Search results for: improved Canny algorithm

Authors: Takuro Kida, Yuichi Kida

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We show a base of the new trend of algorithm mathematically that treats a historical reason of continuous discrimination in the world as well as its solution by introducing new concepts of parallel world that includes an invisible set of errors as its companion. With respect to a matrix operator-filter bank that the matrix operator-analysis-filter bank H and the matrix operator-sampling-filter bank S are given, firstly, we introduce the detailed algorithm to derive the optimum matrix operator-synthesis-filter bank Z that minimizes all the worst-case measures of the matrix operator-error-signals E(ω) = F(ω) − Y(ω) between the matrix operator-input-signals F(ω) and the matrix operator-output signals Y(ω) of the matrix operator-filter bank at the same time. Further, feedback is introduced to the above approximation theory and it is indicated that introducing conversations with feedback does not superior automatically to the accumulation of existing knowledge of signal prediction. Secondly, the concept of category in the field of mathematics is applied to the above optimum signal approximation and is indicated that the category-based approximation theory is applied to the set-theoretic consideration of the recognition of humans. Based on this discussion, it is shown naturally why the narrow perception that tends to create isolation shows an apparent advantage in the short term and, often, why such narrow thinking becomes intimate with discriminatory action in a human group. Throughout these considerations, it is presented that, in order to abolish easy and intimate discriminatory behavior, it is important to create a parallel world of conception where we share the set of invisible error signals, including the words and the consciousness of both worlds.

Keywords: signal prediction, pseudo inverse matrix, artificial intelligence, conditional optimization

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Authors: Hatem I. Mokhtar, Randa A. Abd-El-Salam, Ghada M. Hadad

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An improved nano-composite of self-doped polyaniline nanofibers and silica-coated magnetite nanoparticles were prepared and evaluated for suitability to magnetic dispersive micro solid-phase extraction. The work focused on optimization of the composite capacity to extract four fluoroquinolones (FQs) antibiotics, ciprofloxacin, enrofloxacin, danofloxacin, and difloxacin from water and improvement of composite stability towards acid and atmospheric degradation. Self-doped polyaniline nanofibers were prepared by oxidative co-polymerization of aniline with anthranilic acid. Magnetite nanopariticles were prepared by alkaline co-precipitation and coated with silica by silicate hydrolysis on magnetite nanoparticles surface at pH 6.5. The composite was formed by self-assembly by mixing self-doped polyaniline nanofibers with silica-coated magnetite nanoparticles dispersions in ethanol. The composite structure was confirmed by transmission electron microscopy (TEM). Self-doped polyaniline nanofibers and magnetite chemical structures were confirmed by FT-IR while silica coating of the magnetite was confirmed by Energy Dispersion X-ray Spectroscopy (EDS). Improved stability of the composite magnetic component was evidenced by resistance to degrade in 2N HCl solution. The adsorption capacity of self-doped polyaniline nanofibers based composite was higher than previously reported corresponding composite prepared from polyaniline nanofibers instead of self-doped polyaniline nanofibers. Adsorption-pH profile for the studied FQs on the prepared composite revealed that the best pH for adsorption was in range of 6.5 to 7. Best extraction recovery values were obtained at pH 7 using phosphate buffer. The best solvent for FQs desorption was found to be 0.1N HCl in methanol:water (8:2; v/v) mixture. 20 mL of Spiked water sample with studied FQs were preconcentrated using 4.8 mg of composite and resulting extracts were analysed by HPLC-UV method. The prepared composite represented a suitable adsorbent phase for magnetic dispersive micro-solid phase application.

Keywords: fluoroquinolones, magnetic dispersive micro extraction, nano-composite, self-doped polyaniline nanofibers

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Authors: Zeyad Abdelmageid, Xianbin Wang

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Choosing the operational channel for a WLAN access point (AP) in WLAN networks has been a static channel assignment process initiated by the user during the deployment process of the AP, which fails to cope with the dynamic conditions of the assigned channel at the station side afterward. However, the dramatically growing number of Wi-Fi APs and stations operating in the unlicensed band has led to dynamic, distributed, and often severe interference. This highlights the urgent need for the AP to dynamically select the best overall channel of operation for the basic service set (BSS) by considering the distributed and changing channel conditions at all stations. Consequently, dynamic channel selection algorithms which consider feedback from the station side have been developed. Despite the significant performance improvement, existing channel selection algorithms suffer from very high feedback overhead. Feedback latency from the STAs, due to the high overhead, can cause the eventually selected channel to no longer be optimal for operation due to the dynamic sharing nature of the unlicensed band. This has inspired us to develop our own dynamic channel selection algorithm with reduced overhead through the proposed low-overhead, cluster-based station reporting mechanism. The main idea behind the cluster-based station reporting is the observation that STAs which are very close to each other tend to have very similar channel conditions. Instead of requesting each STA to report on every candidate channel while causing high overhead, the AP divides STAs into clusters then assigns each STA in each cluster one channel to report feedback on. With the proper design of the cluster based reporting, the AP does not lose any information about the channel conditions at the station side while reducing feedback overhead. The simulation results show equal performance and, at times, better performance with a fraction of the overhead. We believe that this algorithm has great potential in designing future dynamic channel selection algorithms with low overhead.

