Search results for: Physalis minima

Authors: Norhanizan U., S. H. Ahmad, N. A. P. Abdullah, G. B. Saleh

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Physalis minima from the family of Solanaceae is one of the promising fruits which contains the high amount of vitamin C and other antioxidants as well. However, it is a perishable fruit where the deterioration process will commence if the fruits are not stored in proper conditions. There is not much work has been carried out to study the effects of storage durations on Physalis fruit. Therefore, this study was conducted to determine the effects of 0, 3, 6, and 9 days of storage on postharvest quality of Physalis minima fruits. Total of 120g of uniform sizes of fruits (2.3 to 2.5g) were used for each replication and the experiment was repeated thrice. The fruits were divided equally into four groups with each group labeled according to the days of storage. The fruits were then stored in the cool room for nine days with temperature maintain at 12 ° C. The fruits were analyzed for weight loss, firmness, color (L*, C* and hue angle), titratable acidity (TA), soluble solids concentrations (SSC), pH and ascorbic acids. Data were analyzed using analysis of variance and means was separated using least significant difference (LSD). The storage durations affect the quality characteristics of the fruits. On the day 9, the average of fruit weight loss and fruit firmness decreased about 21 and 24% respectively. The level of ascorbic acids and titrable acidity were also decreased while the soluble solids concentration increased during storage. Thus, in order to retain the quality of the fruits, it is recommended that the Physalis fruit can be stored only up to 6 days at 12 ° C.

Keywords: fruit quality, Physalis minima, Solanaceae, storage durations

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Authors: Haruna Karamba, Nafisa Muhammad Danyaro

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The study describe the action of natural latex (extract) of two sub-aquatic macrophytes plants i.e., Cassia occidentalis and Physalis angulata which were tested against two water snail species; Bulinus globusus and Lymnaea natalensis, the intermediate host of Bilharziasis (chistosomiasis) in the tropical countries. Bilherziasis is a disease prevalent and endermic to tropical Africa, seriously undermining health status of Nigerian youth. The easiest way to eradicate the disease is to eliminate the secondary host of the pathogen, chistosoma species. Therefore we carried out a research to investigate the molluscicidal effect of the leaf extract of C. occidentalis and P. angulata on mortality rate of B. globusus and L. natalensis water snails using pond water in the laboratory of science laboratory department of Kano State Polytechnic, Nigeria. One hundred and fifty juveniles’ snails were collected from Jakara Dam in the Northeastern part of Kano, Nigeria. The snails were put inside a plastic container and transported immediately to the laboratory where they were transferred into reservoir tank containing pond water and kept for 48 hours to get acclimatized with laboratory environment. Twelve water bathes 2/3 filled with pond water were prepared and kept in the laboratory. Leaf extract of the plants were obtained by blending and homogenizing the leaf tissue from which the extract were obtained and prepared in 10, 20, 30, 40 and 50 ppm, in addition to 0 ppm, which served as control. Ten snails were placed in each of the twelve water bathes. Six water bathes for the species of C. accidentalis extract and other six for P. angulata. The treatment combinations were maintained for 2 days after which the number of living snails present in each water bathes were counted and subsequently at 2 days intervals. The result indicated that extracts from both plants were lethal to the snails as concentration of the extract increases particularly mortality rate was highest at 40 and 50 ppm. Conclusively the toxicity of the extracts from these plants proven lethal to snails and hence can be used as molluscicides for cheap and easy method of eliminating water snails and therefore reducing the incidence of Bilharziasis.

Keywords: schistosomiasis, bilharziasis, Bulinus globusus, Lymnea natalensis, Physalis angulata, Cassia occidentalis, Kano

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Authors: Rian Diana, Naufal Muharam Nurdin, Faisal Anwar, Hadi Riyadi, Ali Khomsan

