Search results for: image correlation

Authors: O. Abusaeeda, J. P. O. Evans, D. Downes

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We demonstrate the synthesis of intermediary views within a sequence of X-ray images that exhibit depth from motion or kinetic depth effect in a visual display. Each synthetic image replaces the requirement for a linear X-ray detector array during the image acquisition process. Scale invariant feature transform, SIFT, in combination with epipolar morphing is employed to produce synthetic imagery. Comparison between synthetic and ground truth images is reported to quantify the performance of the approach. Our work is a key aspect in the development of a 3D imaging modality for the screening of luggage at airport checkpoints. This programme of research is in collaboration with the UK Home Office and the US Dept. of Homeland Security.

Keywords: X-ray, kinetic depth, KDE, view synthesis

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Authors: E. Bal Beşikçi, O. Arslan

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Fuel saving has become one of the most important issue for shipping in terms of fuel economy and environmental impact. Lowering fuel consumption is possible for both new ships and existing ships through enhanced energy efficiency measures, technical and operational respectively. The limitations of applying technical measures due to the long payback duration raise the potential of operational changes for energy efficient ship operations. This study identifies operational energy efficiency measures related voyage performance management. We use ‘noon’ data to examine the correlation between fuel consumption and operational parameters- revolutions per minute (RPM), draft, trim, (beaufort number) BN and relative wind direction, which are used as measures of ship energy efficiency. The results of this study reveal that speed optimization is the most efficient method as fuel consumption depends heavily on RPM. In conclusion, this study will provide ship operators with the strategic approach for evaluating the priority of the operational energy efficiency measures against high fuel prices and carbon emissions.

Keywords: ship, voyage related operational energy Efficiency measures, fuel consumption, pearson's correlation coefficient

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Authors: Balraju Vadlakonda, Narasimha Mangadoddy

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The hydrodynamic behavior in a laboratory column flotation was analyzed using particle image velocimetry. For complete characterization of column flotation, it is necessary to determine the flow velocity induced by bubbles in the liquid phase, the bubble velocity and bubble characteristics:diameter,shape and bubble size distribution. An experimental procedure for analyzing simultaneous, phase-separated velocity measurements in two-phase flows was introduced. The non-invasive PIV technique has used to quantify the instantaneous flow field, as well as the time averaged flow patterns in selected planes of the column. Using the novel particle velocimetry (PIV) technique by the combination of fluorescent tracer particles, shadowgraphy and digital phase separation with masking technique measured the bubble velocity as well as the Reynolds stresses in the column. Axial and radial mean velocities as well as fluctuating components were determined for both phases by averaging the sufficient number of double images. Bubble size distribution was cross validated with high speed video camera. Average turbulent kinetic energy of bubble were analyzed. Different air flow rates were considered in the experiments.

Keywords: particle image velocimetry (PIV), bubble velocity, bubble diameter, turbulent kinetic energy

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Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: deep-learning, image classification, image identification, industrial engineering.

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Authors: Mei-Yi Wu, Shang-Ming Huang

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The online shoppers nowadays often search the product information on the Internet using some keywords of products. To use this kind of information searching model, shoppers should have a preliminary understanding about their interesting products and choose the correct keywords. However, if the products are first contact (for example, the worn clothes or backpack of passengers which you do not have any idea about the brands), these products cannot be retrieved due to insufficient information. In this paper, we discuss and study the applications in E-commerce using image retrieval and text mining techniques. We design a reasonable E-commerce application system containing three layers in the architecture to provide users product information. The system can automatically search and retrieval similar images and corresponding web pages on Internet according to the target pictures which taken by users. Then text mining techniques are applied to extract important keywords from these retrieval web pages and search the prices on different online shopping stores with these keywords using a web crawler. Finally, the users can obtain the product information including photos and prices of their favorite products. The experiments shows the efficiency of proposed system.

