Search results for: stereo-based digital image correlation

Authors: N. P. Tsephe, S. D. Eyono Obono

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Rural tourism is usually associated with rural development because it has strong linkages to rural resources; but it remains underdeveloped compared to urban tourism. This underdevelopment of rural tourism serves as a motivation for this study whose aim is to examine the factors affecting the perceived satisfaction of rural tourists. The objectives of this study are: to identify and design theories and models on rural tourism satisfaction, and to empirically validate these models and theories through a survey of tourists from the Malealea Lodge which is located in the Mafeteng District, in the Mountain Kingdom of Lesotho. Data generated by the collection of questionnaires used by this survey was analyzed quantitatively using descriptive statistics and correlations in SPSS after checking the validity and the reliability of the questionnaire. The main hypothesis behind this study is the relationship between the demographics of rural tourists, the motivation, and their satisfaction of tourists, as supported by existing literature; except that motivation is measured in this study according to three dimensions: push factors, pull factors, and perceived usefulness of ICT's in the rural tourism experience. Findings from this study indicate that among the demographics factors, continent of origin and marital status influence the satisfaction of rural tourists; and their occupation affects their perceptions on the use of ICT's in rural tourism. Moreover, only pull factors were found to influence the satisfaction of rural tourists.

Keywords: digital world, motivation, rural tourism, satisfaction

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Authors: Salem El-Tohami Ashoor

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Here we show that the reduction of [Cr(ArN(CH2)3NAr)2Cl2] (1) where (Ar = 2,6-Pri2C6H3) and in presence of NaCp (2) (Cp= C5H5 = cyclopentadien), with a center coordination η5 interaction between Cp as co-ligand and chromium metal center, this was optimization by using density functional theory (DFT) and then was comparing with experimental data, also other possibility of Cp interacted with ion metal were tested like η1 ,η2 ,η3 and η4 under optimization system. These were carried out under investigation of density functional theory (DFT) calculation, and comparing together. Other methods, explicitly including electron correlation, are necessary for more accurate calculations; MB3LYP ( Becke)( Lee–Yang–Parr ) level of theory often being used to obtain more exact results. These complexes were estimated of electronic energy for molecular system, because it accounts for all electron correlation interactions. The optimised of [Cr(ArN(CH2)3NAr)2(η5-Cp)] (Ar = 2,6-Pri2C6H3 and Cp= C5H5) was found to be thermally more stable than others of chromium cyclopentadienyl. By using Dewar-Chatt-Duncanson model, as a basis of the molecular orbital (MO) analysis and showed the highest occupied molecular orbital (HOMO) and lowest occupied molecular orbital LUMO.

Keywords: Chromium(III) cyclopentadienyl complexes, DFT, MO, HOMO, LUMO

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Authors: F. N. U. Abdullah, A. M. H. Ibrahim, Shuyu Liu

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Hybrid wheat root system is the major plant organ for water and nutrient acquisition. An initial wheat root study with Winrhizo scanner showed that entries with high root surface area but narrow root angle are associated with past drought tolerance in Texas, while those with wide angle can perform best under normal growing conditions. In a hybrid field experiment, commercial heterosis up to 8.3% in grain yield was obtained from diverse parents selected from male and female diverse groups. These hybrids showed promising yield at Greenville and McGregor, Texas, with grain yield up to 4412 Kg ha⁻¹ as compared to best performing commercial varieties ‘TAM-304’ (4075 Kg ha⁻¹) and ‘Gallagher’ (3981 Kg ha⁻¹). Among 130 hybrids produced, a subset of 50 better-performing hybrids and parents was subjected to one-month-old plant root studies scanned with Winrhizo. The results showed a significant positive correlation of grain yield with initial root angle and a negative correlation with root length.

