Modelling web-server Flash Events

A Flash Event (FE) represents a period of time when a web-server experiences a dramatic increase in incoming traffic, either following a newsworthy event that has prompted users to locate and access it, or as a result of redirection from other popular web or social media sites. This usually leads to network congestion and Quality-of-Service (QoS) degradation. These events can be mistaken for Distributed Denial-of-Service (DDoS) attacks aimed at disrupting the server. Accurate detection of FEs and their distinction from DDoS attacks is important, since different actions need to be undertaken by network administrators in these two cases. However, lack of public domain FE datasets hinders research in this area. In this paper we present a detailed study of flash events and classify them into three broad categories. In addition, the paper describes FEs in terms of three key components: the volume of incoming traffic, the related source IP-addresses, and the resources being accessed. We present such a FE model with minimal parameters and use publicly available datasets to analyse and validate our proposed model. The model can be used to generate different types of FE traffic, closely approximating real-world scenarios, in order to facilitate research into distinguishing FEs from DDoS attacks.

Comparison and modification of car following models toward a more accurate microscopic simulation parameters reproduction

Traffic safety studies demand more than what current micro-simulation models can provide as they presume that all drivers of motor vehicles exhibit safe behaviours. Several car-following models are used in various micro-simulation models. This research compares the mainstream car following models’ capabilities of emulating precise driver behaviour parameters such as headways and Time to Collisions. The comparison firstly illustrates which model is more robust in the metric reproduction. Secondly, the study conducted a series of sensitivity tests to further explore the behaviour of each model. Based on the outcome of these two steps exploration of the models, a modified structure and parameters adjustment for each car-following model is proposed to simulate more realistic vehicle movements, particularly headways and Time to Collision, below a certain critical threshold. NGSIM vehicle trajectory data is used to evaluate the modified models performance to assess critical safety events within traffic flow. The simulation tests outcomes indicate that the proposed modified models produce better frequency of critical Time to Collision than the generic models, while the improvement on the headway is not significant. The outcome of this paper facilitates traffic safety assessment using microscopic simulation.

Risk factors to predict the incidence of surgical adverse events following open or laparoscopic surgery for apparent early stage endometrial cancer: results from a randomised controlled trial

Aims: To identify risk factors for major Adverse Events (AEs) and to develop a nomogram to predict the probability of such AEs in individual patients who have surgery for apparent early stage endometrial cancer. Methods: We used data from 753 patients who were randomized to either total laparoscopic hysterectomy or total abdominal hysterectomy in the LACE trial. Serious adverse events that prolonged hospital stay or postoperative adverse events (using common terminology criteria 3+, CTCAE V3) were considered major AEs. We analyzed pre-surgical characteristics that were associated with the risk of developing major AEs by multivariate logistic regression. We identified a parsimonious model by backward stepwise logistic regression. The six most significant or clinically important variables were included in the nomogram to predict the risk of major AEs within 6 weeks of surgery and the nomogram was internally validated. Results: Overall, 132 (17.5%) patients had at least one major AE. An open surgical approach (laparotomy), higher Charlson’s medical co-morbidities score, moderately differentiated tumours on curettings, higher baseline ECOG score, higher body mass index and low haemoglobin levels were associated with AE and were used in the nomogram. The bootstrap corrected concordance index of the nomogram was 0.63 and it showed good calibration. Conclusions: Six pre-surgical factors independently predicted the risk of major AEs. This research might form the basis to develop risk reduction strategies to minimize the risk of AEs among patients undergoing surgery for apparent early stage endometrial cancer.

Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation

Motor unit number estimation (MUNE) is a method which aims to provide a quantitative indicator of progression of diseases that lead to loss of motor units, such as motor neurone disease. However the development of a reliable, repeatable and fast real-time MUNE method has proved elusive hitherto. Ridall et al. (2007) implement a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to produce a posterior distribution for the number of motor units using a Bayesian hierarchical model that takes into account biological information about motor unit activation. However we find that the approach can be unreliable for some datasets since it can suffer from poor cross-dimensional mixing. Here we focus on improved inference by marginalising over latent variables to create the likelihood. In particular we explore how this can improve the RJMCMC mixing and investigate alternative approaches that utilise the likelihood (e.g. DIC (Spiegelhalter et al., 2002)). For this model the marginalisation is over latent variables which, for a larger number of motor units, is an intractable summation over all combinations of a set of latent binary variables whose joint sample space increases exponentially with the number of motor units. We provide a tractable and accurate approximation for this quantity and also investigate simulation approaches incorporated into RJMCMC using results of Andrieu and Roberts (2009).

Extreme sports are good for your health : a phenomenological understanding of fear and anxiety in extreme sport

Extreme sports are traditionally explored from a risk-taking perspective which often assumes that participants do not experience fear. In this paper we explore participants’ experience of fear associated with participation in extreme sports. An interpretive phenomenological method was used with 15 participants. Four themes emerged: experience of fear, relationship to fear, management of fear, and fear and self transformation. Participant’s experience of extreme sports was revealed in terms of intense fear but this fear was integrated and experienced as a potentially meaningful and constructive event in their lives. The findings have implications for understanding fear as a potentially transformative process.

