Search results for: UAV-based hyperspectral data

Authors: Rizwan Sultan, Hasnain Zafar

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Trauma is the leading cause of death among young population not only in USA but Pakistan as well. The high prevalence of disease should result in larger amount of data and larger number of publications resulting in exploring room for improvement in the field. We aimed to review trauma literature generated from Pakistan in journals indexed with PubMed from January 2010 to December 2014. Search using term “Trauma AND Pakistan” filtering for relevant dates and species human was done on Pubmed. The abstracts and articles were reviewed by the authors to collect data on a preformed performa. 114 articles were published from Pakistan during these 5 years. 64% articles were published in international journals. 63% articles were published in journals with impact factor less than 1. 54% articles were published from one of the four provinces of Pakistan. 64% of articles provided level 4 while 14% articles provided level 5 evidence on the topic. 55% articles discussed epidemiology in non-representative populations. Trauma literature from Pakistan is not only lacking significantly but is also of poor quality and is unable to offer conclusions on this particular subject. There is a lot of space for improvement in the upcoming years.

Keywords: trauma, literature, Pakistan, level of evidence

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Authors: Wanhyun Cho, Soonja Kang, Sanggoon Kim, Soonyoung Park

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We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered an efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods.

Keywords: multinomial dirichlet classification model, Gaussian process priors, variational Bayesian approximation, importance sampling, approximate posterior distribution, marginal likelihood evidence

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Authors: Wiqar Hussain Shah

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The electrical and thermal properties of the doped Tellurium Telluride (Tl10Te6) chalcogenide nano-particles are mainly characterized by a competition between metallic (hole doped concentration) and semi-conducting state. We have studied the effects of Sn doping on the electrical and thermoelectric properties of Tl10-xSnxTe6 (1.00 ≤x≤ 2.00), nano-particles, prepared by solid state reactions in sealed silica tubes and ball milling method. Structurally, all these compounds were found to be phase pure as confirmed by the x-rays diffractometery (XRD) and energy dispersive X-ray spectroscopy (EDS) analysis. Additionally crystal structure data were used to model the data and support the findings. The particles size was calculated from the XRD data by Scherrer’s formula. The EDS was used for an elemental analysis of the sample and declares the percentage of elements present in the system. The thermo-power or Seebeck co-efficient (S) was measured for all these compounds which show that S increases with increasing temperature from 295 to 550 K. The Seebeck coefficient is positive for the whole temperature range, showing p-type semiconductor characteristics. The electrical conductivity was investigated by four probe resistivity techniques revealed that the electrical conductivity decreases with increasing temperature, and also simultaneously with increasing Sn concentration. While for Seebeck coefficient the trend is opposite which is increases with increasing temperature. These increasing behavior of Seebeck coefficient leads to high power factor which are increases with increasing temperature and Sn concentration except For Tl8Sn2Te6 because of lowest electrical conductivity but its power factor increases well with increasing temperature.

Keywords: Sn doping in Tellurium Telluride nano-materials, electron holes competition, Seebeck co-efficient, effects of Sn doping on Electrical conductivity, effects on Power factor

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Authors: Athanasia Kouroupa, Karen Irvine, Sivana Mengoni, Shivani Sharma

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Exploring parents’ preferences towards technology-based interventions for children on the autism spectrum can inform future research and support technology design. The study aimed to provide a comprehensive description of parents’ knowledge and preferences about innovative technology to support children on the autism spectrum. Survey data were collected from parents (n = 267) internationally. The survey included information about the use of conventional (e.g., smartphone, iPod, tablets) and non-conventional (e.g., virtual reality, robot) technologies. Parents appeared to prefer conventional technologies such as tablets and dislike non-conventional ones. They highlighted the positive contribution technology brought to the children’s lives during the pandemic. A few parents were equally concerned that the compulsory introduction of technology during the pandemic was associated with elongated time on devices. The data suggested that technology-based interventions are not widely known, need to be financially approachable and achieve a high standard of design to engage users.

Keywords: autism, intervention, preferences, technology

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Authors: Ebenezer Bayode Agboola

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The study investigated the influence of socioeconomic factors on crime perpetuation among inmates of correctional facilities in South West Nigeria. A sample size of two hundred and forty-four inmates was drawn from Ado, Akure and Ilesha correctional facilities. The sample size consisted of both male and female inmates. Individual inmate was drawn through systematic sampling with the use of inmates’ register at the correctional facilities. The study employed a mixed design, which allowed the blend of both quantitative and qualitative methods. For the quantitative method, data was collected through the use of a questionnaire and for the qualitative method; data was collected with the aid of an in-depth interview (ID. Four research questions were raised for the study and analysed descriptively using simple frequency count and percentage. Five research hypotheses were formulated for the study and tested using Analysis of Variance (ANOVA) and Multiple Regressions. Based on the data analysis, findings revealed that there was a significant relationship between family history and perpetuation of crime among inmates. Though no significant relationship was found between employment and the perpetuation of crime, however, the rate of crime perpetuation by individuals was significantly found to be related to peer pressure. Also, the study further found that there was a significant relationship between the use of substances and perpetuation of crime. Lastly, it was found that there was a significant relationship between family history, employment, and peer pressure. The study recommended that Parents should pay adequate attention to their children, especially during the adolescent stage and that the Government should enact relevant laws that will checkmate the rising involvement of young people in cybercrime or internet fraud.

