Search results for: matrix minimization algorithm

Authors: Khaled Nagaty, Heba Nagaty, Gerard McKee

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A signature is a behavioral biometric that is used for authenticating users in most financial and legal transactions. Signatures can be easily forged by skilled forgers. Therefore, it is essential to verify whether a signature is genuine or forged. The aim of any signature verification algorithm is to accommodate the differences between signatures of the same person and increase the ability to discriminate between signatures of different persons. This work presented in this paper proposes an automatic signature verification system to indicate whether a signature is genuine or not. The system comprises four phases: (1) The pre-processing phase in which image scaling, binarization, image rotation, dilation, thinning, and connecting ridge breaks are applied. (2) The feature extraction phase in which global and local features are extracted. The local features are minutiae points, curvature orientation, and curve plateau. The global features are signature area, signature aspect ratio, and Hu moments. (3) The post-processing phase, in which false minutiae are removed. (4) The classification phase in which features are enhanced before feeding it into the classifier. k-nearest neighbors and support vector machines are used. The classifier was trained on a benchmark dataset to compare the performance of the proposed offline signature verification system against the state-of-the-art. The accuracy of the proposed system is 92.3%.

Keywords: signature, ridge breaks, minutiae, orientation

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Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

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Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

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Authors: Karthiyayini Nagarajan, P. V. RamaKrishna

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Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar(SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System(lab bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes.

Keywords: synthetic aperture radar, radio reflection model, lab bench

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Wei-Wen Hu, Yong-Zhi Zhong

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Depositing conductive polypyrrole (PPy) onto elastic polydimethylsiloxane (PDMS) substrate can obtain a highly stretchable conductive film, which can be used to construct a bioreactor to cyclically stretch and electrically stimulate surface cells. However, how to completely harvest these stimulated muscle tissue to repair damaged muscle is a challenge. To address this concern, N-isopropylacrylamide (NIPAAm), a monomer of temperature-sensitive polymer, was added during the polymerization of pyrrole on PDMS so that the resulting P(Py-co-NIPAAm)/PDMS should own both conductivity and thermo-sensitivity. Therefore, cells after stimulation can be completely harvested as cell sheets by reducing temperature. Mouse skeletal myoblast, C2C12 cells, were applied to examine our hypothesis. In electrical stimulation, C2C12 cells on P(Py-co-NIPAAm)/PDMS demonstrated the best myo-differentiation under the electric field of 1 V/cm. Regarding cyclic stretching, the strain equal to or higher than 9% can highly align C2C12 perpendicular to the stretching direction. The Western blotting experiments demonstrated that the cell sheets harvested by cooling reserved more extracellular matrix (ECM) than cells collected by the traditional trypsin digestion method. Immunostaining of myosin heavy chain protein (MHC) indicated that both mechanical and electrical stimuli effectively increased the number of myotubes and the differentiation ratio, and the myotubes can be aligned by cyclic stretching. Stimulated cell sheets can be harvested by cooling, and the alignment of myotubes was still maintained. These results suggested that the deposition of P(Py-co-NIPAAm) on PDMS can be applied to harvest intact cell sheets after cyclic stretching and electrical stimulation, which increased the feasibility of bioreactor for the application of tissue engineering and regenerative medicine.

Keywords: bioreactor, cell sheet, conductive polymer, cyclic stretching, electrical stimulation, muscle tissue engineering, myogenesis, thermosensitive hydrophobicity

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Authors: Wai Ho Darrell Kwok

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Building entities are valuable assets of a society, however, all of them are suffered from the ravages of weather and time. Facilitating onerous maintenance activities is the only way to either maintain or enhance the value and contemporary standard of the premises. By the way, maintenance budget is always bounded by the corresponding threshold limit. In order to optimize the limited resources allocation in carrying out maintenance, there is a substantial need to prioritize maintenance work. This paper reveals the application of Fuzzy AHP in a Facilities Management Company determining the maintenance priorities on the basis of predetermined criteria, viz., Building Status (BS), Effects on Fabrics (EF), Effects on Sustainability (ES), Effects on Users (EU), Importance of Usage (IU) and Physical Condition (PC) in dealing with categorized 8 predominant building components maintenance aspects for building premises. From the case study, it is found that ‘building exterior repainting or re-tiling’, ‘spalling concrete repair works among exterior area’ and ‘lobby renovation’ are the top three maintenance priorities from facilities manager and maintenance expertise personnel. Through the application of the Fuzzy AHP for maintenance priorities decision algorithm, a more systemic and easier comparing scalar linearity factors being explored even in considering other multiple criteria decision scenarios of building maintenance issue.

