Search results for: South Pars Gas complex

Authors: O. I. Nkwonta, B. Dzwairo, J. Adeyemo, A. Jaiyola, N. Sawyerr, F. Otieno

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The effective management of water resources is of great importance to ensure the supply of water resources to support changing water requirements over a selected planning horizon and in a sustainable and cost-effective way. Essentially, the purpose of the water resources planning process is to balance the available water resources in a system with the water requirements and losses to which the system is subjected. In such situations, Water resources yield and planning model can be used to solve those difficulties. It has an advantage over other models by managing model runs, developing a representative system network, modelling incremental sub-catchments, creating a variety of standard system features, special modelling features, and run result output options.

Keywords: complex, water resources, planning, cost effective and management

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Authors: Shuhua Lai, Kairui Chen

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In this paper, we present a fast and efficient mesh coarsening algorithm for 3D triangular meshes. Theis approach can be applied to very complex 3D meshes of arbitrary topology and with millions of vertices. The algorithm is based on the clustering of the input mesh elements, which divides the faces of an input mesh into a given number of clusters for clustering purpose by approximating the Centroidal Voronoi Tessellation of the input mesh. Once a clustering is achieved, it provides us an efficient way to construct uniform tessellations, and therefore leads to good coarsening of polygonal meshes. With proliferation of 3D scanners, this coarsening algorithm is particularly useful for reverse engineering applications of 3D models, which in many cases are dense, non-uniform, irregular and arbitrary topology. Examples demonstrating effectiveness of the new algorithm are also included in the paper.

Keywords: coarsening, mesh clustering, shape approximation, mesh simplification

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Authors: Ashwini Tiwari

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Introduction: Inclusive education for all is a multifaceted approach that requires system thinking and the promotion of a "Culture of Inclusion." Such can only be achieved through the collaboration of multiple stakeholders at the community, regional, state, national, and international levels. Researchers have found effective practices used in inclusive general classrooms are beneficial to all students, including students with disabilities, those who experience challenges academically and socially, and students without disabilities as well. However, to date, no statistically significant effects on the academic performance of students without disabilities in the presence of students with disabilities have been revealed. Therefore, proponents against inclusive education practices, based solely on their beliefs regarding the detrimental effects of students without disabilities, appears to have unfounded perceptions. This qualitative case study examines students' perspectives and beliefs about inclusive education in a middle school in South Texas. More specifically, this study examined students understanding of how inclusive education practices intersect with the classroom community. The data was collected from the students attending fully inclusive classrooms through interviews and focus groups. The findings suggest that peer integration and friendships built during classes are an essential part of schooling for both disabled and non-disabled students. Research Methodology: This qualitative case study used observations and focus group interviews with 12 middle school students attending an inclusive classroom at a public school located in South Texas. The participant of this study includes eight females and five males. All the study participants attend a fully inclusive middle school with special needs peers. Five of the students had disabilities. The focus groups and interviews were conducted during for entire academic year, with an average of one focus group and observation each month. The data were analyzed using the constant comparative method. The data from the focus group and observation were continuously compared for emerging codes during the data collection process. Codes were further refined and merged. Themes emerged as a result of the interpretation at the end of the data analysis process. Findings and discussion: This study was conducted to examine disabled and non-disabled students' perspectives on the inclusion of disabled students. The study revealed that non-disabled students generally have positive attitudes toward their disabled peers. The students in the study did not perceive inclusion as a special provision; rather, they perceived inclusion as a way of instructional practice. Most of the participants in the study spoke about the multiple benefits of inclusion. They emphasized that peer integration and friendships built during classes are an essential part of their schooling. Students believed that it was part of their responsibility to assist their peers in the ways possible. This finding is in line with the literature that the personality of children with disabilities is not determined by their disability but rather by their social environment and its interaction with the child. Interactions with peers are one of the most important socio-cultural conditions for the development of children with disabilities.

Keywords: inclusion, special education, k-12 education, student voices

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Authors: C. Mayr, J. Periya, A. Kariminezhad

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In this paper, we developed a learning framework for the classification of vulnerable road users (VRU) by their range-Doppler signatures. The frequency-modulated continuous-wave (FMCW) radar raw data is first pre-processed to obtain robust object range-Doppler maps per coherent time interval. The complex-valued range-Doppler maps captured from our outdoor measurements are further fed into a convolutional neural network (CNN) to learn the classification. This CNN has gone through a hyperparameter optimization process for improved learning. By learning VRU range-Doppler signatures, the three classes 'pedestrian', 'dog', and 'noise' are classified with an average accuracy of almost 95%. Interestingly, this classification accuracy holds for a combined longitudinal and lateral object trajectories.