Keywords: channel assignment, Wi-Fi networks, clustering, DBSCAN, overhead

Procedia PDF Downloads 104

Authors: Douglas Arthur, Oluwayemisi Ekor, Ernest Obese, Andrew Kissi Agyei, Elvis Ofori Ameyaw

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bjective: Pain negatively impacts every aspect of health, and patients with pain disorders create enormous demands on healthcare systems globally, costing economies up to $635 billion annually. The study was therefore conducted at the Cape Coast Teaching Hospital (CCTH), the only Tertiary Hospital in the Central Region of Ghana and was designed to assess pain disorders in patients between 18 and 90 years attending Urology Clinic. Methods: The study employed a descriptive cross-sectional design, and 149 subjects (16-24, 25-34, 35-44, 45-54, 55-64, 65-90 years) were conveniently selected. The McGill Pain Questionnaire (MPQ), a multidimensional instrument that assesses several aspects of pain by the use of words (descriptors) that the patient chooses to express his/her pain, was used as the primary instrument for data collection. A patient profile form (PPF) was also designed to document the demographics and history of patients. Results: The prevalence of pain disorders was higher among females compared to males. The univariate and multivariate analysis showed that females were more likely to experience pain while being married correlated with a lower likelihood of pain. Again, the 45-54 age group exhibited the highest prevalence of pain disorders. Results from the MPQ showed that half of the patients experienced pain on a daily basis, 15.91% had experienced pain for 3-6 months and 37% experienced pain for more than one year. Pain intensity was described by 25% of the subjects as excruciating for their worst pain experience, followed by 21% for the distressing experience. The most frequently reported area of pain was the abdominal region (22.72%). The co-administration of NSAIDs and opioid compounds was provided for 17.46% of the patients with chronic pain. Conclusion: The treatment interventions improved the pain and associated symptoms such as nausea, improved daily activities and ability to sleep. However, attention and resources should be devoted to 45-54 age group.

Keywords: pain, opioids, distressing, excruciating

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

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Authors: Magdy Roman, Mostafa Shoeib, Mostafa Rostom

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Global vision, whether provided by overhead fixed cameras, on-board aerial vehicle cameras, or satellite images can always provide detailed information on the environment around mobile robots. In this paper, an intelligent vision-based method of path planning and obstacle avoidance for mobile robots is presented. The method integrates visual perception with a new proposed field-based path-planning method to overcome common path-planning problems such as local minima, unreachable destination and unnecessary lengthy paths around obstacles. The method proposes an exponential angle deviation field around each obstacle that affects the orientation of a close robot. As the robot directs toward, the goal point obstacles are classified into right and left groups, and a deviation angle is exponentially added or subtracted to the orientation of the robot. Exponential field parameters are chosen based on Lyapunov stability criterion to guarantee robot convergence to the destination. The proposed method uses obstacles' shape and location, extracted from global vision system, through a collision prediction mechanism to decide whether to activate or deactivate obstacles field. In addition, a search mechanism is developed in case of robot or goal point is trapped among obstacles to find suitable exit or entrance. The proposed algorithm is validated both in simulation and through experiments. The algorithm shows effectiveness in obstacles' avoidance and destination convergence, overcoming common path planning problems found in classical methods.

Keywords: path planning, collision avoidance, convergence, computer vision, mobile robots

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Authors: Shigenori Togashi, Yukako Asano

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We developed an effective microfluidic device for photoreactions with low reflectance and good heat conductance. The performance of this microfluidic device was tested by carrying out a photoreactive synthesis of benzopinacol and acetone from benzophenone and 2-propanol. The yield reached 36% with an irradiation time of 469.2 s and was improved by more than 30% when compared to the values obtained by the batch method. Therefore, the microfluidic device was found to be effective for improving the yields of photoreactions.

Keywords: microfluidic device, photoreaction, black aluminum oxide, benzophenone, yield improvement

Procedia PDF Downloads 234

Authors: Lavinia Flueckiger, Roselind Lieb, Andrea H. Meyer, Cornelia Witthauer, Jutta Mata

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Objective: Health behaviors tend to show a high variability over time within the same person. However, most existing research can only assess a snapshot of a person’s behavior and not capture this natural daily variability. Two intensive longitudinal studies examine the variability in health behavior over one academic year and their implications for other aspects of daily life such as affect and academic performance. Can already a single day of increased physical activity, snacking, or improved sleep have beneficial effects? Methods: In two intensive longitudinal studies with up to 65 assessment days over an entire academic year, university students (Study 1: N = 292; Study 2: N = 304) reported sleep quality, physical activity, snacking, positive and negative affect, and learning goal achievement. Results: Multilevel structural equation models showed that on days on which participants reported better sleep quality or more physical activity than usual, they also reported increased positive affect, decreased negative affect, and better learning goal achievement. Higher day-to-day snacking was only associated with increased positive affect. Both, increased day-to-day sleep quality and physical activity were indirectly associated with better learning goal achievement through changes in positive and negative affect; results for snacking were mixed. Importantly, day-to-day sleep quality was a stronger predictor for affect and learning goal achievement than physical activity or snacking. Conclusion: One day of better sleep or more physical activity than usual is associated with improved affect and academic performance. These findings have important implications for low-threshold interventions targeting the improvement of daily functioning.

