Search results for: principal component

Authors: Samart Porncharoen, Nisa Pakvilai

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The elevated levels of air pollutants in regional atmospheric environments is a significant problem that affects human health in Thailand, particularly in the Chao Phraya River Basin. Of concern are issues surrounding ambient air pollution such as particulate matter, gaseous pollutants and more specifically concerning air pollution along the river. Therefore, the spatio-temporal study of air pollution in this real environment can gain more accurate air quality data for making formalized environmental policy in river basins. In order to inform such a policy, a study was conducted over a period of January –December, 2015 to continually collect measurements of various pollutants in both urban and regional locations in the Chao Phraya River Basin. This study investigated the air pollutants in many diverse environments along the Chao Phraya River Basin, Thailand in 2015. Multivariate Analysis Techniques such as Principle Component Analysis (PCA) and Path analysis were utilised to classify air pollution in the surveyed location. Measurements were collected in both urban and rural areas to see if significant differences existed between the two locations in terms of air pollution levels. The meteorological parameters of various particulates were collected continually from a Thai pollution control department monitoring station over a period of January –December, 2015. Of interest to this study were the readings of SO2, CO, NOx, O3, and PM10. Results showed a daily arithmetic mean concentration of SO2, CO, NOx, O3, PM10 reading at 3±1 ppb, 0.5± 0.5 ppm, 30±21 ppb, 19±16 ppb, and 40±20 ug/m3 in urban locations (Bangkok). During the same time period, the readings for the same measurements in rural areas, Ayutthaya (were 1±0.5 ppb, 0.1± 0.05 ppm, 25±17 ppb, 30±21 ppb, and 35±10 ug/m3respectively. This show that Bangkok were located in highly polluted environments that are dominated source emitted from vehicles. Further, results were analysed to ascertain if significant seasonal variation existed in the measurements. It was found that levels of both gaseous pollutants and particle matter in dry season were higher than the wet season. More broadly, the results show that levels of pollutants were measured highest in locations along the Chao Phraya. River Basin known to have a large number of vehicles and biomass burning. This correlation suggests that the principle pollutants were from these anthropogenic sources. This study contributes to the body of knowledge surrounding ambient air pollution such as particulate matter, gaseous pollutants and more specifically concerning air pollution along the Chao Phraya River Basin. Further, this study is one of the first to utilise continuous mobile monitoring along a river in order to gain accurate measurements during a data collection period. Overall, the results of this study can be used for making formalized environmental policy in river basins in order to reduce the physical effects on human health.

Keywords: air pollution, Chao Phraya river basin, meteorology, seasonal variation, principal component analysis

Procedia PDF Downloads 278

Authors: Morar (Bumbu) Nicoleta Ileana

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Located in the northeastern part of Romania in a cross-border area, Bucovina region, due to historical events that took place here, is characterized by the cohabitation in the same area of a significant number of ethnic communities, represented in 54% by rural population. In addition to providing the natural, economic history and decision makers, the demographic component is responsible for the region's development trajectory to which it belongs. The influence that people exert on rural development is shown by the values of the different demographic indicator. This study will analyze the demographic indicators obtained against a strong database, emphasizing the indicators that favor the rural development of the region and those that prevent it. The study is useful in defining the rightful directions that rural economic development can focus on, also representing an important tool in developing strategies for the development of rural settlements of Bucovina region.

Keywords: Bucovina, development directions, demographic indicators, rural development

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Authors: Damian Gordon, Paul Doyle, Anna Becevel, Tina Baloh

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The goal of this research is to explore the evolution of the concept of “blended learning” over a twenty-year period, to see whether or not the conceptualization has remained consistent or if it has become either more specific or more general. To achieve this goal, the term “blended learning” (and variations) was searched for in various bibliographical repositories for each year 2003-2022 to locate a highly cited paper that is not behind a paywall, to locate unique definitions that would be freely available to all academics each year. Each of the twenty unique definitions is explored to identify how they categorize both the Classroom Component and the Computer Component of blended learning, as well as identify which discipline each definition originates from and which country it comes from to see if there are any significant geographical variations. Based on this analysis, trends that appear in the definitions are noted, as well as an overall interpretation of the notion of “Blended Learning.”

