Search results for: soxhlet extraction method

Authors: Y. Pala, M. O. Ertas

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In this paper, the general Riccati equation is analytically solved by a new transformation. By the method developed, looking at the transformed equation, whether or not an explicit solution can be obtained is readily determined. Since the present method does not require a proper solution for the general solution, it is especially suitable for equations whose proper solutions cannot be seen at first glance. Since the transformed second order linear equation obtained by the present transformation has the simplest form that it can have, it is immediately seen whether or not the original equation can be solved analytically. The present method is exemplified by several examples.

Keywords: Riccati equation, analytical solution, proper solution, nonlinear

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Authors: Nopsarun Vannasirikul

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The purposes of this research paper were to study the current situations of community development, women’s potentials, women’s participation, network, and government policy as well as to study the factors influencing women’s potentials, women’s participation, network, and government policy that have on the community development. The population included the women age of 18 years old who were living in the communities of Bangkok areas. This study was a mix research method of quantitative and qualitative method. A simple random sampling method was utilized to obtain 400 sample groups from 50 districts of Bangkok and to perform data collection by using questionnaire. Also, a purposive sampling method was utilized to obtain 12 informants for an in-depth interview to gain an in-sight information for quantitative method.

Keywords: community development, participation, network, women’s right, management

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Authors: Andrey V. Timofeev

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A method is proposed for stable detection of seismoacoustic sources in C-OTDR systems that guarantee given upper bounds for probabilities of type I and type II errors. Properties of the proposed method are rigorously proved. The results of practical applications of the proposed method in a real C-OTDR-system are presented in this.

Keywords: guaranteed detection, C-OTDR systems, change point, interval estimation

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Authors: W. S. Lawal, T. A. Akande

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Four (4) different methods were employed to detoxified jatropha caucas, they are physical method (if include soaking and drying) chemical method (use of methylated spirit, hexane and methene) biological method,(use of Aspergillus niger and Sunday for 7 days and then baccillus lichifarming) and finally combined method (combination of all these methods). Phobol esther andysis was carried out after the detoxification and was found that combined method is better off (P>0.05). 100 broiler birds was used to further test the effect of detoxified Jatropha by combined method, 50 birds for Jatropha made feed at 10 birds per treatment and was replicated five times, this was also repeated for another 50 birds fed conventional feed, Jatropha made feed was compranded at 8% inclusion level. At the end of the 8th weeks, 8 birds were sacrificed each from each treatment and one bird each was fry, roast, boil and grilled from both conventional and Jatropha fed birds and panelist were served for evaluation. It was found that feeding Jatropha to poultry birds has no effect on the taste of the meat.

Keywords: phobol esther, inclusion level, tolerance level, Jatropha carcass

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Authors: Valentine C. Eze, Adam P. Harvey

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Contamination of groundwater and soil by heavy metals and metalloids through anthropogenic activities and natural occurrence poses serious environmental challenges globally. A possible solution to this problem is through phytoremediation of the contaminants using hyper-accumulating plants. Conventional phytoremediation treats the contaminated hyper-accumulator biomass as a waste stream which adds no value to the heavy metal(loid)s decontamination process. This study investigates strategies for remediation of soil contaminated with arsenic and the extractive chemical routes for recovery of arsenic and phosphorus from the hyper-accumulator biomass. Pteris cretica ferns species were investigated for their uptake of arsenic from soil containing 200 ± 3ppm of arsenic. The Pteris cretica ferns were shown to be capable of hyper-accumulation of arsenic, with maximum accumulations of about 4427 ± 79mg to 4875 ± 96mg of As per kg of the dry ferns. The arsenic in the Pteris cretica fronds was extracted into various solvents, with extraction efficiencies of 94.3 ± 2.1% for ethanol-water (1:1 v/v), 81.5 ± 3.2% for 1:1(v/v) methanol-water, and 70.8 ± 2.9% for water alone. The recovery efficiency of arsenic from the molybdic acid complex process 90.8 ± 5.3%. Phosphorus was also recovered from the molybdic acid complex process at 95.1 ± 4.6% efficiency. Quantitative precipitation of Mg₃(AsO₄)₂ and Mg₃(PO₄)₂ occurred in the treatment of the aqueous solutions of arsenic and phosphorus after stripping at pH of 8 – 10. The amounts of Mg₃(AsO₄)₂ and Mg₃(PO₄)₂ obtained were 96 ± 7.2% for arsenic and 94 ± 3.4% for phosphorus. The arsenic nanoparticles produced from the Mg₃(AsO₄)₂ recovered from the biomass have the average particles diameter of 45.5 ± 11.3nm. A two-stage reduction process – a first step pre-reduction of As(V) to As(III) with L-cysteine, followed by NaBH₄ reduction of the As(III) to As(0), was required to produced arsenic nanoparticles from the Mg₃(AsO₄)₂. The arsenic nanoparticles obtained are potentially valuable for medical applications, while the Mg₃(AsO₄)₂ could be used as an insecticide. The phosphorus contents of the Pteris cretica biomass was recovered as phosphomolybdic acid complex and converted to Mg₃(PO₄)₂, which could be useful in productions of fertilizer. Recovery of these valuable products from phytoremediation biomass would incentivize and drive commercial industries’ participation in remediation of contaminated lands.