Keywords: sleep quality, physical activity, snacking, affect, academic performance, multilevel structural equation model

Procedia PDF Downloads 566

Authors: El Asnaoui Khalid, Aksasse Brahim, Ouanan Mohammed

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This paper presents an unsupervised color image segmentation method. It is based on a hierarchical analysis of 2-D histogram in RGB color space. This histogram minimizes storage space of images and thus facilitates the operations between them. The improved segmentation approach shows a better identification of objects in a color image and, at the same time, the system is fast.

Keywords: image segmentation, hierarchical analysis, 2-D histogram, classification

Procedia PDF Downloads 366

Authors: Ł. Morawiński, C. Jasiński, M. Jurkiewicz, S. Bou Habib, M. Bondyra

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With the development of optics, electronics, and computers, vision systems are increasingly used in various areas of life, science, and industry. Vision systems have a huge number of applications. They can be used in quality control, object detection, data reading, e.g., QR-code, etc. A large part of them is used for measurement purposes. Some of them make it possible to obtain a 3D reconstruction of the tested objects or measurement areas. 3D reconstruction algorithms are mostly based on creating depth maps from data that can be acquired from active or passive methods. Due to the specific appliance in airfield technology, only passive methods are applicable because of other existing systems working on the site, which can be blinded on most spectral levels. Furthermore, reconstruction is required to work long distances ranging from hundreds of meters to tens of kilometers with low loss of accuracy even with harsh conditions such as fog, rain, or snow. In response to those requirements, HRESS (Heliport REmote Safeguard System) was developed; which main part is a rotational head with a two-camera stereovision rig gathering images around the head in 360 degrees along with stereovision 3D reconstruction and point cloud combination. The sub-pixel analysis introduced in the HRESS system makes it possible to obtain an increased distance measurement resolution and accuracy of about 3% for distances over one kilometer. Ultimately, this leads to more accurate and reliable measurement data in the form of a point cloud. Moreover, the program algorithm introduces operations enabling the filtering of erroneously collected data in the point cloud. All activities from the programming, mechanical and optical side are aimed at obtaining the most accurate 3D reconstruction of the environment in the measurement area.

Keywords: airfield monitoring, artificial intelligence, stereovision, 3D reconstruction

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Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

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Authors: Haroon Khan, Chul Min Kim, Sung Yeol Kim, Sanket Goel, Prabhat K. Dwivedi, Ashutosh Sharma, Gyu Man Kim

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Enzymatic biofuel cells (EBFCs) have drawn the attention of researchers due to its demanding application in medical implants. In EBFCs, electricity is produced with the help of redox enzymes. In this study, we report the fabrication of membraneless EBFC with new design of electrodes to overcome microchannel related limitations. The device consists of double layer of electrodes on both sides of Y-shaped microchannel to reduce the effect of oxygen depletion layer and diffusion of fuel and oxidant at the end of microchannel. Moreover, the length of microchannel was reduced by half keeping the same area of multiwalled carbon nanotubes (MWCNT) electrodes. Polydimethylsiloxane (PDMS) stencils were used to pattern MWCNT electrodes on etched Indium Tin Oxide (ITO) glass. PDMS casting was used to fabricate microchannel of the device. Both anode and cathode were modified with glucose oxidase and laccase. Furthermore, these enzymes were covalently bound to carboxyl MWCNTs with the help of EDC/NHS. Glucose used as fuel was oxidized by glucose oxidase at anode while oxygen was reduced to water at the cathode side. The resulted devices were investigated with the help of polarization curves obtained from Chronopotentiometry technique by using potentiostat. From results, we conclude that the performance of double layer EBFC is improved 15 % as compared to single layer EBFC delivering maximum power density of 71.25 µW cm-2 at a cell potential of 0.3 V and current density of 250 µA cm-2 at micro channel height of 450-µm and flow rate of 25 ml hr-1. However, the new device was stable only for three days after which its power output was rapidly dropped by 75 %. This work demonstrates that the power output of membraneless EBFC is improved comparatively, but still efforts will be needed to make the device stable over long period of time.

Keywords: EBFC, glucose, MWCNT, microfluidic

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Authors: Manel Langar, Riad Bourguiba, Jaouhar Mouine

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In this paper, we propose the design and implementation of an enhanced wormhole virtual channel on chip router. It is a heart of a mesh NoC using the XY deterministic routing algorithm. It is characterized by its simple virtual channel allocation strategy which allows reducing area and complexity of connections without affecting the performance. We implemented our router on a Tezzaron process to validate its performances. This router is a basic element that will be used later to design a 3D mesh NoC.

