Search results for: integral transform

Authors: Patcharee Pripdeevech, Sarunpron Khruengsai

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Thirty fungi were tested for their degradation ability on low-density polyethylene (LDPE) plastic film. Biodegradation of all fungi was screened in mineral salt medium broth containing LDPE film as the sole carbon source for 30 days. Diaporthe italiana, Thyrostroma jaczewskii, Colletotrichum fructicola, and Stagonosporopsis citrulli were able to colonize and cover the surface of LDPE film in media. The degradation test result was compared to those obtained from Aspergillus niger. LDPE films cocultured with D. italiana, T. jaczewskii, C. fructicola, S. citrulli, A. niger, and control showed weight loss of 43.90%, 46.34%, 48.78%, 45.12%, 28.78%, and 10.85%, respectively. The tensile strength of degraded LDPE films cocultured with D. italiana, T. jaczewskii, C. fructicola, S. citrulli, A. niger, and control also reduced significantly by 1.56 MPa, 1.78 MPa, 0.43 MPa, 1.86 MPa, 3.34 MPa, and 9.98 MPa, respectively. Analysis of LDPE films by Fourier transform infrared spectroscopy and scanning electron microscopy confirmed the biodegradation by the presence of morphological changes such as cracks, scions, and holes on the surface of the film. These fungi have the ability to break down and consume the LDPE film, especially C. fructicola. These findings showed the potential of fungi in Thailand that play an important role in LDPE film degradation.

Keywords: plastic biodegradation, LDPE film, fungi, Fourier transform infrared, scanning electron microscopy

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Authors: Liaquat Ali, Khurshid Natasha

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Low resource countries are not usually equipped with the organizational tools to implement health care for chronic diseases, and thus, providing effective diabetes care in such countries is a challenging task. Diabetic Association of Bangladesh (BADAS in Bengali acronym) has created a stimulating example to meet this challenge. Starting its journey in 1956 with 39 patients in a small tin shed clinic BADAS, and its affiliated associations now operate 90 hospitals and health centres all over the country. Together, these facilities provide integrated health care to about 1.5 million registered diabetic patients which constitute about 20% of the estimated diabetic population in the country. BADAS has also become a pioneer in health manpower generation in Bangladesh. Along with its affiliates, it now runs 3 Medical Colleges (to generate graduate physicians), 2 Nursing Institutes, and 2 Postgraduate Institutes which conduct 25 postgraduate courses (under the University of Dhaka) in various basic, clinical and public health disciplines. BADAS gives great emphasis on research, which encompasses basic, clinical as well as public health areas. BADAS is an ideal example of public-private partnership in health as most of its infrastructure has been created through government support but it is almost self-reliant in managing its revenue budget which approached approximately 40 million US dollar during 2010. BADAS raises resources by providing high-quality services to the people, both diabetic and non-diabetic. At the same time, BADAS has developed a cross financing model, to support diabetic patients in general and poor diabetic patients (identified through a social welfare network) in particular, through redistribution of the resources. Along with financial sustainability BADAS ensure organizational sustainability through a process of decentralization, community ownership, and democratic management. Presently a large scale pilot project (named as a Health Care Development Project or HCDP) is under implementation under BADAS umbrella with an objective to transform the diabetes care model to a health care model in general. It is expected to create further evidence on providing sustainable (with social safety net) health care delivery for diabetes, and other chronic illnesses as an integral part of general health care delivery in a resource constrained setting.

Keywords: Bangladesh, self sustain, health care, constrain

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Authors: Min Wang, Sergey Utev

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

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

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Authors: Bhargav Baruah, Ali Shemsedin Reshad, Pankaj Tiwari