Keywords: machine learning, radar, signal processing, autonomous driving

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Authors: Yifan Dong, Xincheng Pu

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Rural settlements originate from the accumulation of residential building elements, and their agglomeration forms the settlement pattern and defines the relationship between the settlement and the inside and outside. The settlement boundary is an important part of the settlement pattern. Compared with the simplification of the urban settlement boundary, the settlement of the country is more complex, fuzzy and uncertain, and then presents a rich and diverse boundary morphological phenomenon. In this paper, China traditional rural settlements plane boundary as the research object, using fractal theory and fractal dimension method, quantitative analysis of planar shape boundary settlement, and expounds the research for the architectural design, ancient architecture protection and renewal and development and the significance of the protection of settlements.

Keywords: rural settlement, border, fractal, quantification

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Authors: Yaser E. Hawas, Md. Didarul Alam

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Rollover crashes are complex events entailing interactions of driver, road, vehicle, and environmental factors. The primary objective of this paper is to present an empirical approach that can be used to characterise the rollover crashes and to identify some of the important factors that may lead to rollovers. Among the studied factors are the vehicle types and the rollover occurrence rate after hitting various barrier types. The carried analysis indicated that 71% of the rollover crashes occurred after impact and the type of rollover initiation is “trip/turn over” (nearly 50%). It was also found that light trucks (LTVs) vehicles are more likely to rollover than the sedan vehicles. Barrier impacts are associated with increased incidence of rollover.

Keywords: empirical, hitting barrier, in-depth crash investigation, rollover, severe crash

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Authors: Veronica Sri Lestari, Asmuddin Natsir, Hasmida Karim, Ian Patrick

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The research aimed at investigating the factors affecting consumers’ willingness to pay for chicken meat from biosecure farms. The research was conducted in Makassar City, South Sulawesi Province, Indonesia. Samples were taken using random sampling technique in two supermarkets namely Lotte Mart and Gelael. Total samples were 50 respondents which comprised the chicken meat consumers. To find out the consumers’ willingness to pay for chicken meat from the biosecure farms, the contingent valuation method was utilized. Data were collected through interviews and questionnaires. Probit Logistic was estimated to examine the factors affecting the consumers’ willingness to pay for at the premium price for chicken meat from the biosecure farms. The research indicates that the education and income affect significantly the consumers’ willingness to pay for chicken meat from the biosecure farms (P < 0.05). The results of the study will be beneficial for the policy makers, producers, consumers and those conducting research.

Keywords: biosecure, chicken, farms, consumer, willingness-to-pay

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Authors: T. Alghamdi, G. Alaghband

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In this paper we develop a model that couples Two Concurrent Convolution Neural Network with different filters (TC*CNN) for face recognition and compare its performance to an existing sequential CNN (base model). We also test and compare the quality and performance of the models on three datasets with various levels of complexity (easy, moderate, and difficult) and show that for the most complex datasets, edges will produce the most accurate and efficient results. We further show that in such cases while Support Vector Machine (SVM) models are fast, they do not produce accurate results.

Keywords: Convolution Neural Network, Edges, Face Recognition , Support Vector Machine.

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Authors: Nuruddeen Usman, Ahmad Muhammad Ibrahim

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Management of construction logistics at construction sites becomes increasingly complex with rising construction volume, which made it relatively inefficient in the developing nations even with the technological advancement. The objective of this research is to conceptually synthesise the approaches and challenges befall in the course of construction logistic management, with the aim to proffer possible solution to it. Therefore, this study appraised the glitches associated with both conventional and technological methods of construction logistic management that result in its inefficiency. Thus, this investigation found that, both conventional and the technological issues were due to certain obstacles that affect the construction logistic management which resulted into delays, accidents, fraudulent activities, time and cost overrun. Therefore, this study has developed a framework that might bring a lasting solution to the challenges of construction logistic management.