Keywords: phytoremediation, Pteris cretica, hyper-accumulator, solvent extraction, molybdic acid process, arsenic nanoparticles

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Authors: Kuo-Teng Tsai, You-Min Huang

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In this study, the problem of a spiral fin with a period base temperature is analyzed by using the Adomian decomposition method. The Adomian decomposition method is a useful and practice method to solve the nonlinear energy equation which are associated with the heat radiation. The period base temperature is around a mean value. The results including the temperature distribution and the heat flux from the spiral fin base can be calculated directly. The results also discussed the effects of the dimensionless variables for the temperature variations and the total energy transferred from the spiral fin base.

Keywords: spiral fin, period, adomian decomposition method, nonlinear

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Authors: Bogusław Schreyer

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The paper enquires on the two methods of the wheeled robot braking torque control. Those two methods are applied when the adhesion coefficient under left side wheels is different from the adhesion coefficient under the right side wheels. In case of the select-low (SL) method the braking torque on both wheels is controlled by the signals originating from the wheels on the side of the lower adhesion. In the select-high (SH) method the torque is controlled by the signals originating from the wheels on the side of the higher adhesion. The SL method is securing stable and secure robot behaviors during the braking process. However, the efficiency of this method is relatively low. The SH method is more efficient in terms of time and braking distance but in some situations may cause wheels blocking. It is important to monitor the velocity of all wheels and then take a decision about the braking torque distribution accordingly. In case of the SH method the braking torque slope may require significant decrease in order to avoid wheel blocking.

Keywords: select-high, select-low, torque distribution, wheeled robots

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Authors: Allen A. Espinosa, Larkins A. Trinidad

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The present study investigated collegiate students’ problem solving strategies and misconceptions in solving stoichiometric problems and later on formulate a teaching framework from the result of the study. The study found out that the most prominent strategies among students are the mole method and the proportionality method, which are both algorithmic by nature. Misconception was also noted as some students rely on Avogadro’s number in converting between moles. It is suggested therefore that the teaching of stoichiometry should not be confined to demonstration. Students should be involved in the process of thinking of ways to solve the problem.

Keywords: stoichiometry, Svogadro’s number, mole method, proportionality method

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Authors: Yanjun Chen, Chuanping Hu, Jie Shao, Lin Mei, Chongyang Zhang

