Abstract: Obesity is known to be associated with many clinically important diseases including metabolic syndrome (MetS). Vitamin B12 plays essential roles in fat and protein metabolisms and its cooperation with vitamin B9 is well-known. The aim of this study is to investigate the possible contributions as well as associations of these micronutrients upon obesity and MetS during childhood. A total of 128 children admitted to Namik Kemal University, Medical Faculty, Department of Pediatrics Outpatient Clinics were included into the scope of this study. The mean age±SEM of 92 morbid obese (MO) children and 36 with MetS were 118.3±3.8 months and 129.5±6.4 months, respectively (p > 0.05). The study was approved by Namık Kemal University, Medical Faculty Ethics Committee. Written informed consent forms were obtained from the parents. Demographic features and anthropometric measurements were recorded. WHO BMI-for age percentiles were used. The values above 99 percentile were defined as MO. Components of MetS [waist circumference (WC), fasting blood glucose (FBG), triacylglycerol (TRG), high density lipoprotein cholesterol (HDL-Chol), systolic pressure (SP), diastolic pressure (DP)] were determined. Routine laboratory tests were performed. Serum vitamin B12 concentrations were measured using electrochemiluminescence immunoassay. Vitamin B9 was analyzed by an immunoassay analyzer. Values for vitamin B12 < 148 pmol/L, 148-221 pmol/L, > 221 pmol/L were accepted as low, borderline and normal, respectively. Vitamin B9 levels ≤ 4 mcg/L defined deficiency state. Statistical evaluations were performed by SPSSx Version 16.0. p≤0.05 was accepted as statistical significance level. Statistically higher body mass index (BMI), WC, hip circumference (C) and neck C were calculated in MetS group compared to children with MO. No difference was noted for head C. All MetS components differed between the groups (SP, DP p < 0.001; WC, FBG, TRG p < 0.01; HDL-Chol p < 0.05). Significantly decreased vitamin B9 and vitamin B12 levels were detected (p < 0.05) in children with MetS. In both groups percentage of folate deficiency was 5.5%. No cases were below < 148 pmol/L. However, in MO group 14.3% and in MetS group 22.2% of the cases were of borderline status. In MO group B12 levels were negatively correlated with BMI, WC, hip C and head C, but not with neck C. WC, hip C, head C and neck C were all negatively correlated with HDL-Chol. None of these correlations were observed in the group of children with MetS. Strong positive correlation between FBG and insulin as well as strong negative correlation between TRG and HDL-Chol detected in MO children were lost in MetS group. Deficiency state end-products of both B9 and B12 may interfere with the expected profiles of MetS components. In this study, the alterations in MetS components affected vitamin B12 metabolism and also its associations with anthropometric body measurements. Further increases in vitamin B12 and vitamin B9 deficiency in MetS associated with the increased vitamin B12 as well as vitamin B9 deficiency metabolites may add to MetS parameters.
Abstract: Characterizing the network flow is of fundamental importance to understand the complex dynamics of networks. And passenger flow characteristics of the subway network are very relevant for an effective transportation management in urban cities. In this study, passenger flow of Seoul metropolitan subway network is investigated and characterized through statistical analysis. Traditional betweenness centrality measure considers only topological structure of the network and ignores the transportation factors. This paper proposes a weighted betweenness centrality measure that incorporates monthly passenger flow volume. We apply the proposed measure on the Seoul metropolitan subway network involving 493 stations and 16 lines. Several interesting insights about the network are derived from the new measures. Using Kolmogorov-Smirnov test, we also find out that monthly passenger flow between any two stations follows a power-law distribution and other traffic characteristics such as congestion level and throughflow traffic follow exponential distribution.
Abstract: One of the aims of education is to educate individuals who have embraced universal moral principles and transform universal moral principles into moral values. Character education aims to educate behaviors of individuals in their mental activities to transform moral principles into moral values in their lives. As the result of this education, individuals are expected to develop positive character traits and become morally indifferent individuals. What are the characteristics of the factors that influence character education at this stage? How should character education help individuals develop positive character traits? Which methods are more effective? These questions come to mind when studying character education. Our research was developed within the framework of these questions. The aim of our study is to provide the most effective use of the education factor that affects character. In this context, we tried to explain character definition, character development, character education and the factors affecting character education using qualitative research methods. At this stage, character education programs applied in various countries were examined and a character education program consisting of Islamic values was prepared and implemented in an International Imam Hatip High School in Istanbul. Our application was carried out with the collaboration of school and families. Various seminars were organized in the school and participation of families was ensured. In the last phase of our study, we worked with the students and their families on the effectiveness of the events held during the program. In this study, it was found that activities such as storytelling and theater in character education programs were effective in recognizing wrong behaviors in individuals. It was determined that our program had a positive effect on the quality of education. It was seen that applications of this educational program affected the behavior of the employees in the educational institution.
