Search results for: processing schemes

Authors: Hamid Sharif, Nazish Saleem Abbas, Muhammad Haris Jamil

Abstract:

An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. Because of the complexity of the optimization algorithms, most adaptive filters are digital filters. Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including adaptive noise cancellation and echo cancellation. Acoustic echo cancellation is a common occurrence in today’s telecommunication systems. The signal interference caused by acoustic echo is distracting to both users and causes a reduction in the quality of the communication. In this paper, we review different techniques of adaptive filtering to reduce this unwanted echo. In this paper, we see the behavior of techniques and algorithms of adaptive filtering like Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Variable Step-Size Least Mean Square (VSLMS), Variable Step-Size Normalized Least Mean Square (VSNLMS), New Varying Step Size LMS Algorithm (NVSSLMS) and Recursive Least Square (RLS) algorithms to reduce this unwanted echo, to increase communication quality.

Keywords: adaptive acoustic, echo cancellation, LMS algorithm, adaptive filter, normalized least mean square (NLMS), variable step-size least mean square (VSLMS)

Procedia PDF Downloads 80

Authors: Chigozirim Onwusiribe, Jude Mbanasor

Abstract:

Egusi Melon (Citrullus Lanatus Thunb. Mansf ) is a very important oil seed that serves a major ingredient in the diet of most of the households in Nigeria. Egusi Melon is very nutritious and very important in meeting the food security needs of Nigerians. Egusi Melon is cultivated in most farm enterprise in South East Nigeria but the profitability of its value chain needs to be investigated. This study analyzed the profitability of the Egusi Melon value chain. Specifically this study developed a value chain map for Egusi Melon, analysed the profitability of each stage of the Egusi Melon Value chain and analysed the determinants of the profitability of the Egusi Melon at each stage of the value chain. Multi stage sampling technique was used to select 125 farm enterprises with similar capacity and characteristics. Questionnaire and interview were used to elicit the required data while descriptive statistics, Food and Agriculture Organization Value Chain Analysis Tool, profitability ratios and multiple regression analysis were used for the data analysis. One of the findings showed that the stages of the Egusi Melon value chain are very profitable. Based on the findings, we recommend the provision of grants by government and donor agencies to the farm enterprises through their cooperative societies, this will provide the necessary funds for the local fabrication of value addition and processing equipment to suit their unique value addition needs not met by the imported equipment.

Keywords: value, chain, melon, farm, enterprises

Procedia PDF Downloads 134

Authors: Zohra Mekranfar, Ahmed Saidi, Abdellah Mebrek

Abstract:

This work aims to develop multi-criteria decision making (MCDM) and spatial data warehouse (SDW) methods, which will be integrated into a GIS according to a ‘GIS dominant’ approach. The GIS operating tools will be operational to operate the SDW. The MCDM methods can provide many solutions to a set of problems with various and multiple criteria. When the problem is so complex, integrating spatial dimension, it makes sense to combine the MCDM process with other approaches like data mining, ascending analyses, we present in this paper an experiment showing a geo-decisional methodology of SWD construction, On-line analytical processing (OLAP) technology which combines both basic multidimensional analysis and the concepts of data mining provides powerful tools to highlight inductions and information not obvious by traditional tools. However, these OLAP tools become more complex in the presence of the spatial dimension. The integration of OLAP with a GIS is the future geographic and spatial information solution. GIS offers advanced functions for the acquisition, storage, analysis, and display of geographic information. However, their effectiveness for complex spatial analysis is questionable due to their determinism and their decisional rigor. A prerequisite for the implementation of any analysis or exploration of spatial data requires the construction and structuring of a spatial data warehouse (SDW). This SDW must be easily usable by the GIS and by the tools offered by an OLAP system.

Keywords: data warehouse, GIS, MCDM, SOLAP

Procedia PDF Downloads 177

Authors: Hussein Alahmer, Amr Ahmed

Abstract:

Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

Procedia PDF Downloads 325

Authors: Siddharth Sarma, Ayush Majumdar, Nidhi Sabu, Mufaddal Jiruwaala, Shilpa Paygude

Abstract:

Digital Elevation Models (DEMs) are digital representations of the Earth’s topography, which include information about the elevation, slope, aspect, and other terrain attributes. DEMs play a crucial role in various applications, including terrain analysis, urban planning, and environmental modeling. In this paper, TerraEnhance is proposed, a distinct approach for high-resolution DEM generation using Generative Adversarial Networks (GANs) combined with Real-ESRGANs. By learning from a dataset of low-resolution DEMs, the GANs are trained to upscale the data by 10 times, resulting in significantly enhanced DEMs with improved resolution and finer details. The integration of Real-ESRGANs further enhances visual quality, leading to more accurate representations of the terrain. A post-processing layer is introduced, employing high-pass filtering to refine the generated DEMs, preserving important details while reducing noise and artifacts. The results demonstrate that TerraEnhance outperforms existing methods, producing high-fidelity DEMs with intricate terrain features and exceptional accuracy. These advancements make TerraEnhance suitable for various applications, such as terrain analysis and precise environmental modeling.