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Traditional image classification is based on encoding scheme (e.g. Fisher Vector, Vector of Locally Aggregated Descriptor) with low-level image features (e.g. SIFT, HoG). Compared to these low-level local features, deep convolutional features obtained at the mid-level layer of convolutional neural networks (CNN) have richer information but lack of geometric invariance. For scene classification, there are scattered objects with different size, category, layout, number and so on. It is crucial to find the distinctive objects in scene as well as their co-occurrence relationship. In this paper, we propose a method to take advantage of both deep convolutional features and the traditional encoding scheme while taking object-centric and scene-centric information into consideration. First, to exploit the object-centric and scene-centric information, two CNNs that trained on ImageNet and Places dataset separately are used as the pre-trained models to extract deep convolutional features at multiple scales. This produces dense local activations. By analyzing the performance of different CNNs at multiple scales, it is found that each CNN works better in different scale ranges. A scale-wise CNN adaption is reasonable since objects in scene are at its own specific scale. Second, a fisher kernel is applied to aggregate a global representation at each scale and then to merge into a single vector by using a post-processing method called scale-wise normalization. The essence of Fisher Vector lies on the accumulation of the first and second order differences. Hence, the scale-wise normalization followed by average pooling would balance the influence of each scale since different amount of features are extracted. Third, the Fisher vector representation based on the deep convolutional features is followed by a linear Supported Vector Machine, which is a simple yet efficient way to classify the scene categories. Experimental results show that the scale-specific feature extraction and normalization with CNNs trained on object-centric and scene-centric datasets can boost the results from 74.03% up to 79.43% on MIT Indoor67 when only two scales are used (compared to results at single scale). The result is comparable to state-of-art performance which proves that the representation can be applied to other visual recognition tasks.

Keywords: deep convolutional features, Fisher Vector, multiple scales, scale-specific normalization

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Authors: M. A. Sedghamiz, S. Raeissi

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This study presents three different approaches to estimate bubble point pressures for the binary system of CO2 and ethyl palmitate fatty acid ethyl ester. The first method involves the Peng-Robinson (PR) Equation of State (EoS) with the conventional mixing rule of Van der Waals. The second approach involves the PR EOS together with the Wong Sandler (WS) mixing rule, coupled with the Uniquac Ge model. In order to model the bubble point pressures with this approach, the volume and area parameter for ethyl palmitate were estimated by the Hansen group contribution method. The last method involved the Peng-Robinson, combined with the Wong-Sandler Method, but using NRTL as the GE model. Results using the Van der Waals mixing rule clearly indicated that this method has the largest errors among all three methods, with errors in the range of 3.96–6.22 %. The Pr-Ws-Uniquac method exhibited small errors, with average absolute deviations between 0.95 to 1.97 percent. The Pr-Ws-Nrtl method led to the least errors where average absolute deviations ranged between 0.65-1.7%.

Keywords: bubble pressure, Gibbs excess energy model, mixing rule, CO2 solubility, ethyl palmitate

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Authors: Gianna Zou

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Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed.

Keywords: BART, Bayesian, matching, regression

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Authors: Abdel-Maksoud Abdel-Kader Soliman

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The General Regularized Long Wave (GRLW) equation is solved numerically by giving a new algorithm based on collocation method using quartic B-splines at the mid-knot points as element shape. Also, we use the Fourth Runge-Kutta method for solving the system of first order ordinary differential equations instead of finite difference method. Our test problems, including the migration and interaction of solitary waves, are used to validate the algorithm which is found to be accurate and efficient. The three invariants of the motion are evaluated to determine the conservation properties of the algorithm.

Keywords: generalized RLW equation, solitons, quartic b-spline, nonlinear partial differential equations, difference equations

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Authors: Whitehead-Clarke T., Beynon V., Banks J., Karanjia R., Mudera V., Windsor A., Kureshi A.

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Introduction: Mesh implants are regularly used to help repair both hiatus hernias (HH) and diaphragmatic hernias (DH). In vivo studies are used to test not only mesh safety but increasingly comparative efficacy. Our work examines the field of in vivo mesh testing for HH and DH models to establish current practices and standards. Method: This systematic review was registered with PROSPERO. Medline and Embase databases were searched for relevant in vivo studies. 44 articles were identified and underwent abstract review, where 22 were excluded. 4 further studies were excluded after full text review – leaving 18 to undergo data extraction. Results: Of 18 studies identified, 9 used an in vivo HH model and 9 a DH model. 5 studies undertook mechanical testing on tissue samples – all uniaxial in nature. Testing strip widths ranged from 1-20mm (median 3mm). Testing speeds varied from 1.5-60mm/minute. Upon histology, the most commonly assessed structural and cellular factors were neovascularization and macrophages, respectively (n=9 each). Structural analysis was mostly qualitative, where cellular analysis was equally likely to be quantitative. 11 studies assessed adhesion formation, of which 8 used one of four scoring systems. 8 studies measured mesh shrinkage. Discussion: In vivo studies assessing mesh for HH and DH repair are uncommon. Within this relatively young field, we encourage surgical and materials testing institutions to discuss its standardisation.