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The use of traditional medicine (herbs and medicinal plants) are common among Indonesian people especially the elderly. Few study explore the use of medicinal plants in middle aged people. This study aims to collect information on the use of medicinal plants in middle aged people in rural areas. This cross sectional study included 224 subjects aged 45-59 years old and conducted in Cianjur District, West Java in 2014. Semi-structured questionnaires were used to collect information about preference in treatment of illness, the use of medicinal plants, and their purposes. Information also recorded plant names, parts used, mode of preparation, and dosage. Buying drugs in stall (83.9%) is the first preference in treatment of illness, followed by modern treatment 19.2% (doctors) and traditional treatment 17.0% (herbs/medicinal plants). 87 subjects (38.8%) were using herbs and medicinal plants for curative (66.7%), preventive (31.2%), and rehabilitative (2.1%) purposes. In this study, 48 species are used by the subjects. Physalis minima L. 'cecenet', Orthosiphon aristatus Mic. 'kumis kucing', and Annona muricata 'sirsak' are commonly used for the treatment of hypertension and stiffness. Leaves (64.6%) are the most common part used. Medicinal plants were washed and boiled in a hot water. Subject drinks the herbs with a different dosage. One in three middle aged people used herbal and medicinal plants for curative and preventive treatment particularly hypertension and stiffness. Increasing knowledge about herbal or medicinal plants dosage and their interaction with medical drugs are important to do.Doses vary between 1-3 glasses/day for treatment and 1-2 glasses/months for prevention of diseases.

Keywords: herbs, hypertension, medicinal plants, middle age, rural

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Authors: Nishtha, Richa, Anju Rao

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Plants are the source of herbal medicine and medicinal value of the plants lies in the bioactive phytochemical constituents that produce definite physiological effects on human body. Angiospermic families are known to produce such phytochemical constituents which are termed as secondary plant metabolites. These metabolites include alkaloids, saponins, phenolic compounds, flavonoids, tannins, terpenoids and so on. Solanaceae is one of the important families of Angiosperms known for medicinally important alkaloids such as hyoscyamine, scopolamine, solanine, nicotine, capsaicin etc. Medicinally important species of this family mostly belong to the genera of Datura,Atropa,Solanum,Withania and Nicotiana.Six species such as Datura metel, Solanum torvum, Physalis minima, Cestrum nocturnum, Cestrum diurnum and Nicotiana plumbaginifolia have been collected from different localities of Chandigarh and adjoining areas.Field and anatomical studies helped to identify the plants and their parts used for the study of secondary plant metabolites. Preliminary phytochemcial studies have been done on various parts of plants such as roots, stem and leaves by making aqueous and alcoholic extracts from their powdered forms which showed the presence of alkaloids in almost all the species followed by steroids, flavonoids, terpenoids, tannins etc. HPLC profiles of leaves of Datura metel showed the presence of active compounds such as scopalamine and hyoscyamine and Solanum torvum showed the presence of solanine and solasodine. These alkaloids are important source of drug based medicine used in pharmacognosy. The respective compounds help in treating vomiting, nausea, respiratory disorders, dizziness, asthma and many heart problems.

Keywords: alkaloids, flavanoids, phytochemical constituents, pharmacognosy, secondary metabolites

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Authors: Vishnu Pratap Singh Kirar

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Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: neural network, backpropagation, local minima, fast convergence rate

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Authors: Mary-Luz Olivares-Tenorio, Ruud Verkerk, Matthijs Dekker, Martinus A. J. S. van Boekel

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Cape gooseberry, the fruit of the plant Physalis peruviana L. has gained interest in research given its contents of promising health-promoting compounds like contents. The presence of carotenoids, ascorbic acid, minerals, polyphenols, vitamins and antioxidants. This project aims to study thermal stability of β-carotene, ascorbic acid, catechin and epicatechin and antioxidant activity in the matrix of the Cape Gooseberry. Fruits were obtained from a Colombian field in Cundinamarca. Ripeness stage was 4 (According to NTC 4580, corresponding to mature stage) at the moment of the experiment. The fruits have been subjected to temperatures of 40, 60, 80, 100 and 120°C for several times. β-Carotene, ascorbic acid, catechin and epicatechin content were assessed with HPLC and antioxidant activity with the DPPH method. β-Carotene was stable upon 100°C, and showed some degradation at 120°C. The same behavior was observed for epicatechin. Catechin increased during treatment at 40°C, at 60°C it remained stable and it showed degradation at 80°C, 100°C and 120°C that could be described by a second order kinetic model. Ascorbic acid was the most heat-sensitive of the analyzed compounds. It showed degradation at all studied temperatures, and could be described by a first order model. The activation energy for ascorbic acid degradation in cape gooseberry was 46.0 kJ/mol and its degradation rate coefficient at 100 °C was 6.53 x 10-3 s-1. The antioxidant activity declined for all studied temperatures. Results from this study showed that cape gooseberry is an important source of different health-promoting compounds and some of them are stable to heat. That makes this fruit a suitable raw material for processed products such as jam, juices and dehydrated fruit, giving the consumer a good intake of these compounds.