Keywords: building maintenance, fuzzy AHP, maintenance priority, multi-criteria decision making

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Authors: Cheng-Jhe Lee, Chiun-Chieh Hsu

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Due to the rapid development of the Internet and the increased availability of digital documents, the excessive information on the Internet has led to information overflow problem. In order to solve these problems for effective information retrieval, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document collections because they were originally designed for relational database. Therefore they are impractical in real-world document clustering and require special handling for high dimensionality and high volume. We propose the FIHC (Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering, where the intuition of FIHC is that there exist some common words for each cluster. FIHC uses such words to cluster documents and builds hierarchical topic tree. In this paper, we combine FIHC algorithm with ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results show that our method is more accurate than those of well-known document clustering algorithms.

Keywords: FIHC, documents clustering, ontology, closed frequent itemset

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Authors: Nia Mair Fry, Matthew Profit, Chenfeng Li

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This work focuses on the development and implementation of a fully implicit-implicit, coupled mechanical deformation and porous flow, finite element software tool. The fully implicit software accurately predicts classical fundamental analytical solutions such as the Terzaghi consolidation problem. Furthermore, it can capture other analytical solutions less well known in the literature, such as Gibson’s sedimentation rate problem and Coussy’s problems investigating wellbore stability for poroelastic rocks. The mechanical volume strains are transferred to the porous flow governing equation in an implicit framework. This will overcome some of the many current industrial issues, which use explicit solvers for the mechanical governing equations and only implicit solvers on the porous flow side. This can potentially lead to instability and non-convergence issues in the coupled system, plus giving results with an accountable degree of error. The specification of a fully monolithic implicit-implicit coupled porous media code sees the solution of both seepage-mechanical equations in one matrix system, under a unified time-stepping scheme, which makes the problem definition much easier. When using an explicit solver, additional input such as the damping coefficient and mass scaling factor is required, which are circumvented with a fully implicit solution. Further, improved accuracy is achieved as the solution is not dependent on predictor-corrector methods for the pore fluid pressure solution, but at the potential cost of reduced stability. In testing of this fully monolithic porous media code, there is the comparison of the fully implicit coupled scheme against an existing staggered explicit-implicit coupled scheme solution across a range of geotechnical problems. These cases include 1) Biot coefficient calculation, 2) consolidation theory with Terzaghi analytical solution, 3) sedimentation theory with Gibson analytical solution, and 4) Coussy well-bore poroelastic analytical solutions.

Keywords: coupled, implicit, monolithic, porous media

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Authors: Marek Dlapa

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The algebraic approach is applied to the control of the HiMAT (Highly Maneuverable Aircraft Technology). The objective is to find a robust controller which guarantees robust stability and decoupled control of longitudinal model of a scaled remotely controlled vehicle version of the advanced fighter HiMAT. Control design is performed by decoupling the nominal MIMO (multi-input multi-output) system into two identical SISO (single-input single-output) plants which are approximated by a 4th order transfer function. The algebraic approach is then used for pole placement design, and the nominal closed-loop poles are tuned so that the peak of the µ-function is minimal. As an optimization tool, evolutionary algorithm Differential Migration is used in order to overcome the multimodality of the cost function yielding simple controller with decoupling for nominal plant which is compared with the D-K iteration through simulations of standard longitudinal manoeuvres documenting decoupled control obtained from algebraic approach for nominal plant as well as worst case perturbation.