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This work presents a detailed study on the thermal degradation kinetics of co-pyrolysis of oil shale of Upper Assam, India with rubber seed shell, and lab-scale pyrolysis to investigate the influence of pyrolysis parameters on product yield and composition of products. The physicochemical characteristics of oil shale and rubber seed shell were studied by proximate analysis, elemental analysis, Fourier transform infrared spectroscopy and X-ray diffraction. The physicochemical study showed the mixture to be of low moisture, high ash, siliceous, sour with the presence of aliphatic, aromatic, and phenolic compounds. The thermal decomposition of the oil shale with rubber seed shell was studied using thermogravimetric analysis at heating rates of 5, 10, 20, 30, and 50 °C/min. The kinetic study of the oil shale pyrolysis process was performed on the thermogravimetric (TGA) data using three model-free isoconversional methods viz. Friedman, Flynn Wall Ozawa (FWO), and Kissinger Akahira Sunnose (KAS). The reaction mechanisms were determined using the Criado master plot. The understanding of the composition of Indian oil shale and rubber seed shell and pyrolysis process kinetics can help to establish the experimental parameters for the extraction of valuable products from the mixture. Response surface methodology (RSM) was employed usinf central composite design (CCD) model to setup the lab-scale experiment using TGA data, and optimization of process parameters viz. heating rate, temperature, and particle size. The samples were pre-dried at 115°C for 24 hours prior to pyrolysis. The pyrolysis temperatures were set from 450 to 650 °C, at heating rates of 2 to 20°C/min. The retention time was set between 2 to 8 hours. The optimum oil yield was observed at 5°C/min and 550°C with a retention time of 5 hours. The pyrolytic oil and gas obtained at optimum conditions were subjected to characterization using Fourier transform infrared spectroscopy (FT-IR) gas chromatography and mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR).

Keywords: Indian oil shale, rubber seed shell, co-pyrolysis, isoconversional methods, gas chromatography, nuclear magnetic resonance, Fourier transform infrared spectroscopy

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Authors: Jihad H. Asad

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An expression for the Green’s function (GF) of anisotropic face cantered cubic (IFCC) lattice is evaluated analytically and numerically for a single impurity problem. The density of states (DOS), phase shift and scattering cross section are expressed in terms of complete elliptic integrals of the first kind.

Keywords: lattice Green's function, elliptic integral, physics, cubic lattice

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

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Authors: Bamidele Samson Alobalorun, Ifedotun Roseline Idowu

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Biometrics technologies apply the human body parts for their unique and reliable identification based on physiological traits. The iris recognition system is a biometric–based method for identification. The human iris has some discriminating characteristics which provide efficiency to the method. In order to achieve this efficiency, there is a need for feature extraction of the distinct features from the human iris in order to generate accurate authentication of persons. In this study, an approach for an iris recognition system using 2D Gabor for feature extraction is applied to iris templates. The 2D Gabor filter formulated the patterns that were used for training and equally sent to the hamming distance matching technique for recognition. A comparison of results is presented using two iris image subjects of different matching indices of 1,2,3,4,5 filter based on the CASIA iris image database. By comparing the two subject results, the actual computational time of the developed models, which is measured in terms of training and average testing time in processing the hamming distance classifier, is found with best recognition accuracy of 96.11% after capturing the iris localization or segmentation using the Daughman’s Integro-differential, the normalization is confined to the Daugman’s rubber sheet model.

Keywords: Daugman rubber sheet, feature extraction, Hamming distance, iris recognition system, 2D Gabor wavelet transform

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Authors: Ankur Dixit, Hiroaki Wagatsuma

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The crack detection on a concrete bridge is an important and constant task in civil engineering. Chronically, humans are checking the bridge for inspection of cracks to maintain the quality and reliability of bridge. But this process is very long and costly. To overcome such limitations, we have used a drone with a digital camera, which took some images of bridge deck and these images are processed by morphological component analysis (MCA). MCA technique is a very strong application of sparse coding and it explores the possibility of separation of images. In this paper, MCA has been used to decompose the image into coarse and fine components with the effectiveness of two dictionaries namely anisotropic diffusion and wavelet transform. An anisotropic diffusion is an adaptive smoothing process used to adjust diffusion coefficient by finding gray level and gradient as features. These cracks in image are enhanced by subtracting the diffused coarse image into the original image and the results are treated by Sobel edge detector and binary filtering to exhibit the cracks in a fine way. Our results demonstrated that proposed MCA framework using anisotropic diffusion followed by Sobel operator and binary filtering may contribute to an automation of crack detection even in open field sever conditions such as bridge decks.