Keywords: crime, socio economic factor, inmates, correctional facilities, Southwest

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Authors: Desta Gebretekle Shiferaw, Balakrishna Kalluraya

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Pyrazole is an aromatic five-membered heterocycle with two nitrogen and three carbon atoms in its ring structure. According to the literature, pyrazoline, pyrazole, and thiazole-containing moieties are found in various drug structures and are responsible for nearly all pharmacological effects. The pyrazoline linked to pyrazole moiety carbothioamides was synthesized via the reaction of pyrazole-bearing chalcones (3-(5-chloro-3-methyl-¹-phenyl-1H-pyrazol-4-yl)-¹-(substituted aryl) prop-2-ene-¹-one derivatives) with a nucleophile thiosemicarbohyrazide by heating in ethanol using fused sodium acetate as a catalyst. Then the carbothioamide derivatives were converted into the pyrazoline hybrid to pyrazole and thiazole derivatives by condensing with substituted phenacyl bromide in alcohol in a basic medium. Next, the chemical structure of the newly synthesized molecules was confirmed by IR, 1H-NMR, and mass spectral data. Further, they were screened for their in vitro antioxidant activity. Compared to butylated hydroxy anisole (BHA)., the antioxidant data showed that the synthesized compounds had good to moderate activity.

Keywords: pyrazoline-pyrazole carbothioamide derivatives, pyrazoline-pyrazole-thiazole derivatives, spectral studies, antioxidant activity

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Authors: Bo Wan, Xiu Wen, Lingling An, Xiaoling Ding

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The paper describes a Chinese shadow play animation system based on Kinect. Users, without any professional training, can personally manipulate the shadow characters to finish a shadow play performance by their body actions and get a shadow play video through giving the record command to our system if they want. In our system, Kinect is responsible for capturing human movement and voice commands data. Gesture recognition module is used to control the change of the shadow play scenes. After packaging the data from Kinect and the recognition result from gesture recognition module, VRPN transmits them to the server-side. At last, the server-side uses the information to control the motion of shadow characters and video recording. This system not only achieves human-computer interaction, but also realizes the interaction between people. It brings an entertaining experience to users and easy to operate for all ages. Even more important is that the application background of Chinese shadow play embodies the protection of the art of shadow play animation.

Keywords: hadow play animation, Kinect, gesture recognition, VRPN, HCI

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Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: Edvard P. G. Bruun

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One of the best ways of improving our understanding of reinforced concrete is through large-scale experimental testing. The gathered information is critical in making inferences about structural mechanics and deriving the mathematical models that are the basis for finite element analysis programs and design codes. An effective way of measuring the strains across a region of a specimen is by using a system of surface mounted Linear Variable Differential Transformers (LVDTs). While a single LVDT can only measure the linear strain in one direction, by combining several measurements at known angles a Mohr’s circle of strain can be derived for the whole region under investigation. This paper presents a method that can be used by researchers, which improves the accuracy and removes experimental bias in the calculation of the Mohr’s circle, using four rather than three independent strain measurements. Obtaining high quality strain data is essential, since knowing the angular deviation (shear strain) and the angle of principal strain in the region are important properties in characterizing the governing structural mechanics. For example, the Modified Compression Field Theory (MCFT) developed at the University of Toronto, is a rotating crack model that requires knowing the direction of the principal stress and strain, and then calculates the average secant stiffness in this direction. But since LVDTs can only measure average strains across a plane (i.e., between discrete points), localized cracking and spalling that typically occur in reinforced concrete, can lead to unrealistic results. To build in redundancy and improve the quality of the data gathered, the typical experimental setup for a large-scale shell specimen has four independent directions (X, Y, H, and V) that are instrumented. The question now becomes, which three should be used? The most common approach is to simply discard one of the measurements. The problem is that this can produce drastically different answers, depending on the three strain values that are chosen. To overcome this experimental bias, and to avoid simply discarding valuable data, a more rigorous approach would be to somehow make use of all four measurements. This paper presents the derivation of a method to draw what is effectively a Mohr’s circle of 'best-fit', which optimizes the circle by using all four independent strain values. The four-strain optimized Mohr’s circle approach has been utilized to process data from recent large-scale shell tests at the University of Toronto (Ruggiero, Proestos, and Bruun), where analysis of the test data has shown that the traditional three-strain method can lead to widely different results. This paper presents the derivation of the method and shows its application in the context of two reinforced concrete shells tested in pure torsion. In general, the constitutive models and relationships that characterize reinforced concrete are only as good as the experimental data that is gathered – ensuring that a rigorous and unbiased approach exists for calculating the Mohr’s circle of strain during an experiment, is of utmost importance to the structural research community.

