Search results for: neural activity

Authors: Azza T. Tahera, Eman M. Ahmeda, Nadia A. Khalila, Yassin M. Nissanb

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New 3-(pyridin-4-yl)-[1,2,4] triazolo [4,3-b] pyridazine derivatives 2a-i, 4a,b and 6a,b were designed, synthesized and evaluated as cytotoxic agents. All compounds were investigated for their in vitro cytotoxicity at a single dose 10-5M concentration towards 60 cancer cell lines according to USA NCI protocol. The preliminary screening results showed that the majority of tested compounds exhibited remarkable activity against SR (leukemia) cell panel. Molecular docking for all synthesized compounds was performed on the active site of c-Met kinase. The most active compounds, 2f and 4a were further evaluated at a seven dose level screening and their IC50 as a c-Met kinase inhibitors were determined in vitro.

Keywords: triazolopyridazines, pyridazines, cytotoxic activity, cell panel

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Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

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The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: Ledi Neçaj

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Objectives: The target of this paper is (1) to explain objectively physical activity model throughout three chronic cardiopulmonary conditions, and (2) to study the connection among physical activity dimensions with disease severity, self-reported physical and emotional functioning, and exercise performance. Material and Methods: This is a cross-sectional study of patients in their domestic environment. Patients with cardiopulmonary diseases were: chronic obstructive pulmonary disease (COPD), (n-63), coronary heart failure (n=60), and patients with implantable cardioverter defibrillator (n=60). Main results measures: Seven ambulatory physical activity dimensions (total steps, percentage time active, percentage time ambulating at low, medium, and hard intensity, maximum cadence for 30 non-stop minutes, and peak performance) have been measured with an accelerometer. Results: Subjects with COPD had the lowest amount of ambulatory physical activity compared with topics with coronary heart failure and cardiac dysrhythmias (all 7 interest dimensions, P<.05); total step counts have been: 5319 as opposed to 7464 as opposed to 9570, respectively. Six-minute walk distance becomes correlated (r=.44-.65, P<.01) with all physical activity dimensions inside the COPD pattern, the most powerful correlations being with total steps and peak performance. In topics with cardiac impairment, maximal oxygen intake had the most effective small to slight correlations with five of the physical activity dimensions (r=.22-.40, P<.05). In contrast, correlations among 6-minute walk test distance and physical activity have been higher (r=.48-.61, P<.01) albeit in a smaller pattern of most effective patients with coronary heart failure. For all three samples, self-reported physical and mental health functioning, age, frame mass index, airflow obstruction, and ejection fraction had both exceptionally small and no significant correlations with physical activity. Conclusions: Findings from this study present a profitable benchmark of physical activity patterns in individuals with cardiopulmonary diseases for comparison with future studies. All seven dimensions of ambulatory physical activity have disfavor between subjects with COPD, heart failure, and cardiac dysrhythmias. Depending on the research or clinical goal, the use of one dimension, such as total steps, may be sufficient. Although physical activity had high correlations with performance on a six-minute walk test relative to other variables, accelerometers-based physical activity monitoring provides unique, important information about real-world behavior in patients with cardiopulmonary not already captured with existing measures.

Keywords: ambulatory physical activity, walking, monitoring, COPD, heart failure, implantable defibrillator, exercise performance

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Authors: Alexandra Von Guionneau, William Sloper, Charlotte Burford

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The benefits of exercise for the prevention and management of chronic disease are well established and the importance of primary care practitioners in promoting exercise is becoming increasingly recognized. However, those with severe manifestations of the chronic disease are managed in a secondary care setting. Secondary care practitioners, therefore, have a role to play in promoting physical activity. Methods: In order to assess secondary care doctors’ knowledge of the Chief Medical Officer’s guidelines for physical activity, a 12-question survey was administered to staff working in a district general hospital in South England during team and unit meetings. Questions related to knowledge of the current guidelines for both 19 - 64 year olds and older adults (65 years and above), barriers to exercise discussion or prescription and doctors’ own exercise habits. Responses were collected anonymously and analyzed using SPSS Version 24.0. Results: 96 responses were collected. Doctors taking part in the survey ranged from foundation years (26%) to consultants (40%). 17.7% of participants knew the guidelines for moderate intensity activity for 19 - 64 year olds. Only one participant knew all of the guidance for both 19 - 64 year olds and older adults. While 71.6% of doctors felt they were adequately informed about how to exercise, only 45.6% met the minimum recommended guidance for moderate intensity activity. Conclusion: More work is needed to promote the physical activity guidelines and exercise prescription to doctors working within a secondary care setting. In addition, doctors require more support to personally meet the recommended minimum level of physical activity.