Keywords: hybrids, root studies, heterosis, wheat breeding

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Authors: Rajendar Kandan, Mohammad Zakaria Alli, Hong Ong

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Cloud computing is a business model which provides an easier management of computing resources. Cloud users can request virtual machine and install additional softwares and configure them if needed. However, user can also request virtual appliance which provides a better solution to deploy application in much faster time, as it is ready-built image of operating system with necessary softwares installed and configured. Large numbers of virtual appliances are available in different image format. User can download available appliances from public marketplace and start using it. However, information published about the virtual appliance differs from each providers leading to the difficulty in choosing required virtual appliance as it is composed of specific OS with standard software version. However, even if user choses the appliance from respective providers, user doesn’t have any flexibility to choose their own set of softwares with required OS and application. In this paper, we propose a referenced architecture for dynamically customizing virtual appliance and provision them in an easier manner. We also add our experience in integrating our proposed architecture with public marketplace and Mi-Cloud, a cloud management software.

Keywords: cloud computing, marketplace, virtualization, virtual appliance

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Authors: Mohamed H. Khalifa, Fikry I. El-Shahawi, Nabil A. Mansour

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The cotton leafworm, Spodoptera littoralis (Boisduval) is a major insect pest of vegetables and cotton crops in Egypt, and exhibits different levels of tolerance to certain insecticides. Chlorantraniliprole has been registered recently in Egypt for control this insect. The susceptibilities of three S. littoralis populations collected from El Behaira governorate, north Egypt to chlorantraniliprole were determined by leaf-dipping technique on 4^th instar larvae. Obvious variation of toxicity was observed among the laboratory susceptible, and three field populations with LC₅₀ values ranged between 1.53 µg/ml and 6.22 µg/ml. However, all the three field populations were less susceptible to chlorantraniliprole than a laboratory susceptible population. The most tolerant populations were sampled from El Delengat (ED) Province where S. littoralis had been frequently challenged by insecticides. Certain enzyme activity assays were carried out to be correlated with the mechanism of the observed field population tolerance. All field populations showed significantly enhanced activities of detoxification enzymes compared with the susceptible strain. The regression analysis between chlorantraniliprole toxicities and enzyme activities revealed that the highest correlation is between α-esterase or β-esterase (α-β-EST) activity and collected field strains susceptibility, otherwise this correlation is not significant (P > 0.05). Synergism assays showed the ED and susceptible strains could be synergized by known detoxification inhibitors such as piperonyl butoxide (PBO), triphenyl phosphate (TPP) and diethyl-maleate (DEM) at different levels (1.01-8.76-fold and 1.09-2.94 fold, respectively), TPP showed the maximum synergism in both strains. The results show that there is a correlation between the enzyme activity and tolerance, and carboxylic-esterase (Car-EST) is likely the main detoxification mechanism responsible for tolerance of S. littoralis to chlorantraniliprole.

Keywords: chlorantraniliprole, detoxification enzymes, Egypt, Spodoptera littoralis

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Authors: Shivanand Pawar

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In India, a collective range of measurements had been adopted to control the massive spread of the COVID-19 pandemic, but World Health Organization (2022) revealed 525 930 fatalities and 43,847,065 confirmed cases. There are currently 30,857 cases per million people. Lack of knowledge, attitude and practices are the main causes thought to be increased COVID-19. The present study aims to assess the knowledge, attitude, and practice among Tamil Nadu residents. The participants (N=332) were aged 20 to 50 (mean=42.78, & SD=13.98) and were selected using purposive sampling, and data were collected online using knowledge, attitude and practice scale. Data were analyzed using person correlation and multiple regression analysis. The result found that 31.30% had satisfactory knowledge, 68.70% had non-satisfactory knowledge, followed by 45.20% had a positive attitude, 54.80% had a negative attitude, and 34.30% had a good practice, and 65.70% had poor practice towards COVID-19. Correlation results revealed that age has a negative and significant relationship with Knowledge and Practice towards COVID-19. The current study results contribute to the existing literature on knowledge, attitude and practice of COVID-19 to reduce the COVID-19 cases by managing unhealthy knowledge, attitude and practice to control the massive spread of COVID-19.

