Natural resource wealth “a truly double edged sword?”: a comparative study between Iran and Norway

This is an analytical comparative study done from a historical perspective between Iran and Norway as classic examples of countries touched by resource abundance contrastingly. The study tries to highlight the factors which turn oil riches into a curse so as to serve as a brief practical set of guidelines for countries facing the possibility of a natural resource related revenue increase. This study, however, does not purport to cover all the factors in play, but is rather of an interdisciplinary nature demonstrating the significance of Politics in the fate of Oil-rich countries. An abridged account of the petroleum sector and some other relevant information about the two states is initially given. The study then focuses on historical, economic, management and political differences which could have been influential in the way the two countries have been affected by their riches. A number of key differences are listed and elaborated on in more detail with the aid of examples invoked from similar countries. The study finds that factors such as the political structure of a country as well as its population and timing of oil discoveries, as factors not completely controlled by the state, carry a lot of weight in determining how successful a country is likely to be in managing its resources.Resource riches; resource curse; Norway; Iran

Power market reforms and privatization of the electricity industry in the Iranian energy sector; an uphill struggle?

Following the successful experience of some developed counties in Power market restructuring and reforms, many developing countries have followed suit. Iran has for the last thirty years, since its Islamic revolution of 1979, had an economy dominated by the state, but has been pushed to take some legal steps towards private participation in the electricity sector so as to meet the rapidly rising electricity demand. This paper aims to appraise the stressfulness of Power market restructuring and privatization of electricity industry in Iran. A few years from the commencement of the reforms, the program can be assessed as realistically successful. However, there are plentiful challenges which need to be addressed through legislation. In this study, challenges to competition and Pitfalls of the reforms in the Iranian restructured electricity market will be reviewed. as well as this, a number of recommendations will be offered.Power market restructuring; privatization;Islamic Republic of Iran

Memorization Makes Progress

There are disputes over the role of memorization in language learning. Memorized language, a mainstay of education for almost all of recorded history, was widely repudiated for suppressing creativity, understanding and enjoyment in learners. This paper aims at highlighting the fact that, despite these criticisms, memorization is a helpful strategy which can be employed by the learners and teachers in their process of language learning and teaching. It is discussed that memorization: 1) provides the learner with linguistic data; 2) is the first step to understanding; 3) enhances association in memory; 4) causes cognitive development as a learning strategy; 5) helps noticing; 6) provides rehearsal; 7) is especially helpful in early stages of learning. It is also pointed out that all these become possible when memorization is accompanied and complemented by other strategies and techniques

Mechanobiology of Epithelial Clusters in ECMs of Diverse Mechanical Properties

Cell clusters reside in complex extracellular matrices (ECMs) of varying mechanical properties. Epithelial cells sense and translate the mechanical cues presented by the surrounding ECM into biochemical signals through a process called ‘mechanotransduction’, which controls fundamental aspects of disease and development. During the course of metastasis, mechanical changes in the tumor microenvironment can lead to declustering of epithelial cells through a process called epithelial-to-mesenchymal transition (EMT). Throughout different steps of metastasis, escaped epithelial clusters encounter heterogeneous tissues of varying mechanical properties that ultimately influence their behavior in distant locations within the body. This dissertation investigates the mechanobiology of epithelial clusters inside mechanically heterogeneous tissue microenvironments. Chapter 1 provides an introduction for the mechanobiology of epithelial clusters and describes how mechanical properties of the microenvironment mediates behavior of epithelial cells. Chapter 2 addresses the mechano-regulated epithelial to mesenchymal transition inside matrices of varying stiffness and confinement. Growing evidence suggests that high extracellular matrix (ECM) stiffness induces EMT. Yet, very little is known about how various geometrical parameters of the ECM might influence EMT. To this end, we develop polyacrylamide (PA)-microchannels based matrix platform to culture mammary epithelial cell clusters in ECMs of tunable stiffness and confinement. Our results demonstrate that ECM confinement alone is able to induce EMT in epithelial clusters surrounded by a soft matrix, which otherwise protects against EMT in unconfined environments. Also, we demonstrate that stiffness- and confinement-induced EMT work through cell-matrix adhesions and cytoskeletal polarization, respectively. In chapter 3, we examined the combinatorial effect of phenotypic heterogeneity and matrix heterogeneity in determining the overall migration of the migrating clusters and the motion of individual cells in the cluster. These findings may provide insights into the effect of cellular heterogeneity on the epithelial dynamics during the early stage of cancer progression. In chapter 4, we examined the collective migration of epithelial cells across physically diverse matrices. Although the influence of matrix stiffness on cell migration is well-recognized, it remains unknown whether these matrix-dependent cellular features persist even after cells move to a new microenvironment. We have discovered that epithelial cells primed on a stiff matrix migrate faster, generate higher actomyosin expression, and retain nuclear YAP even after arriving on a soft matrix, as compared to their control behavior on a homogeneously soft matrix. Our results have uncovered a mechanical memory of past matrix stiffness in collective migration of normal and cancer epithelial cells. The depletion of YAP dramatically reduces this memory-dependent migration. These revelations imply that, during metastasis, changes in tumor microenvironment stiffness may influence the future invasion of escaping tumor cells

