Tunnel spoil classification and applicability of lime addition in weak formations for muck reuse

Tunnel construction planning requires careful consideration of the spoil management part, as this involves environmental, economic and legal requirements. In this paper a methodological approach that considers the interaction between technical and geological factors in determining the features of the resulting muck is proposed. This gives indications about the required treatments as well as laboratory and field characterisation tests to be performed to assess muck recovery alternatives. While this reuse is an opportunity for excavations in good quality homogeneous grounds (e.g. granitic mass), it is critical for complex formation. This approach has been validated, at present, for three different geo-materials resulting from a tunnel excavation carried out with a large diameter Earth Pressure Balance Shield (EPB) through a complex geological succession. Physical parameters and technological features of the three materials have been assessed, according to their valorisation potential, for defining re-utilisation patterns. The methodology proved to be effective and the laboratory tests carried out on the three materials allowed the suitability and treatment effectiveness for each muck recovery strategy to be defined. © 2014 Elsevier Ltd.

An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features

The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.

Comparison of granulometric methods and sampling strategies used in marine habitat classification and Ecological Status assessment

Sediment particle size analysis (PSA) is routinely used to support benthic macrofaunal community distribution data in habitat mapping and Ecological Status (ES) assessment. No optimal PSA Method to explain variability in multivariate macrofaunal distribution has been identified nor have the effects of changing sampling strategy been examined. Here, we use benthic macrofaunal and PSA grabs from two embayments in the south of Ireland. Four frequently used PSA Methods and two common sampling strategies are applied. A combination of laser particle sizing and wet/dry sieving without peroxide pre-treatment to remove organics was identified as the optimal Method for explaining macrofaunal distributions. ES classifications and EUNIS sediment classification were robust to changes in PSA Method. Fauna and PSA samples returned from the same grab sample significantly decreased macrofaunal variance explained by PSA and caused ES to be classified as lower. Employing the optimal PSA Method and sampling strategy will improve benthic monitoring. © 2012 Elsevier Ltd.

Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer

Urothelial cancer (UC) is highly recurrent and can progress from non-invasive (NMIUC) to a more aggressive muscle-invasive (MIUC) subtype that invades the muscle tissue layer of the bladder. We present a proof of principle study that network-based features of gene pairs can be used to improve classifier performance and the functional analysis of urothelial cancer gene expression data. In the first step of our procedure each individual sample of a UC gene expression dataset is inflated by gene pair expression ratios that are defined based on a given network structure. In the second step an elastic net feature selection procedure for network-based signatures is applied to discriminate between NMIUC and MIUC samples. We performed a repeated random subsampling cross validation in three independent datasets. The network signatures were characterized by a functional enrichment analysis and studied for the enrichment of known cancer genes. We observed that the network-based gene signatures from meta collections of proteinprotein interaction (PPI) databases such as CPDB and the PPI databases HPRD and BioGrid improved the classification performance compared to single gene based signatures. The network based signatures that were derived from PPI databases showed a prominent enrichment of cancer genes (e.g., TP53, TRIM27 and HNRNPA2Bl). We provide a novel integrative approach for large-scale gene expression analysis for the identification and development of novel diagnostical targets in bladder cancer. Further, our method allowed to link cancer gene associations to network-based expression signatures that are not observed in gene-based expression signatures.

N-opcode analysis for android malware classification and categorization

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.

Deep Head Pose: Gaze-Direction Estimation in Multimodal Video

In this paper we present a convolutional neuralnetwork (CNN)-based model for human head pose estimation inlow-resolution multi-modal RGB-D data. We pose the problemas one of classification of human gazing direction. We furtherfine-tune a regressor based on the learned deep classifier. Next wecombine the two models (classification and regression) to estimateapproximate regression confidence. We present state-of-the-artresults in datasets that span the range of high-resolution humanrobot interaction (close up faces plus depth information) data tochallenging low resolution outdoor surveillance data. We buildupon our robust head-pose estimation and further introduce anew visual attention model to recover interaction with theenvironment. Using this probabilistic model, we show thatmany higher level scene understanding like human-human/sceneinteraction detection can be achieved. Our solution runs inreal-time on commercial hardware

Comparison of the cidal activity of tea tree oil and terpinen-4-ol against clinical bacterial skin isolates and human fibroblast cells

Aim: The aim of this study was to compare both the antimicrobial activity of terpinen-4-ol and tea tree oil (TTO) against clinical skin isolates of meticillin-resistant Staphylococcus aureus (MRSA) and coagulase-negative staphylococci (CoNS) and their toxicity against human fibroblast cells.

Holocene climate change and past Irish societal response

The extent to which North Atlantic Holocene climatic perturbations influenced past human societies is an area of considerable uncertainty and fierce debate. Ireland is ideally placed to help resolve this issue, being occupied for over 9000 yr and located on the eastern Atlantic seaboard, a region dominated by westerly airflow. Irish bog and lake tree populations provide unambiguous evidence of major shifts in surface moisture through the Holocene similar to cycles recorded in the marine realm of the North Atlantic, indicating significant changes in the latitude and intensity of zonal atmospheric circulation across the region. To test for human response to these cycles we summed the probabilities of 465 radiocarbon ages obtained from Irish archaeological contexts and observe enhanced archaeological visibility during periods of sustained wet conditions. These results suggest either increasing density of human populations in key, often defensive locations, and/or the development of subsistence strategies to overcome changing conditions, the latter recently proposed as a significant factor in avoiding societal collapse. Regardless, we demonstrate environmental change is a significantly more important factor in influencing human activity in the landscape than has hitherto been acknowledged.

Description and search labor for information retrieval

Selection power is taken as the fundamental value for information retrieval systems. Selection power is regarded as produced by selection labor, which itself separates historically into description and search labor. As forms of mental labor, description and search labor participate in the conditions for labor and for mental labor. Concepts and distinctions applicable to physical and mental labor are indicated, introducing the necessity of labor for survival, the idea of technology as a human construction, and the possibility of the transfer of human labor to technology. Distinctions specific to mental labor, particular between semantic and syntactic labor, are introduced. Description labor is exemplified by cataloging, classification, and database description, can be more formally understood as the labor involved in the transformation of objects for description into searchable descriptions, and is also understood to include interpretation. The costs of description labor are discussed. Search labor is conceived as the labor expended in searching systems. For both description and search labor, there has been a progressive reduction in direct human labor, with its syntactic aspects transferred to technology, effectively compelled by the high relative costs of direct human labor compared to machine processes.

Energy efficiency in gait, activity, participation, and health status in children with cerebral palsy

The aim of the study was to establish if a relationship exists between the energy efficiency of gait, and measures of activity limitation, participation restriction, and health status in a representative sample of children with cerebral palsy (CP). Secondary aims were to investigate potential differences between clinical subtypes and gross motor classification, and to explore other relationships between the measures under investigation. A longitudinal study of a representative sample of 184 children with ambulant CP was conducted (112 males, 72 females; 94 had unilateral spastic C P, 84 had bilateral spastic C P, and six had non-spastic forms; age range 4-17y; Gross Motor Function Classification System Level I, n=57; Level II, n=91; Level III, n=22; and Level IV, n=14); energy efficiency (oxygen cost) during gait, activity limitation, participation restriction, and health status were recorded. Energy efficiency during gait was shown to correlate significantly with activity limitations; no relationship between energy efficiency during gait was found with either participation restriction or health status. With the exception of psychosocial health, all other measures showed significant differences by clinical subtype and gross motor classification. The energy efficiency of walking is not reflective of participation restriction or health status. Thus, therapies leading to improved energy efficiency may not necessarily lead to improved participation or general health.