Keywords: COVID-19, knowledge, practice, attitude, Fisherman community

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Authors: Shamini Dhana, G. Subrahmanya VRK Rao

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The fashion industry is advancing towards a mindset of zero waste, personalization, creativity, and circularity. The trend of upcycling clothing and materials into personalized fashion is being demanded by the next generation. There is a need for a digital tool to accelerate the process towards mass customization. Dhana’s D/Sphere fashion technology platform uses digital tools to accelerate upcycling. In essence, advanced fashion garments can be designed and developed via reuse, repurposing, recreating activities, and using existing fabric and circulating materials. The D/Sphere platform has the following objectives: to provide (1) An opportunity to develop modern fashion using existing, finished materials and clothing without chemicals or water consumption; (2) The potential for an everyday customer and designer to use the medium of fashion for creative expression; (3) A solution to address the global textile waste generated by pre- and post-consumer fashion; (4) A solution to reduce carbon emissions, water, and energy consumption with the participation of all stakeholders; (5) An opportunity for brands, manufacturers, retailers to work towards zero-waste designs and as an alternative revenue stream. Other benefits of this alternative approach include sustainability metrics, trend prediction, facilitation of disassembly and remanufacture deep learning, and hyperheuristics for high accuracy. A design tool for mass personalization and customization utilizing existing circulating materials and deadstock, targeted to fashion stakeholders will lower environmental costs, increase revenues through up to date upcycled apparel, produce less textile waste during the cut-sew-stitch process, and provide a real design solution for the end customer to be part of circular fashion. The broader impact of this technology will result in a different mindset to circular fashion, increase the value of the product through multiple life cycles, find alternatives towards zero waste, and reduce the textile waste that ends up in landfills. This technology platform will be of interest to brands and companies that have the responsibility to reduce their environmental impact and contribution to climate change as it pertains to the fashion and apparel industry. Today, over 70% of the $3 trillion fashion and apparel industry ends up in landfills. To this extent, the industry needs such alternative techniques to both address global textile waste as well as provide an opportunity to include all stakeholders and drive circular fashion with new personalized products. This type of modern systems thinking is currently being explored around the world by the private sector, organizations, research institutions, and governments. This technological innovation using digital tools has the potential to revolutionize the way we look at communication, capabilities, and collaborative opportunities amongst stakeholders in the development of new personalized and customized products, as well as its positive impacts on society, our environment, and global climate change.

Keywords: circular fashion, deep learning, digital technology platform, personalization

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Authors: Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen

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Behavior recognition has been studied for realizing drivers assisting system and automated navigation and is an important studied field in the intelligent Building. In this paper, a recognition method of behavior recognition separated from a real image was studied. Images were divided into several categories according to the actual weather, distance and angle of view etc. SIFT was firstly used to detect key points and describe them because the SIFT (Scale Invariant Feature Transform) features were invariant to image scale and rotation and were robust to changes in the viewpoint and illumination. My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use SIFT represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for classification of images in order to classify people’s behaviors in the intelligent building in order to give maximum comfort with optimized energy consumption.

Keywords: video analysis, people behavior, intelligent building, classification

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Authors: Chong Ling Ling

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In the Malaysian polytechnic, there are concerns about language proficiency, communicative competence, and employability among Malaysian polytechnic ESL engineering students. This study examined the relationships between communicative competence, language proficiency, and employability using descriptive analysis and inferential statistics. Next, Pearson’s Correlation determines the correlation between communication competence, language proficiency, and employability skills of Malaysian Polytechnic ESL engineering students. The total number of participants was 81 final-year engineering students. The findings revealed high positive correlations between the communicative competence -'I can talk with a friend in English.' and employability skill (r = 0.854, p = .031), also, language proficiency -'I can understand the English songs I listen to' and employability skill (r = 0.887, p = .038). The result is consistent with the theories. The result revealed that for the 81 students, communication competence and language proficiency, and employability skills are firmly and significantly correlated. Thus, it concluded that both communicative competence and language proficiency equally essential to ensure a higher employability rate among Malaysian polytechnic ESL engineering students.

