Search results for: adaptive and non-adaptive spectral estimation

Authors: Nurtati Soewarno, Dian Duhita

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The existence of civilization from the past is recognized by the left objects such as monuments, buildings or even a town. The relics were designed and made well, using the good quality material so it could persist a long period of time. At this moment, those relics are cultural heritage that must be preserved and the authenticity maintained. Indonesia, a country consist of various tribes with many cultural heritages, one of them is the city of Lasem. Lasem city lies in the northern part of Central Java since the Majapahit kingdom era (13th century) poses as a busy harbor city and a trading center. Lasem is one of the residences of Chinese immigrants in Java, seen by the domination of Chinese architectural building styles. The residential was built since the 15th century and the building has the courtyard which is different from other China’s building in another part of Java. This city loses ground since the trade activity experience difficulties during the Japanese colonial era and continues after the Indonesian independence time. Many Chinese people left Lasem city and let the buildings empty not maintained. This paper will present the result of observation to Chinese architectural style buildings in Lasem city which still hold out until this moment. Using typo morphology method, the case study is chosen based on the transformation type. The occurring transformation is parallel with adaptive reuse concept as an effort to revitalize the existence of the buildings. With this concept, it is expected that the buildings could be re functioned and the glory of the foretime Lasem city could be experienced again. Intervention from the local government is expected, issuing regulations, hoping the new building functions won’t ruin the cultural heritage but instead beautifies it.

Keywords: adaptive re-use, brown field area, building transformation, Lasem city

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Authors: Nayla El-Kork, Farah Korjieh, Ahmed Bentiba, Mahmoud Korek

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Ab-initio calculations have been performed to investigate the spectroscopic constants for the diatomic compound BeF. Values of the internuclear distance Re, the harmonic frequency ωe, the rotational constants Be, the electronic transition energy with respect to the ground state Te, the eignvalues Ev, the abscissas of the turning points Rmin, Rmax, the rotational constants Bv and the centrifugal distortion constants Dv have been calculated for the molecule’s ground and excited electronic states. Results are in agreement with experimental data.

Keywords: spectroscopic constant, potential energy curve, diatomic molecule, spectral analysis

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Authors: J. B. Minari, O. B. Oloyede, A. A. Odutuga

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Myeloperoxidase is the most abundant enzyme found in the polymorphonuclear neutrophil and is known to play a central role in the host defense system of the leukocyte. The enzyme has been reported to interact with some drugs to generate free radical which inhibits its activity. This study investigated the effects of artesunate on the activity of the enzyme and the subsequent effect on the host immune system. In investigating the effects of the drugs on myeloperoxidase, the influence of concentration, pH, partition ratio estimation and kinetics of inhibition were studied. This study showed that artesunate is concentration-dependent inhibitor of myeloperoxidase with an IC50 of 0.078mM. Partition ratio estimation showed that 60 enzymatic turnover cycles are required for complete inhibition of myeloperoxidase in the presence of artesunate. The influence of pH on the effect of artesunate on the enzyme showed least activity of myeloperoxidase at physiological pH. The kinetic inhibition studies showed that artesunate caused a competitive inhibition with an increase in the Km value from 0.12mM to 0.26mM and no effect on the Vmax value. The Ki value was estimated to be 2.5mM. The results obtained from this study show that artesunate is a potent inhibitor of myeloperoxidase and it is capable of inactivating the enzyme. It is considered that the inhibition of myeloperoxidase in the presence of artesunate as revealed in this study may partly explain the impairment of polymorphonuclear neutrophil and consequent reduction of the strength of the host defense system against secondary infections.

Keywords: myeloperoxidase, artesunate, inhibition, nuetrophill

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Authors: M. A. Talha, M. Osman Gani, M. Ferdows

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This paper focuses on the study of two dimensional magnetohydrodynamic (MHD) steady incompressible viscous Williamson nanofluid with exponential internal heat generation containing gyrotactic microorganism over a stretching sheet. The governing equations and auxiliary conditions are reduced to a set of non-linear coupled differential equations with the appropriate boundary conditions using similarity transformation. The transformed equations are solved numerically through spectral relaxation method. The influences of various parameters such as Williamson parameter γ, power constant λ, Prandtl number P_r, magnetic field parameter M, Peclet number P_e, Lewis number Le, Bioconvection Lewis number Lb, Brownian motion parameter Nb, thermophoresis parameter Nt, and bioconvection constant σ are studied to obtain the momentum, heat, mass and microorganism distributions. Moment, heat, mass and gyrotactic microorganism profiles are explored through graphs and tables. We computed the heat transfer rate, mass flux rate and the density number of the motile microorganism near the surface. Our numerical results are in better agreement in comparison with existing calculations. The Residual error of our obtained solutions is determined in order to see the convergence rate against iteration. Faster convergence is achieved when internal heat generation is absent. The effect of magnetic parameter M decreases the momentum boundary layer thickness but increases the thermal boundary layer thickness. It is apparent that bioconvection Lewis number and bioconvection parameter has a pronounced effect on microorganism boundary. Increasing brownian motion parameter and Lewis number decreases the thermal boundary layer. Furthermore, magnetic field parameter and thermophoresis parameter has an induced effect on concentration profiles.

