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Karbala International Journal of Modern Science
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  This is an Open Access Journal Open Access journal
ISSN (Print) 2405-609X - ISSN (Online) 2405-6103
Published by Digital Commons Homepage  [8 journals]
  • Prediction of refractive indices and molecular radii of binary mixtures of
           Polyethylene glycol (PEG-200,400) and cyclic ethers: Insight into
           molecular interactions

    • Authors: Monika Dhiman et al.
      Abstract: Using five refractive index mixing rules: Lorentz–Lorenz, Gladstone–Dale, Weiner, Heller, and Arago –Biot; refractive indices of six binary polymer mixtures have been determined at 303.15 K, under atmospheric pressure. The binary mixtures investigated here are PEG-200 + 1,3-Dioxolane, PEG-200 + Oxolane, PEG-200 + Oxane, PEG-400 + 1,3-Dioxolane, PEG-400 + Oxolane, and PEG-400 + Oxane. A good agreement has been observed between the obtained results and respective literature data for all these mixtures. The relative merit of refractive index mixing rules is assessed. Deviation in refractive index and reduced free volume values are also calculated using the refractive index data taken from the literature. Furthermore, the molecular radius of these binary polymer mixtures is computed with the help of the refractive index and molar volume data. In addition, an ideal mixing method is also employed to calculate the molecular radius of these systems.The molecular radius of these binary mixtures is found to be additive with respect to the mole fraction of the pure components. Finally, the results are discussed in terms of the intermolecular interactions among the constituent molecules.
      PubDate: Fri, 11 Nov 2022 09:02:34 PST
  • Interactive virtualisation in Java Script of electromagnetism when
           changing the dynamic, static parameters of ferromagnetics

    • Authors: Adambek Tatenov et al.
      Abstract: Laboratory-based work is necessary for developing the skills of measuring physical quantities, performing physics experiments, and drawing correct conclusions from their observations. Training programs that simulate physics processes and phenomena that cannot always be shown “live”, can significantly help students. The authors developed and implemented the processes of the phenomenon of the physics course “Electromagnetism” – “Study of dynamic hysteresis of ferromagnets” and interactively virtualised using the Java Script computer software environment. The originality of this approach is that it provides a convenient tool for creating a simulation environment for any physics problem. This interactive virtual laboratory development will be introduced into the educational process of the Kazakh National Women’s Pedagogical University.
      PubDate: Fri, 11 Nov 2022 09:02:29 PST
  • An Enhanced Diabetic Foot Ulcer Classification Approach Using GLCM and
           Deep Convolution Neural Network

    • Authors: Hussein A. Ismael et al.
      Abstract: Diabetic Foot Ulcers (DFU) are considered to be a common complication of diabetes, usually resulting in the amputation of lower extremities. Therefore, diagnosing this disease at an early stage is necessary to avoid the accompanying treatment approach, and this results in a significant cost reduction for the patient. To achieve an early diagnosis of this disease, we need to classify a patient's skin as normal or abnormal. A classification process relies heavily on the extracted features. So, we proposed a new technique called CNN_GLCMNet for feature extraction. This technique relies on Convolution Neural Network (CNN) and the Gray-Level Co-Occurrence Matrix (GLCM) techniques to mine abstract features and second-order statistical texture features. Also, Singular Value Decomposition (SVD) is applied to reduce the dimensionality of the obtained features that result from CNN, Next, the GLCM method is applied to extract second-order statistical texture features. Then, these two kinds of features (abstract features and statistical features) are combined and used as input for the classifier. Two classification mechanisms have been adopted in the classification of images into normal and abnormal skin. First, the Deep Neural Network (DNN) classifier achieves the following performance evaluation metrics (accuracy 97.43%, recall 97.25%, specificity 97.59%, precision 97.53%, f1-score 97.38%). Second, the Support Vector Machine (SVM) classifier achieves the following performance evaluation metrics (accuracy 96.93%, recall 96.99%, specificity 96.94%, precision 96.76%, f1-score 96.85%). Since both classifiers have been validated against the DFU dataset using 10-fold cross-validation. The DNN classifiers with our new feature extraction technique achieve better results in terms of accuracy, specificity, precision, recall, and f1-score than in previous work. Furthermore, a comparison of DNN and SVM classifiers finds that DNN gives a better result according to performance metrics.
      PubDate: Fri, 11 Nov 2022 09:02:24 PST
  • Potential use of proteolytic bacteria Paenibacillus dendritiformis (BT7)
           isolated from Batu tannery effluents for the detergent industry