Keywords: exercise is medicine, exercise prescription, physical activity guidelines, exercise habits

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Authors: Igor Astrov, Natalya Berezovski

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This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for vectored thrust aerial vehicle (VTAV). With the SA strategy, we proposed a neural network motion control procedure to address the dynamics variation and performance requirement difference of flight trajectory for a VTAV. This control strategy with using of NARMA-L2 neurocontroller for chosen model of VTAV has been verified by simulation of take-off and forward maneuvers using software package Simulink and demonstrated good performance for fast stabilization of motors, consequently, fast SA with economy in energy can be asserted during search-and-rescue operations.

Keywords: NARMA-L2 neurocontroller, situational awareness, vectored thrust aerial vehicle, aviation

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Authors: Fatma Acheuk, Idir Bitam, Leila Bendifallah, Malika Ramdani, Fethia Barika

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Phytochemical study of the plant Artemisia judaica showed the presence of various groups of natural products: saponins, tannins, coumarins, flavonoids, carbohydrates, and reducer compounds. However, alkaloids are present as traces. The crude ethanol extract of the test plant presented significant insecticidal activity on mosquito larvae in stage I, II and III. The LD50 highlighted the excellent insecticidal effect of the tested extract. Similarly, the LT50 are achieved early with high doses. The results obtained are encouraging and suggest the possibility of using the secondary metabolites of this plant such as bio-insecticide.

Keywords: Atamisia judaica, crud extract, mosquito, insecticidal activity

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Authors: Aschalew Cherie Workneh, K. S. Hari Prasad, Chandra Shekhar Prasad Ojha

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Due to the increase in water scarcity, the crop water stress index (CWSI) is receiving significant attention these days, especially in arid and semiarid regions, for quantifying water stress and effective irrigation scheduling. Nowadays, machine learning techniques such as neural networks are being widely used to determine CWSI. In the present study, the performance of two artificial neural networks, namely, Self-Organizing Maps (SOM) and Feed Forward-Back Propagation Artificial Neural Networks (FF-BP-ANN), are compared while determining the CWSI of rice crop. Irrigation field experiments with varying degrees of irrigation were conducted at the irrigation field laboratory of the Indian Institute of Technology, Roorkee, during the growing season of the rice crop. The CWSI of rice was computed empirically by measuring key meteorological variables (relative humidity, air temperature, wind speed, and canopy temperature) and crop parameters (crop height and root depth). The empirically computed CWSI was compared with SOM and FF-BP-ANN predicted CWSI. The upper and lower CWSI baselines are computed using multiple regression analysis. The regression analysis showed that the lower CWSI baseline for rice is a function of crop height (h), air vapor pressure deficit (AVPD), and wind speed (u), whereas the upper CWSI baseline is a function of crop height (h) and wind speed (u). The performance of SOM and FF-BP-ANN were compared by computing Nash-Sutcliffe efficiency (NSE), index of agreement (d), root mean squared error (RMSE), and coefficient of correlation (R²). It is found that FF-BP-ANN performs better than SOM while predicting the CWSI of rice crops.

