Search results for: neural control

Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

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Authors: Emmanuel Iyamuremye, Jean François Maniriho, Irenee Ndayambaje

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Mathematics is the foundational enabling discipline that underpins science, technology, and engineering disciplines. Science, technology, engineering, and mathematics (STEM) subjects are foreseen as the engine for socio-economic transformation. Rwanda has done reforms in education aiming at empowering and preparing students for the real world job by providing career pathways in science, technology, engineering, and mathematics related fields. While that considered so, the performance in mathematics has remained deplorable in both formative and national examinations. Therefore, this paper aims at exploring the extent to which the engage, explore, explain, elaborate and evaluate (5Es) instructional model contributing towards students’ achievement in mathematics. The present study adopted the pre-test, post-test non-equivalent control group quasi-experimental design. The 5Es instructional model was applied to the experimental group while the control group received instruction with the conventional teaching method for eight weeks. One research-made instrument, mathematics achievement test (MAT), was used for data collection. A pre-test was given to students before the intervention to make sure that both groups have equivalent characteristics. At the end of the experimental period, the two groups have undergone a post-test to ascertain the contribution of the 5Es instructional model. Descriptive statistics and analysis of covariance (ANCOVA) were used for the analysis of the study. For determining the improvement in mathematics, Hakes methods of calculating gain were used to analyze the pre-test and post-test scores. Results showed that students exposed to 5Es instructional model achieved significantly better performance in mathematics than students instructed using the conventional teaching method. It was also found that 5Es instructional model made lessons more interesting, easy and created friendship among students. Thus, 5Es instructional model was recommended to be adopted as a close substitute to the conventional teaching method in teaching mathematics in lower secondary schools in Rwanda.

Keywords: 5Es instructional model, achievement, conventional teaching method, mathematics

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Authors: Chittapon Jantararussamee, Malai Taweechotipatr, Udomsri Showpittapornchai, Wisuit Pradidarcheep

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Hepatic fibrosis is characterized by collagen accumulation in hepatic lobules following wound healing process. If lefts untreated, it could progress into hepatic cirrhosis, portal hypertension, and liver failure. Probiotics comprise of lactic acid bacteria which are crucial components of the intestinal microflora and possess many beneficial properties. The objective of this study is to investigate the hepatoprotective effects of probiotic lactic acid bacteria (mixture of Lactobacillus paracasei, Lactobacillus casei, and Lactobacillus confusus at a ratio of 1: 1: 1) on thioacetamide-induced liver fibrotic rats in term of histomorphology study. Twenty-four male Wistar rats were randomly divided into four groups with 6 rats each: (A) control, (B) fibrotic, (C) fibrotic+probiotic, and (D) probiotic. Group (A) received daily oral administration of distilled water. Group (B and C) were induced by intraperitoneal injection of thioacetamide (TAA) (200 mg/kg BW) 3 times per week for consecutive 8 weeks. In probiotic-treated group (C and D), the number of a mixture of the viable microbial cells at 10⁹ CFU/ml was administered orally daily. After sacrifice, liver tissues were collected and processed for routine histological technique and stained with Sirius red. It was found that the fibrotic rats showed hepatic injury marked by area of inflammation, hydropic degeneration of hepatocytes, and accumulation of myofibroblast-like cells. The collagen fibers were substantially accumulated in the hepatic lobules. Moreover, probiotic-treated group significantly reduced the accumulation of collagen in rats treated by TAA. The liver damage was found to be lesser in the probiotic-treated group. It was noted that the liver tissues of control and probiotics groups were shown to be normal. Administration with probiotic lactic acid bacteria could improve the histomorphology in fibrotic liver and be useful for prevention of hepatic disorders.

Keywords: liver fibrosis, probiotics, lactic acid bacteria, thioacetamide

Procedia PDF Downloads 118

Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

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Authors: Imran Dayan, Ashiqul Khan

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Internal corporate fraud, which is fraud carried out by internal stakeholders of a company, affects the well-being of the organisation just like its external counterpart. Even if such an act is carried out for the short-term benefit of a corporation, the act is ultimately harmful to the entity in the long run. Internal fraud is often carried out by relying upon aberrations from usual business processes. Business processes are the lifeblood of a company in modern managerial context. Such processes are developed and fine-tuned over time as a corporation grows through its life stages. Modern corporations have embraced technological innovations into their business processes, and Enterprise Resource Planning (ERP) systems being at the heart of such business processes is a testimony to that. Since ERP systems record a huge amount of data in their event logs, the logs are a treasure trove for anyone trying to detect any sort of fraudulent activities hidden within the day-to-day business operations and processes. This research utilises the ERP systems in place within corporations to assess the likelihood of prospective internal fraud through developing a framework for measuring the risks of fraud through Process Mining techniques and hence finds risky designs and loose ends within these business processes. This framework helps not only in identifying existing cases of fraud in the records of the event log, but also signals the overall riskiness of certain business processes, and hence draws attention for carrying out a redesign of such processes to reduce the chance of future internal fraud while improving internal control within the organisation. The research adds value by applying the concepts of Process Mining into the analysis of data from modern day applications of business process records, which is the ERP event logs, and develops a framework that should be useful to internal stakeholders for strengthening internal control as well as provide external auditors with a tool of use in case of suspicion. The research proves its usefulness through a few case studies conducted with respect to big corporations with complex business processes and an ERP in place.