Keywords: construction, conventional, logistic, technological

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Authors: Chhavi Rana

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The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

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Authors: Chhavi Rana

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The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

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Authors: Chawarat Rotejanaprasert, Andrew Lawson

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Early detection of unusual health events depends on the ability to detect rapidly any substantial changes in disease, thus facilitating timely public health interventions. To assist public health practitioners to make decisions, statistical methods are adopted to assess unusual events in real time. We introduce a surveillance Kullback-Leibler (SKL) measure for timely detection of disease outbreaks for small area health data. The detection methods are compared with the surveillance conditional predictive ordinate (SCPO) within the framework of Bayesian hierarchical Poisson modeling and applied to a case study of a group of respiratory system diseases observed weekly in South Carolina counties. Properties of the proposed surveillance techniques including timeliness and detection precision are investigated using a simulation study.

Keywords: Bayesian, spatial, temporal, surveillance, prospective

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Authors: G. Khamooshian

Abstract:

One of the main points of application of the mechanisms of the series and parallel is the subject of managing them. The control of this mechanism and similar mechanisms is one that has always been the intention of the scholars. On the other hand, modeling the behavior of the system is difficult due to the large number of its parameters, and it leads to complex equations that are difficult to solve and eventually difficult to control. In this paper, a six-linkage robot has been presented that could be used in different areas such as medical robots. Using these robots needs a robust control. In this paper, the system equations are first found, and then the system conversion function is written. A new controller has been designed for this robot which could be used in other parallel robots and could be very useful. Parallel robots are so important in robotics because of their stability, so methods for control of them are important and the robust controller, especially in parallel robots, makes a sense.

Keywords: 3-RRS, 6 linkage, parallel robot, control

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Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

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Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

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Authors: Ali Safdari-Vaighani

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Pricing financial contracts on several underlying assets received more and more interest as a demand for complex derivatives. The option pricing under asset price involving jump diffusion processes leads to the partial integral differential equation (PIDEs), which is an extension of the Black-Scholes PDE with a new integral term. The aim of this paper is to show how basket option prices in the jump diffusion models, mainly on the Merton model, can be computed using RBF based approximation methods. For a test problem, the RBF-PU method is applied for numerical solution of partial integral differential equation arising from the two-asset European vanilla put options. The numerical result shows the accuracy and efficiency of the presented method.

Keywords: basket option, jump diffusion, ‎radial basis function, RBF-PUM

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Authors: Flavio Campos

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This paper describes an implementation of a STEM curriculum program using robotics as a technological resource at a private school in Brazil. Emphasized the pedagogic and didactic aspects and brings a discussion about STEM curriculum and the perspective of using robotics and the relation between curriculum, science and technologies into the learning process. The results indicate that STEM curriculum integration with robotics as a technological resource in K-12 students learning process has complex aspects, such as relation between time/space, the development of educators and the relation between robotics and other subjects. Therefore, the comprehension of these aspects could indicate some steps that we should consider when integrating STEM basis and robotics into curriculum, which can improve education for science and technology significantly.

Keywords: STEM curriculum, educational robotics, constructionist approach, education and technology

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Authors: Andrés Gomez-Casseres, Rubén Contreras

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In this paper, a bidirectional boost converter operated in Discontinuous Conduction Mode (DCM) is presented as a suitable power conditioning circuit for tuning of kinetic energy harvesters without the need of a battery. A nonlinear control scheme, composed by two linear controllers, is used to control the average value of the input current, enabling the synthesization of complex loads. The converter, along with the control system, is validated through SPICE simulations using the LTspice tool. The converter model and the controller transfer functions are derived. From the simulation results, it was found that the input current distortion increases with the introduced phase shift and that, such distortion, is almost entirely present at the zero-crossing point of the input voltage.

Keywords: average current control, boost converter, electrical tuning, energy harvesting

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Authors: Olga Blasco-Blasco, Vicente Liern

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The development of tourism since the mid-20th century has raised problems of overcrowding, indiscriminate construction in seaside areas and gentrification. Increasingly, the World Tourism Organisation and public institutions are promoting policies that encourage sustainability. From the perspective of sustainability, three types of tourism can be established: traditional tourism, sustainable tourism and sustainable impact tourism. Measuring sustainability is complex due to its multiple dimensions of different relative importance and diversity in nature. In order to try to answer this problem and to identify the benefits of applying policies that promote sustainable tourism, a decision-making analysis will be carried out through the application of a multicriteria analysis method. The proposal is applied to hotel reservations and to the evaluation and management of tourism sustainability in the Spanish Autonomous Communities.