Keywords: communicative competence, employability, language proficiency, Malaysian polytechnic

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Authors: Ahmed H. Abdullah, Pavel Kroupa

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Globular clusters (GC) are important objects for tracing the early evolution of a galaxy. We study the correlation between the cluster population and the global properties of the host galaxy. We found that the correlation between cluster population (NGC) and the baryonic mass (Mb) of the host galaxy are best described as 10 −5.6038Mb. In order to understand the origin of the U -shape relation between the GC specific frequency (SN) and Mb (caused by the high value of SN for dwarfs galaxies and giant ellipticals and a minimum SN for intermediate mass galaxies≈ 1010M), we derive a theoretical model for the specific frequency (SNth). The theoretical model for SNth is based on the slope of the power-law embedded cluster mass function (β) and different time scale (Δt) of the forming galaxy. Our results show a good agreement between the observation and the model at a certain β and Δt. The model seems able to reproduce higher value of SNth of β = 1.5 at the midst formation time scale.

Keywords: galaxies: dwarf, globular cluster: specific frequency, number of globular clusters, formation time scale

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Joanna Dzieńdziora

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Lobbying is an instrument of influence in various decision-making processes. It is also the underestimated issue as a research problem. The lack of research on the modern lobbyist competencies is the most crucial element. The paper presents attempts of finding answers to the following questions: Who should run the lobbying activity? What competencies should a lobbyist possess in order to implement lobbying activities effectively? Searching for answers for the mentioned above questions requires positioning the opportunity to change the image of lobbying in the area of competencies of entities that provide lobbying activities. The aim of the paper is presenting the lobbyist competencies profile in the framework of his professional role. The essence of lobbying activity and its significance in the modern economy as well as areas, the scope of lobbying activities, diagnosis of a modern lobbyist’s competences, lobbyist’s competencies profile that is focused on the professionalization of the lobbying activity, will have been presented in this paper. Indicated research tasks let emerge lobbyist’s competencies in the way that allows identifying and elaborating the lobbyist competencies profile. The profile lets improve lobbying activities. Its elaboration is based on the author’s research results analysis. Taking into consideration the shortages within the theory and research on the lobbying activity, the implementation of this research enables to fill the cognitive gap existing in the theory of management sciences.

Keywords: competencies, competencies profile, lobbying, lobbyist

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Authors: Mahshid Arabi

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With the rapid expansion of digital technologies and the internet, information security has become a critical priority for organizations and individuals. The widespread use of digital tools such as smartphones and internet networks facilitates the storage of vast amounts of data, but simultaneously, vulnerabilities and security threats have significantly increased. The aim of this study is to examine and analyze modern methods of information security management and to develop a comprehensive model to counteract threats and information misuse. This study employs a mixed-methods approach, including both qualitative and quantitative analyses. Initially, a systematic review of previous articles and research in the field of information security was conducted. Then, using the Delphi method, interviews with 30 information security experts were conducted to gather their insights on security challenges and solutions. Based on the results of these interviews, a comprehensive model for information security management was developed. The proposed model includes advanced encryption techniques, machine learning-based intrusion detection systems, and network security protocols. AES and RSA encryption algorithms were used for data protection, and machine learning models such as Random Forest and Neural Networks were utilized for intrusion detection. Statistical analyses were performed using SPSS software. To evaluate the effectiveness of the proposed model, T-Test and ANOVA statistical tests were employed, and results were measured using accuracy, sensitivity, and specificity indicators of the models. Additionally, multiple regression analysis was conducted to examine the impact of various variables on information security. The findings of this study indicate that the comprehensive proposed model reduced cyber-attacks by an average of 85%. Statistical analysis showed that the combined use of encryption techniques and intrusion detection systems significantly improves information security. Based on the obtained results, it is recommended that organizations continuously update their information security systems and use a combination of multiple security methods to protect their data. Additionally, educating employees and raising public awareness about information security can serve as an effective tool in reducing security risks. This research demonstrates that effective and up-to-date information security management requires a comprehensive and coordinated approach, including the development and implementation of advanced techniques and continuous training of human resources.