Keywords: convection flow, similarity, numerical analysis, spectral method, Williamson nanofluid, internal heat generation

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Authors: N. K. Fuloria, S. Fuloria, R. Gupta

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Esterification of p-bromo-m-cresol offered 2-(4-bromo-3-methyl phenoxy)acetate (1), which was hydrazinated to yield 2-(4-bromo-3-methyl phenoxy)aceto hydrazide (2). Compound (2) was reacted with different aromatic aldehydes to yield N-(substituted benzylidiene)-2-(4-bromo-3-methyl phenoxy)acetamide(3a-c). Cyclization of compound (3a-c) with thioglycolic acid yielded 2-(4-bromo-3-methylphenoxy)-N-(4-oxo-2-arylthiazolidin-3-yl) acetamide (4a-c). The newly synthesized compounds were characterized on the basis of spectral studies and evaluated for antibacterial and antifungal activities.

Keywords: imines, thiazolidinone, schiff base, antimicrobial

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Authors: Jaegu Choi, Jae-Mean Koo, Chang-Sung Seok, Byungwoo Moon

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Since massive earthquakes in the world have been reported recently, the safety of nuclear power plants for seismic loading has become a significant issue. Seismic loading is the reverse cyclic loading, consisting of repeated tensile and compression by longitudinal and transverse wave. Up to this time, the study on characteristics of fracture toughness under reverse cyclic loading has been unsatisfactory. Therefore, it is necessary to obtain the fracture toughness under reverse cyclic load for the integrity estimation of nuclear power plants under seismic load. Fracture resistance (J-R) curves, which are used for determination of fracture toughness or integrity estimation in terms of elastic-plastic fracture mechanics, can be derived by the fracture resistance test using single specimen technique. The objective of this paper is to study the effects of reverse cyclic loading on a fracture resistance curve of ESG specimen, having a similar stress gradient compared to the crack surface of the real pipe. For this, we carried out the fracture toughness test under the reverse cyclic loading, while changing incremental plastic displacement. Test results showed that the J-R curves were decreased with a decrease of the incremental plastic displacement.

Keywords: reverse cyclic loading, j-r curve, ESG specimen, incremental plastic displacement

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Authors: Gulsah Olgun, Ozgur Bozdag, Yildirim Ertutar

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This paper mainly focuses on performance assessment of buildings by associating the damage level with the damage cost. For this purpose a methodology is explained and applied to the representative mid-rise concrete building residing in Izmir. In order to consider uncertainties in occurrence of earthquakes, the structural analyses are conducted for all possible earthquakes in the region through the hazard curve. By means of the analyses, probability of the structural response being in different limit states are obtained and used to calculate expected damage cost. The expected damage cost comprises diverse cost components related to earthquake such as cost of casualties, replacement or repair cost of building etc. In this study, inter-story drift is used as an effective response variable to associate expected damage cost with different damage levels. The structural analysis methods performed to obtain inter story drifts are response spectrum method as a linear one, accurate push-over and time history methods to demonstrate the nonlinear effects on loss estimation. Comparison of the results indicates that each method provides similar values of expected damage cost. To sum up, this paper explains an approach which enables to minimize the expected damage cost of buildings and relate performance level to damage cost.

Keywords: expected damage cost, limit states, loss estimation, performance based design

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Authors: Luh Eka Suryani, Purhadi

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Poisson regression is a non-linear regression model with response variable in the form of count data that follows Poisson distribution. Modeling for a pair of count data that show high correlation can be analyzed by Poisson Bivariate Regression. Data, the number of infant mortality and maternal mortality, are count data that can be analyzed by Poisson Bivariate Regression. The Poisson regression assumption is an equidispersion where the mean and variance values are equal. However, the actual count data has a variance value which can be greater or less than the mean value (overdispersion and underdispersion). Violations of this assumption can be overcome by applying Generalized Poisson Regression. Characteristics of each regency can affect the number of cases occurred. This issue can be overcome by spatial analysis called geographically weighted regression. This study analyzes the number of infant mortality and maternal mortality based on conditions in East Java in 2016 using Geographically Weighted Bivariate Generalized Poisson Regression (GWBGPR) method. Modeling is done with adaptive bisquare Kernel weighting which produces 3 regency groups based on infant mortality rate and 5 regency groups based on maternal mortality rate. Variables that significantly influence the number of infant and maternal mortality are the percentages of pregnant women visit health workers at least 4 times during pregnancy, pregnant women get Fe3 tablets, obstetric complication handled, clean household and healthy behavior, and married women with the first marriage age under 18 years.