Keywords: blended learning, definitions of blended learning, e-learning, thematic searches

Procedia PDF Downloads 122

Authors: Etienne Gaborit, Dorothy Durnford, Daniel Deacu, Marco Carrera, Nathalie Gauthier, Camille Garnaud, Vincent Fortin

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A new experimental streamflow forecasting system was recently implemented at the Environment and Climate Change Canada’s (ECCC) Canadian Centre for Meteorological and Environmental Prediction (CCMEP). It relies on CaLDAS (Canadian Land Data Assimilation System) for the assimilation of surface variables, and on a surface prediction system that feeds a routing component. The surface energy and water budgets are simulated with the SVS (Soil, Vegetation, and Snow) Land-Surface Scheme (LSS) at 2.5-km grid spacing over Canada. The routing component is based on the Watroute routing scheme at 1-km grid spacing for the Great Lakes and Nelson River watersheds. The system is run in two distinct phases: an analysis part and a forecast part. During the analysis part, CaLDAS outputs are used to force the routing system, which performs streamflow assimilation. In forecast mode, the surface component is forced with the Canadian GEM atmospheric forecasts and is initialized with a CaLDAS analysis. Streamflow performances of this new system are presented over 2019. Performances are compared to the current ECCC’s operational streamflow forecasting system, which is different from the new experimental system in many aspects. These new streamflow forecasts are also compared to persistence. Overall, the new streamflow forecasting system presents promising results, highlighting the need for an elaborated assimilation phase before performing the forecasts. However, the system is still experimental and is continuously being improved. Some major recent improvements are presented here and include, for example, the assimilation of snow cover data from remote sensing, a backward propagation of assimilated flow observations, a new numerical scheme for the routing component, and a new reservoir model.

Keywords: assimilation system, distributed physical model, offline hydro-meteorological chain, short-term streamflow forecasts

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Authors: Siavash Mirahmadizoghi, Steven Bell, David Simpson

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In this work a new independent component analysis (ICA) based method for noise reduction in evoked potentials is evaluated on for auditory late responses (ALR) captured with a 63-channel electroencephalogram (EEG) from 10 normal-hearing subjects. The performance of the new method is compared with a single channel alternative in terms of signal to noise ratio (SNR), the number of channels with an SNR above an empirically derived statistical critical value and an estimate of the effect of attention on the major components in the ALR waveform. The results show that the multichannel signal processing method can significantly enhance the quality of the ALR signal and also detect the effect of the attention on the ALR better than the single channel alternative.

Keywords: auditory late response (ALR), attention, EEG, independent component analysis (ICA), multichannel signal processing

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Authors: Attila Vámosi, Tamás Mankovits, Dávid Huri, Imre Kocsis, Tamás Szabó

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Non-linear FEM calculations are indispensable when important technical information like operating performance of a rubber component is desired. Rubber bumpers built into air-spring structures may undergo large deformations under load, which in itself shows non-linear behavior. The changing contact range between the parts and the incompressibility of the rubber increases this non-linear behavior further. The material characterization of an elastomeric component is also a demanding engineering task. The shape optimization problem of rubber parts led to the study of FEM based calculation processes. This type of problems was posed and investigated by several authors. In this paper the time demand of certain calculation methods are studied and the possibilities of time reduction is presented.

Keywords: rubber bumper, data acquisition, finite element analysis, support vector regression

Procedia PDF Downloads 467

Authors: Eleni Maria Pavlopoulou, Vasiliki N. Georgiannou, Filippos C. Chortis

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The response of M31 sand stabilized with colloidal silica (CS) aqueous gel is investigated in the laboratory. CS is introduced in the water regime, forming a hydrosol. The low viscosity hydrosol thickens in a controllable manner to form a stable, non-toxic gel; the gel fills the pore space, retains the pore water, and supports the grain structure. The role of colloidal silica on subsequent sand behavior is examined with the aid of direct shear, triaxial, and normal compression tests. Under the examined loading modes, while the strength of the treated sand is enhanced, its stiffness may reduce, and its compressibility increase. However, in most geotechnical problems, the loading conditions are complex, involving changes in both stress magnitude and direction. Rotation of principal stresses (σ1, σ2, σ3) in varying amounts expressed as angle α, (from α=0° to 90°) in concurrence with increasing shear stress loading is commonly encountered in soil structures such as foundations, embankments, underwater slopes. To assess the influence of anisotropy on the response of sands before and after their stabilization, hollow cylinder tests were performed. The behavior of stabilized sand is compared with the characteristic sand behavior, i.e., a reduction in peak stress ratio associated with a softer stress-strain response with the increasing angle a. The influence of the magnitude of the intermediate principal stress (σ2) on the mechanical response of treated and untreated sand is also examined.