Keywords: enterprise resource planning, fraud risk framework, internal corporate fraud, process mining

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Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh

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In brainwaves research using electroencephalogram (EEG) signals, finding the most relevant and effective feature set for identification of activities in the human brain is a big challenge till today because of the random nature of the signals. The feature extraction method is a key issue to solve this problem. Finding those features that prove to give distinctive pictures for different activities and similar for the same activities is very difficult, especially for the number of activities. The performance of a classifier accuracy depends on this quality of feature set. Further, more number of features result in high computational complexity and less number of features compromise with the lower performance. In this paper, a novel idea of the selection of optimal feature set using a heuristically optimized deep autoencoder is presented. Using various feature extraction methods, a vast number of features are extracted from the EEG signals and fed to the autoencoder deep neural network. The autoencoder encodes the input features into a small set of codes. To avoid the gradient vanish problem and normalization of the dataset, a meta-heuristic search algorithm is used to minimize the mean square error (MSE) between encoder input and decoder output. To reduce the feature set into a smaller one, 4 hidden layers are considered in the autoencoder network; hence it is called Heuristically Optimized Deep Autoencoder (HO-DAE). In this method, no features are rejected; all the features are combined into the response of responses of the hidden layer. The results reveal that higher accuracy can be achieved using optimal reduced features. The proposed HO-DAE is also compared with the regular autoencoder to test the performance of both. The performance of the proposed method is validated and compared with the other two methods recently reported in the literature, which reveals that the proposed method is far better than the other two methods in terms of classification accuracy.

Keywords: autoencoder, brainwave signal analysis, electroencephalogram, feature extraction, feature selection, optimization

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Authors: Iuliia Zarubiieva, Joyun Tseng, Vishwesh Kulkarni

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Recent researches has focused on nucleic acids as a substrate for designing biomolecular circuits for in situ monitoring and control. A common approach is to express them by a set of idealised abstract chemical reaction networks (ACRNs). Here, we present new results on how abstract chemical reactions, viz., catalysis, annihilation and degradation, can be used to implement circuit that accurately computes logarithm function using the method of Arithmetic-Geometric Mean (AGM), which has not been previously used in conjunction with ACRNs.

Keywords: chemical reaction networks, ratio computation, stability, robustness

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Authors: Alvarez M., Chapela M. J., Balboa E., Rubianes D., Sinde E., Fernandez de Ana C., Rodríguez-Blanco A.

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The gut microbiota plays an important role as gut inflammation could contribute to colorectal cancer development; however, this role is still not fully understood, and tools able to prevent this progression are yet to be developed. The main objective of this study was to monitor the effects of a mushroom extracts formulation in gut microbial community composition of an Azoxymethane (AOM)/Dextran sodium sulfate (DSS) mice model of inflammation-linked carcinogenesis. For the in vivo study, 41 adult male mice of the C57BL / 6 strain were obtained. 36 of them have been induced in a state of colon carcinogenesis by a single intraperitoneal administration of AOM at a dose of 12.5 mg/kg; the control group animals received instead of the same volume of 0.9% saline. DSS is an extremely toxic polysaccharide sulfate that causes chronic inflammation of the colon mucosa, favoring the appearance of severe colitis and the production of tumors induced by AOM. Induction by AOM/DSS is an interesting platform for chemopreventive intervention studies. This time the model was used to monitor gut microbiota changes as a result of supplementation with a specific mushroom extracts formulation previously shown to have prebiotic activity. The animals have been divided into three groups: (i) Cancer + mushroom extracts formulation experimental group: to which the MicoDigest2.0 mushroom extracts formulation developed by Hifas da Terra S.L has been administered dissolved in drinking water at an estimated concentration of 100 mg / ml. (ii) Control group of animals with Cancer: to which normal water has been administered without any type of treatment. (iii) Control group of healthy animals: these are the animals that have not been induced cancer or have not received any treatment in drinking water. This treatment has been maintained for a period of 3 months, after which the animals were sacrificed to obtain tissues that were subsequently analyzed to verify the effects of the mushroom extract formulation. A microbiological analysis has been carried out to compare the microbial communities present in the intestines of the mice belonging to each of the study groups. For this, the methodology of massive sequencing by molecular analysis of the 16S gene has been used (Ion Torrent technology). Initially, DNA extraction and metagenomics libraries were prepared using the 16S Metagenomics kit, always following the manufacturer's instructions. This kit amplifies 7 of the 9 hypervariable regions of the 16S gene that will then be sequenced. Finally, the data obtained will be compared with a database that makes it possible to determine the degree of similarity of the sequences obtained with a wide range of bacterial genomes. Results obtained showed that, similarly to certain natural compounds preventing colorectal tumorigenesis, a mushroom formulation enriched the Firmicutes and Proteobacteria phyla and depleted Bacteroidetes. Therefore, it was demonstrated that the consumption of the mushroom extracts’ formulation developed could promote the recovery of the microbial balance that is disrupted in the mice model of carcinogenesis. More preclinical and clinical studies are needed to validate this promising approach.