Keywords: DEM, ESRGAN, image upscaling, super resolution, computer vision

Procedia PDF Downloads 8

Authors: Wenhao Wang, Xinzhi Xu, Limin Feng, Wei Cong

Abstract:

This paper comprehensively investigates current development status of global power grids and fossil energy pipelines (oil and natural gas), proposes a standard visual platform of global power and fossil energy based on Geographic Information System (GIS) technology. In this visual platform, a series of systematic visual models is proposed with global spatial data, systematic energy and power parameters. Under this visual platform, the current Global Power Grids Map and Global Fossil Energy Pipelines Map are plotted within more than 140 countries and regions across the world. Using the multi-scale fusion data processing and modeling methods, the world’s global fossil energy pipelines and power grids information system basic database is established, which provides important data supporting global fossil energy and electricity research. Finally, through the systematic and comparative study of global fossil energy pipelines and global power grids, the general status of global fossil energy and electricity development are reviewed, and energy transition in key areas are evaluated and analyzed. Through the comparison analysis of fossil energy and clean energy, the direction of relevant research is pointed out for clean development and energy transition.

Keywords: energy transition, geographic information system, fossil energy, power systems

Procedia PDF Downloads 150

Authors: Mehdi Montakhabrazlighi, Ercan Balikci

Abstract:

The neural network (NN) method is applied to alloy development of single crystal Ni-base Superalloys with low density and improved mechanical strength. A set of 1200 dataset which includes chemical composition of the alloys, applied stress and temperature as inputs and density and time to rupture as outputs is used for training and testing the network. Thermodynamic phase diagram modeling of the screened alloys is performed with Thermocalc software to model the equilibrium phases and also microsegregation in solidification processing. The model is first trained by 80% of the data and the 20% rest is used to test it. Comparing the predicted values and the experimental ones showed that a well-trained network is capable of accurately predicting the density and time to rupture strength of the Ni-base superalloys. Modeling results is used to determine the effect of alloying elements, stress, temperature and gamma-prime phase volume fraction on rupture strength of the Ni-base superalloys. This approach is in line with the materials genome initiative and integrated computed materials engineering approaches promoted recently with the aim of reducing the cost and time for development of new alloys for critical aerospace components. This work has been funded by TUBITAK under grant number 112M783.

Keywords: neural network, rupture strength, superalloy, thermocalc

Procedia PDF Downloads 313

Authors: Yina A. Cifuentes Triana, Andrés M. Pinzón Velásco, Marío E. Velásquez Lozano

Abstract:

In this work the sequencing and genome characterization of a natural isolate of Saccharomyces cerevisiae yeast (strain 202-3), identified with potential for the production of second generation ethanol from sugarcane bagasse hydrolysates is presented. This strain was selected because its capability to consume xylose during the fermentation of sugarcane bagasse hydrolysates, taking into account that many strains of S. cerevisiae are incapable of processing this sugar. This advantage and other prominent positive aspects during fermentation profiles evaluated in bagasse hydrolysates made the strain 202-3 a candidate strain to improve the production of second-generation ethanol, which was proposed as a first step to study the strain at the genomic level. The molecular characterization was carried out by genome sequencing with the Illumina HiSeq 2000 platform paired end; the assembly was performed with different programs, finally choosing the assembler ABYSS with kmer 89. Gene prediction was developed with the approach of hidden Markov models with Augustus. The genes identified were scored based on similarity with public databases of nucleotide and protein. Records were organized from ontological functions at different hierarchical levels, which identified central metabolic functions and roles of the S. cerevisiae strain 202-3, highlighting the presence of four possible new proteins, two of them probably associated with the positive consumption of xylose.