Keywords: algebraic approach, evolutionary computation, genetic algorithms, HiMAT, robust control, structured singular value

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Authors: Sahar Jahani, Meryem Ayse Yucel, David Boas, Seyed Kamaledin Setarehdan

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Near-infrared spectroscopy allows monitoring of oxy- and deoxy-hemoglobin concentration changes associated with hemodynamic response function (HRF). HRF is usually affected by natural physiological hemodynamic (systemic interferences) which occur in all body tissues including brain tissue. This makes HRF extraction a very challenging task. In this study, we used Kalman filter based on a general linear model (GLM) of brain activity to define the proportion of systemic interference in the brain hemodynamic. The performance of the proposed algorithm is evaluated in terms of the peak to peak error (Ep), mean square error (MSE), and Pearson’s correlation coefficient (R2) criteria between the estimated and the simulated hemodynamic responses. This technique also has the ability of real time estimation of single trial functional activations as it was applied to classify finger tapping versus resting state. The average real-time classification accuracy of 74% over 11 subjects demonstrates the feasibility of developing an effective functional near infrared spectroscopy for brain computer interface purposes (fNIRS-BCI).

Keywords: hemodynamic response function, functional near-infrared spectroscopy, adaptive filter, Kalman filter

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Authors: Oluwabukola Oluwaseyi Oduntan

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The English Language plays a central and strategic role in the school system because almost all the school subjects are taught using the English language. However, students’ achievement in this subject at senior secondary school is not encouraging. Therefore, this study examined the effects of oral-written mode of assessment on senior secondary school students’ achievement in a descriptive essay. It also examined the moderating effects of students’ gender and class on students’ achievement in a descriptive essay. The study adopted a pretest-posttest, control group, quasi-experimental design with a 2x2x3 factorial matrix. The participant consisted of 140 Senior Secondary II students drawn from four intact classes from four schools randomly selected from four Local Government Areas randomly selected from Oyo town in Oyo State. Two schools were assigned each to the treatment group and the control group. The following instruments were used for the study: Descriptive Essay Achievement Test (r = 0.78); Descriptive Achievement Test Marking Scheme; Check List of Oral-Written Assessment and Teachers’ Instructional Guide on Descriptive Essay (r = 0.81). Seven null hypotheses guided the study and tested at 0.05 level of significance. Data were analyzed using Analysis of Covariance, Estimated Marginal Means and Scheffe post-hoc test. The result revealed that treatment had a significant main effect on students’ achievement in descriptive essay (F(1,127) = 25.407, P < .05, η2 = .167). Students exposed to oral-written assessment had a higher achievement scores ((x ) ̅= 36.15) than those exposed to written assessment ((x ) ̅= 28.55). There was no significant main effect of gender on students’ achievement in descriptive essay (F₍₁, ₁₂₇₎ = .349, P > .05, η2 = .003). The result also revealed that the effects of class was not significant on students’ students’ achievement in descriptive essay (F₍₁, ₁₂₇₎ = .679, P > .05, η2 = .006). Oral-written mode of assessment enhanced students’ achievement in a descriptive essay. It is, therefore, recommended that teachers and curriculum developers should adopt the use of oral-written assessment for better improvement of students’ achievement in a descriptive essay.

Keywords: class, gender, oral-written assessment, written assessment

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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

Procedia PDF Downloads 130

Authors: Partha P. Gopmandal, Somnath Bhattacharyya, Naren Bag

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In this article, we have studied the effect of pH-regulated surface charge on the electroosmotic flow (EOF) through nanochannel filled with binary symmetric electrolyte solution. The channel wall possesses either an acidic or a basic functional group. Going beyond the widely employed Debye-Huckel linearization, we develop a mathematical model based on Nernst-Planck equation for the charged species, Poisson equation for the induced potential, Stokes equation for fluid flow. A finite volume based numerical algorithm is adopted to study the effect of key parameters on the EOF. We have computed the coupled governing equations through the finite volume method and our results found to be in good agreement with the analytical solution obtained from the corresponding linear model based on low surface charge condition or strong electrolyte solution. The influence of the surface charge density, reaction constant of the functional groups, bulk pH, and concentration of the electrolyte solution on the overall flow rate is studied extensively. We find the effect of surface charge diminishes with the increase in electrolyte concentration. In addition for strong electrolyte, the surface charge becomes independent of pH due to complete dissociation of the functional groups.