Keywords: artificial neural networks; crop water stress index; canopy temperature, prediction capability

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Authors: Lineda Rouissat-Dahane, Abdelkrim Cheriti, Abbderazak Marouf, Reddy Kandappa H., Govender Patrick

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Natural phytochemicals extracted from folk herbal have drawn much attention in complementary and alternative medicine, and the plant kingdom is considered for developing new molluscicide. The aqueous and acetone extract of the aerial parts of some Asteraceae (Anvillea radiata, Bubonium graveolens, Launaea arborescens, Launaea nudicaulis and Warionia saharae) were investigated for its molluscicidal activity against Lymnaea acuminata showed significant molluscicidal activity with a median lethal concentration (LC50) of aqueous extract (8,178mg/ml) and organic extract 0.002μg/mL, which was indicated higher potency than the positive control, (LC50=100mg /mL for aqueous extract ; LC50=11.6 μg/mL for organic extract). Among the extract and their fractions, those of aerial parts of Launaea nudicaulis and Warionia saharae were found to exhibit significant molluscicidal activities. Among different solvent fractions of the acetone extract of Warionia saharae, the dichloromethane (DCM) soluble fraction showed the most potent molluscicidal activity against Lymnaea acuminata. Plants in species Anvillea radiata, Bubonium graveolens, Launaea arborescens, Launaea nudicaulis, and Warionia saharae produce a great variety of Flavonoids, Glucoside flavonoids, and Saponins that confer natural resistance against several pests. Most extracts were found to exhibit significant molluscicidal activity.

Keywords: acetone extract, aqueous extract, Asteraceae, molluscicidal activity, Lymnaea acuminata

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Authors: Mehrdad Nouri Khajavi, Gollamhassan Payganeh, Mohsen Fallah Tafti

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Fault diagnosis is an important aspect of maintaining rotating machinery health and increasing productivity. Many researches has been done in this regards. Many faults such as unbalance, misalignment, looseness, bearing faults, etc. have been considered and diagnosed with different techniques. Most of the researches in fault diagnosis of rotating machinery deal with single fault. Where as in reality faults usually occur simultaneously and it is, therefore, necessary to recognize them at the same time. In this research, two of the most common faults namely unbalance and misalignment have been considered simultaneously with different intensity and then identified and classified with the use of Multi-Layer Perception Neural Network (MLPNN). Processed Vibration signals are used as the input to the MLPNN, and the class of mixed unbalancy, and misalignment is the output of the NN.

Keywords: unbalance, parallel misalignment, combined faults, vibration signals

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Authors: Assia Amri, Naima Layachi, Ali Ladjama

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The characterization of some Algerian honey was carried out on the basis of their physico-chemical properties: moisture,hydroxy methyl furfural, diastase activity, pH,free, total and lactonic acidity, electrical conductivity, minerals and proline content. Studied samples are found to be low in moisture and therefore safe from fermentation, low in HMF level and high in diastase activity. Additionally the diastase activity and the HMF content are widely recognized parameters indicating the freshness of honey. Phenolic compounds present in honey are classified into two groups - simple phenols and polyphenols. The simple phenols in honey are various phenol acids, but polyphenols are various flavonoids and flavonides. The aim of our work was to determine antioxidant properties of various Algerian honey samples–the total phenol content, total flavonoids content, as well as honey anti radical activity.The quality of honey samples differs on account of various factors such as season, packaging and processing conditions, floral source, geographical origin and storage period. It is important that precautions should be taken to ensure standardization and rationalization of beekeeping techniques, manufacturing procedures and storing processes to improve honey quality.

Keywords: honey, physico-chemical characterization, phenolic coumpound, HMF, diastase activity

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Authors: Naghmeh Moradpoor Sheykhkanloo

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Structured Query Language Injection (SQLI) attack is a code injection technique in which malicious SQL statements are inserted into a given SQL database by simply using a web browser. Losing data, disclosing confidential information or even changing the value of data are the severe damages that SQLI attack can cause on a given database. SQLI attack has also been rated as the number-one attack among top ten web application threats on Open Web Application Security Project (OWASP). OWASP is an open community dedicated to enabling organisations to consider, develop, obtain, function, and preserve applications that can be trusted. In this paper, we propose an effective pattern recognition neural network model for detection and classification of SQLI attacks. The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to: 1) classify each generated URL to either a benign URL or a malicious URL and 2) classify the malicious URLs into different SQLI attack categories, and an NN model in order to: 1) detect either a given URL is a malicious URL or a benign URL and 2) identify the type of SQLI attack for each malicious URL. The model is first trained and then evaluated by employing thousands of benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.