Keywords: anisotropic diffusion, coarse component, fine component, MCA, Sobel edge detector and wavelet transform

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Authors: Norhidayu Muhamad Zain

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Gelatin, a purified protein derived mostly from porcine and bovine sources, is used widely in food manufacturing, pharmaceutical, and cosmetic industries. However, the presence of any porcine-related products are strictly forbidden for Muslim and Jewish consumption. Therefore, analytical methods offering reliable results to differentiate the sources of gelatin are needed. The aim of this study was to differentiate the sources of gelatin (porcine and tilapia fish) using Fourier transform infrared- attenuated total reflection (FTIR-ATR) combined with two dimensional infrared (2DIR) correlation analysis. Porcine gelatin (PG) and tilapia fish gelatin (FG) samples were diluted in distilled water at concentrations ranged from 4-20% (w/v). The samples were then analysed using FTIR-ATR and 2DIR correlation software. The results showed a significant difference in the pattern map of synchronous spectra at the region of 1000 cm⁻¹ to 1100 cm⁻¹ between PG and FG samples. The auto peak at 1080 cm⁻¹ that attributed to C-O functional group was observed at high intensity in PG samples compared to FG samples. Meanwhile, two auto peaks (1080 cm⁻¹ and 1030 cm⁻¹) at lower intensity were identified in FG samples. In addition, using 2D correlation analysis, the original broad water OH bands in 1D IR spectra can be effectively differentiated into six auto peaks located at 3630, 3340, 3230, 3065, 2950 and 2885 cm⁻¹ for PG samples and five auto peaks at 3630, 3330, 3230, 3060 and 2940 cm⁻¹ for FG samples. Based on the rule proposed by Noda, the sequence of the spectral changes in PG samples is as following: NH₃⁺ amino acid > CH₂ and CH₃ aliphatic > OH stretch > carboxylic acid OH stretch > NH in secondary amide > NH in primary amide. In contrast, the sequence was totally in the opposite direction for FG samples and thus both samples provide different 2D correlation spectra ranged from 2800 cm-1 to 3700 cm⁻¹. This method may provide a rapid determination of gelatin source for application in food, pharmaceutical, and cosmetic products.

Keywords: 2 dimensional infrared (2DIR) correlation analysis, Fourier transform infrared- attenuated total reflection (FTIR-ATR), porcine gelatin, tilapia fish gelatin

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Authors: M. Ahmadi, M. Kafil, H. Ebrahimi

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Nowadays, induction motors have a significant role in industries. Condition monitoring (CM) of this equipment has gained a remarkable importance during recent years due to huge production losses, substantial imposed costs and increases in vulnerability, risk, and uncertainty levels. Motor current signature analysis (MCSA) is one of the most important techniques in CM. This method can be used for rotor broken bars detection. Signal processing methods such as Fast Fourier transformation (FFT), Wavelet transformation and Empirical Mode Decomposition (EMD) are used for analyzing MCSA output data. In this study, these signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors. Based on wavelet transformation method, an index for fault detection, CF, is introduced which is the variation of maximum to the mean of wavelet transformation coefficients. We find that, in the broken bar condition, the amount of CF factor is greater than the healthy condition. Based on EMD method, the energy of intrinsic mode functions (IMF) is calculated and finds that when motor bars become broken the energy of IMFs increases.

Keywords: broken bar, condition monitoring, diagnostics, empirical mode decomposition, fourier transform, wavelet transform

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Authors: Gurmail Singh

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Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

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Authors: Kaili C. Zhang

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Mental well-being is an integral part of university students’ overall well-being and has been a matter of increasing concern in the UK. This study addressed the impact of university experience on students by investigating the changes students experience in their beliefs, lifestyles, and well-being during their first year of study, as well as the factors contributing to such changes. Using a longitudinal two-wave mixed method design, this project identified importantfactors that contribute to or inhibit these changes. Implications for universities across the UK are discussed.