Architectural Energy Efficiency

Zugleich gedruckt veröffentlicht im Universitätsverlag der TU Berlin unter der ISBN 978-3-7983-2552-4.Energy saving in buildings through cost and energy-intensive measures, such as the application of additional building materials and technologies, is only possible with a great consumption of resources and CO2 emissions for their production. For low energy buildings, the investment costs, including user costs and governmental subsidies, are generally high, and construction is not always economically viable in consideration of the national capital in the present economic conditions of most countries. For these reasons, it is first of all necessary to apply cost and resource-efficient measures to save energy in buildings and then make use of additional cost and energy-intensive measures by improving the thermal envelope, the HVAC system or by installing energy generating systems. One of the most cost effective and ecological methods of energy saving in buildings is the reduction of energy requirements through climate responsive architecture. Due to the fact that energy saving through the optimization of architecture is not only cost-neutral, resource-efficient and carbon-neutral but also has a very high energy-saving potential, the first and most important strategy to save energy should be an optimized and climate responsive design. Energy saving through optimized architectural design is economically and ecologically sustainable. The development of building simulation science in the last decades has made it easier to study the energy performance of buildings. Tools have made it possible to predict the complex behavior of buildings regarding the climate. Except for the comparison of different building typologies to find the most efficient, there are no other methods to achieve energy savings through the architectural design, which can be applied by a variety of building types and climates. Therefore, in order to encourage the optimization of architectural design, it is necessary to improve these methods which represent strategies to significantly reduce the energy demand of buildings. Architectural Energy Efficiency is a parametric method which separately studies the effects of various energy-related architectural factors on the energy demand of buildings by using dynamic energy simulations to find the, from an energy efficiency point of view, optimum value for each of these. The architectural factors include orientation, building elongation, building form, opening ratio in different orientations, sun shading, natural ventilation etc. The research process that led to the formulation of the Architectural Energy Efficiency method is based on a series of simulations carried out by a dynamic simulation software tool (DesignBuilder) to calculate the energy demands of a building with different variants for a single architectural feature. The aim of the simulations is to find an optimum set of energy-related variables that result in the best and most efficient energy performance for a specific building type and climate. This method of efficiency illustrates the effects different architectural features have on the various energy demands of buildings. The criteria are derived from the application of this method for a specific building occupation and climate, and can be applied in the design process of buildings, which leads to improvements of the energy performance and a reduction of resource consumption. As the architectural design affects the heating and cooling as well as the lighting energy demands of buildings, the optimum value of each factor must be based on these three aspects. The heating, cooling and lighting energy demands of buildings all behave very differently. Therefore, these three energy demands together (i. e. the sum of heating, cooling and lighting energy) must also be applied as a criterion to study the building energy performance and find the optimum value for each architectural feature. The criteria for selecting the best variant can not only be based on the total energy demand, but should also consider the primary energy demand, the CO2 emissions, energy costs (for heating, cooling and lighting), life cycle costs, etc. The application of these findings to the architectural design of buildings minimizes the energy demand, the CO2 emissions and energy costs of the building, does not, however, affect the initial building costs. The advantages of energy saving through optimizing the architectural design are not only the improvement of the building’s energy performance, but also the fact that the energy saving is cost and resource-efficient. This means that the energy demand of a building will decrease without increasing the investment costs of the building and without consuming any resources and energy for the production of additional building materials. The cost and resource efficiency contributes towards the economic and ecological sustainability of a building during the full life cycle

Differential railway track settlement in a transition zone – Field measurements and numerical simulations