Keywords: cellulosic ethanol, Saccharomyces cerevisiae, genome sequencing, xylose consumption

Procedia PDF Downloads 320

Authors: Ha-Guk Jeong, Jong-Beom Lee

Abstract:

Cladding process is very typical technology for manufacturing composite materials by the hydrostatic extrusion. Because there is no friction between the metal and the container, it can be easily obtained in uniform flow during the deformation. The general manufacturing process for a metal-matrix composite in the solid state, mixing metal powders and ceramic powders with a suited volume ratio, prior to be compressed or extruded at the cold or hot condition in a can. Since through a plurality of unit processing steps of dispersing the materials having a large difference in their characteristics and physical mixing, the process is complicated and leads to non-uniform dispersion of ceramics. It is difficult and hard to reach a uniform ideal property in the coherence problems at the interface between the metal and the ceramic reinforcements. Metal hybrid composites, which presented in this report, are manufactured through the traditional plastic deformation processes like hydrostatic extrusion, caliber-rolling, and drawing. By the previous process, the realization of uniform macro and microstructure is surely possible. In this study, as a constituent material, aluminum, copper, and titanium have been used, according to the component ratio, excellent characteristics of each material were possible to produce a metal hybrid composite that appears to maximize. MgB₂ superconductor wire also fabricated via the same process. It will be introduced to their unique artistic and thermal characteristics.

Keywords: cladding process, metal-hybrid composites, hydrostatic extrusion, electronic/thermal characteristics

Procedia PDF Downloads 179

Authors: Ahmed Al-Adaileh

Abstract:

Renewable energy sources, domestic storage systems, controllable loads and machine learning technologies will be key components of future smart homes management systems. An energy management scheme that uses a Deep Learning (DL) approach to support the smart home management systems, which consist of a standalone photovoltaic system, storage unit, heating ventilation air-conditioning system and a set of conventional and smart appliances, is presented. The objective of the proposed scheme is to apply DL-based machine learning to predict various running parameters within a smart home's environment to achieve maximum comfort levels for occupants, reduced electricity bills, and less dependency on the public grid. The problem is using Reinforcement learning, where decisions are taken based on applying the Continuous-time Markov Decision Process. The main contribution of this research is the proposed framework that applies DL to enhance the system's supervised dataset to offer unlimited chances to effectively support smart home systems. A case study involving a set of conventional and smart appliances with dedicated processing units in an inhabited building can demonstrate the validity of the proposed framework. A visualization graph can show "before" and "after" results.

Keywords: smart homes systems, machine learning, deep learning, Markov Decision Process

Procedia PDF Downloads 201

Authors: Zhengkun Yi

Abstract:

Surface texture discrimination using artificial tactile sensors has attracted increasing attentions in the past decade as it can endow technical and robot systems with a key missing ability. However, as a major component of texture, roughness has rarely been explored. This paper presents an approach for tactile surface roughness discrimination, which includes two parts: (1) design and fabrication of a bioinspired artificial fingertip, and (2) tactile signal processing for tactile surface roughness discrimination. The bioinspired fingertip is comprised of two polydimethylsiloxane (PDMS) layers, a polymethyl methacrylate (PMMA) bar, and two perpendicular polyvinylidene difluoride (PVDF) film sensors. This artificial fingertip mimics human fingertips in three aspects: (1) Elastic properties of epidermis and dermis in human skin are replicated by the two PDMS layers with different stiffness, (2) The PMMA bar serves the role analogous to that of a bone, and (3) PVDF film sensors emulate Meissner’s corpuscles in terms of both location and response to the vibratory stimuli. Various extracted features and classification algorithms including support vector machines (SVM) and k-nearest neighbors (kNN) are examined for tactile surface roughness discrimination. Eight standard rough surfaces with roughness values (Ra) of 50 μm, 25 μm, 12.5 μm, 6.3 μm 3.2 μm, 1.6 μm, 0.8 μm, and 0.4 μm are explored. The highest classification accuracy of (82.6 ± 10.8) % can be achieved using solely one PVDF film sensor with kNN (k = 9) classifier and the standard deviation feature.

Keywords: bioinspired fingertip, classifier, feature extraction, roughness discrimination

Procedia PDF Downloads 312

Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

Abstract:

Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

Procedia PDF Downloads 149

Authors: Bryan Christian Baybay, M. Francesca Ronario

Abstract:

This study is intended to answer the question “What are the communication styles of selected Filipino gays in breaking their silence on their sexual orientation to their parents?” In this regard, six cases of Filipino gay disclosures were examined through in-depth interviews. The participants were selected through purposive sampling and snowball technique. The theories, Rhetorical Sensitivity of Roderick Hart and Communicator Style of Robert Norton were used to analyze the gathered data and to give support to the communication attitudes, message processing, message rendering and communication styles exhibited in each disclosure. As secondary data and validation, parents and experts in the field of communication, sociology, and psychology were also interviewed and consulted. The study found that Filipino gays vary in the communication styles they use during the disclosure with their parents. All communication styles: impression-leaving, contentious, open, dramatic, dominant, precise, relaxed, friendly, animated, and communicator image were observed by the gays depending on their motivation, relationship and thoughts contemplated. These results lend ideas for future researchers to look into the communication patterns and/or styles of lesbians, bisexuals, transgenders and queers or expand researches on the same subject and the utilization of Social Judgment and Relational Dialectics theories in determining and analyzing LGBTQ communication.