Keywords: carcinogenesis, microbiota, mushroom extracts, inflammation

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Authors: Maryam Radan, Fereshteh Nejad Dehbashi, Vahid Bayati, Mahin Dianat, Seyyed Ali Mard, Zahra Mansouri

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Pulmonary emphysema is a pathological respiratory condition identified by alveolar destruction which leads to limitation of airflow and diminished lung function. A substantial body of evidence suggests that mesenchymal stem cells (MSCs) have the ability to induce tissue repair primarily through a paracrine effect. In this study, we aimed to determine the efficacy of Intratracheal adipose-derived mesenchymal stem cells (ADSCs) therapy in comparison to this approach with that of Intravenous (Systemic) therapy. Fifty adult male Sprague–Dawley rats weighing between 180 and 200 g were used in this experiment. The animals were randomized to Control groups (Intratracheal or Intravenous vehicle), Elastase group (intratracheal administration of porcine pancreatic elastase; 25 U/kg on day 0 and day 10th), Elastase+Intratracheal ADSCs therapy (1x107 Cells, on day 28) and Elastase+Systemic ADSCs therapy (1x107 Cells, on day 28). The rats which not subjected to any treatment, considered as the control. All rats were sacrificed 3 weeks later. Morphometric findings in lung tissues (Mean linear intercept) confirmed the establishment of the emphysema model via alveolar disruption. Contrarily, ADSCs administration partially restored alveolar architecture. These results were associated with improving arterial oxygenation, reducing lung edema, and decreasing lung inflammation with higher significant effects in the Intratracheal therapy route. These results documented that the efficacy of intratracheal ADSCs was comparable with intravenous ADSCs therapy. Accordingly, the obtained data suggested that intratracheal delivery of ADSCs would enhance lung repair in pulmonary emphysema. Moreover, this method provides benefits over a systemic administration, such as the reduction of cell number and the low risk to engraft other organs.

Keywords: mesenchymal stem cell, emphysema, Intratracheal, systemic

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Małgorzata Tomczyńska, Joanna Saluk-Bijak

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Background: Graves’ disease is a frequent organ-specific autoimmune thyroid disease, which characterized by the presence of different kind autoantibodies, that, in most cases, act as agonists of the thyrotropin receptor, leading to hyperthyroidism. Role of platelets and lymphocytes can be modulated in the pathophysiology of thyroid autoimmune diseases. Interference in the physiology of platelets can lead to enhanced activity of these cells. Activated platelets can bind to circulating lymphocytes and to affect lymphocyte adhesion. Platelets and lymphocytes can regulate mutual functions. Therefore, the activation of T lymphocytes, as well as blood platelets, is associated with the development of inflammation and oxidative stress within the target tissue. The present study was performed to investigate a platelet-lymphocyte relation by assessing the degree of their mutual aggregation in whole blood of patients with Graves’ disease. Also, the purpose of this study was to examine the impact of platelet interaction on lymphocyte migration capacity. Methods: 30 patients with Graves’ disease were recruited in the study. The matched 30 healthy subjects were served as the control group. Immunophenotyping of lymphocytes was carried out by flow cytometry method. A CytoSelect™ Cell Migration Assay Kit was used to evaluate lymphocyte migration and adhesion to blood platelets. Visual assessment of lymphocyte-platelet aggregate morphology was done using confocal microscope after magnetic cell isolation by Miltenyi Biotec. Results: The migration and functional responses of lymphocytes to blood platelets were greater in the group of Graves’ disease patients compared with healthy controls. The group of Graves’ disease patients exhibited a reduced T lymphocyte and a higher B cell count compared with controls. Based on microscopic analysis, more platelet-lymphocyte aggregates were found in patients than in control. Conclusions: Studies have shown that in Graves' disease, lymphocytes show increased platelet affinity, more strongly migrating toward them, and forming mutual cellular conglomerates. This may be due to the increased activation of blood platelets in this disease.

Keywords: blood platelets, cell migration, Graves’ disease, lymphocytes, lymphocyte-platelet aggregates

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Authors: Valeria M. Bastos, Myrian A. Costa, Matheus Ambrozio, Nelson F. F. Ebecken

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In this paper a very simple and effective user administration view of computing clusters systems is implemented in order of friendly provide the configuration and monitoring of distributed application executions. The user view, the administrator view, and an internal control module create an illusionary management environment for better system usability. The architecture, properties, performance, and the comparison with others software for cluster management are briefly commented.