Keywords: hiatus, diaphragmatic, hernia, mesh, materials testing, in vivo

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Authors: Sendi Zohra, Belhadjsalah Hedi, Labergere Carl, Saanouni Khemais

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The modeling of mechanical problems including both material and geometric nonlinearities with Finite Element Method (FEM) remains challenging. Meshless methods offer special properties to get rid of well-known drawbacks of the FEM. The main objective of Meshless Methods is to eliminate the difficulty of meshing and remeshing the entire structure by simply insertion or deletion of nodes, and alleviate other problems associated with the FEM, such as element distortion, locking and others. In this study, a robust numerical implementation of an Element Free Galerkin Method for an elastoplastic coupled to damage problem is presented. Several results issued from the numerical simulations by a DynamicExplicit resolution scheme are analyzed and critically compared with Element Finite Method results. Finally, different numerical examples are carried out to demonstrate the efficiency of this method.

Keywords: damage, dynamic explicit, elastoplasticity, isotropic hardening, meshless

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Authors: Safia Hocine, Zina Benouaret, Djamil A¨ıssani

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In this paper, we study the performance of the strong stability method of the univariate classical risk model. We interest to the stability bounds established using two approaches. The first based on the strong stability method developed for a general Markov chains. The second approach based on the regenerative processes theory . By adopting an algorithmic procedure, we study the performance of the stability method in the case of exponential distribution claim amounts. After presenting numerically and graphically the stability bounds, an interpretation and comparison of the results have been done.

Keywords: Marcov chain, regenerative process, risk model, ruin probability, strong stability

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Authors: Michaela Vašková

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The paper is focused on the application of the security audit method on the selected objects of the critical infrastructure. The emphasis is put on security audit method to find gaps in the critical infrastructure security. The theoretical part describes objects of the critical infrastructure. The practical part describes using the security audit method. The main emphasis was put on the protection of the critical infrastructure in the Czech Republic.

Keywords: crisis management, critical infrastructure, object of critical infrastructure, security audit, extraordinary event

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Authors: Neda Azimi, Masoud Rahimi, Faezeh Mohammadi

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Computational fluid dynamics (CFD) simulation was performed to investigate the effect of ferrofluid stimulation on hydrodynamic and mass transfer characteristics of two immiscible liquid phases in a Y-micromixer. The main purpose of this work was to develop a numerical model that is able to simulate hydrodynamic of the ferrofluid flow under magnetic field and determine its effect on mass transfer characteristics. A uniform external magnetic field was applied perpendicular to the flow direction. The volume of fluid (VOF) approach was used for simulating the multiphase flow of ferrofluid and two-immiscible liquid flows. The geometric reconstruction scheme (Geo-Reconstruct) based on piecewise linear interpolation (PLIC) was used for reconstruction of the interface in the VOF approach. The mass transfer rate was defined via an equation as a function of mass concentration gradient of the transported species and added into the phase interaction panel using the user-defined function (UDF). The magnetic field was solved numerically by Fluent MHD module based on solving the magnetic induction equation method. CFD results were validated by experimental data and good agreements have been achieved, which maximum relative error for extraction efficiency was about 7.52 %. It was showed that ferrofluid actuation by a magnetic field can be considered as an efficient mixing agent for liquid-liquid two-phase mass transfer in microdevices.

Keywords: CFD modeling, hydrodynamic, micromixer, ferrofluid, mixing

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Authors: Seyed Amin Vakili, Sahar Sadat Vakili, Seyed Ehsan Vakili, Nader Abdoli Yazdi

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The aim of this paper is to examine the behavior of elastic stability of reinforced and composite concrete struts with axial loads. The objective of this study is to verify the ability of the Modified Newmark Method to include geometric non-linearity in addition to non-linearity due to cracking, and also to show the advantage of the established method to reconsider an ignored minor parameter in mathematical modeling, such as the effect of the cracking by extra geometric bending moment Ny on cross-section properties. The purpose of this investigation is not to present some new results for the instability of reinforced or composite concrete columns. Therefore, no kinds of non-linearity involved in the problem are considered here. Only as mentioned, it is a part of the verification of the new established method to solve two kinds of non-linearity P- δ effect and cracking together simultaneously. However, the Modified Newmark Method can be used to solve non-linearity of materials and time-dependent behavior of concrete. However, since it is out of the scope of this article, it is not considered.