Keywords: anisotropy, colloidal silica, laboratory tests, sands, soil stabilization

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: Kamran Siddique, Hiroyuki Asada, Yoshifumi Ogami

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Micro gas turbine (MGT) nowadays has a wide variety of applications from drones to hybrid electric vehicles. As microfabrication technology getting better, the size of MGT is getting smaller. Overall performance of MGT is dependent on the individual components. Each component’s performance is dependent and interrelated with another component. Therefore, careful consideration needs to be given to each and every individual component of MGT. In this study, the focus is on improving the performance of the compressor in order to improve the overall performance of MGT. Computational Fluid Dynamics (CFD) is being performed using the software FLUENT to analyze the design of a micro compressor. Operating parameters like mass flow rate and RPM, and design parameters like inner blade angle (IBA), outer blade angle (OBA), blade thickness and number of blades are varied to study its effect on the performance of the compressor. Pressure ratio is used as a tool to measure the performance of the compressor. Higher the pressure ratio, better the design is. In the study, target mass flow rate is 0.2 g/s and RPM to be less than or equal to 900,000. So far, a pressure ratio of above 3 has been achieved at 0.2 g/s mass flow rate with 5 rotor blades, 0.36 mm blade thickness, 94.25 degrees OBA and 10.46 degrees IBA. The design in this study differs from a regular centrifugal compressor used in conventional gas turbines such that compressor is designed keeping in mind ease of manufacturability. So, this study proposes a compressor design which has a good pressure ratio, and at the same time, it is easy to manufacture using current microfabrication technologies.

Keywords: computational fluid dynamics, FLUENT microfabrication, RPM

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Authors: Bouchra Nadji, Elaid Bouchetob

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PV systems are common in residential and industrial settings because of their low, upfront costs and operating costs throughout their lifetimes. Buck or boost converters are used in photovoltaic systems, regardless of whether the system is autonomous or connected to the grid. These converters became less appealing because of their low efficiency, inadequate power density, and use of silicon for their power components. Traditional devices based on Si are getting close to reaching their theoretical performance limits, which makes it more challenging to improve the performance and efficiency of these devices. GaN and SiC are the two types of WBG semiconductors with the most recent technological advancements and are available. Tolerance to high temperatures and switching frequencies can reduce active and passive component size. Utilizing high-efficiency dc-dc boost converters is the primary emphasis of this work. These converters are for photovoltaic systems that use wave energy.

Keywords: component, Artificial intelligence, PV System, ANN MPPT, DC-DC converter

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Authors: Abdelhak-Djamel Seriai, Selim Kebir, Allaoua Chaoui

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During the last decades, object oriented program- ming has been massively used to build large-scale systems. However, evolution and maintenance of such systems become a laborious task because of the lack of object oriented programming to offer a precise view of the functional building blocks of the system. This lack is caused by the fine granularity of classes and objects. In this paper, we use a post object-oriented technology namely software components, to propose an approach based on the identification of the functional building blocks of an object oriented system by analyzing its source code. These functional blocks are specified as software components and the result is a multi-layer component based software architecture.

Keywords: software comprehension, software component, object oriented, software architecture, reverse engineering

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Authors: Sammy Kibor, Filip Huyghe, Marc Kochzius, James Kairo

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Goldstripe Sardinella, Sardinella gibbosa, (Bleeker, 1849) is a commercially and ecologically important small pelagic fish common in the Western Indian Ocean region. The present study aimed to assess genetic diversity and population structure of the species in the Kenya-Tanzania transboundary area using mtDNA and msDNA markers. Some 630 bp sequence in the mitochondrial DNA (mtDNA) Cytochrome C Oxidase I (COI) and five polymorphic microsatellite DNA loci were analyzed. Fin clips of 309 individuals from eight locations within the transboundary area were collected between July and December 2018. The S. gibbosa individuals from the different locations were distinguishable from one another based on the mtDNA variation, as demonstrated with a neighbor-joining tree and minimum spanning network analysis. None of the identified 22 haplotypes were shared between Kenya and Tanzania. Gene diversity per locus was relatively high (0.271-0.751), highest Fis was 0.391. The structure analysis, discriminant analysis of Principal component (DAPC) and the pair-wise (FST = 0.136 P < 0.001) values after Bonferroni correction using five microsatellite loci provided clear inference on genetic differentiation and thus evidence of population structure of S. gibbosa along the Kenya-Tanzania coast. This study shows a high level of genetic diversity and the presence of population structure (Φst =0.078 P < 0.001) resulting to the existence of four populations giving a clear indication of minimum gene flow among the population. This information has application in the designing of marine protected areas, an important tool for marine conservation.