Keywords: subjective well-being, beliefs, lifestyles, university students

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Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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Authors: Christine F. Boos, Fernando M. Azevedo

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Electroencephalogram (EEG) is a record of the electrical activity of the brain that has many applications, such as monitoring alertness, coma and brain death; locating damaged areas of the brain after head injury, stroke and tumor; monitoring anesthesia depth; researching physiology and sleep disorders; researching epilepsy and localizing the seizure focus. Epilepsy is a chronic condition, or a group of diseases of high prevalence, still poorly explained by science and whose diagnosis is still predominantly clinical. The EEG recording is considered an important test for epilepsy investigation and its visual analysis is very often applied for clinical confirmation of epilepsy diagnosis. Moreover, this EEG analysis can also be used to help define the types of epileptic syndrome, determine epileptiform zone, assist in the planning of drug treatment and provide additional information about the feasibility of surgical intervention. In the context of diagnosis confirmation the analysis is made using long term EEG recordings with at least 24 hours long and acquired by a minimum of 24 electrodes in which the neurophysiologists perform a thorough visual evaluation of EEG screens in search of specific electrographic patterns called epileptiform discharges. Considering that the EEG screens usually display 10 seconds of the recording, the neurophysiologist has to evaluate 360 screens per hour of EEG or a minimum of 8,640 screens per long term EEG recording. Analyzing thousands of EEG screens in search patterns that have a maximum duration of 200 ms is a very time consuming, complex and exhaustive task. Because of this, over the years several studies have proposed automated methodologies that could facilitate the neurophysiologists’ task of identifying epileptiform discharges and a large number of methodologies used neural networks for the pattern classification. One of the differences between all of these methodologies is the type of input stimuli presented to the networks, i.e., how the EEG signal is introduced in the network. Five types of input stimuli have been commonly found in literature: raw EEG signal, morphological descriptors (i.e. parameters related to the signal’s morphology), Fast Fourier Transform (FFT) spectrum, Short-Time Fourier Transform (STFT) spectrograms and Wavelet Transform features. This study evaluates the application of these five types of input stimuli and compares the classification results of neural networks that were implemented using each of these inputs. The performance of using raw signal varied between 43 and 84% efficiency. The results of FFT spectrum and STFT spectrograms were quite similar with average efficiency being 73 and 77%, respectively. The efficiency of Wavelet Transform features varied between 57 and 81% while the descriptors presented efficiency values between 62 and 93%. After simulations we could observe that the best results were achieved when either morphological descriptors or Wavelet features were used as input stimuli.

Keywords: Artificial neural network, electroencephalogram signal, pattern recognition, signal processing

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Authors: Waleed A. El-Said, Dina M. Fouad, Mohamed H. Aly, Mohamed A. El-Gahami

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Green synthesis of nanomaterials has received increasing attention as an eco-friendly technology in materials science. Here, we have used two types of extractions from green tea leaf (i.e. total extraction and tannin extraction) as reducing agents for a rapid, simple and one step synthesis method of mesoporous silica nanoparticles (MSNPs)/iron oxide (Fe3O4) nanocomposite based on deposition of Fe3O4 onto MSNPs. MSNPs/Fe3O4 nanocomposite were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, energy dispersive X-ray, vibrating sample magnetometer, N2 adsorption, and high-resolution transmission electron microscopy. The average mesoporous silica particle diameter was found to be around 30 nm with high surface area (818 m2/gm). MSNPs/Fe3O4 nanocomposite was used for removing lindane pesticide (an environmental hazard material) from aqueous solutions. Fourier transform infrared, UV-vis, High-performance liquid chromatography and gas chromatography techniques were used to confirm the high ability of MSNPs/Fe3O4 nanocomposite for sensing and capture of lindane molecules with high sorption capacity (more than 89%) that could develop a new eco-friendly strategy for detection and removing of pesticide and as a promising material for water treatment application.

Keywords: green synthesis, mesoporous silica, magnetic iron oxide NPs, adsorption Lindane

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Authors: Atanta Gian

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It is inevitable that globalization has succeeded in transforming the world today. The influence of globalization has emerged in almost every aspect of life nowadays, especially in shaping the perception of the people. It can be seen on how easy for people are affected by the information surrounding them. Due to globalization, the flow of information has become more rapid along with the development of technology. People tend to believe in information that they actually get by themselves, if there is information where most of the people believe it is true, then this information could be categorized as factual and relevant. Therefore if people gain information on what is best for them in terms of daily consumption, then this information could transform their perspective, and it becomes a consideration in selecting their needs for daily consumption. By looking at this trend, the author sees that globalization could be used by Multi-National Corporation (MNC) to enhance the promotion of their products. This is applied by shaping the perspectives of the world regarding what is the best for them. Multi-National Corporation which has better technology in terms of the development of their external promotion could utilize this opportunity to affect people’s perspectives into what they want. In this paper, the author would like to elaborate how globalization is applied by MNC to shape people’s perspective regarding what is the best for them. The author would utilize a case study to analyze on how MNC could transform the perspectives of Asian people regarding the necessary of having a better quality drinking water, which in this case, MNC has shaped the perspective of Asian people in choosing their product by promoting the bottled water as the best choice for them. In the end of this paper, author would come to a conclusion that MNCs are able to shape the world’s perspective regarding the needs of their products which is supported by the globalization that is happening now.

