Slipped capital femoral epiphysis associated with radiation therapy

We reviewed 32 children with 41 radiation-therapy associated slipped capital femoral epiphyses (RTASCFE). Ten were from the authors' institutions and 22 from the literature. Gender distribution was equal. The age at diagnosis of the malignancy was 4.3 +/- 3.1 years; the amount of radiation was 4,240 +/- 1,445 rads. Children with RTASCFE presented younger (10.4 +/- 3.2 years) than a routine SCFE. The average symptom duration was 5 +/- 6 months. Children with RTASCFE are usually thin (median weight, 10th percentile) in contrast to children with typical SCFE, who are usually obese (<95th percentile). The majority (82%) of the slips were mild, compared to routine SCFEs (approximately 50%); 28% were bilateral. There was a positive linear relationship between the age at presentation of the SCFE and the age at diagnosis of the malignancy; there was a negative linear relationship between the age at presentation of the SCFE and the amount of radiation therapy.     

Specific energy optimization in sawing of rocks using Taguchi approah简

This work aims at selecting optimal operating variables to obtain the minimum specific energy （SE） in sawing of rocks. A particular granite was sampled and sawn by a fully automated circular diamond sawblades. The peripheral speed, the traverse speed, the cut depth and the flow rate of cooling fluid were selected as the operating variables. Taguchi approach was adopted as a statistical design of experimental technique for optimization studies. The results were evaluated based on the analysis of variance and signal-to-noise ratio （S/N ratio）. Statistically significant operating variables and their percentage contribution to the process were also determined. Additionally, a statistical model was developed to demonstrate the relationship between SE and operating variables using regression analysis and the model was then verified. It was found that the optimal combination of operating variables for minimum SE is the peripheral speed of 25 m/s, the traverse speed of 70 cm/min, the cut depth of 2 cm and the flow rate of cooling fluid of 100 mL/s. The cut depth and traverse speed were statistically determined as the significant operating variables affecting the SE, respectively. Furthermore, the regression model results reveal that the predictive model has a high applicability for practical applications.     

Geometric correction accuracy of IRS-1D PAN imagery using topographic map versus GPS control points

Geometric correction accuracy of IRS-1D panchromatic imagery was investigated using GPS- and 1?:?25?000 scale topographic map-derived control points. The differentially corrected GPS-derived coordinates provided markedly more accurate results than did uncorrected handheld GPS- and map-derived GCPs. The rms error value of differentially corrected GPS-derived control points based on second-degree polynomial was in the order of ±3?m. Geometric corrections made with second-degree polynomials, using both the map- and uncorrected handheld GPS-derived control points, yielded rms error values in the order of ±5?m. The results revealed that the uncorrected handheld GPS-derived control points can be a valuable alternative to planimetric control for geometric correction of IRS-1C/D panchromatic imagery with one-pixel size accuracy.     

Prioritization methodology of dangerous substances for water quality monitoring with scarce data

Monitoring and control of dangerous substances discharged into receiving waters have attracted more attention lately. Since it is not possible to analyze every single substance, a prioritization methodology is needed for the selection of those to be monitored. Existing well-developed models require significant amount of data for reliable outcomes. This paper presents a methodology to prioritize the dangerous substances having adverse effects on freshwaters in Turkey, where data are scarce. Such a methodology will also serve as a solid model for other countries with limited background data. The adopted methodology enabled the elimination of chemicals to generate a candidate list composed of 608 substances among more than 5000 substances. Further screening and prioritization were conducted using different assessment methods (i.e., Total Hazard Value, Total Impact Value, Combined Monitoring-based, and Modelling-based Priority Setting) to obtain a proposed Final Candidate Specific Pollutants List of 150 dangerous substances. The proposed Candidate National Pollutant List of Turkey was established by combining 45 priority pollutants of the European Union with a list of candidate specific pollutants. According to the outcomes of this study, monitoring and controlling of 195 dangerous substances in freshwaters are recommended. Further detailed studies should be conducted in order to observe the actual levels of these dangerous substances in freshwaters followed by a review of the monitoring list accordingly. Moreover, further revisions might be required in the proposed list due to some possible versatile conditions in terms of sampling points (i.e., change in the location of industries).     

Artificial neural network and regression models for performance prediction of abrasive waterjet in rock cutting

An experimental study is carried out for modeling the rock cutting performance of abrasive waterjet. Kerf angle (KA) is considered as a performance criteria and modeled using artificial neural network (ANN) and regression analysis based on operating variables. Three operating variables, including traverse speed, standoff distance, and abrasive mass flow rate, are studied for obtaining different results for the KA. Data belonging to the trials are used for construction of ANN and regression models. The developed models are then tested using a test data set which is not utilized during construction of models. Additionally, the regression model is validated using various statistical approaches. The results of regression analysis are also used to determine the significant operating variables affecting the KA. Furthermore, the performances of derived models are compared for showing the accuracy levels in prediction of the KA. As a result, it is concluded that both ANN and regression models can give adequate prediction for the KA with an acceptable accuracy level. The compared results reveal also that the corresponding ANN model is more reliable than the regression model. On the other hand, the standoff distance and traverse speed are statistically determined as dominant operating variables on the KA, respectively.     

