Trends in youth’s videogame playing, overall computer use, and communication technology use: The impact of self-esteem and the Big Five personality factors

The objective of the current research is to model trends in video game playing, overall computer use, and communication technology use in a longitudinal sample of youths, aged 11-16 over a 3-year interval. In addition, individual difference characteristics that may be predictive of these trends were included, namely, socio-demographic characteristics (gender, ethnicity, and parental income) and personality characteristics (self-esteem, the Big Five personality factors). Findings suggested that youth increased their overall computer and communication technology use but decreased their videogame playing over time. Many individual differences predicted mean levels of these technologies with fewer predicting slopes. Conclusions, implications, and limitations are discussed.     

Indoor robot gardening: design and implementation

This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. By embedding sensing, computation, and communication into the pots, task allocation in the system is de-centrally coordinated, which makes the system scalable and robust against the failure of a centralized agent. We describe the architecture of this system and present experimental results for navigation, object recognition, and manipulation as well as challenges that lie ahead toward autonomous precision agriculture with multi-robot teams.     

Hierarchical mapping of Northern Eurasian land cover using MODIS data

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-Land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate–vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate–vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate–vegetation relationships for mapping land cover at continental scales.     

Localized coarsening of conforming all-hexahedral meshes

Finite element mesh adaptation methods can be used to improve the efficiency and accuracy of solutions to computational modeling problems. In many applications involving hexahedral meshes, localized modifications which preserve a conforming all-hexahedral mesh are desired. Effective hexahedral refinement methods that satisfy these criteria have recently become available; however, due to hexahedral mesh topology constraints, little progress has been made in the area of hexahedral coarsening. This paper presents a new method to locally coarsen conforming all-hexahedral meshes. The method works on both structured and unstructured meshes and is not based on undoing previous refinement. Building upon recent developments in quadrilateral coarsening, the method utilizes hexahedral sheet and column operations, including pillowing, column collapsing, and sheet extraction. A general algorithm for automated coarsening is presented and examples of models that have been coarsened with this new algorithm are shown. While results are promising, further work is needed to improve the automated process.     

Simulated train driving: fatigue, self-awareness and cognitive disengagement

Fatigue is a serious issue for the rail industry, increasing inefficiency and accident risk. The performance of 20 train drivers in a rail simulator was investigated at low, moderate and high fatigue levels. Psychomotor vigilance (PVT), self-rated performance and subjective alertness were also assessed. Alertness, PVT reaction times, extreme speed violations (>25% above the limit) and penalty brake applications increased with increasing fatigue level. In contrast, fuel use, draft (stretch) forces and braking errors were highest at moderate fatigue levels. Thus, at high fatigue levels, errors involving a failure to act (errors of omission) increased, whereas incorrect responses (errors of commission) decreased. The differential effect of fatigue on error types can be explained through a cognitive disengagement with the virtual train at high fatigue levels. Interaction with the train reduced dramatically, and accident risk increased. Awareness of fatigue-related performance changes was moderate at best. These findings are of operational concern.     

Unconditional lower bounds for learning intersections of halfspaces

We prove new lower bounds for learning intersections of halfspaces, one of the most important concept classes in computational learning theory. Our main result is that any statistical-query algorithm for learning the intersection of $\sqrt{n}$ halfspaces in n dimensions must make $2^{\varOmega (\sqrt{n})}$ queries. This is the first non-trivial lower bound on the statistical query dimension for this concept class (the previous best lower bound was n ^Ω(log?n)). Our lower bound holds even for intersections of low-weight halfspaces. In the latter case, it is nearly tight. We also show that the intersection of two majorities (low-weight halfspaces) cannot be computed by a polynomial threshold function (PTF) with fewer than n ^{Ω(log?n/log?log?n)} monomials. This is the first super-polynomial lower bound on the PTF length of this concept class, and is nearly optimal. For intersections of k=ω(log?n) low-weight halfspaces, we improve our lower bound to $\min\{2^{\varOmega (\sqrt{n})},n^{\varOmega (k/\log k)}\},$ which too is nearly optimal. As a consequence, intersections of even two halfspaces are not computable by polynomial-weight PTFs, the most expressive class of functions known to be efficiently learnable via Jackson’s Harmonic Sieve algorithm. Finally, we report our progress on the weak learnability of intersections of halfspaces under the uniform distribution.     