Keywords: mobile image retrieval, text mining, product information service system, online marketing

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Authors: Abhisek Dash

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This study had three neural networks, one for number segmentation, one for number detection and one for number recognition all of which are coupled to one another. All networks were trained on the MNIST dataset and were convolutional. It was assumed that the images had lighter background and darker foreground. The segmentation network took 28x28 images as input and had sixteen outputs. Segmentation training starts when a dark pixel is encountered. Taking a window(7x7) over that pixel as focus, the eight neighborhood of the focus was checked for further dark pixels. The segmentation network was then trained to move in those directions which had dark pixels. To this end the segmentation network had 16 outputs. They were arranged as “go east”, ”don’t go east ”, “go south east”, “don’t go south east”, “go south”, “don’t go south” and so on w.r.t focus window. The focus window was resized into a 28x28 image and the network was trained to consider those neighborhoods which had dark pixels. The neighborhoods which had dark pixels were pushed into a queue in a particular order. The neighborhoods were then popped one at a time stitched to the existing partial image of the number one at a time and trained on which neighborhoods to consider when the new partial image was presented. The above process was repeated until the image was fully covered by the 7x7 neighborhoods and there were no more uncovered black pixels. During testing the network scans and looks for the first dark pixel. From here on the network predicts which neighborhoods to consider and segments the image. After this step the group of neighborhoods are passed into the detection network. The detection network took 28x28 images as input and had two outputs denoting whether a number was detected or not. Since the ground truth of the bounds of a number was known during training the detection network outputted in favor of number not found until the bounds were not met and vice versa. The recognition network was a standard CNN that also took 28x28 images and had 10 outputs for recognition of numbers from 0 to 9. This network was activated only when the detection network votes in favor of number detected. The above methodology could segment connected and overlapping numbers. Additionally the recognition unit was only invoked when a number was detected which minimized false positives. It also eliminated the need for rules of thumb as segmentation is learned. The strategy can also be extended to other characters as well.

Keywords: convolutional neural networks, OCR, text detection, text segmentation

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Authors: Pouya Molana, Zeinab Alimohammadi

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Determining wind wave characteristics is essential for implementing projects related to Coastal and Marine engineering such as designing coastal and marine structures, estimating sediment transport rates and coastal erosion rates in order to predict significant wave height (H_s), this study applies the third generation spectral wave model, Mike 21 SW, along with CEM model. For SW model calibration and verification, two data sets of meteorology and wave spectroscopy are used. The model was exposed to time-varying wind power and the results showed that difference ratio mean, standard deviation of difference ratio and correlation coefficient in SW model for H_s parameter are 1.102, 0.279 and 0.983, respectively. Whereas, the difference ratio mean, standard deviation and correlation coefficient in The Choice Experiment Method (CEM) for the same parameter are 0.869, 1.317 and 0.8359, respectively. Comparing these expected results it is revealed that the Choice Experiment Method CEM has more errors in comparison to MIKE 21 SW third generation spectral wave model and higher correlation coefficient does not necessarily mean higher accuracy.

Keywords: MIKE 21 SW, CEM method, significant wave height, difference ratio

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Authors: Usman Yakubu, Muktar Haruna

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The paper examines the impact of the audit committee on the earning quality of the listed consumer goods sector in Nigeria. The study used data collected from annual reports and accounts of the 13 sampled companies for the periods 2007 to 2018. Data were analyzed by means of descriptive statistics to provide summary statistics for the variables; also, correlation analysis was carried out using the Pearson correlation technique for the correlation between the dependent and independent variables. Regression was employed using the Generalized Least Square technique since the data has both time series and cross sectional attributes (panel data). It was found out that the audit committee had a positive and significant influence on the earning quality in the listed consumer goods companies in Nigeria. Thus, the study recommends that competency and personal integrity should be the worthwhile attributes to be considered while constituting the committee; this could enhance the quality of accounting information. In addition to that majority of the committee members should be independent directors in order to allow a high level of independency to be exercised.

Keywords: earning quality, corporate governance, audit committee, financial reporting

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Authors: Ahmet Faruk Akyuz, Hasan Sakir Bilge

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Depth estimation using stereo images is a challenging problem in computer vision. Many different studies have been carried out to solve this problem. With advancing machine learning, tackling this problem is often done with neural network-based solutions. The images used in these studies are mostly in the visible spectrum. However, the need to use the Infrared (IR) spectrum for depth estimation has emerged because it gives better results than visible spectra in some conditions. At this point, we recommend using thermal-thermal (IR) image pairs for depth estimation. In this study, we used two well-known networks (PSMNet, FADNet) with minor modifications to demonstrate the viability of this idea.

