Photocatalytic Removal of Ethylene Dichloride Using PAni-TiO2 Nanocomposites Supported on Glass Beads: Process Optimization by RSM-CCD Approach

Ethylene dichloride is one of the most important chlorinated hydrocarbons in the petrochemical industry, which is mainly used to produce vinyl chloride monomer, the main precurser of PVC production. Iran is one of the largest PVC producers in the world. During the production of 1000 kg of ethylene dichloride, about 0.4 m3 wastewater is produced containing 50-200 mg / L of ethylene dichloride. In this study, heterogeneous photocatalysis was used for degradation of this chlorinated hydrocarbon. PAni-TiO2 nanocomposite was immobilized on glass beads by a modified dip coating and heat attachment method. The morphology characteristics were confirmed by scanning electron microscope, energy dispersive X-ray spectroscopy and ultraviolet–visible spectroscopy. A pilot scale packed bed recirculating batch photocatalytic reactor was used for conducting photocatalytic experiments. response surface methodology based on central composite design was used to evaluate and optimize the effect of ethylene dichloride concentration, residence time, pH and coating mass as independent variables on the photocatalytic degradation of ethylene dichloride as the response function. Based on the results, actual and RSM predicted results were well fitted with R2 of 0.9870, adjusted R2 of 0.9718 and predicted R2 of 0.9422. Optimum conditions were the ethylene dichloride concentration of 250 mg/L, reaction time of 240 min, pH of 5 and immobilized mass of 0.5 mg/cm2, which resulted in 88.84% photocatalytic degradation. Kinetic of the photocatalytic degradation at optimal condition followed the Langmuir-Hinshelwood first order reaction with k=0.0095 min-1 with R2=0.9455. Complete photocatalytic degradation of ethylene dichloride was achieved after 360 min. Based on the results, it may be argued that the designed and constructed photocatalytic reactor has the potential for industrialization

Physiological responses of Vetiver plant (Vetiver zizanioides) to municipal waste leachate

Vetiver plant is tolerant to acidity and temperature variations. Has rapid growth for biomass production and has high tolerance to organic and non-organic compounds in municipal waste leachate for example heavy metals. So this plant is good for landfill cultivation. In this study, physiological responses to municipal waste leachate were studied. Statistical design was a randomized complete block and each block treated with different concentrations of latex at levels of zero, 15, 30, 45 and 60 percent compared to the original latex waste. The leachate collected from the Shiraz landfill and brought into the greenhouse. The physiological characterization including leaf area, dry weight, chlorophyll, anthocyanin, proline, soluble sugars and total protein were measured. The result indicated that the dry weight, chlorophyll and anthocyanin decrease with increasing of latex concentration. The leaf area, leaf relative water, soluble sugars and total protein increased with increasing latex concentration. Proline concentration at 15 percent of leachate increased significantly compared to controls, whereas at higher concentrations decreased. According to the results, it is recommended that 45 percent of leachate in a landfill can be used to irrigate Vetiver. This is the maximum concentration of leachate that Vetiver plant can survive as green space. Primary filtration of leachate before using is recommended. If the aim is more growth or perfume application from root, less concentration of leachate is better

Evaluation of expression changes, proteins interaction network, and microRNAs targeting catalase and superoxide dismutase genes under cold stress in rapeseed (

Rapeseed is the third-largest source of plant oil and one of the essential oil plants worldwide. Cold stress is one of the critical factors that affect plant yield. Therefore, improving cold stress tolerance is necessary for yield increase. The present study investigated BnCAT1 and BnCSD1 genes’ expression behavior in a tolerant and sensitive cultivar under cold stress (4 °C). Besides, protein-protein interaction networks of CATs and CSDs enzymes, and their association with other antioxidant enzymes were analyzed. Moreover, the microRNAs targeting BnCAT1 and BnCSD1 genes were predicted. This study indicated many direct and indirect interactions and the association between the components of the plant antioxidant system. However, not only did the CATs and CSDs enzymes have a relationship with each other, but they also interacted directly with ascorbate peroxidase and glutathione reductase enzymes. Also, 23 and 35 effective microRNAs were predicted for BnCAT1 and BnCSD1 genes, respectively. The gene expression results indicated an elevated expression of BnCAT1 and BnCSD1 in both tolerant and sensitive cultivars. However, this increase was more noticeable in the tolerant cultivar. Thus, the BnCSD1 gene had the highest expression in the early hour of cold stress, especially in the 12th h, and the BnCAT1 gene showed the highest expression in the 48th h. This result may indicate a functional relationship between these enzymes

