Lake Urmia restoration success story: A natural trend or a planned remedy?

Lake Urmia is the second-largest hypersaline lake in the world. There has been a drastic water level drop of 7.2 m from 1995 to 2016. Beginning in October 2013, the Lake Urmia Restoration Plan (LURP) launched a 10-year program. An increase in water level and a relative improvement in Lake Urmia condition has been observed since 2017. It is an undecided and controversial issue whether the recent positive trend of Lake Urmia has been due to the LURP activities or it is a natural contribution of climate factors variations. To shed some light on this issue, we examine three other lakes, adjacent to the Lake Urmia basin, with similar rainfall variability to investigate their status during the same period. Van (Turkey), Mosul, and Tharthar (both in Iraq), are evaluated as well as Lake Urmia. Three decades of remotely sensed data including precipitation ($P$), water level ($\mathit{WL}$), and lake extent ($A$) were considered for the mentioned lakes. A significant correlation was observed between standardized $\mathit{WL}-P$, and $A-P$ over the long-term period, especially for the recent three years ($R$² = 0.63–0.87). We show that the cumulative precipitation in the antecedent months played a major role in the improvement of these lakes' situation but with different time lags (up to 6 months for Van and Mosul lakes and up to 36 months for Lake Urmia and Tharthar lake). These findings could inform the planners of LURP to adopt strategies for achieving a sustainable state of Lake Urmia based on a more realistic outlook.     

Thermal–economic–environmental analysis and multi-objective optimization of an internal-reforming solid oxide fuel cell–gas turbine hybrid system

In this article, an internal-reforming solid oxide fuel cell–gas turbine (IRSOFC–GT) hybrid system is modeled and analyzed from thermal (energy and exergy), economic, and environmental points of view. The model is validated using available data in the literature. Utilizing the genetic algorithm optimization technique, multi-objective optimization of modeled system is carried out and the optimal values of system design parameters are obtained. In the multi-objective optimization procedure, the exergy efficiency and the total cost rate of the system (including the capital and maintenance costs, operational cost (fuel cost), and social cost of air pollution for CO, NO_x, and CO₂) are considered as objective functions. A sensitivity analysis is also performed in order to study the effect of variations of the fuel unit cost on the Pareto optimal solutions and their corresponding design parameters. The optimization results indicate that the final optimum design chosen from the Pareto front results in exergy efficiency of 65.60% while it leads to total cost of 3.28 million US$ year⁻¹. It is also demonstrated that the payback time of the chosen design is 6.14 years.     

Speaker independent feature selection for speech emotion recognition: A multi-task approach

Nowadays, automatic speech emotion recognition has numerous applications. One of the important steps of these systems is the feature selection step. Because it is not known which acoustic features of person’s speech are related to speech emotion, much effort has been made to introduce several acoustic features. However, since employing all of these features will lower the learning efficiency of classifiers, it is necessary to select some features. Moreover, when there are several speakers, choosing speaker-independent features is required. For this reason, the present paper attempts to select features which are not only related to the emotion of speech, but are also speaker-independent. For this purpose, the current study proposes a multi-task approach which selects the proper speaker-independent features for each pair of classes. The selected features are then given to the classifier. Finally, the outputs of the classifiers are appropriately combined to achieve an output of a multi-class problem. Simulation results reveal that the proposed approach outperforms other methods and offers higher efficiency in terms of detection accuracy and runtime.

     

Photo-electrochemistry of metallic titanium/mixed phase titanium oxide

In this study, the effect of potassium hydroxide concentration in anodization bath, anodization time, and calcination temperature on the photo-electrochemical behavior of metallic titanium/mixed phase titanium oxide is investigated. Further, the phase structure of a titanium oxide photocatalyst prepared on a titanium electrode through a high-voltage anodization method is examined. The study exploits photo-electrochemical, Fourier transform infrared spectroscopy attenuated total reflectance (FTIR–ATR), X-ray diffraction, and Raman spectroscopic methods to obtain better insights into the mechanism of mixed-phase titanium oxide formation. In this regard, the photo-electrochemical properties of the photocatalysts prepared in single excitation energy, violet light (410 nm), were investigated. The anodization time and the potassium hydroxide concentration in the anodization bath have significant effects on the photo-electrochemical properties of the photocatalysts. The experiments show that the effect of potassium hydroxide concentration is a function of the anodization potential applied, demonstrating different patterns as the anodization potential changes. Furthermore, FTIR-ATR, X-ray diffraction, and Raman spectroscopic studies reveal that the extended anodization times decrease the population of OH-containing groups, leading to lower photo-electrochemical performance. On the other hand, the formation of anatase phases becomes more favorable only in the extended anodization times before application of the calcination process. Additionally, the calcination temperature has a significant impact on the anatase to rutile ratio. Finally, increasing potassium hydroxide concentration leads to the formation of an amorphous titanium oxide layer. It can be concluded that the obtained information might have a significant impact on the preparation of titanium oxide and other metal oxide photocatalysts through the high voltage anodization process.     

Adaptive content-and-deadline aware chunk scheduling in mesh-based P2P video streaming

In mesh-based Peer-to-Peer (P2P) live video streaming systems packet scheduling is an important factor in overall video playback quality. In mesh based P2P video streaming systems, each video sequence is divided into chunks, which are then distributed by multiple suppliers to the receivers. The suppliers need to be coordinated by the receiver through specifying a transmission schedule for each of them. Many previous studies on scheduling of P2P streaming tend to mainly focus on networking issues which strongly depend on a particular P2P architecture such as tree or mesh. These algorithms suffer from some design issues: 1) they are too complex to deploy, 2) they do not take video characteristics into account and 3) they do not have sender-side transmission policy. To address all three of these problems, we propose a new chunk scheduling scheme which consists of two parts: i) receiver-side scheduler and ii) sender-side transmission order scheme. The proposed receiver-side scheduler considers the contribution level of each video frame as well as the frame’s urgency in order to define a priority for each video frame. It attempts to request frames with highest priority from peers which can deliver them in a shorter time. We also design a new chunk transmission order scheme that decides which requested chunk will be sent out first based on its importance to the requesting neighbor. Our simulation results show that the proposed scheduling scheme improves the overall quality of the perceived video in mesh-based P2P video streaming architectures substantially.     

