In this paper we investigate the automatic recognition of emotion in text. We propose a new method for emotion recognition based on the PPM (PPM is short for Prediction by Partial Matching) character-based text compression scheme in order to recognize Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method is very effective when compared with traditional word-based text classification methods. We have also found that our method works best if the sizes of text in all classes used for training are similar, and that performance significantly improves with increased data.

Gingival crevicular fluid (GCF) may reflect the events associated with orthodontic tooth movement. Attempts have been conducted to identify biomarkers reflecting optimum orthodontic force, unwanted sequallea (i.e. root resorption) and accelerated tooth movement. The aim of the present study is to find out a standardized GCF collection, storage and total protein extraction method from apparently healthy gingival sites with orthodontics that is compatible with further high-throughput proteomics. Eighteen patients who required extractions of both maxillary first premolars were recruited in this study. These teeth were randomly assigned to either heavy (225g) or light force (25g), and their site specific GCF was collected at baseline and aft

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

BACKGROUND: Acute appendicitis (AA) remains a complex case even for experienced surgeons. Rate of negative appendectomy is 5–40% and delayed intervention result in perforated appendicitis in 5–30% of cases. AIM: The aim of the study was to evaluate NPT as a marker for the diagnosis of AA concerning its severity. And compare the diagnostic value of it with the ALV scoring system. METHODS: One hundred twenty patients presented with signs and symptoms of AA and underwent appendectomy, only 84 patients proved to be AA by histopathological examination, were included in the study. Blood samples for neopterin (NPT) estimation and Alvarado (ALV) score was calculated. Control group consists of 45 healthy individual. RESULTS: NPT levels were s

Amorphization of drug has been considered as an attractive approach in improving drug solubility and bioavailability. Unlike their crystalline counterparts, amorphous materials lack the long-range order of molecular packing and present the highest energy state of a solid material. Co-amorphous systems (CAM) are an innovative formulation technique by where the amorphous drugs are stabilized via powerful intermolecular interactions by means of a low molecular co-former.

This review highlights the different approaches in the preparation of co-amorphous drug delivery system, the proper selection of the co-formers. In addition, the recent advances in characterization, Industrial scale and formulation will be discussed.

    Nine fish specimens of Thryssa setirostris (Broussonet, 1782) were collected from the Arabian Gulf, during the period from July 2015 to April 2016. Because of the scarcity of this fish and overlapping and ambiguous of its taxonomic characters with other Thryssa spp., a detailed taxonomic study was conducted. The present study includes the most important meristic and morphometric characteristics.

    The mean of the total length of the specimens was 149.67 mm; dorsal fin consists of 12 rays, anal fin with 34-37 rays and pectoral fin with 12-13 rays; Gill rakers were 4 upper,1 medial an.10 lower. The most important character that isolates T. setiristis from

Abstract Introduction: Aphthous stomatitis is a recurring oral ulceration condition that affects a significant proportion of the population. While several factors have been proposed to contribute to its occurrence, the link between caffeine consumption, anxiety levels, and the development of aphthous stomatitis remains unknown. This survey-based study aimed to understand the association between coffee, anxiety, and the development of aphthous stomatitis in this specific demographic, which can provide useful insights for medical management as well as preventive strategies. Material and methods: A structured online questionnaire was distributed via various social media platforms targeting a sample of the Armenian population. The quest

The study is based on the selective binding ability of the drug compound procaine (PRO) on a surface imprinted with nylon 6 (N6) polymer. Physical characterization of the polymer template was performed by X-ray diffraction and DSC thermal analysis. The imprinted polymer showed a high adsorption capacity to trap procaine (237 µg/g) and excellent recognition ability with an imprinted factor equal to 3.2. The method was applied to an extraction column simulating a solid-phase extraction to separate the drug compound in the presence of tinoxicam and nucleosimide separately and in a mixture of them with a recovery rate more than the presence of tinoxicam and nucleosimide separately and in a mixture of them with a recovery rate of more t

Shumblan (SH) is one of the most undesirable aquatic plants widespread in the irrigation channels and water bodies. This work focuses on boosting the biogas potential of shumblan by co-digesting it with other types of wastes without employing any chemical or thermal pretreatments as done in previous studies. A maximum biogas recovery of 378 ml/g VS was reached using shumblan with cow manure as inoculum in a ratio of 1:1. The methane content of the biogas was 55%. Based on volatile solid (VS) and C/N ratios, biogas productions of 518, 434, and 580 ml/g VS were obtained when the shumblan was co-digested with food wastes (SH:F), paper wastes (SH:P), and green wastes (SH:G) respectively. No significant changes of methane contents were observ