Keywords: adaptive bisquare kernel, GWBGPR, infant mortality, maternal mortality, overdispersion

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Authors: H. Yousefnia, S. Zolghadri

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The measurement of organ radiation exposure dose is one of the most important steps to be taken initially, for developing a new radiopharmaceutical. In this study, the dosimetric studies of a novel agent for SPECT-imaging of the bone metastasis, 111In-1,4,7,10-tetraazacyclododecane-1,4,7,10 tetraethylene phosphonic acid (111In-DOTMP) complex, have been carried out to estimate the dose in human organs based on the data derived from rats. The radiolabeled complex was prepared with high radiochemical purity in the optimal conditions. Biodistribution studies of the complex was investigated in the male Syrian rats at selected times after injection (2, 4, 24 and 48 h). The human absorbed dose estimation of the complex was made based on data derived from the rats by the radiation absorbed dose assessment resource (RADAR) method. 111In-DOTMP complex was prepared with high radiochemical purity of >99% (ITLC). Total body effective absorbed dose for 111In-DOTMP was 0.061 mSv/MBq. This value is comparable to the other 111In clinically used complexes. The results show that the dose with respect to the critical organs is satisfactory within the acceptable range for diagnostic nuclear medicine procedures. Generally, 111In-DOTMP has interesting characteristics and can be considered as a viable agent for SPECT-imaging of the bone metastasis in the near future.

Keywords: In-111, DOTMP, Internal Dosimetry, RADAR

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Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: artificial neural networks, digital image processing, pattern recognition, phytosanitary

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Authors: Ahmed G. Mahgoub, Dahlia H. Hafez, Mostafa A. Abu Kiefa

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The Cone Penetration Test (CPT) is a common in-situ test which generally investigates a much greater volume of soil more quickly than possible from sampling and laboratory tests. Therefore, it has the potential to realize both cost savings and assessment of soil properties rapidly and continuously. The principle objective of this paper is to demonstrate the feasibility and efficiency of using artificial neural networks (ANNs) to predict the soil angle of internal friction (Φ) and the soil modulus of elasticity (E) from CPT results considering the uncertainties and non-linearities of the soil. In addition, ANNs are used to study the influence of different parameters and recommend which parameters should be included as input parameters to improve the prediction. Neural networks discover relationships in the input data sets through the iterative presentation of the data and intrinsic mapping characteristics of neural topologies. General Regression Neural Network (GRNN) is one of the powerful neural network architectures which is utilized in this study. A large amount of field and experimental data including CPT results, plate load tests, direct shear box, grain size distribution and calculated data of overburden pressure was obtained from a large project in the United Arab Emirates. This data was used for the training and the validation of the neural network. A comparison was made between the obtained results from the ANN's approach, and some common traditional correlations that predict Φ and E from CPT results with respect to the actual results of the collected data. The results show that the ANN is a very powerful tool. Very good agreement was obtained between estimated results from ANN and actual measured results with comparison to other correlations available in the literature. The study recommends some easily available parameters that should be included in the estimation of the soil properties to improve the prediction models. It is shown that the use of friction ration in the estimation of Φ and the use of fines content in the estimation of E considerable improve the prediction models.

Keywords: angle of internal friction, cone penetrating test, general regression neural network, soil modulus of elasticity

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Authors: Monica Gonzalez Machorro

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Dementia is hard to diagnose because of the lack of early physical symptoms. Early dementia recognition is key to improving the living condition of patients. Speech technology is considered a valuable biomarker for this challenge. Recent works have utilized conventional acoustic features and machine learning methods to detect dementia in speech. BERT-like classifiers have reported the most promising performance. One constraint, nonetheless, is that these studies are either based on human transcripts or on transcripts produced by automatic speech recognition (ASR) systems. This research contribution is to explore a method that does not require transcriptions to detect early Alzheimer’s disease (AD) and mild cognitive impairment (MCI). This is achieved by fine-tuning a pre-trained ASR model for the downstream early AD and MCI tasks. To do so, a subset of the thoroughly studied Pitt Corpus is customized. The subset is balanced for class, age, and gender. Data processing also involves cropping the samples into 10-second segments. For comparison purposes, a baseline model is defined by training and testing a Random Forest with 20 extracted acoustic features using the librosa library implemented in Python. These are: zero-crossing rate, MFCCs, spectral bandwidth, spectral centroid, root mean square, and short-time Fourier transform. The baseline model achieved a 58% accuracy. To fine-tune HuBERT as a classifier, an average pooling strategy is employed to merge the 3D representations from audio into 2D representations, and a linear layer is added. The pre-trained model used is ‘hubert-large-ls960-ft’. Empirically, the number of epochs selected is 5, and the batch size defined is 1. Experiments show that our proposed method reaches a 69% balanced accuracy. This suggests that the linguistic and speech information encoded in the self-supervised ASR-based model is able to learn acoustic cues of AD and MCI.