Keywords: marine connectivity, microsatellites, population genetics, transboundary

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Authors: Anjushi Verma, Tirthankar Gayen

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Although, reliability is an important attribute of quality, especially for mission critical systems, yet, there does not exist any versatile model even today for the reliability assessment of component-based software. The existing Black Box models are found to make various assumptions which may not always be realistic and may be quite contrary to the actual behaviour of software. They focus on observing the manner in which the system behaves without considering the structure of the system, the components composing the system, their interconnections, dependencies, usage frequencies, etc.As a result, the entropy (uncertainty) in assessment using these models is much high.Though, there are some models based on operation profile yet sometimes it becomes extremely difficult to obtain the exact operation profile concerned with a given operation. This paper discusses the drawbacks, deficiencies and limitations of Black Box approaches from the perspective of various authors and finally proposes a conceptual model for the reliability assessment of software.

Keywords: black box, faults, failure, software reliability

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Authors: Krasimir Ivanov, Elitsa Kolentsova, Dimitar Dimitrov

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The development of active and stable catalysts without noble metals for low temperature oxidation of exhaust gases remains a significant challenge. The purpose of this study is to determine the influence of the preparation method on the catalytic activity of the supported copper-manganese mixed oxides in terms of VOCs oxidation. The catalysts were prepared by impregnation of γ-Al2O3 with copper and manganese nitrates and acetates and the possibilities for CO, CH3OH and dimethyl ether (DME) oxidation were evaluated using continuous flow equipment with a four-channel isothermal stainless steel reactor. Effect of the support, Cu/Mn mole ratio, heat treatment of the precursor and active component loading were investigated. Highly active alumina supported Cu-Mn catalysts for CO and VOCs oxidation were synthesized. The effect of preparation conditions on the activity behavior of the catalysts was discussed. The synergetic interaction between copper and manganese species increases the activity for complete oxidation over mixed catalysts. Type of support, calcination temperature and active component loading along with catalyst composition are important factors, determining catalytic activity. Cu/Mn molar ratio of 1:5, heat treatment at 450oC and 20 % active component loading are the best compromise for production of active catalyst for simultaneous combustion of CO, CH3OH and DME.

Keywords: copper-manganese catalysts, CO, VOCs oxidation, exhaust gases

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Authors: Ya-Li Tsai

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Nitrogen is a vital element crucial for crop growth, significantly influencing crop yield. In rice cultivation, farmers often apply substantial nitrogen fertilizer to maximize yields. However, excessive nitrogen application increases the risk of lodging and pest infestation, leading to yield losses. Additionally, conventional flooded irrigation methods consume significant water resources, necessitating precise agricultural and intelligent water management systems. In this study, it leveraged physiological data and field images captured by unmanned aerial vehicles, considering fertilizer treatment and irrigation as key factors. Statistical models incorporating rice physiological data, yield, and vegetation indices from image data were developed. Missing physiological data were addressed using multiple imputation and regression methods, and regression models were established using principal component analysis and stepwise regression. Target nitrogen accumulation at key growth stages was identified to optimize fertilizer application, with the difference between actual and target nitrogen accumulation guiding recommendations for ear dressing dosage. Field experiments conducted in 2022 validated the recommended ear dressing dosage, demonstrating no significant difference in final yield compared to traditional fertilizer levels under alternate wetting and drying irrigation. These findings highlight the efficacy of applying recommended dosages based on fertilizer decision models, offering the potential for reduced fertilizer use while maintaining yield in rice cultivation.