Keywords: thermal stereo matching, deep neural networks, CNN, Depth estimation

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Authors: Timothée Bligny, Clément Codron, Antoine Estruch, Nicolas Girodet, Clément Ginet

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Collateralized Debt Obligations are not as widely used nowadays as they were before 2007 Subprime crisis. Nonetheless there remains an enthralling challenge to optimize cash flows associated with synthetic CDOs. A Gaussian-based model is used here in which default correlation and unconditional probabilities of default are highlighted. Then numerous simulations are performed based on this model for different scenarios in order to evaluate the associated cash flows given a specific number of defaults at different periods of time. Cash flows are not solely calculated on a single bought or sold tranche but rather on a combination of bought and sold tranches. With some assumptions, the simplex algorithm gives a way to find the maximum cash flow according to correlation of defaults and maturities. The used Gaussian model is not realistic in crisis situations. Besides present system does not handle buying or selling a portion of a tranche but only the whole tranche. However the work provides the investor with relevant elements on how to know what and when to buy and sell.

Keywords: synthetic collateralized debt obligation (CDO), credit default swap (CDS), cash flow optimization, probability of default, default correlation, strategies, simulation, simplex

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Authors: Mohammad Zainie Bin Hassan

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Background: Erectile dysfunction (ED) is a complication commonly seen among men with diabetes which can be assessed based upon International Index of Erectile Function (IIEF-5) questionnaire. Creatinine level is a blood test that indicates kidney functionality. Object: To evaluate the association between ED, determined by the IIEF-5scores and Creatinine level in diabetic men attending Temerloh Health Clinic, Pahang, Malaysia.Hence, to identify raising Creatinine level related with ED or not. Methods: All married diabetic patients will be investigated face to face after consented for answering the IIEF-5 questionnaire. Creatinine level will be taken by using standard method.Patients with no sexual partner, refuse to answer the questionnaire, cancer, stroke, heart disease and language barrier will be excluded.Data obtained from IIEF-5 score and Creatinine level will be analyzed by using Pearson correlation. All statistical value determined by p=0.05. ED will be categorized accordingly to IIEF-5 scores: no ED (22-25), mild (17-21), moderate (12-16), severe (8-11) and very severe (1-7). Results: A total of 450 patients were investigated with 385 patients were included (85.6% respondant rate) and 65 patients were excluded in this study with age range from 29 to 85 years old. 7% had no ED, 28% mild ED, 34% moderate ED, 16% severe ED and 15% had very severe ED. There was a significant negative correlation between Creatinine level and IIEF-5 scores (r=-0.218, p <0.001). This result implicated that poor kidney function which indicated by high Creatinine level associated significantly with erectile dysfunction. 93% had ED with a different range of severity which triggers for appropriate aggressive ED management among diabetics. Conclusion: The high level of Creatinine is associated with erectile dysfunction among diabetics in Temerloh Health Clinic.

Keywords: correlation, creatinine level, erectile dysfunction, ED, diabetes

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Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn

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The detection and segmentation of mitochondria from fluorescence microscopy are crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. In the literature, a number of open-source software tools and artificial intelligence (AI) methods have been described for analyzing mitochondrial images, achieving remarkable classification and quantitation results. However, the availability of combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compatibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source python and openCV library, the algorithms are implemented in three stages: pre-processing, image binarization, and coarse-to-fine segmentation. The proposed model is validated using the mitochondrial fluorescence dataset. Ground truth labels generated using a Lab kit were also used to evaluate the performance of our detection and segmentation model. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks conclude the paper.

Keywords: 2D, binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation

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Authors: Yali Zheng, Peipei Luo, Shinan Chen, Jiasheng Hao, Hong Cheng

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Most of traditional visual indoor navigation algorithms and methods only consider the localization in ordinary daytime, while we focus on the indoor re-localization in low light in the paper. As RGB images are degraded in low light, less discriminative infrared and depth image pairs are taken, as the input, by RGB-D cameras, the most similar candidates, as the output, are searched from databases which is built in the bag-of-word framework. Epipolar constraints can be used to relocalize the query infrared and depth image sequence. We evaluate our method in two datasets captured by Kinect2. The results demonstrate very promising re-localization results for indoor navigation system in low light environments.

