Analyzing the determinants of carbon emissions from transportation in European countries: the role of renewable energy and urbanization

Clean Technologies and Environmental Policy - The continuous growth of transport sector and the increase in carbon emissions from transportation attract the attention of policy makers in...     

Selective activation by bryostatin-1 demonstrates unique roles for PKC epsilon in neurite extension and tau phosphorylation

The purpose of this study was to contrast the discharge patterns of the same motor units during movements and during isometric contractions that were produced with comparable torque-time characteristics. Subjects performed elbow flexion and extension movements with predetermined acceleration characteristics. The average acceleration and deceleration profiles for the movements were reproduced in the isometric setting by presenting the kinematic profiles as templates for torque production. Trained subjects were able to match the first agonist (AG1) and antagonist (ANT) electromyographic (EMG) bursts, but tended to produce a smaller second agonist burst (AG2) in the isometric contraction. Twenty-five motor units from triceps brachii were studied. The same motor units (with one exception) were recruited and subsequently discharged in a similar fashion in both the isometric and movement tasks in the AG1 and ANT EMG bursts, with fewer motor unit discharges in the AG2 burst in the isometric contraction. The central control mechanisms appear to be the same for the acceleration phase of movement and isometric contraction, but differ during the deceleration phase.     

Concomitant Crystallization of Copper(II) Sachharinato Complexes with 2-methylpyrazine as a Monomer and an One-dimensional Polymer: Syntheses,Crystal Structures,Spectroscopic and Thermal Properties

The reaction of [Cu(sac)₂(H₂O)₄] · 2H₂O with 2-methylpyrazine (mpyz) leads two complexes, concomitant crystallization of a mononuclear complex [Cu(sac)₂(mpyz)(H₂O)₂] (1) and a polymeric complex [Cu(sac)₂(μ-mpyz)]_n (2). Both complexes have been characterized by elemental analyses, magnetic measurements, FT-IR and ESR, TG-DTA and single-crystal X-ray diffraction analyses. Single-crystal X-ray analyses show that complex 1 consists of discrete molecules in which the copper(II) ions exhibits a square-pyramidal coordination geometry. The individual molecules of 1 are connected into a hydrogen-bonded chain structure, which is further assembled to form a three-dimensional network by π–π stacking interactions. Complex 2 is an 1D coordination polymer in which copper(II) centers are bridged by the mpyz ligand. The chains are further assembled to form two-dimensional frameworks by π–π and C–H···π stacking interactions.     

Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

     

Improvement of flexural bond strength of zirconia-resin cement by surface patterning using sub-nanosecond UV laser

Zirconia is a dental material that shows excellent biocompatibility and high strength in clinical applications. This study aims to evaluate the effects of ultrafast laser applications. The surface nanostructures were classified into three groups. Group 1 was generated using the burst mode, with three different distances between dots: 52 µm (Group 1a), 104 µm (Group 1b), and 156 µm (Group 1c). Group 2 was processed using the scanning mode configuration, with a set of parallel lines. Group 3 was also processed using this scanning configuration creating a set of square-shaped patterning. Group 4 was the control group. After the surface treatments, a pair of zirconia specimens was bonded end to end with resin cement. Flexural bond strength (FBS) test was applied in a universal test machine. Multiple comparisons were performed using a one-way analysis of variance and the Tukey's HSD test. All the samples that were treated with the laser showed higher FBS values than the untreated surface. Using the burst mode, preformed circular-shaped surface on an angle of 90⁰ at 52 µm distance (Group 1a) showed the highest FBS values among all groups (p < .05). Groups 2 and 3 had significantly higher values than 1b and 1c.     

HYDRODYNAMIC STUDIES IN A HALF SLOT-RECTANGULAR SPOUTED BED COLUMN

Experiments were carried out in a half slot-rectangular spouted bed to investigate the effects of slot width and lower section basal angle on column hydrodynamics. Flow regimes, minimum spouting velocity, spouting and maximum pressure drops, and maximum spoutable bed height were determined for 4 mm diameter polyethylene particles. The results are compared with those for conventional cylindrical and rectangular spouted beds. Correlations for each hydrodynamic parameter are developed and compared with equations available in the literature.     

Influence of pitch composition and surface properties of petroleum coke on their interaction during the preparation of carbon/carbon composites

The influence of pitch composition and surface properties of petroleum coke on the interaction of pitch and coke in the composite is studied. The adsorption results and EPR data indicate that recombination of paramagnetic centers and interaction of oxygen functional groups occur during the preparation of the composite. As a result of interaction between petroleum coke and pitch, polar compounds are concentrated in the adsorption layer. The baking criterion is used for the characterization of the baking ability of the pitches obtained as a result of different oxidation treatments of commercial coal tar pitch. It is determined that physico-chemical properties of the obtained pitches influence the value of the “baking criterion”. The kind of thermo-oxidation treatment of the parent pitch should be chosen hence achieving the maximum value of the baking criterion. For this purpose, the maximal part of the pitch has to take part in the adsorption layer.     

Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes

With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid Grasshopper Optimization Algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and Grasshopper Optimization Algorithm. Experimental results are applied in two scenarios; the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26?% enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.

     

Palmprint Recognition by Applying Wavelet-Based Kernel PCA

This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coeffcients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coeffcients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classifier. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.