Keywords: neural networks, pattern recognition, SQL injection attacks, SQL injection attack classification, SQL injection attack detection

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Authors: Debdut Roy, Vidyasagar Guggilla

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In this research, we highlight our exploratory work on modified zeolite based catalysts for catalytic cracking of hydrocarbons for production of light olefin i.e. ethylene and propylene. The work is focused on understanding the catalyst structure and activity correlation. Catalysts are characterized by surface area and pore size distribution analysis, inductively coupled plasma optical emission spectrometry (ICP-OES), Temperature Programmed Desorption (TPD) of ammonia, pyridine Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Thermo-gravimetric Analysis (TGA) and correlated with the catalytic activity. It is observed that the yield of lighter olefins increases with increase of Bronsted acid strength.

Keywords: catalytic cracking, zeolite, propylene, structure-activity correlation

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Authors: Keyvan Molanorouzi, Selina Khoo, Tony Morris

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In recent years, there has been a decline in physical activity among adults. Motivation has been shown to be a crucial factor in maintaining physical activity. The purpose of this study was to whether PA motives measured by the Physical Activity and Leisure Motivation Scale PALMS predicted actual amount of PA at a later time to provide evidence for the construct validity of the PALMS. A quantitative, cross-sectional descriptive research design was employed. The Demographic Form, PALMS, and International Physical Activity Questionnaire Short form (IPAQ-S) questionnaires were used to assess motives and amount for physical activity in adults on two occasions. A sample of 640 (489 male, 151 female) undergraduate students aged 18 to 25 years (mean ±SD; 22.30±8.13 years) took part in the study. Male participants were divided into three types of activities, namely exercise, racquet sport, and team sports and female participants only took part in one type of activity, namely team sports. After 14 weeks, all 640 undergraduate students who had filled in the initial questionnaire (Occasion 1) received the questionnaire via email (Occasion 2). Of the 640 students, 493 (77%; 378 males, 115 females) emailed back the completed questionnaire. The results showed that not only were pertinent sub-scales of PALMS positively related to amount of physical activity, but separate regression analyses showed the positive predictive effect of PALMS motives for amount of physical activity for each type of physical activity among participants. This study supported the construct validity of the PALMS by showing that the motives measured by PALMS did predict amount of PA. This information can be obtained to match people with specific sport or activity which in turn could potentially promote longer adherence to the specific activity.Methods: A quantitative, cross-sectional descriptive research design was employed. The Demographic Form, PALMS, and International Physical Activity Questionnaire Short form (IPAQ-S) questionnaires were used to assess motives and amount for physical activity in adults on two occasions. A sample of 640 (489 male, 151 female) undergraduate students aged 18 to 25 years (mean ±SD; 22.30±8.13 years) took part in the study. Male participants were divided into three types of activities, namely exercise, racquet sport, and team sports and female participants only took part in one type of activity, namely team sports. After 14 weeks, all 640 undergraduate students who had filled in the initial questionnaire (Occasion 1) received the questionnaire via email (Occasion 2). Of the 640 students, 493 (77%; 378 males, 115 females) emailed back the completed questionnaire. Results: The results showed that not only were pertinent sub-scales of PALMS positively related to amount of physical activity, but separate regression analyses showed the positive predictive effect of PALMS motives for amount of physical activity for each type of physical activity among participants. This study supported the construct validity of the PALMS by showing that the motives measured by PALMS did predict amount of PA. Conclusion: This information can be obtained to match people with specific sport or activity which in turn could potentially promote longer adherence to the specific activity.

