Affective News and Networked Publics: The Rhythms of News Storytelling on #Egypt

This study traces the rhythms of news storytelling on Twitter via the #egypt hashtag. Using computational discourse analysis, we examine news values and the form of news exhibited in #egypt from January 25 to February 25, 2011, pre‐ and post‐resignation of Hosni Mubarak. Results point to a hybridity of old and newer news values, with emphasis on the drama of instantaneity, the crowdsourcing of elites, solidarity, and ambience. The resulting stream of news combines news, opinion, and emotion to the point where discerning one from the other is difficult and doing so misses the point. We offer a theory of affective news to explain the distinctive character of content produced by networked publics in times of political crisis.     

A new texture and shape based technique for improving meningioma classification

Over the past decade, computer‐aided diagnosis is rapidly growing due to the availability of patient data, sophisticated image acquisition tools and advancement in image processing and machine learning algorithms. Meningiomas are the tumors of brain and spinal cord. They account for 20% of all the brain tumors. Meningioma subtype classification involves the classification of benign meningioma into four major subtypes: meningothelial, fibroblastic, transitional, and psammomatous. Under the microscope, the histology images of these four subtypes show a variety of textural and structural characteristics. High intraclass and low interclass variabilities in meningioma subtypes make it an extremely complex classification problem. A number of techniques have been proposed for meningioma subtype classification with varying performances on different subtypes. Most of these techniques employed wavelet packet transforms for textural features extraction and analysis of meningioma histology images. In this article, a hybrid classification technique based on texture and shape characteristics is proposed for the classification of meningioma subtypes. Meningothelial and fibroblastic subtypes are classified on the basis of nuclei shapes while grey‐level co‐occurrence matrix textural features are used to train a multilayer perceptron for the classification of transitional and psammomatous subtypes. On the whole, average classification accuracy of 92.50% is achieved through the proposed hybrid classifier; which to the best of our knowledge is the highest. Microsc. Res. Tech. 77:862–873, 2014. © 2014 Wiley Periodicals, Inc.     

Effect of high temperature and the role of sulfate on adsorption behavior and effectiveness of AMPS®‐based cement fluid loss polymers

The fluid loss control performance of 2‐acrylamido‐2‐methylpropane sulfonic acid (AMPS®)‐based copolymers added to cement slurries was studied at 27 and 100°C, respectively. It was found that effectiveness of these fluid loss additives solely relies on achievement of a high adsorbed amount on the surface of cement. At elevated temperature (100°C), CaAMPS®‐N,N‐dimethyl acrylamide copolymer (CaAMPS®‐co‐NNDMA) exhibits reduced adsorption and hence decreased fluid loss control of the cement slurry. The reason behind this behavior is poor calcium binding capability of the sulfonate anchor groups, which coordinate with calcium atoms present on the mineral surface. Whereas, an increase in the sulfate concentration present in cement pore solution instigates partial coiling of CaAMPS®‐co‐NNDMA and causes only a slight influence on the performance of this copolymer. The elevated sulfate content results from thermal degradation of ettringite, a cement hydrate mineral produced during the early stages of cement hydration. Incorporation of minor amounts (～ 1.3 mol %) of maleic anhydride into this copolymer produces a terpolymer, which exhibits higher and more stable adsorption, even at high temperature. This effect is owed to the presence of homopolymer blocks of polycarboxylates distributed along the polymer trunk. On mineral surfaces, they present much stronger anchor groups than sulfonate functionalities, as evidenced by their higher calcium binding capability. Consequently, fluid loss performance of CaAMPS®‐co‐NNDMA‐co‐MA is little affected by temperature. Understanding the influence of temperature on the physicochemical interactions occurring between additives and the mineral surface can help to design more effective admixtures suitable for high temperature applications. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Polyelectrolyte complexes from polyethylene imine/acetone formaldehyde sulfite polycondensates: A novel reagent for effective fluid loss control of oil well cement slurries