Keywords: sustainable tourism, multicriteria analysis, flexible optimization, composite indicators

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Authors: Mary Gouva, Elena Dragioti, Evangelia Kotrsotsiou

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Family identification has the potential to play a very decisive role in psychopathology. In this study we aimed to investigate the impact of family identifications on female psychopathology. A community sample of 101 women (mean age 20.81 years, SD = 0.91 ranged 20-25) participated to the present study. The girls completed a) the Symptom Check-List Revised (SCL-90) and b) questionnaire concerning socio-demographic information and questions for family identifications. The majority of women reported that they matched to the father in terms of identifications (47.1%). Age and birth order were not contributed on family identifications (F(5) =2.188, p=.062 and F(3)=1.244, p=.299 respectively). Multivariate analysis by using MANCOVA found statistical significant associations between family identifications and domains of psychopathology as provided by SCL-90 (P<05). Our results highlight the role of identifications especially on father and female psychopathology as well as replicate the Freudian perception about the female Oedipus complex.

Keywords: family identification, psychoanalysis, psychopathology, women

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Authors: Sven Tackenberg, Sönke Duckwitz, Andreas Petz, Christopher M. Schlick

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This paper introduces an extension to the well-established Resource-Constrained Project Scheduling Problem (RCPSP) to apply it to complex maintenance problems. The problem is to assign technicians to a team which has to process several tasks with multi-level skill requirements during a work shift. Here, several alternative activities for a task allow both, the temporal shift of activities or the reallocation of technicians and tools. As a result, switches from one valid work process variant to another can be considered and may be selected by the developed evolutionary algorithm based on the present skill level of technicians or the available tools. An additional complication of the observed scheduling problem is that the locations of the construction sites are only temporarily accessible during a day. Due to intensive rail traffic, the available time slots for maintenance and repair works are extremely short and are often distributed throughout the day. To identify efficient working periods, a first concept of a Bayesian network is introduced and is integrated into the extended RCPSP with pre-emptive and non-pre-emptive tasks. Thereby, the Bayesian network is used to calculate the probability of a maintenance task to be processed during a specific period of the shift. Focusing on the domain of maintenance of the railway infrastructure in metropolitan areas as the most unproductive implementation process at construction site, the paper illustrates how the extended RCPSP can be applied for maintenance planning support. A multi-criteria evolutionary algorithm with a problem representation is introduced which is capable of revising technician-task allocations, whereas the duration of the task may be stochastic. The approach uses a novel activity list representation to ensure easily describable and modifiable elements which can be converted into detailed shift schedules. Thereby, the main objective is to develop a shift plan which maximizes the utilization of each technician due to a minimization of the waiting times caused by rail traffic. The results of the already implemented core algorithm illustrate a fast convergence towards an optimal team composition for a shift, an efficient sequence of tasks and a high probability of the subsequent implementation due to the stochastic durations of the tasks. In the paper, the algorithm for the extended RCPSP is analyzed in experimental evaluation using real-world example problems with various size, resource complexity, tightness and so forth.

Keywords: maintenance management, scheduling, resource constrained project scheduling problem, genetic algorithms

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Authors: Agostinho Antunes

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The completion of the human genome sequencing in 2003 opened a new perspective into the importance of whole genome sequencing projects, and currently multiple species are having their genomes completed sequenced, from simple organisms, such as bacteria, to more complex taxa, such as mammals. This voluminous sequencing data generated across multiple organisms provides also the framework to better understand the genetic makeup of such species and related ones, allowing to explore the genetic changes underlining the evolution of diverse phenotypic traits. Here, recent results from our group retrieved from comparative evolutionary genomic analyses of selected marine animal species will be considered to exemplify how gene novelty and gene enhancement by positive selection might have been determinant in the success of adaptive radiations into diverse habitats and lifestyles.

Keywords: comparative genomics, adaptive evolution, bioinformatics, phylogenetics, genome mining

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Authors: K. L. Kabemba, R. F. Sandenberg

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The usual way to desorb gold is by elution with a hot concentrated alkaline solution of sodium cyanide. The high temperature is necessary because the dielectric constant of water decreases with increasing temperature hence the electrostatic forces between charcoal and the gold cyanide complex decreases. High alkalinity and a high concentration of cyanide are necessary for gold desorption because both OH- and CN- ions are preferentially adsorbed. The rate of elution increases with increasing anion concentration but decreases with increasing cation concentration that means the rate of elution passes through a maximum as the concentration of the eluting salt (NaCN, for example) is increased. The anion that gives the best results, the cyanide ion, decomposes fairly rapidly at elevated temperatures (40% in 6 hours, 90% in 24 hours at 95°C).