Keywords: stability, buckling, modified newmark method, reinforced

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Authors: Shivankar Agrawal, Indira Sarangthem

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Staphylococcus aureus and Methicillin-resistant Staphylococcus aureus (MRSA) remains one of the major causes of healthcare-associated and community-onset infections worldwide. Hence the search for non-toxic natural compounds having antibacterial activity has intensified for future drug development. The exploration of less studied niches of Earth can highly increase the possibility to discover novel bioactive compounds. Therefore, in this study, the cultivable fraction of fungi from the sediments of natural hot springs has been studied to mine potential fungal candidates with antibacterial activity against the human pathogen Staphylococcus aureus and Methicillin-resistant Staphylococcus aureus. We isolated diverse strains of thermophilic fungi from a collection of samples from sediment. Following a standard method, we isolated a promising thermophilic fungus strain IBSD19, identified as Acrophialophora levis, possessing the potential to produce an anti-Staphylococcus aureus agent. The growth conditions were optimized and scaled to fermentation, and its produced extract was subjected to chemical extraction. The ethyl acetate fraction was found to display significant activity against Staphylococcus aureus and MRSA with a minimum inhibitory concentration (MIC) of 0.5 mg/ml and 4 mg/ml, respectively. The cell membrane integrity assay and SEM suggested that the fungal metabolites cause bacteria clustering and further lysis of the cell.

Keywords: antibacterial activity, antioxidant, fungi, Staphylococcus aureus, MRSA, thermophiles

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Authors: Ting Lu, Ya-Ru Diao, Fei Fan, Ye Xue, Lei Shi, Xian-e Tang, Meng-jun Zhan, Zhen-hua Deng

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Accurate adult age estimation (AAE) is a significant and challenging task in forensic and archeology fields. Attempts have been made to explore optimal adult age metrics, and the rib is considered a potential age marker. The traditional way is to extract age-related features designed by experts from macroscopic or radiological images followed by classification or regression analysis. Those results still have not met the high-level requirements for practice, and the limitation of using feature design and manual extraction methods is loss of information since the features are likely not designed explicitly for extracting information relevant to age. Deep learning (DL) has recently garnered much interest in imaging learning and computer vision. It enables learning features that are important without a prior bias or hypothesis and could be supportive of AAE. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. Chest CT data were reconstructed using volume rendering (VR). Retrospective data of 2500 patients aged 20.00-69.99 years were obtained between December 2019 and September 2021. Five-fold cross-validation was performed, and datasets were randomly split into training and validation sets in a 4:1 ratio for each fold. Before feeding the inputs into networks, all images were augmented with random rotation and vertical flip, normalized, and resized to 224×224 pixels. ResNeXt was chosen as the DL baseline due to its advantages of higher efficiency and accuracy in image classification. Mean absolute error (MAE) was the primary parameter. Independent data from 100 patients acquired between March and April 2022 were used as a test set. The manual method completely followed the prior study, which reported the lowest MAEs (5.31 in males and 6.72 in females) among similar studies. CT data and VR images were used. The radiation density of the first costal cartilage was recorded using CT data on the workstation. The osseous and calcified projections of the 1 to 7 costal cartilages were scored based on VR images using an eight-stage staging technique. According to the results of the prior study, the optimal models were the decision tree regression model in males and the stepwise multiple linear regression equation in females. Predicted ages of the test set were calculated separately using different models by sex. A total of 2600 patients (training and validation sets, mean age=45.19 years±14.20 [SD]; test set, mean age=46.57±9.66) were evaluated in this study. Of ResNeXt model training, MAEs were obtained with 3.95 in males and 3.65 in females. Based on the test set, DL achieved MAEs of 4.05 in males and 4.54 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. Those results showed that the DL of the ResNeXt model outperformed the manual method in AAE based on CT reconstruction of the costal cartilage and the developed system may be a supportive tool for AAE.

