Investigation of bar parameters occurred by cross-shore sediment transport

ABSTRACTCross-shore sediment transport is very important factor in the design of coastal structures, and the beach profile is mainly affected by a number of parameters, such as wave height and period, beach slope, and the material properties of the bed. In this study cross-shore sediment movement was investigated using a physical model and various offshore bar geometric parameters were determined by the resultant erosion profile. The experiments on cross-shore sediment transport carried out in a laboratory wave channel for initial base slopes of 1/8, 1/10 and 1/15. Using the regular waves with different deep-water wave steepness generated by a pedal-type wave generator, the geometrical of sediment transport rate and considerable characteristics of beach profiles under storm conditions and bar parameters affecting on-off shore sediment transport are investigated for the beach materials with the medium diameter of d50=0.25, 0.32, 0.45, 0.62 and 0.80 mm. Non-dimensional equations were obtained by using linear and non-linear regression methods through the experimental data and were compared with previously developed equations in the literature. The results have shown that the experimental data fitted well to the proposed equations with respect to the previously developed equations

An Experimental Study on Cross-Shore Sediment Transport

The cross-shore sediment transport in a coastal region causes the important changes in beach morphological properties. The accurate estimation of the cross-shore sediment transport is important for the designing of the marine structures such as seawalls, jetties, breakwaters etc, and the preventing coastal erosion and accretion due to on-off shore sediment transportation. In this study, the experiments on cross- shore sediment transport carried out in a laboratory wave channel for initial beach slopes of 1/8, 1/10 and 1/15. Using the regular waves with different deep-water wave steepness generated by a pedal-type wave generator, the geometrical characteristics of beach profiles under storm conditions and the parameters affecting on-off shore sediment transport are investigated for the beach materials having medium diameters of d50=0.25, 0.32, 0.45, 0.62 and 0.80 mm. The experimental results obtained from this study compared with previous experimental work and found to be of the same magnitude as the experimental measurements and followed the expected basic trend

Numerical Investigation of Dam-Break Flow over a Bottom Obstacle Using Eulerian Finite Element Method

Dam-break flows can cause major destructions in case settlements located atthe downstream area. Since many people live in the settlements, investigations regarding the dam-break flow have great importance.Dam break flow characteristics can become variousbased on different downstream conditions. In this study, an investigation was made relating tothe dam break flow in a channel with symmetrical triangular-shaped bottom obstacle using Finite Element Method (FEM) formulation. Numerical results of the present study were compared with experimental results. It was concluded that numerical and experimental results are in good agreement

Forecasting of Suspended Sediment in Rivers Using Artificial Neural Networks Approach

Suspended sediment estimation is important to the water resources management and water quality problem. In this article, artificial neural networks (ANN), M5tree (M5T) approaches and statistical approaches such as Multiple Linear Regression (MLR), Sediment Rating Curves (SRC) are used for estimation daily suspended sediment concentration from daily temperature of water and streamflow in river. These daily datas were measured at Iowa station in US. These prediction aproaches are compared to each other according to three statistical criteria, namely, mean square errors (MSE), mean absolute relative error (MAE) and correlation coefficient (R). When the results are compared ANN approach have better forecasts suspended sediment than the other estimation methods

Of the importance of a leaf: the ethnobotany of sarma in Turkey and the Balkans

BACKGROUND: Sarma - cooked leaves rolled around a filling made from rice and/or minced meat, possibly vegetables and seasoning plants - represents one of the most widespread feasting dishes of the Middle Eastern and South-Eastern European cuisines. Although cabbage and grape vine sarma is well-known worldwide, the use of alternative plant leaves remains largely unexplored. The aim of this research was to document all of the botanical taxa whose leaves are used for preparing sarma in the folk cuisines of Turkey and the Balkans. Methods: Field studies were conducted during broader ethnobotanical surveys, as well as during ad-hoc investigations between the years 2011 and 2014 that included diverse rural communities in Croatia, Bosnia and Herzegovina, Serbia, Kosovo, Albania, Macedonia, Bulgaria, Romania, and Turkey. Primary ethnobotanical and folkloric literatures in each country were also considered. Results: Eighty-seven botanical taxa, mainly wild, belonging to 50 genera and 27 families, were found to represent the bio-cultural heritage of sarma in Turkey and the Balkans. The greatest plant biodiversity in sarma was found in Turkey and, to less extent, in Bulgaria and Romania. The most commonly used leaves for preparing sarma were those of cabbage (both fresh and lacto-fermented), grape vine, beet, dock, sorrel, horseradish, lime tree, bean, and spinach. In a few cases, the leaves of endemic species (Centaurea haradjianii, Rumex gracilescens, and R. olympicus in Turkey) were recorded. Other uncommon sarma preparations were based on lightly toxic taxa, such as potato leaves in NE Albania, leaves of Arum, Convolvulus, and Smilax species in Turkey, of Phytolacca americana in Macedonia, and of Tussilago farfara in diverse countries. Moreover, the use of leaves of the introduced species Reynoutria japonica in Romania, Colocasia esculenta in Turkey, and Phytolacca americana in Macedonia shows the dynamic nature of folk cuisines. Conclusion: The rich ethnobotanical diversity of sarma confirms the urgent need to record folk culinary plant knowledge. The results presented here can be implemented into initiatives aimed at re-evaluating folk cuisines and niche food markets based on local neglected ingredients, and possibly also to foster trajectories of the avant-garde cuisines inspired by ethnobotanical knowledge