Keywords: automatic speech recognition, early Alzheimer’s recognition, mild cognitive impairment, speech impairment

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Authors: Zeinab Farhat, Nicolas Errien, Romuald Wernert, Véronique Verriele, Frédéric Amiard, Philippe Daniel

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Ovarian cancer, also known as the silent killer, is the fifth most common cancer among women worldwide, and its death rate is higher than that of other gynecological cancers. The low survival rate of women with high-grade serous ovarian carcinoma highlights the critical need for the development of new methods for early detection and diagnosis of the disease. The aim of this study was to evaluate if Raman spectroscopy combined with chemometric methods such as Principal Component Analysis (PCA) could differentiate between cancerous and normal tissues from different types of samples, such as paraffin embedding, chemical deparaffinized, formalin-fixed and fresh samples of the same normal and malignant ovarian tissue. The method was applied specifically to two critical spectral regions: the signature region (860-1000 〖cm〗^(-1)) and the high-frequency region (2800-3100 〖cm〗^(-1) ). The mean spectra of paraffin-embedded in normal and malignant tissues showed almost similar intensity. On the other hand, the mean spectra of normal and cancer tissues from chemical deparaffinized, formalin-fixed, and fresh samples show significant intensity differences. These spectral differences reflect variations in the molecular composition of the tissues, particularly lipids and proteins. PCA, which was applied to distinguish between cancer and normal tissues, was performed on whole spectra and on selected regions—the PCA score plot of paraffin-embedded shows considerable overlap between the two groups. However, the PCA score of chemicals deparaffinized, formalin-fixed, and fresh samples showed a good discrimination of tissue types. Our findings were validated by analyses of a set of samples whose status (normal and cancerous) was not previously known. The results of this study suggest that Raman Spectroscopy associated with PCA methods has the capacity to provide clinically significant differentiation between normal and cancerous ovarian tissues.

Keywords: Raman spectroscopy, ovarian cancer, signal processing, Principal Component Analysis, classification

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Authors: Fatima Zohra Mahi, Luca Varani

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We present an analytical model for the calculation of the sensitivity, the spectral current noise and the detectivity for an optically illuminated In0.53Ga0.47As n+nn+ diode. The photocurrent due to the excess carrier is obtained by solving the continuity equation. Moreover, the current noise level is evaluated at room temperature and under a constant voltage applied between the diode terminals. The analytical calculation of the current noise in the n+nn+ structure is developed. The responsivity and the detectivity are discussed as functions of the doping concentrations and the emitter layer thickness in one-dimensional homogeneous n+nn+ structure.

Keywords: detectivity, photodetectors, continuity equation, current noise

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Authors: Mahsa Mousavi, Hamid Reza Pourshaghaghi, Mohammad Tahghighi, Henk Corporaal

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Recently, Statistic Random-Access Memory-based (SRAM-based) Field-Programmable Gate Arrays (FPGAs) are widely used in aeronautics and space systems where high dependability is demanded and considered as a mandatory requirement. Since design’s circuit is stored in configuration memory in SRAM-based FPGAs; they are very sensitive to Single Event Upsets (SEUs). In addition, the adverse effects of SEUs on the electronics used in space are much higher than in the Earth. Thus, developing fault tolerant techniques play crucial roles for the use of SRAM-based FPGAs in space. However, fault tolerance techniques introduce additional penalties in system parameters, e.g., area, power, performance and design time. In this paper, an accurate estimation of configuration memory vulnerability to SEUs is proposed for approximate-tolerant applications. This vulnerability estimation is highly required for compromising between the overhead introduced by fault tolerance techniques and system robustness. In this paper, we study applications in which the exact final output value is not necessarily always a concern meaning that some of the SEU-induced changes in output values are negligible. We therefore define and propose Approximate-based Configuration Memory Vulnerability Factor (ACMVF) estimation to avoid overestimating configuration memory vulnerability to SEUs. In this paper, we assess the vulnerability of configuration memory by injecting SEUs in configuration memory bits and comparing the output values of a given circuit in presence of SEUs with expected correct output. In spite of conventional vulnerability factor calculation methods, which accounts any deviations from the expected value as failures, in our proposed method a threshold margin is considered depending on user-case applications. Given the proposed threshold margin in our model, a failure occurs only when the difference between the erroneous output value and the expected output value is more than this margin. The ACMVF is subsequently calculated by acquiring the ratio of failures with respect to the total number of SEU injections. In our paper, a test-bench for emulating SEUs and calculating ACMVF is implemented on Zynq-7000 FPGA platform. This system makes use of the Single Event Mitigation (SEM) IP core to inject SEUs into configuration memory bits of the target design implemented in Zynq-7000 FPGA. Experimental results for 32-bit adder show that, when 1% to 10% deviation from correct output is considered, the counted failures number is reduced 41% to 59% compared with the failures number counted by conventional vulnerability factor calculation. It means that estimation accuracy of the configuration memory vulnerability to SEUs is improved up to 58% in the case that 10% deviation is acceptable in output results. Note that less than 10% deviation in addition result is reasonably tolerable for many applications in approximate computing domain such as Convolutional Neural Network (CNN).