Keywords: NoC, mesh, router, 3D NoC

Procedia PDF Downloads 554

Authors: Adam Majczak, Michał Bialy, Zbigniew Czyż, Zdzislaw Kaminski

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Increasingly stringent emission standards for passenger cars require us to find alternative drives. The share of electric vehicles in the sale of new cars increases every year. However, their performance and, above all, range cannot be today successfully compared to those of cars with a traditional internal combustion engine. Battery recharging lasts hours, which can be hardly accepted due to the time needed to refill a fuel tank. Therefore, the ways to reduce the adverse features of cars equipped with electric motors only are searched for. One of the methods is a combination of an electric engine as a main source of power and a small internal combustion engine as an electricity generator. This type of drive enables an electric vehicle to achieve a radically increased range and low emissions of toxic substances. For several years, the leading automotive manufacturers like the Mazda and the Audi together with the best companies in the automotive industry, e.g., AVL have developed some electric drive systems capable of recharging themselves while driving, known as a range extender. An electricity generator is powered by a Wankel engine that has seemed to pass into history. This low weight and small engine with a rotating piston and a very low vibration level turned out to be an excellent source in such applications. Its operation as an energy source for a generator almost entirely eliminates its disadvantages like high fuel consumption, high emission of toxic substances, or short lifetime typical of its traditional application. The operation of the engine at a constant rotational speed enables a significant increase in its lifetime, and its small external dimensions enable us to make compact modules to drive even small urban cars like the Audi A1 or the Mazda 2. The algorithm to maintain a constant speed was investigated on the engine dynamometer with an eddy current brake and the necessary measuring apparatus. The research object was the Aixro XR50 rotary engine with the electronic power supply developed at the Lublin University of Technology. The load torque of the engine was altered during the research by means of the eddy current brake capable of giving any number of load cycles. The parameters recorded included speed and torque as well as a position of a throttle in an inlet system. Increasing and decreasing load did not significantly change engine speed, which means that control algorithm parameters are correctly selected. This work has been financed by the Polish Ministry of Science and Higher Education.

Keywords: electric vehicle, power generator, range extender, Wankel engine

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Authors: Ciaran Smiddy, Fergus Nugent, Karen Fitzmaurice

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A measure of prescribing and administration of Antimicrobial Prophylaxis before and during Covid-19(2019 vs. 2021) was desired to assess how these were affected by Covid-19. Antimicrobial Prophylaxis was assessed for 60 patients, under 3 Orthopaedic Consultants, against local guidelines. The study found that compliance with guidelines improved significantly, from 60% to 83%, but Appropriate use of Vancomycin reduced from 37% to 29%.

Keywords: antimicrobial stewardship, prescribing, spinal surgery, vancomycin

Procedia PDF Downloads 160

Authors: Jaehong Yu, Seoung Bum Kim

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Clustering analysis can facilitate the extraction of intrinsic patterns in a dataset and reveal its natural groupings without requiring class information. For effective clustering analysis in high dimensional datasets, unsupervised dimensionality reduction is an important task. Unsupervised dimensionality reduction can generally be achieved by feature extraction or feature selection. In many situations, feature selection methods are more appropriate than feature extraction methods because of their clear interpretation with respect to the original features. The unsupervised feature selection can be categorized as feature subset selection and feature ranking method, and we focused on unsupervised feature ranking methods which evaluate the features based on their importance scores. Recently, several unsupervised feature ranking methods were developed based on ensemble approaches to achieve their higher accuracy and stability. However, most of the ensemble-based feature ranking methods require the true number of clusters. Furthermore, these algorithms evaluate the feature importance depending on the ensemble clustering solution, and they produce undesirable evaluation results if the clustering solutions are inaccurate. To address these limitations, we proposed an ensemble-based feature ranking method with random subspace and multiple-k ensemble (FRRM). The proposed FRRM algorithm evaluates the importance of each feature with the random subspace ensemble, and all evaluation results are combined with the ensemble importance scores. Moreover, FRRM does not require the determination of the true number of clusters in advance through the use of the multiple-k ensemble idea. Experiments on various benchmark datasets were conducted to examine the properties of the proposed FRRM algorithm and to compare its performance with that of existing feature ranking methods. The experimental results demonstrated that the proposed FRRM outperformed the competitors.

Keywords: clustering analysis, multiple-k ensemble, random subspace-based feature evaluation, unsupervised feature ranking

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Authors: Omran Al Balushi, Haitham Al Rawahi

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Enterprise Asset Management (eAM) has been implemented across different Generation, Transmission and Distribution subsidiaries in Nama Group companies. As part of Nama group, Oman Electricity Transmission Company (OETC) was the first company to implement this system. It was very important for OETC to implement and maintain such a system to achieve its business objectives and for effective operations, which will also support the delivery of the asset management strategy. Enterprise Asset Management (eAM) addresses the comprehensive asset maintenance requirements of Oman Electricity Transmission Company (OETC). OETC needs to optimize capacity and increase utilization, while lowering unit production. E-AM will enable OETC to adopt this strategy. Implementation of e-AM has improved operation performance with preventive and scheduled maintenance as well as it increased safety. Implementation of e-AM will also enable OETC to create optimal asset management strategy which will increase revenue and decrease cost by effectively monitoring operational data such as maintenance history and operation conditions. CMMS (Computerised Maintenance Management System) is the main software and the back-bone of e-AM system. It is used to provide an improved working practice to properly establish information and data flow related to maintenance activities. Implementation of e-AM system was one of the factors that supported OETC to achieve ISO55001 Certificate on fourth quarter of 2016. Also, full implementation of e-AM system will result in strong integration between CMMS and Geographical Information Systems (GIS) application and it will improve OETC to build a reliable maintenance strategy for all asset classes in its Transmission network. In this paper we will share our experience and knowledge of implementing such a system and how it supported OETC’s management to make decisions. Also we would highlight the challenges and difficulties that we encountered during the implementation of e-AM. Also, we will list some features and advantages of e-AM in asset management, preventive maintenance and maintenance cost management.

