Identification methods of key contributing factors in crashes with high numbers of fatalities and injuries in China

Objectives In China, “serious road traffic crashes” (SRTCs) are those in which there are 10-30 fatalities, 50-100 serious injuries or a total cost of 50-100 million RMB ($US8-16m), and “particularly serious road traffic crashes” (PSRTCs) are those which are more severe or costly. Due to the large number of fatalities and injuries as well as the negative public reaction they elicit, SRTCs and PSRTCs have become great concerns to China during recent years. The aim of this study is to identify the main factors contributing to these road traffic crashes and to propose preventive measures to reduce their number. Methods 49 contributing factors of the SRTCs and PSRTCs that occurred from 2007 to 2013 were collected from the database “In-depth Investigation and Analysis System for Major Road traffic crashes” (IIASMRTC) and were analyzed through the integrated use of principal component analysis and hierarchical clustering to determine the primary and secondary groups of contributing factors. Results Speeding and overloading of passengers were the primary contributing factors, featuring in up to 66.3% and 32.6% of accidents respectively. Two secondary contributing factors were road-related: lack of or nonstandard roadside safety infrastructure, and slippery roads due to rain, snow or ice. Conclusions The current approach to SRTCs and PSRTCs is focused on the attribution of responsibility and the enforcement of regulations considered relevant to particular SRTCs and PSRTCs. It would be more effective to investigate contributing factors and characteristics of SRTCs and PSRTCs as a whole, to provide adequate information for safety interventions in regions where SRTCs and PSRTCs are more common. In addition to mandating of a driver training program and publicisation of the hazards associated with traffic violations, implementation of speed cameras, speed signs, markings and vehicle-mounted GPS are suggested to reduce speeding of passenger vehicles, while increasing regular checks by traffic police and passenger station staff, and improving transportation management to increase income of contractors and drivers are feasible measures to prevent overloading of people. Other promising measures include regular inspection of roadside safety infrastructure, and improving skid resistance on dangerous road sections in mountainous areas.

Cluster based core vector machine

Core Vector Machine(CVM) is suitable for efficient large-scale pattern classification. In this paper, a method for improving the performance of CVM with Gaussian kernel function irrespective of the orderings of patterns belonging to different classes within the data set is proposed. This method employs a selective sampling based training of CVM using a novel kernel based scalable hierarchical clustering algorithm. Empirical studies made on synthetic and real world data sets show that the proposed strategy performs well on large data sets.

Crop stage classification of hyperspectral data using unsupervised techniques

The presence of a large number of spectral bands in the hyperspectral images increases the capability to distinguish between various physical structures. However, they suffer from the high dimensionality of the data. Hence, the processing of hyperspectral images is applied in two stages: dimensionality reduction and unsupervised classification techniques. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The selected dimensions are classified using Niche Hierarchical Artificial Immune System (NHAIS). The NHAIS combines the splitting method to search for the optimal cluster centers using niching procedure and the merging method is used to group the data points based on majority voting. Results are presented for two hyperspectral images namely EO-1 Hyperion image and Indian pines image. A performance comparison of this proposed hierarchical clustering algorithm with the earlier three unsupervised algorithms is presented. From the results obtained, we deduce that the NHAIS is efficient.

The relationship between classification of multi-domain proteins using an alignment-free approach and their functions: a case study with immunoglobulins

Establishing functional relationships between multi-domain protein sequences is a non-trivial task. Traditionally, delineating functional assignment and relationships of proteins requires domain assignments as a prerequisite. This process is sensitive to alignment quality and domain definitions. In multi-domain proteins due to multiple reasons, the quality of alignments is poor. We report the correspondence between the classification of proteins represented as full-length gene products and their functions. Our approach differs fundamentally from traditional methods in not performing the classification at the level of domains. Our method is based on an alignment free local matching scores (LMS) computation at the amino-acid sequence level followed by hierarchical clustering. As there are no gold standards for full-length protein sequence classification, we resorted to Gene Ontology and domain-architecture based similarity measures to assess our classification. The final clusters obtained using LMS show high functional and domain architectural similarities. Comparison of the current method with alignment based approaches at both domain and full-length protein showed superiority of the LMS scores. Using this method we have recreated objective relationships among different protein kinase sub-families and also classified immunoglobulin containing proteins where sub-family definitions do not exist currently. This method can be applied to any set of protein sequences and hence will be instrumental in analysis of large numbers of full-length protein sequences.