Keywords: reinforced concrete, shell tests, Mohr’s circle, experimental research

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Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: neural networks, pattern learning, security, wireless sensor networks

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Authors: Mary Joy C. Montenegro, Voltaire M. Mistades

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This action research was implemented to enhance the teaching of linear motion and to improve students' conceptual understanding and engagement using a developed 7R-based module called 'module on vectors and one-dimensional kinematics' (MVOK). MVOK was validated in terms of objectives, contents, format, and language used, presentation, usefulness, and overall presentation. The validation process revealed a value of 4.7 interpreted as 'Very Acceptable' with a substantial agreement (0. 60) from the validators. One intact class of 46 Grade 12 STEM students from one of the public schools in Paranaque City served as the participants of this study. The students were taught using the module during the first semester of the academic year 2019–2020. Employing the mixed-method approach, quantitative data were gathered using pretest/posttest, activity sheets, problem sets, and survey form, while qualitative data were obtained from surveys, interviews, observations, and reflection log. After the implementation, there was a significant difference of 18.4 on students’ conceptual understanding as shown in their pre-test and post-test scores on the 24-item test with a moderate Hake gain equal to 0.45 and an effect size of 0.83. Moreover, the scores on activity and problem sets have a 'very good' to 'excellent' rating, which signifies an increase in the level of students’ conceptual understanding. There also exists a significant difference between the mean scores of students’ engagement overall (t= 4.79, p = 0.000, p < 0.05) and in the dimension of emotion (t = 2.51, p = 0.03) and participation/interaction (t = 5.75, p = 0.001). These findings were supported by gathered qualitative data. Positive views were elicited from the students since it is an accessible tool for learning and has well-detailed explanations and examples. The results of this study may substantiate that using MVOK will lead to better physics content understanding and higher engagement.

Keywords: conceptual understanding, engagement, linear motion, module

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Authors: Gaurav Kumar, Shilpa

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Digital libraries have been established all over the world to create, maintain and to preserve the digital materials. This paper focuses on operational digital preservation systems specifically in educational organizations in India. It considers the broad range of digital objects including e-journals, technical reports, e-records, project documents, scientific data, etc. This paper describes the main objectives, process and technological issues involved in preservation of digital materials. Digital preservation refers to the various methods of keeping digital materials alive for the future. It includes everything from electronic publications on CD-ROM to Online database and collections of experimental data in digital format maintains the ability to display, retrieve and use digital collections in the face of rapidly changing technological and organizational infrastructures elements. This paper exhibits the importance and objectives of digital preservation. The necessities of preservation are hardware and software technology to interpret the digital documents and discuss various aspects of digital preservation.

Keywords: preservation, digital preservation, digital dark age, conservation, archive, repository, document, information technology, hardware, software, organization, machine readable format

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Authors: Keiwoma Mark Yila, Mathew Lamrana Siaffa Gboku, Mohamed Sahr Lebbie, Lamin Ibrahim Kamara

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Rainfall and temperature are the important variables which are often used to trace climate variability and change. A perception study and analysis of climatic data were conducted to assess the changes in rainfall and temperature and their impact on crop production in Moyamba district, Sierra Leone. For the perception study, 400 farmers were randomly selected from farmer-based organizations (FBOs) in 4 chiefdoms, and 30 agricultural extension workers (AWEs) in the Moyamba district were purposely selected as respondents. Descriptive statistics and Kendall’s test of concordance was used to analyze the data collected from the farmers and AEWs. Data for the analysis of variability and trends of rainfall and temperature from 1991 to 2020 were obtained from the Sierra Leone Meteorological Agency and Njala University and grouped into monthly, seasonal and annual time series. Regression analysis was used to determine the statistical values and trend lines for the seasonal and annual time series data. The Mann-Kendall test and Sen’s Slope Estimator were used to analyze the trends' significance and magnitude, respectively. The results of both studies show evidence of climate change in the Moyamba district. A substantial number of farmers and AEWs perceived a decrease in the annual rainfall amount, length of the rainy season, a late start and end of the rainy season, an increase in the temperature during the day and night, and a shortened harmattan period over the last 30 years. Analysis of the meteorological data shows evidence of variability in the seasonal and annual distribution of rainfall and temperature, a decreasing and non-significant trend in the rainy season and annual rainfall, and an increasing and significant trend in seasonal and annual temperature from 1991 to 2020. However, the observed changes in rainfall and temperature by the farmers and AEWs partially agree with the results of the analyzed meteorological data. The majority of the farmers perceived that; adverse weather conditions have negatively affected crop production in the district. Droughts, high temperatures, and irregular rainfall are the three major adverse weather events that farmers perceived to have contributed to a substantial loss in the yields of the major crops cultivated in the district. In response to the negative effects of adverse weather events, a substantial number of farmers take no action due to their lack of knowledge and technical or financial capacity to implement climate-sensitive agricultural (CSA) practices. Even though few farmers are practising some CSA practices in their farms, there is an urgent need to build the capacity of farmers and AEWs to adapt to and mitigate the negative impacts of climate change. The most priority support needed by farmers is the provision of climate-resilient crop varieties, whilst the AEWs need training on CSA practices.