Keywords: goldenberry, health-promoting compounds, phytochemical, processing, heat treatment

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Authors: Oliver Ohneiser, Francesca De Crescenzio, Gianluca Di Flumeri, Jan Kraemer, Bruno Berberian, Sara Bagassi, Nicolina Sciaraffa, Pietro Aricò, Gianluca Borghini, Fabio Babiloni

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An increasing degree of automation in air traffic will also change the role of the air traffic controller (ATCO). ATCOs will fulfill significantly more monitoring tasks compared to today. However, this rather passive role may lead to Out-Of-The-Loop (OOTL) effects comprising vigilance decrement and less situation awareness. The project MINIMA (Mitigating Negative Impacts of Monitoring high levels of Automation) has conceived a system to control and mitigate such OOTL phenomena. In order to demonstrate the MINIMA concept, an experimental simulation set-up has been designed. This set-up consists of two parts: 1) a Task Environment (TE) comprising a Terminal Maneuvering Area (TMA) simulator as well as 2) a Vigilance and Attention Controller (VAC) based on neurophysiological data recording such as electroencephalography (EEG) and eye-tracking devices. The current vigilance level and the attention focus of the controller are measured during the ATCO’s active work in front of the human machine interface (HMI). The derived vigilance level and attention trigger adaptive automation functionalities in the TE to avoid OOTL effects. This paper describes the full-scale experimental set-up and the component development work towards it. Hence, it encompasses a pre-test whose results influenced the development of the VAC as well as the functionalities of the final TE and the two VAC’s sub-components.

Keywords: automation, human factors, air traffic controller, MINIMA, OOTL (Out-Of-The-Loop), EEG (Electroencephalography), HMI (Human Machine Interface)

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Authors: Shreya Goyal

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The study aims to build user-friendly software to understand patient arrival patterns and compute the number of potential patients who will visit a particular health facility for a given period by using a machine learning algorithm. The underlying machine learning algorithm used in this study is the Support Vector Machine (SVM). Accurate prediction of patient arrival allows hospitals to operate more effectively, providing timely and efficient care while optimizing resources and improving patient experience. It allows for better allocation of staff, equipment, and other resources. If there's a projected surge in patients, additional staff or resources can be allocated to handle the influx, preventing bottlenecks or delays in care. Understanding patient arrival patterns can also help streamline processes to minimize waiting times for patients and ensure timely access to care for patients in need. Another big advantage of using this software is adhering to strict data protection regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States as the hospital will not have to share the data with any third party or upload it to the cloud because the software can read data locally from the machine. The data needs to be arranged in. a particular format and the software will be able to read the data and provide meaningful output. Using software that operates locally can facilitate compliance with these regulations by minimizing data exposure. Keeping patient data within the hospital's local systems reduces the risk of unauthorized access or breaches associated with transmitting data over networks or storing it in external servers. This can help maintain the confidentiality and integrity of sensitive patient information. Historical patient data is used in this study. The input variables used to train the model include patient age, time of day, day of the week, seasonal variations, and local events. The algorithm uses a Supervised learning method to optimize the objective function and find the global minima. The algorithm stores the values of the local minima after each iteration and at the end compares all the local minima to find the global minima. The strength of this study is the transfer function used to calculate the number of patients. The model has an output accuracy of >95%. The method proposed in this study could be used for better management planning of personnel and medical resources.

Keywords: machine learning, SVM, HIPAA, data

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Authors: Wullapa Wongsinlatam

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Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples.