Keywords: caustic, cyanide, gold, temperature

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Authors: Mads H. Clausen

Abstract:

Plant cell walls are structurally complex and contain a larger number of diverse carbohydrate polymers. These plant fibers are a highly valuable bio-resource and the focus of food, energy and health research. We are interested in studying the interplay of plant cell wall carbohydrates with proteins such as enzymes, cell surface lectins and antibodies. However, detailed molecular level investigations of such interactions are hampered by the heterogeneity and diversity of the polymers of interest. To circumvent this, we target well-defined oligosaccharides with representative structures that can be used for characterizing protein-carbohydrate binding. The presentation will highlight chemical syntheses of plant cell wall oligosaccharides from our group and provide examples from studies of their interactions with proteins.

Keywords: oligosaccharides, carbohydrate chemistry, plant cell walls, carbohydrate-acting enzymes

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

Abstract:

Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Carole A. South-Winter

Abstract:

Research-based learning gives students the opportunity to experience problems that require critical thinking and idea development. The skills they gain in working through these problems 'hands-on,' develop into attributes that benefit their careers in the professional field. The partnerships developed between students and industries give advantages to both sides. The students gain knowledge and skills that will increase their likelihood of success in the future and the industries are given research on new advancements that will give them a competitive advantage in their given field of work. The future of these partnerships is dependent on the success of current programs, enabling the enhancement and improvement of the research efforts. Once more students can complete research, there will be an increase in reliability of the results for each industry. The overall goal is to continue the support for research-based learning and the partnerships formed between students and industries.

Keywords: global healthcare, industry partnerships, research-driven decisions, short-term study abroad

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Authors: A. J. Regan, F. J. Lidgey, M. Betteridge, P. Georgiou, C. Toumazou, K. Hayatleh, J. R. Dibble

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Our goal is development of an algorithm capable of predicting the directional trend of the Standard and Poor’s 500 index (S&P 500). Extensive research has been published attempting to predict different financial markets using historical data testing on an in-sample and trend basis, with many authors employing excessively complex mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, we moved to an out-of-sample strategy based on linear regression analysis of an extensive set of financial data correlated with historical closing prices of the S&P 500. We are pleased to report a directional trend accuracy of greater than 55% for tomorrow (t+1) in predicting the S&P 500.

Keywords: financial algorithm, GPU, S&P 500, stock market prediction

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Authors: Badri Gechbaia

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Adaptive management as a fundamental element of the concept of the assurance of economy`s sustainability to the economic security of the system-synergetic type has been considered. It has been proved that the adaptive sustainable development is a transitional phase from the extensive one and later on from the rapid growth to the sustainable development. It has been determined that the adaptive system of the strategic assurance of the sustainability of the economy to the economic security threats is formed on the principles of the domination in its complex of the subsystems with weightier adaptive characteristics that negate the destructive influence of external and internal environmental factors on the sustainability of the national economy.

Keywords: adaptive management, adaptive properties, economic security, strategic assurance

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Authors: Kristina Pflug, Markus Busch

Abstract:

Being able to predict polymer properties and processing behavior based on the applied operating reaction conditions in one of the key challenges in modern polymer reaction engineering. Especially, for cost-intensive processes such as the high-pressure polymerization of low-density polyethylene (LDPE) with high safety-requirements, the need for simulation-based process optimization and product design is high. A multi-scale modelling approach was set-up and validated via a series of high-pressure mini-plant autoclave reactor experiments. The approach starts with the numerical modelling of the complex reaction network of the LDPE polymerization taking into consideration the actual reaction conditions. While this gives average product properties, the complex polymeric microstructure including random short- and long-chain branching is calculated via a hybrid Monte Carlo-approach. Finally, the processing behavior of LDPE -its melt flow behavior- is determined in dependence of the previously determined polymeric microstructure using the branch on branch algorithm for randomly branched polymer systems. All three steps of the multi-scale modelling approach can be independently validated against analytical data. A triple-detector GPC containing an IR, viscosimetry and multi-angle light scattering detector is applied. It serves to determine molecular weight distributions as well as chain-length dependent short- and long-chain branching frequencies. 13C-NMR measurements give average branching frequencies, and rheological measurements in shear and extension serve to characterize the polymeric flow behavior. The accordance of experimental and modelled results was found to be extraordinary, especially taking into consideration that the applied multi-scale modelling approach does not contain parameter fitting of the data. This validates the suggested approach and proves its universality at the same time. In the next step, the modelling approach can be applied to other reactor types, such as tubular reactors or industrial scale. Moreover, sensitivity analysis for systematically varying process conditions is easily feasible. The developed multi-scale modelling approach finally gives the opportunity to predict and design LDPE processing behavior simply based on process conditions such as feed streams and inlet temperatures and pressures.