Abstract: Dosimetry is an indispensable and precious factor in patient treatment planning to minimize the absorbed dose in vital tissues. In this study, compartmental model was used in order to estimate the human absorbed dose of 177Lu-DOTATOC from the biodistribution data in wild type rats. For this purpose, 177Lu-DOTATOC was prepared under optimized conditions and its biodistribution was studied in male Syrian rats up to 168 h. Compartmental model was applied to mathematical description of the drug behaviour in tissue at different times. Dosimetric estimation of the complex was performed using radiation absorbed dose assessment resource (RADAR). The biodistribution data showed high accumulation in the adrenal and pancreas as the major expression sites for somatostatin receptor (SSTR). While kidneys as the major route of excretion receive 0.037 mSv/MBq, pancreas and adrenal also obtain 0.039 and 0.028 mSv/MBq. Due to the usage of this method, the points of accumulated activity data were enhanced, and further information of tissues uptake was collected that it will be followed by high (or improved) precision in dosimetric calculations.
Abstract: The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p1), fractured layer (p2), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable gr (gravitational resistance) can be deduced from the elevation and apparent resistivity pa. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.
Abstract: With rapid development of industrialization and urbanization, water environmental deterioration is widespread in majority of urban rivers, which seriously affects city image and life satisfaction of residents. As an emergency measure to improve water quality, clean water diversion is introduced for water environmental management. Lubao River and Southwest River, two urban rivers in typical plain tidal river network, are identified as technically and economically feasible for the application of clean water diversion. One-dimensional hydrodynamic-water quality model is developed to simulate temporal and spatial variations of water level and water quality, with satisfactory accuracy. The mathematical model after calibration is applied to investigate hydrodynamic and water quality variations in rivers as well as determine the optimum operation scheme of water diversion. Assessment system is developed for evaluation of positive and negative effects of water diversion, demonstrating the effectiveness of clean water diversion and the necessity of pollution reduction.
Abstract: Renewable energy sources and distributed power generation units already have an important role in electrical power generation. A mixture of different technologies penetrating the electrical grid, adds complexity in the management of distribution networks. High penetration of distributed power generation units creates node over-voltages, huge power losses, unreliable power management, reverse power flow and congestion. This paper presents an optimization algorithm capable of reducing congestion and power losses, both described as a function of weighted sum. Two factors that describe congestion are being proposed. An upgraded selective particle swarm optimization algorithm (SPSO) is used as a solution tool focusing on the technique of network reconfiguration. The upgraded SPSO algorithm is achieved with the addition of a heuristic algorithm specializing in reduction of power losses, with several scenarios being tested. Results show significant improvement in minimization of losses and congestion while achieving very small calculation times.
Abstract: This article examines the presence of Greek immigrant artists (painters and sculptors) in Paris during 1970-2010. The aim is to highlight their presence in the French capital through archival research in the daily and periodical press as well as present the impact of their artistic activity on the French intellectual life and society. At the same time, their contribution to the development of cultural life in Greece becomes apparent. The integration of those migrant artists into an environment of cultural coexistence and the understanding of the social phenomenon of their migration, in the context of postmodernity, are being investigated. The cultural relations between the two countries are studied in the context of support mechanisms, such as the Greek community, cultural institutions, museums and galleries. The recognition of the Greek artists by the French society and the social dimension in the context of their activity in Paris, are discussed in terms of the assimilation theory. Since the 1970s, and especially since the fall of the dictatorship in Greece, in opposition to the prior situation, artists' contacts with their homeland have been significantly enhanced, with most of them now travelling to Paris, while others work in parallel in both countries. As a result, not only do the stages of the development of their work through their pursuits become visible, but, most importantly, the artistic world becomes informed about the multifaceted expression of art through the succession of various contemporary currents. Thus, the participation of Greek artists in the international cultural landscape is demonstrated.
Abstract: South Africa is one of the few countries that have stopped using the same Enumeration Areas (EAs) for census enumeration and dissemination. The advantage of this change is that confidentiality issue could be addressed for census dissemination as the design of geographic unit for collection is mainly to ensure that this unit is covered by one enumerator. The objective of this paper was to evaluate the performance of automated zone design output areas against non-zone design developed geographies using the 2001 census data, and 2011 census to some extent, as the main input. The comparison of the Automated Zone-design Tool (AZTool) census output areas with the Small Area Layers (SALs) and SubPlaces based on confidentiality limit, population distribution, and degree of homogeneity, as well as shape compactness, was undertaken. Further, SPSS was employed for validation of the AZTool output results. The results showed that AZTool developed output areas out-perform the existing official SAL and SubPlaces with regard to minimum population threshold, population distribution and to some extent to homogeneity. Therefore, it was concluded that AZTool program provides a new alternative to the creation of optimised census output areas for dissemination of population census data in South Africa.