In a transition zone between two different railway track forms, there is a discontinuity in track structure leading to a gradient in track stiffness. Examples include transitions between different superstructures, e.g., slab track to ballasted track, and/or between different substructures, e.g., embankment to a bridge or tunnel structure. Differences in loading and support conditions at the interfaces between track superstructure and substructure on either side of the transition may lead to differential track settlement and an irregularity in longitudinal rail level soon after construction because of densification of ballast and consolidation in the subsoil. This results in an amplification of the dynamic traffic loading along the transition. To ensure the safety of railway operation and reduce maintenance costs, it is necessary to monitor the condition of the transition zone and detect any operational change at an early stage.A methodology for the simulation of long-term differential track settlement, the development of voided sleepers leading to a redistribution of rail seat loads, and the evolving irregularity in vertical track geometry at a transition between two track forms, is presented. For a prescribed traffic load, the accumulated settlement is predicted using an iterative approach. It is based on a time-domain model of vertical dynamic vehicle–track interaction to calculate the contact forces between sleepers and ballast in the short term. These are used in an empirical model to determine the long-term settlement of the ballast and subgrade below each sleeper. Gravity loads and state-dependent track conditions are accounted for. The methodology is applied to a transition zone between a ballasted track and a slab track that is subjected to heavy haul traffic. The influence of higher axle loads and the implementation of under sleeper pads on sleeper settlement is assessed. Based on fibre Bragg grating sensors, a setup for in-situ long-term condition monitoring of track bed degradation in a transition zone has been developed and implemented to provide data for verification and calibration of the simulation model. The system is designed for measurements in an operational railway track in harsh conditions in the north of Sweden. The instrumentation along the transition comprises four clusters, each with an optical strain gauge array on the rail web in one sleeper bay, and an accelerometer and a displacement transducer on the sleeper end. Two additional accelerometers are installed far from the transition zone to measure a reference state. Combined, the data should not only provide details on long-term settlements, but also the change in dynamic response it underpins

Automated Bridge Inspection for Concrete Surface Defect Detection Using Deep Neural Network Based on LiDAR Scanning

Structural inspection and maintenance of bridges are essential to improve the safety and sustainability of the infrastructure systems. Visual inspection using non-equipped eyes is the principal method of detecting surface defects of bridges, which is time-consuming, unsafe, and encounters inspectors falling risks. Therefore, there is a need for automated bridge inspection. Recently, Light Detection and Ranging (LiDAR) scanners are used for detecting surface defects. LiDAR scanners can collect high-quality 3D point cloud datasets. In order to automate the process of structural inspection, it is important to collect proper datasets and use an efficient approach to analyze them and find the defects. Deep Neural Networks (DNNs) have been recently used for detecting 3D objects within 3D point clouds. PointNet and PointNet++ are deep neural networks for classification, part segmentation, and semantic segmentation of point clouds that are modified and adapted in this work to detect surface concrete defects. The research contributions are: (1) Designing a LiDAR-equipped UAV platform for structural inspection using an affordable 2D scanner for data collection, and (2) Proposing a method for detecting concrete surface defects using deep neural networks based on LiDAR generated point clouds. Training and testing datasets are collected from four concrete bridges in Montréal and annotated manually. The point cloud dataset prepared in five areas, which contain more than 51 million points and 2,572 annotated defects in four classes of crack, light spalling, medium spalling, and severe spalling. The accuracies of 75% (adapted PointNet) and 79% (adapted PointNet++) in detecting defects are achieved in binary semantic segmentation

Quantifying the reliability of four global datasets for drought monitoring over a semiarid region

Drought is one of the most relevant natural disasters, especially in arid regions such as Iran. One of the requirements to access reliable drought monitoring is long-term and continuous high-resolution precipitation data. Different climatic and global databases are being developed and made available in real time or near real time by different agencies and centers; however, for this purpose, these databases must be evaluated regionally and in different local climates. In this paper, a near real-time global climate model, a data assimilation system, and two gridded gauge-based datasets over Iran are evaluated. The ground truth data include 50 gauges from the period of 1980 to 2010. Drought analysis was carried out by means of the Standard Precipitation Index (SPI) at 2-, 3-, 6-, and 12-month timescales. Although the results show spatial variations, overall the two gauge-based datasets perform better than the models. In addition, the results are more reliable for the western portion of the Zagros Range and the eastern region of the country. The analysis of the onsets of the 6-month moderate drought with at least 3 months’ persistence indicates that all datasets have a better performance over the western portion of the Zagros Range, but display poor performance over the coast of the Caspian Sea. Base on the results of this study, the Modern-Era Retrospective Analysis for Research and Applications (MERRA) dataset is a preferred alternative for drought analysis in the region when gauge-based datasets are not available

The Impact of Firm's Social Media Applications on Green Supply Chain Management

Social Media Applications (SMA) has become a common tool for networking and communication and also content sharing. As a result, firms utilize SMA for organizational purposes. Then again, Green Supply Chain Management (GSCM) is a popular subject in the area of operations management for both researchers as well as practitioners. However, the influences of SMA on GSCM practices and finally on organizational performance are not well understood. SMA can expedite information flow and knowledge sharing amidst supply chains and also may assist organizations for greening their SCM practices. In this research, we empirically investigate the impact of SMA capabilities on GSCM practices. Data collected from 206 manufacturing managers were analyzed applying a structural equation modeling methodology. Results confirm that the adoption of SMA in supply chains by manufacturing firms positively affect the GSCM practices among the chain