Keywords: artificial neural networks, back propagation algorithm, time series, local minima problem, metaheuristic optimization

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Authors: Bingxi Huang, Yiqing Li, Marek Morzynski, Bernd R. Noack

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We propose a Kriging-based global optimization method for active flow control with multiple actuation parameters. This method is designed to converge quickly and avoid getting trapped into local minima. We follow the model-free explorative gradient method (EGM) to alternate between explorative and exploitive steps. This facilitates a convergence similar to a gradient-based method and the parallel exploration of potentially better minima. In contrast to EGM, both kinds of steps are performed with Kriging surrogate model from the available data. The explorative step maximizes the expected improvement, i.e., favors regions of large uncertainty. The exploitive step identifies the best location of the cost function from the Kriging surrogate model for a subsequent weight-biased linear-gradient descent search method. To verify the effectiveness and robustness of the improved Kriging-based optimization method, we have examined several comparative test problems of varying dimensions with limited evaluation budgets. The results show that the proposed algorithm significantly outperforms some model-free optimization algorithms like genetic algorithm and differential evolution algorithm with a quicker convergence for a given budget. We have also performed direct numerical simulations of the fluidic pinball (N. Deng et al. 2020 J. Fluid Mech.) on three circular cylinders in equilateral-triangular arrangement immersed in an incoming flow at Re=100. The optimal cylinder rotations lead to 44.0% net drag power saving with 85.8% drag reduction and 41.8% actuation power. The optimal results for active flow control based on this configuration have achieved boat-tailing mechanism by employing Coanda forcing and wake stabilization by delaying separation and minimizing the wake region.

Keywords: direct numerical simulations, flow control, kriging, stochastic optimization, wake stabilization

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Authors: Rayenne Djemil

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The mechanism of oxidation reaction of linoleic acid C18: 2 (9 cis12) by singlet oxygen 1O2 were theoretically investigated via using quantum chemical methods. We explored the four reaction pathways at PM3, Hartree-Fock HF and, B3LYP functional associated with the base 6-31G (d) level. The results are in favor of the first and the last reaction ways. The transition states were found by QST3 method. Thus the pathways between the transition state structures and their corresponding minima have been identified by the IRC calculations. The thermodynamic study showed that the four ways of oxidation of linoleic acid are spontaneous, exothermic and, the enthalpy values confirm that conjugate hydroperoxydes are the most favorable products.

Keywords: echanism, quantum mechanics, oxidation, linoleic acid H

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Authors: Stoyan Nedeltchev, Markus Schubert

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By means of the ultrafast X-ray tomography facility, data were obtained at different superficial gas velocities U_G in a bubble column (0.1 m in ID) operated with an air-deionized water system at ambient conditions. Raw reconstructed images were treated by both the information entropy (IE) and the reconstruction entropy (RE) algorithms in order to identify the main transition velocities in a bubble column. The IE values exhibited two well-pronounced minima at U_G=0.025 m/s and U_G=0.085 m/s identifying the boundaries of the homogeneous, transition and heterogeneous regimes. The RE extracted from the central region of the column’s cross-section exhibited only one characteristic peak at U_G=0.03 m/s, which was attributed to the transition from the homogeneous to the heterogeneous flow regime. This result implies that the transition regime is non-existent in the core of the column.

Keywords: bubble column, ultrafast X-ray tomography, information entropy, reconstruction entropy

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Authors: Moqbil S. Alenazi, Magdy. M. Elkhateeb

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A comprehensive photometric study and evolutionary state for the newly discovered Beta Lyr systems V0487 Lac, V0566 Hya, and V0666 Lac were carried out by means of their first photometric observations. New times of minima were estimated from the observed light curves, and first (O-C) curves were established for all systems. A windows interface version of the Wilson and Devinney code (W-D) based on model atmospheres and a pass band prescription have been used for the radiative treatment. The accepted models reveal some absolute parameters for the studied systems, which are used in adopting the spectral type of the system's components and their evolutionary status. Distances to each system were calculated, and physical properties were estimated. Locations of the systems on the theoreticalmass–luminosity and mass–radius relations revealed a good fit for all systems components except for the secondary component of the system V0487 Lac.