Abstract: Developing countries are nowadays confronted with great challenges related to domestic sanitation services in view of the imminent water scarcity. Contemporary sanitation technologies established in these countries are likely to pose health risks unless waste management standards are followed properly. This paper provides a solution to sustainable sanitation with the development of an innovative toilet system, called Nano Membrane Toilet (NMT), which has been developed by Cranfield University and sponsored by the Bill & Melinda Gates Foundation. The particular technology converts human faeces into energy through gasification and provides treated wastewater from urine through membrane filtration. In order to evaluate the environmental profile of the NMT system, a deterministic life cycle assessment (LCA) has been conducted in SimaPro software employing the Ecoinvent v3.3 database. The particular study has determined the most contributory factors to the environmental footprint of the NMT system. However, as sensitivity analysis has identified certain critical operating parameters for the robustness of the LCA results, adopting a stochastic approach to the Life Cycle Inventory (LCI) will comprehensively capture the input data uncertainty and enhance the credibility of the LCA outcome. For that purpose, Monte Carlo simulations, in combination with an artificial neural network (ANN) model, have been conducted for the input parameters of raw material, produced electricity, NOX emissions, amount of ash and transportation of fertilizer. The given analysis has provided the distribution and the confidence intervals of the selected impact categories and, in turn, more credible conclusions are drawn on the respective LCIA (Life Cycle Impact Assessment) profile of NMT system. Last but not least, the specific study will also yield essential insights into the methodological framework that can be adopted in the environmental impact assessment of other complex engineering systems subject to a high level of input data uncertainty.
Abstract: With the rapid development of urbanization process in China, its environmental protection pressure is severely tested. So, analyzing and optimizing the landscape pattern is an important measure to ease the pressure on the ecological environment. This paper takes Wuhan Urban Development Zone as the research object, and studies its landscape pattern evolution and quantitative optimization strategy. First, remote sensing image data from 1990 to 2015 were interpreted by using Erdas software. Next, the landscape pattern index of landscape level, class level, and patch level was studied based on Fragstats. Then five indicators of ecological environment based on National Environmental Protection Standard of China were selected to evaluate the impact of landscape pattern evolution on the ecological environment. Besides, the cost distance analysis of ArcGIS was applied to simulate wildlife migration thus indirectly measuring the improvement of ecological environment quality. The result shows that the area of land for construction increased 491%. But the bare land, sparse grassland, forest, farmland, water decreased 82%, 47%, 36%, 25% and 11% respectively. They were mainly converted into construction land. On landscape level, the change of landscape index all showed a downward trend. Number of patches (NP), Landscape shape index (LSI), Connection index (CONNECT), Shannon's diversity index (SHDI), Aggregation index (AI) separately decreased by 2778, 25.7, 0.042, 0.6, 29.2%, all of which indicated that the NP, the degree of aggregation and the landscape connectivity declined. On class level, the construction land and forest, CPLAND, TCA, AI and LSI ascended, but the Distribution Statistics Core Area (CORE_AM) decreased. As for farmland, water, sparse grassland, bare land, CPLAND, TCA and DIVISION, the Patch Density (PD) and LSI descended, yet the patch fragmentation and CORE_AM increased. On patch level, patch area, Patch perimeter, Shape index of water, farmland and bare land continued to decline. The three indexes of forest patches increased overall, sparse grassland decreased as a whole, and construction land increased. It is obvious that the urbanization greatly influenced the landscape evolution. Ecological diversity and landscape heterogeneity of ecological patches clearly dropped. The Habitat Quality Index continuously declined by 14%. Therefore, optimization strategy based on greenway network planning is raised for discussion. This paper contributes to the study of landscape pattern evolution in planning and design and to the research on spatial layout of urbanization.
Abstract: In this paper, the single-depot capacitated
location-routing problem under uncertainty is presented. The
problem aims to find the optimal location of a single depot and
the routing of vehicles to serve the customers when the parameters
may change under different circumstances. This problem has many
applications, especially in the area of supply chain management and
distribution systems. To get closer to real-world situations, travel time
of vehicles, the fixed cost of vehicles usage and customers’ demand
are considered as a source of uncertainty. A combined approach
including robust optimization and stochastic programming was
presented to deal with the uncertainty in the problem at hand. For
this purpose, a mixed integer programming model is developed and
a heuristic algorithm based on Variable Neighborhood Search(VNS)
is presented to solve the model. Finally, the computational results
are presented and future research directions are discussed.
