Direct electrochemistry for the imidazole complex of microperoxidase-11 in dimethyl sulfoxide solution at naked electrode substrates including glassy carbon, gold, and platinum

The effect of the total water content on the persistence and rate of direct heterogeneous electron transfer between the imidazole complex of microperoxidase-11 (im-MP-11) and naked gold, platinum, and glassy carbon (GC) in dimethyl sulfoxide (DMSO) solutions containing 0.1 M tetra-n-butylammonium perchlorate was investigated using cyclic voltammetry. Electron transfer between im-MP-11 and Au, Pt, and GC has been found to be persistent for more than 1 h and at least quasi-reversible [k(s)' = (8.7 ± 0.1) × 10(-4) cm/s (Au), k(s)' = (7.2 ± 1.3) × 10(-4) cm/s (Pt), and k(s)' = (5.7 ± 1.0) × 10(-4) cm/s (GC)] in dimethyl sulfoxide containing an absolute water content between 0.1 and 1.8%. The heterogeneous electron transfer rate constant is independent of the total water content of the DMSO solution when between 0.1 and 1.8% water is present.     

PAC Learning of One-Dimensional Patterns

Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We consider the problem of PAC-learning the concept class of geometric patterns where the target geometric pattern is a configuration ofk points on the real line. Each instance is a configuration ofn points on the real line, where it is labeled according to whether or not it visually resembles the target pattern. To capture the notion of visual resemblance we use the Hausdorff metric. Informally, two geometric patternsP andQ resemble each other under the Hausdorff metric if every point on one pattern is close to some point on the other pattern. We relate the concept class of geometric patterns to the landmark matching problem and then present a polynomial-time algorithm that PAC-learns the class of one-dimensional geometric patterns. We also present some experimental results on how our algorithm performs.     

The effects of different roster schedules on sleep in miners

Shiftwork involving early morning starts and night work can affect both sleep and fatigue. This study aimed to assess the impact of different rostering schedules at an Australian mine site on sleep and subjective sleep quality. Participants worked one of four rosters;

4 × 4 (n = 14) 4D4O4N4O

7 × 4 (n = 10) 7D4O7N40

10 × 5 (n = 17) 5D5N50

14 × 7 (n = 12) 7D7N70

Sleep (wrist actigraphy and sleep diaries) was monitored for a full roster cycle including days off. Total sleep time (TST) was longer on days off (7.0 ± 1.9) compared to sleep when on day (6.0 ± 1.0) and nightshifts (6.2 ± 1.6). Despite an increase in TST on days off, this may be insufficient to recover from the severe sleep restriction occurring during work times. Restricted sleep and quick shift-change periods may lead to long-term sleep loss and associated fatigue.     

Sleep in a live-in mining operation: The influence of start times and restricted non-work activities

The amount of sleep obtained between shifts is influenced by numerous factors including the length of work and rest periods, the timing of the rest period relative to the endogenous circadian cycle and personal choices about the use of non-work time. The current study utilised a real-world live-in mining environment to examine the amount of sleep obtained when access to normal domestic, family and social activities was restricted. Participants were 29 mining operators (26 male, average age 37.4 ± 6.8 years) who recorded sleep, work and fatigue information and wore an activity monitor for a cycle of seven day shifts and seven night shifts (both 12 h) followed by either seven or fourteen days off. During the two weeks of work participants lived on-site. Total sleep time was significantly less (p < 0.01) while on-site on both day (6.1 ± 1.0 h) and night shifts (5.7 ± 1.5 h) than days off (7.4 ± 1.4 h). Further, night shift sleep was significantly shorter than day-shift sleep (p < 0.01). Assessment of subjective fatigue ratings showed that the sleep associated with both days off and night shifts had a greater recovery value than sleep associated with day shifts (p < 0.01). While on-site, participants obtained only 6 h of sleep indicating that the absence of competing domestic, family and social activities did not convert to more sleep. Factors including shift start times and circadian influences appear to have been more important.     

The impact of map orientation and generalisation on congestion decisions: a comparison of schematic-egocentric and topographic-allocentric maps

Map information for drivers is usually presented in an allocentric-topographic form (as with printed maps) or in an egocentric-schematic form (as with road signs). The advent of new variable message boards on UK motorways raises the possibility of presenting road maps to reflect congestion ahead. Should these maps be allocentric-topographic or egocentric-schematic? This was assessed in an eye tracking study, with participants viewing maps of a motorway network in order to identify whether any congestion was relevant to their intended route. The schematic-egocentric maps were responded to most accurately with shorter fixation durations suggesting easier processing. In particular, the driver's entrance and intended exit from the map were attended to more in the allocentric maps. Individual differences in mental rotation ability also seem to contribute to poor performance on allocentric maps. The results favour schematic-egocentric maps for roadside congestion information, but also provide theoretical insights into map-rotation and individual differences. Statement of Relevance: This study informs designers and policy makers about optimum representations of traffic congestion on roadside variable message signs and, furthermore, demonstrates that individual differences contribute to problems with processing certain sign types. Schematic-egocentric representations of a motorway network produced the best results, as noted in behavioural and eye movement measures.     

