Paleoseismological investigations along the Kera fault zone, Western Crete: implications for seismic hazard assessment

The island of Crete is the principal landmass in the Aegean arc system. Collision of the Euroasian plate in the north and the African plate in the south gives rise to the subduction related deformation along the Hellenic arc. As a result of the complex deformation, the area is characterized by high seismic activity. Paleoseismic investigations performed along the Kera fault scarp, which is part of a N-S oriented fault system along the Spatha peninsula (NW-Crete), show clear evidence of repeated normal faulting events. Five distinct episodes of faulting are observed. The first two are probably of Middle-Miocene or younger age representing older tectonic episodes, whereas the last three indicate co-seismic displacements most likely during the Pleistocene and Holocene. This is in good agreement with the previous estimates of Holocene average slip rate and the recurrence time estimate of large earthquakes in the order of ca. lmm/yr and 3000yrs, respectively. The Kera fault represents a NE-SW oriented bend in a N-S fault system and therefore has a minor left-lateral strike-slip component. During the 1980's at least three earthquakes could be associated with the Kera fault. More recently, in 1999, there were three small (with magnitudes between 3.0-4.5) offshore events that are probably associated with the same fault system in the offshore extension (to the north) of the N-S oriented faults along the Spatha peninsula. The existence of these earthquakes as well as the recent paleoseismic results clearly demonstrates the need of revising the seismic hazard assessment of the area. The length of the N-S oriented fault system, where the Kera fault represents the middle segment, reaches to a total of 30 km., and is capable of generating an earthquake of magnitude in the range 6.0-6.7. Such a (shallow) earthquake occurring at a short distance to the densely populated north-western coast of Crete is likely to have significant consequences

An Exploration of Lean and BIM synergies with a focus on SMEs in Construction

Small and Medium Enterprises (SME) account for 99.7% of the Irish Construction Industry and contribute to 68% of all employment in the sector. These organisations now find themselves facing the challenge of returning to productive business post the Covid 19 shutdown. More than ever, SMEs must modernise and adapt their business models to embrace new ways of working, such as Lean Construction and Building Information Modelling (BIM), in the absence of clear business incentives. It has proved difficult to persuade SMEs to change their ways of working due to limited finances, internal resources and above all, the cultural shift required to embrace new ways of working. The vast bulk of Irish construction SMEs are accustomed to working in a sector that produces low product quality, budget overruns, and substantial construction waste. When partnered with lean construction, BIM can address many of these issues, as the two processes can work together to target and eliminate waste while streamlining the value stream. The primary goals of lean construction are to maximise value and minimise waste. Therefore, BIM can be seen as a lean tool that helps eliminate waste and, at the same time, increases business opportunities and promotes sustainability. This paper will explore the synergies between Lean and BIM in the context of construction SMEs through a literature review. The findings will address a number of barriers to entry for SMEs, focusing on how digital technologies, such as BIM can complement lean construction in targeting major types of wastes. Some of the barriers identified include financial and legal concerns, lack of implementation strategies/guides, knowledge retainment, training impendiments, software and hardware restrictions, as well as employee resistance

Sensitivity to temporal structure facilitates perceptual analysis of complex auditory scenes

The notion that sensitivity to the statistical structure of the environment is pivotal to perception has recently garnered considerable attention. Here we investigated this issue in the context of hearing. Building on previous work (Sohoglu and Chait, 2016a; elife), stimuli were artificial 'soundscapes' populated by multiple (up to 14) simultaneous streams ('auditory objects') comprised of tone-pip sequences, each with a distinct frequency and pattern of amplitude modulation. Sequences were either temporally regular or random. We show that listeners' ability to detect abrupt appearance or disappearance of a stream is facilitated when scene streams were characterized by a temporally regular fluctuation pattern. The regularity of the changing stream as well as that of the background (non-changing) streams contribute independently to this effect. Remarkably, listeners benefit from regularity even when they are not consciously aware of it. These findings establish that perception of complex acoustic scenes relies on the availability of detailed representations of the regularities automatically extracted from multiple concurrent streams

Non-Parametric Extraction of Implied Asset Price Distributions

Extracting the risk neutral density (RND) function from option prices is well defined in principle, but is very sensitive to errors in practice. For risk management, knowledge of the entire RND provides more information for Value-at-Risk (VaR) calculations than implied volatility alone [1]. Typically, RNDs are deduced from option prices by making a distributional assumption, or relying on implied volatility [2]. We present a fully non-parametric method for extracting RNDs from observed option prices. The aim is to obtain a continuous, smooth, monotonic, and convex pricing function that is twice differentiable. Thus, irregularities such as negative probabilities that afflict many existing RND estimation techniques are reduced. Our method employs neural networks to obtain a smoothed pricing function, and a central finite difference approximation to the second derivative to extract the required gradients. This novel technique was successfully applied to a large set of FTSE 100 daily European exercise (ESX) put options data and as an Ansatz to the corresponding set of American exercise (SEI) put options. The results of paired t-tests showed significant differences between RNDs extracted from ESX and SEI option data, reflecting the distorting impact of early exercise possibility for the latter. In particular, the results for skewness and kurtosis suggested different shapes for the RNDs implied by the two types of put options. However, both ESX and SEI data gave an unbiased estimate of the realised FTSE 100 closing prices on the options' expiration date. We confirmed that estimates of volatility from the RNDs of both types of option were biased estimates of the realised volatility at expiration, but less so than the LIFFE tabulated at-the-money implied volatility.Comment: Paper based on Application of Physics in Financial Analysis,APFA5, Conference Presentation, Torino, Italy. 11.5 Page

Increased Belief Instability in Psychotic Disorders Predicts Treatment Response to Metacognitive Training

BACKGROUND AND HYPOTHESIS: In a complex world, gathering information and adjusting our beliefs about the world is of paramount importance. The literature suggests that patients with psychotic disorders display a tendency to draw early conclusions based on limited evidence, referred to as the jumping-to-conclusions bias, but few studies have examined the computational mechanisms underlying this and related belief-updating biases. Here, we employ a computational approach to understand the relationship between jumping-to-conclusions, psychotic disorders, and delusions. STUDY DESIGN: We modeled probabilistic reasoning of 261 patients with psychotic disorders and 56 healthy controls during an information sampling task-the fish task-with the Hierarchical Gaussian Filter. Subsequently, we examined the clinical utility of this computational approach by testing whether computational parameters, obtained from fitting the model to each individual's behavior, could predict treatment response to Metacognitive Training using machine learning. STUDY RESULTS: We observed differences in probabilistic reasoning between patients with psychotic disorders and healthy controls, participants with and without jumping-to-conclusions bias, but not between patients with low and high current delusions. The computational analysis suggested that belief instability was increased in patients with psychotic disorders. Jumping-to-conclusions was associated with both increased belief instability and greater prior uncertainty. Lastly, belief instability predicted treatment response to Metacognitive Training at the individual level. CONCLUSIONS: Our results point towards increased belief instability as a key computational mechanism underlying probabilistic reasoning in psychotic disorders. We provide a proof-of-concept that this computational approach may be useful to help identify suitable treatments for individual patients with psychotic disorders