Beyond core object recognition: Recurrent processes account for object recognition under occlusion

Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information. Deep neural networks are shown to achieve human-level performance in these tasks, and explain the primate brain representation. On the other hand, object recognition under more challenging conditions (i.e. beyond the core recognition problem) is less characterized. One such example is object recognition under occlusion. It is unclear to what extent feedforward and recurrent processes contribute in object recognition under occlusion. Furthermore, we do not know whether the conventional deep neural networks, such as AlexNet, which were shown to be successful in solving core object recognition, can perform similarly well in problems that go beyond the core recognition. Here, we characterize neural dynamics of object recognition under occlusion, using magnetoencephalography (MEG), while participants were presented with images of objects with various levels of occlusion. We provide evidence from multivariate analysis of MEG data, behavioral data, and computational modelling, demonstrating an essential role for recurrent processes in object recognition under occlusion. Furthermore, the computational model with local recurrent connections, used here, suggests a mechanistic explanation of how the human brain might be solving this problem

The Relevance Sample-Feature Machine: A Sparse Bayesian Learning Approach To Joint Feature-Sample Selection

This paper introduces a novel sparse Bayesian machine-learning algorithm for embedded feature selection in classification tasks. Our proposed algorithm, called the relevance sample feature machine (RSFM), is able to simultaneously choose the relevance samples and also the relevance features for regression or classification problems. We propose a separable model in feature and sample domains. Adopting a Bayesian approach and using Gaussian priors, the learned model by RSFM is sparse in both sample and feature domains. The proposed algorithm is an extension of the standard RVM algorithm, which only opts for sparsity in the sample domain. Experimental comparisons on synthetic as well as benchmark data sets show that RSFM is successful in both feature selection (eliminating the irrelevant features) and accurate classification. The main advantages of our proposed algorithm are: less system complexity, better generalization and avoiding overfitting, and less computational cost during the testing stage. © 2013 IEEE

The Relevance Sample-Feature Machine: A Sparse Bayesian Learning Approach to Joint Feature-Sample Selection

This paper introduces a novel sparse Bayesian machine-learning algorithm for embedded feature selection in classification tasks. Our proposed algorithm, called the relevance sample feature machine (RSFM), is able to simultaneously choose the relevance samples and also the relevance features for regression or classification problems. We propose a separable model in feature and sample domains. Adopting a Bayesian approach and using Gaussian priors, the learned model by RSFM is sparse in both sample and feature domains. The proposed algorithm is an extension of the standard RVM algorithm, which only opts for sparsity in the sample domain. Experimental comparisons on synthetic as well as benchmark data sets show that RSFM is successful in both feature selection (eliminating the irrelevant features) and accurate classification. The main advantages of our proposed algorithm are: less system complexity, better generalization and avoiding overfitting, and less computational cost during the testing stage. © 2013 IEEE

The impact of addictive behaviors on adolescents psychological well-being : the mediating effect of perceived peer support

Studies exploring excessive Internet use and gambling are rapidly expanding concerns regarding its impact on mental health, especially in young people due to the increased prevalence of Internet and gambling addictions. Research suggests that perceived peer support plays a significant role in adolescents' psychological well-being. However, no empirical study has dealt with the mediating effect of perceived peer support on the relationship between Internet and gambling addictions and psychological well-being. Thus, the present study aimed to examine whether perceived peer support mediates the relation between Internet and gambling addictions and psychological well-being of adolescents. A sample of 347 Iranian adolescents aged 14 to 18 (Mean age 16.14, 50.4% male) who were studying in Kuala Lumpur, Malaysia participated in this study. Subjective Vitality Scale (SVS), Compulsive Internet Use Scale (CIUS), Six-item Social Support Questionnaire (SSQ6), and The South Oaks Gambling Screen (SOGS) were used to collect data. Mediation analyses showed a significant indirect effect of compulsive Internet use and problem gambling on psychological well-being through perceived peer support. The total effects of compulsive Internet use and problem gambling on psychological well-being were negative. This study implies the significance of strengthening the knowledge about the impact of peer relationships among adolescents