    • Authors: Chandran Masi et al.
      Abstract: This research was aimed at identifying a bacterium that can produce alkaline proteases. As a result, bacteria that produce proteases were isolated from Batu tannery effluents, tested for protease synthesis on skim milk agar plates, and validated with a protease assay. Microscopic and molecular phylogenetic analyses identified Paenibacillus dendritiformis (BT7) as the bacterial isolate with the highest alkaline protease production. The isolate's maximum enzyme production was obtained by 2% inoculum size, 40°C temperature, 9.0 pH, and a 48-hour incubation time with production media components such as glucose, casein, MgCl2, and 2% NaCl. The maximal enzyme activity was 270 U/mL under all optimum culture conditions. Concentrated ammonium sulfate precipitation (75%) and dialysis were employed to obtain a cell-free, partially purified protease. The specific activity of the dialysate, which accounts for 3% of the enzyme yield, was discovered to be 134 U/mL. The partially purified protease was used for application in blood stain removal. It was studied and found that the alkaline protease resistance under stringent conditions is very stable with bleach detergent. Also, this enzyme could clean blood-stained fabrics. This study shows that the alkaline protease from Paenibacillus dendritiformis - BT7 could be used in various ways in the detergent industry that are good for the environment.
      PubDate: Fri, 11 Nov 2022 09:02:19 PST
  • Sustainable material for urea delivery based on chitosan cross-linked by
           glutaraldehyde saturated toluene: Characterization and determination of
           the release rate mathematical model

    • Authors: Jayanudin Jayanudin et al.
      Abstract: The aims of this study were to characterize the urea-loaded chitosan microspheres and determine the release kinetic constants and diffusion coefficients. An emulsion cross-linking method was used to prepare the urea-loaded chitosan microspheres. Urea was dissolved in a solution of chitosan then put into vegetable oil and stirred to form an emulsion. Glutaraldehyde saturated toluene (GST) was added into the emulsion dropwise while continuously stirring for the solidification process. Chitosan microspheres filled with urea were washed, dried, and then analyzed. Characterization of the urea-loaded chitosan microspheres was conducted using a scanning electron microscope (SEM), Raman spectroscopy, X-ray diffraction, and particle size distribution. The cumulative release analysis was used to determine the amount of urea released from the chitosan microspheres and determine the release kinetic constants and diffusion coefficients. The chitosan microspheres had a good spherical geometry with a smooth surface and crystallinity of 95.5 - 98.18%. They had a diameter in the range of 125.31 - 153.65 m and a cumulative release value in the range of 38.22 - 48.06%. Based on the kinetic analysis, the best kinetic models were models of Korsmeyer-Peppas, Peppas-Sahlin, and simple power law with the burst effect resulting in the highest R2 of 0.99. The diffusion coefficient obtained was in the range of 5.439 × 10-11 - 7.512 × 10-11 cm2/sec.
      PubDate: Fri, 11 Nov 2022 09:02:14 PST
  • Magnéli Phase Titanium Sub-Oxide Production using a Hydrothermal

    • Authors: Mohanad Q. Fahem et al.
      Abstract: One gram of TiO2 nanoparticles, size of 30-50 nm and 20 ml of 3M of NaOH as the suspension were utilized in a hy-drothermal process using three homemade reactors of different surface areas but of the same capacity to synthesise tita-nium sub-oxide Ti6O11. X-ray diffraction, Raman spectroscopy, and field-emission scanning electron microscopy (FE-SEM) were employed to characterise the samples. When the temperature was raised to 363 K (90 °C) for 6 h and the surface area changed, X-ray diffraction revealed the development of sub-oxide titanium (Ti6O11) with a triclinic Magnéli phase from TiO2 nanoparticles. FE-SEM revealed consistent hierarchical structures with grass-like planar ge-ometries. In titanium, a new phase has been discovered.
      PubDate: Fri, 11 Nov 2022 09:02:09 PST
  • Controlling the heterogeneous vancomycin intermediated Staphylococcus
           aureus (hVISA) through the use of Rosmarinus officinalis L. leaves extract