Keywords: communication attitudes, communication styles, Filipino gays, self-disclosure, sexual orientation

Procedia PDF Downloads 523

Authors: M. Crozet, D. Roudil, T. Branger, S. Boden, P. Peerani, B. Russell, M. Herranz, L. Aldave de la Heras

Abstract:

The EURATOM work program project INSIDER (Improved Nuclear Site Characterization for Waste minimization in Decommissioning under Constrained Environment) was launched in June 2017. This 4-year project has 18 partners and aims at improving the management of contaminated materials arising from decommissioning and dismantling (D&D) operations by proposing an integrated methodology of characterization. This methodology is based on advanced statistical processing and modelling, coupled with adapted and innovative analytical and measurement methods, with respect to sustainability and economic objectives. In order to achieve these objectives, the approaches will be then applied to common case studies in the form of Inter-laboratory comparisons on matrix representative reference samples and benchmarking. Work Package 6 (WP6) ‘Performance analysis and overall uncertainty’ is in charge of the analysis of the benchmarking on real samples, the organisation of inter-laboratory comparison on synthetic certified reference materials and the establishment of overall uncertainty budget. Assessment of the outcome will be used for providing recommendations and guidance resulting in pre-standardization tests.

Keywords: decommissioning, sampling strategy, research and development, characterization, European project

Procedia PDF Downloads 364

Authors: Angela Maria Fasnacht

Abstract:

Detection of anomalies due to contaminants’ presence, also known as early detection systems in water treatment plants, has become a critical point that deserves an in-depth study for their improvement and adaptation to current requirements. The design of these systems requires a detailed analysis and processing of the data in real-time, so it is necessary to apply various statistical methods appropriate to the data generated, such as Spearman’s Correlation, Factor Analysis, Cross-Correlation, and k-fold Cross-validation. Statistical analysis and methods allow the evaluation of large data sets to model the behavior of variables; in this sense, statistical treatment or analysis could be considered a vital step to be able to develop advanced models focused on machine learning that allows optimized data management in real-time, applied to early detection systems in water treatment processes. These techniques facilitate the development of new technologies used in advanced sensors. In this work, these methods were applied to identify the possible correlations between the measured parameters and the presence of the glyphosate contaminant in the single-pass system. The interaction between the initial concentration of glyphosate and the location of the sensors on the reading of the reported parameters was studied.

Keywords: glyphosate, emergent contaminants, machine learning, probes, sensors, predictive

Procedia PDF Downloads 121

Authors: Ebenezer Joseph, Veena Easvaradoss, S. Sundar Manoharan, David Chandran, Sumathi Chandrasekaran, T. R. Uma

Abstract:

Perceptual reasoning is the ability that incorporates fluid reasoning, spatial processing, and visual motor integration. Several theories of cognitive functioning emphasize the importance of fluid reasoning. The ability to manipulate abstractions and rules and to generalize is required for reasoning tasks. This study, funded by the Cognitive Science Research Initiative, Department of Science and Technology, Government of India, analyzed the effect of 1-year chess training on the perceptual reasoning of children. A pretest–posttest with control group design was used, with 43 (28 boys, 15 girls) children in the experimental group and 42 (26 boys, 16 girls) children in the control group. The sample was selected from children studying in two private schools from South India (grades 3 to 9), which included both the genders. The experimental group underwent weekly 1-hour chess training for 1 year. Perceptual reasoning was measured by three subtests of WISC-IV INDIA. Pre-equivalence of means was established. Further statistical analyses revealed that the experimental group had shown statistically significant improvement in perceptual reasoning compared to the control group. The present study clearly establishes a correlation between chess learning and perceptual reasoning. If perceptual reasoning can be enhanced in children, it could possibly result in the improvement of executive functions as well as the scholastic performance of the child.