Keywords: data protection, digital technologies, information security, modern management

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Authors: Silvia Elias

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The 'Femme Fatale' figure has become synonymous with a mysterious and seductive woman whose charms captivate her lovers into bonds of irresistible desire, often leading them to compromise or downfall. Originally, a Femme Fatale typically uses her beauty to lead men to their destruction but in modern literature, she represents a direct attack on traditional womanhood and the nuclear family as she refuses to abide by the pillars of mainstream society creating an image of a strong independent woman who defies the control of men and rejects the institution of the family. This research aims at discussing the differences and similarities between the femme fatale and the New Woman and how they are perceived by the audience. There is often confusion between the characteristics that define a New Woman and a Femme Fatale since both women desire independence, challenge typical gender role casting, push against the limits of the patriarchal society and take control of their sexuality. The study of the femme fatale remains appealing in modern times because the fear of gender equality gives life to modern femme fatale versions and post-modern literary works introduce their readers to new versions of the deadly seductress. One that does not fully depend on her looks to destroy men. The idea behind writing this paper was born from reading David Mamet's two-character play Oleanna (1992) and tracing the main female protagonist/antagonist's transformation from a helpless inarticulate girl into a powerful controlling negotiator who knows how to lead a bargain and maintain the upper hand.

Keywords: Circe, David, Eve, evolution, feminist, femme fatale, gender, Mamet, new, Odysseus, Oleanna, power, Salome, schema, seduction, temptress, woman

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Authors: M. Saadi, S. E. H. Abaidia, M. Y. Mokeddem.

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We present the results of a systematic and comparative study of the bulk, the structural properties, and phonon calculations of aluminum alloys using several exchange–correlations functional theory (DFT) with different plane-wave basis pseudo potential techniques. Density functional theory implemented by the Vienna Ab Initio Simulation Package (VASP) technique is applied to calculate the bulk and the structural properties of several structures. The calculations were performed for within several exchange–correlation functional and pseudo pententials available in this code (local density approximation (LDA), generalized gradient approximation (GGA), projector augmented wave (PAW)). The lattice dynamic code “PHON” developed by Dario Alfè was used to calculate some thermodynamics properties and phonon dispersion relation frequency distribution of Aluminium alloys using the VASP LDA PAW and GGA PAW results. The bulk and structural properties of the calculated structures were compared to different experimental and calculated works.

Keywords: DFT, exchange-correlation functional, LDA, GGA, pseudopotential, PAW, VASP, PHON, phonon dispersion

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Authors: Faizan Asghar, Mohammad Jamal Khattak

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Aging, in general, refers to changes in rheological characteristics of asphalt mixture due to changes in chemical composition over the course of construction and service life of the pavement. The main goal of this study was to investigate the impact of oxidation on fatigue characteristics of a novel HMA composite fabricated with a combination of crumb rubber (CRM) and polyvinyl alcohol (PVA) fiber subject to aging of 7 and 14 days. A flexural beam fatigue test was performed to evaluate several characteristics of control, CRM modified, PVA reinforced, and novel rubber-fiber HMA composite. Experimental results revealed that aging had a significant impact on the fatigue performance of novel HMA composite. It was found that a suitable proportion of CRM and PVA radically affected the performance of novel rubber-fiber HMA in resistance to fracture and fatigue cracking when subjected to long-term aging. The developed novel HMA composite containing 2% CRM and 0.2% PVA presented around 29 times higher resistance to fatigue cracking for a period of 7 days of aging. To develop a cumulative plastic deformation level of 250 micros, such a mixture required over 50 times higher cycles than control HMA. Moreover, the crack propagation rate was reduced by over 90%, with over 12 times higher energy required to propagate a unit crack length in such a mixture compared to conventional HMA. Further, digital imaging correlation analyses revealed a more twisted and convoluted fracture path and higher strain distribution in rubber-fiber HMA composite. The fatigue performance after long-term aging of such novel HMA composite explicitly validates the ability to withstand load repetition that could lead to an extension in the service life of pavement infrastructure and reduce taxpayers’ dollars spent.