    • Authors: Osamah Z. Al-Hayali et al.
      Abstract: Treatment failure and persistent infection are two serious problems that result from Staphylococcus aureus having decreased vancomycin susceptibility. The present study investigates the effects of Rosmarinus officinalis L. leaves extract on the growth and quorum sensing (QS) of heterogeneous vancomycin-intermediate S. aureus hVISA isolates. Real-time RT-polymerase chain reaction (PCR) is used to determine the transcriptional changes of the accessory gene regulator (agr) in the (hVISA) strain after and before being treated with the R. officinalis leaves extract. The antibacterial activity of the EOs was determined through the use of a microtitre plate test (MtP); with a minimum inhibitory concentration (MIC) of 0.7 mg/ml against hVISA, a rosemary leaves extract has a high level of inhibitory activity. However, the extraction of essential oils is done in a soxhlet apparatus by using ethanol 96%. Gas chromatography and mass spectrometry (GC/MS) and revealed the most common chemicals containing verbenone, 36.20%, and 1,8-cineol (Eucalyptol), 12.14%. Finally, after treatment with sub-MIC concentration, hld gene levels significantly decreased in hVISA strains (a mean of 0.4-fold) in comparison to the control strains (1.0-fold) in late-exponential phases, regardless of whether the agr mutation is present. The down regulation of the hld gene may be a significant genetic event in the hVISA strain.
      PubDate: Fri, 11 Nov 2022 09:02:04 PST
  • Characterization of Algerian apricots (Prunus armeniaca) using
           morphological and pomological markers

    • Authors: Kaouther Boutiti et al.
      Abstract: The aim of this study was to evaluate the diversity of an Algerian apricot germplasm. This Algerian apricot was characterized by a green-yellow skin, a red ground color, and a light orange flesh color in general. Besides, highly positive and negative significant correlations were revealed between the studied characters. Whereby, the principal component analysis explained 81% of the variability. Fruit, stone and leaves dimensions were the main features that explained evidentially the majority of variability. Moreover, the cluster analysis divided the accessions into two major groups. Thus, Algerian accessions selected in this study may have the potential to be used in apricot breeding programs in the future.
      PubDate: Fri, 11 Nov 2022 09:01:59 PST
  • Predicting Users’ Personality on Social Media: A Comparative Study of
           Different Machine Learning Techniques

    • Authors: Ali Saadi Al-Fallooji et al.
      Abstract: The use of social media sites (SMSs) becomes ubiquitous worldwide as the number of users is noticeably increasing. This has led to exploiting such sites by market, business, and educational companies to deliver content that meets users’ personal needs. However, this requires identifying users’ personalities to respond to their individual preferences. This research aims at (1) analyzing users' posts on SMSs to predict their personality based on the Meyers-Briggs Type Indicator (MBTI) model, (2) comparing the performance accuracy of different preprocessing and data mining techniques, and (3) improving the prediction accuracy of users' personality types. The used dataset includes 8668 records in which each raw contains fifty posts. Three data mining techniques are applied namely, support vector machine (SVM), logistic regression (LR), and lightGBM. The findings suggest that lightGBM with the application of stemming, lemmatization, and grid search optimization as well as removing stop-words outperformed other techniques. The prediction accuracies for the four personality dimensions namely, Introversion-Extroversion (I-E), Intuition-Sensing (N-S), Feeling-Thinking (F-T), and Judging-Perceiving (J-P) are 100.0%. The research outcomes are promising as the four dimensions of MBTI have been identified effectively. Such outcomes are also compared with earlier research on personality prediction. This study can help SMSs providers, businesses, and educational institutions adapt their online sites based on users’ posts, tweets, and comments that can be used to predict their personality behavior.
      PubDate: Fri, 11 Nov 2022 09:01:54 PST
  • Investigation of a Solar Space Heating System Based on an Evacuated Tube
           Collector for Baghdad Climatic Conditions