Keywords: indoor navigation, low light, RGB-D camera, vision based

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Authors: Fauzi Jarushi, Sinan Mohsin AlKaabi

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The Standard Penetration Test (SPT) is the most common insitu test for soil investigations. On the other hand, the Cone Penetration Test (CPT) is considered one of the best investigation tools. Due to the fast and accurate results that can be obtained it complaints the SPT in many applications like field explorations, design parameters, and quality control assessments. Many soil index and engineering properties have been correlated to both of SPT and CPT. Various foundation design methods were developed based on the outcome of these tests. Therefore it is vital to correlate these tests to each other so that either one of the tests can be used in the absence of the other, especially for preliminary evaluation and design purposes. The primary purpose of this study was to investigate the relationships between the SPT and CPT for different types of soil in Florida. Data for this research were collected from number of projects sponsored by the Florida Department of Transportation (FDOT), six sites served as the subject of SPT-CPT correlations. The correlations were established between the cone resistance (qc) and the SPT blows (i.e., N) for various soils. A positive linear relationship was found between fs and N for various soils. In general, qc versus N showed higher correlation coefficients than fs versus N. qc/N ratios were developed for different soil types and compared to literature values, the results of this research revealed higher ratios than literature values.

Keywords: in situ tests, correlation, SPT, CPT

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Authors: Ankit Sharma, Mudit Sachan, Anurag Prakash

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Rapid growth in data acquisition technologies through drones, have led to advances and interests in collecting high-resolution images of geological fields. Being advantageous in capturing high volume of data in short flights, a number of challenges have to overcome for efficient analysis of this data, especially while data acquisition, image interpretation and processing. We introduce a method that allows effective mapping of geological fields using photogrammetric data of surfaces, drainage area, water bodies etc, which will be captured by airborne vehicles like UAVs, we are not taking satellite images because of problems in adequate resolution, time when it is captured may be 1 yr back, availability problem, difficult to capture exact image, then night vision etc. This method includes advanced automated image interpretation technology and human data interaction to model structures and. First Geological structures will be detected from the primary photographic dataset and the equivalent three dimensional structures would then be identified by digital elevation model. We can calculate dip and its direction by using the above information. The structural map will be generated by adopting a specified methodology starting from choosing the appropriate camera, camera’s mounting system, UAVs design ( based on the area and application), Challenge in air borne systems like Errors in image orientation, payload problem, mosaicing and geo referencing and registering of different images to applying DEM. The paper shows the potential of using our method for accurate and efficient modeling of geological structures, capture particularly from remote, of inaccessible and hazardous sites.

Keywords: digital elevation model, mapping, photogrammetric data analysis, geological structures

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Authors: Zhaoxu Lu, Yufeng Ma, Linying Gao, Li Wang, Mei Qiang

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Mercury (Hg) is a well-recognized environmental pollutant known by its toxicity of development and neurotoxicity, which may result in adverse health outcomes. However, the mechanisms underlying the teratogenic effects of Hg are not well understood. Imprinting genes are emerging regulators for fetal development subject to environmental pollutants impacts. In this study, we examined the association between paternal preconception Hg exposures and the alteration of DNA methylation of imprinting genes in human sperm DNA. A total of 618 men aged from 22 to 59 was recruited from the Reproductive Medicine Clinic of Maternal and Child Care Service Center and the Urologic Surgery Clinic of Shanxi Academy of Medical Sciences during April 2015 and March 2016. Demographic information was collected using questionnaires. Urinary Hg concentrations were measured using a fully-automatic double-channel hydride generation atomic fluorescence spectrometer. And methylation status in the DMRs of imprinting genes H19, Meg3 and Peg3 of sperm DNA were examined by bisulfite pyrosequencing in 243 participants. Spearman’s rank and multivariate regression analysis were used for correlation analysis between sperm DNA methylation status of imprinting genes and urinary Hg levels. The median concentration of Hg for participants overall was 9.09μg/l (IQR: 5.54 - 12.52μg/l; range = 0 - 71.35μg/l); no significant difference was found in median concentrations of Hg among various demographic groups (p > 0.05). The proportion of samples that a beyond intoxication criterion (10μg/l) for urinary Hg was 42.6%. Spearman’s rank correlation analysis indicates a negative correlation between urinary Hg concentrations and average DNA methylation levels in the DMRs of imprinted genes H19 (rs=﹣0.330, p = 0.000). However, there was no such a correlation found in genes of Peg3 and Meg3. Further, we analyzed of correlation between methylation level at each CpG site of H19 and Hg level, the results showed that three out of 7 CpG sites on H19 DMR, namely CpG2 (rs =﹣0.138, p = 0.031), CpG4 (rs =﹣0.369, p = 0.000) and CpG6 (rs=﹣0.228, p = 0.000), demonstrated a significant negative correlation between methylation levels and the levels of urinary Hg. After adjusting age, smoking, drinking, intake of aquatic products and education by multivariate regression analysis, the results have shown a similar correlation. In summary, mercury nonoccupational environmental exposure in reproductive-aged men associated with altered DNA methylation outcomes at DMR of imprinting gene H19 in sperm, implicating the susceptibility of the developing sperm for environmental insults.