Keywords: electroosmosis, finite volume method, functional group, surface charge

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Authors: Ariful Islam, Showkat Ahmad Lone

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Statistical distributions have long been employed in the assessment of semiconductor devices and product reliability. The power function-distribution is one of the most important distributions in the modern reliability practice and can be frequently preferred over mathematically more complex distributions, such as the Weibull and the lognormal, because of its simplicity. Moreover, it may exhibit a better fit for failure data and provide more appropriate information about reliability and hazard rates in some circumstances. This study deals with estimating information about failure times of items under step-stress partially accelerated life tests for competing risk based on adoptive type-I progressive hybrid censoring criteria. The life data of the units under test is assumed to follow Mukherjee-Islam distribution. The point and interval maximum-likelihood estimations are obtained for distribution parameters and tampering coefficient. The performances of the resulting estimators of the developed model parameters are evaluated and investigated by using a simulation algorithm.

Keywords: adoptive progressive hybrid censoring, competing risk, mukherjee-islam distribution, partially accelerated life testing, simulation study

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Authors: Violeta Damjanovic-Behrendt

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This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. The presented solution implements the repeated Stackelberg Security Game (SSG) and a model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution. In this way, we combine GT and ML algorithms for discovering optimal cyber security policies. The proposed security optimization components will be validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model.

Keywords: security, internet of things, cloud computing, stackelberg game, machine learning, naive q-learning

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Authors: Taufik Rizkiandi, Teuku Yuri M. Zagloel, Andri Dwi Setiawan

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Distribution and growth in the transportation and warehousing business sector decreased by 15,04%. There was a decrease in Gross Domestic Product (GDP) contribution level from rank 7 of 4,41% in 2019 to 3,81% in rank 8 in 2020. A decline in the transportation and warehousing business sector contributes to GDP, resulting in oil and gas companies implementing an efficient supply chain strategy to ensure the availability of goods, especially lubricants. Fluctuating demand for lubricants and warehouse service time limits are essential things that are taken into account in determining an efficient route. Add depots points as a solution so that demand for lubricants is fulfilled (not stock out). However, adding a depot will increase operating costs and storage costs. Therefore, it is necessary to optimize the addition of depots using the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). This research case study was conducted at an oil and gas company that produces lubricants from 2019 to 2021. The study results obtained the optimal route and the addition of a depot with a minimum additional cost. The total cost remains efficient with the addition of a depot when compared to one depot from Jakarta.

Keywords: CVRPTW, optimal route, depot, tabu search algorithm

Procedia PDF Downloads 126

Authors: Adel Dalilottojari, Bahman Delalat, Frances J. Harding, Michaelia P. Cockshell, Claudine S. Bonder, Nicolas H. Voelcker

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Endothelial Progenitor Cell (EPC) based therapies continue to be of interest to treat ischemic events based on their proven role to promote blood vessel formation and thus tissue re-vascularisation. Current strategies for the production of clinical-grade EPCs requires the in vitro isolation of EPCs from peripheral blood followed by cell expansion to provide sufficient quantities EPCs for cell therapy. This study aims to examine the use of different biomolecules to significantly improve the current strategy of EPC capture and expansion on collagen type I (Col I). In this study, four different biomolecules were immobilised on a surface and then investigated for their capacity to support EPC capture and proliferation. First, a cell microarray platform was fabricated by coating a glass surface with epoxy functional allyl glycidyl ether plasma polymer (AGEpp) to mediate biomolecule binding. The four candidate biomolecules tested were Col I, collagen type II (Col II), collagen type IV (Col IV) and vascular endothelial growth factor A (VEGF-A), which were arrayed on the epoxy-functionalised surface using a non-contact printer. The surrounding area between the printed biomolecules was passivated with polyethylene glycol-bisamine (A-PEG) to prevent non-specific cell attachment. EPCs were seeded onto the microarray platform and cell numbers quantified after 1 h (to determine capture) and 72 h (to determine proliferation). All of the extracellular matrix (ECM) biomolecules printed demonstrated an ability to capture EPCs within 1 h of cell seeding with Col II exhibiting the highest level of attachment when compared to the other biomolecules. Interestingly, Col IV exhibited the highest increase in EPC expansion after 72 h when compared to Col I, Col II and VEGF-A. These results provide information for significant improvement in the capture and expansion of human EPC for further application.