Keywords: intelligent fertilizer management, nitrogen top and ear dressing fertilizer, rice, yield optimization

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Authors: Zouhair Haddi, Stephane Delliaux, Jean-Francois Pons, Ismail Kechaf, Jean-Claude De Haro, Mustapha Ouladsine

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Atrial fibrillation (AF) has been considered as the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Nowadays, telemedical approaches targeting cardiac outpatients situate AF among the most challenged medical issues. The automatic, early, and fast AF detection is still a major concern for the healthcare professional. Several algorithms based on univariate analysis have been developed to detect atrial fibrillation. However, the published results do not show satisfactory classification accuracy. This work was aimed at resolving this shortcoming by proposing multivariate data analysis methods for automatic AF detection. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR intervals window and then four specific features were calculated. Two pattern recognition methods, i.e., Principal Component Analysis (PCA) and Learning Vector Quantization (LVQ) neural network were used to develop classification models. PCA, as a feature reduction method, was employed to find important features to discriminate between AF and Normal Sinus Rhythm. Despite its very simple structure, the results show that the LVQ model performs better on the analyzed databases than do existing algorithms, with high sensitivity and specificity (99.19% and 99.39%, respectively). The proposed AF detection holds several interesting properties, and can be implemented with just a few arithmetical operations which make it a suitable choice for telecare applications.

Keywords: atrial fibrillation, multivariate data analysis, automatic detection, telemedicine

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Authors: Roya Kelishadi, Mostafa Amini-Rarani, Mostafa Qorbani

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Introduction: As a health-related behavior, physical activity (PA) has an unequal distribution relating to individual's socioeconomic status. This study aimed to assess socioeconomic inequality in PA among Iranian students and their parents at national level and according to socioeconomic status (SES) of the living regions. Method: This study was conducted as part of a national surveillance program conducted among 14400 Iranian students and their parents. Non-linear principal component analysis was used to construct the households' socioeconomic status, and the concentration index approach was applied to measure inequality in father, mother, and student’s PA. Results: The data of 13313 students and their parents were complete for the current study. At national level and SES regions, students had more PA than their parents (except in the lowest SES region), and fathers have more PA than mothers. The lowest means of mother and student's PA were find in the highest SES region. At national level, the concentration indices of father and mother’s PA were -0.050 (95 % CI: -0.067 ~ -0.030) and -0.028 (95% CI: -0.044 ~ -0.012), respectively; indicating pro-poor inequality and, the CI value of student PA was nearly equal to zero (P > 0.05). At SES regions, father and mother's PA were more concentrated in the poor, except for lower middle region. Regional concentration indices for students reveal that inequality not statistically significant at all regions. Conclusion: This study suggests that reliable evidence that comparing different aspects of inequality of PA, based on socioeconomic status and residence areas of students and their parents, could be used for better planning for health promotion programs. Moreover, given the average of mother's and student’s PA in the richer regions were low, it can be suggested that richer focused-PA planning may further increase the level of PA across higher SES and, consequently, reduce inequality in PA. These findings can be applied in the health system services.

Keywords: concentration index, health system services, physical activity, socioeconomic inequality

Procedia PDF Downloads 152

Authors: Manjula Weerasekera, Chris Sissons, Lisa Wong, Sally Anderson, Ann Holmes, Richard Cannon

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The aim of this study was to characterize bacterial population and yeasts in saliva by Polymerase chain reaction followed by denaturing gradient gel electrophoresis (PCR-DGGE) and measure yeast levels by culture. PCR-DGGE was performed to identify oral bacteria and yeasts in 24 saliva samples. DNA was extracted and used to generate DNA amplicons of the V2–V3 hypervariable region of the bacterial 16S rDNA gene using PCR. Further universal primers targeting the large subunit rDNA gene (25S-28S) of fungi were used to amplify yeasts present in human saliva. Resulting PCR products were subjected to denaturing gradient gel electrophoresis using Universal mutation detection system. DGGE bands were extracted and sequenced using Sanger method. A potential relationship was evaluated between groups of bacteria identified by cluster analysis of DGGE fingerprints with the yeast levels and with their diversity. Significant interpersonal variation of salivary microbiome was observed. Cluster and principal component analysis of the bacterial DGGE patterns yielded three significant major clusters, and outliers. Seventeen of the 24 (71%) saliva samples were yeast positive going up to 10³ cfu/mL. Predominately, C. albicans, and six other species of yeast were detected. The presence, amount and species of yeast showed no clear relationship to the bacterial clusters. Microbial community in saliva showed a significant variation between individuals. A lack of association between yeasts and the bacterial fingerprints in saliva suggests the significant ecological person-specific independence in highly complex oral biofilm systems under normal oral conditions.