Keywords: fault tolerance, FPGA, single event upset, approximate computing

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Authors: Rachida Ouysse

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This paper proposes a constrained principal components (CnPC) estimator for efficient estimation of large-dimensional factor models when errors are cross sectionally correlated and the number of cross-sections (N) may be larger than the number of observations (T). Although principal components (PC) method is consistent for any path of the panel dimensions, it is inefficient as the errors are treated to be homoskedastic and uncorrelated. The new CnPC exploits the assumption of bounded cross-sectional dependence, which defines Chamberlain and Rothschild’s (1983) approximate factor structure, as an explicit constraint and solves a constrained PC problem. The CnPC method is computationally equivalent to the PC method applied to a regularized form of the data covariance matrix. Unlike maximum likelihood type methods, the CnPC method does not require inverting a large covariance matrix and thus is valid for panels with N ≥ T. The paper derives a convergence rate and an asymptotic normality result for the CnPC estimators of the common factors. We provide feasible estimators and show in a simulation study that they are more accurate than the PC estimator, especially for panels with N larger than T, and the generalized PC type estimators, especially for panels with N almost as large as T.

Keywords: high dimensionality, unknown factors, principal components, cross-sectional correlation, shrinkage regression, regularization, pseudo-out-of-sample forecasting

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Authors: Ferran Paulí Pla, Pere Palacín Farré, Albert Fornells Herrera, Pol Toldrà Fernández

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Poor image acquisition is limiting the promising growth of industrial vision in food control. In recent years, the food industry has witnessed a significant increase in the implementation of automation in quality control through artificial vision, a trend that continues to grow. During the packaging process, some defects may appear, compromising the proper sealing of the products and diminishing their shelf life, sanitary conditions and overall properties. While failure to detect a defective product leads to major losses, food producers also aim to minimize over-rejection to avoid unnecessary waste. Thus, accuracy in the evaluation of the products is crucial, and, given the large production volumes, even small improvements have a significant impact. Recently, efforts have been focused on maximizing the performance of classification neural networks; nevertheless, their performance is limited by the quality of the input data. Monochrome linear backlight systems are most commonly used for online inspections of food packaging thermo-sealing zones. These simple acquisition systems fit the high cadence of the production lines imposed by the market demand. Nevertheless, they provide a limited amount of data, which negatively impacts classification algorithm training. A desired situation would be one where data quality is maximized in terms of obtaining the key information to detect defects while maintaining a fast working pace. This work presents a backlight hyperspectral imaging system designed and implemented replicating an industrial environment to better understand the relationship between visual data quality and spectral illumination range for a variety of packed food products. Furthermore, results led to the identification of advantageous spectral bands that significantly enhance image quality, providing clearer detection of defects.

Keywords: artificial vision, food packaging, hyperspectral imaging, image acquisition, quality control

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Authors: Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung

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Lightweight unmanned aerial vehicle (UAV) loading with novel sensors offers a low cost approach for data acquisition in complex environment. This study established a framework for applying UAV system in complex environment quick mapping and assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area Mai Po Inner Deep Bay Ramsar Site, Hong Kong. The study area was part of shallow bay with flat terrain and the major species including reedbed and four mangroves: Kandelia obovata, Aegiceras corniculatum, Acrostichum auerum and Acanthus ilicifolius. Other species involved in various graminaceous plants, tarbor, shrub and invasive species Mikania micrantha. In particular, invasive species climbed up to the mangrove canopy caused damage and morphology change which might increase species distinguishing difficulty. Hyperspectral images were acquired by Headwall Nano sensor with spectral range from 400nm to 1000nm and 0.06m spatial resolution image. A sequence of multi-view RGB images was captured with 0.02m spatial resolution and 75% overlap. Hyperspectral image was corrected for radiative and geometric distortion while high resolution RGB images were matched to generate maximum dense point clouds. Furtherly, a 5 cm grid digital surface model (DSM) was derived from dense point clouds. Multiple feature reduction methods were compared to identify the efficient method and to explore the significant spectral bands in distinguishing different species. Examined methods including stepwise discriminant analysis (DA), support vector machine (SVM) and minimum noise fraction (MNF) transformation. Subsequently, spectral subsets composed of the first 20 most importance bands extracted by SVM, DA and MNF, and multi-source subsets adding extra DSM to 20 spectrum bands were served as input in maximum likelihood classifier (MLC) and SVM classifier to compare the classification result. Classification results showed that feature reduction methods from best to worst are MNF transformation, DA and SVM. MNF transformation accuracy was even higher than all bands input result. Selected bands frequently laid along the green peak, red edge and near infrared. Additionally, DA found that chlorophyll absorption red band and yellow band were also important for species classification. In terms of 3D data, DSM enhanced the discriminant capacity among low plants, arbor and mangrove. Meanwhile, DSM largely reduced misclassification due to the shadow effect and morphological variation of inter-species. In respect to classifier, nonparametric SVM outperformed than MLC for high dimension and multi-source data in this study. SVM classifier tended to produce higher overall accuracy and reduce scattered patches although it costs more time than MLC. The best result was obtained by combining MNF components and DSM in SVM classifier. This study offered a precision species distribution survey solution for inaccessible wetland area with low cost of time and labour. In addition, findings relevant to the positive effect of DSM as well as spectral feature identification indicated that the utility of UAV-borne hyperspectral and photogrammetry deriving 3D data is promising in further research on wetland species such as bio-parameters modelling and biological invasion monitoring.

