Search results for: image correlation

Authors: Pablo Javier Ortega-Rodriguez, Francisco Jose Pozuelos-Estrada

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Recently, there has been a renewed interest in school autonomy in the United Kingdom and its impact on students' outcomes. English principals have a pivotal role in decision-making. The aim of this paper is to explore the correlation between the type of school (public or private) and the considerable responsibilities of English principals which participated in PISA 2015. The final sample consisted of 419 principals. Descriptive data (percentages and means) were generated for the variables related to professional autonomy. Pearson's chi-square test was used to determine if there is an association between the type of school and principals' responsibilities for relevant tasks. Statistical analysis was performed using SPSS software, version 22. Findings suggest a significant correlation between the type of school and principals' responsibility for firing teachers and formulating the school budget. This study confirms that the type of school is not associated with principals' responsibility for choosing which textbooks are used at school. The present study establishes a quantitative framework for defining four models of professional autonomy and some proposals to improve school autonomy in the United Kingdom.

Keywords: decision making, principals, professional autonomy, school autonomy

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Authors: Ebasa Girma, Nathnael Minyelshowa, Lebsework Negash

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The use of unmanned aerial vehicles (UAVs) in precision agriculture has seen a huge increase recently. As such, systems that aim to apply various algorithms on the field need a structured framework of abstractions. This paper defines the various tasks of the UAVs in precision agriculture and models them into an architectural framework. The presented architecture is built on the context that there will be minimal physical intervention to do the tasks defined with multiple coordinated and cooperative UAVs. Various tasks such as image processing, path planning, communication, data acquisition, and field mapping are employed in the architecture to provide an efficient system. Besides, different limitation for applying Multi-UAVs in precision agriculture has been considered in designing the architecture. The architecture provides an autonomous end-to-end solution, starting from mission planning, data acquisition, and image processing framework that is highly efficient and can enable farmers to comprehensively deploy UAVs onto their lands. Simulation and field tests show that the architecture offers a number of advantages that include fault-tolerance, robustness, developer, and user-friendliness.

Keywords: deep learning, multi-UAVs, precision agriculture, UAVs architecture

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Authors: Anne Hovgaard Jorgensen

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This study investigates how Muslim fathers in Denmark are struggling to overcome notions of mistrust from teachers and educators. Starting from school-home-cooperation (parent conferences, school-home communication, etc.), the study finds that many Muslim fathers do not feel acknowledged as a resource in the upbringing of their children. To explain these experiences further, the study suggest the notion of ‘mistrusted masculinity’ to grasp the controlling image these fathers meet in various schools and child-care-institutions in the Danish Welfare state. The paper is based on 9 months of fieldwork in a Danish school, a social housing area and in various ‘father groups’ in Denmark. Additional, 50 interviews were conducted with fathers, children, mothers, schoolteachers, and educators. By using Connell's concepts 'hegemonic' and 'marginalized' masculinity as steppingstones, the paper argues that these concepts might entail a too static and dualistic picture of gender. By applying the concepts of 'emergent masculinity' and 'emergent fatherhood' the paper brings along a long needed discussion of how Muslim men in Denmark are struggling to overcome and change the controlling images of them as patriarchal and/or ignorant fathers regarding the upbringing of their children. As such, the paper shows how Muslim fathers are taking action to change this controlling image, e.g. through various ‘father groups’. The paper is inspired by the phenomenological notions of ‘experience´ and in the light of this notion, the paper tells the fathers’ stories about their upbringing of their children and aspirations for fatherhood. These stories share light on how these fathers take care of their children in everyday life. The study also shows that the controlling image of these fathers have affected how some Muslim fathers are actually being fathers. The study shows that fear of family-interventions from teachers or social workers e.g. have left some Muslim fathers in a limbo, being afraid of scolding their children, and being confused of ‘what good parenting in Denmark is’. This seems to have led to a more lassie fair upbringing than these fathers actually wanted. This study is important since anthropologists generally have underexposed the notion of fatherhood, and how fathers engage in the upbringing of their children. Over more, the vast majority of qualitative studies of fatherhood have been on white middleclass fathers, living in nuclear families. In addition, this study is crucial at this very moment due to the major refugee crisis in Denmark and in the Western world in general. A crisis, which has resulted in a vast number of scare campaigns against Islam from different nationalistic political parties, which enforces the negative controlling image of Muslim fathers.

Keywords: fatherhood, Muslim fathers, mistrust, education

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Authors: Saeed Eskandari, Seyed Rasoul Mehdikhani

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Radiation sources have been used in many industries, such as gamma sources in medical imaging. These waves have destructive effects on humans and the environment. It is very important to detect and find the source of these waves because these sources cannot be seen by the eye. A portable robot has been designed and built with the purpose of revealing radiation sources that are able to scan the place from 5 to 20 meters away and shows the location of the sources according to the intensity of the waves on a two-dimensional digital image. The operation of the robot is done by measuring the pixels separately. By increasing the image measurement resolution, we will have a more accurate scan of the environment, and more points will be detected. But this causes a lot of time to be spent on scanning. In this paper, to overcome this challenge, we designed a method that can optimize this time. In this method, a small number of important points of the environment are measured. Hence the remaining pixels are predicted and estimated by regression algorithms in machine learning. The research method is based on comparing the actual values of all pixels. These steps have been repeated with several other radiation sources. The obtained results of the study show that the values estimated by the regression method are very close to the real values.