Keywords: eclipsing binaries, light curve modelling, evolutionary state

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Authors: S. Dargouthi, S. Boughdiri, B. Tangour

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This work invoked two complementary parts. In the first, we performed a theoretical study of various dimers of molecular of titanium dioxide. Five structures were examined. Three among them, the (T), (C) and (T/P) isomers, may be considered as stable compounds because they represent absolute minima on their potential energy surfaces. (T) and (C) may coexist because they are separted by only 6.5 kcal mol-1 but (T/P) dimer is in a metastable state from an energetic point of view. Non bonded dimer (P) transforms into its homologue (O) which has been considered as transitory specie with low lifetime which evolves to (T) structure. In the second part, we highlight the possible stabilization of (T), (C) and (P) dimers by encapsulation in carbon nanotubes. This indicates the probable role that plays this transitory specie the polymerization process of molecular TiO2. Confinement is suitable to control the fast evolution process and could towards the synthesis of new titanium dioxide nanostructured materials. An alternative description of TiO2 polymorphs (Rutie, anatase et Brookite) is proposed from (T), (C) and (T/P) dimmers motifs.

Keywords: titanium dioxide, carbon nanotube, confinement. encapsulation, transitory specie

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Authors: J. Yang, Y. Ma, X. Zhang, S. Li, Y. Zhang

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The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.

Keywords: degree, initial cluster center, k-means, minimum spanning tree

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Authors: S. N. Derrar, M. Sekkal-Rahal

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The Nonlinear optics (NLO) technique is widely used in the field of biological imaging. In fact, grafting biological entities with a high NLO response on tissues and cells enhances the NLO responses of these latter, and ameliorates, consequently, their biological imaging quality. In this optics, we carried out a theoretical study, in the aim of analyzing the peptide bonds created between alanine amino acid and both unnatural amino acids: L-Dopa and Azatryptophan, respectively. Ramachandran plots have been performed for these systems, and their structural parameters have been analyzed. The NLO responses of these peptides have been reported by calculating the first hyperpolarizability values of all the minima found on the plots. The use of such unnatural amino acids as endogenous probing molecules has been investigated through this study. The Density Functional Theory (DFT) has been used for structural properties, while the Second-order Møller-Plesset Perturbation Theory (MP2) has been employed for the NLO calculations.

Keywords: biological imaging, hyperpolarizability, nonlinear optics, probing molecule

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Authors: Baoshan Wei, Shuai Han, Xing Zhang

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Local obstacle avoidance is critical for mobile robot navigation. It is a challenging task to ensure path optimality and safety in cluttered environments. We proposed an Environment Aware Dynamic Window Approach in this paper to cope with the issue. The method integrates environment characterization into Dynamic Window Approach (DWA). Two strategies are proposed in order to achieve the integration. The local goal strategy guides the robot to move through openings before approaching the final goal, which solves the local minima problem in DWA. The adaptive control strategy endows the robot to adjust its state according to the environment, which addresses path safety compared with DWA. Besides, the evaluation shows that the path generated from the proposed algorithm is safer and smoother compared with state-of-the-art algorithms.

Keywords: adaptive control, dynamic window approach, environment aware, local obstacle avoidance, mobile robots

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Authors: V. S. S. Kumar, B. Vikram

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The development of effective decision support tools that adopted in the construction industry is vital in the world we live in today, since it can lead to substantial cost reduction and efficient resource consumption. Solving the time-cost trade off problems and its related variants is at the heart of scientific research for optimizing construction planning problems. In general, the classical optimization techniques have difficulties in dealing with TCT problems. One of the main reasons of their failure is that they can easily be entrapped in local minima. This paper presents an investigation on the application of meta-heuristic techniques to two particular variants of the time-cost trade of analysis, the time-cost trade off problem (TCT), and time-cost trade off optimization problem (TCO). In first problem, the total project cost should be minimized, and in the second problem, the total project cost and total project duration should be minimized simultaneously. Finally it is expected that, the optimization models developed in this paper will contribute significantly for efficient planning and management of construction project.

Keywords: fuzzy sets, uncertainty, optimization, time cost trade off problems

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Authors: Zidane Abderrezzaq

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In contrast to recent successful developments in macro monetary policies, the modelling, measurement and management of systemic financial stability has remained problematical. Indeed, the focus of most effort has been on improving individual, rather than systemic, bank risk management; the Basel II objective has been to bring regulatory bank capital into line with the (sophisticated) banks’ assessment of their own economic capital. Even at the individual bank level there are concerns over appropriate diversification allowances, differing objectives of banks and regulators, the need for a buffer over regulatory minima, and the distinction between expected and unexpected losses (EL and UL). At the systemic level the quite complex and prescriptive content of Basel II raises dangers of ‘endogenous risk’ and procyclicality. Simulations suggest that this latter could be a serious problem. In an extension to the main analysis we study how liquidity effects interact with banking structure to produce a greater chance of systemic breakdown. We finally consider how the risk of contagion might depend on the degree of asymmetry (tiering) inherent in the structure of the banking system. A number of our results have important implications for public policy, which this paper also draws out.