Keywords: CMMS, Maintenance Management, Asset Management, Maintenance Strategy

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Authors: Jocelyn Li, Xiangyang Li

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We have employed THINC-it® to study the acute effects of Spravato on the cognitive function of patients with severe major depression disorder (MDD). The scores of the four tasks (Spotter, Symbol Check, Code Breaker, Trails) found in THINC-it® were used to measure cognitive function throughout treatment. The patients who participated in this study have tried more than 3 antidepressants without significant improvement before they began Spravato treatment. All patients received 3 doses of 28 mg Spravato 5 minutes apart (84 mg total per treatment) during this study with THINC-it®. The data were collected before the first Spravato administration (T0), 1 hour after the first Spravato administration (T1), and 2 hours after the first Spravato administration (T2) during each treatment. The following data were from 13 patients, with a total of 226 trials in a 2-3 month period. Spravato at 84 mg reduced the scores of Trails, Code Breaker, Symbol Check, and Spotter at T1 by 10-20% in all patients with one exception for a minority of patients in Spotter. At T2, the scores of Trails, Symbol Check, and Spotter were back to 97% of T0 while the score of Code Breaker was back to 92%. Interestingly, we found that the score of Spotter was consistently increased by 17% at T1 in the same 30% of patients in each treatment. We called this change reverse response while the pattern of the other patients, a decline (T1) and then recovery (T2), was called non-reverse response. We also compared the scores at T0 between the first visit and the fifth visit. The T0 scores of all four tasks were improved at visit 5 when compared to visit 1. The scores of Trails, Code Breaker, and Symbol Check at T0 were increased by 14%, 33%, and 14% respectively at visit 5. The score of Code Breaker, which had two trends, improved by 9% in reverse response patients compared to a 27% improvement in non-reverse response patients. To our knowledge, this is the first study done on the impact of Spravato on cognitive function change in major depression patients at this time frame. Whether we can predict future responses to Spravato with THINC-it® merits further study.

Keywords: Spravato, THINC-it, major depressive disorder, cognitive function

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Authors: Niroshan K. Walgama Wellalage, Tieling Zhang, Richard Dwight

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State-based Markov deterioration models (SMDM) sometimes fail to find accurate transition probability matrix (TPM) values, and hence lead to invalid future condition prediction or incorrect average deterioration rates mainly due to drawbacks of existing nonlinear optimization-based algorithms and/or subjective function types used for regression analysis. Furthermore, a set of separate functions for each condition state with age cannot be directly derived by using Markov model for a given bridge element group, which however is of interest to industrial partners. This paper presents a new approach for generating Homogeneous SMDM model output, namely, the Modified Weibull approach, which consists of a set of appropriate functions to describe the percentage condition prediction of bridge elements in each state. These functions are combined with Bayesian approach and Metropolis Hasting Algorithm (MHA) based Markov Chain Monte Carlo (MCMC) simulation technique for quantifying the uncertainty in model parameter estimates. In this study, factors contributing to rail bridge deterioration were identified. The inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered accordingly based on the real operational experience. Network level deterioration model for a typical bridge element group was developed using the proposed Modified Weibull approach. The condition state predictions obtained from this method were validated using statistical hypothesis tests with a test data set. Results show that the proposed model is able to not only predict the conditions in network-level accurately but also capture the model uncertainties with given confidence interval.

Keywords: bridge deterioration modelling, modified weibull approach, MCMC, metropolis-hasting algorithm, bayesian approach, Markov deterioration models

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Authors: Rindra Rantoson, Hichem Nouira, Nabil Anwer, Charyar Mehdi-Souzani

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Multiple measurements by means of various data acquisition systems are generally required to measure the shape of freeform workpieces for accuracy, reliability and holisticity. The obtained data are aligned and fused into a common coordinate system within a registration technique involving coarse and fine registrations. Standardized iterative methods have been established for fine registration such as Iterative Closest Points (ICP) and its variants. For coarse registration, no conventional method has been adopted yet despite a significant number of techniques which have been developed in the literature to supply an automatic rough matching between data sets. Two main issues are addressed in this paper: the coarse registration and the fine registration. For coarse registration, two novel automated methods based on the exploitation of discrete curvatures are presented: an enhanced Hough Transformation (HT) and an improved Ransac Transformation. The use of curvature features in both methods aims to reduce computational cost. For fine registration, a new variant of ICP method is proposed in order to reduce registration error using curvature parameters. A specific distance considering the curvature similarity has been combined with Euclidean distance to define the distance criterion used for correspondences searching. Additionally, the objective function has been improved by combining the point-to-point (P-P) minimization and the point-to-plane (P-Pl) minimization with automatic weights. These ones are determined from the preliminary calculated curvature features at each point of the workpiece surface. The algorithms are applied on simulated and real data performed by a computer tomography (CT) system. The obtained results reveal the benefit of the proposed novel curvature-based registration methods.