Keywords: physical activity, motivation, level of physical activity, type of physical activity

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Authors: Sinarwati Mohamad Suhaili, Naomie Salim, Mohamad Nazim Jambli

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Sequence-to-sequence (seq2seq) models augmented with attention mechanisms are playing an increasingly important role in automated customer service. These models, which are able to recognize complex relationships between input and output sequences, are crucial for optimizing chatbot responses. Central to these mechanisms are neural attention weights that determine the focus of the model during sequence generation. Despite their widespread use, there remains a gap in the comparative analysis of different attention weighting functions within seq2seq models, particularly in the domain of chatbots using the Customer Support Twitter (CST) dataset. This study addresses this gap by evaluating four distinct attention-scoring functions—dot, multiplicative/general, additive, and an extended multiplicative function with a tanh activation parameter — in neural generative seq2seq models. Utilizing the CST dataset, these models were trained and evaluated over 10 epochs with the AdamW optimizer. Evaluation criteria included validation loss and BLEU scores implemented under both greedy and beam search strategies with a beam size of k=3. Results indicate that the model with the tanh-augmented multiplicative function significantly outperforms its counterparts, achieving the lowest validation loss (1.136484) and the highest BLEU scores (0.438926 under greedy search, 0.443000 under beam search, k=3). These results emphasize the crucial influence of selecting an appropriate attention-scoring function in improving the performance of seq2seq models for chatbots. Particularly, the model that integrates tanh activation proves to be a promising approach to improve the quality of chatbots in the customer support context.

Keywords: attention weight, chatbot, encoder-decoder, neural generative attention, score function, sequence-to-sequence

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Authors: Maryam Kheirollahpour, Mahmoud Danaee, Amir Faisal Merican, Asma Ahmad Shariff

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Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy.

Keywords: hybrid model, structural equation modeling, artificial neural networks, eating behavior patterns

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Authors: Maryam Nejat Dehkordi, Per Lincoln, Hassan Momtaz

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Interaction of Schiff base complexes of iron and manganese (iron [N, N’ Bis (5-(triphenyl phosphonium methyl) salicylidene) -1, 2 ethanediamine) chloride, [Fe Salen]Cl, manganese [N, N’ Bis (5-(triphenyl phosphonium methyl) salicylidene) -1, 2 ethanediamine) acetate) with DNA were investigated by spectroscopic and isothermal titration calorimetry techniques (ITC). The absorbance spectra of complexes have shown hyper and hypochromism in the presence of DNA that is indication of interaction of complexes with DNA. The linear dichroism (LD) measurements confirmed the bending of DNA in the presence of complexes. Furthermore, isothermal titration calorimetry experiments approved that complexes bound to DNA on the base of both electrostatic and hydrophobic interactions. Furthermore, ITC profile exhibits the existence of two binding phases for the complex. Antibacterial activity of ligand and complexes were tested in vitro to evaluate their activity against the gram positive and negative bacteria.

Keywords: Schiff base complexes, ct-DNA, linear dichroism (LD), isothermal titration calorimetry (ITC), antibacterial activity

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Authors: Gabriel S. Adesina, Ruixue Cheng, Geetika Aggarwal, Michael Short

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With the global shift towards sustainability and technological advancements, electric Hybrid vehicles (EHVs) are increasingly being seen as viable alternatives to traditional internal combustion (IC) engine vehicles, which also require efficient cooling systems. The electric Automotive Water Pump (AWP) has been introduced as an alternative to IC engine belt-driven pump systems. However, current control methods for AWPs typically employ fixed gain settings, which are not ideal for the varying conditions of dynamic vehicle environments, potentially leading to overheating issues. To overcome the limitations of fixed gain control, this paper proposes implementing an artificial neural network (ANN) for managing the AWP in EHVs. The proposed ANN provides an intelligent, adaptive control strategy that enhances the AWP's performance, supported through MATLAB simulation work illustrated in this paper. Comparative analysis demonstrates that the ANN-based controller surpasses conventional PID and fuzzy logic-based controllers (FLC), exhibiting no overshoot, 0.1secs rapid response, and 0.0696 IAE performance. Consequently, the findings suggest that ANNs can be effectively utilized in EHVs.