Keywords: climate change, crop productivity, farmer’s perception, rainfall, temperature, Sierra Leone

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Authors: Didier Auroux, Vladimir Groza

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This work is part of STEEP Marie-Curie ITN project, and it focuses on the identification of unknown parameters of the proposed generic Abrasive WaterJet Milling (AWJM) PDE model, that appears as an ill-posed inverse problem. The necessity of studying this problem comes from the industrial milling applications where the possibility to predict and model the final surface with high accuracy is one of the primary tasks in the absence of any knowledge of the model parameters that should be used. In this framework, we propose the identification of model parameters by minimizing a cost function, measuring the difference between experimental and numerical solutions. The adjoint approach based on corresponding Lagrangian gives the opportunity to find out the unknowns of the AWJM model and their optimal values that could be used to reproduce the required trench profile. Due to the complexity of the nonlinear problem and a large number of model parameters, we use an automatic differentiation software tool (TAPENADE) for the adjoint computations. By adding noise to the artificial data, we show that in fact the parameter identification problem is highly unstable and strictly depends on input measurements. Regularization terms could be effectively used to deal with the presence of data noise and to improve the identification correctness. Based on this approach we present results in 2D and 3D of the identification of the model parameters and of the surface prediction both with self-generated data and measurements obtained from the real production. Considering different types of model and measurement errors allows us to obtain acceptable results for manufacturing and to expect the proper identification of unknowns. This approach also gives us the ability to distribute the research on more complex cases and consider different types of model and measurement errors as well as 3D time-dependent model with variations of the jet feed speed.

Keywords: Abrasive Waterjet Milling, inverse problem, model parameters identification, regularization

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Authors: Hicham Amar, Mustapha El Ghorfi, Yassine Taha, Abdellatif Elghali, Rachid Hakkou, Mostafa Benzaazoua

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Phosphate mine waste rock (PMWR) generated during ore extraction is continuously increasing, resulting in a significant environmental footprint. The main objectives of this study consist of i) elaboration of the sampling strategy of PMWR piles, ii) a mineralogical and chemical characterization of PMWR piles, and iii) 3D block model creation to evaluate the potential valorization of the existing PMWR. Destructive drilling using reverse circulation from 13 drills was used to collect samples for chemical (X-ray fluorescence analysis) and mineralogical assays. The 3D block model was created based on the data set, including chemical data of the realized drills using Datamine RM software. The optical microscopy observations showed that the sandy phosphate from drills in the PMWR piles is characterized by the abundance of carbonate fluorapatite with the presence of calcite, dolomite, and quartz. The mean grade of composite samples was around 19.5±2.7% for P₂O₅. The mean grade of P₂O₅ exhibited an increasing tendency by depth profile from bottom to top of PMWR piles. 3D block model generated with chemical data confirmed the tendency of the mean grades’ variation and may allow a potential selective extraction according to %P₂O₅. The 3D block model of P₂O₅ grade is an efficient sampling approach that confirmed the variation of P₂O₅ grade. This integrated approach for PMWR management will be a helpful tool for decision-making to recover the residual phosphate, adopting the circular economy and sustainability in the phosphate mining industry.

Keywords: 3D modelling, reverse circulation drilling, circular economy, phosphate mine waste rock, sampling

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Authors: Washima Tuleun

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Cybercrime is a global concern, and Nigeria is not immune to its effects. This paper investigates the cybercrimes and significant cyber-attacks that have targeted businesses and institutions in Nigeria, examining their various forms and the financial and economic impacts they have on individuals, businesses, and the nation as a whole. As technological advancements rapidly evolve and online services gain widespread adoption, there has been a corresponding rise in cyber-related attacks. These attacks often target personal data, exploit system vulnerabilities, and result in the theft of sensitive information, leading to financial losses, reputational damage, and broader impacts on organizations. The study conducts a thorough review of existing literature, case studies, and statistical data to provide a comprehensive understanding of Nigeria’s cybercrime landscape. Additionally, it assesses the efforts by both the government and the private sector to address these challenges and offers recommendations for more effective strategies to mitigate and reduce their impact.