Keywords: discrete curvature, RANSAC transformation, hough transformation, coarse registration, ICP variant, point-to-point and point-to-plane minimization combination, computer tomography

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Authors: Aitor Bilbao, Dragos Axinte, John Billingham

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The inverse problem in Energy Beam (EB) Processes consists of defining the control parameters, in particular the 2D beam path (position and orientation of the beam as a function of time), to arrive at a prescribed solution (freeform surface). This inverse problem is well understood for conventional machining, because the cutting tool geometry is well defined and the material removal is a time independent process. In contrast, EB machining is achieved through the local interaction of a beam of particular characteristics (e.g. energy distribution), which leads to a surface-dependent removal rate. Furthermore, EB machining is a time-dependent process in which not only the beam varies with the dwell time, but any acceleration/deceleration of the machine/beam delivery system, when performing raster paths will influence the actual geometry of the surface to be generated. Two different EB processes, Abrasive Water Machining (AWJM) and Pulsed Laser Ablation (PLA), are studied. Even though they are considered as independent different technologies, both can be described as time-dependent processes. AWJM can be considered as a continuous process and the etched material depends on the feed speed of the jet at each instant during the process. On the other hand, PLA processes are usually defined as discrete systems and the total removed material is calculated by the summation of the different pulses shot during the process. The overlapping of these shots depends on the feed speed and the frequency between two consecutive shots. However, if the feed speed is sufficiently slow compared with the frequency, then consecutive shots are close enough and the behaviour can be similar to a continuous process. Using this approximation a generic continuous model can be described for both processes. The inverse problem is usually solved for this kind of process by simply controlling dwell time in proportion to the required depth of milling at each single pixel on the surface using a linear model of the process. However, this approach does not always lead to the good solution since linear models are only valid when shallow surfaces are etched. The solution of the inverse problem is improved by using a discrete adjoint optimization algorithm. Moreover, the calculation of the Jacobian matrix consumes less computation time than finite difference approaches. The influence of the dynamics of the machine on the actual movement of the jet is also important and should be taken into account. When the parameters of the controller are not known or cannot be changed, a simple approximation is used for the choice of the slope of a step profile. Several experimental tests are performed for both technologies to show the usefulness of this approach.

Keywords: abrasive waterjet machining, energy beam processes, inverse problem, pulsed laser ablation

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Authors: Min Wang, Sergey Utev

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The lessons from the 2007-09 global financial crisis have driven scientific research, which considers the design of new methodologies and financial models in the global market. The quantum mechanics approach was introduced in the unpredictable stock market modeling. One famous quantum tool is Feynman path integral method, which was used to model insurance risk by Tamturk and Utev and adapted to formalize the path-dependent option pricing by Hao and Utev. The research is based on the path-dependent calculation method, which is motivated by the Feynman path integral method. The path calculation can be studied in two ways, one way is to label, and the other is computational. Labeling is a part of the representation of objects, and generating functions can provide many different ways of representing share price paths. In this paper, the recent works on graphical theoretical construction of individual share price path via matroid is presented. Firstly, a study is done on the knowledge of matroid, relationship between lattice path matroid and Tutte polynomials and ways to connect points in the lattice path matroid and Tutte polynomials is suggested. Secondly, It is found that a general binary tree can be validly constructed from a connected lattice path matroid rather than general lattice path matroid. Lastly, it is suggested that there is a way to represent share price paths via a general binary tree, and an algorithm is developed to construct share price paths from general binary trees. A relationship is also provided between lattice integer points and Tutte polynomials of a transversal matroid. Use this way of connection together with the algorithm, a share price path can be constructed from a given connected lattice path matroid.

Keywords: combinatorial construction, graphical representation, matroid, path calculation, share price, Tutte polynomial

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Authors: Abdur Raheem, Ado Yakub

Abstract:

Urban violence in the form of ethnic and religious conflicts have been on the increase in many African cities in the recent years of which most of them are the result of intense and bitter competition for political power, the control of limited economic, social and environmental resources. In Nigeria, the emergence of the Boko Haram insurgency in most parts of the north eastern parts have ignited violence, bloodshed, refuge exodus and internal migration. Not only do the persistent attacks of the sect create widespread insecurity and fear, it has also stifled normal processes of trade and investments most especially real property investment which is acclaimed to accelerate the economic cycle, thus the need to evolve strategies for an improved property market in such areas. This paper, therefore, examines the impact of these social crisis on effective and efficient utilization of real properties as a resource towards the development of the economy, using a descriptive analysis approach where particular emphasis was based on trends in residential housing values; volume of estimated property transactions and real estate investment decisions by affected individuals. Findings indicate that social crisis in the affected areas have been a clog on the wheels of property development and investment as properties worth hundreds of millions have been destroyed thereby having great impact on property values. Based on these findings, recommendations were made to include the need to strategically continue investing in property during such times, the need for Nigerian government to establish an active conflict monitoring and management unit for prompt response, encourage community and neighbourhood policing to ameliorate security challenges in Nigeria.

