Preparation and characterization of new photoactive polyamides containing 4‐(4‐dimethylaminophenyl)urazole units

4‐(4‐dimethylaminophenyl)‐1,2,4‐triazolidine‐3,5‐dione ( DAPTD ) was prepared from 4‐dimethylaminobenzoic acid in five steps. The compound DAPTD was reacted with excess acetyl chloride in N,N‐dimethylacetamide (DMAc) solution and gave 1,2‐bisacetyl‐4‐[4‐(dimethylaminophenyl)]‐1,2,4‐triazolidine‐3,5‐dione as a model compound. Solution polycondensation reactions of monomer with succinyl chloride (SucC), suberoyl chloride (SubC), and sebacoyl chloride (SebC) were performed under conventional solution polymerization techniques in the presence of triethylamine and pyridine as a catalyst in N‐methylpyrrolidone (NMP) and led to the formation of novel aliphatic polyamides. These novel polyamides have inherent viscosities in the range of 0.09–0.21 dL/g in N,N‐dimethylformamide (DMF) at 25°C. Fluorimetric studies of the model compound as well as polymers were performed. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 947–954, 2007     

Synthesis of Photoactive Polyureas Derived from 4-(4-Dimethylaminophenyl)-1,2,4-triazolidine-3,5-dione and Diisocyanates

Summary 4-(4-Dimethylaminophenyl)-1,2,4-triazolidine-3,5-dione (DAPTD) was prepared from 4-dimethylaminobenzoic acid in five steps. The reaction of monomer DAPTD with n-isopropylisocyanate was performed at room temperature in N,N-dimethylacetamide (DMAc) solution and the resulting bis-urea derivative was obtained in high yield and was finally used as a model compound for polymerization reaction. The step-growth polymerization reactions of monomer with hexamethylene diisocyanate (HMDI), isophorone diisocyanate (IPDI) and toluene-2,4-diisocyanate (TDI) were performed in DMAc solution in the presence of pyridine, triethylamine or dibutyltin dilurate as catalysts. Some physical properties and structural characterization of these novel polyureas are reported. Fluorimetric studies of the model compound as well as polymers were performed.     

Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22 %, 4.45 %, and 19.56 %, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35 % of that obtained in subjective evaluation by human judges.     

Modulation Based Combination of High Level Features Generated from SCCF & Contourlet Transforms for CBIR Applications

The need for suitable and cost-effective technologies rise with the growth of the internet of things (IoT) applications. These aim at handling voluminous data transmission in addition to minimum energy and latency cost constraints. LoRa networks are recommended for applications in confined spaces, long ranges, and less battery consumption requirements. However, the end devices in these networks communicate to all gateways in their ranges, thereby expediting energy unproductively in redundant transmissions. In our article, we explore the possibilities of whether LoRa networks could employ the advantages of clustering and propose two algorithms, path-based and data-centric, for such networks. We suggest that LoRaWAN technology with clustering can be apt for long-range, low power consumption IoT applications in the future. We study the impact of network density, node range, and cluster range on the energy consumption in data transmissions. The algorithms are compared with the inherent star-based communication of LoRa networks based on energy consumed, and our results show that, for dense deployments, clustering becomes advantageous.

     

Dynamic estimation of the modeling error statistics in Diffuse Optical Tomography

Diffuse Optical Tomography (DOT) is a non-invasive imaging technique that suffers from a typical large-scale and ill-posed inverse problem with low spatial resolution. In DOT, the inverse problem is computationally intensive and decreasing the computation complexity and making it well-posed is the one of the most challenging research areas. More precisely, one of the well-known complexity reduction techniques is defined as applying modelling error originated from discretization of forward problem. Applying the discretization error in Bayesian inference has already been discussed; the method in which the likelihood is modified by an off-line prior density estimation. This paper implements a new method to enhance the modelling error approach using an iterative scheme to update statistical parameters of modelling discrepancy in DOT. The algorithm is very similar to Ensemble Kalman Filter. Moreover, the reconstruction process in the applied method is conducted by a small sample size rather than off-line method. Hence, the computation complexity is decreased and the algorithm converges in few iterations. The efficiency of the proposed method is illustrated by simulations.     