Keywords: forensic anthropology, age determination by the skeleton, costal cartilage, CT, deep learning

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Authors: Siti Noor Farwina Anwar, Hailiza Kamarulhaili

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In this paper, we present the idea of implementing the Integer Sub-Decomposition (ISD) method on elliptic curves with j-invariant 1728. The ISD method was proposed in 2013 to compute scalar multiplication in elliptic curves, which remains to be the most expensive operation in Elliptic Curve Cryptography (ECC). However, the original ISD method only works on integer number field and solve integer scalar multiplication. By extending the method into the complex quadratic field, we are able to solve complex multiplication and implement the ISD method on elliptic curves with j-invariant 1728. The curve with j-invariant 1728 has a unique discriminant of the imaginary quadratic field. This unique discriminant of quadratic field yields a unique efficiently computable endomorphism, which later able to speed up the computations on this curve. However, the ISD method needs three endomorphisms to be accomplished. Hence, we choose all three endomorphisms to be from the same imaginary quadratic field as the curve itself, where the first endomorphism is the unique endomorphism yield from the discriminant of the imaginary quadratic field.

Keywords: efficiently computable endomorphism, elliptic scalar multiplication, j-invariant 1728, quadratic field

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Authors: Robert Ogbanje Okwori

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The research studied expository and simulation method of teaching woodwork at Federal University of Technology, Minna, Niger State, Nigeria. The purpose of the study was to compare expository and simulation method of teaching woodwork and determine the method that is more effective in improving performance of students in woodwork. Two research questions and two hypotheses were formulated to guide the study. Fifteen objective questions and two theory questions were used for data collection. The questions set were on structure of timber. The study used the quasi experimental design. The population of the study consisted of 25 woodwork students of Federal University of Technology, Minna, Niger State, Nigeria and three hundred (300) level students were used for the study. The lesson plans for expository method and questions were validated by three lecturers in the Department of Industrial and Technology Education, Federal University of Technology, Minna, Nigeria. The validators checked the appropriates of test items and all the corrections and inputs were effected before administration of the instrument. Data obtained were analyzed using mean, standard deviation and t-test statistical tool. The null hypotheses were formulated and tested using t-test statistics at 0.05 level of significance. The findings of the study showed that simulation method of teaching has improved students’ performance in woodwork and the performance of the students was not influenced by gender. Based on the findings of the study, it was concluded that there was a significant difference in the mean achievement scores of students taught woodwork using simulation method. This implies that simulation method is more effective than expository method of teaching woodwork. Therefore, woodwork teachers should adopt simulation method of teaching woodwork towards better performance. It was recommended that simulation method should be used by woodwork lecturers to teach woodwork since students perform better using the method and also the teachers needs to be trained and re-trained in using simulation method for teaching woodwork. Teachers should be encouraged to use simulation method for their instructional delivery because it will allow them to identify their areas of strength and weakness when imparting knowledge to woodwork students. Government and different agencies should assist in procuring materials and equipment for wood workshops to enable students effectively practice what they have been taught using simulation method.

Keywords: comparative, expository, simulation, woodwork

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Authors: Dariusz Jacek Jakóbczak

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Mathematics and computer science are interested in methods of 2D curve interpolation and extrapolation using the set of key points (knots). A proposed method of Hurwitz- Radon Matrices (MHR) is such a method. This novel method is based on the family of Hurwitz-Radon (HR) matrices which possess columns composed of orthogonal vectors. Two-dimensional curve is interpolated via different functions as probability distribution functions: polynomial, sinus, cosine, tangent, cotangent, logarithm, exponent, arcsin, arccos, arctan, arcctg or power function, also inverse functions. It is shown how to build the orthogonal matrix operator and how to use it in a process of curve reconstruction.