Estimates of site response based on spectral ratio between horizontal and vertical components of ambient vibrations in the source zone of 2001 Bhuj earthquake

We investigated the site response characteristics of Kachchh rift basin over the meizoseismal area of the 2001, Mw 7.6, Bhuj (NW India) earthquake using the spectral ratio of the horizontal and vertical components of ambient vibrations. Using the available knowledge on the regional geology of Kachchh and well documented ground responses from the earthquake, we evaluated the H/V curves pattern across sediment filled valleys and uplifted areas generally characterized by weathered sandstones. Although our HIV curves showed a largely fuzzy nature, we found that the hierarchical clustering method was useful for comparing large numbers of response curves and identifying the areas with similar responses. Broad and plateau shaped peaks of a cluster of curves within the valley region suggests the possibility of basin effects within valley. Fundamental resonance frequencies (f(0)) are found in the narrow range of 0.1-2.3 Hz and their spatial distribution demarcated the uplifted regions from the valleys. In contrary, low HIV peak amplitudes (A(0) = 2-4) were observed on the uplifted areas and varying values (2-9) were found within valleys. Compared to the amplification factors, the liquefaction indices (kg) were able to effectively indicate the areas which experienced severe liquefaction. The amplification ranges obtained in the current study were found to be comparable to those obtained from earthquake data for a limited number of seismic stations located on uplifted areas; however the values on the valley region may not reflect their true amplification potential due to basin effects. Our study highlights the practical usefulness as well as limitations of the HIV method to study complex geological settings as Kachchh. (C) 2014 Elsevier Ltd. All rights reserved.

Neighborhoods attributes as determinants of children's academic achievement

Resumen: Los estudios sobre los efectos del vecindario sobre los logros educativos han confirmado la existencia de estos efectos particularmente en la adolescencia. Una deficiencia común de la investigación empírica hasta la fecha, la falta de información en múltiples contextos, se aborda en este trabajo mediante el uso de encuestas escolares de datos para obtener una mayor comprensión sobre el efecto de la pobreza en los niños que cursan el nivel primario de enseñanza en los EE.UU. Este trabajo propone la utilización de un modelo jerárquico lineal de clasificación cruzada para tomar en cuenta en forma apropiada la estructura anidada de la información ya que los niños pertenecen simultáneamente a los dos grupos, el barrio y la escuela. Los resultados que se presentan, basados en la encuesta ECLS-K, una muestra de más de 20000 niños en aproximadamente 1.000 vecindarios y 1200 escuelas 1200, pone de manifiesto la asociación entre la composición socioeconómica del vecindario y los resultados académicos de los estudiantes. Este estudio proporciona evidencia a favor de las teorías de la socialización y epidémica. La presencia de adultos con buen nivel educativo en el vecindario así como la mediana de ingresos tienen impacto positivo en el logro del estudiante. De la misma forma, elevados niveles de pobreza tienen una influencia significativa, pero negativa en los resultados académicos. Sin embargo, el impacto se produce cuando se supera el umbral de 30% de hogares pobres en el vecindario. Los resultados agregados son invariantes a distintas especificaciones en términos de variables, esto no sucede cuando se analizan subgrupos clasificados según su origen étnico, género y estatus socio-económico.

Distribution and sighting frequency of reef fishes in the Florida Keys National Marine Sanctuary

