Bibliometric analysis on the papers dedicated to microplastics in wastewater treatments

The presence of microplastics (MPs) in the environment is becoming a problem for soils and seas, as well as for the food chain of animals and humans. The scientific community has been called upon to contribute to solving the problem and several papers have been published, especially in the last decade. The aim of this work is to carry out a bibliometric analysis of the scientific literature dedicated to the problem of MPs, highlighting its course over the years, and to identify the sectors to which the research could be profitably addressed. The VOSviewer software has been used to perform the analysis of the data in which specific maps were used to represent the network of the relationships among countries, journals, organizations, authors, and keywords related to the investigated topic and subtopics. The results of the survey demonstrated that during the investigated range of time, most attention has been paid to the individuation of the MPs, and to marine pollution, while a gap seems to exist in the possible advanced oxidation processes specifically addressing the degradation of MPs and their derivates

Analytical Case Study of Seismic Performance of Retrofit Strategies for Reinforced Concrete Frames : Steel Bracing with Shear Links Versus Column Jacketing

The effectiveness of seismic retrofitting applied to enhance seismic performance is assessed for a five-storey reinforced concrete (RC) frame building structure as built in Jordan in mid 80s. The response of the structure is evaluated using nonlinear static and dynamic analysis with synthetic ground motion records for rock base. FEMA 356 criteria are used to evaluate the seismic performance of the case study building. Two approaches are used for seismic evaluation: global-level evaluation (drift values) and member-level evaluation using three performance levels (immediate occupancy, life safety and collapse prevention). Based on the seismic evaluation results, two possible retrofit techniques are applied to improve the seismic performance of the structure, including the addition of RC column jackets and the addition of eccentric steel bracing. SAP 2000 is used to perform linear and nonlinear dynamic analysis, whereas plastic hinge analysis is performed by Response 2000. This study shows that adding new structural elements as steel members to an existing RC building proves to be effective in enhancing performance and reducing cost than adding RC elements. Even more, the eccentric bracing proves to outperform the column jacketing drift limit, plastic hinge limit and cost effectiveness

EEG-based image classification using an efficient geometric deep network based on functional connectivity

To ensure that the FC-GDN is properly calibrated for the EEG-ImageNet dataset, we subject it to extensive training and gather all of the relevant weights for its parameters. Making use of the FC-GDN pseudo-code. The dataset is split into a "train" and "test" section in Kfold cross-validation. Ten-fold recommends using ten folds, with one fold being selected as the test split at each iteration. This divides the dataset into 90% training data and 10% test data. In order to train all 10 folds without overfitting, it is necessary to apply this procedure repeatedly throughout the whole dataset. Each training fold is arrived at after several iterations. After training all ten folds, results are analyzed. For each iteration, the FC-GDN weights are optimized by the SGD and ADAM optimizers. The ideal network design parameters are based on the convergence of the trains and the precision of the tests. This study offers a novel geometric deep learning-based network architecture for classifying visual stimulation categories using electroencephalogram (EEG) data from human participants while they watched various sorts of images. The primary goals of this study are to (1) eliminate feature extraction from GDL-based approaches and (2) extract brain states via functional connectivity. Tests with the EEG-ImageNet database validate the suggested method's efficacy. FC-GDN is more efficient than other cutting-edge approaches for boosting classification accuracy, requiring fewer iterations. In computational neuroscience, neural decoding addresses the problem of mind-reading. Because of its simplicity of use and temporal precision, Electroencephalographys (EEG) are commonly employed to monitor brain activity. Deep neural networks provide a variety of ways to detecting brain activity. Using a Function Connectivity (FC) - Geometric Deep Network (GDN) and EEG channel functional connectivity, this work directly recovers hidden states from high-resolution temporal data. The time samples taken from each channel are utilized to represent graph signals on a topological connection network based on EEG channel functional connectivity. A novel graph neural network architecture evaluates users' visual perception state utilizing extracted EEG patterns associated to various picture categories using graphically rendered EEG recordings as training data. The efficient graph representation of EEG signals serves as the foundation for this design. Proposal for an FC-GDN EEG-ImageNet test. Each category has a maximum of 50 samples. Nine separate EEG recorders were used to obtain these images. The FC-GDN approach yields 99.4% accuracy, which is 0.1% higher than the most sophisticated method presently availabl

Neural network to investigate gaming addiction and its impact on health effects during the COVID-19 Pandemic

The Playing games become a serious issue and may have adverse effects on the quality of life of children. The research aims at identify in the factors and degree of influence which lead to gaming addiction and its impact on the quality of life of world children employing a comprehensive. Our method collects 2,526 children and adults’ data for five significant regions globally contain schools and universities in municipal and non-municipal areas. The research also aims to investigate the effect that gaming addiction has on the quality of life of children. Structural equation test and the (NNM) were uutilized to analyze the data. The results indicate some differences between boys and girls as to what factors lead to gaming addiction. The average Root Means Square Error (RMSE) of the neural network model is relatively low (.0103 for male training data and .0113 for male examining data, while for females it was .0103 for exercising data and .0104 for examining data), But gaming addiction was found to harm the life for both genders. Discussions comprising both academic as well as practical perspectives are also presented