Keywords: cybersecurity, telecommunications engineering, information technology, threat intelligence, vulnerability management, computing

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Authors: Shreya Srivastava, Sushovan Ghosh, Sagnik Dey

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Aerosols directly affect Earth’s radiation budget by scattering and absorbing incoming solar radiation and outgoing terrestrial radiation. While the uncertainty in aerosol radiative forcing (ARF) has decreased over the years, it is still higher than that of greenhouse gas forcing, particularly in the South Asian region, due to high heterogeneity in their chemical properties. Understanding the Spatio-temporal heterogeneity of aerosol composition is critical in improving climate prediction. Studies using satellite data, in-situ and aircraft measurements, and models have investigated the Spatio-temporal variability of aerosol characteristics. In this study, we have taken aerosol data from Multi-angle Imaging Spectro-Radiometer (MISR) level-2 version 23 aerosol products retrieved at 4.4 km and radiation data from Clouds and the Earth’s Radiant Energy System (CERES, spatial resolution=1ox1o) for 21 years (2000-2021) over the Indian subcontinent. MISR aerosol product includes size and shapes segregated aerosol optical depth (AOD), Angstrom exponent (AE), and single scattering albedo (SSA). Additionally, 74 aerosol mixtures are included in version 23 data that is used for aerosol speciation. We have seasonally mapped aerosol optical and microphysical properties from MISR for India at quarter degrees resolution. Results show strong Spatio-temporal variability, with a constant higher value of AOD for the Indo-Gangetic Plain (IGP). The contribution of small-size particles is higher throughout the year, spatially during winter months. SSA is found to be overestimated where absorbing particles are present. The climatological map of short wave (SW) ARF at the top of the atmosphere (TOA) shows a strong cooling except in only a few places (values ranging from +2.5o to -22.5o). Cooling due to aerosols is higher in the absence of clouds. Higher negative values of ARF are found over the IGP region, given the high aerosol concentration above the region. Surface ARF values are everywhere negative for our study domain, with higher values in clear conditions. The results strongly correlate with AOD from MISR and ARF from CERES.

Keywords: aerosol Radiative forcing (ARF), aerosol composition, single scattering albedo (SSA), CERES

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Authors: Mohamad Mahdi Namdari

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In today's rapidly changing and evolving business competition, companies and organizations require advanced and efficient tools to manage their markets and sales networks. Big data analysis, quick response in competitive markets, process and operations optimization, and forecasting customer behavior are among the concerns of executive managers. Artificial intelligence, as one of the emerging technologies, has provided extensive capabilities in this regard. The use of artificial intelligence in market and sales network management can lead to improved efficiency, increased decision-making accuracy, and enhanced customer satisfaction. Specifically, AI algorithms can analyze vast amounts of data, identify complex patterns, and offer strategic suggestions to improve sales performance. However, many companies are still distant from effectively leveraging this technology, and those that do face challenges in fully exploiting AI's potential in market and sales network management. It appears that the general public's and even the managerial and academic communities' lack of knowledge of this technology has caused the managerial structure to lag behind the progress and development of artificial intelligence. Additionally, high costs, fear of change and employee resistance, lack of quality data production processes, the need for updating structures and processes, implementation issues, the need for specialized skills and technical equipment, and ethical and privacy concerns are among the factors preventing widespread use of this technology in organizations. Clarifying and explaining this technology, especially to the academic, managerial, and elite communities, can pave the way for a transformative beginning. The aim of this research is to elucidate the capacities of artificial intelligence in market and sales network management, identify its opportunities and benefits, and examine the existing challenges and obstacles. This research aims to leverage AI capabilities to provide a framework for enhancing market and sales network performance for managers. The results of this research can help managers and decision-makers adopt more effective strategies for business growth and development by better understanding the capabilities and limitations of artificial intelligence.

Keywords: artificial intelligence, market management, sales network, big data analysis, decision-making, digital marketing

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Authors: A. U. Ugwu

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Post-Apartheid South African education system had witnessed waves of curriculum reforms. Accordingly, there have been evidences of responsiveness towards local and global challenges of sustainable development over the past decade. In other words, the curriculum shows sensitivity towards issues of Sustainable Development (SD). Moreover, the paradigm of Sustainable Development Goals (SDGs) was introduced by the UNESCO in year 2015. The SDGs paradigm is essentially a vision towards actualizing sustainability in all aspects of the global society. Education for Sustainable Development (ESD) in retrospect entails teaching and learning to actualize the intended UNESCO 2030 SDGs. This paper explores how teaching and learning of ESD can be improved, by drawing from local context of the South African schooling system. Preservice natural sciences and technology teachers in their 2nd to 4th years of study at a university’s college of education in South Africa were contacted as participants of the study. Using qualitative case study research design, the study drew from the views and experiences of five (5) purposively selected participants from a broader study, aiming to closely understating how ESD is implemented pedagogically in teaching and learning. The inquiry employed questionnaires and a focus group discussion as qualitative data generation tools. A qualitative data analysis of generated data was carried out using content and thematic analysis, underpinned by interpretive paradigm. The result of analyzed data, suggests that ESD pedagogy at the location where this research was conducted is largely influenced by contextual factors. Furthermore, the result of the study shows that there is a critical need to employ/adopt local experiences or occurrences while teaching sustainable development. Certain pedagogical approaches such as the use of videos relative to local context should also be considered in order to achieve a more realistic application. The paper recommends that educational institutions through teaching and learning should implement ESD by drawing on local contexts and problems, thereby foregrounding constructivism, appreciating and fostering students' prior knowledge and lived experiences.