Keywords: automotive water pump, cooling system, electric hybrid vehicles, artificial neural networks, PID control, fuzzy logic control, IAE, MATLAB

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Authors: T. Hojageldiyev, Y. Bolmammedov, S. Gurbanaliyev

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It is known that drugs used in the treatment of tuberculosis show toxic effect to organism especially to liver besides its therapeutic effect. Because of ineffectiveness of drugs used in the treatment regimen of tuberculosis against multidrug resistance (MDR) and extensively drug-resistance (XDR) tuberculosis requires the development of new treatment methods and new, novel drugs. Considering the usage of Artemisia absinthium in traditional medicine in treatment of wounds which suggests its antibacterial activity it seems that, also it may have significant antimycobacterial activity. The objective of present study was to evaluate antibacterial activity of ethanolic extract of A. absinthium against M. tuberculosis. In this study, the effect of ethanolic extract of A. absinthium was tested against tuberculosis and pharmaco-toxicological properties evaluated on laboratory animals. The 20%, 40%, 70% and 96% ethanolic extracts of A. absinthium prepared then its bacteriostatic and bactericidal activities were evaluated by validated methods. Data were analyzed by GraphPad Prism 7.0 at the level P < 0.05. Results showed that ethanolic extracts of A. absinthium show no toxicological properties with having high LD50. All concentrations of extract show high bacteriostatic activity on M. tuberculosis. 96% ethanolic extract has highest bactericidal effect among other concentrations. By conducting further studies, as a result of our study, antimycobacterial drug can be prepared from A. absinthium.

Keywords: Artemisia absinthium, antimycobacterial, ethanolic extract, Mycobacteria tuberculosis

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Authors: Ikram Mohamed Eltayeb Elsiddig, Abdel Khalig Muddather, Hiba Abdel Rahman Ali, Saad Mohamed Hussein Ayoub

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Anogeissusleiocarpus (Combretaceae) is well known for its medicinal uses in African traditional medicine, for treating many human diseases mainly skin diseases and infections.Mycetoma disease is a fungal and/ or bacterial skin infection, mainly cause by Madurella mycetomatis fungus.This study was carried out in vitro to investigate the antifungal activity of Anogeissusleiocarpus leaf extracts against the isolated pathogenicMadurellamycetomatis, by using the NCCLS modified method compared to Ketoconazole standard drug and MTT assay. The bioactive fraction was subjected to chemical analysis implementing different chromatographic analytical methods (TLC, HPLC, and LC-MS/MS). The results showed significance antifungal activity of A. leiocarpus leaf extractsagainst the isolated pathogenicM. mycetomatis, compared to negative and positive controls. The chloroform fraction showed the highest antifungal activity.The chromatographic analysis of the chloroform fraction with the highest activity showed the presence of important bioactive compounds such as ellagic and flavellagic acids derivatives, flavonoids and stilbenoid, which are well known for their antifungal activity.

Keywords: Anogeissus leiocarpus, crude extracts and fractions of Anogeissus leiocarpus, in vitrosusceptibility of Madurella mycetomatis, Madurella mycetomatis

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Authors: Salam Labidi, Sonia Machraoui, Souha Gharbi

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Radium isotopes (226Ra, 228Ra) and uranium isotopes (234U, 238U) activity concentrations were determined in most popular Tunisian bottled mineral waters samples. Activity concentrations of uranium were studied by radiochemical separation procedures followed by alpha spectrometry and that of radium isotopes by gamma-ray spectrometry. The activity concentrations of 238U, 234U, 226Ra and 228Ra in water samples varied in range 3.3 - 22.5 mBq.L−1, 4.0 - 34.2 mBq L−1, 2.0 - 67.0 mBq L−1 and 2.0 - 30.2 mBq L−1, respectively. These values are comparable with those reported for many other countries in the world for different types of water. Based on the activity concentration results obtained in this study, the estimated annual ingestion dose rates for three different age groups (babies, children and adults) due to the ingestion of radium and uranium isotopes through drinking water are lower than the limit of intake prescribed by WHO. The annual doses exceed the recommended value of 0.1 mSv y-1 in one case for babies.