Preemptive cloud resource allocation modeling of processing jobs

Cloud computing allows execution and deployment of different types of applications such as interactive databases or web-based services which require distinctive types of resources. These applications lease cloud resources for a considerably long period and usually occupy various resources to maintain a high quality of service (QoS) factor. On the other hand, general big data batch processing workloads are less QoS-sensitive and require massively parallel cloud resources for short period. Despite the elasticity feature of cloud computing, fine-scale characteristics of cloud-based applications may cause temporal low resource utilization in the cloud computing systems, while process-intensive highly utilized workload suffers from performance issues. Therefore, ability of utilization efficient scheduling of heterogeneous workload is one challenging issue for cloud owners. In this paper, addressing the heterogeneity issue impact on low utilization of cloud computing system, conjunct resource allocation scheme of cloud applications and processing jobs is presented to enhance the cloud utilization. The main idea behind this paper is to apply processing jobs and cloud applications jointly in a preemptive way. However, utilization efficient resource allocation requires exact modeling of workloads. So, first, a novel methodology to model the processing jobs and other cloud applications is proposed. Such jobs are modeled as a collection of parallel and sequential tasks in a Markovian process. This enables us to analyze and calculate the efficient resources required to serve the tasks. The next step makes use of the proposed model to develop a preemptive scheduling algorithm for the processing jobs in order to improve resource utilization and its associated costs in the cloud computing system. Accordingly, a preemption-based resource allocation architecture is proposed to effectively and efficiently utilize the idle reserved resources for the processing jobs in the cloud paradigms. Then, performance metrics such as service time for the processing jobs are investigated. The accuracy of the proposed analytical model and scheduling analysis is verified through simulations and experimental results. The simulation and experimental results also shed light on the achievable QoS level for the preemptively allocated processing jobs.     

A comparison of Zayandehrood River water values for agriculture and the environment

River water management is challenging not only since they are open systems with changing physical structures, but also because the water values are mostly unknown over varied sectors. If policymakers grasp water values, water management will be more efficient. This research intends to examine the values of water in agriculture, which receives the most substantial portion of water resources, with the values of water in the environment in Isfahan located in the Zayandehrood River basin of Iran. The consequences of contingent valuation and production function methods revealed that per cubic metre value of water is 13 times higher in the environment than agriculture. The government should reconsider the higher value of the environment despite it is a non‐market value. The contingent valuation model additionally proved that women exhibited 21% more willingness to pay than men in order to protect the environment; however, they are paid less by 36%.     

Probabilistic design of ground improvement by vertical drains for soil of spatially variable coefficient of consolidation

The design of soil consolidation via prefabricated vertical drains (PVDs) has been traditionally carried out deterministically and thus can be misleading due to the ignorance of the uncertainty associated with the inherent variability of soil properties. To treat such uncertainty in the course of design of soil improvement by PVDs, more rational probabilistic methods are necessary. In this paper, a simplified probabilistic method is proposed in which the inherent variability of the coefficient of consolidation, which is the most significant uncertain soil parameter that affects the consolidation process, is considered. An easy-to-use design procedure and charts are provided for routine use by practitioners.     

Prediction of carbon dioxide separation from gas mixtures in petroleum industries using the Levenberg–Marquardt algorithm

In this study, two mathematical models for hydrate formation process to separate carbon dioxide by a combination of two different kinds of organic and surfactant promoters are presented. Promoters such as sodium dodecyl sulfate, sodium dodecyl benzene sulfonate, and dodecyl trimethyl ammonium chloride as surfactant promoters; also, tetrahydrofuran, cyclopentane, 1,3-dioxolane, and 2-methyl tetrahydrofuran as organic promoters have been used in recent years. The results showed that a combination of 3000 ppm of surfactant promoters and 4 wt% organic promoters had the highest separation rate of carbon dioxide and; consequently, the investigated models were based on this optimum condition. As a matter of fact, by using these simulations the hydrate formation behavior was predicted with high accuracy; moreover, conducting consuming experiments is not essential anymore. To sum up, in the present research both Vandermonde matrix model and Levenberg-Marquardt algorithm were applied to predict the hydrate formation behavior; in addition, their results were precisely considered and validated with experimental data.     

Morphology and crystallization behavior of nylon 6‐clay/neat nylon 6 bicomponent nanocomposite fibers

Nylon 6‐clay hybrid/neat nylon 6, sheath/core bicomponent nanocomposite fibers containing 4 wt % of clay in sheath section, were melt spun at different take‐up speeds. Their molecular orientation and crystalline structure were compared to those of neat nylon 6 fibers. Moreover, the morphology of the bicomponent fibers and dispersion of clay within the fibers were analyzed using scanning electron microscopy and transmission electron microscopy (TEM), respectively. Birefringence measurements showed that the orientation development in sheath part was reasonably high while core part showed negligibly low birefringence. Results of differential scanning calorimetry showed that crystallinity of bicomponent fibers was lower than that of neat nylon 6 fibers. The peaks of γ‐crystalline form were observed in the wide‐angle X‐ray diffraction of bicomponent and neat nylon 6 fibers in the whole take‐up speed, while α‐crystalline form started to appear at high speeds in bicomponent fibers. TEM micrographs revealed that the clay platelets were individually and evenly dispersed in the nylon 6 matrix. The neat nylon 6 fibers had a smooth surface while striped pattern was observed on the surface of bicomponent fibers containing clay. This was speculated to be due to thermal shrinkage of the core part after solidification of the sheath part in the spin‐line. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2014 , 131, 39996.