Keywords: social crisis, property market, economy, resources, north-eastern Nigeria

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Authors: Amani Tahat, Serdal Kirmizialtin

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Trajectory fragment methods such as Markov State Models (MSM), Milestoning (MS) and Transition Path sampling are the prime choice of extending the timescale of all atom Molecular Dynamics simulations. In these approaches, a set of structures that covers the accessible phase space has to be chosen a priori using cluster analysis. Structural clustering serves to partition the conformational state into natural subgroups based on their similarity, an essential statistical methodology that is used for analyzing numerous sets of empirical data produced by Molecular Dynamics (MD) simulations. Local transition kernel among these clusters later used to connect the metastable states using a Markovian kinetic model in MSM and a non-Markovian model in MS. The choice of clustering approach in constructing such kernel is crucial since the high dimensionality of the biomolecular structures might easily confuse the identification of clusters when using the traditional hierarchical clustering methodology. Of particular interest, in the case of MS where the milestones are very close to each other, accurate determination of the milestone identity of the trajectory becomes a challenging issue. Throughout this work we present two cluster analysis methods applied to the cis–trans isomerism of dinucleotide AA. The choice of nucleic acids to commonly used proteins to study the cluster analysis is two fold: i) the energy landscape is rugged; hence transitions are more complex, enabling a more realistic model to study conformational transitions, ii) Nucleic acids conformational space is high dimensional. A diverse set of internal coordinates is necessary to describe the metastable states in nucleic acids, posing a challenge in studying the conformational transitions. Herein, we need improved clustering methods that accurately identify the AA structure in its metastable states in a robust way for a wide range of confused data conditions. The single linkage approach of the hierarchical clustering available in GROMACS MD-package is the first clustering methodology applied to our data. Self Organizing Map (SOM) neural network, that also known as a Kohonen network, is the second data clustering methodology. The performance comparison of the neural network as well as hierarchical clustering method is studied by means of computing the mean first passage times for the cis-trans conformational rates. Our hope is that this study provides insight into the complexities and need in determining the appropriate clustering algorithm for kinetic analysis. Our results can improve the effectiveness of decisions based on clustering confused empirical data in studying conformational transitions in biomolecules.

Keywords: milestoning, self organizing map, single linkage, structure clustering

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Authors: Panrapee Suttiwan, Rewadee Watakakosol Arunya Tuicomepee, Sakkaphat T. Ngamake

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Alcohol consumption ranks among the top six of health-risk behaviors that lead to disability and death among Thai teenagers. Underage drinkers have higher health risks than their non-drinking peers do. This study, therefore, aimed to explore salient beliefs of Thai teenagers with alcohol reduction and cessation based on the Theory of Planned Behaviour theoretical framework. Participants were 225 high-school and vocational school students, most of whom (60.9%) consumed alcohol almost daily (5-6 times / week), and one-third of whom (33.8%) reported habitual moderate drink. The average age was 16.5 (SD = 0.9), and the average age of the first use of alcohol was 13.7 (SD = 2.2). Instrument was an open-ended questionnaire that elicited beliefs about having alcohol reduction / cessation in the past 12 months. Findings revealed salient benefit beliefs of alcohol reduction / cessation among the teens such as improved physical and mental health, accident and violence avoidance, less sexual risks, money and time saving, better academic performance, and improved relationships. In contrast, the teens identified several disadvantage beliefs such as deteriorating health, social awkwardness, lack of little fun, excitement, and experience, physical uneasiness, stress, and lack of self-confidence. Salient normative groups for alcohol reduction / cessation included parents, elder relatives, siblings, close friends, teachers, boy / girlfriends, and seniors / juniors at school. Situations influencing alcohol reduction / cessation included quarrels with boy / girlfriends, family conflicts, peer pressure, partying and socializing, festive holidays and anniversary celebration, and visiting entertainment places, etc. This study provides empirical evidence that help to identify normative attitudes towards alcohol reduction / cessation and may thus be an important knowledge for public health campaigns seeking to reduce alcohol consumption in this population.

Keywords: alcohol consumption reduction, cessation, salient belief, Thai teenagers

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Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis

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Authors: Vongsathorn Ngampuak, Yutachai Chookaew, Wipawee Dejtisakdi