Keywords: bacteria, denaturing gradient gel electrophoresis, oral biofilm, yeasts

Procedia PDF Downloads 218

Authors: Nguyen The Hoang Anh, Tran Huy Hoang, Vu Tat Thang, T. T. Quyen Bui

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Mental state classification is an important step for realizing a control system based on electroencephalography (EEG) signals which could benefit a lot of paralyzed people including the locked-in or Amyotrophic Lateral Sclerosis. Considering that EEG signals are nonstationary and often contaminated by various types of artifacts, classifying thoughts into correct mental states is not a trivial problem. In this work, our contribution is that we present and realize a novel model which integrates different techniques: Independent component analysis (ICA), relative wavelet energy, and support vector machine (SVM) for the same task. We applied our model to classify thoughts in two types of experiment whether with two or three mental states. The experimental results show that the presented model outperforms other models using Artificial Neural Network, K-Nearest Neighbors, etc.

Keywords: EEG, ICA, SVM, wavelet

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Authors: Brandon Foggo, Nanpeng Yu

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Power distribution circuits undergo frequent network topology changes that are often left undocumented. As a result, the documentation of a circuit’s connectivity becomes inaccurate with time. The lack of reliable circuit connectivity information is one of the biggest obstacles to model, monitor, and control modern distribution systems. To enhance the reliability and efficiency of electric power distribution systems, the circuit’s connectivity information must be updated periodically. This paper focuses on one critical component of a distribution circuit’s topology - the secondary transformer to phase association. This topology component describes the set of phase lines that feed power to a given secondary transformer (and therefore a given group of power consumers). Finding the documentation of this component is call Phase Identification, and is typically performed with physical measurements. These measurements can take time lengths on the order of several months, but with supervised learning, the time length can be reduced significantly. This paper compares several such methods applied to Phase Identification for a large range of real distribution circuits, describes a method of training data selection, describes preprocessing steps unique to the Phase Identification problem, and ultimately describes a method which obtains high accuracy (> 96% in most cases, > 92% in the worst case) using only 5% of the measurements typically used for Phase Identification.

Keywords: distribution network, machine learning, network topology, phase identification, smart grid

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Authors: Shahram Nowrouzieh, Abbas Rezaei Asl, Mohamad Ali Jafari

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Cotton, as a strategic crop, plays an important role in providing human food and clothing need, because of its oil, protein, and fiber. Iran has been one of the largest cotton producers in the world in the past, but unfortunately, for economic reasons, its production is reduced now. One of the ways to reduce the cost of cotton production is to expand the mechanization of cotton harvesting. Iranian farmers do not accept the function of cotton harvesters. One reason for this lack of acceptance of cotton harvesting machines is the number of field losses on these machines. So, the majority of cotton fields are harvested by hand. Although the correct setting of the harvesting machine is very important in the cotton losses, the morphological properties of the cotton plant also affect the performance of cotton harvesters. In this study, the effect of some cotton morphological properties such as the height of the cotton plant, number, and length of sympodial and monopodial branches, boll dimensions, boll weight, number of carpels and bracts angle were evaluated on the performance of cotton picker. In this research, the efficiency of John Deere 9920 spindle Cotton picker is investigated on five different Iranian cotton cultivars. The results indicate that there was a significant difference between the five cultivars in terms of machine harvest efficiency. Golestan cultivar showed the best cotton harvester performance with an average of 87.6% of total harvestable seed cotton and Khorshid cultivar had the least cotton harvester performance. The principal component analysis showed that, at 50.76% probability, the cotton picker efficiency is affected by the bracts angle positively and by boll dimensions, the number of carpels and the height of cotton plants negatively. The seed cotton remains (in the plant and on the ground) after harvester in PCA scatter plot were in the same zone with boll dimensions and several carpels.

Keywords: cotton, bract, harvester, carpel

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Authors: Peyman Sheikholharam Mashhadi

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Difficulties in analyzing real world images in classical image processing and machine vision framework have motivated researchers towards considering the biology-based vision. It is a common belief that mammalian visual cortex has been adapted to the statistics of the real world images through the evolution process. There are two well-known successful models of mammalian visual cortical cells: Independent Component Analysis (ICA) and Independent Subspace Analysis (ISA). In this paper, we statistically analyze the dependencies which remain in the components after applying these models to the natural images. Also, we investigate the response of feature detectors to gratings with various parameters in order to find optimal parameters of the feature detectors. Finally, the selectiveness of feature detectors to phase, in both models is considered.

