Predicting and interpreting identification errors in military vehicle training using multidimensional scaling

We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification.

Practitioner Summary: Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.     

FA composition and regiospecific analysis of Acer saccharum (sugar maple tree) and Acer saccharinum (silver maple tree) seed oils

GC analysis was performed to determine regiospecific distribution and FA composition in seed oils of the Aceraceae species, Acer saccharum and A. saccharinum. The oil content in the seeds was low at 5.0% in A. saccharum and 5.8% in A. saccharinum, and the main FA were linoleic (30.8 and 29.4%), oleic (21.3 and 27.6%), palmitic (10.1 and 10.5%), and cis-vaccenic (9.4 and 7.9%) acids, respectively. In addition, both oils contained long-chain monoenes of the n−9 and n−7 groups, including 11-eicosenoic, 13-docosenoic, 15-tetracosenoic, 13-eicosenoic, and 15-docosenoic acids, whereas γ-linolenic acid accounted for 0.8% of total FA in A. saccharum, and 0.5% in A. saccharinum. Regiospecific analysis, performed using the methodology of dibutyroyl derivatives of MAG, indicated that linoleic, oleic, and linolenic acids were mainly esterified at the internal position of TAG in both seed oils, whereas long-chain monoenes of the n−7 group were almost exclusively esterified on the external positions.     

Processing and morphology of molecularly imprinted nylon thin films

The characterization of thin, selectively imprinted films of nylon‐6 was performed. Amino acids were used as template molecules. Spin‐cast films were prepared with sizes ranging from 2 μm to 300 nm, depending on the nylon and template concentration in the casting solution. The morphological characteristics of the film surface were examined by atomic force microscopy, and the structure within the films was observed by freeze‐fracture scanning electron microscopy. The film activity was clearly coordinated with the appearance of nanometer‐sized pores both on the surface and within the film. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 2919–2926, 2006     

Thermal and mechanical properties of a polypropylene nanocomposite

An experimental polypropylene (PP) nanocomposite, containing approximately 4 wt % of an organophilic montmorillonite clay, was prepared and characterized, and its properties were compared with those of talc‐filled (20–40 wt %) compositions. Weight reduction, with maintained or even improved flexural and tensile moduli, especially at temperatures up to 70°C, was a major driving force behind this work. By a comparison with the analytical data from a nylon 6 (PA‐6) nanocomposite, it was found that the PP nanocomposite contained well‐dispersed, intercalated clay particles; however, X‐ray diffraction, transmission electron microscopy, dynamic mechanical analysis, and permeability measurements confirmed that exfoliation of the clay in PP was largely absent. The increased glass‐transition temperature (T_g) of a PA‐6 nanocomposite, which possessed fully exfoliated particles, indicated the molecular character of the matrix–particle interaction, whereas the PP nanocomposite exhibited simple matrix–filler interactions with no increase in T_g. The PP nanocomposite exhibited a weight reduction of approximately 12% in comparison with the 20% talc‐filled PP, while maintaining comparable stiffness. Undoubtedly, considerable advantages may be available if a fully exfoliated PP nanocomposite is fabricated; however, with the materials available, a combination of talc, or alternative reinforcements, and nanocomposite filler particles may provide optimum performance. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 90: 1639–1647, 2003     

Learning perceptually grounded word meanings from unaligned parallel data

In order for robots to effectively understand natural language commands, they must be able to acquire meaning representations that can be mapped to perceptual features in the external world. Previous approaches to learning these grounded meaning representations require detailed annotations at training time. In this paper, we present an approach to grounded language acquisition which is capable of jointly learning a policy for following natural language commands such as “Pick up the tire pallet,” as well as a mapping between specific phrases in the language and aspects of the external world; for example the mapping between the words “the tire pallet” and a specific object in the environment. Our approach assumes a parametric form for the policy that the robot uses to choose actions in response to a natural language command that factors based on the structure of the language. We use a gradient method to optimize model parameters. Our evaluation demonstrates the effectiveness of the model on a corpus of commands given to a robotic forklift by untrained users.     