Keywords: regression, machine learning, scan radiation, robot

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Authors: X. Wang

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Timely and accurate information on urban building damage caused by earthquake is important basis for disaster assessment and emergency relief. Very high resolution (VHR) remotely sensed imagery containing abundant fine-scale information offers a large quantity of data for detecting and assessing urban building damage in the aftermath of earthquake disasters. However, the accuracy obtained using spectral features alone is comparatively low, since building damage, intact buildings and pavements are spectrally similar. Therefore, it is of great significance to detect urban building damage effectively using multi-source data. Considering that in general height or geometric structure of buildings change dramatically in the devastated areas, a novel multi-stage urban building damage detection method, using bi-temporal spectral, height and corner information, was proposed in this study. The pre-event height information was generated using stereo VHR images acquired from two different satellites, while the post-event height information was produced from airborne LiDAR data. The corner information was extracted from pre- and post-event panchromatic images. The proposed method can be summarized as follows. To reduce the classification errors caused by spectral similarity and errors in extracting height information, ground surface, shadows, and vegetation were first extracted using the post-event VHR image and height data and were masked out. Two different types of building damage were then extracted from the remaining areas: the height difference between pre- and post-event was used for detecting building damage showing significant height change; the difference in the density of corners between pre- and post-event was used for extracting building damage showing drastic change in geometric structure. The initial building damage result was generated by combining above two building damage results. Finally, a post-processing procedure was adopted to refine the obtained initial result. The proposed method was quantitatively evaluated and compared to two existing methods in Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010, using pre-event GeoEye-1 image, pre-event WorldView-2 image, post-event QuickBird image and post-event LiDAR data. The results showed that the method proposed in this study significantly outperformed the two comparative methods in terms of urban building damage extraction accuracy. The proposed method provides a fast and reliable method to detect urban building collapse, which is also applicable to relevant applications.

Keywords: building damage, corner, earthquake, height, very high resolution (VHR)

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Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie

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Being able to identify biological particles such as spores, viruses, or pollens is important for health care professionals, as it allows for appropriate therapeutic management of patients. Optical microscopy is a technology widely used for the analysis of these types of microorganisms, because, compared to other types of microscopy, it is not expensive. The analysis of an optical microscope slide is a tedious and time-consuming task when done manually. However, using machine learning and computer vision, this process can be automated. The first step of an automated microscope slide image analysis process is segmentation. During this step, the biological particles are localized and extracted. Very often, the use of an automatic thresholding method is sufficient to locate and extract the particles. However, in some cases, the particles are not extracted individually because they are stuck to other biological elements. In this paper, we propose a stuck particles separation method based on the use of the Sobel operator and thresholding. We illustrate it by applying it to the separation of 813 images of adjacent pollen grains. The method correctly separated 95.4% of these images.

Keywords: image segmentation, stuck particles separation, Sobel operator, thresholding

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Authors: Auwal Abubakar, Ahmed Ahidjo, Shazril Imran Shaukat, Noor Khairiah A. Karim, Gokula Kumar Appalanaido, Hafiz Mohd Zin

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Background: The use of optical surface imaging systems (OSISs) is increasingly becoming popular in radiotherapy practice, especially during breast cancer treatment. This study reviews the accuracy of the available commercial OSISs for breast radiotherapy. Method: A literature search was conducted and identified the available commercial OSISs from different manufacturers that are integrated into radiotherapy practice for setup verification during breast radiotherapy. Studies that evaluated the accuracy of the OSISs during breast radiotherapy using cone beam computed tomography (CBCT) as a reference were retrieved and analyzed. The physics and working principles of the systems from each manufacturer were discussed together with their respective strength and limitations. Results: A total of five (5) different commercially available OSISs from four (4) manufacturers were identified, each with a different working principle. Six (6) studies were found to evaluate the accuracy of the systems during breast radiotherapy in conjunction with CBCT as a goal standard. The studies revealed that the accuracy of the system in terms of mean difference ranges from 0.1 to 2.1 mm. The correlation between CBCT and OSIS ranges between 0.4 and 0.9. The limit of agreements obtained using bland Altman analysis in the studies was also within an acceptable range. Conclusion: The OSISs have an acceptable level of accuracy and could be used safely during breast radiotherapy. The systems are non-invasive, ionizing radiation-free, and provide real-time imaging of the target surface at no extra concomitant imaging dose. However, the system should only be used to complement rather than replace x-ray-based image guidance techniques such as CBCT.

Keywords: optical surface imaging system, Cone beam computed tomography (CBCT), surface guided radiotherapy, Breast radiotherapy

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Authors: Ashwani Kumar Aggarwal

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NIR Light is non-ionizing and can pass easily through living tissues such as breast without any harmful effects. Therefore, use of NIR light for imaging the biological tissue and to quantify its optical properties is a good choice over other invasive methods. Optical tomography involves two steps. One is the forward problem and the other is the reconstruction problem. The forward problem consists of finding the measurements of transmitted light through the tissue from source to detector, given the spatial distribution of absorption and scattering properties. The second step is the reconstruction problem. In X-ray tomography, there is standard method for reconstruction called filtered back projection method or the algebraic reconstruction methods. But this method cannot be applied as such, in optical tomography due to highly scattering nature of biological tissue. A hybrid algorithm for reconstruction has been implemented in this work which takes into account the highly scattered path taken by photons while back projecting the forward data obtained during Monte Carlo simulation. The reconstructed image suffers from blurring due to point spread function. This blurred reconstructed image has been enhanced using a digital filter which is optimal in mean square sense.