Keywords: biomolecules, cell microarray platform, cell therapy, endothelial progenitor cells, high throughput screening

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Authors: Andrew R. Winters, Gregor J. Gassner

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A high-order numerical magentohydrodynamics (MHD) solver built upon a non-linear entropy stable numerical flux function that supports eight traveling wave solutions will be described. The method is designed to treat the divergence-free constraint on the magnetic field in a similar fashion to a hyperbolic divergence cleaning technique. The solver is especially well-suited for flows involving strong discontinuities due to its strong stability without the need to enforce artificial low density or energy limits. Furthermore, a new formulation of the numerical algorithm to guarantee positivity of the pressure during the simulation is described and presented. By construction, the solver conserves mass, momentum, and energy and is entropy stable. High spatial order is obtained through the use of a third order limiting technique. High temporal order is achieved by utilizing the family of strong stability preserving (SSP) Runge-Kutta methods. Main attributes of the solver are presented as well as details on an implementation of the new solver into the multi-physics, multi-scale simulation code FLASH. The accuracy, robustness, and computational efficiency is demonstrated with a variety of numerical tests. Comparisons are also made between the new solver and existing methods already present in FLASH framework.

Keywords: entropy stability, finite volume scheme, magnetohydrodynamics, pressure positivity

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Authors: Seyyed Masoud Seyyedoshohadaei, Borhanuddin Mohd Ali

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To support group mobility, the NEMO Basic Support Protocol has been standardized as an extension of Mobile IP that enables an entire network to change its point of attachment to the Internet. Using NEMO in IEEE 802.16e (WiMax) networks causes latency in handover procedure and affects seamless communication of real-time applications. To decrease handover latency and service disruption time, an integrated scheme named Optimized Fast NEMO (OFNEMO) was introduced by authors of this paper. In OFNEMO a pre-establish multi tunnels concept, cross function optimization and cross layer design are used. In this paper, an analytical model is developed to evaluate total cost consisting of signaling and packet delivery costs of the OFNEMO compared with RFC3963. Results show that OFNEMO increases probability of predictive mode compared with RFC3963 due to smaller handover latency. Even though OFNEMO needs extra signalling to pre-establish multi tunnel, it has less total cost thanks to its optimized algorithm. OFNEMO can minimize handover latency for supporting real time application in moving networks.

Keywords: fast mobile IPv6, handover latency, IEEE802.16e, network mobility

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Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

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Authors: Isiaka Oladele Oladipo, Olusegun Adewale Ajayi

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The blood pressure fitness of female college students has been implicated in sedentary lifestyles. This study was designed to determine the effects of the Aerobic Dance Circuit Training Programme (ADCT) on blood pressure variables (Diastolic Blood Pressure (DBP) and Systolic Blood Pressure (SBP). Participants’ Pretest-Posttest control group quasi-experimental design using a 2x2x4 factorial matrix was adopted, while one (1) research question and two (2) research hypotheses were formulated. Seventy (70) untrained obese students-volunteers age 21.10±2.46 years were purposively selected from Oyo town, Nigeria; Emmanuel Alayande College of Education (experimental group and Federal College of Education (special) control group. The participants’ BMI, weight (kg), height (m), systolic bp(mmHg), and diastolic bp (mmHg) were measured before and completion of ADCT. Data collected were analysed using a pie chart, graph, percentage, mean, frequency, and standard deviation, while a t-test was used to analyse the stated hypotheses set at the critical level of 0.05. There were significant mean differences in baseline and post-treatment values of blood pressure variables in terms of SBP among the experimental group 136.49mmHg and 131.66mmHg; control group 130.82mmHg and 130.56mmHg (crit-t=2.00, cal.t=3.02, df=69, p<.0, the hypothesis was rejected; while DBP experimental group 88.65mmHg and 82.21mmHg; control group 69.91mmHg and 72.66mmHg (crit-t=2.00, cal.t=1.437, df=69, p>.05) in which the hypothesis was accepted). It was revealed from the findings that participants’ SBP decrease from week 4 to week 12 of ADCT indicated an effective reduction in blood pressure variables of obese female students. Therefore, the study confirmed that the use of ADCT is safe and effective in the management of blood pressure for the healthy benefit of obesity.

