Length-weight relationship, condition factor and relative condition factor of Alosa braschnikowi and A. caspia in the southeast of the Caspian Sea (Goharbaran)

The main objectives of the present study were to determine the species composition of Caspian shad, genus Alosa and to estimate the LWR, CF, and Kn of A. braschnikowi and A. caspia during different months in the southeastern coast of the Caspian Sea. Two fishing methods, small mesh size beach seine and gillnet were used from December 2013 through July 2014. A. braschnikowi and A. caspia, were distinguished in the southeastern part of the Caspian Sea (Goharbaran), consisting of 57.1% and 42.9% of the Alosa catch, respectively. The slopes (b values) of the length-weight regression were 3.241 and 2.844 which were significantly different from 3 (P<0.05), indicating positive and negative allometric growth, respectively. The average CF of A. braschnikowi and A. caspia were calculated as 0.72 ± 0.12 and 0.83 ± 0.13, respectively. The average CF for both species were significantly different among months (P<0.001). There was a significantly negative correlation between size classes and CF of A. caspia. The Kn was greater than 1 for A. braschnikowi and lower than 1 for A. caspia indicating good well-being of A. braschnikowi as opposed to A. caspia in the southeastern Caspian Sea

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations

Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-Adjusted life-years for 29 cancer groups, 1990 to 2017 : A systematic analysis for the global burden of disease study

Importance: Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. Objective: To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. Evidence Review: We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-Adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. Findings: In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572000 deaths and 15.2 million DALYs), and stomach cancer (542000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601000 deaths and 17.4 million DALYs), TBL cancer (596000 deaths and 12.6 million DALYs), and colorectal cancer (414000 deaths and 8.3 million DALYs). Conclusions and Relevance: The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer care. © 2019 American Medical Association. All rights reserved.Peer reviewe

Autonomous off-line robot programming for powder suction operation with PythonOCC

While a powder bed 3D printer device is easy to use, the cleaning task after each print is a tedious job. Consequently, a proper approach is to employ an industrial robot for this task. The robot should be programmed quickly and efficiently with the off-line robot programming (OLP) method. In this paper, an OLP system based on Python and OpenCasCade libraries is introduced to generate robot trajectories for cleaning the printer powder bed immediately and autonomously. The cleaning operation is divided into three sub-operations: top layer raster, raster from the offset, and offset oriented. Several algorithms are employed to satisfy sub-operations autonomously from a CAD model. Raster path, wire, and yaw angle calculators are essential algorithms. Finally, a graphical simulation illustrates the operation efficiency. The proposed system can generate a cleaning path immediately and due to utilizing open resource libraries, there is a wide range of applicable personalization

Radial Fatigue Analysis of Automotive Wheel Rim(ISO 3006)

Due to many unexpected harsh environmental conditions of the road, automotive wheel is a vital part to ensure the vehicle safety and performance. ISO-3006 provides a comprehensive fatigue life experiment to validate proper wheels. This article is investigating a car wheel under the dynamic radial fatigue test of the ISO standard. This study aims to compare five different commercially available materials of the wheel concerning the ISO test conditions. As the wheel rim weight has a great impact on the performance of the vehicle, this comparison is considering the weight of the wheel made of various materials. The test is simulated via ANSYS software with a dens mesh to ensure the highest possible accuracy of results. Among selected materials, the CFRP is demonstrating the best fatigue strength to weight ratio in ISO radial fatigue test

Data-driven compressive strength prediction of steel fiber reinforced concrete (SFRC) subjected to elevated temperatures using stacked machine learning algorithms

Experimental studies using a substantial number of datasets can be avoided by employing efficient methods to predict the mechanical properties of construction materials. The correlation between the mechanical attributes and structural performance of these structures can be determined using an efficient mathematical model. In this study, a large data-rich framework is constructed with data from 307 experiments conducted between 2000 and 2022 and reported in the literature to predict the compressive strength (CS) of steel fiber-reinforced concrete (SFRC) subjected to high temperatures. The collected data are utilized for training the proposed models using the SciKit, Tree-based Pipeline Optimization Tool (TPOT), and AutoKeras libraries in Python, followed by hyperparameter tuning and k-fold cross-validation. After performing the feature selection analysis, several machine learning (ML) algorithms are developed and compared. Out of 7 different leaderboard combinations, the best stacked pipeline including support vector machine, random forest, gradient boosting machine, extra tree regression, and K-nearest neighbors, is found to provide the most accurate solution. In addition, the results obtained using the stacked ML are compared with those obtained using an artificial neural network algorithm. Moreover, the accuracy of each method is determined through a comparative study. The stacked ML pipeline with optimum hyperparameters yields the highest accuracy (R2 = 0.92). The proposed stacked technique serves as an accurate and adaptable attribute evaluation tool for researchers to predict the CS of SFRCs subjected to elevated temperatures in construction applications