Keywords: big data, computing clusters, administration view, user view

Procedia PDF Downloads 314

Authors: Elena Vinogradova, Aleksei Pleshakov, Aleksei Yakovlev

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During the flight of a supersonic aircraft under various conditions (altitude, Mach, etc.), it becomes necessary to coordinate the operating modes of the air intake and engine. On the supersonic aircraft, it’s been done by changing various control factors (the angle of rotation of the wedge panels and etc.). This paper investigates the possibility of using modern optimization methods to determine the optimal position of the supersonic air intake wedge panels in order to maximize the total pressure recovery coefficient. Modern software allows us to conduct auto-optimization, which determines the optimal position of the control elements of the investigated product to achieve its maximum efficiency. In this work, the flow in the supersonic aircraft inlet has investigated and optimized the operation of the flaps of the supersonic inlet in an aircraft in a 2-D setting. This work has done using ANSYS CFX software. The supersonic aircraft inlet is a flat adjustable external compression inlet. The braking surface is made in the form of a three-stage wedge. The IOSO NM software package was chosen for optimization. Change in the position of the panels of the input device is carried out by changing the angle between the first and second steps of the three-stage wedge. The position of the rest of the panels is changed automatically. Within the framework of the presented work, the position of the moving air intake panel was optimized under fixed flight conditions of the aircraft under a certain engine operating mode. As a result of the numerical modeling, the distribution of total pressure losses was obtained for various cases of the engine operation, depending on the incoming flow velocity and the flight altitude of the aircraft. The results make it possible to obtain the maximum total pressure recovery coefficient under given conditions. Also, the initial geometry was set with a certain angle between the first and second wedge panels. Having performed all the calculations, as well as the subsequent optimization of the aircraft input device, it can be concluded that the initial angle was set sufficiently close to the optimal angle.

Keywords: optimal angle, optimization, supersonic air intake, total pressure recovery coefficient

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Authors: Wassie Molla, Klaas Frankena, Mart De Jong

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Lumpy skin disease (LSD) is a severe viral disease of cattle, which often occurs in epidemic form. It is caused by lumpy skin disease virus of the genus capripoxvirus of family poxviridae. Mathematical models play important role in the study of infectious diseases epidemiology. They help to explain the dynamics and understand the transmission of an infectious disease within a population. Understanding the transmission dynamics of lumpy skin disease between animals is important for the implementation of effective prevention and control measures against the disease. This study was carried out in central and north-western part of Ethiopia with the objectives to understand LSD outbreak dynamics, quantify the transmission between animals and herds, and estimate the disease reproduction ratio in dominantly crop-livestock mixed and commercial herd types. Field observation and follow-up study were undertaken, and the transmission parameters were estimated based on a SIR epidemic model in which individuals are susceptible (S), infected and infectious (I), and recovered and immune or dead (R) using the final size and generalized linear model methods. The result showed that a higher morbidity was recorded in infected crop-livestock (24.1%) mixed production system herds than infected commercial production (17.5%) system herds whereas mortality was higher in intensive (4.0%) than crop-livestock (1.5%) system and the differences were statistically significant. The transmission rate among animals and between herds were 0.75 and 0.68 per week, respectively in dominantly crop-livestock production system. The transmission study undertaken in dominantly crop-livestock production system highlighted the presence of statistically significant seasonal difference in LSD transmission among animals. The reproduction numbers of LSD in dominantly crop-livestock production system were 1.06 among animals and 1.28 between herds whereas it varies from 1.03 to 1.31 among animals in commercial production system. Though the R estimated for LSD in different production systems at different localities is greater than 1, its magnitude is low implying that the disease can be easily controlled by implementing the appropriate control measures.

Keywords: commercial, crop-livestock, Ethiopia, LSD, reproduction number, transmission

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Authors: Hafiza Javaria Ashraf, Asim Abbasi, Muhammad Hussnain Babar, Muhammad Sufyan

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Brevicoryne brassicae (Aphid) is not only the major biotic constraint of rapeseed crop but also transmits 20 different viral pathogens that cause diseases in crucifers. Aphids cause major losses to rapeseed by stunting growth and yield, with real damage being contamination of harvested heads. The misuse of pesticides has led to tremendous economic losses and hazards to human health and environmental pollution. Thus, newer approaches for pest control are continuously being sought. The naturally occurring, biologically active plant-based products seem to have a prominent role in the development of future commercial pesticides not only for increased productivity but their eco-friendly nature. The present experiment was carried out in Research Area of Ayub Agriculture Research Institute, Faisalabad to check the efficacy of different botanicals against rapeseed aphid. The tested botanicals were, neem seed extract, neem leaf extract, dathora seed extract, kaner leaf extract and aak leaf extract. Insecticide, advantage 20 EC served as the positive control in the experiment. Data was recorded before and after 1, 3 and 7 days of treatment application. The results of the experiment revealed that neem seed extract exhibited maximum mortality (48.42%) followed by dathora (45.54%) and kaner leaf extract (40.29%) after 7 days of treatment application. However minimum mortality i.e. 26.64% was observed in case of aak leaf extract. Advantage encountered maximum mortality i.e. 86.14%. All treatments caused maximum mortality after 7 days of treatment application. In case of pollinators maximum population reduction was observed in case of insecticide (74.29%) while minimum reduction was observed in neem leaf extract (11.57%). Hence it was concluded that unlike insecticides, plant based products can be a better option for regulating pests and conserving beneficial insect fauna.