Keywords: context, education for sustainable development, natural sciences and technology preservice teachers, qualitative research, sustainable development goals

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Authors: A. Senthil Kumar, V. Murali Bhaskaran

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In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

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Authors: S. Lungtae, C. Noknoi

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The main purpose of this research is to analyze the marketing strategies of the various five-star rated herbal businesses of One Tambon One Product (OTOP) entrepreneurs in Songkhla province, Thailand. This includes the targeting, positioning and marketing mix in order to develop marketing strategies for OTOP entrepreneurs. The data were collected from the presidents of herbal-product enterprises in Songkhla province. The products of all these enterprises were selected as five-star herbal products for the OTOP project in 2012. In-depth interviews were conducted, and content analysis was used to analyze the data. The research found that the community enterprises should 1) increase the range of product sizes offered, 2) increase their distribution channels, 3) publicize more to inform consumers about their identities and products, 4) undertake promotional activities during the festival, and 5) choose salespeople who are knowledgeable about the features of their products.

Keywords: marketing mix, market positioning, marketing strategies, target market.

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Authors: David Glassmeyer

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Quantitative reasoning is necessary to robustly understand mathematical concepts ranging from elementary to university levels. Quantitative reasoning involves identifying and representing quantities and the relationships between these quantities. Without reasoning quantitatively, students often resort to memorizing formulas and procedures, which have negative impacts when they encounter mathematical topics in the future. This study investigated how high school students’ quantitative reasoning could be fostered within a unit on arc measure and angle relationships. Arc measure, or the measure of a central angle that cuts off a portion of a circle’s circumference, is often confused with arclength. In this study, the researcher redesigned an activity to clearly distinguish arc measure and arc length by using a quantitative reasoning framework. Data were collected from high school students to determine if this approach impacted their understanding of these concepts. Initial data indicates the approach was successful in supporting students’ quantitative reasoning of these topics. Implications for the work are that teachers themselves may also benefit from considering mathematical definitions from a quantitative reasoning framework and can use this activity in their own classrooms.

Keywords: arc length, arc measure, quantitative reasoning, student content knowledge

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Authors: Eun Kyong Park

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This study explores the relationships between immigrant parents’ beliefs about bilingualism, family literacy practices, and their children’s identity development in Sydney, Australia. This project examines how these parents’ ideological beliefs and knowledge are related to their provision of family literacy practices and management of the environment for their bilingual children based on family language policy (FLP). This is a follow-up study of the author’s prior thesis that presented Korean immigrant mothers’ beliefs and decision-making in support of their children’s bilingualism. It includes fathers’ perspectives within the participating families as a whole by foregrounding their perceptions of bilingual and identity development. It adopts a qualitative approach with twelve immigrant mothers and fathers living in a Korean-Australian community whose child attends one of the communities Korean language programs. This time, it includes introspective and self-evocative auto-ethnographic data. The initial data set collected from the first part of this study demonstrated the mothers provided rich, diverse, and specific family literacy activities for their children. These mothers selected specific practices to facilitate their child’s bilingual development at home. The second part of data has been collected over a three month period: 1) a focus group interview with mothers; 2) a brief self-report of fathers; 3) the researcher’s reflective diary. To analyze these multiple data, thematic analysis and coding were used to reveal the parents’ ideologies surrounding bilingualism and bilingual identities. It will highlight the complexity of language and literacy practices in the family domain interrelated with sociocultural factors. This project makes an original contribution to the field of bilingualism and FLP and a methodological contribution by introducing auto-ethnographic input of this community’s lived practices. This project will empower Korean-Australian immigrant families and other multilingual communities to reflect their beliefs and practices for their emerging bilingual children. It will also enable educators and policymakers to access authentic information about how bilingualism is practiced within these immigrant families in multiple ways and to help build the culturally appropriate partnership between home and school community.