Keywords: mineral water, natural radioactivity, radiation dose, radium, uranium

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Authors: T. Goldstein, E. Serok, J. D. Kark

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Background and Aim: The prevalence of obesity is increasing worldwide and in Israel. To meet this challenge, our study tests a new educational approach through a controlled school-based trial to achieve an improvement in eating habits, aerobic activity, and reduced obesity in Grades 2-3. Methods and Design: A cluster randomized controlled trial allocated 4 elementary schools (3rd and 2nd-grade classes each) to intervention or control groups. This allocation was switched with the next cohort of children. Recruitment was in first grade, randomization at the beginning of second grade, evaluation of results at the end of second grade and the beginning of third grade — intervention: 5 joint parent-children classroom activities on health topics and 5 educational workshops for parents only. Alfred Adler's concepts were guiding principles. Subjects: Of 743 children in 23-second grade classes, parents provided informed consent for 508 (68%). Information of retention health habits continued for third grade. Additional parental approvals were required. Parents provided informed consent for third-grade follow-up for 432. Results: At the end of 2nd grade, the amount of aerobic activity increased in the intervention group in comparison with the control group, the difference being marginally statistically significant (p=0.061). There is a significant difference between the groups in the percentage of "no activity being done" reported at the end of second grade when in the experimental group, the percentage is lower than the control. There are differences between genders in the percentage of aerobic activity at the end of second grade (p=0.044) and in the third grade (p < 0.0001). Height increased significantly (p=0.030 ), and waist circumference declined significantly (p=0.021) in the intervention compared with the control group. There were no significant between-group differences in BMI and weight. Conclusion: There were encouraging changes in aerobic activity and in anthropometric measurements. To maintain changes over longer periods, refreshing these nutrition and activity themes annually in school using the model is required.

Keywords: aerobic activity, child obesity, Alfred Adler, schoolchildren

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Authors: Gurmail Singh

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Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

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Authors: Tawqeer Ali Syed, Prakash Chandra

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This study aims to evaluate the antileishmanial activity of Nigella sativa black seed extract. This well-known plant extract was taken from the botanical garden of Kashmir. Materials and Methods: The methanolic extracts of these plants were screened for their antileishmanial activity against Leishmania major using 3‑(4.5‑dimethylthiazol‑2yl)‑2.5‑diphenyltetrazolium bromide assay or MTT assay. Results: The methanolic extract of Nigella sativa showed potential antileishmanial activity at an inhibition% value of 80.29% ± 0.65%. IC 50 was calculated after 48 hours to be 964.3 µg/ml. Conclusion: Considering these results, these medicinal plants from Kashmir could serve as potential drug sources for antileishmanial compounds.

Keywords: MTT assay, antileishmanial, cell viability, Nigella sativa

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Authors: Ramalingam Boobalan, Chinpiao Chen, Jui-I. Chiao

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A series of erlotinib analogues that have structural modification at 6,7-alkoxyl positions is efficiently synthesized. The key reactions that involved in synthesis are one-pot oxime formation-dehydration for the formation of nitrile, quinazoline ring formation reaction between aniline and o-cyanoaniline via formamidine intermediate, Fe/NH4Cl catalyzed reduction-hetereocyclization-reductive ring opening reaction for the formation of o-aminobenzamide, high yielding seal tube reactions for O-demethylation, sodium iodide substitution, ammonia substitution. The in vitro anti-tumor activity of synthesized compounds is studied in two non-small cell lung cancer (NSCLC) cell lines (A549 and H1975). Among the synthesized compounds, the iodo compound 6 (ETN-6) exhibits higher anti-cancer activity compared to erlotinib. An efficient method is developed for the conjugation of erlotinib analogue-4, alcohol compound, with protein, bovine serum albumin (BSA), via succinic acid linker. The in vitro anti-tumor activity of the protein attached erlotinib analogue, 8 (ETN-4-Suc-BSA), showed stronger inhibitory activity in both A549 and H1975 NSCLC cell lines.