Keywords: Aphid, mortality, plant based, pollinators

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Authors: Bakytzhan Bimbetov, Abay Zhangabilov, Saule Aitbaeva, Galymzhan Meirambekov

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Mare’s milk (saumal) contains in total about 40 biological components necessary for the human body. The most significant among them are amino acids, fats, carbohydrates, enzymes (lysozyme, amylase), more minerals and vitamins which are well balanced with each other. In Kazakhstan, Company "Eurasia Invest Ltd.” produces a freeze-dried saumal in form of powder by the use of modern German innovative technology by means of evaporating at low temperature (-35°C) with an appropriate pasteurization. Research of freeze-dried biomilk for the qualitative content showed that main ingredients of freshly drown milk are being preserved. We are currently studying medical and dietary properties of freeze-dried mare's milk for diseases of the digestive system, including for nonalcoholic steatohepatitis (NASH) and liver cirrhosis (LC) viral etiology. The studied group consisted of 14 patients with NASH, and 7 patients with LC viral etiology of Class A severity degree as per Child-Pugh. Patients took freeze-dried saumal, preliminary dissolved in boiled warm water (24 g. powder per 200 ml water) 3-4 times a day for a month in conjunction with basic therapy. The results were compared to a control group (11 patients with NASH and LC) who received only basic therapy without mare’s milk. Results of preliminary research showed an improvement of subjective and objective conditions of all patients, but more significant improvement of clinical symptoms and syndromes were observed in the treatment group compared to the control one. Patients with NASH significantly over time compared to the beginning of therapy decreased asthenic and dyspeptic syndromes (p<0,01). Hepatomegaly, identified on the basis of ultrasound prior to treatment was observed in 92,8±2,4% of patients, and after combination therapy hepatomegaly the rate decreased by 14,3%, amounting to 78,5±2,8%. Patients with LC also noted the improvement of asthenic (p<0,01) and dyspeptic (p<0,05) syndromes and hemorrhagic syndrome (nosebleeds and bleeding gums when brushing your teeth, p<0,05), and jaundice. Laboratory study also showed improvement in the research group, but more significant changes were observed in the experimental group. Group of patients with NASH showed a significant improvement of index in cytolysis in conjunction with a combination therapy (p<0,05). In the control group, these indicators were also improved, but they were not statistically reliable (p>0,05). Markers of liver failure were additionally studied during the study of laboratory parameters in patients with liver cirrhosis, in particular, bilirubin, albumin and prothrombin index (PTI). Combined therapy with the use of basic treatment and mare's milk showed a significant improvement in cytolysis and bilirubin (p<0,05). In our opinion, a very important and interesting fact is that, in conjunction with basic therapy, the use of mare's milk revealed an improvement of liver function in the form of normalized PTI and albumin in patients with liver cirrhosis viral etiology. Results of this work have shown therapeutic efficiency of the use of mare's milk in complex treatment of patients with liver disease and require further in-depth study.

Keywords: liver cirrhosis, non-alcohol steatohepatitis, saumal, mare’s milk

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Authors: Mohammad A. Alia, Abdelfatah Aref Tamimi, Omaima N. A. Al-Allaf

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This paper reviews a comparison study on the most common used authentication methods. Some of these methods are actually based on cryptography. In this study, we show the main cryptographic services. Also, this study presents a specific discussion about authentication service, since the authentication service is classified into several categorizes according to their methods. However, this study gives more about the real life example for each of the authentication methods. It talks about the simplest authentication methods as well about the available biometric authentication methods such as voice, iris, fingerprint, and face authentication.

Keywords: information security, cryptography, system access control, authentication, network security

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Samar Saadeldin Abdelmotalab Omer, Ikram Mohammed Eltayeb Elsiddig, Amna Beshir Medani Ahmed, Saad Mohammed Hussein Ayoub

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Many herbal medicinal products are considered as potential hypocholesterolemic agents with encouraging safety profiles, however, only a limited amount of clinical research exists to support their efficacy. The present study was designed to compare the antihypercholesterolaemic and antioxidant activities of the crude ethanolic extracts of Citrus reticulata peel, Zingiber officinale rhizome, and Sesamum indicum seeds. Forty-five rats were used throughout the experiment, which were divided into nine groups, five rats in each as follows; normal control group (normal rats fed with standard normal diet), rats fed hypercholesterolemic diet consisting of 1% cholesterol and 10% saturated animal fat, which were further divided into eight groups; hypercholesterolemic control group (rats only fed hypercholesterolemic diet), groups 3,4,5,6,7, and 8 were given Citrus reticulata, Zingiber officinale, and Sesamum indicum ethanolic extracts at doses of (250mg/kg and 500mg/kg, respectively) orally; and group 9 rats were given atorvastatin (0.18mg/kg) orally as a reference antihypercholesterolaemic drug. Blood samples were obtained four weeks following treatment from the retro-orbital venous plexus after fasting overnight from all groups and the lipid profile (serum total cholesterol (TC), high-density-lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), and triglycerides levels) was measured and the risk ratio (TC/HDL-C) was assessed. The antioxidant activity of the three plant extracts was determined using DPPH free-radical assay. Results of in vivo and in vitro antihypercholesterolaemic and antioxidant assay, respectively, revealed that the three extracts possess comparable antioxidant and anti-hypercholesterolaemic activities.