Keywords: consumption, globalisation, influence, information technology, multi-national corporations

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Authors: Julius Ilawe Osayi, Peter Osifo

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Solid waste disposal, such as used tires is a global challenge as well as energy crisis due to rising energy demand amidst price uncertainty and depleting fossil fuel reserves. Therefore, the effectiveness of pyrolysis as a disposal method that can transform used tires into liquid fuel and other end-products has made the process attractive to researchers. Although used tires have been converted to liquid fuel using pyrolysis, there is the need to improve on the liquid fuel properties. Hence, this paper reports the investigation of zeolite NaY synthesized from kaolin, a locally abundant soil material in the Benin metropolis as a suitable catalyst and its effect on the properties of pyrolytic oil produced from used tires. The pyrolysis process was conducted for a range of 1 to 10 wt.% of catalyst concentration to used tire at a temperature of 600 oC, a heating rate of 15oC/min and particle size of 6mm. Although no significant increase in pyrolytic oil yield was observed compared to the previously investigated non-catalytic pyrolysis of a used tire. However, the Fourier transform infrared (FTIR), Nuclear Magnetic Resonance (NMR); and Gas chromatography-mass spectrometry (GC-MS) characterization results revealed the pyrolytic oil to possess an improved physicochemical and fuel properties alongside valuable industrial chemical species. This confirms the possibility of transforming kaolin into a catalyst suitable for improved fuel properties of the liquid fraction obtainable from thermal cracking of hydrocarbon materials.

Keywords: catalytic pyrolysis, fossil fuel, kaolin, pyrolytic oil, used tyres, Zeolite NaY

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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Interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of liver, spleen and kidneys is regarded as a major primary step in the computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for the abdominal organ segmentation data using mathematical morphology. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. Our algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.

Keywords: anisotropic diffusion filter, CT images, morphological filter, mosaic image, simultaneous organ segmentation, the watershed algorithm

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Authors: Hare Krishna Nigam

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In this paper, for the first time, (E,q)(C,2) product summability method is introduced and two quite new results on degree of approximation of the function f belonging to Lip (alpha,r)class and W(L(r), xi(t)) class by (E,q)(C,2) product means of Fourier series, has been obtained.

Keywords: Degree of approximation, (E, q)(C, 2) means, Fourier series, Lebesgue integral, Lip (alpha, r)class, W(L(r), xi(t))class of functions

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Authors: Roger-Claude Liwanga

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This article reflects on the normative aspects of the jurisprudence on the protection of human rights in times of war that the International Court of Justice (ICJ) developed in 2005 in the Case Concerning Armed Activities on the Territory of the Congo (Democratic Republic of Congo v. Uganda). The article focuses on theories raised in connection with the Democratic Republic of Congo (DRC)'s claim of the violation of human rights of its populations by Uganda as opposed to the violation of its territorial integrity claims. The article begins with a re-visitation of the doctrine of state extraterritorial responsibility for violations of human rights by suggesting that a state's accountability for the breach of its international obligations is not territorially confined but rather transcends the State's national borders. The article highlights the criteria of assessing the State's extraterritorial responsibility, including the circumstances: (1) where the concerned State has effective control over the territory of another State in the context of belligerent occupation, and (2) when the unlawful actions committed by the State's organs on the occupied territory can be attributable to that State. The article also analyzes the ICJ's opinions articulated in DRC v. Uganda with reference to the relationship between human rights law and humanitarian law, and it contends that the ICJ had revised the traditional interaction between these two bodies of law to the extent that human rights law can no longer be excluded from applying in times of war as both branches are complementary rather than exclusive. The article correspondingly looks at the issue of reparations for victims of human rights violations. It posits that reparations for victims of human rights violations should be integral (including restitution, compensation, rehabilitation, satisfaction, and guarantees of non-repetition). Yet, the article concludes by emphasizing that reparations for victims were not integral in DRC v. Uganda because: (1) the ICJ failed to set a reasonable timeframe for the negotiations between the DRC and Uganda on the amount of compensation, resulting in Uganda paying no financial reparation to the DRC since 2005; and (2) the ICJ did not request Uganda to domestically prosecute the perpetrators of human rights abuses.