Keywords: chess, cognition, intelligence, perceptual reasoning

Procedia PDF Downloads 356

Authors: Alaa Al Hawari, Mohammad Khader, Wael El Hasan, Mahmoud Alijla, Ammar Manawi, Abdelbaki Benamour

Abstract:

The production of aluminium alloys and ingots -starting from the processing of alumina to aluminium, and the final cast product- was studied using a Life Cycle Assessment (LCA) approach. The studied aluminium supply chain consisted of a carbon plant, a reduction plant, a casting plant, and a power plant. In the LCA model, the environmental loads of the different plants for the production of 1 ton of aluminium metal were investigated. The impact of the aluminium production was assessed in eight impact categories. The results showed that for all of the impact categories the power plant had the highest impact only in the cases of Human Toxicity Potential (HTP) the reduction plant had the highest impact and in the Marine Aquatic Eco-Toxicity Potential (MAETP) the carbon plant had the highest impact. Furthermore, the impact of the carbon plant and the reduction plant combined was almost the same as the impact of the power plant in the case of the Acidification Potential (AP). The carbon plant had a positive impact on the environment when it comes to the Eutrophication Potential (EP) due to the production of clean water in the process. The natural gas based power plant used in the case study had 8.4 times less negative impact on the environment when compared to the heavy fuel based power plant and 10.7 times less negative impact when compared to the hard coal based power plant.

Keywords: life cycle assessment, aluminium production, supply chain, ecological impacts

Procedia PDF Downloads 532

Authors: Karisma Karisma, Pardis Moslemzadeh Tehrani

Abstract:

This study aims to conduct a high-level strategic dialogue on the lack of consensus, consistency, and legal certainty regarding blockchain-based energy prosumerism so that appropriate institutional and governance structures can be put in place to address the inadequacies and gaps in the legal and regulatory framework. The drive to achieve national and global decarbonization targets is a driving force behind climate goals and policies under the Paris Agreement. In recent years, efforts to ‘demonopolize’ and ‘decentralize’ energy generation and distribution have driven the energy transition toward decentralized systems, invoking concepts such as ownership, sovereignty, and autonomy of RE sources. The emergence of individual and collective forms of prosumerism and the rapid diffusion of blockchain is expected to play a critical role in the decarbonization and democratization of energy systems. However, there is a ‘regulatory void’ relating to individual and collective forms of prosumerism that could prevent the rapid deployment of blockchain systems and potentially stagnate the operationalization of blockchain-enabled energy sharing and trading activities. The application of broad and facile regulatory fixes may be insufficient to address the major regulatory gaps. First, to the authors’ best knowledge, the concepts and elements circumjacent to individual and collective forms of prosumerism have not been adequately described in the legal frameworks of many countries. Second, there is a lack of legal certainty regarding the creation and adaptation of business models in a highly regulated and centralized energy system, which inhibits the emergence of prosumer-driven niche markets. There are also current and prospective challenges relating to the legal status of blockchain-based platforms for facilitating energy transactions, anticipated with the diffusion of blockchain technology. With the rise of prosumerism in the energy sector, the areas of (a) network charges, (b) energy market access, (c) incentive schemes, (d) taxes and levies, and (e) licensing requirements are still uncharted territories in many countries. The uncertainties emanating from this area pose a significant hurdle to the widespread adoption of blockchain technology, a complementary technology that offers added value and competitive advantages for energy systems. The authors undertake a conceptual and theoretical investigation to elucidate the lack of consensus, consistency, and legal certainty in the study of blockchain-based prosumerism. In addition, the authors set an exploratory tone to the discussion by taking an analytically eclectic approach that builds on multiple sources and theories to delve deeper into this topic. As an interdisciplinary study, this research accounts for the convergence of regulation, technology, and the energy sector. The study primarily adopts desk research, which examines regulatory frameworks and conceptual models for crucial policies at the international level to foster an all-inclusive discussion. With their reflections and insights into the interaction of blockchain and prosumerism in the energy sector, the authors do not aim to develop definitive regulatory models or instrument designs, but to contribute to the theoretical dialogue to navigate seminal issues and explore different nuances and pathways. Given the emergence of blockchain-based energy prosumerism, identifying the challenges, gaps and fragmentation of governance regimes is key to facilitating global regulatory transitions.

Keywords: blockchain technology, energy sector, prosumer, legal and regulatory.