Efficiency evaluation of the photocatalytic degradation of zinc oxide nanoparticles immobilized on modified zeolites in the removal of styrene vapor from air

Styrene monomer is a volatile organic compound that has many applications in plastics, rubber, and paint manufacturing industries. Exposure to styrene vapor has certain effects, including suppression of the central nervous system, loss of concentration, weakness and fatigue, and nausea and there is a possibility of carcinogenesis in long-term exposure. Therefore, it is necessary to control and eliminate this vapor. The aim of this study was to investigate the performance of zinc oxide nanoparticles on modified natural zeolites in removing styrene vapor from the air. Natural zeolites of clinoptilolite were modified using hydrochloric acid and diphenyldichlorosilane. Next, zinc oxide nanoparticles with different ratios of 3, 5, and 10 wt% were stabilized on the zeolites. To determine their characteristics, samples were used from BET, SEM and XRD analyses. The input styrene concentration and the ratio of nanoparticles stabilized on zeolites were studied as effective functional parameters on the removal process. The efficiency results of natural zeolites (Ze) and modified zeolites (Mze) in styrene adsorption from the air show that the styrene breakthrough in the bed of MZe compared to that of Ze increases approximately two times. Also, the results showed that the removal by the process of UV/MZe-ZnO 3%, UV/MZe-ZnO 5%, and UV/MZe-ZnO 10%, was 36.5%, 40%, and 26%, respectively. From the results it can be concluded that MZe can increase the efficiency of photocatalytic degradation. Clinoptilolites of Iran can be used as an adsorbent to remove polluted air in industries that have low concentrations and flow rates.     

Heats and input variables selection for designing a water detection framework applicable to industrial electric arc furnaces

This paper describes the development of “heats” and input variables selection models that are incorporated into a water detection framework for an industrial steelmaking electric arc furnace (EAF). The selection models in this work are developed based on latent variable methods. The latent variable methods used in this work are multiway principal component analysis (MPCA) and multiway projection to latent structures (MPLS). The particular problems related to latent variable methods discussed in this paper include data preprocessing, including alignment, unfolding method, centering, and scaling. The outcome of the heats selection model is heats with normal operation and the outcome of the input variables selection model is variables that are highly correlated with the off-gas water vapour. The water detection framework and developed models are useful in practical settings for the prediction of water leakage and the design of appropriate fault detection and diagnosis strategies.     

Blood supply chain network design considering blood group compatibility under uncertainty

This paper addresses the design of a blood supply chain (SC) network considering blood group compatibility. To this aim, a bi-objective mathematical programming model is developed which minimises the total cost as well as the maximum unsatisfied demand. Due to uncertain nature of some input parameters, two novel robust possibilistic programming models are proposed based on credibility measure. The data of a real case study are then used to illustrate the applicability and performance of the proposed models as well as validating the proposed robust possibilistic programming approach. The obtained results show the superiority of the developed models and significant cost savings compared to current existed blood SC network.     

Online monitoring of transit-time ultrasonic flowmeters based on fusion of optical observation

It is well-known that transit-time ultrasonic flowmeters (TTUF) are unsuitable for bubble-contained flows. This paper presents a new online monitoring method based on optical observation, which monitors the functionality of the TTUF in the presence of bubbles in the fluid. The method avoids the unnecessary Emergency Shutdowns (ESD) due to bubble presence in the fluid by bubble detection. The proposed method accomplishes bubble identification through a combination of image processing and wavelet analysis. In addition, a new method is proposed which estimates single bubble size in horizontal pipes using a data fusion approach.     

Development of Al356/SiCp cast composites by injection of SiCp containing composite powders

One of the great challenges of producing cast metal matrix composites is the agglomeration tendency of the reinforcements. This would normally result in poor distribution of the particles, high porosity content, and low mechanical properties. In the present work, a new method for uniform distribution of very fine SiC particles with average size of less than 3 μm was employed. The key idea was to allow for gradual in situ release of properly wetted SiC particles in the liquid metal. For this purpose, SiC particles were injected into the melt in three different forms, i.e., untreated SiC_p, milled particulate Al–SiC_p composite powder, and milled particulate Al–SiC_p–Mg composite powder. The resultant composite slurries were then cast from either fully liquid (stir casting) or semisolid (compocasting) state. Consequently, the effects of the casting method and the type of the injected powder on the microstructural characteristics as well as the mechanical properties of the cast composites were investigated. The results showed that the distribution of SiC particles in the matrix and the porosity content of the composites were greatly improved by injecting milled composite powders instead of untreated-SiC particles into the melt. Casting from semisolid state instead of fully liquid state had similar effects. The average size of SiC particles incorporated into the matrix was also significantly reduced from about 8 to 3 μm by injecting milled composite powders. The ultimate tensile strength, yield strength and elongation of Al356/5 vol.%SiC_p composite manufactured by compocasting of the (Al–SiC_p–Mg)_cp injected melt were increased by 90%, 103% and 135%, respectively, compared to those of the composite manufactured by stir casting of the untreated-SiC_p injected melt.