-10 self-report psychological distress scale, a subjective mood rating (1-10), and the depression, anxiety and hostility items of the clinician-rated Brief Psychiatric Rating Scale. Timeline followback methods were used to collect information on drug use and life stress in the past month. Self-reported sleep quality was also assessed. Ecstasy use was not associated with subacute depressive, anxiety or aggressive symptoms. Rather, lowered mood and increased psychological distress were associated with self-reported hours and quality of sleep obtained during the 3-day follow up. These findings highlight the importance of considering sleep disruption in understanding the short-term mood effects of ecstasy use.

Clinical trial of the Norton Scale in acute care settings

This study investigated the effect of using Norton Scale assessment data in the nursing care of patients at risk of developing pressure ulcers. The results indicated that incorporating the Norton Scale in care planning resulted in benefits to patients through earlier and more effective nursing interventions.

The factor structure of the COPE Questionnaire in a sample of clinically depressed and anxious adults

The Coping Orientation to Problems Experienced is a multidimensional scale designed to assess how people respond to stress. The COPE has been validated in a variety of populations displaying variations in factor structure. However, in terms of mental health populations, it has only been validated in alcohol-dependent samples. This paper investigated the factor structure of the COPE in a sample of adults diagnosed with depression and anxiety. Two hundred and seventy-one patients attending cognitive behaviour therapy for anxiety and depression completed the COPE. Confirmatory factor analysis found a poor fit for both lower order and higher order factors based upon the Lyne and Roger (2000) study. Exploratory factor analyses identified six primary subscales (Active Planning, Social Support, Denial, Acceptance, Disengagement, Restraint) which explained approximately 60% of the variance in coping. These 6 subscales may assist researchers and clinicians to validly measure coping in anxious and depressed adults.

Teacher Reporting Attitudes Scale (TRAS): confirmatory and exploratory factor analyses with a Malaysian sample.

The Teacher Reporting Attitude Scale (TRAS) is a newly developed tool to assess teachers’ attitudes toward reporting child abuse and neglect. This article reports on an investigation of the factor structure and psychometric properties of the short form Malay version of the TRAS. A self-report cross-sectional survey was conducted with 667 teachers in 14 randomly selected schools in Selangor state, Malaysia. Analyses were conducted in a 3-stage process using both confirmatory (stages 1 and 3) and exploratory factor analyses (stage 2) to test, modify, and confirm the underlying factor structure of the TRAS in a non-Western teacher sample. Confirmatory factor analysis did not support a 3-factor model previously reported in the original TRAS study. Exploratory factor analysis revealed an 8-item, 4-factor structure. Further confirmatory factor analysis demonstrated appropriateness of the 4-factor structure. Reliability estimates for the four factors—commitment, value, concern, and confidence—were moderate. The modified short form TRAS (Malay version) has potential to be used as a simple tool for relatively quick assessment of teachers’ attitudes toward reporting child abuse and neglect. Cross-cultural differences in attitudes toward reporting may exist and the transferability of newly developed instruments to other populations should be evaluated.

General belongingness, workplace belongingness, and depressive symptoms

Research has shown that a strong relationship exists between belongingness and depressive symptoms; however, the contribution of specific types of belongingness remains unknown. Participants (N=369) completed the sense of belonging instrument, psychological sense of organizational membership, and the depression scale of the depression anxiety stress scales. Factor analysis demonstrated that workplace and general belongingness are distinct constructs. When regressed onto depressive symptoms, these belongingness types made independent contributions, together accounting for 45% of variance, with no moderation effects evident. Hence, general belongingness and specific workplace belongingness appear to have strong additive links to depressive symptoms. These results add support to the belongingness hypothesis and sociometer theory and have significant implication for depression prevention and treatment

Utility of the Mild Brain Injury Atypical Symptoms Scale to detect symptom exaggeration : an analogue simulation study