Nonlinear pedagogy underpins intrinsic motivation in sports coaching

A key challenge for sports coaches is to provide performers with learning environments that result in sustainable motivation. In this paper, we will demonstrate that programmes based around the principles of Nonlinear Pedagogy can support the three basic psychological needs that underpin self-determined motivation. Coaches can therefore ensure that practice sessions provide for intrinsic motivation with its associated motivational and emotional benefits.

Nutritional status in Parkinson's disease patients undergoing deep-brain stimulation surgery : a pilot study

People with Parkinson’s disease (PD) are at higher risk of malnutrition due to PD symptoms and pharmacotherapy side effects. Poorer outcomes are associated with higher amounts of weight loss (>5%) and lower levels of fat free mass. When pharmacotherapy is no longer effective for symptom control, deep-brain stimulation (DBS) surgery may be considered. People with PD scheduled for DBS surgery were recruited from a Brisbane neurological clinic (n=11 out of 16). The Scale for Outcomes of Parkinson’s disease –Autonomic (SCOPA-AUT), Modified Constipation Assessment Scale (MCAS), and a 3-day food diary were mailed to participants’ homes for completion prior to hospital admission. During admission, the Patient-Generated Subjective Global Assessment (PG-SGA), weight, height and body composition were assessed. Mean(±s.d.) PD duration from diagnosis and time since occurrence of PD symptoms was 9.0(±8.0) and 12(±8.8) years, respectively. Five participants reported unintentional weight loss (average loss of 15.6%). PD duration but not years since symptom onset significantly predicted PG-SGA scores (β=4.2, t(8)=2.7, p<.05). Both were positively correlated with PG-SGA score (r = .667, r=.587). On average, participants classified as well-nourished (SGA-A) (n=4) were younger, had shorter disease durations, lower PG-SGA scores, higher body mass (BMI) and fat free mass (FFMI) indices when compared to malnourished participants (SGA-B) (n=7). They also reported fewer non-motor symptoms on the SCOPA-AUT and MCAS. Three participants had previously received dietetic advice but not in relation to PD. These findings demonstrate that malnutrition remains unrecognised and untreated in this group despite unintentional weight loss and a high prevalence of malnutrition.

Prevalence of malnutrition in community-dwelling adults with Parkinson's disease

People with Parkinson’s disease (PD) have been reported to be at higher risk of malnutrition than an age-matched population due to PD motor and non-motor symptoms and pharmacotherapy side effects. The prevalence of malnutrition in PD has yet to be well-defined. Community-dwelling people with PD, aged > 18 years, were recruited (n = 97, 61 M, 36 F). The Patient-Generated Subjective Global Assessment (PGSGA) was used to assess nutritional status, the Parkinson’s Disease Questionnaire (PDQ-39) was used to assess quality of life, and the Beck’s Depression Inventory (BDI) was used to measure depression. Levodopa equivalent doses (LEDs) were calculated based on reported Parkinson’s disease medication. Weight, height, mid-arm circumference (MAC) and calf circumference were measured. Cognitive function was measured using the Addenbrooke’s Cognitive Examination. Average age was 70.0 (9.1, 35–92) years. Based on SGA, 16 (16.5%) were moderately malnourished (SGA B) while none were severely malnourished (SGA C). The well-nourished participants (SGA A) had a better quality of life, t(90) = −2.28, p < 0.05, and reported less depressive symptoms, t(94)= −2.68, p < 0.05 than malnourished participants. Age, years since diagnosis, cognitive function and LEDs did not signifi cantly differ between the groups. The well-nourished participants had lower PG-SGA scores, t(95) = −5.66, p = 0.00, higher BMIs, t(95) = 3.44, p < 0.05, larger MACs, t(95) = 3.54, p < 0.05 and larger calf circumferences, t(95) = 2.29, p < 0.05 than malnourished participants. Prevalence of malnutrition in community-dwelling adults with PD in this study is comparable to that in other studies with community-dwelling adults without PD and is higher than other PD studies where a nutritional status assessment tool was used. Further research is required to understand the primary risk factors for malnutrition in this group.

Risk factors for malnutrition in community-dwelling people with Parkinson’s disease