Keywords: epigenetics, genomic imprinting gene, DNA methylation, mercury, transgenerational effects, sperm

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Authors: Masayuki Mori

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This study investigates the relationship between teachers’ perceptions of the usability of classroom space and various elements, including both physical and non-physical, of classroom environments. With the introduction of the GIGA School funding program in Japan in 2019, understanding its impact on learning in classroom space is crucial. The program enabled local educational authorities (LEA) to make it possible to provide one PC/tablet for each student of both elementary and junior high schools. Moreover, at the same time, the program also supported LEA to purchase other electronic devices for educational purposes such as electronic whiteboards, large displays, and real image projectors. A nationwide survey was conducted using random sampling methodology among 100 junior high schools to collect data on classroom space. Of those, 60 schools responded to the survey. The survey covered approximately fifty items, including classroom space size, class size, and educational electronic devices owned. After the data compilation, statistical analysis was used to identify correlations between the variables and to explore the extent to which classroom environment elements influenced teachers’ perceptions. Furthermore, decision tree analysis was applied to visualize the causal relationships between the variables. The findings indicate a significant negative correlation between class size and teachers’ evaluation of usability. In addition to the class size, the way students stored their belongings also influenced teachers’ perceptions. As for the placement of educational electronic devices, the installation of a projector produced a small negative correlation with teachers’ perceptions. The study suggests that while the GIGA School funding program is not significantly influential, traditional educational conditions such as class size have a greater impact on teachers’ perceptions of the usability of classroom space. These results highlight the need for awareness and strategies to integrate various elements in designing the learning environment of the classroom for teachers and students to improve their learning experience.

Keywords: classroom space, GIGA School, questionnaire survey, teachers’ perceptions

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Authors: Wael M Albadri, Hassnen M Jafer, Ehab H Sfoog

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Relationship between undrained shear strength (Su) and over consolidation ratio (OCR) of clay soil (marine clay) is very important in the field of geotechnical engineering to estimate the settlement behaviour of clay and to prepare a small scale physical modelling test. In this study, a relationship between shear strength and OCR parameters was determined using the laboratory vane shear apparatus and the fully automatic consolidated apparatus. The main objective was to establish non-linear correlation formula between shear strength and OCR and comparing it with previous studies. Therefore, in order to achieve this objective, three points were chosen to obtain 18 undisturbed samples which were collected with an increasing depth of 1.0 m to 3.5 m each 0.5 m. Clay samples were prepared under undrained condition for both tests. It was found that the OCR and shear strength are inversely proportional at similar depth and at same undrained conditions. However, a good correlation was obtained from the relationships where the R2 values were very close to 1.0 using polynomial equations. The comparison between the experimental result and previous equation from other researchers produced a non-linear correlation which has a similar pattern with this study.

Keywords: shear strength, over-consolidation ratio, vane shear test, clayey soil

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Authors: Alisher Ibragimov, Sofya Senotrusova, Aleksandra Beliaeva, Egor Ushakov, Yuri Markin

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Mammography plays a critical role in screening for breast cancer in women, and artificial intelligence has enabled the automatic detection of diseases in medical images. Many of the current techniques used for mammogram analysis focus on a single view (mediolateral or craniocaudal view), while in clinical practice, radiologists consider multiple views of mammograms from both breasts to make a correct decision. Consequently, computer-aided diagnosis (CAD) systems could benefit from incorporating information gathered from multiple views. In this study, the introduce a method based on a Siamese neural network (SNN) model that simultaneously analyzes mammographic images from tri-view: bilateral and ipsilateral. In this way, when a decision is made on a single image of one breast, attention is also paid to two other images – a view of the same breast in a different projection and an image of the other breast as well. Consequently, the algorithm closely mimics the radiologist's practice of paying attention to the entire examination of a patient rather than to a single image. Additionally, to the best of our knowledge, this research represents the first experiments conducted using the recently released Vietnamese dataset of digital mammography (VinDr-Mammo). On an independent test set of images from this dataset, the best model achieved an AUC of 0.87 per image. Therefore, this suggests that there is a valuable automated second opinion in the interpretation of mammograms and breast cancer diagnosis, which in the future may help to alleviate the burden on radiologists and serve as an additional layer of verification.