Keywords: least-squares optimization, filtering, tomography, laser interaction, light scattering

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Authors: Piyush Sharma, Devi Prasad Sharma, Sulabh Bansal

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Diseases are fairly common in fruits and vegetables because of the changing climatic and environmental circumstances. Crop diseases, which are frequently difficult to control, interfere with the growth and output of the crops. Accurate disease detection and timely disease control measures are required to guarantee high production standards and good quality. In India, apples are a common crop that may be afflicted by a variety of diseases on the fruit, stem, and leaves. It is fungi, bacteria, and viruses that trigger the early symptoms of leaf diseases. In order to assist farmers and take the appropriate action, it is important to develop an automated system that can be used to detect the type of illnesses. Machine learning-based image processing can be used to: this research suggested a system that can automatically identify diseases in apple fruit and apple plants. Hence, this research utilizes the hybrid SVM-RBN model. As a consequence, the model may produce results that are more effective in terms of accuracy, precision, recall, and F1 Score, with respective values of 96%, 99%, 94%, and 93%.

Keywords: fruit plant disease, crop disease, machine learning, image processing, SVM-RBN

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Authors: Xiaohua Zhu, Lingling Ma, Yongguang Zhao

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Leaf Area Index (LAI) is a key structural characteristic of crops and plays a significant role in precision agricultural management and farmland ecosystem modeling. However, LAI retrieved from different resolution data contain a scaling bias due to the spatial heterogeneity and model non-linearity, that is, there is scale effect during multi-scale LAI estimate. In this article, a typical farmland in semi-arid regions of Chinese Inner Mongolia is taken as the study area, based on the combination of PROSPECT model and SAIL model, a multiple dimensional Look-Up-Table (LUT) is generated for multiple crops LAI estimation from unmanned aerial vehicle (UAV) hyperspectral data. Based on Taylor expansion method and computational geometry model, a scale transfer model considering both difference between inter- and intra-class is constructed for scale effect analysis of LAI inversion over inhomogeneous surface. The results indicate that, (1) the LUT method based on classification and parameter sensitive analysis is useful for LAI retrieval of corn, potato, sunflower and melon on the typical farmland, with correlation coefficient R2 of 0.82 and root mean square error RMSE of 0.43m2/m-2. (2) The scale effect of LAI is becoming obvious with the decrease of image resolution, and maximum scale bias is more than 45%. (3) The scale effect of inter-classes is higher than that of intra-class, which can be corrected efficiently by the scale transfer model established based Taylor expansion and Computational geometry. After corrected, the maximum scale bias can be reduced to 1.2%.

Keywords: leaf area index (LAI), scale effect, UAV-based hyperspectral data, look-up-table (LUT), remote sensing

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Authors: Cosmin-Gabriel Porumb-Ghiurco, Dumitrana Fiț-Iordache, Szőke Árpád

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The literature incorporating geographical or tourist-geographical themes and explicit references to the Carpathian area is extremely abundant. Through this paper, we attempt to “undermine” the traditional, tourist-geographical approaches of the Carpathian Arch by targeting an aspect often regarded as marginal but which, if examined, even only empirically, takes the form of a vast problem with multidisciplinary vocation. Therefore, we propose a more extravagant yet pro-touristic approach to the Romanian Carpathian geo-space. Consequently, the explicit goal of this approach consists precisely in broadening the multidisciplinary, essentially geographic scope of the research, the vision and mental representation of the Carpathian area by advancing a lever that would symbolize a different kind of unification between geography and tourism on a more intimate, subtle, mythological and archetypal level. The spiritual and mercantile dimensions of the tourism field in general and of the local Carpathian tourism can meld harmoniously together in order to create a common territorial reality of referral and favorable perspectives for the consolidation of their symbiotic relationship.

Keywords: tourist image, mythical geography, collective imaginary, spiritual patrimony, Carpathians

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Authors: Mohammad Hossain Mohammadi, Saif Al Ameri, Sana Ziaei, Jinane Mounsef

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Automated enumeration of the number of hand fingers is widely used in several motion gaming and distance control applications, and is discussed in several published papers as a starting block for hand recognition systems. The automated finger enumeration technique should not only be accurate, but also must have a fast response for a moving-picture input. The high performance of video in motion games or distance control will inhibit the program’s overall speed, for image processing software such as Matlab need to produce results at high computation speeds. Since an automated finger enumeration with minimum error and processing time is desired, a comparative study between two finger enumeration techniques is presented and analyzed in this paper. In the pre-processing stage, various image processing functions were applied on a real-time video input to obtain the final cleaned auto-cropped image of the hand to be used for the two techniques. The first technique uses the known morphological tool of skeletonization to count the number of skeleton’s endpoints for fingers. The second technique uses a radial distance method to enumerate the number of fingers in order to obtain a one dimensional hand representation. For both discussed methods, the different steps of the algorithms are explained. Then, a comparative study analyzes the accuracy and speed of both techniques. Through experimental testing in different background conditions, it was observed that the radial distance method was more accurate and responsive to a real-time video input compared to the skeletonization method. All test results were generated in Matlab and were based on displaying a human hand for three different orientations on top of a plain color background. Finally, the limitations surrounding the enumeration techniques are presented.