Abstract: The field of wireless sensor networks (WSN) engages a lot of associates in the research community as an interdisciplinary field of interest. This type of network is inexpensive, multifunctionally attributable to advances in micro-electromechanical systems and conjointly the explosion and expansion of wireless communications. A mobile ad hoc network is a wireless network without fastened infrastructure or federal management. Due to the infrastructure-less mode of operation, mobile ad-hoc networks are gaining quality. During this work, we have performed an efficient performance study of the two major routing protocols: Ad hoc On-Demand Distance Vector Routing (AODV) and Dynamic Source Routing (DSR) protocols. We have used an accurate simulation model supported NS2 for this purpose. Our simulation results showed that AODV mitigates the drawbacks of the DSDV and provides better performance as compared to DSDV.
Abstract: Integrated resilience engineering (IRE) is capable of returning banking systems to the normal state in extensive economic circumstances. In this study, information system of a large bank (with several branches) is assessed and optimized under severe economic conditions. Data envelopment analysis (DEA) models are employed to achieve the objective of this study. Nine IRE factors are considered to be the outputs, and a dummy variable is defined as the input of the DEA models. A standard questionnaire is designed and distributed among executive managers to be considered as the decision-making units (DMUs). Reliability and validity of the questionnaire is examined based on Cronbach's alpha and t-test. The most appropriate DEA model is determined based on average efficiency and normality test. It is shown that the proposed integrated design provides higher efficiency than the conventional RE design. Results of sensitivity and perturbation analysis indicate that self-organization, fault tolerance, and reporting culture respectively compose about 50 percent of total weight.
Abstract: Datasets or collections are becoming important assets by themselves and now they can be accepted as a primary intellectual output of a research. The quality and usage of the datasets depend mainly on the context under which they have been collected, processed, analyzed, validated, and interpreted. This paper aims to present a collection of program educational objectives mapped to student’s outcomes collected from self-study reports prepared by 32 engineering programs accredited by ABET. The manual mapping (classification) of this data is a notoriously tedious, time consuming process. In addition, it requires experts in the area, which are mostly not available. It has been shown the operational settings under which the collection has been produced. The collection has been cleansed, preprocessed, some features have been selected and preliminary exploratory data analysis has been performed so as to illustrate the properties and usefulness of the collection. At the end, the collection has been benchmarked using nine of the most widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k-Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. To recap, the benchmark has achieved promising results by utilizing preliminary exploratory data analysis performed on the collection, proposing new trends for research and providing a baseline for future studies.
Abstract: The new emerging Visible Light Communication
(VLC) technology has been subjected to intensive investigation,
evaluation, and lately, deployed in the context of convoy-based
applications for Intelligent Transportations Systems (ITS). The
technology limitations were defined and supported by different
solutions proposals to enhance the crucial alignment and mobility
limitations. In this paper, we propose the incorporation of VLC
technology and Lane-Centering (LC) technique to assure the
VLC-connectivity by keeping the autonomous vehicle aligned to
the lane center using vision-based lane detection in a convoy-based
formation. Such combination can ensure the optical communication
connectivity with a lateral error less than 30 cm. As soon as the
road lanes are detectable, the evaluated system showed stable
behavior independently from the inter-vehicle distances and
without the need for any exchanged information of the remote
vehicles. The evaluation of the proposed system is verified using
VLC prototype and an empirical result of LC running application
over 60 km in Madrid M40 highway.
Abstract: Obesity as a global health challenge motivates pharmaceutical industries to produce anti-obesity drugs. However, effectiveness of these agents is remained unclear. Because of popularity of dietary supplements, the aim of this study was tp investigate the effects of Conjugated Linoleic Acid (CLA) and L-carnitine (LC) on serum glucose, triglyceride, cholesterol and weight changes in diet induced obese rats. 48 male Wistar rats were randomly divided into two groups: Normal fat diet (n=8), and High fat diet (HFD) (n=32). After eight weeks, the second group which was maintained on HFD until the end of study, was subdivided into four categories: a) 500 mg Corn Oil (as control group), b) 500 mg CLA, c) 200 mg LC, d) 500 mg CLA+ 200 mg LC.All doses are planned per kg body weights, which were administered by oral gavage for four weeks. Body weights were measured and recorded weekly by means of a digital scale. At the end of the study, blood samples were collected for biochemical markers measurement. SPSS Version 16 was used for statistical analysis. At the end of 8th week, a significant difference in weight was observed between HFD and NFD group. After 12 weeks, LC significantly reduced weight gain by 4.2%. Trend of weight gain in CLA and CLA+LC groups was insignificantly decelerated. CLA+LC reduced triglyceride level significantly, but just CLA had significant influence on total cholesterol and insignificant decreasing effect on FBS. Our results showed that an obesogenic diet in a relative short time led to obesity and dyslipidemia which can be modified by LC and CLA to some extent.