Large-scale in vivo synthesis of the carbohydrate moieties of gangliosides GM1 and GM2 by metabolically engineered Escherichia coli

Two metabolically engineered Escherichia coli strains have been constructed to produce the carbohydrate moieties of gangliosides GM2 (GalNAcbeta-4(NeuAcalpha-3)Galbeta-4Glc; Gal = galactose, Glc = glucose, Ac = acetyl) and GM1 (Galbeta-3GalNAcbeta-4(NeuAcalpha-3)Galbeta-4Glc. The GM2 oligosaccharide-producing strain TA02 was devoid of both beta-galactosidase and sialic acid aldolase activities and overexpressed the genes for CMP-NeuAc synthase (CMP = cytidine monophosphate), alpha-2,3-sialyltransferase, UDP-GlcNAc (UDP = uridine diphosphate) C4 epimerase, and beta-1,4-GalNAc transferase. When this strain was cultivated on glycerol, exogenously added lactose and sialic acid were shown to be actively internalized into the cytoplasm and converted into GM2 oligosaccharide. The in vivo synthesis of GM1 oligosaccharide was achieved by taking a similar approach but using strain TA05, which additionally overexpressed the gene for beta-1,3-galactosyltransferase. In high-cell-density cultures, the production yields for the GM2 and GM1 oligosaccharides were 1.25 g L(-1) and 0.89 g L(-1), respectively.     

Sustainable conducting polymer composites: study of mechanical and tribological properties of natural fiber reinforced PVA composites with carbon nanofillers

ABSTRACT

Ball milled jute fiber (JF) was added to Polyvinyl Alcohol (PVA)/20 wt.% multi-layer graphene (MLG) composites in various proportions (0, 5, 10, 15 and 20 wt.%) to prepare sustainable and biodegradable conducting polymer composites. Also, PVA/17.5wt.%MLG/2.5wt.%MWCNT/20wt.% JF composite was prepared for comparison purpose. A dynamic mechanical analysis of the composites was conducted to analyze their viscoelastic nature. The electrical conductivity of the composites was measured to study their suitability for various applications. Jute reinforcement increased the electrical conductivity of PVA/MLG nanocomposites. The PVA/20wt.%JF/17.5wt.%MLG/2.5wt.%MWCNT hybrid composite had the highest electrical conductivity of 3.64 × 10^?4 S/cm among all the composites prepared. Multilayered structures of the hybrid composite films were made by hot-pressing, and their effectiveness in electromagnetic interference shielding was tested. The shielding effectiveness of the composites decreased with jute addition. The wear resistance of PVA/MLG/JF composites increased with an increase in the jute content up to an optimum value of 10 wt.%, and then it started deteriorating.     

Enhanced Fracture Toughness in Layered Microcomposites of Ce-ZrO2 and Al2O3

Laminar composites, containing layers of Ce-ZrO₂ and either Al₂O₃ or a mixture of Al₂O₃ and Ce-ZrO₂, have been fabricated using a colloidal method that allowed formation of layers with thicknesses as small as 10 μm. Strong interactions between these layers and the martensitic transformation zones surrounding cracks and indentations have been observed. In both cases, the transformation zones spread along the region adjacent to the layer, resulting in an increased fracture toughness. The enhanced fracture toughness was observed for cracks growing parallel to the layers as well as for those that were oriented normal to the layers.     

Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease

Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)—15 normal controls (NC), 16 early MCI, and 17 early stage AD—are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.     

Evaluation of hydraulic excavator Human–Machine Interface concepts using NASA TLX

This study evaluated newly proposed Human–Machine Interface (HMI) design concepts for improving the ergonomics of hydraulic excavators. The design concepts were based on an augmented interaction technique which involved the use of heads-up display (HUD) and coordinated control as HMI elements. Two alternative HMI designs were elaborated in order to separately evaluate the ergonomic impacts of the head-up display and the coordinated control by comparing them to the standard HMI design. The effectiveness of these three HMI designs in terms of the reduction of the operators' mental and physical workload were assessed by conducting experiments utilizing human subjects, ages 23–35 years. The National Aeronautics and Space Administration's Task Load Index (NASA TLX) method was used for collecting subjective workload scores based on a weighted average of ratings of six factors: Mental Demand, Physical Demand, Temporal Demand, Own Performance, Effort, and Frustration Level. The results showed that the type of HMI design affects different aspects of the operator's workload. Indeed, it showed how the proposed augmented interaction is an effective solution for reducing the ergonomic gaps in terms of mental workload, and to a lesser extent the physical workload, subjected by the standard HMI design.