Keywords: comparative study, hand recognition, fingertip detection, skeletonization, radial distance, Matlab

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Authors: Constantin Haita

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The chalcolithic tell settlements from south-eastern Romania, attributed to Gumelnița culture, are characterised by a well-defined surface, marked often by delimitation structures, a succession of many layers of construction, destruction, and rebuilding, and a well-structured area of occupation: built spaces, passage areas, waste zones. Settlements of tell type are located in the river valleys –on erosion remnants, alluvial bars or small islands, at the border of the valleys– on edges or prominences of Pleistocene terraces, lower Holocene terraces, and banks of lakes. This study integrates data on the geographical position, the morphological background, and the general stratigraphy of these important settlements. The correlation of the spatial distribution with the geomorphological units of each area of evolution creates an image of the natural landscape in which they occurred. The sedimentological researches achieved in the floodplain area of Balta Ialomiței showed important changes in the alluvial activity of Danube, after the Chalcolithic period (ca. 6500 - 6000 BP), to Iron Age and Middle Ages. The micromorphological analysis, consisting in thin section interpretation, at the microscopic scale, of sediments and soils in an undisturbed state, allowed the interpretation of the identified sedimentary facies, in terms of mode of formation and anthropic activities. Our studied cases reflect some distinct situations, correlating either with the geomorphological background or with the vertical development, the presence of delimiting structures and the internal organization. The characteristics of tells from this area bring significant information about the human habitation of Lower Danube in Prehistory.

Keywords: chalcolithic, micromorphology, Romania, sedimentology, tell settlements

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Authors: Thanh Noi Phan, Martin Kappas, Jan Degener

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The currently launched Sentinel 2 (S2) satellite (June, 2015) bring a great potential and opportunities for land use/cover map applications, due to its fine spatial resolution multispectral as well as high temporal resolutions. So far, there are handful studies using S2 real data for land cover classification. Especially in northern Vietnam, to our best knowledge, there exist no studies using S2 data for land cover map application. The aim of this study is to provide the preliminary result of land cover classification using Sentinel -2 data with a rising state – of – art classifier, Random Forest. A case study with heterogeneous land use/cover in the eastern of Hanoi Capital – Vietnam was chosen for this study. All 10 spectral bands of 10 and 20 m pixel size of S2 images were used, the 10 m bands were resampled to 20 m. Among several classified algorithms, supervised Random Forest classifier (RF) was applied because it was reported as one of the most accuracy methods of satellite image classification. The results showed that the red-edge and shortwave infrared (SWIR) bands play an important role in land cover classified results. A very high overall accuracy above 90% of classification results was achieved.

Keywords: classify algorithm, classification, land cover, random forest, sentinel 2, Vietnam

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Authors: Sonia Pajaron, Flaviano Sentina, Ranulfo Etulle

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Learning a second language calls for a total commitment from the learner whose response is necessary to successfully send and receive linguistic messages. It is relevant to virtually every aspect of human behaviour which is even more challenging when the components on -affective domains- are involved in second language learning. This study investigated the association between the identified affective attributes and second language performance of the one hundred seventeen (117) randomly selected third year maritime students. A descriptive-correlational method was utilized to generate data on their affective attributes while composition writing (2 series) and IELTS-based interview was done for speaking test. Additionally, to establish the respondents’ English language profile, data on their high school grades (GPA), entrance exam results in English subject (written) as well as in the interview was extracted as baseline information. Data were subjected to various statistical treatment (average means, percentages and pearson-r moment coefficient correlation) and found out that, Nautical Science and Marine Engineering students were found to have average high school grade, entrance test results, both written and in the interview turned out to be very satisfactory at 50% passing percentage. Varied results were manifested in their affective attributes towards learning the second language. On attitude, nautical science students had true positive attitude while marine engineering had only a moderate positive one. Secondly, the former were positively motivated to learn English while the latter were just moderately motivated. As regards anxiety, both groups embodied a moderate level of anxiety in the English language. Finally, data showed that nautical science students exuded real confidence while the marine engineering group had only moderate confidence with the second language. Respondents’ English academic achievement (GWA) was significantly correlated with confidence and speaking with anxiety towards the second language among the students from the nautical science group with moderate positive and low negative degree of correlation, respectively. On the other hand, the marine engineering students’ speaking test result was significantly correlated with anxiety and self-confidence with a moderate negative and low positive degree of correlation, respectively while writing was significantly correlated with motivation bearing a low positive degree of correlation.

Keywords: affective attributes, second language, second language performance, anxiety, attitude, self-confidence and motivation

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Authors: M. C. Paliwal, A. K. Jain, S. K. Katiyar

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Classification of satellite imagery is very important for the assessment of its accuracy. In order to determine the accuracy of the classified image, usually the assumed-true data are derived from ground truth data using Global Positioning System. The data collected from satellite imagery and ground truth data is then compared to find out the accuracy of data and error matrices are prepared. Overall and individual accuracies are calculated using different methods. The study illustrates advanced classification and accuracy assessment of land use/land cover mapping using satellite imagery. IRS-1C-LISS IV data were used for classification of satellite imagery. The satellite image was classified using the software in fourteen classes namely water bodies, agricultural fields, forest land, urban settlement, barren land and unclassified area etc. Classification of satellite imagery and calculation of accuracy was done by using ERDAS-Imagine software to find out the best method. This study is based on the data collected for Bhopal city boundaries of Madhya Pradesh State of India.