Keywords: breast cancer, computer-aided diagnosis, deep learning, multi-view mammogram, siamese neural network

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Authors: Shih-Ching Wang, Mark Lang

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This paper examines the secondary effects and consequences of special displays on subsequent shopping behavior. Special displays are studied as a prominent form of in-store or shopper marketing activity. Two experiments are performed using special value and special quality-oriented displays in an online simulated store environment. The impact of exposure to special displays on mindsets and resulting product choices are tested in a shopping task. Impact on store image is also tested. The experiments find that special displays do trigger shopping mindsets that affect product choices and shopping basket composition and value. There are intended and unintended positive and negative effects found. Special value displays improve store price image but trigger a price sensitive shopping mindset that causes more lower-priced items to be purchased, lowering total basket dollar value. Special natural food displays improve store quality image and trigger a quality-oriented mindset that causes fewer lower-priced items to be purchased, increasing total basket dollar value. These findings extend the theories of product categorization, mind-sets, and price sensitivity found in communication research into the retail store environment. Findings also warn retailers to consider the total effects and consequences of special displays when designing and executing in-store or shopper marketing activity.

Keywords: special displays, mindset, shopping behavior, price consciousness, product categorization, store image

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Authors: Dionisios Panagiotaras, Pavlos Avramidis, Dimitrios Papoulis, Dionysios Koulougliotis, Dionisis C. Christodoulopoulos, Dimitra Lekka, Despoina Nifora, Denisa Drouvari, Alexandra Skalioti

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This study delineates the geochemical and mineralogical characteristics of soils collected from woodland and forest areas affected by the fires of August 2021 at the Ilia prefecture, Greece. The mineralogical composition of the samples consists of quartz, calcite, albite, oligoclase, anorthite (feldspars), smectite, kaolinite and illite (clays). Quartz ranges from 38.21% to 57.49% with an average of 48.43%, calcite ranges from 2.55% to 25.09% with an average of 13.92%, feldspars ranges from 7.76% to 25.87% with an average of 17.02% and clays ranges from 4.39% to 43.43% with an average of 20.63%. Geochemical analyses of the soil samples applied for total organic carbon (TOC), total nitrogen (TN), total phosphorous (TP), Cu, Zn, Mn and Fe. Statistical analysis of the data shows a positive correlation between clays and Zn, Mn, Fe. TOC and TN show a strong positive correlation, while Fe shows a strong negative correlation with calcite.

Keywords: soils, geochemistry, mineralogy, woodland, forest

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Authors: Michael Udey Udam

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Background: This study investigated the impact of cloud resources, a component of cloud computing, on science teachers’ effectiveness in secondary schools in Cross River State. Three (3) research questions and three (3) alternative hypotheses guided the study. Method: The descriptive survey design was adopted for the study. The population of the study comprised 1209 science teachers in public secondary schools of Cross River state. Sample: A sample of 487 teachers was drawn from the population using a stratified random sampling technique. The researcher-made structured questionnaire with 18 was used for data collection for the study. Research question one was answered using the Pearson Product Moment Correlation, while research question two and the hypotheses were answered using the Analysis of Variance (ANOVA) statistics in the Statistical Package for Social Sciences (SPSS) at a 0.05 level of significance. Results: The results of the study revealed that there is a positive correlation between the utilization of cloud resources in teaching and teaching effectiveness among science teachers in secondary schools in Cross River state; there is a negative correlation between gender and utilization of cloud resources among science teachers in secondary schools in Cross River state; and that there is a significant correlation between teaching experience and the utilization of cloud resources among science teachers in secondary schools in Cross River state. Conclusion: The study justifies the effectiveness of the Cross River state government policy of introducing cloud computing into the education sector. The study recommends that the policy should be sustained.

