Construction informatics in Turkey: strategic role of ICT and future research directions

Construction Informatics deals with subjects ranging from strategic management of ICTs to interoperability and information integration in the construction industry. Studies on defining research directions for Construction Informatics have a history over 20 years. The recent studies in the area highlight the priority themes for Construction Informatics research as interoperability, collaboration support, intelligent sites and knowledge sharing. In parallel, today it is widely accepted in the Architecture/Engineering/Construction (AEC) industry that ICT is becoming a strategic asset for any organisation to deliver business improvement and achieve sustainable competitive advantage. However, traditionally the AEC industry has approached investing in ICT with a lack of strategic focus and low level of priority to the business. This paper presents a recent study from Turkey that is focused on two themes. The first theme investigates the strategic role of ICT implementations from an industrial perspective, and explores if organisations within the AEC industry view ICT as a strategic resource for their business practice. The second theme investigates the ‘perspective of academia’ in terms of future research directions of Construction Informatics. The results of the industrial study indicates that ICT is seen as a value-adding resource, but a shift towards the recognition of the importance of ICT in terms of value adding in winning work and achieving strategic competitive advantage is observed. On the other hand, ICT Training is found to be the theme of highest priority from the academia point of view

Traumatic neuroma after torticollis surgery: a rare occurrence

We report a 15 years old girl who admitted to our hospital with signs of recurrent torticollis after two failed operations and consistent pain at the side of surgery. The past operations were performed at 1 and 6 years of age and she has been suffering pain from previous incisions with neck movements. At physical examination, the sternocleidomastoid (SCM) muscle behaved like a fibrous band, restricting the neck movements and resulting in pain. The operation was indicated for the fibrotic SCM. At operation two separate incisions were performed on each end of the SCM to remove all of the fibrotic muscle. The histopathological examination demonstrated a traumatic neuroma which respectively correlates with the pain symptoms. The patient discharged on the second postoperative day and physiotherapy was started. The patient is symptom free one year after the surgery. This case demonstrates a rare occurrence of traumatic neuroma after torticollis surgery, which can manifest with pain.Keywords: neuroma, torticollis, traum

Modeling brain connectivity dynamics in functional Magnetic Resonance Imaging via Particle Filtering

Interest in the studying of functional connections in the brain has grown considerably in the last decades, as many studies have pointed out that alterations in the interaction among brain areas can play a role as markers of neurological diseases. Most studies in this field treat the brain network as a system of connections stationary in time, but dynamic features of brain connectivity can provide useful information, both on physiology and pathological conditions of the brain. In this paper, we propose the application of a computational methodology, named Particle Filter (PF), to study non-stationarities in brain connectivity in functional Magnetic Resonance Imaging (fMRI). The PF algorithm estimates time-varying hidden parameters of a first-order linear time-varying Vector Autoregressive model (VAR) through a Sequential Monte Carlo strategy. On simulated time series, the PF approach effectively detected and enabled to follow time-varying hidden parameters and it captured causal relationships among signals. The method was also applied to real fMRI data, acquired in presence of periodic tactile or visual stimulations, in different sessions. On these data, the PF estimates were consistent with current knowledge on brain functioning. Most importantly, the approach enabled to detect statistically significant modulations in the cause-effect relationship between brain areas, which correlated with the underlying visual stimulation pattern presented during the acquisition

Operation of Faddeev-Kernel in Configuration Space

We present a practical method to solve Faddeev three-body equations at energies above three-body breakup threshold as integral equations in coordinate space. This is an extension of previously used method for bound states and scattering states below three-body breakup threshold energy. We show that breakup components in three-body reactions produce long-range effects on Faddeev integral kernels in coordinate space, and propose numerical procedures to treat these effects. Using these techniques, we solve Faddeev equations for neutron-deuteron scattering to compare with benchmark solutions.Comment: 20 pages, 8 figures, to be published in Few-Body System

Cauchy-Rician model for backscattering in urban SAR images

This paper presents a new statistical model for urban scene SAR images by combining the Cauchy distribution, which is heavy-tailed, with the Rician back-scattering. The literature spans various well-known models most of which are derived under the assumption that the scene consists of multitudes of random reflectors. This idea specifically fails for urban scenes since they accommodate a heterogeneous collection of strong scatterers such as buildings, cars, wall corners. Moreover, when it comes to analysing their statistical behaviour, due to these strong reflectors, urban scenes include a high number of high amplitude samples, which implies that urban scenes are mostly heavy-tailed. The proposed Cauchy-Rician model contributes to the literature by leveraging non-zero location (Rician) heavy-tailed (Cauchy) signal components. In the experimental analysis, the Cauchy-Rician model is investigated in comparison to state-of-the-art statistical models that include G0, generalized gamma, and the lognormal distribution. The numerical analysis demonstrates the superior performance and flexibility of the proposed distribution for modelling urban scenes

Measures of Learning, Memory and Processing Speed Accurately Predict Smoking Status in Short-term Abstinent Treatment-seeking Alcohol-dependent Individuals

Aim: Chronic cigarette smoking appears to adversely affect several domains of neurocognition in those with alcohol use disorders (AUDs). The primary goal of this study was to identify which measures commonly used to assess neurocognition in AUDs accurately predict smoking status of individuals seeking treatment of alcohol dependence. Methods: Treatment-seeking alcohol-dependent participants (ALC; n = 92) completed a comprehensive neuropsychological battery after 33 ± 9 days of abstinence. Measures significantly different between smoking and non-smoking ALC were entered as predictors in binary logistic regression and discriminant analysis models, with smoking status as the dependent variable. Results: Smoking ALC performed significantly worse than non-smoking ALC on measures assessing processing speed, auditory–verbal and visuospatial learning and memory. Using these measures as predictors, a logistic regression model accurately classified 91% of smokers and non-smokers into their respective groups overall and accounted for 68% of the variance in smoking status. The discriminant analysis confirmed the findings from the logistic regression. In smoking ALC, smoking chronicity was inversely related to performance on multiple measures after controlling for lifetime alcohol consumption. Conclusions: Measures of processing speed, learning and memory robustly predicted the smoking status of ALC with high sensitivity and specificity during early abstinence. The results identified specific measures within a comprehensive neurocognitive battery that discriminated smoking and non-smoking alcohol-dependent individuals with a high sensitivity and specificity. The association of greater smoking chronicity and poorer performance on multiple measures after control for alcohol consumption suggests that chronic smoking adds an additional burden to neurocognitive function in those with alcohol dependence

Thinking about Eating Food Activates Visual Cortex with Reduced Bilateral Cerebellar Activation in Females with Anorexia Nervosa: An fMRI Study

Background: Women with anorexia nervosa (AN) have aberrant cognitions about food and altered activity in prefrontal cortical and somatosensory regions to food images. However, differential effects on the brain when thinking about eating food between healthy women and those with AN is unknown. Methods: Functional magnetic resonance imaging (fMRI) examined neural activation when 42 women thought about eating the food shown in images: 18 with AN (11 RAN, 7 BPAN) and 24 age-matched controls (HC). Results: Group contrasts between HC and AN revealed reduced activation in AN in the bilateral cerebellar vermis, and increased activation in the right visual cortex. Preliminary comparisons between AN subtypes and healthy controls suggest differences in cortical and limbic regions. Conclusions: These preliminary data suggest that thinking about eating food shown in images increases visual and prefrontal cortical neural responses in females with AN, which may underlie cognitive biases towards food stimuli and ruminations about controlling food intake. Future studies are needed to explicitly test how thinking about eating activates restraint cognitions, specifically in those with restricting vs. binge-purging AN subtypes