Keywords: human rights law, humanitarian law, civilian protection, extraterritorial responsibility

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Authors: Lingerew Nebere Kassie, Ping-Yu Chang, Hsin-Hua Huang, , Chaw-Son Chen

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Transient electromagnetic (TEM) data plays a crucial role in hydrogeological and environmental applications, providing valuable insights into geological structures and resistivity variations. However, the presence of noise often hinders the interpretation and reliability of these data. Our study addresses this issue by utilizing a FASTSNAP system for the TEM survey, which operates at different modes (low, medium, and high) with continuous adjustments to discretization, gain, and current. We employ a denoising approach that processes the raw data obtained from each acquisition mode to improve signal quality and enhance data reliability. We use a signal-averaging technique for each mode, increasing the signal-to-noise ratio. Additionally, we utilize wavelet transform to suppress noise further while preserving the integrity of the underlying signals. This approach significantly improves the data quality, notably suppressing severe noise at late times. The resulting denoised data exhibits a substantially improved signal-to-noise ratio, leading to increased accuracy in parameter estimation. By effectively denoising TEM data, our study contributes to a more reliable interpretation and analysis of underground structures. Moreover, the proposed denoising approach can be seamlessly integrated into existing ground-based TEM data processing workflows, facilitating the extraction of meaningful information from noisy measurements and enhancing the overall quality and reliability of the acquired data.

Keywords: data quality, signal averaging, transient electromagnetic, wavelet transform

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Authors: Alla Levina, Sergey Taranov

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The classical approach to the providing noise immunity and integrity of information that process in computing devices and communication channels is to use linear codes. Linear codes have fast and efficient algorithms of encoding and decoding information, but this codes concentrate their detect and correct abilities in certain error configurations. To protect against any configuration of errors at predetermined probability can robust codes. This is accomplished by the use of perfect nonlinear and almost perfect nonlinear functions to calculate the code redundancy. The paper presents the error-correcting coding scheme using biorthogonal wavelet transform. Wavelet transform applied in various fields of science. Some of the wavelet applications are cleaning of signal from noise, data compression, spectral analysis of the signal components. The article suggests methods for constructing linear codes based on wavelet decomposition. For developed constructions we build generator and check matrix that contain the scaling function coefficients of wavelet. Based on linear wavelet codes we develop robust codes that provide uniform protection against all errors. In article we propose two constructions of robust code. The first class of robust code is based on multiplicative inverse in finite field. In the second robust code construction the redundancy part is a cube of information part. Also, this paper investigates the characteristics of proposed robust and linear codes.

Keywords: robust code, linear code, wavelet decomposition, scaling function, error masking probability

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Authors: Atena Tazikeh Lemeski, Ismail Ozdamar

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This study presents a detailed analysis of harmonic distortion in a grid-connected photovoltaic (PV) system using real-time data captured from a solar power plant. Harmonics introduced by inverters in PV systems can degrade power quality and lead to increased Total Harmonic Distortion (THD), which poses challenges such as transformer overheating, increased power losses, and potential grid instability. This research addresses these issues by applying Fast Fourier Transform (FFT) to identify significant harmonic components and employing notch filters to target specific frequencies, particularly the 3rd harmonic (150 Hz), which was identified as the largest contributor to THD. Initial analysis of the unfiltered voltage signal revealed a THD of 21.15%, with prominent harmonic peaks at 150 Hz, 250 Hz and 350 Hz, corresponding to the 3rd, 5th, and 7th harmonics, respectively. After implementing the notch filters, the THD was reduced to 5.72%, demonstrating the effectiveness of this approach in mitigating harmonic distortion without affecting the fundamental frequency. This paper provides practical insights into the application of real-time filtering techniques in PV systems and their role in improving overall grid stability and power quality. The results indicate that targeted harmonic mitigation is crucial for the sustainable integration of renewable energy sources into modern electrical grids.