This study analyzed species richness, distribution, and sighting frequency of selected reef fishes to describe species assemblage composition, abundance, and spatial distribution patterns among sites and regions (Upper Keys, Middle Keys, Lower Keys, and Dry Tortugas) within the Florida Keys National Marine Sanctuary (FKNMS) barrier reef ecosystem. Data were obtained from the Reef Environmental Education Foundation (REEF) Fish Survey Project, a volunteer fish-monitoring program. A total of 4,324 visual fish surveys conducted at 112 sites throughout the FKNMS were used in these analyses. The data set contained sighting information on 341 fish species comprising 68 families. Species richness was generally highest in the Upper Keys sites (maximum was 220 species at Molasses Reef) and lowest in the Dry Tortugas sites. Encounter rates differed among regions, with the Dry Tortugas having the highest rate, potentially a result of differences in the evenness in fishes and the lower diversity of habitat types in the Dry Tortugas region. Geographic coverage maps were developed for 29 frequently observed species. Fourteen of these species showed significant regional variation in mean sighting frequency (%SF). Six species had significantly lower mean %SF and eight species had significantly higher mean %SF in the Dry Tortugas compared with other regions. Hierarchical clustering based on species composition (presence-absence) and species % SF revealed interesting patterns of similarities among sites that varied across spatial scales. Results presented here indicate that phenomena affecting reef fish composition in the FKNMS operate at multiple spatial scales, including a biogeographic scale that defines the character of the region as a whole, a reef scale (~50-100 km) that include meso-scale physical oceanographic processes and regional variation in reef structure and associated reef habitats, and a local scale that includes level of protection, cross-shelf location and a suite of physical characteristics of a given reef. It is likely that at both regional and local scales, species habitat requirements strongly influence the patterns revealed in this study, and are particularly limiting for species that are less frequently observed in the Dry Tortugas. The results of this report serve as a benchmark for the current status of the reef fishes in the FKNMS. In addition, these data provide the basis for analyses on reserve effects and the biogeographic coupling of benthic habitats and fish assemblages that are currently underway. (PDF contains 61 pages.)

A novel brain partition highlights the modular skeleton shared by structure and function

Elucidating the intricate relationship between brain structure and function, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Recent progress in neuroimaging has helped advance our understanding of this important issue, with diffusion images providing information about structural connectivity (SC) and functional magnetic resonance imaging shedding light on resting state functional connectivity (rsFC). Here, we adopt a systems approach, relying on modular hierarchical clustering, to study together SC and rsFC datasets gathered independently from healthy human subjects. Our novel approach allows us to find a common skeleton shared by structure and function from which a new, optimal, brain partition can be extracted. We describe the emerging common structure-function modules (SFMs) in detail and compare them with commonly employed anatomical or functional parcellations. Our results underline the strong correspondence between brain structure and resting-state dynamics as well as the emerging coherent organization of the human brain.

Structure learning in a sensorimotor association task.

Learning is often understood as an organism's gradual acquisition of the association between a given sensory stimulus and the correct motor response. Mathematically, this corresponds to regressing a mapping between the set of observations and the set of actions. Recently, however, it has been shown both in cognitive and motor neuroscience that humans are not only able to learn particular stimulus-response mappings, but are also able to extract abstract structural invariants that facilitate generalization to novel tasks. Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects. Using regression and reinforcement learning models we show that the observed facilitation cannot be explained by these basic models of learning stimulus-response associations. We show, however, that the observed data can be explained by a hierarchical Bayesian model that performs structure learning. In line with previous results from cognitive tasks, this suggests that hierarchical Bayesian inference might provide a common framework to explain both the learning of specific stimulus-response associations and the learning of abstract structures that are shared by different task environments.

TRANSIENT SCANNING ELECTRON BEAM ANNEALING METHODS USED TO STUDY DIFFUSION AND DEFECTS IN IMPLANTED SILICON.

Rapid thermal annealing of arsenic and boron difluoride implants, such as those used for source/drain regions in CMOS, has been carried out using a scanning electron beam annealer, as part of a study of transient diffusion effects. Three types of e-beam anneal have been performed, with peak temperatures in the range 900 -1200 degree C; the normal isothermal e-beam anneals, together with sub-second fast anneals and 'dual-pulse' anneals, in which the sample undergoes an isothermal pre-anneal followed by rapid heating to the required anneal temperature is less than 0. 5s. The diffusion occuring during these anneal cycles has been modelled using SPS-1D, an implant and diffusion modelling program developed by one of the authors. This has been modified to incorporate simulated temperature vs. time cycles for the anneals. Results are presented applying the usual equilibrium clustering model, a transient point-defect enhancement to the diffusivity proposed recently by Fair and a new dynamic clustering model for arsenic. Good agreement with SIMS measurements is obtained using the dynamic clustering model, without recourse to a transient defect model.

