Multiple Representation Approach to Geometric Model Construction From Range Data

A method is presented for constructing geometric design data from noisy 3-D sensor measurements of physical parts. In early processing phase, RLTS regression filters stemming from robust estimation theory are used for separating the desired part of the signal in contaminated sensor data from undesired part. Strategies for producing a complete 3-D data set from partial views are studied. Multiple representations are used in model construction because there is no single representation that would be most appropriate in all situations. In particular, surface triangulation, NURBS, and super-ellipsoids are employed in order to represent efficiently polygonal and irregular shapes, free form surfaces and standard primitive solids. The size of the required control point mesh for spline description is estimated using a surface characterization process. Surfaces of arbitrary topology are modeled using triangulation and trimmed NURBS. A user given tolerance value is driving refinement of the obtained surface model. The resulting model description is a procedural CAD model which can convey structural information in addition to low level geometric primitives. The model is translated to IGES standard product data exchange format to enable data sharing with other processes in concurrent engineering environment. Preliminary results on view registration using simulated data are shown. Examples of model construction using both real and simulated data are also given

eV-SCALE STERILE NEUTRINOS FROM 331-MODEL WITH FROGGATT-NIELSEN AND LINEAR SEESAW MECHANISMS

We consider the properties of sterile neutrino sector of an extension of the Standard Model, (SM) based on SU(3)C ? SU(3)L ? U(1)X gauge symmetry (331-model). Our model has built-in Froggatt-Nielsen mechanism (FN) for generating the Yukawa terms as effective operators and subsequently the correct fermion mass hierarchies, unexplained in the Standard Model. In addition, it has another built-in property, the linear seesaw mechanism. The model is very economical, containing three scalar triplets and and six sterile neutrinos. The medium-heavy eV-scale sterile neutrinos mix with the active neutrinos, and the disappearance of active flavours would be a ?smoking gun? of this model, among many others. New physics scale is assumed to be ? 7 TeV.Peer reviewe

The effects of a competitive exclusion product and two probiotics on Salmonella colonization and nutrient digestibility in broiler chickens

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Toe pressure and toe brachial index are predictive of cardiovascular mortality regardless of the most diseased arterial segment in symptomatic lower-extremity artery disease—A retrospective cohort study

Objective Although lower extremity arterial disease (LEAD) is most often multisegmental, the predominant disease location and risk factors differ between patients. Ankle-brachial index (ABI), toe-brachial index (TBI), and toe pressure (TP) are predictive of outcome in LEAD patients. Previously, we reported a classification method defining the most diseased arterial segment (MDAS); crural (CR), femoropopliteal (FP), or aortoiliac (AOI). Current study aimed to analyze the associations between MDAS, peripheral pressure measurements and cardiovascular mortality. Materials and methods We reviewed retrospectively 729 consecutive LEAD patients (Rutherford 2–6) who underwent digital subtraction angiography between January, 2009 to August, 2011 and had standardized peripheral pressure measurements. Results In Cox Regression analyses, cardiovascular mortality was associated with MDAS and noninvasive pressure indices as follows; MDAS AOI, TP 1.30 (HR 6.71, 95% CI 1.89–23.8), and MDAS CR, TP <30 mmHg (HR 4.26, 95% CI 2.19–8.27), TBI <0.25 (HR 7.71, 95% CI 1.86–32.9), and ABI <0.25 (HR 2.59, 95% CI 1.15–5.85). Conclusions Symptomatic LEAD appears to be multisegmental with severe infrapopliteal involvement. Because of this, TP and TBI are strongly predictive of cardiovascular mortality and they should be routinely measured despite the predominant disease location or clinical presentation.Peer reviewe

Approximate Semantic Matching Over Linked Data Streams

In the Internet of Things (IoT),data can be generated by all kinds of smart things. In such context, enabling machines to process and understand such data is critical. Semantic Web technologies, such as Linked Data, provide an effective and machine-understandable way to represent IoT data for further processing. It is a challenging issue to match Linked Data streams semantically based on text similarity as text similarity computation is time consuming. In this paper, we present a hashing-based approximate approach to efficiently match Linked Data streams with users’ needs. We use the Resource Description Framework (RDF) to represent IoT data and adopt triple patterns as user queries to describe users’ data needs. We then apply locality-sensitive hashing techniques to transform semantic data into numerical values to support efficient matching between data and user queries. We design a modified k nearest neighbors (kNN) algorithm to speedup the matching process. The experimental results show that our approach is up to five times faster than the traditional methods and can achieve high precisions and recalls

19F NMR spectroscopy monitors ligand binding to recombinantly fluorine-labelled b'x from human protein disulphide isomerase (hPDI)

We report a protein-observe (19)F NMR-based ligand titration binding study of human PDI b'x with ?-somatostatin that also emphasises the need to optimise recombinant protein fluorination when using 5- or 6-fluoroindole. This study highlights a recombinant preference for 5-fluoroindole over 6-fluoroindole; most likely due to the influence of fluorine atomic packing within the folded protein structure. Fluorination affords a single (19)F resonance probe to follow displacement of the protein x-linker as ligand is titrated and provides a dissociation constant of 23 ± 4 ?M

Alcohol intoxication and lack of helmet use are common in electric scooter-related traumatic brain injuries : a consecutive patient series from a tertiary university hospital

Purpose Clinicians have increasingly encountered traumatic brain injuries (TBI) related to electric scooter (ES) accidents. In this study, we aim to identify the modifiable risk factors for ES-related TBIs. Methods A retrospective cohort of consecutive patients treated for ES-related traumatic brain injuries in a tertiary university hospital between May 2019 and September 2021 was identified and employed for the study. The characteristics of the accidents along with the clinical and imaging findings of the injuries were collected from the patient charts. Results During the study period, 104 TBIs related to ES accidents were identified. There was a high occurrence of accidents late at night and on Saturdays. In four cases, the patient's helmet use was mentioned (3.8%). Seventy-four patients (71%) were intoxicated. At the scene of the accident, seventy-seven (74%) of the patients had a Glasgow Coma Scale score of 13-15, three patients (3%) had a score of 9-12, and two patients (2%) had a score of 3-8. The majority (83%) of TBIs were diagnosed as concussions. Eighteen patients had evidence of intracranial injuries in the imagining. Two patients required neurosurgical procedures. The estimated population standardized incidence increased from 7.0/100,000 (95% CI 3.5-11/100,000) in 2019 to 27/100,000 (95% CI 20-34/100,000) in 2021. Conclusions Alcohol intoxication and the lack of a helmet were common in TBIs caused by ES accidents. Most of the accidents occurred late at night. Targeting these modifiable factors could decrease the incidence of ES-related TBIs.Peer reviewe