Procedia PDF Downloads 181

Authors: Regina Chua

Abstract:

To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

Procedia PDF Downloads 94

Authors: Alparslan A. Balta, Hilmi Yurdakul, Orkun Tunckan, Servet Turan, Arife Yurdakul

Abstract:

Si2N2O (Sinoite) is an inorganic-based oxynitride material that reveals promising phosphor candidates for white light-emitting diodes (WLEDs). However, there is now limited knowledge to explain the synthesis of Si2N2O for this purpose. Here, to the best of authors’ knowledge, we report the first time the production of Si2N2O based phosphors by CeO2, SiO2, Si3N4 from main starting powders, and Li2O sintering additive through spark plasma sintering (SPS) route. The processing parameters, e.g., pressure, temperature, and sintering time, were optimized to reach the monophase Si2N2O containing samples. The lattice parameter, crystallite size, and amount of formation phases were characterized in detail by X-ray diffraction (XRD). Grain morphology, particle size, and distribution were analyzed by scanning and transmission electron microscopes (SEM and TEM). Cathodoluminescence (CL) in SEM and photoluminescence (PL) analyses were conducted on the samples to determine the excitation, and emission characteristics of Ce3+ activated Si2N2O. Results showed that the Si2N2O phase in a maximum 90% ratio was obtained by sintering for 15 minutes at 1650oC under 30 MPa pressure. Based on the SEM-CL and PL measurements, Ce3+: Si2N2O phosphor shows a broad emission summit between 400-700 nm that corresponds to white light. The present research was supported by TUBITAK under project number 217M667.

Keywords: cerium, oxynitride, phosphors, sinoite, Si₂N₂O

Procedia PDF Downloads 107

Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

Abstract:

Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

Procedia PDF Downloads 139

Authors: Gajendra Kumar Mourya, Dinesh Bhatia, Akash Handique, Sunita Warjri, Syed Achaab Amir

Abstract:

Nowadays, Medical imaging has become an integral part of modern healthcare. Abdominal CT images are an invaluable mean for abdominal organ investigation and have been widely studied in the recent years. Diagnosis of liver pathologies is one of the major areas of current interests in the field of medical image processing and is still an open problem. To deeply study and diagnose the liver, segmentation of liver is done to identify which part of the liver is mostly affected. Manual segmentation of the liver in CT images is time-consuming and suffers from inter- and intra-observer differences. However, automatic or semi-automatic computer aided segmentation of the Liver is a challenging task due to inter-patient Liver shape and size variability. In this paper, we present a technique for automatic segmenting the liver from CT images using Random Forest Classifier. Random forests or random decision forests are an ensemble learning method for classification that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes of the individual trees. After comparing with various other techniques, it was found that Random Forest Classifier provide a better segmentation results with respect to accuracy and speed. We have done the validation of our results using various techniques and it shows above 89% accuracy in all the cases.

Keywords: CT images, image validation, random forest, segmentation

Procedia PDF Downloads 313

Authors: Poonam Singh, Surendra Prasad, William Aalbersberg

Abstract:

Iron the most essential trace element in human nutrition. Its deficiency has serious health consequences and is a major public health threat worldwide. The common deficiencies in Fiji population reported are of Fe, Ca and Zn. It has also been reported that 40% of women in Fiji are iron deficient. Therefore, we have been studying the bioavailability of iron in commonly consumed Fiji foods. To study the bioavailability it is essential to assess the iron contents in raw foods. This paper reports the iron contents and its bioavailability in commonly consumed foods by multicultural population of Fiji. The food samples (rice, breads, wheat flour and breakfast cereals) were analyzed by atomic absorption spectrophotometer for total iron and its bioavailability. The white rice had the lowest total iron 0.10±0.03 mg/100g but had high bioavailability of 160.60±0.03%. The brown rice had 0.20±0.03 mg/100g total iron content but 85.00±0.03% bioavailable. The white and brown breads showed the highest iron bioavailability as 428.30±0.11 and 269.35 ±0.02%, respectively. The Weetabix and the rolled oats had the iron contents 2.89±0.27 and 1.24.±0.03 mg/100g with bioavailability of 14.19±0.04 and 12.10±0.03%, respectively. The most commonly consumed normal wheat flour had 0.65±0.00 mg/100g iron while the whole meal and the Roti flours had 2.35±0.20 and 0.62±0.17 mg/100g iron showing bioavailability of 55.38±0.05, 16.67±0.08 and 12.90±0.00%, respectively. The low bioavailability of iron in certain foods may be due to the presence of phytates/oxalates, processing/storage conditions, cooking method or interaction with other minerals present in the food samples.