Deriving evidence theoretical functions in multivariate data spaces: a systematic approach.

The mathematical theory of evidence is a generalization of the Bayesian theory of probability. It is one of the primary tools for knowledge representation and uncertainty and probabilistic reasoning and has found many applications. Using this theory to solve a specific problem is critically dependent on the availability of a mass function (or basic belief assignment). In this paper, we consider the important problem of how to systematically derive mass functions from the common multivariate data spaces and also the ensuing problem of how to compute the various forms of belief function efficiently. We also consider how such a systematic approach can be used in practical pattern recognition problems. More specifically, we propose a novel method in which a mass function can be systematically derived from multivariate data and present new methods that exploit the algebraic structure of a multivariate data space to compute various belief functions including the belief, plausibility, and commonality functions in polynomial-time. We further consider the use of commonality as an equality check. We also develop a plausibility-based classifier. Experiments show that the equality checker and the classifier are comparable to state-of-the-art algorithms.     

The use of foreign patenting as an internationally comparable science and technology output indicator

Foreign patenting activity in some of the world major patent systems is being compared between countries and industries and is found to be, with a few notable exceptions, relatively unbiased. Furthermore, a brief dynamic analysis of the foreign patenting activity in the USA of a number of OECD-countries in 41 industrial sectors in terms of ‘Revealed Technological Advantage’ indices suggests that foreign patent data might provide a very useful addition to the arsenal of Science and Technology Output Indicators.     

Learning from examples with unspecified attribute values

A challenging problem within machine learning is how to make good inferences from data sets in which pieces of information are missing. While it is valuable to have algorithms that perform well for specific domains, to gain a fundamental understanding of the problem, one needs a “theory” about how to learn with incomplete data. The important contribution of such a theory is not so much the specific algorithmic results, but rather that it provides good ways of thinking about the problem formally. In this paper we introduce the unspecified attribute value (UAV) learning model as a first step towards a theoretical framework for studying the problem of learning from incomplete data in the exact learning framework.In the UAV learning model, an example x is classified positive (resp., negative) if all possible assignments for the unspecified attributes result in a positive (resp., negative) classification. Otherwise the classification given to x is “?” (for unknown). Given an example x in which some attributes are unspecified, the oracle UAV-MQ responds with the classification of x. Given a hypothesis h, the oracle UAV-EQ returns an example x (that could have unspecified attributes) for which h(x) is incorrect.We show that any class of functions learnable in Angluin’s exact model using the MQ and EQ oracles is also learnable in the UAV model using the MQ and UAV-EQ oracles as long as the counterexamples provided by the UAV-EQ oracle have a logarithmic number of unspecified attributes. We also show that any class learnable in the exact model using the MQ and EQ oracles is also learnable in the UAV model using the UAV-MQ and UAV-EQ oracles as well as an oracle to evaluate a given boolean formula on an example with unspecified attributes. (For some hypothesis classes such as decision trees and unate formulas the evaluation can be done in polynomial time without an oracle.) We also study the learnability of a universal class of decision trees under the UAV model and of DNF formulas under a representation-dependent variation of the UAV model.     

Developing an Automated Enzyme Immunoassay for High-Throughput Screening of Bovine Serum Antibodies to Neospora Caninum

An enzyme-linked immunosorbent assay (ELISA) for Neospora caninum antibodies was automated with a robotic workstation, the Beckman Coulter Biomek 2000, to screen 200 bovine sera. Comparing these results with manually run ELISA data, a 95.92% agreement (K = 0.9592) between the two assays was obtained. The automated assay was specific and sensitive with excellent positive and negative predictive values. The results were repeatable and reproducible. The automation flexibility was high and the operation complexity was minimal. High-throughput screening (HTS) for bovine antibodies to Neospora caninum was achieved. The assay was developed according to the internationally recognized ISO17025 standard requirements.     

双享空间的幽默合辑

Extract荷兰Den Bosh的一怕老建筑,最近迎来自己的新生。那是座建于20世纪的双层Loft,在设计师Roderick Vos的改造下,它的空间一半被用于零售展示,一半用于办公思考。它模糊了设计师与顾客、艺术与生活的界限,令每一位到访者都能在幽默而单纯的氛围中,领略双享空间的专业设计精神。