Keywords: bilingualism, beliefs, identity, family language policy, Korean immigrant parents in Australia

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Authors: Wadsana Sarakarn, Pimonpan Charoensri, Baliya Chaiyara

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Objectives: To study the outcomes of the developed nursing model to prevent early postpartum hemorrhage (PPH). Materials and Methods: The analytical study was conducted in Sunpasitthiprasong Hospital during October 1st, 2015, until May 31st, 2017. After review the prevalence, risk factors, and outcomes of postpartum hemorrhage of the parturient who gave birth in Sunpasitthiprasong Hospital, the nursing model was developed under research regulation of Kemmis&McTaggart using 4 steps of operating procedures: 1) analyzing problem situation and gathering 2) creating the plan 3) noticing and performing 4) reflecting the result of the operation. The nursing model consisted of the screening tools for risk factors associated with PPH, the clinical nursing practice guideline (CNPG), and the collecting bag for measuring postpartum blood loss. Primary outcome was early postpartum hemorrhage. Secondary outcomes were postpartum hysterectomy, maternal mortality, personnel’s practice, knowledge, and satisfaction of the nursing model. The data were analyzed by using content analysis for qualitative data and descriptive statistics for quantitative data. Results: Before using the nursing model, the prevalence of early postpartum hemorrhage was under estimated (2.97%). There were 5 cases of postpartum hysterectomy and 2 cases of maternal death due to postpartum hemorrhage. During the study period, there was 22.7% prevalence of postpartum hemorrhage among 220 pregnant women who were vaginally delivered at Sunpasitthiprasong Hospital. No maternal death or postpartum hysterectomy was reported after using the nursing model. Among 16 registered nurses at the delivery room who evaluated using of the nursing model, they reported the high level of practice, knowledge, and satisfaction Conclusion: The nursing model for the prevention of early PPH is effective to decrease early PPH and other serious complications.

Keywords: the development of a nursing model, prevention of postpartum hemorrhage, pregnant women, postpartum hemorrhage

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Authors: Richard J. Hanowski, Rebecca L. Hammond

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Very few studies have been conducted to investigate safety issues associated with motorcoach/bus operations. The current study investigates the impact that roadway infrastructure, including locality, roadway grade, traffic flow and traffic density, have on bus safety. A naturalistic driving study was conducted in the U.S.A that involved 43 motorcoaches. Two fleets participated in the study and over 600,000 miles of naturalistic driving data were collected. Sixty-five bus drivers participated in this study; 48 male and 17 female. The average age of the drivers was 49 years. A sophisticated data acquisition system (DAS) was installed on each of the 43 motorcoaches and a variety of kinematic and video data were continuously recorded. The data were analyzed by identifying safety critical events (SCEs), which included crashes, near-crashes, crash-relevant conflicts, and unintentional lane deviations. Additionally, baseline (normative driving) segments were also identified and analyzed for comparison to the SCEs. This presentation highlights the need for bus safety research and the methods used in this data collection effort. With respect to elements of roadway infrastructure, this study highlights the methods used to assess locality, roadway grade, traffic flow, and traffic density. Locality was determined by manual review of the recorded video for each event and baseline and was characterized in terms of open country, residential, business/industrial, church, playground, school, urban, airport, interstate, and other. Roadway grade was similarly determined through video review and characterized in terms of level, grade up, grade down, hillcrest, and dip. The video was also used to make a determination of the traffic flow and traffic density at the time of the event or baseline segment. For traffic flow, video was used to assess which of the following best characterized the event or baseline: not divided (2-way traffic), not divided (center 2-way left turn lane), divided (median or barrier), one-way traffic, or no lanes. In terms of traffic density, level-of-service categories were used: A1, A2, B, C, D, E, and F. Highlighted in this abstract are only a few of the many roadway elements that were coded in this study. Other elements included lighting levels, weather conditions, roadway surface conditions, relation to junction, and roadway alignment. Note that a key component of this study was to assess the impact that driver distraction and fatigue have on bus operations. In this regard, once the roadway elements had been coded, the primary research questions that were addressed were (i) “What environmental condition are associated with driver choice of engagement in tasks?”, and (ii) “what are the odds of being in a SCE while engaging in tasks while encountering these conditions?”. The study may be of interest to researchers and traffic engineers that are interested in the relationship between roadway infrastructure elements and safety events in motorcoach bus operations.

Keywords: bus safety, motorcoach, naturalistic driving, roadway infrastructure

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Authors: S. Mohamed Rabeek, Mi Kyoung Park, M. Annamalai Arasu

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This paper presents a design of a wireless read out system for tracking the frequency shift of the polymer coated piezoelectric micro electromechanical resonator due to gas absorption. The measure of this frequency shift indicates the percentage of a particular gas the sensor is exposed to. It is measured using an oscillator and an FPGA based frequency counter by employing the resonator as a frequency determining element in the oscillator. This system consists of a Gas Sensing Wireless Readout (GSWR) and an USB Wireless Transceiver (UWT). GSWR consists of an oscillator based on a trans-impedance sustaining amplifier, an FPGA based frequency readout, a sub 1GHz wireless transceiver and a micro controller. UWT can be plugged into the computer via USB port and function as a wireless module to transfer gas sensor data from GSWR to the computer through its USB port. GUI program running on the computer periodically polls for sensor data through UWT - GSWR wireless link, the response from GSWR is logged in a file for post processing as well as displayed on screen.