Keywords: aerobic dance circuit training, fitness lifestyles, obese college female students, systolic blood pressure, diastolic blood pressure

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Authors: Kritiyaporn Kunsook

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Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

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Authors: Mohammadreza Heydariazad

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Productivity growth of wind energy as a clean source needed to achieve improved strategy in production and transmission and management of wind resources in order to increase quality of power and reduce costs. New technologies based on power converters that cause changing turbine speed to suit the wind speed blowing turbine improve extraction efficiency power from wind. This article introduces variable speed wind turbines and optimization of power, and presented methods to use superconducting inductor in the composition of power converter and is proposed the dc measurement for the wind farm and especially is considered techniques available to them. In fact, this article reviews mechanisms and function, changes of wind speed turbine according to speed control strategies of various types of wind turbines and examines power possible transmission and ac from producing location to suitable location for a strong connection integrating wind farm generators, without additional cost or equipment. It also covers main objectives of the dynamic control of wind turbines, and the methods of exploitation and the ways of using it that includes the unique process of these components. Effective algorithm is presented for power control in order to extract maximum active power and maintains power factor at the desired value.

Keywords: wind energy, generator, superconducting inductor, wind turbine power

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Authors: Nuri Caglayan, H. Kursat Celik

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There are many factors that influence the health and productivity of the animals in livestock production fields, including temperature, humidity, carbon dioxide (CO2), ammonia (NH3), hydrogen sulfide (H2S), physical activity and particulate matter. High NH3 concentrations reduce feed consumption and cause daily weight gain. At high concentrations, H2S causes respiratory problems and CO2 displace oxygen, which can cause suffocation or asphyxiation. Good air quality in livestock facilities can have an impact on the health and well-being of animals and humans. Air quality assessment basically depends on strictly given limits without taking into account specific local conditions between harmful gases and other meteorological factors. The stated limitations may be eliminated. using controlling systems based on neural networks and fuzzy logic. This paper describes a fuzzy logic based ventilation algorithm, which can calculate different fan speeds under pre-defined boundary conditions, for removing harmful gases from the production environment. In the paper, a fuzzy logic model has been developed based on a Mamedani’s fuzzy method. The model has been built on MATLAB software. As the result, optimum fan speeds under pre-defined boundary conditions have been presented.

Keywords: air quality, fuzzy logic model, livestock housing, fan speed

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Authors: Rizwan Rizwan

Abstract:

This paper provides an overview of Federated Learning (FL) and its application in enhancing data security, privacy, and efficiency. FL utilizes three distinct architectures to ensure privacy is never compromised. It involves training individual edge devices and aggregating their models on a server without sharing raw data. This approach not only provides secure models without data sharing but also offers a highly efficient privacy--preserving solution with improved security and data access. Also we discusses various frameworks used in FL and its integration with machine learning, deep learning, and data mining. In order to address the challenges of multi--party collaborative modeling scenarios, a brief review FL scheme combined with an adaptive gradient descent strategy and differential privacy mechanism. The adaptive learning rate algorithm adjusts the gradient descent process to avoid issues such as model overfitting and fluctuations, thereby enhancing modeling efficiency and performance in multi-party computation scenarios. Additionally, to cater to ultra-large-scale distributed secure computing, the research introduces a differential privacy mechanism that defends against various background knowledge attacks.

Keywords: federated learning, differential privacy, gradient descent strategy, convergence, stability, threats

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Authors: Reza Eslami-Farsani, Hamed Khosravi, Saba Fayazzadeh

Abstract:

Lightweight and efficient structures have the aim to enhance the efficiency of the components in various industries. Toward this end, composites are one of the most widely used materials because of durability, high strength and modulus, and low weight. One type of the advanced composites is grid-stiffened composite (GSC) structures which have been extensively considered in aerospace, automotive, and aircraft industries. They are one of the top candidates for replacing some of the traditional components which are used here. Although there are a good number of published surveys on the design aspects and fabrication of GSC structures, little systematic work has been reported on their material modification to improve their properties, to our knowledge. Matrix modification using nanoparticles is an effective method to enhance the flexural properties of the fibrous composites. In the present study, a silane coupling agent (3-glycidoxypropyltrimethoxysilane/3-GPTS) was introduced onto the silica (SiO2) nanoparticle surface and its effects on the three-point flexural response of isogrid E-glass/epoxy composites were assessed. Based on the fourier transform infrared spectrometer (FTIR) spectra, it was inferred that the 3-GPTS coupling agent was successfully grafted onto the surface of SiO2 nanoparticles after modification. Flexural test revealed an improvement of 16%, 14%, and 36% in stiffness, maximum load and energy absorption of the isogrid specimen filled with 3 wt.% 3-GPTS/SiO2 compared to the neat one. It would be worth mentioning that in these structures, a considerable energy absorption was observed after the primary failure related to the load peak. Also, 3-GPTMS functionalization had a positive effect on the flexural behavior of the multiscale isogrid composites. In conclusion, this study suggests that the addition of modified silica nanoparticles is a promising method to improve the flexural properties of the grid-stiffened fibrous composite structures.

Keywords: isogrid-stiffened composite panels, silica nanoparticles, surface modification, flexural properties, energy absorption

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Authors: Sam Yurdakul, Nick Baverstock, Jim Mills

Abstract:

TDT 064 (FLEXISEQ®) is a drug-free gel used to treat osteoarthritis (OA)-associated pain and joint stiffness. It contains ultra-deformable phospholipid Sequessome™ vesicles, which can pass through the skin barrier intact. In six randomized OA studies, topical TDT 064 was well tolerated and improved joint pain, physical function and stiffness. In the largest study, these TDT 064-mediated effects were statistically significantly greater than oral placebo and equivalent to celecoxib. To understand the therapeutic effects of TDT 064, we investigated the localisation of the drug-free vesicles within rat synovial joints. TDT 064 containing DiO-labelled Sequessome™ vesicles was applied to the knees of four 6-week-old CD® hairless rats (10 mg/kg/ joint), 2–3 times/day, for 3 days (representing the recommended clinical dose). Eighteen hours later, the animals and one untreated control were sacrificed, and the knee joints isolated, flash frozen and embedded in Acrytol Mounting Media™. Approximately 15 sections (10 µm) from each joint were analysed by fluorescence microscopy. To investigate whether the localisation of DiO fluorescence was associated with intact vesicles, an anti-PEG monoclonal antibody (mAb) was used to detect Tween, a constituent of Sequessome™ vesicles. Sections were visualized at 484 nm (DiO) and 647 nm (anti-PEG mAb) and analysed using inForm 1.4 (Perkin Elmer, Inc.). Significant fluorescence was observed at 484 nm in sections from TDT 064-treated animals. No non-specific fluorescence was observed in control sections. Fluorescence was detected as discrete vesicles on the cartilage surfaces, inside the cartilaginous matrix and within the synovial space. The number of DiO-labelled vesicles in multiple fields of view was consistent and >100 in sections from four different treated knees. DiO and anti-PEG mAb co-localised within the collagenous tissues in four different joint sections. Under higher magnification (40x), vesicles were seen in the intercellular spaces of the synovial joint tissue, but no fluorescence was seen inside cells. These data suggest that the phospholipid vesicles in TDT 064 localize at the surface of the joint cartilage; these vesicles may therefore be supplementing the phospholipid deficiency reported in OA and acting as a biolubricant within the synovial joint.

Keywords: joint pain, osteoarthritis, phospholipid vesicles, TDT 064

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Authors: Surendra Agrawal, Pravina Gurjar, Supriya Bhide, Ram Gaud

Abstract:

Levamisole hydrochloride is a prominent anticancer drug in the treatment of colon cancer but resulted in toxic effects due poor bioavailability and poor cellular uptake by tumor cells. Levamisole is an unstable drug. Incorporation of this molecule in solid lipids may minimize their exposure to the aqueous environment and partly immobilize the drug molecules within the lipid matrix-both of which may protect the encapsulated drugs against degradation. The objectives of the study were to enhance bioavailability by sustaining drug release and to reduce the toxicities associated with the therapy. Solubility of the drug was determined in different lipids to select the components of Solid Lipid Nanoparticles (SLN). Pseudoternary phase diagrams were created using aqueous titration method. Formulations were subjected to particle size and stability evaluation to select the final test formulations which were characterized for average particle size, zeta potential, and in-vitro drug release and percentage transmittance to optimize the final formulation. SLN of Levamisole hydrochloride was prepared by Nanoprecipitation method. Glyceryl behenate (Compritol 888 ATO) was used as core comprising of Tween 80 as surfactant and Lecithin as co-surfactant in (1:1) ratio. Entrapment efficiency (EE) was found to be 45.89%. Particle size was found in the range of 100-600 nm. Zeta potential of the formulation was -17.0 mV revealing the stability of the product. In-vitro release study showed that 66 % drug released in 24 hours in pH 7.2 which represent that formulation can give controlled action at the intestinal environment. In pH 5.0 it showed 64% release indicating that it can even release drug in acidic environment of tumor cells. In conclusion, results revealed SLN to be a promising approach to sustain the drug release so as to increase bioavailability and cellular uptake of the drug with reduction in toxic effects as dose has been reduced with controlled delivery.

Keywords: SLN, nanoparticulate delivery of levamisole, pharmacy, pharmaceutical sciences

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Authors: Erica Elice Uy, Toshihiro Noda, Kentaro Nakai, Jonathan Dungca

Abstract:

Deformation in a conventional cyclic triaxial test is normally measured by using point-wise measuring device. In this study, non-contact measurement technique was applied to be able to monitor and measure the occurrence of non-homogeneous behavior of the soil under cyclic loading. Non-contact measurement is executed through image processing. Two-dimensional measurements were performed using Lucas and Kanade optical flow algorithm and it was implemented Labview. In this technique, the non-homogeneous deformation was monitored using a mirrorless camera. A mirrorless camera was used because it is economical and it has the capacity to take pictures at a fast rate. The camera was first calibrated to remove the distortion brought about the lens and the testing environment as well. Calibration was divided into 2 phases. The first phase was the calibration of the camera parameters and distortion caused by the lens. The second phase was to for eliminating the distortion brought about the triaxial plexiglass. A correction factor was established from this phase. A series of consolidated undrained cyclic triaxial test was performed using a coarse soil. The results from the non-contact measurement technique were compared to the measured deformation from the linear variable displacement transducer. It was observed that deformation was higher at the area where failure occurs.

Keywords: cyclic loading, non-contact measurement, non-homogeneous, optical flow

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Authors: B. Hammouti, H. Oudda, A. Benabdellah, A. Benayada, A. Aouniti

Abstract:

Corrosion behaviour of carbon steel was studied in various concentrated HClO4 solutions. To explain the acid attack in relation of H+ activity, new sensor was realised: two carbon paste electrodes (CPE) were constructed by incorporating ferrocene (Fc) and orthoquinone into the carbon paste matrix and crossed by weak current to stabilize potential difference. The potentiometric method at imposed weak current between these two electrodes permits the in situ determination of both concentration and acidity level of various concentrated HClO4 solutions. The different factors affecting the potential at imposed current as current intensity, temperature and H+ ion concentration are studied. The potentials measured between ferrocene and chloranil electrodes are directly linked to the acid concentration. The acidity Ri(H) function defined represents the determination of the H+ activity and constitutes the extend of pH is concentrated acid solutions. Ri(H) has been determined and compared to Strehlow Ro(H), Janata HGF and Hammett Ho functions. The collected data permit to give a scale of strength of mineral concentrated acids at a given concentration. Ri(H) is numerically equal to the thermodynamic Ro(H), but deviated from Hammett functions based on indicator determination. The CPE electrode with inserted ferrocene in presence of ferricinium (Fc+) ion in concentrated HClO4 at various concentrations is realized without junction potential and may plays the role of a practical reference electrode (FRE) in concentrated acids. Fc+ was easily prepared in biphasic medium HClO4-acid by the quantitative oxidation of ferrocene by the ortho-chloranil (oQ). Potential of FRE is stable with time. The variation of equilibrium potential of the interface Fc/ Fc+ at various concentrations of Fc+ (10-4 - 2 10-2 M) obeyed to the Nernst equation with a slope 0.059 Volt per decade. Corrosion rates obtained by weight loss and electrochemical techniques were then easily linked to acidity level.

Keywords: ferrocene, strehlow, concentrated acid, corrosion, Generalised pH, sensor carbon paste electrode

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