Keywords: statistics, independent component analysis, independent subspace analysis, phase, natural images

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Authors: Ricardo Bragança, Tom Vinaimont

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This paper discusses the introduction of processes of self-determined learning (heutagogy) into a graduate course on financial modeling, using elements of entangled pedagogy and Biggs’ constructive alignment. To encourage learners to take control of their own learning journey and develop critical thinking and problem-solving skills, each session in the course receives tailor-made media-enhanced pedagogical assets. The design of those assets specifically supports entangled pedagogy, which opposes technological or pedagogical determinism in support of the collaborative integration of pedagogy and technology. Media assets for each of the ten sessions in this course consist of three components. The first component in this three-pronged approach is a game-cut-like cinematographic representation that introduces the context of the session. The second component represents a character from an open-source-styled community that encourages self-determined learning. The third component consists of a character, which refers to the in-person instructor and also aligns learning outcomes and assessment tasks, using Biggs’ constructive alignment, to the cinematographic and open-source-styled component. In essence, the course's metamorphosis helps students apply the concepts they've studied to actual financial modeling issues. The audio-visual media assets create a storyline throughout the course based on gamified and real-world applications, thus encouraging student engagement and interaction. The structured entanglement of pedagogy and technology also guides the instructor in the design of the in-class interactions and directs the focus on outcomes and assessments. The transformation process of this graduate course in financial modeling led to an institutional teaching award in 2021. The transformation of this course may be used as a model for other courses and programs in many disciplines to help with intended learning outcomes integration, constructive alignment, and Assurance of Learning.

Keywords: innovative education, active learning, entangled pedagogy, heutagogy, constructive alignment, project based learning, financial modeling, graduate business education

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Authors: Goran Sporiš, Dinko Vuleta, Stefan Lovro

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The purpose of the present study was to determine the associations between health-related quality of life (HRQOL) and physical activity (PA) in different domains. In this cross-sectional study, participants were 169 Croatian police officers (mean age 35.14±8.95 yrs, mean height 180.93±7.53 cm, mean weight 88.39±14.05 kg, mean body-mass index 26.90±3.39 kg/m2). The dependent variables were two general domains extracted from the HRQOL questionnaire: (1) physical component scale (PCS) and (2) mental component scale (MCS). The independent variables were job-related, transport, domestic and leisure-time PA, along with other factors: age, body-mass index, smoking status, psychological distress, socioeconomic status and time spent in sedentary behaviour. The associations between dependent and independent variables were analyzed by using multiple regression analysis. Significance was set up at p < 0.05. PCS was positively associated with leisure-time PA (β 0.28, p < 0.001) and socioeconomic status (SES) (β 0.16, p=0.005), but inversely associated with job-related PA (β -0.15, p=0.012), domestic-time PA (β -0.14, p=0.014), age (β -0.12, p=0.050), psychological distress (β -0.43, p<0.001) and sedentary behaviour (β -0.15, p=0.009). MCS was positively associated with leisure-time PA (β 0.19, p=0.013) and SES (β 0.20, p=0.002), while inversely associated with age (β -0.23, p=0.001), psychological distress (β -0.27, p<0.001) and sedentary behaviour (β -0.22, p=0.001). Our results added new information about the associations between domain-specific PA and both physical and mental component scale in police officers. Future studies should deal with the same associations in other stressful occupations.

Keywords: health, fitness, police force, relations

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Stephen V. Crowder, Jonathan W. Lane

Abstract:

With short production development times, there is an increased need to demonstrate product reliability relatively quickly with minimal testing. In such cases there may be few if any observed failures. Thus it may be difficult to assess reliability using the traditional reliability test plans that measure only time (or cycles) to failure. For many components, degradation measures will contain important information about performance and reliability. These measures can be used to design a minimal test plan, in terms of number of units placed on test and duration of the test, necessary to demonstrate a reliability goal. In this work we present a case study involving an electronic component subject to degradation. The data, consisting of 42 degradation paths of cycles to failure, are first used to estimate a reliability function. Bootstrapping techniques are then used to perform power studies and develop a minimal reliability test plan for future production of this component.