Keywords: systemic stability, financial regulation, credit risk, systemic risk

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Authors: Paffrath Meinhard, Zhou Yayun, Guo Yiqing, Ertl Harald

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Winding geometry design is an important part of power transformer electrical design. Conventionally, the winding geometry is designed manually, which is a time-consuming job because it involves many iteration steps in order to meet all cost, manufacturing and electrical requirements. Here a method is presented which automatically generates the winding geometry for given user parameters and allows the user to interactively set and change parameters. To achieve this goal, the winding problem is transferred to a mixed integer nonlinear optimization problem. The relevant geometrical design parameters are defined as optimization variables. The cost and other requirements are modeled as constraints. For the solution, a stochastic ant colony optimization algorithm is applied. It is well-known, that an optimizer can get stuck in a local minimum. For the winding problem, we present efficient strategies to come out of local minima, furthermore a reduced variable search range helps to accelerate the solution process. Numerical examples show that the optimization result is delivered within seconds such that the user can interactively change the variable search area and constraints to improve the design.

Keywords: ant colony optimization, mixed integer nonlinear programming, power transformer, winding design

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Authors: Tong He, Long Chen, Irag Mantegh, Wen-Fang Xie

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This paper proposes an image-based obstacle avoidance and tracking target identification strategy in GPS-degraded or GPS-denied environment for an Unmanned Aerial Vehicle (UAV). The traditional force algorithm for obstacle avoidance could produce local minima area, in which UAV cannot get away obstacle effectively. In order to eliminate it, an artificial potential approach based on harmonic potential is proposed to guide the UAV to avoid the obstacle by using the vision system. And image-based visual servoing scheme (IBVS) has been adopted to implement the proposed obstacle avoidance approach. In IBVS, the pixel accuracy is a key factor to realize the obstacle avoidance. In this paper, the deep reinforcement learning framework has been applied by reducing pixel errors through constant interaction between the environment and the agent. In addition, the combination of OpenTLD and Tensorflow based on neural network is used to identify the type of tracking target. Numerical simulation in Matlab and ROS GAZEBO show the satisfactory result in target identification and obstacle avoidance.

Keywords: image-based visual servoing, obstacle avoidance, tracking target identification, deep reinforcement learning, artificial potential approach, neural network

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Authors: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Muhammad Faisal Fateh

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In this work, the strength of bio-inspired computational intelligence based technique is exploited for parameter estimation for the periodic signals using Continuous Differential Evolution (CDE) by defining an error function in the mean square sense. Multidimensional and nonlinear nature of the problem emerging in sinusoidal signal models along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of CDE to ensure convergence and avoid trapping in local minima. In the proposed scheme of Continuous Differential Evolution based Signal Parameter Estimation (CDESPE), unknown adjustable weights of the signal system identification model are optimized utilizing CDE algorithm. The performance of CDESPE model is validated through statistics based various performance indices on a sufficiently large number of runs in terms of estimation error, mean squared error and Thiel’s inequality coefficient. Efficacy of CDESPE is examined by comparison with the actual parameters of the system, Genetic Algorithm based outcomes and from various deterministic approaches at different signal-to-noise ratio (SNR) levels.

Keywords: parameter estimation, bio-inspired computing, continuous differential evolution (CDE), periodic signals

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Authors: Jia-Ming Kok, Javaan Chahl

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An optimisation method using both global and local optimisation is implemented to determine the flapping profile which will produce the most lift for an experimental wing-actuation system. The optimisation method is tested using a numerical quasi-steady analysis. Results of an optimised flapping profile show a 20% increase in lift generated as compared to flapping profiles obtained by high speed cinematography of a Sympetrum frequens dragonfly. Initial optimisation procedures showed 3166 objective function evaluations. The global optimisation parameters - initial sample size and stage one sample size, were altered to reduce the number of function evaluations. Altering the stage one sample size had no significant effect. It was found that reducing the initial sample size to 400 would allow a reduction in computational effort to approximately 1500 function evaluations without compromising the global solvers ability to locate potential minima. To further reduce the optimisation effort required, we increase the local solver’s convergence tolerance criterion. An increase in the tolerance from 0.02N to 0.05N decreased the number of function evaluations by another 20%. However, this potentially reduces the maximum obtainable lift by up to 0.025N.