Keywords: resolution, accuracy assessment, land use mapping, satellite imagery, ground truth data, error matrices

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Authors: Mingxin Li, Liya Ni

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Autonomous parking is a valuable feature applicable to many robotics applications such as tour guide robots, UV sanitizing robots, food delivery robots, and warehouse robots. With auto-parking, the robot will be able to park at the charging zone and charge itself without human intervention. As compared to self-driving vehicles, auto-parking is more challenging for a small-scale mobile robot only equipped with a front camera due to the camera view limited by the robot’s height and the narrow Field of View (FOV) of the inexpensive camera. In this research, auto-parking of a small-scale mobile robot with a front camera only was achieved in a four-step process: Firstly, transfer learning was performed on the AlexNet, a popular pre-trained convolutional neural network (CNN). It was trained with 150 pictures of empty parking slots and 150 pictures of occupied parking slots from the view angle of a small-scale robot. The dataset of images was divided into a group of 70% images for training and the remaining 30% images for validation. An average success rate of 95% was achieved. Secondly, the image of detected empty parking space was processed with edge detection followed by the computation of parametric representations of the boundary lines using the Hough Transform algorithm. Thirdly, the positions of the entrance point and center of available parking space were predicted based on the robot kinematic model as the robot was driving closer to the parking space because the boundary lines disappeared partially or completely from its camera view due to the height and FOV limitations. The robot used its wheel speeds to compute the positions of the parking space with respect to its changing local frame as it moved along, based on its kinematic model. Lastly, the predicted entrance point of the parking space was used as the reference for the motion control of the robot until it was replaced by the actual center when it became visible again by the robot. The linear and angular velocities of the robot chassis center were computed based on the error between the current chassis center and the reference point. Then the left and right wheel speeds were obtained using inverse kinematics and sent to the motor driver. The above-mentioned four subtasks were all successfully accomplished, with the transformed learning, image processing, and target prediction performed in MATLAB, while the motion control and image capture conducted on a self-built small scale differential drive mobile robot. The small-scale robot employs a Raspberry Pi board, a Pi camera, an L298N dual H-bridge motor driver, a USB power module, a power bank, four wheels, and a chassis. Future research includes three areas: the integration of all four subsystems into one hardware/software platform with the upgrade to an Nvidia Jetson Nano board that provides superior performance for deep learning and image processing; more testing and validation on the identification of available parking space and its boundary lines; improvement of performance after the hardware/software integration is completed.

Keywords: autonomous parking, convolutional neural network, image processing, kinematics-based prediction, transfer learning

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Authors: Zongyao Sha

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Green vegetation plays a vital role in energy flows and matter cycles in terrestrial ecosystems, and vegetation phenology may not only be influenced by but also impose active feedback on climate changes. The phenological events of vegetation, such as the start of the season (SOS), end of the season (EOS), and length of the season (LOS), can respond to climate changes and affect gross primary productivity (GPP). Here we coupled satellite remote sensing imagery with FLUXNET observations to systematically map the shift of SOS, EOS, and LOS in global vegetated areas and explored their response to climate fluctuations and feedback on GPP during the last two decades. Results indicated that SOS advanced significantly, at an average rate of 0.19 days/year at a global scale, particularly in the northern hemisphere above the middle latitude (≥30°N) and that EOS was slightly delayed during the past two decades, resulting in prolonged LOS in 72.5% of the vegetated area. The climate factors, including seasonal temperature and precipitation, are attributed to the shifts in vegetation phenology but with a high spatial and temporal difference. The study revealed interactions between vegetation phenology and climate changes. Both temperature and precipitation affect vegetation phenology. Higher temperature as a direct consequence of global warming advanced vegetation green-up date. On the other hand, 75.9% and 20.2% of the vegetated area showed a positive correlation and significant positive correlation between annual GPP and length of vegetation growing season (LOS), likely indicating an enhancing effect on vegetation productivity and thus increased carbon uptake from the shifted vegetation phenology. Our study highlights a comprehensive view of the vegetation phenology changes of the global terrestrial ecosystems during the last two decades. The interactions between the shifted vegetation phenology and climate changes may provide useful information for better understanding the future trajectory of global climate changes. The feedback on GPP from the shifted vegetation phenology may serve as an adaptation mechanism for terrestrial ecosystems to mitigate global warming through improved carbon uptake from the atmosphere.

Keywords: vegetation phenology, growing season, NPP, correlation analysis

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Authors: Santosh Kumar, M. Muralidhar

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The term ‘ergonomics’ is derived from two Greek words: ‘ergon’, meaning work and ‘nomoi’, meaning natural laws. Ergonomics is the study of how working conditions, machines and equipment can be arranged in order that people can work with them more efficiently. In this research communication an attempt has been made to study the effect of hand-arm vibrational exposure on the workers of a gear manufacturing plant by comparison of potential Carpal Tunnel Syndrome (CTS) symptoms and effect of different exposure levels of vibration on occurrence of CTS in actual industrial environment. Chi square test and correlation analysis have been considered for statistical analysis. From Chi square test, it has been found that the potential CTS symptoms occurrence is significantly dependent on the level of vibrational exposure. Data analysis indicates that 40.51% workers having potential CTS symptoms are exposed to vibration. Correlation analysis reveals that potential CTS symptoms are significantly correlated with exposure to level of vibration from handheld tools and to repetitive wrist movements.

