Wind energy resource assessment for five locations in Saudi Arabia

The analysis of recently collected wind data at five sites in Saudi Arabia namely, Dhulum, Arar, Yanbu, Gassim and Dhahran is presented. The five sites represent different geographically and climatologically conditions. The data collected over a period spanned between 1995 and 2002 with different collection periods for each site. Daily, monthly and frequency profiles of the wind speed at the sites showed that Dhulum and Arar sites have higher wind energy potential with annual wind speed average of 5.7 and 5.4 m/s and speeds higher than 5 m/s for 60 and 47% of the time, respectively. The two sites are candidates for remote area wind energy applications. The costal site's, i.e. Yanbu and Dhahran wind speed data indicated that the two sites have lower annual wind speed averages and wind blows at speed higher than 5 m/s during afternoon hours. That makes the two sites candidates for grid connected wind systems for electrical load peak shaving. The data of Gassim site showed that the site has the lowest wind energy potential compared to the others. The annual energy produced by a Nordex N43 wind machine is estimated to be 1080, 990, 730, 454 and 833 MWh for Dhulum, Arar, Yanbu, Gassim and Dhahran, respectively. The analysis showed that the estimated annual energy produced by the machine based on 10 min averaged data is 2.5% higher than the estimated energy based on 30 min averaged data.     

Bacillus subtilis: As an Efficient Bacterial Strain for the Reclamation of Water Loaded with Textile Azo Dye,Orange II

The azo dye orange II is used extensively in the textile sector for coloring fabrics. High concentrations of it are released into aqueous environments through textile effluents. Therefore, its removal from textile wastewater and effluents is necessary. Herein, initially, we tested 11 bacterial strains for their capabilities in the degradation of orange II dye. It was revealed in the preliminary data that B. subtilis can more potently degrade the selected dye, which was thus used in the subsequent experiments. To achieve maximum decolorization, the experimental conditions were optimized whereby maximum degradation was achieved at: a 25 ppm dye concentration, pH 7, a temperature of 35 °C, a 1000 mg/L concentration of glucose, a 1000 mg/L urea concentration, a 666.66 mg/L NaCl concentration, an incubation period of 3 days, and with hydroquinone as a redox mediator at a concentration of 66.66 mg/L. The effects of the interaction of the operational factors were further confirmed using response surface methodology, which revealed that at optimum conditions of pH 6.45, a dye concentration of 17.07 mg/L, and an incubation time of 9.96 h at 45.38 °C, the maximum degradation of orange II can be obtained at a desirability coefficient of 1, estimated using the central composite design (CCD). To understand the underlying principles of degradation of the metabolites in the aliquot mixture at the optimized condition, the study steps were extracted and analyzed using GC-MS(Gas Chromatography Mass Spectrometry), FTIR(Fourier Transform Infrared Spectroscopy), ¹H and carbon 13 NMR(Nuclear Magnetic Resonance Spectroscopy). The GC-MS pattern revealed that the original dye was degraded into o-xylene and naphthalene. Naphthalene was even obtained in a pure state through silica gel column isolation and confirmed using ¹H and ¹³C NMR spectroscopic analysis. Phytotoxicity tests on Vigna radiata were also conducted and the results confirmed that the dye metabolites were less toxic than the parent dye. These results emphasize that B. subtilis should be used as a potential strain for the bioremediation of textile effluents containing orange II and other toxic azo dyes.     

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

Scientometrics - In-text citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata,...     

Detail preserving impulsive noise removal

Most image processing applications require noise elimination. For example, in applications where derivative operators are applied, any noise in the image can result in serious errors. Impulsive noise appears as a sprinkle of dark and bright spots. Transmission errors, corrupted pixel elements in the camera sensors, or faulty memory locations can cause impulsive noise. Linear filters fail to suppress impulsive noise. Thus, non-linear filters have been proposed. Windyga's peak-and-valley filter, introduced to remove impulsive noise, identifies noisy pixels and then replaces their values with the minimum or maximum value of their neighbors depending on the noise (dark or bright). Its main disadvantage is that it removes fine image details. In this work, a variation of the peak-and-valley filter is proposed to overcome this problem. It is based on a recursive minimum–maximum method, which replaces the noisy pixel with a value based on neighborhood information. This method preserves constant and edge areas even under high impulsive noise probability. Finally, a comparison study of the peak-and-valley filter, the median filter, and the proposed filter is carried-out using different types of images. The proposed filter outperforms other filters in the noise reduction and the image details preservation. However, it operates slightly slower than the peak-and-valley filter.     