Keywords: flapping wing, optimisation, quasi-steady model, dragonfly

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Authors: Orhun Aydin, Mark V. Janikas, Rodrigo Alves, Renato Assuncao

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In this paper, a tree-based perturbation methodology for regionalization inference is presented. Regionalization is a constrained optimization problem that aims to create groups with similar attributes while satisfying spatial contiguity constraints. Similar to any constrained optimization problem, the spatial constraint may hinder convergence to some global minima, resulting in spatially contiguous members of a group with dissimilar attributes. This paper presents a general methodology for rigorously perturbing spatial constraints through the use of random spanning trees. The general framework presented can be used to quantify the effect of the spatial constraints in the overall regionalization result. We compare several types of stochastic spanning trees used in inference problems such as fuzzy regionalization and determining the number of regions. Performance of stochastic spanning trees is juxtaposed against the traditional permutation-based hypothesis testing frequently used in spatial statistics. Inference results for fuzzy regionalization and determining the number of regions is presented on the Local Area Personal Incomes for Texas Counties provided by the Bureau of Economic Analysis.

Keywords: regionalization, constrained clustering, probabilistic inference, fuzzy clustering

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Authors: Manel Bouloudenine, Karima Djeddou, Hadjer Ben Manser, Hana Soualah Alila, Mohmed Bououdina

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This survey research involves the elaboration and characterization of silver nanoparticles for therapeutic and environmental applications. The silver nanoparticles "Ag NPs" were synthesized by reducing AgNO3 with microwaves. The characterization of nanoparticles was done by using Transmission Electron Microscopy " TEM ", Energy Dispersive Spectroscopy "EDS", Selected Area Electron Diffraction "SEAD", UV-Visible Spectroscopy and Dynamic Light Scattering "DLS". Transmission Electron Microscopy and Electron Diffraction have confirmed the nanoscale, the shape, and the crystalline quality of as synthesized silver nanoparticles. Elementary analysis has proved the purity of Ag NPs and the presence of the Surface Plasmon Resonance phenomenon "SPR". A strong absorption shift was observed in the visible range of the UV-visible spectrum of as synthesized Ag NPs, which indicates the presence of metallic silver. When the strong absorption in the ultraviolet range of the spectrum has revealed the presence of ionic Ag NPs ionic Ag aggregates species. The autocorrelation function measured by the Dynamic Light Scattering has shown a strong monodispersed character of Ag NPs, which is indicated by the presence of a single size population, with a minima and a maxima laying between 40 and 111 nm. Related to other research, our results confirm the performance properties of as synthesized Ag NPs, which allows them to be performing in many technological applications, including therapeutic and environmental ones.

Keywords: silvers nanoparticles, microwaves, EDS, TEM

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Authors: M. Dhana Lakshmi, S. Sakthivel Murugan

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As light passes from source to observer in the water medium, it is scattered by the suspended particulate matter. This scattering effect will plague the captured images with non-uniform illumination, blurring details, halo artefacts, weak edges, etc. To overcome this, pixel normalization with an Amended Unsharp Mask (AUM) filter is proposed to enhance the degraded image. To validate the robustness of the proposed technique irrespective of atmospheric light, the considered datasets are collected on dual locations. For those images, the maxima and minima pixel intensity value is computed and normalized; then the AUM filter is applied to strengthen the blurred edges. Finally, the enhanced image is obtained with good illumination and contrast. Thus, the proposed technique removes the effect of scattering called de-hazing and restores the perceptual information with enhanced edge detail. Both qualitative and quantitative analyses are done on considering the standard non-reference metric called underwater image sharpness measure (UISM), and underwater image quality measure (UIQM) is used to measure color, sharpness, and contrast for both of the location images. It is observed that the proposed technique has shown overwhelming performance compared to other deep-based enhancement networks and traditional techniques in an adaptive manner.