Keywords: grid-connected photovoltaic systems, fast Fourier transform, harmonic filtering, inverter-induced harmonics

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Authors: Iftikhar Ahmad, A. Benallegue, A. El Hadri

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In this paper, we have proposed an observer for the reconstruction and a control law for the rejection application of unknown bounded external disturbance in a dynamical system. The strategy of both the observer and the controller is designed like a second order sliding mode with a proportional-integral (PI) term. Lyapunov theory is used to prove the exponential convergence and stability. Simulations results are given to show the performance of this method.

Keywords: non-linear systems, sliding mode observer, disturbance rejection, nonlinear control

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Authors: Ju Shan Lin

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The challenges for traditional industries in Taiwan the past few years are the changes of overall domestic and foreign industry structure, the entrepreneurs not only need to keep on improving their profession skills but also continuously research and develop new products. It is also necessary for the all traditional industries to keep updating the business strategy, let the enterprises continue to progress, and won't be easily replaced by the other industries. The traditional industry in Taiwan attach great importance to the field of enterprises upgrading and innovation in recent years, by the enterprise innovation and transformation can enhance the overall business situation also enable them to obtain more additional profits than in the past. Except the original industry structure's need to transform and upgrade, the brand's business and marketing strategy are also essential. This study will take a certain plastic company as case analysis, for the brand promotion of traditional industries, brand values and business innovation model for further exploration. It will also be mentioned that the other traditional industries cases which were already achieved success on the enterprise's upgrading and innovation, at the same time, the difficulties which they faced with and the way they overcome will be explored as well. This study will use the case study method combined with expert interviews to discuss and analyze this certain plastic company's current business situation, the existing products and the possible trends in the future. Looking forward to providing an innovative business model that will enable this plastic company to upgrade its corporate image and the brand could transform successfully.

Keywords: brand marketing strategy, enterprise upgrade, industrial transformation, traditional industry

Procedia PDF Downloads 239

Authors: Asep Sukohar, Ramadhan Triyandi, Muhammad Iqbal, Sahidin, Suharyani

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Introduction: Resveratrol is a class of bioactive chemicals found in melinjo, which has a wide range of biological actions. The purpose of this study is to determine the linarin content of the melinjo fraksi by using preparative-high-performance liquid chromatography (prep-HPLC). Method: Extraction used the soxhletation method with 96% ethanol solvent. Fractionation used ethyl acetate and ethanol in a ratio of 1:1. Tracing of linarin compound used prep-HPLC with a mobile phase ratio of distilled water: methanol (55: 45, v/v). The presence of linarin was detected using a wavelength of 215 nm. Fourier Transform Infrared (FTIR) was used to identify the functional groups of compound. Result: The retention time required to elute the ethyl acetate fraction was 2.601 minutes. Compound separation identification using Fourier Transform Infrared Spectroscopy - Quest Attenuated Total Reflectance (FTIR - QATR) has a similarity value range with standards from 0 to 1000. The elution results of the ethyl acetate fraction have similar values with the standard compounds linarin (668), resveratrol (578), and catechin (455). Conclusion: Tracing for active compound in the ethyl acetate fraction of Gnetum Gnemon L. using prep-HPLC showed a strong suspicion of the presence of linarin compound.

Keywords: Gnetum gnemon L., linarin, prep-HPLC, fraction ethyl acetate

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Authors: W. Ketren, J. Wannapeera, Z. Heishun, A. Ryuichi, K. Toshiteru, M. Kouichi, O. Hideaki

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Degradative solvent extraction is the method developed for biomass upgrading by dewatering and fractionation of biomass under the mild condition. However, the conversion mechanism of the degradative solvent extraction method has not been fully understood so far. The rice straw was treated in 1-methylnaphthalene (1-MN) at a different solvent-treatment temperature varied from 250 to 350 ^oC with the residence time for 60 min. The liquid membrane-Fourier Transform Infrared Spectroscopy (FTIR) technique is applied to study the processing mechanism in-depth without separation of the solvent. It has been found that the strength of the oxygen-hydrogen stretching  (3600-3100 cm^-1) decreased slightly with increasing temperature in the range of 300-350 ^oC. The decrease of the hydroxyl group in the solvent soluble suggested dehydration reaction taking place between 300 and 350 ^oC. FTIR spectra in the carbonyl stretching region (1800-1600 cm^-1) revealed the presence of esters groups, carboxylic acid and ketonic groups in the solvent-soluble of biomass. The carboxylic acid increased in the range of 200 to 250 ^oC and then decreased. The prevailing of aromatic groups showed that the aromatization took place during extraction at above 250 ^oC. From 300 to 350 ^oC, the carbonyl functional groups in the solvent-soluble noticeably decreased. The removal of the carboxylic acid and the decrease of esters into the form of carbon dioxide indicated that the decarboxylation reaction occurred during the extraction process.