Efficient Resource Allocation in Fog Computing Using QTCS Model

Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be shared. The embedded devices deployed with the Internet of Things (IoT) technology have increased since the past few years, and these devices generate huge amount of data. The devices in IoT can be remotely connected and might be placed in different locations which add to the network delay. Real time applications require high bandwidth with reduced latency to ensure Quality of Service (QoS). To achieve this, fog computing plays a vital role in processing the request locally with the nearest available resources by reduced latency. One of the major issues to focus on in a fog service is managing and allocating resources. Queuing theory is one of the most popular mechanisms for task allocation. In this work, an efficient model is designed to improve QoS with the efficacy of resource allocation based on a Queuing Theory based Cuckoo Search (QTCS) model which will optimize the overall resource management process.     

Privacy management of patient physiological parameters

Patient Physiological Parameters (PPPs) seem to be the most extensively accessed and utilized Personal Health Information (PHI) in hospitals, and their utilization by the various medical entities for treatment and diagnosis creates a real threat to patient privacy. This study aims to investigate whether PPPs access in a hospital environment violates patient privacy. If so, to what extent can we manage patient privacy while accessing PPPs in this environment? We investigated this question by analyzing questionnaire-based data from two Asian countries: Group A (China) and Group B (Pakistan). For data collection, we targeted those medical entities which were directly dealing with PPPs in their routine tasks. Results suggest that patient type directly influences the collection of PPPs: Group A (one-time?=?1.9, follow-up?=?1.06) and Group B (one-time?=?2.0 and follow-up?=?1.9). Both groups agreed that patients have the right to control their own PPPs. In both, doctors are the most trusted entity: for Group A, the Pearson Chi-Square with one degree of freedom is 1.414, p?=?0.234, whereas for Group B, the Pearson Chi-Square with three degrees of freedom is 4.511, p?=?0.11. Most of the Group A entities (92%) are familiar with unauthorized access of PPPs, while in Group B the level was only 35%. In Group B, only 35% of entities stated the purpose, specification and use limitations of PPPs. Doctors in both groups showed a high utilization of PPPs read authorization rights. This empirical evidence about PPPs usage in both countries will benefit health technology and improve policy on patient privacy.     

Stability limits and consolute critical conditions for liquid mixtures

This work addresses the determination of stability limits and consolute critical conditions for multicomponent liquid mixtures. Using the NRTL model, thermodynamic stability and criticality criteria are implemented for ternary liquid mixtures to predict stability limit loci and critical composition at its specified temperature and pressure. The method is general and applicable for the prediction of spinodal curves and critical points for liquid–liquid-phase transitions in multicomponent liquid mixture using liquid–liquid equilibrium data even over a limited range of composition. Stability limits’ loci for 18 aqueous and 3 nonaqueous ternary liquid mixtures were used in validating the method. For ternary systems that were studied over the whole composition range including the critical zone, where experimental compositions at the critical point were available, the predicted results agree with the experimental measurements within 0.7?mol%.     

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed. Hence, this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis. This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment. This can be particularly helpful when dealing with smaller datasets. Furthermore, our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms. This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts. The experimental results validate that our model offers excellent results with a validation accuracy of 92.5% and an F1 measure of 0.92.     

H∞ filtering of discrete-time interconnected systems with failed interconnections

The H_∞ filtering problem for interconnected systems is addressed in this paper. In order to facilitate the stability analysis of interconnected systems under the conditions of failed interconnections, a strategy related to switched systems is introduced. Assume that the interconnections among these subsystems disconnect based on the mode-dependent average dwell time (MDADT), a switching signal is implemented to denote the failed interconnections cases. In the aid of the MDADT method, a filter is designed such that the fault detection (FD) system is asymptotically stable and satisfies the weighted H_∞ performance. Finally, a numerical simulation is provided to show the effectiveness of the proposed filter.     

Can social media usage of scientific literature predict journal indices of AJG,SNIP and JCR? An altmetric study of economics

Scientometrics - Altmetrics are often praised as an alternative or complement to classic bibliometric metrics, especially in the social sciences discipline. However, empirical investigations of...