Screening of serum samples from Wegener's granulomatosis patients using antibody microarrays

Wegener's Granulomatosis (WG) is an idiopathic granulomatosis autoimmune vasculitis that primarily affects small vessels and is associated with glomerulonephritis and pulmonary granulomatous vasculitis. Anti‐neutrophil cytoplasmic auto‐antibodies (cANCA) against proteinase‐3 are used to identify WG, but ANCA titers are not present in some patients with the localized disease. The objective of this study was to develop an antibody array to help identify protein expression patterns in serum from patients with WG as compared to normals. The arrays were tested for limits of detection, background, and cross reactivity using standard proteins. The arrays were hybridized with either normal patient serum (n = 30) or with serum samples from a population of WG patients (n = 26) that were age and sex matched. Data analysis and curve fitting of the standard dilution series calculated r² values and determined a sensitivity of <50 pg/mL for the majority of proteins. A total of 24 proteins were assessed. Several statistically significant increases (p<0.05) were seen in the expression of: angiotensin converting enzyme‐I, IFN‐γ, IL‐8, s‐ICAM‐1 and s‐VCAM in WG patients as compared to controls. Utilizing the antibody microarray technology has led to the identification of potential biomarkers of vascular injury in the serum of WG patients.     

Balancing systematic and flexible exploration of social networks

Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks     

The confused world of sulfate attack on concrete

External sulfate attack is not completely understood. Part I identifies the issues involved, pointing out disagreements, and distinguishes between the mere occurrence of chemical reactions of sulfates with hydrated cement paste and the damage or deterioration of concrete; only the latter are taken to represent sulfate attack. Furthermore, sulfate attack is defined as deleterious action involving sulfate ions; if the reaction is physical, then, it is physical sulfate attack that takes place. The discussion of the two forms of sulfate attack leads to a recommendation for distinct nomenclature. Sulfate attack on concrete structures in service is not widespread, and the amount of laboratory-based research seems to be disproportionately large. The mechanisms of attack by different sulfates—sodium, calcium, and magnesium—are discussed, including the issue of topochemical and through-solution reactions. The specific aspects of the action of magnesium sulfate are discussed, and the differences between laboratory conditions and field exposure are pointed out.Part II discusses the progress of sulfate attack and its manifestations. This is followed by a discussion of making sulfate-resisting concrete. One of the measures is to use Type V cement, and this topic is extensively discussed. Likewise, the influence of w/c on sulfate resistance is considered. The two parameters are not independent of one another. Moreover, the cation in the sulfate salt has a strong bearing on the efficiency of the Type V cement. Recent interpretations of the Bureau of Reclamation tests, both long term and accelerated, are evaluated, and it appears that they need reworking.Part III reviews the standards and guides for the classification of the severity of exposure of structures to sulfates and points out the lack of calibration of the various classes of exposure. A particular problem is the classification of soils because much depends on the extraction ratio of sulfate in the soil: there is a need for a standardized approach. Taking soil samples is discussed, with particular reference to interpreting highly variable contents of sulfates. The consequences of disturbed drainage of the soil adjacent to foundations and of excessive irrigation, coupled with the use of fertilizer, are described. Whether concrete has undergone sulfate attack can be established by determining the change in the compressive strength since the time of placing the concrete. The rejection of this method and the reliance on determining the tensile strength of concrete because of “layered damage” are erroneous. Scanning electron microscopy (SEM) should not be the primary, and certainly not the first, method of determining whether sulfate attack has occurred. Mathematical modeling will be of help in the future but, at present, cannot provide guidance on the sulfate resistance of concrete in structures.Part IV presents conclusions and an overview of the situation, with consideration of future improvements. Appendix A contains the classification of exposure to sulfate given by various codes and guides.