Keywords: digital surface model (DSM), feature reduction, hyperspectral, photogrammetric point cloud, species mapping, unmanned aerial vehicle (UAV)

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Authors: A. Prajapati, K. L. Koo, F. Ghassemi, M. Mulimakwenda

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In the UK, as the volume of non-linear loads connected to transmission grid continues to rise steeply, the harmonic distortion levels on transmission network are becoming a serious concern for the network owners and system operators. This paper outlines the findings of the study conducted to verify the proposal that the harmonic mitigation could be optimized and can be managed economically and effectively at the transmission network level by the Transmission Owner (TO) instead of the individual polluter connected to the grid. Harmonic mitigation studies were conducted on selected regions of the transmission network in England for recently connected offshore wind power plants to strategize and optimize selected harmonic filter options. The results – filter volume and capacity – were then compared against the mitigation measures adopted by the individual connections. Estimation ratios were developed based on the actual installed and optimal proposed filters. These estimation ratios were then used to derive harmonic filter requirements for future contracted connections. The study has concluded that a saving of 37% in the filter volume/capacity could be achieved if the TO is to centrally manage the harmonic mitigation instead of individual polluter installing their own mitigation solution.

Keywords: C-type filter, harmonics, optimization, offshore wind farms, interconnectors, HVDC, renewable energy, transmission owner

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Authors: Bushra Nabi, Saleha Rehman, Sanjula Baboota, Javed Ali

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Aim: The objective of research undertaken is analytical method validation (HPLC method) of an anti-HIV drug Elvitegravir (EVG). Additionally carrying out the forced degradation studies of the drug under different stress conditions to determine its stability. It is envisaged in order to determine the suitable technique for drug estimation, which would be employed in further research. Furthermore, comparative pharmacokinetic profile of the drug from lipid architectonics and drug suspension would be obtained post oral administration. Method: Lipid Architectonics (LA) of EVR was formulated using probe sonication technique and optimized using QbD (Box-Behnken design). For the estimation of drug during further analysis HPLC method has been validation on the parameters (Linearity, Precision, Accuracy, Robustness) and Limit of Detection (LOD) and Limit of Quantification (LOQ) has been determined. Furthermore, HPLC quantification of forced degradation studies was carried out under different stress conditions (acid induced, base induced, oxidative, photolytic and thermal). For pharmacokinetic (PK) study, Albino Wistar rats were used weighing between 200-250g. Different formulations were given per oral route, and blood was collected at designated time intervals. A plasma concentration profile over time was plotted from which the following parameters were determined:

Keywords: AIDS, Elvitegravir, HPLC, nanostructured lipid carriers, pharmacokinetics

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Authors: Jeron Maheswaran, Sudath C. Siriwardane

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In fatigue analysis, the structural detail of tubular joint has taken great attention among engineers. The DNV-RP-C203 is covering this topic quite well for simple and clear joint cases. For complex joint and geometry, where joint classification isn’t available and limitation on validity range of non-dimensional geometric parameters, the challenges become a fact among engineers. The classification of joint is important to carry out through the fatigue analysis. These joint configurations are identified by the connectivity and the load distribution of tubular joints. To overcome these problems to some extent, this paper compare the fatigue life of tubular joints in offshore jacket according to the stress concentration factors (SCF) in DNV-RP-C203 and finite element method employed Abaqus/CAE. The paper presents the geometric details, material properties and considered load history of the jacket structure. Describe the global structural analysis and identification of critical tubular joints for fatigue life estimation. Hence fatigue life is determined based on the guidelines provided by design codes. Fatigue analysis of tubular joints is conducted using finite element employed Abaqus/CAE [4] as next major step. Finally, obtained SCFs and fatigue lives are compared and their significances are discussed.

Keywords: fatigue life, stress-concentration factor, finite element analysis, offshore jacket structure

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Authors: M. H. M. Rashid

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A Banach space operator T obeys property (gm) if the isolated points of the spectrum σ(T) of T which are eigenvalues are exactly those points λ of the spectrum for which T − λI is a left Drazin invertible. In this article, we study the stability of property (gm), for a bounded operator acting on a Banach space, under perturbation by finite rank operators, by nilpotent operators, by quasi-nilpotent operators, or more generally by algebraic operators commuting with T.

