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《Journal of endodontics》2020,46(5):662-667
IntroductionThe aim of this study was to evaluate the amount of root canal dentin removed and apical transportation occurrence after instrumentation of mesiobuccal canals of maxillary molars with ProTaper Next (PTN [Dentsply Maillefer, Ballaigues, Switzerland]), OneShape (OS [MicroMega, Besançon, France]), and EdgeFile (EF [Edge Endo, Albuquerque, NM]) rotary systems.MethodsTwenty-seven mesiobuccal canals of maxillary molars were used. Canals were randomly divided into 3 groups for canal preparation: PTN, EF X3, or OS (n = 9 for each group). Micro–computed tomographic imaging was used to measure apical transportation (mm) and the volume of dentin removed (mm3). The amount of dentin removed was measured for the coronal portion and for the whole canal length. Superposition of pre- and postoperative cross-sectional apical slices were used to measure apical transportation at 1 mm from the apex; the differences were evaluated using the Kruskal-Wallis test and Wilcoxon analysis. The Spearman correlation coefficient was used to display the relationship between variables for each group. The significance level was set at P < .05.ResultsThe percentages of the amount of dentin removed on the coronal portion and the amount removed for the whole canal length were statistically similar between groups (P > .05). The average amount of apical transportation for the PTN, OS, and EF X3 were 0.197, 0.263, and 0.218 mm, respectively. Statistically, there were no significant differences between the 3 rotary instruments for apical transportation.ConclusionsThe amount of dentin removed for the coronal third portion and the whole canal length was similar for the PTN, OS, and EF X3 rotary instruments. Although there were differences in the sizes of apical enlargement, no apical transportation was observed in any of the instrumentation systems.  相似文献   
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Introduction

The aim of this study was to evaluate the root canal shaping effect of instruments manufactured from nickel titanium, M-Wire, and Gold wire with different glide path preparation techniques.

Methods

One hundred thirty-five mesiobuccal canals of extracted human maxillary molars were randomly divided into 3 equal groups (N = 45) for glide path preparation with K-files (KF) (Dentsply Sirona, Ballaigues, Switzerland), One G (OG) files (Micro-Mega, Besançon, France), and ProGlider (PG) files (Dentsply Sirona). Specimens of each glide path group were further divided equally into 3 groups for instrumentation with ProTaper Next (PTN, Dentsply Sirona), One Shape (OS, Micro-Mega), and WaveOne Gold (WOG, Dentsply Sirona) systems (n = 15). Micro–computed tomographic imaging was used to scan teeth before instrumentation and after shaping to compare centering ratio and canal transportation values at the apical, midroot, and coronal levels and the overall changes in canal volume. Data sets were statistically analyzed (analysis of variance and Kruskal-Wallis H tests).

Results

The centering ratios for all groups were statistically similar at all levels. Apical canal transportation was significantly high for K/OS and K/PTN (P = .003). Midroot canal transportation was significantly high for K/PTN, K/OS, and OG/OS (P = .0003). Coronal canal transportation was significantly high for K/PTN and K/OS (P = .011). The highest change in canal volume was observed with all PTN groups and the lowest with PG/WOG (P = .06).

Conclusions

WOG manufactured from Gold wire combined with PG showed better root canal shaping ability and removed less dentin from the canal walls. The nickel-titanium (OS) and M-Wire (PTN) instruments used in combination with KF significantly transported more canals. PTN removed the most dentin from the canal walls regardless of the GPP technique.  相似文献   
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Purpose: The aim of this study was to investigate the driving performance of drivers with autism spectrum disorders under complex driving conditions.

Method: Seventeen drivers with autism spectrum disorders and 18 typically developed drivers participated in a driving simulator trial. Prior to the assessment, participants completed the Driving Behaviour Questionnaire and measurements of cognitive and visual-motor ability. The driving simulation involved driving in an urban area with dense traffic and unpredictable events.

Results: In comparison with the typically developed group, drivers with autism spectrum disorders reported significantly more lapses in driving, committed more mistakes on the driving simulator, and were slower to react in challenging situations, such as driving through intersections with abrupt changes in traffic lights. However, they were also less likely to tailgate other vehicles, as measured by time-to-collision between vehicles, on the driving simulator.

Conclusions: The performances of licensed drivers with autism spectrum disorders appeared to be safer in respect to car-following distance but were poorer in their response to challenging traffic situations. Driver education for individuals with autism spectrum disorders should focus on quick identification of hazards, prompt execution of responses, and effective allocation of attention to reduce lapses in driving.

  • Implications for rehabilitation
  • Drivers with autism spectrum disorders reported significantly more lapses during driving.