Brief self-report symptom checklists are often used to screen for postconcussional disorder (PCD) and posttraumatic stress disorder (PTSD) and are highly susceptible to symptom exaggeration. This study examined the utility of the five-item Mild Brain Injury Atypical Symptoms Scale (mBIAS) designed for use with the Neurobehavioral Symptom Inventory (NSI) and the PTSD Checklist–Civilian (PCL–C). Participants were 85 Australian undergraduate students who completed a battery of self-report measures under one of three experimental conditions: control (i.e., honest responding, n = 24), feign PCD (n = 29), and feign PTSD (n = 32). Measures were the mBIAS, NSI, PCL–C, Minnesota Multiphasic Personality Inventory–2, Restructured Form (MMPI–2–RF), and the Structured Inventory of Malingered Symptomatology (SIMS). Participants instructed to feign PTSD and PCD had significantly higher scores on the mBIAS, NSI, PCL–C, and MMPI–2–RF than did controls. Few differences were found between the feign PCD and feign PTSD groups, with the exception of scores on the NSI (feign PCD > feign PTSD) and PCL–C (feign PTSD > feign PCD). Optimal cutoff scores on the mBIAS of ≥8 and ≥6 were found to reflect “probable exaggeration” (sensitivity = .34; specificity = 1.0; positive predictive power, PPP = 1.0; negative predictive power, NPP = .74) and “possible exaggeration” (sensitivity = .72; specificity = .88; PPP = .76; NPP = .85), respectively. Findings provide preliminary support for the use of the mBIAS as a tool to detect symptom exaggeration when administering the NSI and PCL–C.

A known-groups evaluation of the Response Bias Scale in a neuropsychological setting

We evaluated the Minnesota Multiphasic Personality Inventory-Second Edition (MMPI-2) Response Bias Scale (RBS). Archival data from 83 individuals who were referred for neuropsychological assessment with no formal diagnosis (n = 10), following a known or suspected traumatic brain injury (n = 36), with a psychiatric diagnosis (n = 20), or with a history of both trauma and a psychiatric condition (n = 17) were retrieved. The criteria for malingered neurocognitive dysfunction (MNCD) were applied, and two groups of participants were formed: poor effort (n = 15) and genuine responders (n = 68). Consistent with previous studies, the difference in scores between groups was greatest for the RBS (d = 2.44), followed by two established MMPI-2 validity scales, F (d = 0.25) and K (d = 0.23), and strong significant correlations were found between RBS and F (rs = .48) and RBS and K (r = −.41). When MNCD group membership was predicted using logistic regression, the RBS failed to add incrementally to F. In a separate regression to predict group membership, K added significantly to the RBS. Receiver-operating curve analysis revealed a nonsignificant area under the curve statistic, and at the ideal cutoff in this sample of >12, specificity was moderate (.79), sensitivity was low (.47), and positive and negative predictive power values at a 13% base rate were .25 and .91, respectively. Although the results of this study require replication because of a number of limitations, this study has made an important first attempt to report RBS classification accuracy statistics for predicting poor effort at a range of base rates.

A confirmatory factor analysis of the Observer Alexithymia Scale in treatment seeking alcohol-dependent patients

Confirmatory factor analyses evaluated the factorial validity of the Observer Alexithymia Scale (OAS) in an alcohol-dependent sample. Observation was conducted by clinical psychologists. All models examined were rejected, given their poor fit. Given the psychometric limitations of the OAS shown in this study, the OAS may not be the most appropriate measure to use early in treatment among alcohol-dependent individuals.

Common DC link in residential LV network to improve the penetration level of Small-Scale Embedded Generators

This paper presents a new approach for network upgrading to improve the penetration level of Small Scale Generators in residential feeders. In this paper, it is proposed that a common DC link can be added to LV network to alleviate the negative impact of increased export power on AC lines, allowing customers to inject their surplus power with no restrictions to the common DC link. In addition, it is shown that the proposed approach can be a pathway from current AC network to future DC network.

