Characteristics and Composition of Watermelon Seed Oil and Solvent Extraction Parameters Effects

Watermelon seed oil characteristics were evaluated to determine whether this oil could be exploited as an edible oil. Hexane extraction of watermelon seeds produced yields of 50% (w/w) oil. The refractive index, saponification and iodine value were 1.4712 (at 25 °C), 200 mg KOH/g and 156 g I/100 g, respectively. The acid and peroxide values were 2.4 mg KOH/g and 3.24 mequiv/kg, respectively. The induction time of the oil was also 5.14 h at 110 °C, which was measured for the first time. Total unsaturation contents of the oil was 81.6%, with linoleic acid (18:2) being the dominant fatty acid (68.3%). Considering that the watermelon seed oil was highly unsaturated, the relatively high induction time might indicate the presence of natural antioxidants. In addition, the influence of extraction parameters on extraction of oil from watermelon seed with hexane as a solvent was studied at several temperatures (40, 50, and 60 °C), times (1, 2, and 3 h) and solvent/kernel ratios (1:1, 2:1, and 3:1). The oil yield was primarily affected by the solvent/kernel ratio and then time and temperature, respectively. The protein content of the oil-free residue was 47%.     

Helical metallomacrocyclic nickel(II) complexes of a tetradentate N2S2 ligand derived from N-methyldithiocarbazic acid

The structure of the tetradentate sulphur–nitrogen chelating agent, 1,2-phenylenebis(methylene) bis(1-methylhydrazinecarbodithioate) (NSSN) has been determined by X-ray diffraction. In the solid state, the two arms of the ligand remain in E configurations about the hydrazinic C–N bonds. The ligand reacts with hydrated nickel(II) salts in a 3:2 ligand-to-metal ratio yielding dimeric triple helical complexes of general formula, [Ni₂(NSSN)₃]X₄·nH₂O (X = Cl, Br, I, NCS; n = 5, 6, 9) which contain two nickel(II) ions in distorted octahedral environments. An X-ray crystallographic structure determination of the [Ni₂(NSSN)₃]Cl₄·9H₂O complex shows that it is a metallomacrocycle in which each ligand acts as a bis-bidentate chelating agent providing NS donors of one arm to one nickel(II) ion and NS donors of the second arm to a second nickel(II) ion. Each nickel(II) ion adopts a distorted octahedral geometry with a NiN₃S₃ coordination core as shown by their common electronic spectra.     

Detection of microscopic glaucoma through fundus images using deep transfer learning approach

Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detected since there are no symptoms, but it can be detected using tonometry, ophthalmoscopy, and perimeter. However, advances in artificial intelligence approaches have permitted machine learning techniques to diagnose at an early stage. Numerous methods have been proposed using Machine Learning to diagnose glaucoma with different data sets and techniques but these are complex methods. Although, medical imaging instruments are used as glaucoma screening methods, fundus imaging specifically is the most used screening technique for glaucoma detection. This study presents a novel DenseNet and DarkNet combination to classify normal and glaucoma affected fundus image. These frameworks have been trained and tested on three data sets of high-resolution fundus (HRF), RIM 1, and ACRIMA. A total of 658 images have been used for healthy eyes and 612 images for glaucoma-affected eyes classification. It has also been observed that the fusion of DenseNet and DarkNet outperforms the two CNN networks and achieved 99.7% accuracy, 98.9% sensitivity, 100% specificity for the HRF database. In contrast, for the RIM1 database, 89.3% accuracy, 93.3% sensitivity, 88.46% specificity has been attained. Moreover, for the ACRIMA database, 99% accuracy, 100% sensitivity, 99% specificity has been achieved. Therefore, the proposed method is robust and efficient with less computational time and complexity compared to the literature available.     

Characterization of a Cohesive Soil Bed using a Cone Pressuremeter

A new cone pressuremeter has been developed at the University of Engineering and Technology, Lahore-Pakistan. The new device is called the Akbar Pressuremeter (APMT). This paper is based on the APMT testing of a cohesive soil deposit comprised of low plastic lean clay (CL) to sandy silty clay (CL-ML). The APMT testing, using a full-displacement technique, standard penetration tests and undisturbed samples, was carried out at two locations. The soil strength and type were determined using the undisturbed samples. The applied pressure-cavity strain curves of the APMT tests performed at different levels were analysed to determine soil strength and stiffness. The undrained shear strength of the undisturbed samples was determined in the laboratory by unconfined compression tests. This paper provides a comparison of the parameters interpreted from the pressuremeter and those determined from other field and laboratory methods.     