Keywords: anti hypercholesterolemic effects, antioxidant activity, HDL, LDL, TC, TGs, citrus reticulata, sesamum indicum, zingiber officinale

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Authors: Z. Wochyński, K. A. Sobiech, Z. Kobos

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To C-Reactive Proteins include ferritin, transferrin, and ceruloplasmin- metalloproteins. The study aimed at assessing an effect of training on the Special Aerial Gymnastics Instruments (SAGI) on changes of serum ferritin, transferrin, and ceruloplasmin and cadets’ physical fitness in comparison with a control group. Fifty-five cadets in the mean age 20 years were included into this study. They were divided into two groups: Group A (N=41) trained on SAGI and Group B (N=14) trained according the standard program of physical education (control group). In both groups, blood was a material for assays. Samples were collected twice before and after training at the start of the program (training I), during (training II), and after education program completion (training III). Commercially available kits were used to assay blood serum ferritin, transferrin, and ceruloplasmin. Cadets’ physical fitness was evaluated with exercise tests before and after education program completion. In Group A, serum post-exercise ferritin decreased statistically insignificantly in training I and II and increased in training III in comparison with pre-exercise values. In Group B, post-exercise serum ferritin decreased statistically insignificantly in training I and III and significantly increased in training II in comparison with the pre-exercise values. In Group A, serum transferrin decreased statistically insignificantly in training I, and significantly increased in training II, whereas in training III it increased insignificantly in comparison with pre-exercise values. In Group B, post-exercise serum transferrin increased statistically significantly in training I, II, and III in comparison with pre-exercise values. I n Group A, serum ceruloplasmin decreased in all three series in comparison with pre-exercise values. In Group B, serum ceruloplasmin increased significantly in training II. It was showed that the training on SAGI significantly decreased serum ceruloplasmin in Group A in all three series of assays and did not produce significant changes in serum ferritin also was showed significant increase in serum transferrin.

Keywords: special aerial gymnastics instruments, ferritin, ceruloplasmin, transferrin

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Authors: Anika Chebrolu

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Mono-targeted drugs can be of limited efficacy against complex diseases. Recently, multi-target drug design has been approached as a promising tool to fight against these challenging diseases. However, the scope of current computational approaches for multi-target drug design is limited. DeepLig presents a de-novo drug discovery platform that uses reinforcement learning to generate and optimize novel, potent, and multitargeted drug candidates against protein targets. DeepLig’s model consists of two networks in interplay: a generative network and a predictive network. The generative network, a Stack- Augmented Recurrent Neural Network, utilizes a stack memory unit to remember and recognize molecular patterns when generating novel ligands from scratch. The generative network passes each newly created ligand to the predictive network, which then uses multiple Graph Attention Networks simultaneously to forecast the average binding affinity of the generated ligand towards multiple target proteins. With each iteration, given feedback from the predictive network, the generative network learns to optimize itself to create molecules with a higher average binding affinity towards multiple proteins. DeepLig was evaluated based on its ability to generate multi-target ligands against two distinct proteins, multi-target ligands against three distinct proteins, and multi-target ligands against two distinct binding pockets on the same protein. With each test case, DeepLig was able to create a library of valid, synthetically accessible, and novel molecules with optimal and equipotent binding energies. We propose that DeepLig provides an effective approach to design multi-targeted drug therapies that can potentially show higher success rates during in-vitro trials.

Keywords: drug design, multitargeticity, de-novo, reinforcement learning

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Authors: N. Nalini, Bhanu Prakash Gopularam

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The term data security refers to the degree of resistance or protection given to information from unintended or unauthorized access. The core principles of information security are the confidentiality, integrity and availability, also referred as CIA triad. Cloud computing services are classified as SaaS, IaaS and PaaS services. With cloud adoption the confidential enterprise data are moved from organization premises to untrusted public network and due to this the attack surface has increased manifold. Several cloud computing platforms like OpenStack, Eucalyptus, Amazon EC2 offer users to build and configure public, hybrid and private clouds. While the traditional encryption based on PKI infrastructure still works in cloud scenario, the management of public-private keys and trust certificates is difficult. The Identity based Public Key Cryptography (also referred as ID-PKC) overcomes this problem by using publicly identifiable information for generating the keys and works well with decentralized systems. The users can exchange information securely without having to manage any trust information. Another advantage is that access control (role based access control policy) information can be embedded into data unlike in PKI where it is handled by separate component or system. In OpenStack cloud platform the keystone service acts as identity service for authentication and authorization and has support for public key infrastructure for auto services. In this paper, we explain OpenStack security architecture and evaluate the PKI infrastructure piece for data confidentiality. We provide method to integrate ID-PKC schemes for securing data while in transit and stored and explain the key measures for safe guarding data against security attacks. The proposed approach uses JPBC crypto library for key-pair generation based on IEEE P1636.3 standard and secure communication to other cloud services.

Keywords: data confidentiality, identity based cryptography, secure communication, open stack key stone, token scoping

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Authors: Adel Salem Bahakeem, Ahmad Jamal, Mir Md. Maruf Morshed, Elwaleed Awad Khidir

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Direct Current (DC) servo motors, or simply DC motors, play an important role in many industrial applications such as manufacturing of plastics, precise positioning of the equipment, and operating computer-controlled systems where speed of feed control, maintaining the position, and ensuring to have a constantly desired output is very critical. These parameters can be controlled with the help of control systems such as the Proportional Integral Derivative (PID) controller. The aim of the current work is to investigate the effects of Proportional (P) and Integral (I) controllers on the steady state and transient response of the DC motor. The controller gains are varied to observe their effects on the error, damping, and stability of the steady and transient motor response. The current investigation is conducted experimentally on a servo trainer CE 110 using analog PI controller CE 120 and theoretically using Simulink in MATLAB. Both experimental and theoretical work involves varying integral controller gain to obtain the response to a steady-state input, varying, individually, the proportional and integral controller gains to obtain the response to a step input function at a certain frequency, and theoretically obtaining the proportional and integral controller gains for desired values of damping ratio and response frequency. Results reveal that a proportional controller helps reduce the steady-state and transient error between the input signal and output response and makes the system more stable. In addition, it also speeds up the response of the system. On the other hand, the integral controller eliminates the error but tends to make the system unstable with induced oscillations and slow response to eliminate the error. From the current work, it is desired to achieve a stable response of the servo motor in terms of its angular velocity subjected to steady-state and transient input signals by utilizing the strengths of both P and I controllers.