Keywords: iron, bioavailability, Fiji foods, in vitro technique, human nutrition

Procedia PDF Downloads 529

Authors: Kumaran Narayanan, Pei-Sheng Liew

Abstract:

This paper adapts the bacteriophage N15 protelomerase enzyme to assemble linear chromosomes as vectors for gene expression in human cells. Phage N15 has the unique ability to replicate as a linear plasmid with telomeres in E. coli during its prophage stage of life-cycle. The virus-encoded protelomerase enzyme cuts its circular genome and caps its ends to form hairpin telomeres, resulting in a linear human-chromosome-like structure in E. coli. In mammalian cells, however, no enzyme with TelN-like activities has been found. In this work, we show for the first-time transfer of the protelomerase from phage into human and mouse cells and demonstrate recapitulation of its activity in these hosts. The function of this enzyme is assayed by demonstrating cleavage of its target DNA, followed by detecting telomere formation based on its resistance to recBCD enzyme digestion. We show protelomerase expression persists for at least 60 days, which indicates limited silencing of its expression. Next, we show that an intact human β-globin gene delivered on this linear chromosome accurately retains its expression in the human cellular environment for at least 60 hours, demonstrating its stability and potential as a vector. These results demonstrate that the N15 protelomerse is able to function in mammalian cells to cut and heal DNA to create telomeres, which provides a new tool for creating novel structures by DNA resolution in these hosts.

Keywords: chromosome, beta-globin, DNA, gene expression, linear vector

Procedia PDF Downloads 192

Authors: S. Petrovska, D. Jonkus, D. Smiltiņa

Abstract:

κ-casein is one of milk proteins which are very important for milk processing. Genotypes of κ-casein affect milk yield, fat, and protein content. The main factors which affect local Latvian dairy breed milk yield and composition are analyzed in research. Data were collected from 88 Latvian brown and 82 Latvian blue cows in 2015. AA genotype was 0.557 in Latvian brown and 0.232 in Latvian blue breed. BB genotype was 0.034 in Latvian brown and 0.207 in Latvian blue breed. Highest milk yield was observed in Latvian brown (5131.2 ± 172.01 kg), significantly high fat content and fat yield also was in Latvian brown (p < 0.05). Significant differences between κ-casein genotypes were not found in Latvian brown, but highest milk yield (5057 ± 130.23 kg), protein content (3.42 ± 0.03%), and protein yield (171.9 ± 4.34 kg) were with AB genotype. Significantly high fat content was observed in Latvian blue breed with BB genotype (4.29 ± 0.17%) compared with AA genotypes (3.42 ± 0.19). Similar tendency was found in protein content – 3.27 ± 0.16% with BB genotype and 2.59 ± 0.16% with AA genotype (p < 0.05). Milk yield increases by increasing parity. We did not obtain major tendency of changes of milk fat and protein content according parity.

Keywords: dairy cows, κ-casein, milk productivity, polymorphism

Procedia PDF Downloads 270

Authors: Shadrack Mambo, Karim Djouani, Yskandar Hamam, Barend van Wyk, Patrick Siarry

Abstract:

This paper discusses the current trends in medical image registration techniques and addresses the need to provide a solid theoretical foundation for research endeavours. Methodological analysis and synthesis of quality literature was done, providing a platform for developing a good foundation for research study in this field which is crucial in understanding the existing levels of knowledge. Research on medical image registration techniques assists clinical and medical practitioners in diagnosis of tumours and lesion in anatomical organs, thereby enhancing fast and accurate curative treatment of patients. Literature review aims to provide a solid theoretical foundation for research endeavours in image registration techniques. Developing a solid foundation for a research study is possible through a methodological analysis and synthesis of existing contributions. Out of these considerations, the aim of this paper is to enhance the scientific community’s understanding of the current status of research in medical image registration techniques and also communicate to them, the contribution of this research in the field of image processing. The gaps identified in current techniques can be closed by use of artificial neural networks that form learning systems designed to minimise error function. The paper also suggests several areas of future research in the image registration.

Keywords: image registration techniques, medical images, neural networks, optimisaztion, transformation

Procedia PDF Downloads 177

Authors: Arkadiusz Urzędowski, Dorota Wójcicka-Migasiuk, Andrzej Sachajdak, Magdalena Paśnikowska-Łukaszuk

Abstract:

Due to the impact of climate changes and inevitability to reduce greenhouse gases, the need to use low-carbon and sustainable construction has increased. In this work, it is investigated how texture of the surface building materials and radiative heat transfer phenomenon in flat multilayer can be correlated. Attempts to test the surface emissivity are taken however, the trustworthiness of measurement results remains a concern since sensor size and thickness are common problems. This paper presents an experimental method to studies surface emissivity with use self constructed thermal sensors and thermal imaging technique. The surface of building materials was modified by mechanical and chemical treatment affecting the reduction of the emissivity. For testing the shaping surface of materials and mapping its three-dimensional structure, scanning profilometry were used in a laboratory. By comparing the results of laboratory tests and performed analysis of 3D computer fluid dynamics software, it can be shown that a change in the surface coverage of materials affects the heat transport by radiation between layers. Motivated by recent advancements in variational inference, this publication evaluates the potential use a dedicated data processing approach, and properly constructed temperature sensors, the influence of the surface emissivity on the phenomenon of radiation and heat transport in the entire partition can be determined.