  • Drivers with autism spectrum disorders were observed to be poorer in traffic scenarios requiring critical response.

  • Driver education for individuals with autism spectrum disorders should focus on managing anxiety and effective attention allocation while driving.

  • Driving simulators can be used as a safe means for training critical response to challenging traffic scenarios.

  相似文献   
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Public transportation systems are an essential component of major cities. The widespread use of smart cards for automated fare collection in these systems offers a unique opportunity to understand passenger behavior at a massive scale. In this study, we use network-wide data obtained from smart cards in the London transport system to predict future traffic volumes, and to estimate the effects of disruptions due to unplanned closures of stations or lines. Disruptions, or shocks, force passengers to make different decisions concerning which stations to enter or exit. We describe how these changes in passenger behavior lead to possible overcrowding and model how stations will be affected by given disruptions. This information can then be used to mitigate the effects of these shocks because transport authorities may prepare in advance alternative solutions such as additional buses near the most affected stations. We describe statistical methods that leverage the large amount of smart-card data collected under the natural state of the system, where no shocks take place, as variables that are indicative of behavior under disruptions. We find that features extracted from the natural regime data can be successfully exploited to describe different disruption regimes, and that our framework can be used as a general tool for any similar complex transportation system.Well-designed transportation systems are a key element in the economic welfare of major cities. Design and planning of these systems requires a quantitative understanding of traffic patterns and relies on the ability to predict the effects of disruptions to such patterns, both planned and unplanned (1).There is a long history of analytic and modeling approaches to the study of traffic patterns (2), for example using simulated scenarios in simple transportation systems (3), and analysis of real traffic data in complex systems, either focusing on a small samples (4) or using more aggregate data (5, 6). Here we take this approach to the next level by making use of smart-card data and incident logs to (i) predict traffic patterns and (ii) estimate the effect of unplanned disruptions on these patterns. We analyzed 70 d of smart-card transactions from the London transportation network, composed of ∼10 million unique IDs and 6 million transactions per day on average, resulting in one of the largest statistical analyses of transportation systems to date.A related literature deals with various aspects of dynamics in complex networks and complex systems in general (79), using a variety of data sources, from emails (10) to the circulation of bank notes (11) to online experiments on Amazon Turk (12). More recently, a number of analyses have leveraged mobile phone data as proxies for mobility (4, 1315).However, smart-card technology allows us to obtain large samples of passenger location and movements without requiring noisy and potentially unreliable proxies such as mobile Global Positioning System traces (16), while also leveraging a more structured environment that imposes hard constraints on patterns of urban mobility (17). In particular, these constraints of the system allow us to identify a global model of passenger behavior under local line and station closures.  相似文献   
7.
Introduction: The chances of health care waste (Biomedical waste) coming in contact with the health care workers, patients, visitors, sanitary workers, waste handlers, public, rag pickers and animals during transportation are high. Materials and Methods: The study was conducted over a period of seven months (April 2013–October 2013) in a 500-bedded hospital where the average quantum of biomedical waste is 0.8 kg/bed/day. The issues related to transportation of health care waste from 39 generation sites to the health care waste storage site inside the hospital (intramural transfer) were addressed and analysed in a predesigned proforma. Results: The biomedical waste management team inspected the generation sites in the hospital on a daily basis and conformance to the procedures was checked. It was found that waste was collected at scheduled timings in 99.6% occasions; however, compliance to wearing personal protective equipment (PPE) was poor and ranged from 1.22−1.84%. Conclusion: Transportation of health care waste is a crucial step in its management. Regular training program for all the sections of health care workers with special emphasis on waste handlers is needed.  相似文献   
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A comparison of the preparation ability of two root canal instrumentation systems in oval‐shaped canals using micro‐computed tomography was undertaken. Thirty extracted, single‐rooted, human mandibular premolars with radiographically similar canal morphology were selected, allocated to two groups (N = 15) and prepared with TRUShape or Vortex Blue (VB). Each sample was subjected to three scans (20 μm resolution): pre‐preparation and after preparation to sizes #30 and #40. Three‐dimensional data sets were evaluated for canal volume, surface area and surface treatment. Matched axial slices in apical, middle and coronal root thirds were evaluated for cross‐sectional area, roundness and transportation. Preparation with both instruments increased canal volumes and surface areas similarly and significantly (P < 0.001) with no significant difference between groups. TRUShape significantly enhanced surface treatment at both apical sizes (P < 0.05). Transportation exceeded 100 μm in only eight out of 90 cross sections. Both instruments performed similarly during preparation. TRUShape, however, significantly enhanced surface treatment.  相似文献   
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