Using mesoscopic models to design strong and tough biomimetic polymer networks

Using computational modeling, we investigate the mechanical properties of polymeric materials composed of coiled chains, or "globules", which encompass a folded secondary structure and are cross-linked by labile bonds to form a macroscopic network. In the presence of an applied force, the globules can unfold into linear chains and thereby dissipate energy as the network is deformed; the latter attribute can contribute to the toughness of the material. Our goal is to determine how to tailor the labile intra- and intermolecular bonds within the network to produce material exhibiting both toughness and strength. Herein, we use the lattice spring model (LSM) to simulate the globules and the cross-linked network. We also utilize our modified Hierarchical Bell model (MHBM) to simulate the rupture and reforming of N parallel bonds. By applying a tensile deformation, we demonstrate that the mechanical properties of the system are sensitive to the values of N in and N out, the respective values of N for the intra- and intermolecular bonds. We find that the strength of the material is mainly controlled by the value of N out, with the higher value of N out providing a stronger material. We also find that, if N in is smaller than N out, the globules can unfold under the tensile load before the sample fractures and, in this manner, can increase the ductility of the sample. Our results provide effective strategies for exploiting relatively weak, labile interactions (e.g., hydrogen bonding or the thiol/disulfide exchange reaction) in both the intra- and intermolecular bonds to tailor the macroscopic performance of the materials. © 2011 American Chemical Society.

MULTIVARIATE CLASSIFICATION BASED ON METAL CONTENTS IN HUMAN SENILE CATARACT LENSES

Multivariate classification methods were used to evaluate data on the concentrations of eight metals in human senile lenses measured by atomic absorption spectrometry. Principal components analysis and hierarchical clustering separated senile cataract lenses, nuclei from cataract lenses, and normal lenses into three classes on the basis of the eight elements. Stepwise discriminant analysis was applied to give discriminant functions with five selected variables. Results provided by the linear learning machine method were also satisfactory; the k-nearest neighbour method was less useful.

影响生育价值观因素的研究－传统回归分析与多层线性模型分析结果的比较

Based on the research of predictors of VOC, this study explores the predictive effect of factors, such as generation, urban/rural context, collectivism/individualism orientation, family value, independent/interdependent self, adult attachment, on the Emotional and Traditional factors of VOC. Considering the hierarchical data structure of the VOC study, which resulted from the original research design, this dissertation applies Hierarchical Linear Model (HLM) after using traditional regression. A comparison between the results from the tow statistical methods is made, and the results are as follows: 1) Reliability coefficients of questionnaires used in this study are satisfactory, and most of them can be used in further research. 2) Samples from different generation and urban/rural context show significant differences on the score of collectivism/individualism orientation, family value, independent/interdependent self, adult attachment, and VOC. 3) Regression equations with VOC as outcome variable differ from each other when using data from sample with restricted generation or urban/rural context. 4) Results by HLM shows that interdependent self and mother identity have positive effect on emotional factor of VOC. Emotional factor’s variation on family level is not significant. 5) Results by HLM shows that Individualism, Interdependent Self and Grandmother Identity can predict Traditional factor of VOC. Traditional factor’s variation is significant on family level, which can be explained by family income and it’s area-urban or rural. Based on the results above, the researcher concludes that a) generation identity and urban/rural context have important effect on VOC; b) Interdependent Self is an important predictive factor of VOC’s Emotional factor, which is nearly subjective to other factors; d) VOC’s traditional factor varies with other factors, which show its strong relation with culture and tradition; e) more exact results can be gotten from HLM analysis, which beyond tradition regression.

Near-Optimal Distributed Failure Circumscription

Small failures should only disrupt a small part of a network. One way to do this is by marking the surrounding area as untrustworthy --- circumscribing the failure. This can be done with a distributed algorithm using hierarchical clustering and neighbor relations, and the resulting circumscription is near-optimal for convex failures.

Error-driven active learning in growing radial basis function networks for early robot learning

Q. Meng and M.H. Lee, 'Error-driven active learning in growing radial basis function networks for early robot learning', 2006 IEEE International Conference on Robotics and Automation (IEEE ICRA 2006), 2984-90, Orlando, Florida, USA.