Keywords: CTS symptoms, hand-arm vibration, ergonomics, physical tests

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Authors: Dongqi Li, Yunjia Huang, Tomo Inoue, Kohei Inoue

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With urbanization, urban cultural heritage is facing the impact and destruction of modernization and urbanization. Many historical areas are losing their historical information and regional cultural characteristics, so it is necessary to carry out systematic color planning for historical areas in conservation. As an early focus on urban color planning, Japan has a systematic approach to urban color planning. Hence, this paper selects five merchant towns from the category of important traditional building preservation areas in Japan as the subject of this study to explore the color structure and emotion of this type of historic area. First, the image semantic segmentation method identifies the buildings, roads, and landscape environments. Their color data were extracted for color composition and emotion analysis to summarize their common features. Second, the obtained Internet evaluations were extracted by natural language processing for keyword extraction. The correlation analysis of the color structure and keywords provides a valuable reference for conservation decisions for this historic area in the town. This paper also combines the color structure and Internet evaluation results with generative adversarial networks to generate predicted images of color structure improvements and color improvement schemes. The methods and conclusions of this paper can provide new ideas for the digital management of environmental colors in historic districts and provide a valuable reference for the inheritance of local traditional culture.

Keywords: historic districts, color planning, semantic segmentation, natural language processing

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Authors: Elizabeth Malebogo Mosepele

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Road transport has become increasingly common in the world; people rely on road networks for transportation purpose on a daily basis. However, environmental impact of roads on surrounding landscapes extends their potential effects even further. This study investigates the impact of road network on natural vegetation. The study will provide baseline knowledge regarding roadside vegetation and would be helpful in future for conservation of biodiversity along the road verges and improvements of road verges. The general hypothesis of this study is that the amount and condition of road side vegetation could be explained by road network conditions. Remote sensing techniques were used to analyze vegetation conditions. Landsat 8 OLI image was used to assess vegetation cover condition. NDVI image was generated and used as a base from which land cover classes were extracted, comprising four categories viz. healthy vegetation, degraded vegetation, bare surface, and water. The classification of the image was achieved using the supervised classification technique. Road networks were digitized from Google Earth. For observed data, transect based quadrats of 50*50 m were conducted next to road segments for vegetation assessment. Vegetation condition was related to road network, with the multinomial logistic regression confirming a significant relationship between vegetation condition and road network. The null hypothesis formulated was that 'there is no variation in vegetation condition as we move away from the road.' Analysis of vegetation condition revealed degraded vegetation within close proximity of a road segment and healthy vegetation as the distance increase away from the road. The Chi Squared value was compared with critical value of 3.84, at the significance level of 0.05 to determine the significance of relationship. Given that the Chi squared value was 395, 5004, the null hypothesis was therefore rejected; there is significant variation in vegetation the distance increases away from the road. The conclusion is that the road network plays an important role in the condition of vegetation.

Keywords: Chi squared, geographic information system, multinomial logistic regression, remote sensing, road side vegetation

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Authors: Shishay T. Kidanu

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The study area is located in Southwestern Missouri and is mainly underlain by Mississippian Age limestone which is highly susceptible to karst processes. The area is known for the presence of various karst features like caves, springs and more importantly Sinkholes. Sinkholes are one of the most common karst features and the primary hazard in karst areas. Investigating the subsurface structure and development mechanism of existing sinkholes enables to understand their long-term impact and chance of reactivation and also helps to provide effective mitigation measures. In this study ERT (Electrical Resistivity Tomography), MASW (Multichannel Analysis of Surface Waves) and borehole control data have been used to image the subsurface structure and investigate the development mechanism of a sinkhole in Southwestern Missouri. The study shows that the main process responsible for the development of the sinkhole is the downward piping of fine grained soils. Furthermore, the study reveals that the sinkhole developed along a north-south oriented vertical joint set characterized by a vertical zone of water seepage and associated fine grained soil piping into preexisting fractures.

Keywords: ERT, Karst, MASW, sinkhole

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Authors: Qianhua He, Weili Zhou, Aiwu Chen

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A sparse representation speech denoising method based on adapted stopping residue error was presented in this paper. Firstly, the cross-correlation between the clean speech spectrum and the noise spectrum was analyzed, and an estimation method was proposed. In the denoising method, an over-complete dictionary of the clean speech power spectrum was learned with the K-singular value decomposition (K-SVD) algorithm. In the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the adjusted noise spectrum, and the orthogonal matching pursuit (OMP) approach was applied to reconstruct the clean speech spectrum from the noisy speech. Finally, the clean speech was re-synthesised via the inverse Fourier transform with the reconstructed speech spectrum and the noisy speech phase. The experiment results show that the proposed method outperforms the conventional methods in terms of subjective and objective measure.

Keywords: speech denoising, sparse representation, k-singular value decomposition, orthogonal matching pursuit

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Authors: Jeff Chak-Fu Wong, Tony Chun Yin Yip

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The transition from secondary school to university seems exciting for many first-year students but can be more challenging than expected. Enabling instructors to know students’ learning habits and styles enhances their understanding of the students’ learning backgrounds, allows teachers to provide better support for their students, and has therefore high potential to improve teaching quality and learning, especially in any mathematics-related courses. The aim of this research is to collect students’ data using online surveys, to analyze students’ factors using learning analytics and educational data mining and to discover the characteristics of the students at risk of falling behind in their studies based on students’ previous academic backgrounds and collected data. In this paper, we use correlation-based distance methods and mutual information for measuring student factor relationships. We then develop a factor network using the Minimum Spanning Tree method and consider further study for analyzing the topological properties of these networks using social network analysis tools. Under the framework of mutual information, two graph-based feature filtering methods, i.e., unsupervised and supervised infinite feature selection algorithms, are used to analyze the results for students’ data to rank and select the appropriate subsets of features and yield effective results in identifying the factors affecting students at risk of failing. This discovered knowledge may help students as well as instructors enhance educational quality by finding out possible under-performers at the beginning of the first semester and applying more special attention to them in order to help in their learning process and improve their learning outcomes.