Keywords: degradation measure, time to failure distribution, bootstrap, computational science

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Authors: Randa Ismail Sharafeddine

Abstract:

The Islamic economic system is distinctive in that it implicitly recognizes money as a separate, independent component of production capable of assuming risk and so entitled to the same reward as other Entrepreneurial Factors of Production (EFP). Conventional theory does not identify money capital explicitly as a component of production; rather, interest is recognized as a reward for capital, the interest rate is the cost of money capital, and it is also seen as a cost of physical capital. The conventional theory of production examines how diverse non-entrepreneurial resources (Land, Labor, and Capital) are selected; however, the economic theory community is largely unaware of the reasons why these resources choose to remain as non-entrepreneurial resources as opposed to becoming entrepreneurial resources. Should land, labor, and financial asset owners choose to work for others in return for rent, income, or interest, or should they engage in entrepreneurial risk-taking in order to profit. This is a decision made often in the actual world, but it has never been effectively treated in economic theory. This article will conduct a critical analysis of the conventional classification of factors of production and propose a classification for resource allocation and income distribution (Rent, Wages, Interest, and Profits) that is more rational, even within the conventional theoretical framework for evaluating and developing production and distribution theories. Money is an essential component of production in an Islamic economy, and it must be used to sustain economic activity.

Keywords: financial capital, production theory, distribution theory, economic activity, riba avoidance, institution of participation

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Authors: Fariba Dortaj, Reza Asadinejad, Akram Dortaj, Atena Baziyar

Abstract:

The aim of this study was to determine the effect of distance education (based on MOOC) on the components of academic engagement of Kerman PNU. The research was quasi-experimental method that cluster sampling with an appropriate volume was used in this study (one class in experimental group and one class in controlling group). Sampling method is single-stage cluster sampling. The statistical society is students of Kerman Payam Noor University, which) were selected 40 of them as sample (20 students in the control group and 20 students in experimental group). To test the hypothesis, it was used the analysis of univariate and Co-covariance to offset the initial difference (difference of control) in the experimental group and the control group. The instrument used in this study is academic engagement questionnaire of Zerang (2012) that contains component of cognitive, behavioral and motivational engagement. The results showed that there is no significant difference between mean scores of academic components of academic engagement in experimental group and the control group on the post-test, after elimination of the pre-test. The adjusted mean scores of components of academic engagement in the experimental group were higher than the adjusted average of scores after the test in the control group. The use of technology-based education in distance education has been effective in increasing cognitive engagement, motivational engagement and behavioral engagement among students. Experimental variable with the effect size 0.26, predicted 26% of cognitive engagement component variance. Experimental variable with the effect size 0.47, predicted 47% of the motivational engagement component variance. Experimental variable with the effect size 0.40, predicted 40% of behavioral engagement component variance. So teaching with technology (MOOC) has a positive impact on increasing academic engagement and academic performance of students in educational technology. The results suggest that technology (MOOC) is used to enrich the teaching of other lessons of PNU.

Keywords: educational technology, distance education, components of academic engagement, mooc technology

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Authors: Razieh Rezaeekalantari

Abstract:

Objective: The purpose of this study was to investigate the prediction of parenting orientations in terms of the components of couples' sexual review. Methods: This was a descriptive correlational research method. The population consisted of 500 couples referring to Sari Health Center. Two hundred and fifteen (215) people were selected randomly by using Krejcie-Morgan-sample-size-table. For data collection, the childbearing orientations scale and the Multidimensional Sexual Self-Concept Questionnaire were used. Result: For data analysis, the mean and standard deviation were used and to analyze the research hypothesis regression correlation and inferential statistics were used. Conclusion: The findings indicate that there is not a significant relationship between the tendency to childbearing and the predictive value of sexual review (r = 0.84) with significant level (sig = 219.19) (P < 0.05). So, with 95% confidence, we conclude that there is not a meaningful relationship between sexual orientation and tendency to child-rearing.

Keywords: couples referring, health center, sexual review component, parenting orientations

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Authors: Helena Dvořáčková, Mikajlo Irina, Záhora Jaroslav, Elbl Jakub

Abstract:

Biochar can be produced from the waste matter and its application has been associated with returning of carbon in large amounts into the soil. The impacts of this material on physical and chemical properties of soil have been described. The biggest part of the research work is dedicated to the hypothesis of this material’s toxic effects on the soil life regarding its effect on the soil biological component. At present, it has been worked on methods which could eliminate these undesirable properties of biochar. One of the possibilities is to mix biochar with organic material, such as compost, or focusing on the natural processes acceleration in the soil. In the experiment has been used as the addition of compost as well as the elimination of toxic substances by promoting microbial activity in aerated water environment. Biochar was aerated for 7 days in a container with a volume of 20 l. This way modified biochar had six times higher biomass production and reduce mineral nitrogen leaching. Better results have been achieved by mixing biochar with compost.

Keywords: leaching of nitrogen, soil, biochar, compost

Procedia PDF Downloads 321