Tuning of magnetic and electronic states by control of oxygen content in lanthanum strontium cobaltites

We report on the magnetic, resistive, and structural studies of perovskite La$_{1/3}$Sr$_{2/3}$CoO$_{3-\delta}$. By using the relation of synthesis temperature and oxygen partial pressure to oxygen stoichiometry obtained from thermogravimetric analysis, we have synthesized a series of samples with precisely controlled $\delta=0.00-0.49$. These samples show three structural phases at $\delta=0.00-0.15$, $\approx0.25$, $\approx0.5$, and two-phase behavior for other oxygen contents. The stoichiometric material with $\delta=0.00$ is a cubic ferromagnetic metal with the Curie temperature $T_{\rm C}=274$ K. The increase of $\delta$ to 0.15 is followed by a linear decrease of $T_{\rm C}$ to $\approx$ 160 K and a metal-insulator transition near the boundary of the cubic structure range. Further increase of $\delta$ results in formation of a tetragonal $2a_p\times 2a_p \times 4a_p$ phase for $\delta\approx 0.25$ and a brownmillerite phase for $\delta\approx0.5$. At low temperatures, these are weak ferromagnetic insulators (canted antiferromagnets) with magnetic transitions at $T_{\rm m}\approx230$ and 120 K, respectively. At higher temperatures, the $2a_p\times 2a_p \times 4a_p$ phase is $G$-type antiferromagnetic between 230 K and $\approx$360 K. Low temperature magnetic properties of this system for $\delta<1/3$ can be described in terms of a mixture of Co$^{3+}$ ions in the low-spin state and Co$^{4+}$ ions in the intermediate-spin state and a possible spin transition of Co$^{3+}$ to the intermediate-spin state above $T_{\rm C}$. For $\delta>1/3$, there appears to be a combination of Co$^{2+}$ and Co$^{3+}$ ions, both in the high-spin state with dominating antiferromagnetic interactions.Comment: RevTeX, 9 pages, 7 figures, to be published in Physical Review

Effect of potential and chlorides on photoelectrochemical removal of diethyl phthalate from water

Removal of persistent pollutants from water by photoelectrocatalysis has emerged as a promising powerful process. Applied potential plays a key role in the photocatalytic activity of the semi-conductor as well as the possible presence of chloride ions in the solution. This work aims to investigate these effects on the photoelectrocatalytic oxidation of diethyl phthalate (DEP) by using TiO2 nanotubular anodes under solar light irradiation. PEC tests were performed at constant potentials under different concentration of NaCl. The process is able to remove DEP following a pseudo-first order kinetics: values of kapp of 1.25 × 10−3 min−1 and 1.56 × 10−4 min−1 have been obtained at applied potentials of 1.8 and 0.2 V, respectively. Results showed that, depending on the applied potential, the presence of chloride ions in the solution affects the degradation rate resulting in a negative effect: the presence of 500 mM of Cl− reduces the value of kapp by 50 and 80% at 0.2 and 1.8 V respectively

The Importance of Call Delays and Cash Flow Positions in Evaluating the Information Content of Convertible Preferred Stock Calls

We examine a sample of in-the-money convertible preferred stock calls and find that they are delayed. We find that the length of the call delay does not depend on the relation between the preferred stock dividends and the pro rata common dividends to be paid on conversion. Thus, our evidence suggests that preferred stock calls may be used for signaling purposes. In support of this, we find that only delayed calls (i.e., those with potential signaling elements) are viewed negatively by equity investors. We also show that, in responding to delayed call announcements, investors appear to react to two distinct information elements. First, price responses to delayed calls are increasingly negative the larger the cash flow disadvantage to calling. In other words, common investors respond more negatively to calls when the forced conversion results in convertible holders receiving larger dividends than were previously required. Second, both cash flow advantage and cash flow disadvantage firms experience significant downward shifts in earnings growth during post-call periods, suggesting that delayed calls are timely signals of decreasing profitability

Dynamic nuclear polarization enhanced solid-state NMR studies of surface modification of gamma-alumina

Dynamic nuclear polarization (DNP) gives large (>100-fold) signal enhancements in solid-state NMR spectra via the transfer of spin polarization from unpaired electrons from radicals implanted in the sample. This means that the detailed information about local molecular environment available for bulk samples from solid-state NMR spectroscopy can now be obtained for dilute species, such as sites on the surfaces of catalysts and catalyst supports. In this paper we describe a DNP-enhanced solid-state NMR study of the widely used catalyst gamma-alumina which is often modified at the surface by the incorporation of alkaline earth oxides in order to control the availability of catalytically active penta-coordinate surface Al sites. DNP-enhanced 27Al solid-state NMR allows surface sites in gamma-alumina to be observed and their 27Al NMR parameters measured. In addition changes in the availability of different surface sites can be detected after incorporation of BaO