Type 1 Autoimmune Pancreatitis in Europe:Clinical Profile and Response to Treatment

Background &amp; Aims: Autoimmune pancreatitis (AIP) is an immune-mediated disease of the pancreas with distinct pathophysiology and manifestations. Our aims were to characterize type 1 AIP in a large pan-European cohort and study the effectiveness of current treatment regimens. Methods: We retrospectively analyzed adults diagnosed since 2005 with type 1 or not-otherwise-specified AIP in 42 European university hospitals. Type 1 AIP was uniformly diagnosed using specific diagnostic criteria. Patients with type 2 AIP and those who had undergone pancreatic surgery were excluded. The primary end point was complete remission, defined as the absence of clinical symptoms and resolution of the index radiologic pancreatic abnormalities attributed to AIP. Results: We included 735 individuals with AIP (69% male; median age, 57 years; 85% White). Steroid treatment was started in 634 patients, of whom 9 (1%) were lost to follow-up. The remaining 625 had a 79% (496/625) complete, 18% (111/625) partial, and 97% (607/625) cumulative remission rate, whereas 3% (18/625) did not achieve remission. No treatment was given in 95 patients, who had a 61% complete (58/95), 19% partial (18/95), and 80% cumulative (76/95) spontaneous remission rate. Higher (≥0.4 mg/kg/day) corticosteroid doses were no more effective than lower (&lt;0.4 mg/kg/day) doses (odds ratio, 0.428; 95% confidence interval, 0.054–3.387) and neither was a starting dose duration &gt;2 weeks (odds ratio, 0.908; 95% confidence interval, 0.818–1.009). Elevated IgG4 levels were independently associated with a decreased chance of complete remission (odds ratio, 0.639; 95% confidence interval, 0.427–0.955). Relapse occurred in 30% of patients. Relapses within 6 months of remission induction were independent of the steroid-tapering duration, induction treatment duration, and total cumulative dose. Conclusions: Patients with type 1 AIP and elevated IgG4 level may need closer monitoring. For remission induction, a starting dose of 0.4 mg/kg/day for 2 weeks followed by a short taper period seems effective. This study provides no evidence to support more aggressive regimens.</p

Type 1 Autoimmune Pancreatitis in Europe:Clinical Profile and Response to Treatment

Background &amp; Aims: Autoimmune pancreatitis (AIP) is an immune-mediated disease of the pancreas with distinct pathophysiology and manifestations. Our aims were to characterize type 1 AIP in a large pan-European cohort and study the effectiveness of current treatment regimens. Methods: We retrospectively analyzed adults diagnosed since 2005 with type 1 or not-otherwise-specified AIP in 42 European university hospitals. Type 1 AIP was uniformly diagnosed using specific diagnostic criteria. Patients with type 2 AIP and those who had undergone pancreatic surgery were excluded. The primary end point was complete remission, defined as the absence of clinical symptoms and resolution of the index radiologic pancreatic abnormalities attributed to AIP. Results: We included 735 individuals with AIP (69% male; median age, 57 years; 85% White). Steroid treatment was started in 634 patients, of whom 9 (1%) were lost to follow-up. The remaining 625 had a 79% (496/625) complete, 18% (111/625) partial, and 97% (607/625) cumulative remission rate, whereas 3% (18/625) did not achieve remission. No treatment was given in 95 patients, who had a 61% complete (58/95), 19% partial (18/95), and 80% cumulative (76/95) spontaneous remission rate. Higher (≥0.4 mg/kg/day) corticosteroid doses were no more effective than lower (&lt;0.4 mg/kg/day) doses (odds ratio, 0.428; 95% confidence interval, 0.054–3.387) and neither was a starting dose duration &gt;2 weeks (odds ratio, 0.908; 95% confidence interval, 0.818–1.009). Elevated IgG4 levels were independently associated with a decreased chance of complete remission (odds ratio, 0.639; 95% confidence interval, 0.427–0.955). Relapse occurred in 30% of patients. Relapses within 6 months of remission induction were independent of the steroid-tapering duration, induction treatment duration, and total cumulative dose. Conclusions: Patients with type 1 AIP and elevated IgG4 level may need closer monitoring. For remission induction, a starting dose of 0.4 mg/kg/day for 2 weeks followed by a short taper period seems effective. This study provides no evidence to support more aggressive regimens.</p

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting