Enhancement of the properties of polymer blends by hydrogen bonding and transesterification

The importance of polymer rigidity on the extent of intermolecular hydrogen bonding in the blends of copolymers of 4-vinylphenol and styrene (PS-co-PVPh) and polyethers has been studied utilizing FT-IR spectroscopy. A series of polyethers containing mesogens with different rigidity including a liquid crystalline poly ether (LCP) are synthesized using phase transfer polyesterification and blended with PS-co-PVPh. The extent of intermolecular hydrogen bonding between the hydroxyl group ofPS-co-PVPh and the ethereal oxygen is correlated to the rigidity of polyethers. The results of this study indicate that extent of intermolecular hydrogen bonding in blends increases as the flexibility of polyethers increases, causing a shift in the frequency of the hydrogen bonded hydroxyl band towards lower frequency. The rigidity of the polyethers inhibits the formation of intermolecular hydrogen bonds, however this effect is not dramatic. It must be recognized that the LCP utilized in this study can not be considered as a rod-like LCP, as it contains flexible aliphatic spacers. Therefore, the result shows that the rigidity of LCP does not dramatically affect the formation of intermolecular hydrogen bonds is only applicable to non-rodlike LCP. The data also show that a lower concentration of the polyether in the blends induces better mixing and higher extent of intermolecular hydrogen bonding due to better dispersion of polyether in PS-co-PVPh. This suggests that a one-phase system may exist in the region of the phase diagram of the blends that are rich in PS-co-PVPh. The result also shows that the extent of intermolecular hydrogen bonding between PSco-PVPh and polyethers decreases at the higher temperatures. The concept of functional group accessibility of hydroxyl groups for intermolecular hydrogen bonding is also examined in these blends. The amount of intermolecular hydrogen bonds increases with an increase in spacing between the hydroxyl groups. The transesterification Reaction between poly(carbonate) (PC) and thermoplastic liquid crystalline poly (hydroxy benzoate)-poly(ethylene terephthalate) (PHB-PET) and its consequences on the blend morphology and mechanical properties of the blend has also been investigated. The transesterification reaction between PC and PHB-PET upon annealing at 260 °C is characterized and quantified by 13C NMR spectroscopy, showing peaks at 120.9 ppm, 148.3 ppm, and 165.9 ppm corresponding to bisphenol-A terephthalate and bisphenol-A oxybenzoate diads, respectively. These peaks are the result of the initial formation of block copolymer and eventual formation of random copolymer at the interface as the mole fraction of corresponding diads increases. Polarized optical microscopy and tensile measurements reveal that there is a direct correlation between the loss of liquid crystallinity character and mechanical properties of the blend to the extent of transesterification reaction. The results of this study indicate a trade-off between the loss of liquid crystallinity of the blend and its strength resulting from transesterification upon annealing

A New MCMC Sampling Based Segment Model for Radar Target Recognition

One of the main tools in radar target recognition is high resolution range profile (HRRP)‎. ‎However‎, ‎it is very sensitive to the aspect angle‎. ‎One solution to this problem is to assume the consecutive samples of HRRP identically independently distributed (IID) in small frames of aspect angles‎, ‎an assumption which is not true in reality‎. ‎However, b‎‎ased on this assumption‎, ‎some models have been developed to characterize the sequential information contained in the multi-aspect radar echoes‎. ‎Therefore‎, ‎they only consider the short dependency between consecutive samples‎. ‎Here‎, ‎we propose an alternative model‎, ‎the segment model‎, ‎to address the shortcomings of these assumptions‎. ‎In addition‎, ‎using a Markov chain Monte-Carlo (MCMC) based Gibbs sampler as an iterative approach to estimate the parameters of the segment model‎, ‎we will show that the proposed method is able to estimate the parameters with quite satisfying accuracy and computational load‎

Three essays on institutions, religion, and economic development

This dissertation consists of three essays that provide empirical evidence on the importance of institutions, informal institutions and particularly religion in economic development. In the first chapter, the relationship between religious adherence rate in the U.S. counties and entrepreneurship is examined. After controlling for spatial dependencies, results indicate that more religious counties, meaning counties with higher rate of religious membership, have lower entrepreneurial potentials. Chapter 2 investigates the relevance of institutional quality in efficiency of public spending. This study is delivered in the state level. By using a nonparametric method, efficiency of public spending is calculated and then its relationship with some indicators of institutional quality is tested. It is shown that states with better judicial system and less restrictive labor market present more effective public spending. In the last chapter the role of state religiosity in the political and societal conflict is studied. The hypothesis states that if a country has a religious government, the chance and magnitude of having a domestic conflict due to religious heterogeneity would increase. After controlling for several covariates, there is not enough evidence found to reject the null hypothesis. Therefore religious heterogeneity in countries with religious state seems to be associated with larger magnitude of conflict

Developing a Phenomenographic Argument for The Exit-Level Prospective Teachers’ Conceptions About Teaching and Learning

This study presents a phenomenographic argument regarding Turkish prospective teachers’ (PTs) beliefs about teaching and learning varying according to their experience. A phenomenographic research study was conducted in order to capture and interpret the PTs’ explanations and documentation regarding the conceptions of instruction. The participants were 54 senior pre-service student teachers. Qualitative data was gathered through phenomenographic interviewing. The data was analysed interpretatively, and categories of description were derived to establish an outcome space for supporting the phenomenographic argument. The outcome space incorporates three hierarchical levels of the PTs’ conceptions of teaching and learning: subject-centred teaching, individual-centred learning, and learner-centred teaching-learning. It was revealed that the PTs held considerably conventional, idealistic teaching and learning conceptions. Moreover, there was a sharp distinction between the individual-centred and learner-centred conceptions of the participant PTs. Concrete recommendations for teacher preparation are offered to enhance the PTs’ shallow conceptions of teaching and learning.

Artificial Intelligence Approach for Seismic Control of Structures

Abstract In the first part of this research, the utilization of tuned mass dampers in the vibration control of tall buildings during earthquake excitations is studied. The main issues such as optimizing the parameters of the dampers and studying the effects of frequency content of the target earthquakes are addressed. Abstract The non-dominated sorting genetic algorithm method is improved by upgrading generic operators, and is utilized to develop a framework for determining the optimum placement and parameters of dampers in tall buildings. A case study is presented in which the optimal placement and properties of dampers are determined for a model of a tall building under different earthquake excitations through computer simulations. Abstract In the second part, a novel framework for the brain learning-based intelligent seismic control of smart structures is developed. In this approach, a deep neural network learns how to improve structural responses during earthquake excitations using feedback control. Abstract Reinforcement learning method is improved and utilized to develop a framework for training the deep neural network as an intelligent controller. The efficiency of the developed framework is examined through two case studies including a single-degree-of-freedom system and a high-rise building under different earthquake excitation records. Abstract The results show that the controller gradually develops an optimum control policy to reduce the vibrations of a structure under an earthquake excitation through a cyclical process of actions and observations. Abstract It is shown that the controller efficiently improves the structural responses under new earthquake excitations for which it was not trained. Moreover, it is shown that the controller has a stable performance under uncertainties

Geochemistry of rare earth elements in the Baba Ali magnetite skarn deposit, western Iran – a key to determine conditions of mineralisation

The Baba Ali skarn deposit, situated 39 km to the northwest of Hamadan (Iran), is the result of a syenitic pluton that intruded and metamorphosed the diorite host rock. Rare earth element (REE) values in the quartz syenite and diorite range between 35.4 and 560 ppm. Although the distribution pattern of REEs is more and less flat and smooth, light REEs (LREEs) in general show higher concentrations than heavy REEs (HREEs) in different lithounits. The skarn zone reveals the highest REE-enriched pattern, while the ore zone shows the maximum depletion pattern. A comparison of the concentration variations of LREEs (La–Nd), middle REEs (MREEs; Sm–Ho) and HREEs (Er–Lu) of the ore zone samples to the other zones elucidates two important points for the distribution of REEs: 1) the distribution patterns of LREEs and MREEs show a distinct depletion in the ore zone while representing a great enrichment in the skarn facies neighbouring the ore body border and decreasing towards the altered diorite host rock; 2) HREEs show the same pattern, but in the exoskarn do not reveal any distinct increase as observed for LREEs and MREEs. The ratio of La/Y in the Baba Ali skarn ranges from 0.37 to 2.89. The ore zone has the highest La/Y ratio. In this regard the skarn zones exhibit two distinctive portions: 1) one that has La/Y >1 beingadjacent to the ore body and; 2) another one with La/Y < 1 neighbouring altered diorite. Accordingly, the Baba Ali profile, from the quartz syenite to the middle part of the exoskarn, demonstrates chiefly alkaline conditions of formation, with a gradual change to acidic towards the altered diorite host rocks. Utilising three parameters, Ce/Ce*, Eu/Eu* and (Pr/Yb)n, in different minerals implies that the hydrothermal fluids responsible for epidote and garnet were mostly of magmatic origin and for magnetite, actinolite and phlogopite these were of magmatic origin with low REE concentration or meteoric water involved

Subsequence Based Deep Active Learning for Named Entity Recognition

Active Learning (AL) has been successfully applied to Deep Learning in order to drastically reduce the amount of data required to achieve high performance. Previous works have shown that lightweight architectures for Named Entity Recognition (NER) can achieve optimal performance with only 25% of the original training data. However, these methods do not exploit the sequential nature of language and the heterogeneity of uncertainty within each instance, requiring the labelling of whole sentences. Additionally, this standard method requires that the annotator has access to the full sentence when labelling. In this work, we overcome these limitations by allowing the AL algorithm to query subsequences within sentences, and propagate their labels to other sentences. We achieve highly efficient results on OntoNotes 5.0, only requiring 13% of the original training data, and CoNLL 2003, requiring only 27%. This is an improvement of 39% and 37% compared to querying full sentences

Passive Control of Tall Buildings Using Distributed Multiple Tuned Mass Dampers

The vibration control of the tall building during earthquake excitations is a challenging task due to their complex seismic behavior. This paper investigates the optimum placement and properties of the Tuned Mass Dampers (TMDs) in tall buildings, which are employed to control the vibrations during earthquakes. An algorithm was developed to spend a limited mass either in a single TMD or in multiple TMDs and distribute them optimally over the height of the building. The Non-dominated Sorting Genetic Algorithm (NSGA – II) method was improved by adding multi-variant genetic operators and utilized to simultaneously study the optimum design parameters of the TMDs and the optimum placement. The results showed that under earthquake excitations with noticeable amplitude in higher modes, distributing TMDs over the height of the building is more effective in mitigating the vibrations compared to the use of a single TMD system. From the optimization, it was observed that the locations of the TMDs were related to the stories corresponding to the maximum modal displacements in the lower modes and the stories corresponding to the maximum modal displacements in the modes which were highly activated by the earthquake excitations. It was also noted that the frequency content of the earthquake has significant influence on the optimum location of the TMDs

The role of a blue ocean strategy on performance evaluation

This paper develops a balanced scorecard (BSC) in order to prepare a comprehensive tool for performance evaluation. In this way, an experimental test is conducted in the Resorts of Ramsar Green City located in the north of Iran, in which the factors of a blue ocean strategy influence on the dimensions of the BSC. The sample number of this study consists of 90 managers and experts of the employees who work for Resorts of Ramsar Green City. The acquired data are analyzed with using the t-test. The obtained results show that the blue ocean strategy changes in the objects and the scales of the BSC