    • Authors: Alaa H. Shneishil et al.
      Abstract: The current work aims at evaluating the overall experimental performance of the solar space heating system regarding the local meteorological conditions of Baghdad. Two types of systems have been considered, namely, the thermal radiator with and without a fan. Indeed, a thermal radiator is connected to evacuate the tube solar collector through a circulating pump to distribute the heat into a room with dimensions of (3×3 m2). The experimental measurements for the current research were carried out during a period of six days in February and March, i.e., under mild conditions where the weather ranged between sunny and cloudy during the day. In order to find out the system performance, the solar radiation intensity, wind speed, inlet and outlet temperatures from solar collectors, inlet and outlet temperatures from thermal radiators, room temperature, and ambient temperature have been measured using specific instruments. The results revealed that, under sunny conditions, the room temperature of the system without a fan changes from 16.2 oC to 23.9 oC with a temperature difference of 7.7 oC. However, when a fan is used, the temperature difference reaches 10 oC. Such an improvement in the performance of the solar heating system that characterized the current work in comparison with its counterpart research is a direct result of the adoption of two main ideas. The first of them is the use of a modern metal heat exchanger that contains vertical and horizontal tubes. In addition to that, it is equipped with a fan that helps to increase the heat exchange between the heat radiator and the room space due to forced convection. Furthermore, the fan works to distribute the heat homogeneously inside the room, thus increasing the efficiency of the system. The second idea, however, is the use of a modern system represented by software that analyzes the results and evaluates the values of solar radiation and wind speed.
      PubDate: Fri, 11 Nov 2022 09:01:50 PST
  • RDLNN-based Image Forgery Detection and Forged Region Detection Using MOT

    • Authors: Akram Hatem Saber et al.
      Abstract: Image forgery detection TEMPhas become an emerging research area due to the increasing number of forged images circulating on the internet and other social media, which leads to legal and social issues. Image forgery detection includes the classification of an image as forged or authentic and as well as localizing the forgery wifin the image. In this paper, we propose a Regression Deep Learning Neural Network (RDLNN) based image forgery detection followed by Modified Otsu Thresholding (MOT) algorithm to detect the forged region. The proposed model comprises five steps that are preprocessing, image decomposition, feature extraction, classification and block matching. In the preprocessing step, the RGB images are converted to YCbCr color format. Then, the images are decomposed using the new Polar Dyadic Wavelet Transform (PDyWT), followed by the extraction of important features. The classification phase called RDLNN effectively classifies the normal image and the forged image. For localization of the forgery, the forged image is divided into a number of blocks, and then Genetic Three Step Search (GTSS) algorithm is exploited to identify the dissimilar blocks. To get the exact forged region in the image, the dissimilar blocks are analyzed by the Modified Otsu Thresholding (MOT) algorithm. The proposed algorithm is compared wif widely used image forgery detection algorithms. The results show that the proposed method improves the forgery detection accuracy and precision by at least 6.04% and 3.77%, respectively, as compared to the already existent techniques such as ANFIS, KNN, ANN, and SVM. Moreover, the training time of the proposed network is lower by at least 64.3 % than the above existing techniques
      PubDate: Fri, 11 Nov 2022 09:01:45 PST
  • A Clustering Approach Based on Fuzzy C-Means in Wireless Sensor Networks
           for IoT Applications

    • Authors: Ali Mohammed Kadhim Abdulzahra et al.
      Abstract: Sensor nodes in Wireless Sensor Network (WSN)-based Internet of Things (IoT) networks are often battery-powered, resulting in supplying relatively low energy. Energy efficiency in WSN-based IoT systems is a critical challenge as the IoT becomes more sophisticated owing to its widespread adoption. Clustering-based routing approaches are well-known approaches that have distinct benefits in terms of efficient communication, scalability, and network lifespan extension. In this research, we present a novel clustering technique for WSN-based IoT systems based on Fuzzy C-Means (FCM). To pick the best Cluster Head (CH), the method uses an FCM technique to build the clusters and a reduction in the total energy spent on each cluster. Rather than replacing CHs for dynamic clustering at each period in this study, we plan to use an energy threshold to hypothesize the dynamicity of CH dependent on existing energy levels, therefore increasing the sensor network lifespan
      PubDate: Fri, 11 Nov 2022 09:01:40 PST
  • Evaluating Accuracy of Solar Ground Station System Data

    • Authors: Emad Jaleel Mahdi et al.
      Abstract: The solar radiation data is crucial for many solar applications. In this regard, this manuscript analyzed and compared the correctness of ground station data with data from the PVGIS. Empirical equations were adopted to build software. This software calculates the global, direct, diffuse, and rates of solar radiation data on two axes. The result of the analysis was employed to assess the compatibility, dependability, and confidence of these types of data. The comparison showed that vastly variations in some curves from dawn to sunset. In sum, every site exhibit variation in solar radiation station rates. These results could serve as a starting point for users of solar data when analyzing the expected uncertainty for a given data
      PubDate: Fri, 11 Nov 2022 09:01:35 PST
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