Prediction of the plastic viscosity of self-compacting steel fibre reinforced concrete

Micromechanical constitutive models are used to predict the plastic viscosity of self-compacting steel fibre reinforced concrete (SCFRC) from the measured plastic viscosity of the paste. The concrete is regarded as a two-phase composite in which the solid phase is suspended in a viscous liquid phase. The liquid matrix phase consists of cement, water and any viscosity modifying agent (VMA) to which the solids (fine and coarse aggregates and fibres) are added in succession. The predictions are shown to correlate very well with available experimental data. Comments are made on the practical usefulness of the predicted plastic viscosity in simulating the flow of SCFRC.     

A new and applicable method to calculate mass and heat transfer coefficients and efficiency of industrial distillation columns containing structured packings

Most of the methods developed for efficiency estimation of distillation columns were based on the empirical mass transfer and hydraulic relations correlated to laboratory data. Therefore, these methods cannot estimate efficiency of industrial columns with sufficient accuracy. In this paper, a new and applicable method was developed for calculation of efficiency (and mass and heat transfer coefficients) of distillation columns containing structured packings. This method has potential advantages; e.g., it can calculate efficiency without using any empirical mass transfer and hydraulic correlations and models, and without the need to estimate the operational and hydraulic parameters of column. Therefore, it will be free of errors, limitations, and complexities of such empirical items. In addition, precision of the method does not decrease with increasing complexity of operating conditions and design parameters of column. The method can be used for efficiency calculation of any structured packing, including new ones, in distillation columns.     

Optimal visual sensor placement for coverage based on target location profile

The proper placement of visual sensors across a sensor field for covering targets with arbitrary location and orientation is a mission-critical decision in surveillance applications. The specifics of sensor deployment in these applications not only determine the maximum achievable coverage, but it also affects the quality of the target’s appearance in cameras for subsequent use in vision tasks. However, the inaccuracies inherent in localization techniques and the lack of knowledge regarding the target orientation render existing proposals insufficient for real-life scenarios. In this paper, we address both challenges. First, we extend the conventional point representation of targets with a circular model to account for full-angle coverage of targets with known location yet with unknown orientation from all directions. We then assume, in the absence of precise location information, a trajectory profile of targets could instead be generated through the importance sampling of the environment, indicating spots where the target is most likely located. This profile-based abstraction enables us to capture the uncertainty in target’s location by encircling every agglomeration of proximal samples within one cluster. Each cluster can then be viewed as a virtual macroscopic circular target for which we formulate the coverage problem in terms of a Binary Integer Programming (BIP) model. We have also taken into account the presence of obstruction in between multiple targets by calculating the angles of view of the sensors in an occlusion-dependant manner, effectively determining optimal placement for maximal instead of full-angle coverage. Evaluation results derived from our simulation experiments reveal that the proposed mechanism can effectively achieve high coverage accuracy with minimum number of deployed sensors.     

Effects of slip and heat transfer on the peristaltic flow of a third order fluid in an inclined asymmetric channel

The present investigation is concerned with the peristaltic flow of an incompressible and magnetohydrodynamic MHD third order fluid in an inclined asymmetric channel. Both thermal and velocity slip conditions have been taken into account. The channel walls are maintained at different temperatures. The governing differential systems subjected to their boundary conditions have been solved for small Deborah number. In the derived solution expressions, the long wavelength and low Reynolds number assumptions are utilized. Graphical results are presented for the pressure rise, longitudinal velocity and temperature. Comparison with the previous published work regarding viscous fluid and no-slip case is performed. Pumping and trapping phenomena are displayed graphically just to examine the various interesting aspects of the present work.     

语义词特征提取及其在维吾尔文文本分类中的应用

基于机器学习的文本分类中,维吾尔文传统分词方法表现出非常明显的不足和局限性。该文使用另外一种维吾尔文自动分词方法dme-TS。dme-TS中,不再以词间空格作为切分标记提取词特征,而是用一种组合统计量(dme)来度量文本中相邻单词之间的关联程度,并以dme度量的弱关联的词间位置作为切分点,提取对学习算法真正有意义的语义词特征。实验结果表明,用dme-TS提取文本特征可以降低特征空间的维度,同时也能有效的提高传统以单词为特征的分类算法的性能。