When used by itself, polyethylene imine (PEI) does not perform well as cement fluid loss additive. Its combination with acetone formaldehyde sulfite (AFS) polycondensate, however, exhibits excellent filtration control. The mechanism underlying this synergistic effect was studied and the conditions producing best results were determined. For optimum performance, PEI and AFS must be reacted with each other to yield a polyelectrolyte complex (PEC) (d ～ 5–10 μm), which effectively plugs the pores of the cement filter cake. Composition, size, and effectiveness of the PEC are strongly influenced by the anionic charge amount of the AFS dispersant. Ionic interactions between cationic imine functionalities of PEI and anionic sulfonate groups existing in AFS were confirmed by conductivity, infrared, zeta potential, and particle size measurements. For AFS samples possessing different degrees of sulfonation, the largest particle size and hence best fluid loss performance of the PEC was found to occur at a PEI:AFS molar ratio, which corresponds to neutral charge. Occurrence of large PEC particles (d ～ 5 μm) within the cement filter cake pores was visualized by scanning electron microscopy, and their stability in highly alkaline cement pore solution was confirmed by particle size measurement. Other anionic polyelectrolytes may be used to yield such PECs with PEI to provide effective fluid loss control for cement slurries. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Intergenerational family predictors of the Black–White achievement gap.

The authors examined intergenerational family predictors of the Black–White achievement gap among 4,406 adolescents from the National Longitudinal Survey of Youth. An intergenerational model of the process by which family factors contribute to the achievement gap was also tested. The results showed that the ethnic gaps in socioeconomic status (SES) and achievement had significantly reduced over the past few generations. Moreover, measures of grandparent SES, mothers’ achievement, parent SES, and a comprehensive set of reliable parenting practices explained all of the ethnic differences in achievement scores. Parenting practices such as creating a school-oriented home environment, allowing adolescents to make decisions, and not burdening them with too many chores had particularly important effects on the achievement gap. The authors conclude that adjusting for these differences would eliminate the ethnic achievement gap. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

In vitro synergism of antimutagenic and antioxidant activities of Phoenix dactylifera fruit

The availability of total phenolics of date flesh (Pheonix dactylifera) in aqueous and acidified methanol and in vitro synergism of their antimutagenic and antioxidant activities were investigated. The antimutagenic activities of date flesh extracts tested against Salmonella strains TA-98 and TA-100 using Ames bacterial testing were in the range of 36.47 to 79.74% against the standard mutagens potassium dichromate and sodium azide. The antioxidant activities assessed as maximal inhibitory concentrations (IC₅₀) for DPPH radicals and linoleic acid peroxidation inhibition were 812.90 to 2,076.10 μg/mL and 54.43 to 80.89%, respectively. The total phenolic contents of date flesh extracts (35.76 to 114.09mg/g of gallic acid equivalents) were found to be correlated with the biological activities. Use of 0.5 N acidified methanol was efficient for extraction of phenolic compounds with retained antioxidant and antimutagen activities. Dates are a good candidate as a source for development of chemotherapeutic drugs, nutracueticals, and functional food ingredients.     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.     

Physico-chemical analysis of tannery solid waste and structural characterization of its isolated humic acids after composting

In Marrakech, solid by-products from tanneries are highly polluting, generating large amounts of nitrogenous and organic matter. In the present study composting is tested as a cost-effective method for waste management to overcome many of the environmental hazards and produce a stable, rich material for soil fertilization. Two composting trials were conducted after neutralization by ammonia or lime. The aim of the neutralization was to avoid the antimicrobial effects of the acidity in the tannery waste, thus ensuring correct composting. Different techniques such as elemental analysis and ¹³C NMR spectroscopy were applied to analyse humic acids isolated from raw and composted materials, and to monitor the process of tannery waste composting, and the stability and maturity of the final product according to the means of neutralization. Comparison of data showed similar behaviour in both trials, but the composting process appeared to be more complete following neutralization with lime. The C, H and N content decreased, while the O increased. The FTIR and ¹³C NMR spectra show the decrease of aliphatic compounds demonstrated by the reduction of absorbance around 2922 cm⁻¹ and of the resonance in the C-alkyl area around 0–55 ppm. The humic acids newly formed during composting were richer in the O–N alkyl and oxidized aromatic structures that increased almost twofold on composting after neutralization with lime. The first principal component axis PC1 (54%) separated C-aliphatic, C-carboxylic and other less stable and less polycondensed compounds such as polyphenols from the more polycondensed O–N alkyl and oxidized C-aromatic compounds.