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Dunaliella salina has great potential as a system for generating commercially valuable products, including beta-carotene, pharmaceuticals, and biofuels. Our goal is to improve this potential by enhancing growth rate and other properties of D. salina under optimal growth conditions. We used ethyl methane sulfonate (EMS) to generate random mutants in D. salina KU11, a strain classified in Thailand. In a preliminary experiment, we first treated D. salina cells with 0%, 0.8%, 1.0%, 1.2%, 1.44% and 1.66% EMS to generate a killing curve. After that, we randomly picked 30 candidates from approximately 300 isolated survivor colonies from the 1.44% EMS treatment (which permitted 30% survival) as an initial test of the mutant screen. Among the 30 survivor lines, we found that 2 strains (mutant #17 and #24) had significantly improved growth rates and cell number accumulation at stationary phase approximately up to 1.8 and 1.45 fold, respectively, 2 strains (mutant #6 and #23) had significantly decreased growth rates and cell number accumulation at stationary phase approximately down to 1.4 and 1.35 fold, respectively, while 26 of 30 lines had similar growth rates compared with the wild type control. We also analyzed cell size for each strain and found there was no significant difference comparing all mutants with the wild type. In addition, mutant #24 had shown an increase of biomass accumulation approximately 1.65 fold compared with the wild type strain on day 5 that was entering early stationary phase. From these preliminary results, it could be feasible to identify D. salina mutants with significant improved growth rate, cell accumulation and biomass production compared to the wild type for the further study; this makes it possible to improve this microorganism as a platform for biotechnology application.

Keywords: Dunaliella salina, ethyl methyl sulfonate, growth rate, biomass

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Authors: Zayneb Kthiri, Walid Hamada

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The application of organic fertilizers is generally known to be useful to sustain soil fertility and plant growth, especially in poor soils, with less than 1% of organic matter, as it is very common in our Tunisian fields. Therefore, we focused on evaluating the effect of a new released liquid organic fertilizer named Solorga (with 5% of organic matter) compared to a reference product (Espartan: Kimitec, Spain) on tomato plant growth and physiology. Both fertilizers, derived from plant decomposition, were applied at an early stage in hydroponic system and under greenhouse. In hydroponic system, after 14 days of their application by root feeding, a significant difference was observed between treatments. Indeed, Solorga improved shoots and roots length, as well as the biomass respectively, by 45%, 27%, and 27.8% increase rate, while compared to control plants. However, Espartan induced less the measured parameters while compared to untreated control. Moreover, Solorga significantly increased the chlorophyll content by 42% compared to control and by 32% compared to Espartan. In the greenhouse, after 20 days of treatments, the results showed a significant effect of both fertilizers on SPAD index and the number of flowers blossom. Solorga increased the amount of chlorophyll present in the leaf by 7% compared to Espartan as well as the plant height under greenhouse. Moreover, the number of flowers blossom increased by 15% in plants treated with Solorga while compared to Espartan. Whereas, there is no notable difference between both organic fertilizers on the fruits blossom and the number of fruits per blossom. In conclusion, even though there is a difference in the organic matter between both fertilizers, Solorga improved better the plant growth in controlled conditions in hydroponic system while compared to Espartan. Altogether the obtained results are encouraging for the use of Solorga as a soil enriching source of organic matter to help plants to boost their growth and help them to overcome abiotic stresses linked to soil fertility.

Keywords: tomato, plant growth, organic fertilizer, hydroponic system, greenhouse

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Authors: Amr Mohamed Bakry Ibrahim, Yingzhou Ni, Hao Cheng, Li Liang

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Tuna oil (omega-3 oil) has become increasingly popular in the last ten years, because it is considered one of the treasures of food which has many beneficial health effects for the humans. Nevertheless, the susceptibility of omega-3 oils to oxidative deterioration, resulting in the formation of oxidation products, in addition to organoleptic problems including “fishy” flavors, have presented obstacles to the more widespread use of tuna oils in the food industry. This study sought to evaluate the potential impact of Mentha piperita oil on physicochemical characteristics and oxidative stability of tuna oil microcapsules formed by spray drying using the partial substitution to whey protein isolate by carboxymethyl cellulose and pullulan. The emulsions before the drying process were characterized regarding size and ζ-potential, viscosity, surface tension. Confocal laser scanning microscopy showed that all emulsions were sphericity and homogeneous distribution without any visible particle aggregation. The microcapsules obtained after spray drying were characterized regarding microencapsulation efficiency, water activity, color, bulk density, flowability, scanning surface morphology and oxidative stability. The microcapsules were spherical shape had low water activity (0.11-0.23 aw). The microcapsules containing both tuna oil and Mentha piperita oil were smaller than others and addition of pullulan into wall materials improved the morphology of microcapsules. Microencapsulation efficiency of powdered oil ranged from 90% to 94%. Using Mentha piperita oil in the process of microencapsulation tuna oil enhanced the oxidative stability using whey protein isolate only or with carboxymethyl cellulose or pullulan as wall materials, resulting in improved storage stability and mask fishy odor. Therefore, it is foreseen using tuna-Mentha piperita oil mixture microcapsules in the applications of the food industries.

Keywords: Mentha piperita oil, microcapsule, tuna oil, whey protein isolate

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Authors: M. Nekri, A. Khelladi

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The world wide web network is a network with a complex topology, the main properties of which are the distribution of degrees in power law, A low clustering coefficient and a weak average distance. Modeling the web as a graph allows locating the information in little time and consequently offering a help in the construction of the research engine. Here, we present a model based on the already existing probabilistic graphs with all the aforesaid characteristics. This work will consist in studying the web in order to know its structuring thus it will enable us to modelize it more easily and propose a possible algorithm for its exploration.

Keywords: clustering coefficient, preferential attachment, small world, web community

Procedia PDF Downloads 264