Keywords: biomass waste, degradative solvent extraction, mechanism, upgrading

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Authors: Hamidreza Akbari

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In this paper, for detection of inclined eccentricity in an induction motor, time–frequency analysis of the stator startup current is carried out. For this purpose, the discrete wavelet transform is used. Data are obtained from simulations, using winding function approach. The results show the validity of the approach for detecting the fault and discriminating with respect to other faults.

Keywords: induction machine, fault, DWT, electric

Procedia PDF Downloads 350

Authors: Arth J. Shah, Madhu R. Kamble

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Voice biometric liveness detection is customized to certify an authentication process of the voice data presented is genuine and not a recording or synthetic voice. With the rise of deepfakes and other equivalently sophisticated spoofing generation techniques, it’s becoming challenging to ensure that the person on the other end is a live speaker or not. Voice Liveness Detection (VLD) system is a group of security measures which detect and prevent voice spoofing attacks. Motivated by the recent development of the Kolmogorov-Arnold Network (KAN) based on the Kolmogorov-Arnold theorem, we proposed KAN for the VLD task. To date, multilayer perceptron (MLP) based classifiers have been used for the classification tasks. We aim to capture not only the compositional structure of the model but also to optimize the values of univariate functions. This study explains the mathematical as well as experimental analysis of KAN for VLD tasks, thereby opening a new perspective for scientists to work on speech and signal processing-based tasks. This study emerges as a combination of traditional signal processing tasks and new deep learning models, which further proved to be a better combination for VLD tasks. The experiments are performed on the POCO and ASVSpoof 2017 V2 database. We used Constant Q-transform, Mel, and short-time Fourier transform (STFT) based front-end features and used CNN, BiLSTM, and KAN as back-end classifiers. The best accuracy is 91.26 % on the POCO database using STFT features with the KAN classifier. In the ASVSpoof 2017 V2 database, the lowest EER we obtained was 26.42 %, using CQT features and KAN as a classifier.

Keywords: Kolmogorov Arnold networks, multilayer perceptron, pop noise, voice liveness detection

Procedia PDF Downloads 39

Authors: Hossein Navid, Maryam Adeli Khadem, Shahin Oustan, Mahmoud Zareie

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Among the nutrients needed by the plants, three elements containing nitrate, phosphorus and potassium are more important. The objective of this research was measuring these nutrient amounts in soil using Fourier transform infrared spectroscopy in range of 400- 4000 cm-1. Soil samples for different soil types (sandy, clay and loam) were collected from different areas of East Azerbaijan. Three types of fertilizers in conventional farming (urea, triple superphosphate, potassium sulphate) were used for soil treatment. Each specimen was divided into two categories: The first group was used in the laboratory (direct measurement) to extract nitrate, phosphorus and potassium uptake by colorimetric method of Olsen and ammonium acetate. The second group was used to measure drug absorption spectrometry. In spectrometry, the small amount of soil samples mixed with KBr and was taken in a small pill form. For the tests, the pills were put in the center of infrared spectrometer and graphs were obtained. Analysis of data was done using MINITAB and PLSR software. The data obtained from spectrometry method were compared with amount of soil nutrients obtained from direct drug absorption using EXCEL software. There were good fitting between these two data series. For nitrate, phosphorus and potassium R2 was 79.5%, 92.0% and 81.9%, respectively. Also, results showed that the range of MIR (mid-infrared) is appropriate for determine the amount of soil nitrate and potassium and can be used in future research to obtain detailed maps of land in agricultural use.

Keywords: nitrate, phosphorus, potassium, soil nutrients, spectroscopy

Procedia PDF Downloads 403