Keywords: DC servo motor, proportional controller, integral controller, controller gain optimization, Simulink

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Authors: Julia Langen

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This article describes the challenges of a wireless charging system for a cab waiting lane in a public space and presents a concept for solving them. In this concept, multiple cabs can be charged simultaneously and during stopping and rolling. Particular technical challenges are a coil topology that meets the EMF requirements and an intelligent control concept that allows the individual coil segments to be switched on and off. The charging concept explained here is currently being implemented as a pilot project, so that initial results on the operation can be presented.

Keywords: charge lane, inductive charging solution, smart city, wireless power transfer

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Authors: Mohammed Abu Shquier

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This paper introduces a word segmentation method using the novel BP-LSTM-CRF architecture for processing semantic output training. The objective of web morphological analysis tools is to link a formal morpho-syntactic description to a lemma, along with morpho-syntactic information, a vocalized form, a vocalized analysis with morpho-syntactic information, and a list of paradigms. A key objective is to continuously enhance the proposed system through an inductive learning approach that considers semantic influences. The system is currently under construction and development based on data-driven learning. To evaluate the tool, an experiment on homograph analysis was conducted. The tool also encompasses the assumption of deep binary segmentation hypotheses, the arbitrary choice of trigram or n-gram continuation probabilities, language limitations, and morphology for both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), which provide justification for updating this system. Most Arabic word analysis systems are based on the phonotactic morpho-syntactic analysis of a word transmitted using lexical rules, which are mainly used in MENA language technology tools, without taking into account contextual or semantic morphological implications. Therefore, it is necessary to have an automatic analysis tool taking into account the word sense and not only the morpho-syntactic category. Moreover, they are also based on statistical/stochastic models. These stochastic models, such as HMMs, have shown their effectiveness in different NLP applications: part-of-speech tagging, machine translation, speech recognition, etc. As an extension, we focus on language modeling using Recurrent Neural Network (RNN); given that morphological analysis coverage was very low in dialectal Arabic, it is significantly important to investigate deeply how the dialect data influence the accuracy of these approaches by developing dialectal morphological processing tools to show that dialectal variability can support to improve analysis.

Keywords: NLP, DL, ML, analyser, MSA, RNN, CNN

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Authors: Xiaowei Zhang, Min Xu, Saeid Habibi, Fengjun Yan, Ryan Ahmed

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Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-Ion (Li-ion) batteries are increasingly being deployed in EVs because of their high energy density, high cell-level voltage, and low rate of self-discharge. Since Li-ion batteries represent the most expensive component in the EV powertrain, accurate monitoring and control strategies must be executed to ensure their prolonged lifespan. The Battery Management System (BMS) has to accurately estimate parameters such as the battery State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). In order for the BMS to estimate these parameters, an accurate and control-oriented battery model has to work collaboratively with a robust state and parameter estimation strategy. Since battery physical parameters, such as the internal resistance and diffusion coefficient change depending on the battery state-of-life (SOL), the BMS has to be adaptive to accommodate for this change. In this paper, an extensive battery aging study has been conducted over 12-months period on 5.4 Ah, 3.7 V Lithium polymer cells. Instead of using fixed charging/discharging aging cycles at fixed C-rate, a set of real-world driving scenarios have been used to age the cells. The test has been interrupted every 5% capacity degradation by a set of reference performance tests to assess the battery degradation and track model parameters. As battery ages, the combined model parameters are optimized and tracked in an offline mode over the entire batteries lifespan. Based on the optimized model, a state and parameter estimation strategy based on the Extended Kalman Filter (EKF) and the relatively new Smooth Variable Structure Filter (SVSF) have been applied to estimate the SOC at various states of life.

Keywords: lithium-ion batteries, genetic algorithm optimization, battery aging test, parameter identification

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Authors: Mengjia Zhi, Xingmei Wei, Xiang Gao, Shiyang Liu, Zhiran Huang, Li Yang, Jing Sun