Keywords: cloud resources, science teachers, effectiveness, secondary school

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Authors: Kumator Josiphiah Taku

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Sustainability in construction is essential to the economic construction and can be achieved by the use of locally available construction materials. This research work, thus, uses locally available materials –calcined clay and Sandcrete SPR-300 superplasticizer in the production of Self Compacting Concrete (SCC) by investigating the correlation between the superplasticizer dosage and the fresh and hardened states properties of a grade 50 SCC made by incorporating a Calcined Clay (CC) – Portland Limestone Cement (PLC) blend as the cementitious matter at 20% replacement of PLC with CC and using CC as filler. The superplasticizer dosage was varied from 0.4 to 3.0% by weight of cementitious material and the slump, v-funnel, L-box and strength parameters investigated. The result shows a positive correlation between the increased dosage of the superplasticizer and the fresh and hardened states properties of the SCC up to 2% dosage. The J¬Spread¬, t¬500J¬, Slump flow, L-box H¬2¬/H¬1 ¬ratio and strength, all increases with SP dosage while the V-funnel flow decreased with SP dosage. Overall, SP ratio of 0.5 to 2.0 can be used in improving the properties of SCC produced using calcined clay both as filler and cementitious material.

Keywords: calcined clay, compressive strength, fresh-state properties of SCC, self compacting concrete, superplasticizer dosage

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Authors: Tatsuya Takino, Izuru Nomura, Yuji Kageyama, Shin Nagata, Hiroyuki Kamata

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We propose a method of detecting the craters from the image of the lunar surface. This proposal assumes that it is applied to SLIM (Smart Lander for Investigating Moon) working group aiming at the pinpoint landing on the lunar surface and investigating scientific research. It is difficult to equip and use high-performance computers for the small space probe. So, it is necessary to use a small computer with an exclusive hardware such as FPGA. We have studied the crater detection using principal component analysis (PCA), In this paper, We implement detection algorithm into the FPGA, and the detection is performed on the data that was captured from the CMOS camera.

Keywords: crater detection, PCA, FPGA, image processing

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Authors: Cristina Azocar, Ivana Markova

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A survey of 565 male undergraduates examined the effects of exposure to social networking sites and entertainment media on young men’s body image. Exposure to social and to entertainment media was found to have negative effects on men’s body satisfaction, social comparison, and thin ideal internalization. Findings indicated significant differences in those men who were more exposed to social and to entertainment media than those who were not as exposed. Consistent with past studies, gay men were found to be more dissatisfied with their bodies than straight men. Gay men compared themselves to other better-looking individuals and internalized ideal body types seen in media significantly more than their straight counterparts. Surprisingly, straight men seem to care as much about their physical attractiveness/appearance as gay men do, but only in public settings such as at the beach, at athletic events (including gyms) and social events. Although on average ethnic groups were more similar than different, small but significant differences occurred with Asian men indicating significantly higher body dissatisfaction than White/European men and Middle Eastern/Arab men their counterparts. The study increases our knowledge about SNS and entertainment use and its associated body image, and body satisfaction affects among low-income ethnic minority men.

Keywords: body dissatisfaction, body image, entertainment media, gay men, race and ethnicity, social economic status, social comparison, social media

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Authors: Chen Chan Wei

Abstract:

The purpose of this study was to investigate the impact of swim 1500m, 10000m run, VO2 max, and body fat on Olympic distance triathlon competition performance. The subjects were thirteen college triathletes with endurance training, with an average age, height and weight of 20.61±1.04 years (mean ± SD), 171.76±8.54 cm and 65.32±8.14 kg respectively. All subjects were required to take the tests of swim 1500m, run 10000m, VO2 max, body fat, and participate in the Olympic distance triathlon competition. First, the swim 1500m test was taken in the standardized 50m pool, with a depth of 2m, and the 10000m run test on the standardized 400m track. After three days, VO2 max was tested with the MetaMax 3B and body fat was measured with the DEXA machine. After two weeks, all 13 subjects joined the Olympic distance triathlon competition at the 2016 New Taipei City Asian Cup. The relationships between swim 1500m, 10000m run, VO2 max, body fat test, and Olympic distance triathlon competition performance were evaluated using Pearson's product-moment correlation. The results show that 10000m run and body fat had a significant positive correlation with Olympic distance triathlon performance (r=.830, .768), but VO2 max has a significant negative correlation with Olympic distance triathlon performance (r=-.735). In conclusion, for improved non-draft Olympic distance triathlon performance, triathletes should focus on running than swimming training and can be measure VO2 max to prediction triathlon performance. Also, managing body fat can improve Olympic distance triathlon performance. In addition, swimming performance was not significantly correlated to Olympic distance triathlon performance, possibly because the 2016 New Taipei City Asian Cup age group was not a drafting competition. The swimming race is the shortest component of Olympic distance triathlons. Therefore, in a non-draft competition, swimming ability is not significantly correlated with overall performance.