Keywords: Weyl's Theorem, Weyl Spectrum, Polaroid operators, property (gm)

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Authors: S. Farid F. Mojtahedi, Ali Khosravi, Behnaz Naeimian, S. Adel A. Hosseini

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Land subsidence is a gradual settling or sudden sinking of the land surface from changes that take place underground. There are different causes of land subsidence; most notably, ground-water overdraft and severe weather conditions. Subsidence of the land surface due to ground water overdraft is caused by an increase in the intergranular pressure in unconsolidated aquifers, which results in a loss of buoyancy of solid particles in the zone dewatered by the falling water table and accordingly compaction of the aquifer. On the other hand, exploitation of underground water may result in significant changes in degree of saturation of soil layers above the water table, increasing the effective stress in these layers, and considerable soil settlements. This study focuses on estimation of soil moisture at surface using different methods. Specifically, different methods for the estimation of moisture content at the soil surface, as an important term to solve Richard’s equation and estimate soil moisture profile are presented, and their results are discussed through comparison with field measurements obtained from Yanco1 station in south-eastern Australia. Surface soil moisture is not easy to measure at the spatial scale of a catchment. Due to the heterogeneity of soil type, land use, and topography, surface soil moisture may change considerably in space and time.

Keywords: artificial neural network, empirical method, remote sensing, surface soil moisture, unsaturated soil

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Authors: Alaa H. Thabet, Ehab F. Badran, Moustafa H. Aly

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With the rapid expand of wireless digital communications, demand for wireless systems that are reliable and have a high spectral efficiency have increased too. FBMC scheme based on the OFDM/OQAM has been recognized for its good performance to achieve high data rates. Fast Fourier Transforms (FFT) has been used to produce the orthogonal sub-carriers. Due to the drawbacks of OFDM -FFT based system which are the high peak-to-average ratio (PAR) and the synchronization. In this paper, Wavelet Packet Transform (WPT) is used in the place of FFT, and show better performance.

Keywords: OQAM-OFDM, wavelet packet transform, PAPR, FFT

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Authors: Adewale I. Adeolu, Vivian U. Oleforuh-Okoleh, Sylvester N. Ibe

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Heritability and repeatability estimates are needed for the genetic evaluation of livestock populations and consequently for the purpose of upgrading or improvement. Pooled data on 604 progeny from three consecutive parities of purebred rabbit breeds (Chinchilla, Dutch and New Zealand white) raised in Derived Savanna Zone of Nigeria were used to estimate heritability and repeatability for pre-weaning body weights between 1st and 8th week of age. Traits studied include Individual kit weight at birth (IKWB), 2nd week (IK2W), 4th week (IK4W), 6th week (IK6W) and 8th week (IK8W). Nested random effects analysis of (Co)variances as described by Statistical Analysis System (SAS) were employed in the estimation. Respective heritability estimates from the sire component (h2s) and repeatability (R) as intra-class correlations of repeated measurements from the three parties for IKWB, IK2W, IK4W and IK8W are 0.59±0.24, 0.55±0.24, 0.93±0.31, 0.28±0.17, 0.64±0.26 and 0.12±0.14, 0.05±0.14, 0.58±0.02, 0.60±0.11, 0.20±0.14. Heritability and repeatability (except R for IKWB and IK2W) estimates are moderate to high. In conclusion, since pre-weaning body weights in the present study tended to be moderately to highly heritable and repeatable, improvement of rabbits raised in derived savanna zone can be realized through genetic selection criterions.

Keywords: heritability, nested design, parity, pooled data, repeatability

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Nnadi Ezekiel Oluwaseun Ejiofor

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This research investigates the effects of poor cost estimation, material cost variations, and payment punctuality on the financial health and execution of construction projects in Nigeria. To achieve the objectives of the study, a quantitative research approach was employed, and data was gathered through an online survey of 74 construction industry professionals consisting of quantity surveyors, contractors, and other professionals. The study surveyed input on cost estimation errors, price fluctuations, and payment delays, among other factors. The responses of the respondents were analyzed using a five-point Likert scale and the Relative Importance Index (RII). The findings demonstrated that the errors in cost estimating in the Bill of Quantity (BOQ) have a high degree of negative impact on the reputation and image of the participants in the projects. The greatest effect was experienced on the likelihood of obtaining future endeavors for contractors (mean value = 3.42), followed by the likelihood of obtaining new commissions by quantity surveyors (mean value = 3.40). The level of inaccuracy in costing that undershoots exposes them to risks was most serious in terms of easement of construction and effects of shortage of funds to pursue bankruptcy (hence fears of mean value = 3.78). There was also considerable financial damage as a result of cost underestimation, whereby contractors suffered the worst loss in profit (mean value = 3.88). Every expense comes with its own peculiar risk and uncertainty. Pressure on the cost of materials and every other expense attributed to the building and completion of a structure adds risks to the performance figures of a project. The greatest weight (mean importance score = 4.92) was attributed to issues like market inflation in building materials, while the second greatest weight (mean importance score = 4.76) was due to increased transportation charges. On the other hand, delays in payments arising from issues of the clients like poor availability of funds (RII=0.71) and contracting issues such as disagreements on the valuation of works done (RII=0.72) or other reasons were also found to lead to project delays and additional costs. The results affirm the importance of proper cost estimation on the health of organization finances and project risks and finishes within set time limits. As for the suggestions, it is proposed to progress on the methods of costing, engender better communications with the stakeholders, and manage the delays by way of contracting and financial control. This study enhances the existing literature on construction project management by suggesting ways to deal with adverse cost inaccuracies and availability of materials due to delays in payments which, if addressed, would greatly improve the economic performance of the construction business.