In the elderly, the risks for protein-energy malnutrition from older age, dementia, depression and living alone have been well-documented. Other risk factors including anorexia, gastrointestinal dysfunction, loss of olfactory and taste senses and early satiety have also been suggested to contribute to poor nutritional status. In Parkinson’s disease (PD), it has been suggested that the disease symptoms may predispose people with PD to malnutrition. However, the risks for malnutrition in this population are not well-understood. The current study’s aim was to determine malnutrition risk factors in community-dwelling adults with PD. Nutritional status was assessed using the Patient-Generated Subjective Global Assessment (PG-SGA). Data about age, time since diagnosis, medications and living situation were collected. Levodopa equivalent doses (LDED) and LDED per kg body weight (mg/kg) were calculated. Depression and anxiety were measured using the Beck’s Depression Inventory (BDI) and Spielberger Trait Anxiety questionnaire, respectively. Cognitive function was assessed using the Addenbrooke’s Cognitive Examination (ACE-R). Non-motor symptoms were assessed using the Scales for Outcomes in Parkinson's disease-Autonomic (SCOPA-AUT) and Modified Constipation Assessment Scale (MCAS). A total of 125 community-dwelling people with PD were included, average age of 70.2±9.3(35-92) years and average time since diagnosis of 7.3±5.9(0–31) years. Average body mass index (BMI) was 26.0±5.5kg/m2. Of these, 15% (n=19) were malnourished (SGA-B). Multivariate logistic regression analysis revealed that older age (OR=1.16, CI=1.02-1.31), more depressive symptoms (OR=1.26, CI=1.07-1.48), lower levels of anxiety (OR=.90, CI=.82-.99), and higher LDED per kg body weight (OR=1.57, CI=1.14-2.15) significantly increased malnutrition risk. Cognitive function, living situation, number of prescription medications, LDED, years since diagnosis and the severity of non-motor symptoms did not significantly influence malnutrition risk. Malnutrition results in poorer health outcomes. Proactively addressing the risk factors can help prevent declines in nutritional status. In the current study, older people with PD with depression and greater amounts of levodopa per body weight were at increased malnutrition risk.

Twitter as a technology for audiencing and fandom : the #Eurovision phenomenon

Amongst the most prominent uses of Twitter at present is its role in the discussion of widely televised events: Twitter’s own statistics for 2011, for example, list major entertainment spectacles (the MTV Music Awards, the BET Awards) and sports matches (the UEFA Champions League final, the FIFA Women’s World Cup final) amongst the events generating the most tweets per second during the year (Twitter, 2011). User activities during such televised events constitute a specific, unique category of Twitter use, which differs clearly from the other major events which generate a high rate of tweets per second (such as crises and breaking news, from the Japanese earthquake and tsunami to the death of Steve Jobs), as preliminary research has shown. During such major media events, by contrast, Twitter is used most predominantly as a technology of fandom instead: it serves in the first place as a backchannel to television and other streaming audiovisual media, enabling users offer their own running commentary on the universally shared media text of the event broadcast as it unfolds live. Centrally, this communion of fans around the shared text is facilitated by the use of Twitter hashtags – unifying textual markers which are now often promoted to prospective audiences by the broadcasters well in advance of the live event itself. This paper examines the use of Twitter as a technology for the expression of shared fandom in the context of a major, internationally televised annual media event: the Eurovision Song Contest. It constitutes a highly publicised, highly choreographed media spectacle whose eventual outcomes are unknown ahead of time and attracts a diverse international audience. Our analysis draws on comprehensive datasets for the ‘official’ event hashtags, #eurovision, #esc, and #sbseurovision. Using innovative methods which combine qualitative and quantitative approaches to the analysis of Twitter datasets containing several hundreds of thousands, we examine overall patterns of participation to discover how audiences express their fandom throughout the event. Minute-by-minute tracking of Twitter activity during the live broadcasts enables us to identify the most resonant moments during each event; we also examine the networks of interaction between participants to detect thematically or geographically determined clusters of interaction, and to identify the most visible and influential participants in each network. Such analysis is able to provide a unique insight into the use of Twitter as a technology for fandom and for what in cultural studies research is called ‘audiencing’: the public performance of belonging to the distributed audience for a shared media event. Our work thus contributes to the examination of fandom practices led by Henry Jenkins (2006) and other scholars, and points to Twitter as an important new medium facilitating the connection and communion of such fans.

Systematic variation of the severity of motor vehicle accident-related traumatic brain injury vignettes produces different post-concussion symptom reports

This study investigated the specificity of the post-concussion syndrome (PCS) expectation-as-etiology hypothesis. Undergraduate students (n = 551) were randomly allocated to one of three vignette conditions. Vignettes depicted either a very mild (VMI), mild (MI), or moderate-to-severe (MSI) motor vehicle-related traumatic brain injury (TBI). Participants reported the PCS and PTSD symptoms that they imagined the depicted injury would produce. Secondary outcomes (knowledge of mild TBI, and the perceived undesirability of TBI) were also assessed. After data screening, the distribution of participants by condition was: VMI (n = 100), MI (n = 96), and MSI (n = 71). There was a significant effect of condition on PCS symptomatology, F(2, 264) = 16.55, p < .001. Significantly greater PCS symptomatology was expected in the MSI condition compared to the other conditions (MSI > VMI; medium effect, r = .33; MSI > MI; small-to-medium effect, r = .22). The same pattern of group differences was found for PTSD symptoms, F(2, 264) = 17.12, p < .001. Knowledge of mild TBI was not related to differences in expected PCS symptoms by condition; and the perceived undesirability of TBI was only associated with reported PCS symptomatology in the MSI condition. Systematic variation in the severity of a depicted TBI produces different PCS and PTSD symptom expectations. Even a very mild TBI vignette can elicit expectations of PCS symptoms.