Keywords: gas sensor, GSWR, micromechanical system, UWT, volatile emissions

Procedia PDF Downloads 485

Authors: A. Abdelnasser, M. Kumral, B. Zoheir, P. Weihed, M. Budakoglu, L. Gumus

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Atud gold deposits located at the central part of the Egyptian Eastern Desert of Egypt. It represents the vein-type gold mineralization at the Arabian-Nubian Shield in North Africa. Furthermore, this Au mineralization was closely associated with intense hydrothermal alteration haloes along the NW-SE brittle-ductile shear zone at the mined area. This study reports new data about the mineral chemistry of the hydrothermal and metamorphic minerals as well as the geothermobarometry of the metamorphism and determines the paragenetic interrelationship between Au-bearing sulfides and gangue minerals in Atud gold mine by using the electron microprobe analyses (EMPA). These analyses revealed that the ore minerals associated with gold mineralization are arsenopyrite, pyrite, chalcopyrite, sphalerite, pyrrhotite, tetrahedrite and gersdorffite-cobaltite. Also, the gold is highly associated with arsenopyrite and As-bearing pyrite as well as sphalerite with an average ~70 wt.% Au (+26 wt.% Ag) whereas it occurred either as disseminated grains or along microfractures of arsenopyrite and pyrite in altered wallrocks and mineralized quartz veins. Arsenopyrite occurs as individual rhombic or prismatic zoned grains disseminated in the quartz veins and wallrock and is intergrown with euhedral arsenian pyrite (with ~2 atom % As). Pyrite is As-bearing pyrite that occurs as disseminated subhedral or anhedral zoned grains replacing by chalcopyrite in some samples. Inclusions of sphalerite and pyrrhotite are common in the large pyrite grains. Secondary minerals such as sericite, calcite, chlorite and albite are disseminated either in altered wallrocks or in quartz veins. Sericite is the main secondary and alteration mineral associated with Au-bearing sulfides and calcite. Electron microprobe data of the sericite show that its muscovite component is high in all analyzed flakes (XMs= an average 0.89) and the phengite content (Mg+Fe a.p.f.u.) varies from 0.10 to 0.55 and from 0.13 to 0.29 in wallrocks and mineralized veins respectively. Carbonate occurs either as thin veinlets or disseminated grains in the mineralized quartz vein and/or the wallrocks. It has higher amount of calcite (CaCO3) and low amount of MgCO3 as well as FeCO3 in the wallrocks relative to the quartz veins. Chlorite flakes are associated with arsenopyrite and their electron probe data revealed that they are generally Fe-rich composition (FeOt 20.64–20.10 wt.%) and their composition is clinochlore either pycnochlorite or ripidolite with Al (iv) = 2.30-2.36 pfu and 2.41-2.51 pfu and with narrow range of estimated formation temperatures are (289–295°C) and (301-312°C) for pycnochlorite and ripidolite respectively. Albite is accompanied with chlorite with an Ab content is high in all analyzed samples (Ab= 95.08-99.20).

Keywords: micro-analytical data, mineral chemistry, EMPA, Atud gold deposit, Egypt

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Authors: Ali Abdrhman M. Ukasha

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Data security needed in data transmission, storage, and communication to ensure the security. The single step parallel contour extraction (SSPCE) method is used to create the edge map as a key image from the different Gray level/Binary image. Performing the X-OR operation between the key image and each bit plane of the original image for image pixel values change purpose. The Arnold transform used to changes the locations of image pixels as image scrambling process. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Gary level image and completely reconstructed without any distortion. Also shown that the analyzed algorithm have extremely large security against some attacks like salt & pepper and JPEG compression. Its proof that the Gray level image can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services.

Keywords: SSPCE method, image compression-salt- peppers attacks, bitplanes decomposition, Arnold transform, lossless image encryption

Procedia PDF Downloads 441

Authors: Bahareh Golchin, Nooshin Riahi

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One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

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Authors: Chen-Yin Kuo, Yung-Hsin Lee

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Previous studies applying residual income valuation (RIV) model generally use panel data and single-equation model to forecast stock prices. Unlike these, this paper uses Taiwan longitudinal data to estimate multi-equation time-series models such as Vector Autoregressive (VAR), Vector Error Correction Model (VECM), and conduct out-of-sample forecasting. Further, this work assesses their forecasting performance by two instruments. In favor of extant research, the major finding shows that VECM outperforms other three models in forecasting for three stock sectors over entire horizons. It implies that an error correction term containing long-run information contributes to improve forecasting accuracy. Moreover, the pattern of composite shows that at longer horizon, VECM produces the greater reduction in errors, and performs substantially better than VAR.

Keywords: residual income valuation model, vector error correction model, out of sample forecasting, forecasting accuracy

Procedia PDF Downloads 319