Keywords: crumb rubber, PVA fibers, dry process, aging, performance testing, fatigue life

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Authors: Tienfuan Kerh, Hsienchang Lu, Rob Saunders

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Shoreline erosion problems caused by global warming and sea level rising may result in losing of land areas, so it should be examined regularly to reduce possible negative impacts. Initially in this study, three sets of survey images obtained from the years of 1990, 2001, and 2010, respectively, are digitalized by using graphical software to establish the spatial coordinates of six major beaches around the island of Taiwan. Then, by overlaying the known multi-period images, the change of shoreline can be observed from their distribution of coordinates. In addition, the neural network approximation is used to develop a model for predicting shoreline variation in the years of 2015 and 2020. The comparison results show that there is no significant change of total sandy area for all beaches in the three different periods. However, the prediction results show that two beaches may exhibit an increasing of total sandy areas under a statistical 95% confidence interval. The proposed method adopted in this study may be applicable to other shorelines of interest around the world.

Keywords: digitalized shoreline coordinates, survey image overlaying, neural network approximation, total beach sandy areas

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Authors: Billy Zou

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Statement of the Problem: The burden of mental illness and problem behaviors in adolescence has risen worldwide. While less than 50% of teens have access to traditional mental health care, more than 73% have smartphones. Internet-based interventions offer advantages such as cost-effectiveness, availability, and flexibility. Methodology & Theoretical Orientation: A literature review was done using a PubMed search with the words mental health app yielding 2113 results. 103 articles that met inclusion criteria were reviewed, and findings were then described and synthesized. Findings: 1. Computer-based CBT was found to be effective for OCD, depression, social phobia, and panic disorder. 2. Web-based psychoeducation reduced problem behavior and improved parental well-being. 3. There is limited evidence for mobile-phone-based apps, but preliminary results suggest computer-based interventions are transferrable to mobile apps. 4. Adherence to app-based treatment was correlated with impressions about the user interface Conclusion & Significance: There is evidence for the effectiveness of computer-based programs in filling the significant gaps that currently exist in mental health delivery in the United States and internationally. There is also potential and theoretical validity for mobile-based apps to do the same, though more data is needed.

Keywords: children's mental health, mental health app, child and adolecent psychiatry, digital therapy

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Farhad Hekmatipour, M. A. Akhavan-Behabadi, Farzad Hekmatipour

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In this paper, experiments were conducted to investigate the heat transfer of Copper Oxide-Heat Transfer Oil (CuO-HTO) nanofluid laminar flow in vertical smooth and microfin tubes as the surface temperature is constant. The effect of adding the nanoparticle to base fluid and Richardson number on the heat transfer enhancement is investigated as Richardson number increases from 0.1 to 0.7. The experimental results demonstrate that the combined forced-natural convection heat transfer rate may be improved significantly with an increment of mass nanoparticle concentration from 0% to 1.5%. In this experiment, a correlation is also proposed to predict the mixed convection heat transfer rate of CuO-HTO nanofluid flow. The maximum deviation of both correlations is less than 14%. Moreover, a correlation is presented to estimate the Nusselt number inside vertical smooth and microfin tubes as Rayleigh number is between 2´105 and 6.8´106 with the maximum deviation of 12%.

Keywords: mixed convection, heat transfer, nanofluid, vertical tube, microfin tube

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

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Authors: Jan Henrik Wosnitza

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The logistic regression has developed into a standard approach for estimating conditional probabilities in a wide range of applications including credit risk prediction. The article at hand contributes to the current literature on logistic regression fourfold: First, it is demonstrated that the binary logistic regression automatically meets its model assumptions under very general conditions. This result explains, at least in part, the logistic regression's popularity. Second, the requirement of homoscedasticity in the context of binary logistic regression is theoretically substantiated. The variances among the groups of defaulted and non-defaulted obligors have to be the same across the level of the aggregated default indicators in order to achieve linear logits. Third, this article sheds some light on the question why nonlinear logits might be superior to linear logits in case of a small amount of data. Fourth, an innovative methodology for estimating correlations between obligor-specific log-odds is proposed. In order to crystallize the key ideas, this paper focuses on the example of credit risk prediction. However, the results presented in this paper can easily be transferred to any other field of application.