Effects of mixing techniques and dentin moisture conditions on push-out bond strength of ProRoot MTA and Biodentine

Objective: To evaluate the influence of manual and mechanical mixing techniques as well as the effects of moisture on the push-out bond strength of ProRoot MTA (Dentsply Tulsa Dental, Tulsa, OK, USA) and Biodentine (Septodont, Saint Maur des Fosses, France) to radicular dentin.

Material and methods: Two hundred and forty dentin discs were assigned into three groups with respect to the moisture condition tested: (1) dry, (2) paper points, (3) wet. The discs were further divided into four subgroups according to the calcium silicate cements (CSCs) and mixing techniques used: (1) ProRoot MTA mixed manually, (2) ProRoot MTA mixed mechanically, (3) Biodentine mixed manually, and (4) Biodentine mixed mechanically. Bond strengths of the cements to root canal dentin were measured using a push-out test setup. The data were statistically analyzed using three-way ANOVA and Bonferroni post hoc test p = 0.05.

Results: The data indicated that the push-out bond strength values were significantly affected by CSCs, mixing techniques, and moisture conditions (p < 0.001). Dry conditions caused a significant decrease in bond strength values for both CSCs (p < 0.001). The mean bond strength of Biodentine was significantly higher than that of ProRoot MTA, regardless of the mixing techniques and moisture conditions (p < 0.001). Mechanical mixing favored bond strength values statistically compared to manual mixing (p < .001).

Conclusion: The mixing techniques and moisture conditions have an effect on the push-out bond strengths of ProRoot MTA and Biodentine. Dry samples and manual mixing of cements deteriorate the push-out bond strengths values.     

Towards a verifiable real-time, autonomic, fault mitigation framework for large scale real-time systems

Designing autonomic fault responses is difficult, particularly in large-scale systems, as there is no single ‘perfect’ fault mitigation response to a given failure. The design of appropriate mitigation actions depend upon the goals and state of the application and environment. Strict time deadlines in real-time systems further exacerbate this problem. Any autonomic behavior in such systems must not only be functionally correct but should also conform to properties of liveness, safety and bounded time responsiveness. This paper details a real-time fault-tolerant framework, which uses a reflex and healing architecture to provide fault mitigation capabilities for large-scale real-time systems. At the heart of this architecture is a real-time reflex engine, which has a state-based failure management logic that can respond to both event- and time-based triggers. We also present a semantic domain for verifying properties of systems, which use this framework of real-time reflex engines. Lastly, a case study, which examines the details of such an approach, is presented.     

Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study

A comparison of agricultural crop maps from independent field-based classifications of the Satellite Pour l'Observation de la Terre (SPOT) 4 multispectral (XS), SPOT5 XS, IKONOS XS, QuickBird XS and QuickBird pan-sharpened (PS) images is presented. An agricultural area within the north-west section of Turkey was analysed for field-based crop identification. The SPOT4 XS, SPOT5 XS, IKONOS XS and QuickBird images were collected in similar climatic conditions during July and August 2004. The classification of each image was carried out separately on a per-field basis on all bands and the coincident bands that are green, red and near-infrared (NIR). To examine the effect of filtering on field-based classification, the images were each filtered using the 3?×?3, 5?×?5, 7?×?7 and 9?×?9 mean filter and the filtered bands were also classified on per-field basis. For the unfiltered images, IKONOS XS provided the highest overall accuracies of 88.9% and 88.1% for the all-bands and the coincident bands classifications, respectively. On average, IKONOS XS performed slightly better than QuickBird XS and QuickBird PS, while it outperformed SPOT4 XS and SPOT5 XS. The use of filtered images in field-based classification reduced the accuracies for SPOT4 XS, SPOT5 XS, IKONOS XS and QuickBird XS. The results of this study indicate that smoothing images prior to classification does not improve the accuracies for the field-based classification. On the contrary, the accuracies for the filtered QuickBird PS images indicated a slight improvement. On the whole, both IKONOS and QuickBird images produced quite promising results for field-based crop mapping, yielding overall accuracies above 83%.     

Detection of collapsed buildings caused by the 1999 Izmit,Turkey earthquake through digital analysis of post-event aerial photographs

In this study, the post-earthquake aerial photographs were digitally processed and analysed to detect collapsed buildings caused by the Izmit, Turkey earthquake of 17 August 1999. The selected area of study encloses part of the city of Golcuk, which is one of the urban areas most strongly hit by the earthquake. The analysis relies on the idea that if a building is collapsed, then it will not have corresponding shadows. The boundaries of the buildings were available and stored in a Geographical Information System (GIS) as vector polygons. The vector building polygons were used to match the shadow casting edges of the buildings with their corresponding shadows and to perform analyses in a building-specific manner. The shadow edges of the buildings were detected through a Prewitt edge detection algorithm. For each building, the agreement was then measured between the shadow producing edges of the building polygons and the thresholded edge image based on the percentage of shadow edge pixels. If the computed percentage value was below a preset threshold then the building being assessed was declared as collapsed. Of the 80 collapsed buildings, 74 were detected correctly, providing 92.50% producer's accuracy. The overall accuracy was computed as 96.15%. The results show that the detection of the collapsed buildings through digital analysis of post-earthquake aerial photographs based on shadow information is quite encouraging. It is also demonstrated that determining the optimum threshold value for separating the collapsed from uncollapsed buildings is important.