Keywords: students' academic performance, correlation-based distance method, social network analysis, feature selection, graph-based feature filtering method

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Authors: Devatha Kalyan Kumar, R. Poovarasan

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In this paper, we have taken certain important factors and health parameters of diabetes patients especially among children by birth (pediatric congenital) where using the above three metrics methods we are going to assess the importance of each attributes in the dataset and thereby determining the most highly responsible and co-related attribute causing diabetics among young patients. We use cost optimization, control chart and Spearmen methodologies for the real-time application of finding the data efficiency in this diabetes dataset. The Spearmen methodology is the correlation methodologies used in software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher, then there is a higher chance of occurrence of the risk in the software. With the use of control; chart mean, variance and standard deviation of data are calculated. With the use of Cost optimization model, we find to optimize the variables. Hence we choose the Spearmen, control chart and cost optimization methods to assess the data efficiency in diabetes datasets.

Keywords: correlation, congenital diabetics, linear relationship, monotonic function, ranking samples, pediatric

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Authors: Shalini TV, Gangadharan GG, Sriranjini S Jaideep, ASN Seshasayee, Awadhesh Pandit

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Introduction: Prakriti (body-mind constitution of an individual) is a conventional, customized and unique understanding of which is essential for the personalized medicine described in Ayurveda, Indian System of Medicine. Based on the Doshas( functional, bio humoral unit in the body), individuals are categorized into three major Prakriti- Vata, Pitta, and Kapha. The human gut microbiome hosts plenty of highly diverse and metabolically active microorganisms, mainly dominated by the bacteria, which are known to influence the physiology of an individual. Few researches have shown the correlation between the Prakriti and the biochemical parameters. In this study, an attempt was made to explore any correlation between the Prakriti (phenotype of an individual) with the Genetic makeup of the gut microbiome in healthy individuals. Materials and methods: 270 multi-ethnic, healthy volunteers of both sex with the age group between 18 to 40 years, with no history of antibiotics in the last 6 months were recruited into three groups of Vata, Pitta, and Kapha. The Prakriti of the individual was determined using Ayusoft, a software designed by CDAC, Pune, India. The volunteers were subjected to initial screening for the assessment of their height, weight, Body Mass Index, Vital signs and Blood investigations to ensure they are healthy. The stool and saliva samples of the recruited volunteers were collected as per the standard operating procedure developed, and the bacterial DNA was isolated using Qiagen kits. The extracted DNA was subjected to 16s rRNA sequencing using the Illumina kits. The sequencing libraries are targeting the variable V3 and V4 regions of the 16s rRNA gene. Paired sequencing was done on the MiSeq system and data were analyzed using the CLC Genomics workbench 11. Results: The 16s rRNA sequencing of the V3 and V4 regions showed a diverse pattern in both the oral and stool microbial DNA. The study did not reveal any specific pattern of bacterial flora amongst the Prakriti. All the p-values were more than the effective alpha values for all OTUs in both the buccal cavity and stool samples. Therefore, there was no observed significant enrichment of an OTU in the patient samples from either the buccal cavity or stool samples. Conclusion: In healthy volunteers of multi-ethnicity, due to the influence of the various factors, the correlation between the Prakriti and the gut microbiome was not seen.

Keywords: gut microbiome, ayurveda Prakriti, sequencing, multi-ethnic urban population

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Authors: Samuel Abraham Berhane, Lingbing Bu

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This paper presents the spatiotemporal variability of aerosols, clouds, and precipitation within the major cities in Eritrea and it investigates the relationship between aerosols, clouds, and precipitation concerning the presence of aerosols over the study region. In Eritrea, inadequate water supplies will have both direct and indirect adverse impacts on sustainable development in areas such as health, agriculture, energy, communication, and transport. Besides, there exists a gap in the knowledge on suitable and potential areas for cloud seeding. Further, the inadequate understanding of aerosol-cloud-precipitation (ACP) interactions limits the success of weather modification aimed at improving freshwater sources, storage, and recycling. Spatiotemporal variability of aerosols, clouds, and precipitation involve spatial and time series analysis based on trend and anomaly analysis. To find the relationship between aerosols and clouds, a correlation coefficient is used. The spatiotemporal analysis showed larger variations of aerosols within the last two decades, especially in Assab, indicating that aerosol optical depth (AOD) has increased over the surrounding Red Sea region. Rainfall was significantly low but AOD was significantly high during the 2011 monsoon season. Precipitation was high during 2007 over most parts of Eritrea. The correlation coefficient between AOD and rainfall was negative over Asmara and Nakfa. Cloud effective radius (CER) and cloud optical thickness (COT) exhibited a negative correlation with AOD over Nakfa within the June–July–August (JJA) season. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model that is used to find the path and origin of the air mass of the study region showed that the majority of aerosols made their way to the study region via the westerly and the southwesterly winds.