Keywords: correlation, credit risk estimation, default correlation, homoscedasticity, logistic regression, nonlinear logistic regression

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Authors: Ali Ashraf, Ghazaleh Aghaee, Sedigheh Samimian, Mohaya Farzin

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Objectives: Previous studies have indicated the positive effect of physical activity and sports on different aspects of health, such as muscle endurance and sleep cycle. However, in university students, particularly medical students, who have limited time and a stressful lifestyle, there have been limited studies exploring this matter with proven statistical results. In this regard, this study aims to find out how regular physical activity can influence the academic performance of medical students during their internship period. Methods: This was a descriptive-analytical study. Overall, 160 medical students (including 80 women and 88 men) voluntarily participated in the study. The Baecke Physical Activity Questionnaire was applied to determine the student’s physical activity levels. The student's academic performance was determined based on their total average academic scores. The data were analyzed in SPSS version 16 software using the independent t-test, Pearson correlation, and linear regression. Results: The average age of the students was 26.0±1.5 years. Eighty-eight students (52.4%) were male, and 142 (84.5%) were single. The student's mean total average academic score was 16.2±1.2, and their average physical activity score was 8.3±1.1. The student's average academic score was not associated with their gender (P=0.427), marital status (P=0.645), and age (P=0.320). However, married students had a significantly lower physical activity level compared to single students (P=0.020). The results indicated a significant positive correlation between student's physical activity levels and average academic scores (r=+0.410 and P<0.001). This correlation was independent of the student’s age, gender, and marital status based on the regression analysis. Conclusion: The results of the current study suggested that the physical activity level in medical students was low to moderate in most cases, and there was a significant direct relationship between student’s physical activity level and academic performance, independent of age, gender, and marital status.

Keywords: exercise, education, physical activity, academic performance

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Authors: Sher Muhammad, Mirza Muhammad Waqar

Abstract:

It is necessary to monitor and identify mangroves types and spatial extent near coastal areas because it plays an important role in coastal ecosystem and environmental protection. This research aims at identifying and mapping mangroves types along Karachi coast ranging from 24.79 to 24.85 degree in latitude and 66.91 to 66.97 degree in longitude using hyperspectral remote sensing data and techniques. Image acquired during February, 2012 through Hyperion sensor have been used for this research. Image preprocessing includes geometric and radiometric correction followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analyzed by visualizing it in n-dimensions for end-member extraction. Well-distributed clusters on the n-dimensional scatter plot have been selected with the region of interest (ROI) tool as end members. These end members have been used as an input for classification techniques applied to identify and map mangroves species including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), and Spectral Information Diversion (SID). Only two types of mangroves namely Avicennia Marina (white mangroves) and Avicennia Germinans (black mangroves) have been observed throughout the study area.

Keywords: mangrove, hyperspectral, hyperion, SAM, SFF, SID

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Authors: Destriyanti Sugiarti, Ririn A Wulandari

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Background: Malaria disease was widespread in many countries, both tropical and sub-tropical. Tasikmalaya is a region that experienced an increase in malaria cases over the last 5 years and highest in 2013, a total of 168 positive cases of malaria. Tasikmalaya region consists of coastal and mountain areas, it has a potential place for Anopheles mosquito breeding, i.e swamp, lagoon, andrice fields.The purpose of this study was to determine the correlation of environmental risk factors with the incidence of malaria in Tasikmalaya. Methods: The design of the study is case control study with 140 samples in 5 sub-district (Cineam, Cikatomas, Cipatujah, Salopa, and Jatiwaras). This study examines the environmental factors that influence the incidence of malaria in Tasikmalaya District in 2013. The research used 14 variables: individual characteristics (education, knowledge, occupation) and environmental risk factors (mobility to endemic areas, use mosquito nets, use of wire gauze at home, use mosquito repellent, repellent use, the presence of a large animal in a cage, breeding place, the presence of larvae, temperature and humidity chamber). Results: Results demonstrated an association between occupation (0.22; 0.10-0.47), the mobility of the population to the endemic areas (37.4; 14.29-98.18) ,the presence of larvae (5.26; 1.41-19.74), and the room temperature with optimum temperature for mosquito breeding is 25-30oC (3.25; 1.62- 6.50). Conclusion: The dominant factor affecting the spread of malaria in Tasikmalaya is the mobility of the population to endemic areas. The results of the study suggest migration survey conducted activity and health promotion for preventive efforts against malaria in malaria-endemic areas, and to encourage people to behave healthy life by freeing environment of mosquito larvae and protect themselves from mosquito bites.