Keywords: cost estimation, construction project management, material price fluctuations, payment delays, financial impact

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Authors: Umesh Kumar Singh, Chanchala Joshi

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With increasing dependency on IT infrastructure, the main objective of a system administrator is to maintain a stable and secure network, with ensuring that the network is robust enough against malicious network users like attackers and intruders. Security risk management provides a way to manage the growing threats to infrastructures or system. This paper proposes a framework for risk level estimation which uses vulnerability database National Institute of Standards and Technology (NIST) National Vulnerability Database (NVD) and the Common Vulnerability Scoring System (CVSS). The proposed framework measures the frequency of vulnerability exploitation; converges this measured frequency with standard CVSS score and estimates the security risk level which helps in automated and reasonable security management. In this paper equation for the Temporal score calculation with respect to availability of remediation plan is derived and further, frequency of exploitation is calculated with determined temporal score. The frequency of exploitation along with CVSS score is used to calculate the security risk level of the system. The proposed framework uses the CVSS vectors for risk level estimation and measures the security level of specific network environment, which assists system administrator for assessment of security risks and making decision related to mitigation of security risks.

Keywords: CVSS score, risk level, security measurement, vulnerability category

Procedia PDF Downloads 321

Authors: Yi-Ling Chen, Dung-Ying Lin

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In the manufacturing process, computer numerical control (CNC) machining plays a crucial role. CNC enables precise machinery control through computer programs, achieving automation in the production process and significantly enhancing production efficiency. However, traditional CNC production lines often require manual intervention for loading and unloading operations, which limits the production line's operational efficiency and production capacity. Additionally, existing CNC automation systems frequently lack sufficient intelligence and fail to achieve optimal configuration efficiency, resulting in the need for substantial time to reconfigure production lines when producing different products, thereby impacting overall production efficiency. Using the NSGA-II algorithm, we generate production line layout configurations that consider field constraints and select robotic arm specifications from an arm list. This allows us to calculate loading and unloading times for each job order, perform demand allocation, and assign processing sequences. The NSGA-II algorithm is further employed to determine the optimal processing sequence, with the aim of minimizing demand completion time and maximizing average machine utilization. These objectives are used to evaluate the performance of each layout, ultimately determining the optimal layout configuration. By employing this method, it enhance the configuration efficiency of CNC production lines and establish an adaptive capability that allows the production line to respond promptly to changes in demand. This will minimize production losses caused by the need to reconfigure the layout, ensuring that the CNC production line can maintain optimal efficiency even when adjustments are required due to fluctuating demands.

Keywords: evolutionary algorithms, multi-objective optimization, pareto optimality, layout optimization, operations sequence

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Authors: Kizito Ugochukwu Nwajeri

Abstract:

This paper explores the application of hybrid block methods for solving boundary value problems (BVPs), which are prevalent in various fields such as science, engineering, and applied mathematics. Traditionally, numerical approaches such as finite difference and shooting methods, often encounter challenges related to stability and convergence, particularly in the context of complex and nonlinear BVPs. To address these challenges, we propose a hybrid block method that integrates features from both single-step and multi-step techniques. This method allows for the simultaneous computation of multiple solution points while maintaining high accuracy. Specifically, we employ a combination of polynomial interpolation and collocation strategies to derive a system of equations that captures the behavior of the solution across the entire domain. By directly incorporating boundary conditions into the formulation, we enhance the stability and convergence properties of the numerical solution. Furthermore, we introduce an adaptive step-size mechanism to optimize performance based on the local behavior of the solution. This adjustment allows the method to respond effectively to variations in solution behavior, improving both accuracy and computational efficiency. Numerical tests on a variety of boundary value problems demonstrate the effectiveness of the hybrid block methods. These tests showcase significant improvements in accuracy and computational efficiency compared to conventional methods, indicating that our approach is robust and versatile. The results suggest that this hybrid block method is suitable for a wide range of applications in real-world problems, offering a promising alternative to existing numerical techniques.

Keywords: hybrid block methods, boundary value problem, polynomial interpolation, adaptive step-size control, collocation methods

Procedia PDF Downloads 31