Abstract: Multi-modal film boiling simulations are carried out on adaptive octree grids. The liquid-vapor interface is captured using the volume-of-fluid framework adjusted to account for exchanges of mass, momentum, and energy across the interface. Surface tension effects are included using a volumetric source term in the momentum equations. The phase change calculations are conducted based on the exact location and orientation of the interface; however, the source terms are calculated using the mixture variables to be consistent with the one field formulation used to represent the entire fluid domain. The numerical model on octree representation of the computational grid is first verified using test cases including advection tests in severely deforming velocity fields, gravity-based instabilities and bubble growth in uniformly superheated liquid under zero gravity. The model is then used to simulate both single and multi-modal film boiling simulations. The octree grid is dynamically adapted in order to maintain the highest grid resolution on the instability fronts using markers of interface location, volume fraction, and thermal gradients. The method thus provides an efficient platform to simulate fluid instabilities with or without phase change in the presence of body forces like gravity or shear layer instabilities.
Abstract: Electricity prices have sophisticated features such as
high volatility, nonlinearity and high frequency that make forecasting
quite difficult. Electricity price has a volatile and non-random
character so that, it is possible to identify the patterns based on the
historical data. Intelligent decision-making requires accurate price
forecasting for market traders, retailers, and generation companies.
So far, many shallow-ANN (artificial neural networks) models have
been published in the literature and showed adequate forecasting
results. During the last years, neural networks with many hidden
layers, which are referred to as DNN (deep neural networks) have
been using in the machine learning community. The goal of this
study is to investigate electricity price forecasting performance of the
shallow-ANN and DNN models for the Turkish day-ahead electricity
market. The forecasting accuracy of the models has been evaluated
with publicly available data from the Turkish day-ahead electricity
market. Both shallow-ANN and DNN approach would give successful
result in forecasting problems. Historical load, price and weather
temperature data are used as the input variables for the models.
The data set includes power consumption measurements gathered
between January 2016 and December 2017 with one-hour resolution.
In this regard, forecasting studies have been carried out comparatively
with shallow-ANN and DNN models for Turkish electricity markets
in the related time period. The main contribution of this study
is the investigation of different shallow-ANN and DNN models
in the field of electricity price forecast. All models are compared
regarding their MAE (Mean Absolute Error) and MSE (Mean Square)
results. DNN models give better forecasting performance compare to
shallow-ANN. Best five MAE results for DNN models are 0.346,
0.372, 0.392, 0,402 and 0.409.
Abstract: Load forecasting has become crucial in recent years
and become popular in forecasting area. Many different power
forecasting models have been tried out for this purpose. Electricity
load forecasting is necessary for energy policies, healthy and reliable
grid systems. Effective power forecasting of renewable energy load
leads the decision makers to minimize the costs of electric utilities
and power plants. Forecasting tools are required that can be used
to predict how much renewable energy can be utilized. The purpose
of this study is to explore the effectiveness of LSTM-based neural
networks for estimating renewable energy loads. In this study, we
present models for predicting renewable energy loads based on
deep neural networks, especially the Long Term Memory (LSTM)
algorithms. Deep learning allows multiple layers of models to learn
representation of data. LSTM algorithms are able to store information
for long periods of time. Deep learning models have recently been
used to forecast the renewable energy sources such as predicting
wind and solar energy power. Historical load and weather information
represent the most important variables for the inputs within the
power forecasting models. The dataset contained power consumption
measurements are gathered between January 2016 and December
2017 with one-hour resolution. Models use publicly available data
from the Turkish Renewable Energy Resources Support Mechanism.
Forecasting studies have been carried out with these data via deep
neural networks approach including LSTM technique for Turkish
electricity markets. 432 different models are created by changing
layers cell count and dropout. The adaptive moment estimation
(ADAM) algorithm is used for training as a gradient-based optimizer
instead of SGD (stochastic gradient). ADAM performed better than
SGD in terms of faster convergence and lower error rates. Models
performance is compared according to MAE (Mean Absolute Error)
and MSE (Mean Squared Error). Best five MAE results out of
432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting
performance of the proposed LSTM models gives successful results
compared to literature searches.