Keywords: 2D data interpolation, hurwitz-radon matrices, MHR method, probabilistic modeling, curve extrapolation

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Authors: Zhe Li, Aihua Cai

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Aiming at the problem of high-density clutter and interference affecting radar detection target track initiation in ECM and complex radar mission, the traditional radar target track initiation method has been difficult to adapt. To this end, we propose a multi-algorithm fusion learning track initiation algorithm, which transforms the track initiation problem into a true-false track discrimination problem, and designs an algorithm based on 1DCNN(One-Dimensional CNN)combined with Bi-LSTM (Bi-Directional Long Short-Term Memory )for fusion classification. The experimental dataset consists of real trajectories obtained from a certain type of three-coordinate radar measurements, and the experiments are compared with traditional trajectory initiation methods such as rule-based method, logical-based method and Hough-transform-based method. The simulation results show that the overall performance of the multi-algorithm fusion learning track initiation algorithm is significantly better than that of the traditional method, and the real track initiation rate can be effectively improved under high clutter density with the average initiation time similar to the logical method.

Keywords: track initiation, multi-algorithm fusion, 1DCNN, Bi-LSTM

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Authors: Dong Liu

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By sending a high-frequency probe wave and a low-frequency pump wave to a specimen, the vibroacoustic method evaluates the defect’s severity according to the modulation index of the received signal. Many studies experimentally proved the significant sensitivity of the modulation index to the tiny contact type defect. However, it has also been found that the modulation index was highly affected by the frequency of probe or pump waves. Therefore, the chirp signal has been introduced to the VAM method since it can assess multiple frequencies in a relatively short time duration, so the robustness of the VAM method could be enhanced. Consequently, the signal processing method needs to be modified accordingly. Various studies utilized different algorithms or combinations of algorithms for processing the VAM signal method by chirp excitation. These signal process methods were compared and used for processing a VAM signal acquired from the steel samples.

Keywords: vibroacoustic modulation, nonlinear acoustic modulation, nonlinear acoustic NDT&E, signal processing, structural health monitoring

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Authors: Girgis Younan, Ikram Eltayeb

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Geigeria alata belongs to the family Asteraceae, is an effective plant traditionally used in Sudan as a therapy for hepatic disease and as an antiepileptic, antispasmodic and to treat cough and intestinal complaints.The liver is responsible for many critical functions within the body and any liver disease or injury will result in the loss of those functions leading to significant damage in the body. Liver diseases cause increase in liver enzymes (AST, ALP ALT) and total bilirubin and a decrease in total blood protein level. The objective of this study is to investigate the hepato-protective activity of Geigeria alata leaves ethanolic extract. The plant leaves were extracted using 96% ethanol using Soxhlet apparatus. The hepatoprotective effect was determined using 25 wistar rats, the rats was divided to 5 groups, each group contain 5 rats: [Normal control group] receiving purified water, liver damage was induced in wistar rats by administering a 1:1 (v/v) mixture of CCl4 (1.25 ml/kg) and olive oil once at day four of the experiment [negative control group]. Two doses of extract [400mg/kg and 200mg/kg] was applied daily for 7 days, and standard drug Silymarin (200 mg/kg) were administered daily for 7 days to CCl4-treated rats. The degree of hepato-protective activity was evaluated by determining the hepatic marker enzymes AST, ALP, ALT, total Bilirubin and total proteins (TP). Results have shown that, the extract of G.alata leaves reduced the level of liver enzymes ALT, AST, ALP, total bilirubin and increased the level of total proteins. Since the levels of liver enzymes; bilirubin and total protein are considered as markers of liver function, the extract has proven to reduce the detrimental effects of liver toxicity induced using CCl4. The hepato-protective effect of extract on liver was found to be dose dependent, where the 400mg/kg dose of the extract exhibited higher activity than 200mg/kg dose. In addition, the effect of the higher dose (400mg/kg) of the extract was found to be higher than Silymarin standard drug. The result concludes that, G.alata leaves extract was found to exhibit profound hepato-protective activity, which justifies the traditional use of the plant for the treatment of hepatic diseases.

