6,794 research outputs found

    Design knowledge capture for the space station

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    The benefits of design knowledge availability are identifiable and pervasive. The implementation of design knowledge capture and storage using current technology increases the probability for success, while providing for a degree of access compatibility with future applications. The space station design definition should be expanded to include design knowledge. Design knowledge should be captured. A critical timing relationship exists between the space station development program, and the implementation of this project

    An approach to design knowledge capture for the space station

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    The design of NASA's space station has begun. During the design cycle, and after activation of the space station, the reoccurring need will exist to access not only designs, but also deeper knowledge about the designs, which is only hinted in the design definition. Areas benefiting from this knowledge include training, fault management, and onboard automation. NASA's Artificial Intelligence Office at Johnson Space Center and The MITRE Corporation have conceptualized an approach for capture and storage of design knowledge

    Considerations for a design and operations knowledge support system for Space Station Freedom

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    Engineering and operations of modern engineered systems depend critically upon detailed design and operations knowledge that is accurate and authoritative. A design and operations knowledge support system (DOKSS) is a modern computer-based information system providing knowledge about the creation, evolution, and growth of an engineered system. The purpose of a DOKSS is to provide convenient and effective access to this multifaceted information. The complexity of Space Station Freedom's (SSF's) systems, elements, interfaces, and organizations makes convenient access to design knowledge especially important, when compared to simpler systems. The life cycle length, being 30 or more years, adds a new dimension to space operations, maintenance, and evolution. Provided here is a review and discussion of design knowledge support systems to be delivered and operated as a critical part of the engineered system. A concept of a DOKSS for Space Station Freedom (SSF) is presented. This is followed by a detailed discussion of a DOKSS for the Lyndon B. Johnson Space Center and Work Package-2 portions of SSF

    Performance, lean meat proportion and behaviour of fattening pigs given a liquid diet at different animal/feeding-place ratios

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    Sensor feeding is a liquid feeding system for fattening pigs that is operated with a restricted animal/feeding-place ratio (AFR). The aim of the present study was to quantify the effect of three different AFRs (4:1, 7:1 and 13:1, calculated with a feeding space of 33 cm per animal) on the performance and behaviour of fattening pigs (mean initial weight 26·3 (s.d. 3·3) kg, live weight at slaughter 102 (s.d. 5) kg). The pigs were housed in groups of 40 and each AFR was tested with seven groups (21 groups in total). The daily weight gain of the individual pigs was calculated from the beginning of the experiments until slaughter. Additionally, the lean meat percentage was recorded (AutoFOM). Feeding behaviour was observed by means of 24-h video recording at the ages of 14 and 17 weeks with scan sampling every 5 min. The daily weight gain decreased with increasing AFR ( P<0·01) and females had lower weight gains than barrows ( P<0·001). The lean meat proportion was influenced by the AFR ( P<0·01) and sex of the pigs ( P<0·001). Proportions were highest with the AFR 13:1 and in females. The average number of pigs feeding simultaneously was highest for the AFR of 4:1 ( P<0·01). Moreover, the ingestion rate per day (kg/min) increased with increasing AFR ( P<0·05). The average number of pigs waiting behind other pigs feeding at the trough was highest with the AFR 13:1 ( P<0·001).In conclusion, growth performance and pig behaviour were negatively affected by an AFR of 13:1, which cannot be recommended for use with this feeding system. With an AFR of 4:1 lean meat values were lo

    Strengths and Weaknesses in the Intellectual Profile of Different Subtypes of Specific Learning Disorder

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    The present study analyzes whether and how the most common diagnoses within the specific learning disorder (SLD) category are characterized by different intellectual profiles. The issue is relevant to the current debate on the unitary versus decomposable nature of the SLD category and may help define specific interventions. Intellectual profiles were obtained using the Wechsler Intelligence Scale for Children–IV (WISC-IV) on 1,049 children diagnosed with SLD using the ICD-10 codes. Four major subsamples were compared: reading disorder, spelling disorder, disorder of arithmetical skills, and mixed disorder of scholastic skills. The four main WISC-IV indexes (verbal comprehension, perceptual reasoning, working memory, and processing speed) were considered. Results showed that all SLD subgroups share similar weaknesses in working memory and processing speed, but they also showed that the subgroups are characterized by partly different intellectual profiles. These specificities should be considered in the definition of SLD

    Gender Differences in the First Impressions of Autistic Adults

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    Prior research has shown that less favourable first impressions are formed of autistic adults by non‐autistic observers. Autistic females may present differently to autistic males and could engage in more camouflaging behaviours, which could affect these first impressions. However, research has not yet tested for gender differences in the first impressions of autistic adults. In the current study, non‐autistic observers (n = 205) viewed either 10‐sec video clips or text transcripts in the context of a mock job interview by 10 autistic females and 10 autistic males, matched to 10 non‐autistic females and 10 non‐autistic males. They then rated each stimulus on personality traits (e.g., awkwardness) and behavioural intentions (e.g., “I would start a conversation with this person”). Non‐autistic observers were blind to diagnostic status of the individuals in either modality. Results showed that first impressions were less favourable overall of autistic adults in the video modality. Furthermore, autistic females were rated more favourably than autistic males in the video modality across most traits—but autistic females were also rated less favourably than both non‐autistic females and males. Some judgements were also made in the text modality, whereby more favourable first impressions were made of autistic males on the basis of speech content. Understanding the first impressions that both autistic females and males make has important implications for diagnostic services and employment prospects

    The unitary ability of IQ and indexes in WAIS-IV

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    Lichtenberger and Kaufman (2009, p. 167) defined unitary ability as ‘an ability […] that is represented by a cohesive set of scaled scores, each reflecting slightly different or unique aspects of the ability’. Flanagan and Kaufman (2009) and Lichtenberger and Kaufman (2012) used a difference of 23 IQ points between the highest score (Max) and the lowest score (Min) obtained by a subject in the four Indexes of the WAIS-IV to define unitarity of the total IQ score. A similar method has been used to assess the unitary ability of the four Indexes, with a threshold of 5 points. Such difference scores (of 23 for IQ and 5 for Indexes) are considered high and infrequent and the authors therefore conclude that the corresponding Full-Scale IQ score or Index score is uninterpretable. In this paper we argue that these thresholds are inappropriate because they are based on the wrong standard deviation. The main aim of this study was to establish variability thresholds for IQ and the WAIS-IV Indexes for the American standardization sample and to compare these thresholds with those for the Italian standardization sample. We also consider an alternative approach to determining whether an IQ score represents a unitary ability, based on the maximum difference score for the 10 core subtests that contribute to Full-Scale IQ scores
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