Keywords: heat transfer, surface roughness, surface emissivity, radiation

Procedia PDF Downloads 96

Authors: Karan Vishavjit, Aakash Lakra, Shafaq Khan

Abstract:

The COVID-19 pandemic has pushed healthcare professionals to use big data analytics as a vital tool for tracking and evaluating the effects of contagious viruses. To effectively analyze huge datasets, efficient NoSQL databases are needed. The analysis of post-COVID-19 health and well-being outcomes and the evaluation of the effectiveness of government efforts during the pandemic is made possible by this research’s integration of several datasets, which cuts down on query processing time and creates predictive visual artifacts. We recommend applying sharding and indexing technologies to improve query effectiveness and scalability as the dataset expands. Effective data retrieval and analysis are made possible by spreading the datasets into a sharded database and doing indexing on individual shards. Analysis of connections between governmental activities, poverty levels, and post-pandemic well being is the key goal. We want to evaluate the effectiveness of governmental initiatives to improve health and lower poverty levels. We will do this by utilising advanced data analysis and visualisations. The findings provide relevant data that supports the advancement of UN sustainable objectives, future pandemic preparation, and evidence-based decision-making. This study shows how Big Data and NoSQL databases may be used to address problems with global health.

Keywords: big data, COVID-19, health, indexing, NoSQL, sharding, scalability, well being

Procedia PDF Downloads 70

Authors: Ekaterina Tikhonova

Abstract:

This research is aimed to evaluate the effectiveness of T&D (Training and Development) on the example of two T&D programs for the Executive TOP Management run in 2012, 2015-2016 in Komos Group. This study is commissioned to research the effectiveness of two similar corporate T&D programs (within one company) in two periods of time (2012, 2015-2016) through evaluating the programs’ effectiveness using the four-level Kirkpatrick’s model of evaluating T&D programs and calculating ROI as an instrument for T&D program measuring by Phillips’ formula. The research investigates the correlation of two figures: the ROI calculated and the rating percentage scale per the ROI implementation (Wagle’s scale). The study includes an assessment of feedback 360 (Kirkpatrick's model) and Phillips’ ROI Methodology that provides a step-by-step process for collecting data, summarizing and processing the collected information. The data is collected from the company accounting data, the HR budgets, MCFO and the company annual reports for the research periods. All analyzed data and reports are organized and presented in forms of tables, charts, and graphs. The paper also gives a brief description of some constrains of the research considered. After ROI calculation, the study reveals that ROI ranges between the average implementation (65% to 75%) by Wagle’s scale that can be considered as a positive outcome. The paper also gives some recommendations how to use ROI in practice and describes main benefits of ROI implementation.

Keywords: ROI, organizational performance, efficacy of T&D program, employee performance

Procedia PDF Downloads 250

Authors: Muhammad Suhail Ibrahim, Muhammad Nadeem, Waseem Khalid, Ammara Ainee, Taleeha Roheen, Sadaf Javaria, Aftab Ahmed, Hira Fatima, Mian Nadeem Riaz, Muhammad Zubair Khalid, Isam A. Mohamed Ahmed J, Moneera O. Aljobair

Abstract:

This study was executed to explore the efficacy of ultrasound-assisted extraction of dietary fiber from oat cultivar S2000. Extraction (variables time, temperature and amplitude) was optimized by using response surface methodology (RSM) conducted by Box Behnken Design (BBD). The effect of time, temperature and amplitude were studied at three levels. It was observed that time and temperature exerted more impact on extraction efficiency as compared to amplitude. The highest yield of total dietary fiber (TDF), soluble dietary fiber (SDF) and In-soluble dietary fiber (IDF) fractions were observed under ultrasound processing for 20 min at 40 ◦C with 80% amplitude. Characterization of extracted dietary fiber showed that it had better crystallinity, thermal properties and good fibrous structure. It also showed better functional properties as compared to traditionally extracted dietary fiber. Furthermore, dietary fibers from oats may offer high-value utilization and the expansion of comprehensive utilization in functional food and nutraceutical development.

Keywords: extraction, ultrasonication, response surface methodology, box behnken design

Procedia PDF Downloads 50