Keywords: Environmental risk factors, malaria, correlation, Anopheles

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Authors: Toby Gibbs, Suwaree Yordchim

Abstract:

This research analyzes factors affecting the success of Litecoin Value within Thailand and develops a guideline for self-reliance for effective business implementation. Samples in this study included 119 people through surveys. The results revealed four main factors affecting the success as follows: 1) Future Career training should be pursued in applied Litecoin development. 2) Didn't grasp the concept of a digital currency or see the benefit of a digital currency. 3) There is a great need to educate the next generation of learners on the benefits of Litecoin within the community. 4) A great majority didn't know what Litecoin was. The guideline for self-reliance planning consisted of 4 aspects: 1) Development planning: by arranging meet up groups to conduct further education on Litecoin and share solutions on adoption into every day usage. Local communities need to develop awareness of the usefulness of Litecoin and share the value of Litecoin among friends and family. 2) Computer Science and Business Management staff should develop skills to expand on the benefits of Litecoin within their departments. 3) Further research should be pursued on how Litecoin Value can improve business and tourism within Thailand. 4) Local communities should focus on developing Litecoin awareness by encouraging street vendors to accept Litecoin as another form of payment for services rendered.

Keywords: litecoin, mining, confirmations, payment method

Procedia PDF Downloads 184

Authors: Bo Wang

Abstract:

As the first high-resolution civil optical satellite, ZY-3 satellite is able to obtain high-resolution multi-view images with three linear array sensors. The images can be used to generate Digital Elevation Models (DEM) through dense matching of stereo images. However, due to the clouds, forest, water and buildings covered on the images, there are some problems in the dense matching results such as outliers and areas failed to be matched (matching holes). This paper introduced an algorithm to verify the accuracy of DEM that generated by ZY-3 satellite with Shuttle Radar Topography Mission (SRTM). Since the accuracy of SRTM (Internal accuracy: 5 m; External accuracy: 15 m) is relatively uniform in the worldwide, it may be used to improve the accuracy of ZY-3 DEM. Based on the analysis of mass DEM and SRTM data, the processing can be divided into two aspects. The registration of ZY-3 DEM and SRTM can be firstly performed using the conjugate line features and area features matched between these two datasets. Then the ZY-3 DEM can be refined by eliminating the matching outliers and filling the matching holes. The matching outliers can be eliminated based on the statistics on Local Vector Binning (LVB). The matching holes can be filled by the elevation interpolated from SRTM. Some works are also conducted for the accuracy statistics of the ZY-3 DEM.

Keywords: ZY-3 satellite imagery, DEM, SRTM, refinement

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Authors: M. Owlia, M. H. Azarsa, M. Khabbazan, A. Mirbagheri

Abstract:

Proprioceptive neuromuscular facilitation exercises are a series of stretching techniques that are commonly used in rehabilitation and exercise therapy. Assessment of these exercises for true maneuvering requires extensive experience in this field and could not be down with patients themselves. In this paper, we developed software that uses Microsoft Kinect sensor, a spherical color marker, and real-time image processing methods to evaluate patient’s performance in generating true patterns of movements. The software also provides the patient with a visual feedback by showing his/her avatar in a Virtual Reality environment along with the correct path of moving hand, wrist and marker. Primary results during PNF exercise therapy of a patient in a room environment shows the ability of the system to identify any deviation of maneuvering path and direction of the hand from the one that has been performed by an expert physician.

Keywords: image processing, Microsoft Kinect, proprioceptive neuromuscular facilitation, upper extremities assessment, virtual reality

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Authors: Insaf Bellamine, Hamid Tairi

Abstract:

Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

Procedia PDF Downloads 378

Authors: Zachary Pan

Abstract:

Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

Procedia PDF Downloads 103