Keywords: anti-cancer, BSA, EGFR, Erlotinib

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Authors: Ulrike Dowie, Ralph Grothmann

Abstract:

Rational planning decisions are based upon forecasts. Precise forecasting has, therefore, a central role in business. The prediction of customer demand is a prime example. This paper introduces recurrent neural networks to model customer demand and combines the forecast with uncertainty measures to derive decision support of the demand planning department. It identifies and describes the keys to the successful implementation of an AI-based solution: bringing together data with business knowledge, AI methods, and user experience, and applying agile software development practices.

Keywords: agile software development, AI project success factors, deep learning, demand forecasting, forecast uncertainty, neural networks, supply chain management

Procedia PDF Downloads 191

Authors: A. Fahim, Mutahira N. Naseem

Abstract:

SPAM is any unwanted electronic message or material in any form posted to many people. As the world is growing as global world, social networking sites play an important role in making world global providing people from different parts of the world a platform to meet and express their views. Among different social networking sites facebook become the leading one. With increase in usage different users start abusive use of facebook by posting or creating ways to post spam. This paper highlights the potential spam types nowadays facebook users faces. This paper also provide the reason how user become victim to spam attack. A methodology is proposed in the end discusses how to handle different types of spam.

Keywords: artificial neural networks, facebook spam, social networking sites, spam filter

Procedia PDF Downloads 372

Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi, Morvarid Lalenoor

Abstract:

Background: Plantar pressure distribution measurement has been used for a long time to assess foot disorders. Plantar pressure is an important component affecting the foot and ankle function and Changes in plantar pressure distribution could indicate various foot and ankle disorders. Morphologic and mechanical properties of the foot may be important factors affecting the plantar pressure distribution. Accurate and early measurement may help to reduce the prevalence of pes planus. With recent developments in technology, new techniques such as machine learning have been used to assist clinicians in predicting patients with foot disorders. Significance of the study: This study proposes a neural network learning-based flat foot classification methodology using static foot pressure distribution. Methodologies: Data were collected from 895 patients who were referred to a foot clinic due to foot disorders. Patients with pes planus were labeled by an experienced physician based on clinical examination. Then all subjects (with and without pes planus) were evaluated for static plantar pressures distribution. Patients who were diagnosed with the flat foot in both feet were included in the study. In the next step, the leg length was normalized and the network was trained for plantar pressure mapping images. Findings: From a total of 895 image data, 581 were labeled as pes planus. A computational neural network (CNN) ran to evaluate the performance of the proposed model. The prediction accuracy of the basic CNN-based model was performed and the prediction model was derived through the proposed methodology. In the basic CNN model, the training accuracy was 79.14%, and the test accuracy was 72.09%. Conclusion: This model can be easily and simply used by patients with pes planus and doctors to predict the classification of pes planus and prescreen for possible musculoskeletal disorders related to this condition. However, more models need to be considered and compared for higher accuracy.

Keywords: foot disorder, machine learning, neural network, pes planus

Procedia PDF Downloads 362

Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

Abstract:

Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

Procedia PDF Downloads 200

Authors: Meriem Mossaddek, El Mehdi Laadissi, El Mehdi Loualid, Chouaib Ennawaoui, Sohaib Bouzaid, Abdelowahed Hajjaji

Abstract:

An electrochemical system is a subset of mechatronic systems that includes a wide variety of batteries and nickel-cadmium, lead-acid batteries, and lithium-ion. Those structures have several non-linear behaviors and uncertainties in their running range. This paper studies an effective technique for modeling Lithium-Ion (Li-Ion) batteries using a Nonlinear Auto-Regressive model with exogenous input (NARX). The Artificial Neural Network (ANN) is trained to employ the data collected from the battery testing process. The proposed model is implemented on a Li-Ion battery cell. Simulation of this model in MATLAB shows good accuracy of the proposed model.

Keywords: lithium-ion battery, neural network, energy storage, battery model, nonlinear models

Procedia PDF Downloads 115

Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

Abstract:

Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

Procedia PDF Downloads 188