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Background: To understand the consumption trend of opioid analgesics and the consumption adequacy of opioid analgesic treatment for moderate to severe pain in China, as well as the pain control level of China with international perspective. Importance: To author’s best knowledge, this is the first study in China to measure the adequacy of opioid analgesic treatment for moderate to severe pain considering disease pattern and with the standardized pain treatment guideline. Methods: A retrospective analysis was carried out to show the consumption frequency (daily defined doses, DDDs) of opioid analgesics and its trend in China from 2006 to 2016. Adequacy of consumption measure (ACM) was used to measure the number of needed morphine equivalents and the overall adequacy of opioid analgesic treatment of moderate to severe pain in China, and compared with international data. Results: The consumption frequency of opioid analgesics (DDDs) in China increased from 13,200,000 DDDs in 2006 to 44,200,000 DDDs in 2016, and showed an increasing trend. The growth rate was faster at first, especially in 2013, then slowed down, decreased slightly in 2015. The ACM of China increased from 0.0032 in 2006 to 0.0074 in 2016, with an overall trend of growth. The ACM level of China has been always a very poor level during 2006-2016. Conclusion: The consumption of opioid analgesics for the treatment of moderate to severe pain in China has always been inadequate. There is a huge gap between China and the international level. There are many reasons behind this problem, which lie in different aspects, including medical staff, patients and the public, health systems and social & cultural aspects. It is necessary to strengthen the training and education of medical staff and the patients, to use mass media to disseminate scientific knowledge of pain management, to encourage communications between doctors and patients, to improve regulatory system for the controlled medicines and the overall health systems, and to balance the regulatory goal for avoidance of abuse, and the social goal of meeting the increasing needs of the people for better life.

Keywords: opioid analgesics, adequate consumption measure, pain control, China

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Authors: R. Fardid, R. Coppes

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Introduction: In mediastinal radiotherapy and to a lesser extend also in total-body irradiation (TBI) radiation exposure may lead to development of cardiac diseases. Radiation-induced heart disease is dose-dependent and it is characterized by a loss of cardiac function, associated with progressive heart cells degeneration. We aimed to determine the in-vivo radiation effects on fibronectin, ColaA1, ColaA2, galectin and TGFb1 gene expression levels in left ventricle heart tissues of rats after irradiation. Material and method: Four non-treatment adult Wistar rats as control group (group A) were selected. In group B, 4 adult Wistar rats irradiated to 20 Gy single dose of 150 Mev proton beam locally in heart only. In heart plus lung irradiate group (group C) 4 adult rats was irradiated by 50% of lung laterally plus heart radiation that mentioned in before group. At 8 weeks after radiation animals sacrificed and left ventricle heart dropped in liquid nitrogen for RNA extraction by Absolutely RNA® Miniprep Kit (Stratagen, Cat no. 400800). cDNA was synthesized using M-MLV reverse transcriptase (Life Technologies, Cat no. 28025-013). We used Bio-Rad machine (Bio Rad iQ5 Real Time PCR) for QPCR testing by relative standard curve method. Results: We found that gene expression of fibronectin in group C significantly increased compared to control group, but it was not showed significant change in group B compared to group A. The levels of gene expressions of Cola1 and Cola2 in mRNA did not show any significant changes between normal and radiation groups. Changes of expression of galectin target significantly increased only in group C compared to group A. TGFb1 expressions in group C more than group B showed significant enhancement compared to group A. Conclusion: In summary we can say that 20 Gy of proton exposure of heart tissue may lead to detectable damages in heart cells and may distribute function of them as a component of heart tissue structure in molecular level.

Keywords: gene expression, heart damage, proton irradiation, radiotherapy

Procedia PDF Downloads 480

Authors: M. Sayyour, M. Abdulkareem, O. Osman, S. Salmeh

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Emphatic sounds in Arabic are /tˤ/, /sˤ/, /dˤ/, and /ðˤ/. They involve a secondary articulation in the pharynx area as opposed to their counterparts: /t/,/s/,/d/and /ð/. Although they are present in most Arabic dialects, some dialects have lost this class as a historical development, such as Maltese Arabic. It has been found that there is a difference in the pronunciation of these emphatic sounds between the two genders, arguing that males tend to produce more evident emphasis than females. This study builds on these studies by trying to investigate whether listeners perceive fully emphatic sounds as more masculine and less emphatic sounds as more feminine. Furthermore, the study aims to find out which is more important in this perception process: the emphatic consonant itself or the vowel following it. To test this, natural and manipulated tokens of two male and two female speakers were used. The natural tokens include words that have emphatic consonant and emphatic vowel and tokens that have plain consonant and plain vowel. The manipulated tokens include words that have emphatic consonant but central vowel and plain consonant followed by the same central vowel. These manipulated tokens allow us to see whether the consonant will still affect the perception even if the vowel is controlled. Another group of words that contained no emphatic sounds was used as a control group. The total number of tokens (natural, manipulated, and control) are 160 tokens. After that, 60 university students (30 males and 30 females) listened to these tokens and responded by choosing a specific character that they think is likely to produce each token. The characters’ descriptions are carefully written with two degrees of femininity and two degrees of masculinity. The preliminary results for the femininity level showed that the highest degree of femininity was for tokens that contain a plain consonant and a plain vowel. The lowest level of femininity was given for tokens that have fully emphatic consonant and vowel. For the manipulated tokens that contained plain consonant and central vowel, the femininity degree was high which indicates that the consonant is more important than the vowel, while for the manipulated tokens that contain emphatic consonant and a central vowel, the femininity level was higher than that for the tokens that have emphatic consonant and emphatic vowel, which indicates that the vowel is more important for the perception of emphatic consonants. These results are interpreted in light of feminist linguistic theories, linguistic expectations, performed gender and linguistic change theories.

Keywords: Emphatic sounds, gender studies, perception, sociophonetics

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