Where were you? Development of a time-geographic approach for activity destination re-construction

With the use of individual-level travel survey datasets describing the detailed activities of households, it is possible to analyze human movements with a high degree of precision. However, travel survey data are not without quality issues. Potential exists for origins and destinations of reported trips not to be geo-referenced, perhaps due to misreported information or inconsistencies in spatial address databases, which can limit the usefulness of the survey data. From an analytical standpoint, this is a serious problem because a single unreferenced stop in a trip record in effect renders that individual’s data useless, especially in cases where analyzing chains of activity locations is of interest. This paper presents a framework and basic computational approach for exploring unlocatable activity locations inherent to travel surveys. Derived from recent work in developing a network-based, probabilistic time geography, the proposed methods are able to estimate the likely locations of missing trip origins and destinations. The methods generate probabilistic potential path trees which are used to visualize and quantify potential locations for the unreferenced destinations. The methods are demonstrated with simulated survey data from a smaller metropolitan area.     

Broadband branchline coupler for S-band

The design of an S-band, broadband, microstrip 3 dB, 90 degrees branchline coupler is described and measured results for a prototype version are given. The design uses broadbanding stubs and three branches, with the central branch split into two of twice the characteristic impedance and separated by half wavelength transmission lines. This is a new technique to improve the bandwidth of which a theoretical account was given by Mayer (see ibid., vol.26, no.18, p.1477-8, 1990). Measured results for the prototype coupler show return loss greater than 17 dB and isolation greater than 16.5 dB, over a percentage bandwidth of 45%.<>     

Influence of emergent macrophyte （Phragmites australis） density on water turbulence and erosion of organic-rich sediment

The effects of the emergent macrophyte Phragmites australis on water turbulence, bottom shear velocity and water turbidity were experimentally studied with the stem densities 0 stems/m 2 -100 stems/m 2 and using organic-rich sediment. It is found that the maximum root-mean-square (rms) velocity and shear velocity decrease linearly with increasing stem density. By affecting the height of peak turbulence values, emergent plants can thus lower the frequency of sediment erosion events. Additionally, the slope of the rms turbulence increment decreases with increasing stem density. Thus, also the duration of strong turbulence is affected by the density of emergent macrophytes. The critical shear velocity for sediment resuspension is 0.0035 m/s-0.0055 m/s and not affected by the stem density. In the absence of macrophytes, water flow causes a 6.3-fold increment in turbidity, but with macrophytes turbidity values remain lower. The experiments suggest that even relatively low densities of Phragmites can have substantial effects on water turbulence and consequently on water quality.     

Analysing infrequently sampled animal tracking data by incorporating generalized movement trajectories with kernel density estimation

When analysing the movements of an animal, a common task is to generate a continuous probability density surface that characterises the spatial distribution of its locations, termed a home range. Traditional kernel density estimation (KDE), the Brownian Bridges kernel method, and time-geographic density estimation are all commonly used for this purpose, although their applicability in some practical situations is limited. Other studies have argued that KDE is inappropriate analysing moving objects, while the latter two methods are only suitable for tracking data collected at frequent enough intervals such that an object’s movement pattern can be adequately represented using a space–time path created by connecting consecutive points. This research formulates and evaluates KDE using generalised movement trajectories approximated by Delaunay triangulation (KDE-DT) as a method for analysing infrequently sampled animal tracking data. In this approach, a DT is constructed from a point pattern of tracking data in order to approximate the network of movement trajectories for an animal. This network represents the generalised movement patterns of an animal rather than its specific, individual trajectories between locations. Then, kernel density estimates are calculated with distances measured using that network. First, this paper describes the method and then applies it to generate a probability density surface for a Florida panther from radio-tracking data collected three times per week. Second, the performance of the technique is evaluated in the context of delineating wildlife home ranges and core areas from simulated animal locational data. The results of the simulations suggest that KDE-DT produces more accurate home range estimates than traditional KDE, which was evaluated with the same data in a previous study. In addition to animal home range analysis, the technique may be useful for characterising a variety of spatial point patterns generated by objects that move through continuous space, such as pedestrians or ships.     

Mining indirect antagonistic communities from social interactions

Antagonistic communities refer to groups of people with opposite tastes, opinions, and factions within a community. Given a set of interactions among people in a community, we develop a novel pattern mining approach to mine a set of antagonistic communities. In particular, based on a set of user-specified thresholds, we extract a set of pairs of communities that behave in opposite ways with one another. We focus on extracting a compact lossless representation based on the concept of closed patterns to prevent exploding the number of mined antagonistic communities. We also present a variation of the algorithm using a divide and conquer strategy to handle large datasets when main memory is inadequate. The scalability of our approach is tested on synthetic datasets of various sizes mined using various parameters. Case studies on Amazon, Epinions, and Slashdot datasets further show the efficiency and the utility of our approach in extracting antagonistic communities from social interactions.     

Finite state automata representing two-dimensional subshifts

A new type of two-dimensional automaton has been defined to recognize a class of two-dimensional shifts of finite type having the property that every admissible block found within the related local picture language can be extended to a point of the subshift. Here it is shown that this automaton accurately represents the image of the represented two-dimensional shift of finite type under a block code. It is then shown that these automata can be used to check for a certain type of two-dimensional transitivity in the factor language of the corresponding shift space and how this relates to periodicity in the two-dimensional case. The paper closes with a notion of “follower sets” that are used to reduce the size of the automata representing two-dimensional sofic shifts.     

The treatment of disordered money behaviors: Results of an open clinical trial.

While destructive financial behaviors permeate our culture, the field of psychology has remained relatively silent on the issue. This article argues for the need for psychologists to identify disordered money behaviors as treatable psychological problems and provide the public with effective approaches to treatment. It describes one such experimental treatment utilizing experiential therapy and examines treatment outcomes of 33 individuals with problematic financial behaviors who participated in a 6-day experiential therapy program. Following treatment, participants showed significant and lasting reductions in psychological distress, anxiety, and worry about money and finance-related situations and showed measurable signs of better overall financial health. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Role of Nucleocytoplasmic Transport Defects in Amyotrophic Lateral Sclerosis

There is ample evidence that nucleocytoplasmic-transport deficits could play an important role in the pathology of amyotrophic lateral sclerosis (ALS). However, the currently available data are often circumstantial and do not fully clarify the exact causal and temporal role of nucleocytoplasmic transport deficits in ALS patients. Gaining this knowledge will be of great significance in order to be able to target therapeutically nucleocytoplasmic transport and/or the proteins involved in this process. The availability of good model systems to study the nucleocytoplasmic transport process in detail will be especially crucial in investigating the effect of different mutations, as well as of other forms of stress. In this review, we discuss the evidence for the involvement of nucleocytoplasmic transport defects in ALS and the methods used to obtain these data. In addition, we provide an overview of the therapeutic strategies which could potentially counteract these defects.     

Compatible natural gradient policy search

Machine Learning - Trust-region methods have yielded state-of-the-art results in policy search. A common approach is to use KL-divergence to bound the region of trust resulting in a natural...     

Tangential X-Ray Radiography for Pitting Geometry Analysis of Outside Wall of Insulated Steel Pipes

Russian Journal of Nondestructive Testing - The piping systems used for the transportation of liquids and gases in power stations, petroleum petrochemical facilities, and chemical plants are...