Keywords: alata, extract, geigeria, hepatoprotective

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Authors: Seyran Naghdi

Abstract:

Background: Orphan drug development is limited by very high costs attributed to the research and development and small size market. How health policymakers address this challenge to consider both supply and demand sides need to be explored for directing the policies and plans in the right way. The price is an important signal for pharmaceutical companies’ profitability and the patients’ accessibility as well. Objective: This study aims to find out the orphan drugs' price-setting patterns and approaches in health systems through a systematic review of the available evidence. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) approach was used. MedLine, Embase, and Web of Sciences were searched via appropriate search strategies. Through Medical Subject Headings (MeSH), the appropriate terms for pricing were 'cost and cost analysis', and it was 'orphan drug production', and 'orphan drug', for orphan drugs. The critical appraisal was performed by the Joanna-Briggs tool. A Cochrane data extraction form was used to obtain the data about the studies' characteristics, results, and conclusions. Results: Totally, 1,197 records were found. It included 640 hits from Embase, 327 from Web of Sciences, and 230 MedLine. After removing the duplicates, 1,056 studies remained. Of them, 924 studies were removed in the primary screening phase. Of them, 26 studies were included for data extraction. The majority of the studies (>75%) are from developed countries, among them, approximately 80% of the studies are from European countries. Approximately 85% of evidence has been produced in the recent decade. Conclusions: There is a huge variation of price-setting among countries, and this is related to the specific pharmacological market structure and the thresholds that governments want to intervene in the process of pricing. On the other hand, there is some evidence on the availability of spaces to reduce the very high costs of orphan drugs development through an early agreement between pharmacological firms and governments. Further studies need to focus on how the governments could incentivize the companies to agree on providing the drugs at lower prices.

Keywords: orphan drugs, orphan drug production, pricing, costs, cost analysis

Procedia PDF Downloads 163

Authors: Abhinav Bajpayee, Shubham Damke, Rupal Ranjan, Neha Bharti

Abstract:

Polymer flooding is a dramatic improvement in water flooding and quickly becoming one of the EOR technologies. Used for improving oil recovery. With the increasing energy demand and depleting oil reserves EOR techniques are becoming increasingly significant .Since most oil fields have already begun water flooding, chemical EOR technique can be implemented by using fewer resources than any other EOR technique. Polymer helps in increasing the viscosity of injected water thus reducing water mobility and hence achieves a more stable displacement .Polymer flooding helps in increasing the injection viscosity as has been revealed through field experience. While the injection of a polymer solution improves reservoir conformance the beneficial effect ceases as soon as one attempts to push the polymer solution with water. It is most commonly applied technique because of its higher success rate. In polymer flooding, a water-soluble polymer such as Polyacrylamide is added to the water in the water flood. This increases the viscosity of the water to that of a gel making the oil and water greatly improving the efficiency of the water flood. It also improves the vertical and areal sweep efficiency as a consequence of improving the water/oil mobility ratio. Polymer flooding plays an important role in oil exploitation, but around 60 million ton of wastewater is produced per day with oil extraction together. Therefore the treatment and reuse of wastewater becomes significant which can be carried out by electro dialysis technology. This treatment technology can not only decrease environmental pollution, but also achieve closed-circuit of polymer flooding wastewater during crude oil extraction. There are three potential ways in which a polymer flood can make the oil recovery process more efficient: (1) through the effects of polymers on fractional flow, (2) by decreasing the water/oil mobility ratio, and (3) by diverting injected water from zones that have been swept. It has also been suggested that the viscoelastic behavior of polymers can improve displacement efficiency Polymer flooding may also have an economic impact because less water is injected and produced compared with water flooding. In future we need to focus on developing polymers that can be used in reservoirs of high temperature and high salinity, applying polymer flooding in different reservoir conditions and also combine polymer with other processes (e.g., surfactant/ polymer flooding).

Keywords: fractional flow, polymer, viscosity, water/oil mobility ratio

Procedia PDF Downloads 400

Authors: Ravneil Nand

Abstract:

Cooperative coevolution uses problem decomposition methods to solve a larger problem. The problem decomposition deals with breaking down the larger problem into a number of smaller sub-problems depending on their method. Different problem decomposition methods have their own strengths and limitations depending on the neural network used and application problem. In this paper we are introducing a new problem decomposition method known as Hidden-Neuron Level Decomposition (HNL). The HNL method is competing with established problem decomposition method in time series prediction. The results show that the proposed approach has improved the results in some benchmark data sets when compared to the standalone method and has competitive results when compared to methods from literature.

Keywords: cooperative coevaluation, feed forward network, problem decomposition, neuron, synapse

Procedia PDF Downloads 335

Authors: M. Devaki, K. B. Jayanthi

Abstract:

The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

Procedia PDF Downloads 89