Keywords: triathletes, olympic, non-drafting, correlation

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Authors: İsmail Ay

Abstract:

In this study, it was aimed to determine the relations between need for psychological help and psychological symptoms. The sample of the study consists of 530 university students getting educated in University of Atatürk in 2015-2016 academic years. Need for Psychological Help Scale and Brief Symptom Inventory were used to collect data in the study. In data analysis, correlation analysis and structural equation model with latent variables were used. Normality and homogeneity analyses were used to analyze the basic conditions of parametric tests. The findings obtained from the study show that as the psychological symptoms increase, need for psychological help also increases. The findings obtained through the study were approached according to the literature.

Keywords: psychological symptoms, need for psychological help, structural equation model, correlation

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Authors: Nathenal Thomas

Abstract:

In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

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Authors: Ayala Amir

Abstract:

The paper presents interdisciplinary research which draws on the literary study and the cultural study of photography to explore a literary genre defined by nostalgia – the photographed book for children. This genre, which was popular in the second half of the 20th century, presents the romantic, nostalgic image of childhood created in the visual arts in the 18th century (as suggested by Ann Higonnet). At the same time, it capitalizes on the nostalgia inherent in the event of photography as formulated by Jennifer Green-Lewis: photography frames a moment in the present while transforming it into a past longed for in the future. Unlike Freudian melancholy, nostalgia is an effect that enables representation by acknowledging the loss and containing it in the very experience of the object. The representation and preservation of the lost object (nature, childhood, innocence) are in the center of the genre of children's photography books – a modern version of ancient pastoral. In it, the unique synergia of word and image results in a nostalgic image of childhood in an era already conquered by modernization. The nostalgic effect works both in the representation of space – an Edenic image of nature already shadowed by its demise, and of time – an image of childhood imbued by what Gill Bartholnyes calls the "looking backward aesthetics" – under the sign of loss. Little critical attention has been devoted to this genre with the exception of the work of Bettina Kümmerling-Meibauer, who noted the nostalgic effect of the well-known series of photography books by Astrid Lindgren and Anna Riwkin-Brick. This research aims to elaborate Kümmerling-Meibauer's approach using the theories of the study of photography, word-image studies, as well as current studies of childhood. The theoretical perspectives are implemented in the case study of photography books created in one of the most innovative social structures in our time – the Israeli Kibbutz. This communal way of life designed a society where children will experience their childhood in a parentless rural environment that will save them from the fate of the Oedipal fall. It is suggested that in documenting these children in a fictional format, photographers and writers, images and words cooperated in creating nostalgic works situated on the border between nature and culture, imagination and reality, utopia and its realization in history.

Keywords: nostalgia, photography , childhood, children's books, kibutz

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Authors: Wissem Abdaoui, Nizar M. Mhaidat, Ilhem Mokhtari, Adel Gouri

Abstract:

Colorectal cancer (CRC) is one of the most common tumors all over the world. Obesity, considered a risk factor of CRC, is characterized by a high level of secreted cytokines from adipose tissue. Among these inflammatory molecules, leptin is considered the key mediator for CRC cancer development and progression by activation of mitogenic and anti apoptotic signaling pathways. Gene expression can be significantly modulated by alterations in DNA methylation patterns. The aim of this study is to investigate the impact of leptin gene methylation on CRC prognosis and sensitivity to chemotherapy. The study involved 70 CRC tissue samples collected from King Abdullah University Hospital (KAUH) from which only 53 was analyzed because of bisulfate fragmentation and low yield of DNA extracted from FFPE tissues. A total of 22 blood samples were collected from healthy volunteers and enrolled as a control group. Leptin promoter methylation was analyzed by methylation specific PCR after bisulfate conversion. Results revealed that the incidence of leptin gene methylation was significantly higher in CRC patients in comparison to that of controls (P < 0.05). The correlation between patient’s demographics and leptin gene methylation was not significant (P < 0.05). However, a significant correlation between leptin gene methylation status and early cancer stages (I, II and III) was found in male but not in female (p < 0.05). Moreover, a significant correlation was found between leptin promoter methylation and early tumor localization T1-2 (p < 0.05). The correlation between epigenetic regulation of leptin and chemosensitivity was not significant. Taken together, these results suggest the possibility to use leptin gene methylation as a biomarker for the evaluation of CRC prognosis and metastasis.

Keywords: colorectal cancer, obesity, leptin, DNA methylation, disease prognosis, bisulfate conversion, chemoresistance

Procedia PDF Downloads 376