Keywords: low-density polyethylene, multi-scale modelling, polymer properties, reaction engineering, rheology

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Authors: Kamran Taghizadeh

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This study was aimed at evaluating the effect of IMS including occupational health system, environmental management system, and safety and health system on the social responsibility (case study of NISOC`s oil and gas production companies). This study`s objectives include evaluating the IMS situation and its effect on social responsibility in addition of providing appropriate solutions based on the study`s hypotheses as a basis for future. Data collection was carried out by library and field studies as well as a questionnaire. The stratified random method was the sampling method and a sample of 285 employees in addition to the collected data (from the questionnaire) were analyzed by inferential statistics methods using SPSS software. Finally, results of regression and fitted model at a significance level of 5% confirmed all hypotheses meaning that IMS and its items have a significant effect on social responsibility.

Keywords: social responsibility, integrated management, oil and gas production companies, regression

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Authors: Brajesh

Abstract:

Introduction: It is generally believed that women's lack power to making decision may restrict their use of modern contraceptives practices. However, few studies have examined the different dimensions of women's empowerment and contraceptive use in Asian content. Pervasive gendered inequities and norms regarding the subordination of women give Asian men disproportionately more power than women, particularly in relation to the sex. We hypothesize that lack of sexual empowerment may pose an important barrier to reproductive health and adoption of family planning methods. Using the Demographic Health Survey, we examine the association between women’s sexual empowerment and contraceptive use in Nepal, Bangladesh and Pakistan. Objectives: To understand the trend and pattern of contraceptive choices and use among women due to sexual empowerment in selected south Asian countries. To examine the association between women’s sexual empowerment and contraceptive practices among non-pregnant married and partnered women in Nepal, Bangladesh and Pakistan. Methods: Data came from the latest round of Demographic and Health Surveys conducted between 2010-12 in and during deacde1990 -92 in Nepal, Bangladesh and Pakistan. Responses from married or cohabiting women aged 15-49 years were analyzed for six dimensions of empowerment and the current use of female-only methods or couple of methods. Bi-variate and multivariate multinomial regressions were used to identify associations between the empowerment dimensions and method use. Results: Positive associations were found between the overall empowerment score and method use in all countries (relative risk ratios, 1.1-1.3). In multivariate analysis, household economic decision-making was associated with the use of either female-only or couple methods (relative risk ratios -1. 1 for all), as was agreement on fertility preferences (RRR-1.3-1.6) and the ability to negotiate sexual activity (RRR -1. 1-1.2). In Bangladesh, women's negative attitudes toward domestic violence were correlated with the use of couple of methods (RRR -1. 1). Increasing levels of sexual empowerment were found to be associated with use of contraceptives, even after adjusting for demographic predictors of contraceptive use. This association is moderated by the wealth. Formal education, increasing wealth, and being in an unmarried partnership are associated with contraceptive use, whereas women who identify as being Muslim are less likely to use contraceptives than those who identify as being Hindus or other. These findings suggest that to achieve universal access to reproductive health services, gendered disparities in sexual empowerment, particularly among economically disadvantaged women, need to be better addressed. Conclusions: Intervention programs aimed at increasing contraceptive use may need to involve different approaches, including promoting couples' discussion of fertility preferences and family planning, improving women's self-efficacy in negotiating sexual activity and increasing their economic independence. Policies are needed to encourage the rural families to give their girls a chance of attending higher level education and professional course so that can get a better job opportunity and can economically support their family as son are expected to do.

Keywords: reproductive and child health (RCH), relative risk ratios (RRR), demographic and health survey (DHS), women’s sexual empowerment (WSE)

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