Keywords: underwater drone imagery, pixel normalization, thresholding, masking, unsharp mask filter

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

Abstract:

In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: Juan Antonio Mondragon Sanchez, Ruben Santamaria

Abstract:

The DNA G-quadruplex is a four-stranded DNA structure conformed by stacked planes of four base paired guanines (G-quartet). The guanine rich DNA sequences are present in many sites of genomic DNA and can potentially lead to the formation of G-quadruplexes, especially at the 3'-terminus of the human telomeric DNA with many TTAGGG repeats. The formation and stabilization of a G-quadruplex by small ligands at the telomeric region can inhibit the telomerase activity. In turn, the ligands can be used to regulate oncogene expression making the G-quadruplex an attractive target for anticancer therapy. Clearly, the G-quadruplex structured in the telomeric DNA is of fundamental importance for rational drug design. In this context, we investigate two G-quadruplex structures, the first follows from the sequence TTAGGG(TTAGGG)3TT (HUT1), and the second from AAAGGG(TTAGGG)3AA (HUT2), both in a K+ solution. We determine the free energy surfaces of the HUT1 and HUT2 structures and investigate their conformations using replica-exchange metadynamics simulations. The carbonyl-carbonyl distances belonging to different guanines residues are selected as the main collective variables to determine the free energy surfaces. The surfaces exhibit two main local minima, compatible with experiments on the conformational transformations of HUT1 and HUT2 under substitution of the K+ ions by the Na+ ions. The conformational transitions are not observed in short MD simulations without the use of the metadynamics approach. The results of this work should be of help to understand the formation and stability of human telomeric G-quadruplex in environments including the presence of K+ and Na+ ions.

Keywords: g-quadruplex, metadynamics, molecular dynamics, replica-exchange

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Authors: G. Shahzadi, A. Soulaimani

Abstract:

This research work aims to identify the physical parameters of the constitutive soil model in the design of a rockfill dam by inverse analysis. The best parameters of the constitutive soil model, are those that minimize the objective function, defined as the difference between the measured and numerical results. The Finite Element code (Plaxis) has been utilized for numerical simulation. Polynomial and neural network-based response surfaces have been generated to analyze the relationship between soil parameters and displacements. The performance of surrogate models has been analyzed and compared by evaluating the root mean square error. A comparative study has been done based on objective functions and optimization techniques. Objective functions are categorized by considering measured data with and without uncertainty in instruments, defined by the least square method, which estimates the norm between the predicted displacements and the measured values. Hydro Quebec provided data sets for the measured values of the Romaine-2 dam. Stochastic optimization, an approach that can overcome local minima, and solve non-convex and non-differentiable problems with ease, is used to obtain an optimum value. Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) are compared for the minimization problem, although all these techniques take time to converge to an optimum value; however, PSO provided the better convergence and best soil parameters. Overall, parameter identification analysis could be effectively used for the rockfill dam application and has the potential to become a valuable tool for geotechnical engineers for assessing dam performance and dam safety.

Keywords: Rockfill dam, parameter identification, stochastic analysis, regression, PLAXIS

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Authors: A. Cerrato Casado, C. Guigou, P. Jean

Abstract:

In this investigation, the particle swarm optimization (PSO) algorithm is used to perform the inversion of the dispersion curves in the spectral analysis of surface waves (SASW) method. This inverse problem usually presents complicated solution spaces with many local minima that make difficult the convergence to the correct solution. PSO is a metaheuristic method that was originally designed to simulate social behavior but has demonstrated powerful capabilities to solve inverse problems with complex space solution and a high number of variables. The dispersion curve of the synthetic soils is constructed by the vertical flexibility coefficient method, which is especially convenient for soils where the stiffness does not increase gradually with depth. The reason is that these types of soil profiles are not normally dispersive since the dominant mode of Rayleigh waves is usually not coincident with the fundamental mode. Multiple synthetic soil profiles have been tested to show the characteristics of the convergence process and assess the accuracy of the final soil profile. In addition, the inversion procedure is applied to multiple real soils and the final profile compared with the available information. The combination of the vertical flexibility coefficient method to obtain the dispersion curve and the PSO algorithm to carry out the inversion process proves to be a robust procedure that is able to provide good solutions for complex soil profiles even with scarce prior information.

Keywords: dispersion, inverse problem, particle swarm optimization, SASW, soil profile

Procedia PDF Downloads 152