Efficient route update and maintenance processes for multipath routing in large-scale industrial wireless sensor networks

Reliable communications is one of the major concerns in wireless sensor networks (WSNs). Multipath routing is an effective way to improve communication reliability in WSNs. However, most of existing multipath routing protocols for sensor networks are reactive and require dynamic route discovery. If there are many sensor nodes from a source to a destination, the route discovery process will create a long end-to-end transmission delay, which causes difficulties in some time-critical applications. To overcome this difficulty, the efficient route update and maintenance processes are proposed in this paper. It aims to limit the amount of routing overhead with two-tier routing architecture and introduce the combination of piggyback and trigger update to replace the periodic update process, which is the main source of unnecessary routing overhead. Simulations are carried out to demonstrate the effectiveness of the proposed processes in improvement of total amount of routing overhead over existing popular routing protocols.

Numerical modelling and full scale testing of load bearing steel walls under real fires

Fire safety has become an important part in structural design due to the ever increasing loss of properties and lives during fires. Fire rating of load bearing wall systems made of Light gauge Steel Frames (LSF) is determined using fire tests based on the standard time-temperature curve given in ISO 834. However, modern residential buildings make use of thermoplastic materials, which mean considerably high fuel loads. Hence a detailed fire research study into the performance of load bearing LSF walls was undertaken using a series of realistic design fire curves developed based on Eurocode parametric curves and Barnett’s BFD curves. It included both full scale fire tests and numerical studies of LSF walls without any insulation, and the recently developed externally insulated composite panels. This paper presents the details of fire tests first, and then the numerical models of tested LSF wall studs. It shows that suitable finite element models can be developed to predict the fire rating of load bearing walls under real fire conditions. The paper also describes the structural and fire performances of externally insulated LSF walls in comparison to the non-insulated walls under real fires, and highlights the effects of standard and real fire curves on fire performance of LSF walls.

On the statistical determination of optimal camera configurations in large scale surveillance networks

The selection of optimal camera configurations (camera locations, orientations etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we introduce a statistical formulation of the optimal selection of camera configurations as well as propose a Trans-Dimensional Simulated Annealing (TDSA) algorithm to effectively solve the problem. We compare our approach with a state-of-the-art method based on Binary Integer Programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than 2 alternative heuristics designed to deal with the scalability issue of BIP.

University Student Depression Inventory (USDI) : confirmatory factor analysis and review of psychometric properties

Background: The 30-item USDI is a self-report measure that assesses depressive symptoms among university students. It consists of three correlated three factors: Lethargy, Cognitive-Emotional and Academic motivation. The current research used confirmatory factor analysis to asses construct validity and determine whether the original factor structure would be replicated in a different sample. Psychometric properties were also examined. Method: Participants were 1148 students (mean age 22.84 years, SD = 6.85) across all faculties from a large Australian metropolitan university. Students completed a questionnaire comprising of the USDI, the Depression Anxiety Stress Scale (DASS) and Life Satisfaction Scale (LSS). Results: The three correlated factor model was shown to be an acceptable fit to the data, indicating sound construct validity. Internal consistency of the scale was also demonstrated to be sound, with high Cronbach Alpha values. Temporal stability of the scale was also shown to be strong through test-retest analysis. Finally, concurrent and discriminant validity was examined with correlations between the USDI and DASS subscales as well as the LSS, with sound results contributing to further support the construct validity of the scale. Cut-off points were also developed to aid total score interpretation. Limitations: Response rates are unclear. In addition, the representativeness of the sample could be improved potentially through targeted recruitment (i.e. reviewing the online sample statistics during data collection, examining the representativeness trends and addressing particular faculties within the university that were underrepresented). Conclusions: The USDI provides a valid and reliable method of assessing depressive symptoms found among university students.

Evaluation of Attacks and Countermeasures in Large Scale Networks

Computer worms represent a serious threat for modern communication infrastructures. These epidemics can cause great damage such as financial losses or interruption of critical services which support lives of citizens. These worms can spread with a speed which prevents instant human intervention. Therefore automatic detection and mitigation techniques need to be developed. However, if these techniques are not designed and intensively tested in realistic environments, they may cause even more harm as they heavily interfere with high volume communication flows. We present a simulation model which allows studies of worm spread and counter measures in large scale multi-AS topologies with millions of IP addresses.