Keywords: aerosol-cloud-precipitation, aerosol optical depth, cloud effective radius, cloud optical thickness, HYSPLIT

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Authors: Fitsumbrhan Tajebe, Fadil Murad, Mitikie Tigabie, Mareye Abebaw, Tadele Alemu, Sefanit Abate, Rezika Mohammedw, Arega Yeshanew, Elias Shiferaw

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Background: Visceral leshimaniasis is a parasitic disease characterized by a systemic infection of phagocytic cells. Hematological parameters of these patients may be affected by the progress of the disease or treatment. Thus, the current study aimed to assess the hematological profiles of visceral leishmaniasis patients before and after treatment. Method: An institutional based retrospective cohort study was conducted among visceral leishmaniasis patients at University of Gondar Comprehensive Specialized Referral Hospital Leishmaniasis Research and Treatment Center from 2013 to 2018. Hematological profiles before initiation and after completion of treatment were extracted from registration book. Descriptive statics was presented using frequency and percentage. Paired t-test and Wilcoxon Signed rank test were used for comparing mean difference for normally and non- normally distributed data, respectively. Spearman and Pearson correlation analysis was used to describe the correlation of hematological parameters with different variables. P value < 0.05 was considered as statistically significant. Result: Except absolute nerutrophil count, post treatment hematological parameters show a significant increment compared to pretreatment one. The prevalence of anemia, leucopenia and thrombocytopenia was 85.5%, 83.4% and 75.8% prior to treatment and it was 58.3%, 38.2% and 19.2% after treatment, respectively. Moreover, parasite load of the disease showed statistically significant negative correlation with hematological profiles mainly with white blood cell and red blood cell. Conclusion: Majority of hematological profiles of patients with active VL have been restored after treatment, which might be associated with treatment effect on parasite proliferation and concentration of parasite in visceral organ, which directly affect hematological profiles.

Keywords: visceral leshimaniasis, hematological profile, anti-leshimanial drug, Gondar

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Authors: Corinne Zurmuehle, Andreas Christoph Weber

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In 2019, the Swiss Federal Institute of Sport Magglingen (SFISM) conducted a mixed-methods study on the Swiss elite sports system, which yielded a large quantity of research data. In a quantitative online survey, 1151 elite sports athletes, 542 coaches, and 102 Performance Directors of national sports federations (NF) have submitted their perceptions of the national support measures of the Swiss elite sports system. These data provide an essential database for the further development of the Swiss elite sports system. The results were published in a report presenting the results divided into 40 Olympic summer and 14 winter sports (Olympic classification). The authors of this paper assume that, in practice, this division is too unspecific to assess where further measures would be needed. The aim of this paper is to find appropriate parameters for data visualization in order to identify disparities in sports promotion that allow an assessment of where further interventions by Swiss Olympic (NF umbrella organization) are required. Method: First, the variable 'salary earned from sport' was defined as a variable to measure the impact of elite sports promotion. This variable was chosen as a measure as it represents an important indicator for the professionalization of elite athletes and therefore reflects national level sports promotion measures applied by Swiss Olympic. Afterwards, the variable salary was tested with regard to the correlation between Olympic classification [a], calculating the Eta coefficient. To estimate the appropriate parameters for data visualization, the correlation between salary and four further parameters was analyzed by calculating the Eta coefficient: [a] sport; [b] prioritization (from 1 to 5) of the sports by Swiss Olympic; [c] gender; [d] employment level in sports. Results & Discussion: The analyses reveal a very small correlation between salary and Olympic classification (ɳ² = .011, p = .005). Gender demonstrates an even small correlation (ɳ² = .006, p = .014). The parameter prioritization was correlating with small effect (ɳ² = .017, p = .001) as did employment level (ɳ² = .028, p < .001). The highest correlation was identified by the parameter sport with a moderate effect (ɳ² = .075, p = .047). The analyses show that the disparities in sports promotion cannot be determined by a particular parameter but presumably explained by a combination of several parameters. We argue that the possibility of combining parameters for data visualization should be enabled when the analysis is provided to Swiss Olympic for further strategic decision-making. However, the inclusion of multiple parameters massively multiplies the number of graphs and is therefore not suitable for practical use. Therefore, we suggest to apply interactive dashboards for data visualization using Business Intelligence Software. Practical & Theoretical Contribution: This contribution provides the first attempt to use Business Intelligence Software for strategic decision-making in national level sports regarding the prioritization of national resources for sports and athletes. This allows to set specific parameters with a significant effect as filters. By using filters, parameters can be combined and compared against each other and set individually for each strategic decision.

Keywords: data visualization, business intelligence, Swiss elite sports system, strategic decision-making

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Authors: Muhammad Waseem Akram

Abstract:

The current study attempts to analyze the impact of business constraints on the growth potential and performance of Small and Medium Enterprises (SMEs) in the electrical industry of Pakistan. Primary data have been utilized for the study collected from the electrical industry cluster in Sargodha, Pakistan. OLS regression is used to assess the impact of business constraints on the performance of SMEs by controlling the effect of Technology Level, Innovations, and Firm Size. To associate business constraints with the growth potential of SMEs, the study utilized Tetrachoric Correlation and Logistic Regression. Findings reveal that all the business constraints negatively affect the performance of SMEs in the electrical industry except Political Instability. Results of Tetrachoric Correlation show that all the business constraints are negatively correlated with the growth potential of SMEs. Logistic Regression results show that Energy Constraint, Inflation and Price Instability, and Bad Business Practices, all three business constraints cause to reduce the probability of income growth in sample SMEs.

Keywords: SMEs, business constraints, performance, growth potential

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