Search results for: image rotation

Authors: Caio Marcelo Nunes, Anderson da Silva Soares, Gustavo Teodoro Laureano, Clarimar Jose Coelho

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This paper presents a methodology to pixel recognition in aerial images using fuzzy $c$-means algorithm. This algorithm is a alternative to recognize areas considering uncertainties and inaccuracies. Traditional clustering technics are used in recognizing of multispectral images of earth's surface. This technics recognize well-defined borders that can be easily discretized. However, in the real world there are many areas with uncertainties and inaccuracies which can be mapped by clustering algorithms that use fuzzy sets. The methodology presents in this work is applied to multispectral images obtained from Landsat-5/TM satellite. The pixels are joined using the $c$-means algorithm. After, a classification process identify the types of surface according the patterns obtained from spectral response of image surface. The classes considered are, exposed soil, moist soil, vegetation, turbid water and clean water. The results obtained shows that the fuzzy clustering identify the real type of the earth's surface.

Keywords: aerial images, fuzzy clustering, image processing, pattern recognition

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Authors: Faci Youcef, Ahmed Mebtouche, Djillali Allou, Maalem Badredine

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The inclination of the bolt in a fastened joint of composite material during a tensile test can be influenced by several parameters, including material properties, bolt diameter and length, the type of composite material being used, the size and dimensions of the bolt, bolt preload, surface preparation, the design and configuration of the joint, and finally testing conditions. These parameters should be carefully considered and controlled to ensure accurate and reliable results during tensile testing of composite materials with fastened joints. Our work focuses on the effect of the stacking sequence and the geometry of specimens. An experimental test is carried out to obtain the inclination of a bolt during a tensile test of a composite material using acoustic emission and digital image correlation. Several types of damage were obtained during the load. Digital image correlation techniques permit the obtaining of the inclination of bolt angle value during tensile test. We concluded that the inclination of the bolt during a tensile test of a composite material can be related to the damage that occurs in the material. It can cause stress concentrations and localized deformation in the material, leading to damage such as delamination, fiber breakage, matrix cracking, and other forms of failure.

Keywords: damage, inclination, analyzed, carbon

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Authors: Asmau Mukhtar Ahmed, Olga Duran

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Hydrocarbon (HC) spills constitute a significant problem that causes great concern to the environment. With the latest technology (hyperspectral images) and state of the earth techniques (image processing tools), hydrocarbon spills can easily be detected at an early stage to mitigate the effects caused by such menace. In this study; a controlled laboratory experiment was used, and clay soil was mixed and homogenized with different hydrocarbon types (diesel, bio-diesel, and petrol). The different mixtures were scanned with HYSPEX hyperspectral camera under constant illumination to generate the hypersectral datasets used for this experiment. So far, the Short Wave Infrared Region (SWIR) has been exploited in detecting HC spills with excellent accuracy. However, the Near-Infrared Region (NIR) is somewhat unexplored with regards to HC contamination and how it affects the spectrum of soils. In this study, Deep Neural Network (DNN) was applied to the controlled datasets to detect and quantify the amount of HC spills in soils in the Near-Infrared Region. The initial results are extremely encouraging because it indicates that the DNN was able to identify features of HC in the Near-Infrared Region with a good level of accuracy.

Keywords: hydrocarbon, Deep Neural Network, short wave infrared region, near-infrared region, hyperspectral image

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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: 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: 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: A. C. Galvis-Castro, R. D. Tovar, R. Salgado, M. Prezzi

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It is generally accepted that the shaft capacity of piles in the sand is lower for tensile loading that for compressive loading. So far, very little attention has been paid to the role of the influence of the installation method on the tensile to compressive shaft capacity ratio. The objective of this paper is to analyze the effect of installation method on the tensile to compressive shaft capacity of piles in dense sand as observed in tests on half-circular model pile tests in a half-circular calibration chamber with digital image correlation (DIC) capability. Model piles are either monotonically jacked, jacked with multiple strokes or pre-installed into the dense sand samples. Digital images of the model pile and sand are taken during both the installation and loading stages of each test and processed using the DIC technique to obtain the soil displacement and strain fields. The study provides key insights into the mobilization of shaft resistance in tensile and compressive loading for both displacement and non-displacement piles.

Keywords: digital image correlation, piles, sand, shaft resistance

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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: Hiroshi Ikeda

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In this research, a questionnaire survey was conducted to measure nap, drowsiness and fatigue of drivers who work long shifts, to discuss about the work environment and health conditions for taxi and bus drivers who work at night time. The questionnaire sheet used for this research was organized into the following categories: tension/tiredness, drowsiness while driving, and the nap situation during night-time work. The number of taxi drivers was 127 and the number of bus drivers was 40. Concerning the results of a comparison of nap hours of taxi and bus drivers, the taxi drivers’ nap hours are overwhelmingly shorter, and also the frequency of drivers who feel drowsiness is higher. The burden on bus drivers does not change because of the system of a two-driver rotation shift. In particular, the working environment of the taxi driver may lead to greater fatigue accumulation than the bus driver’s environment.

Keywords: bus and taxi, drowsiness, fatigue, nap

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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: Daniel Icaza, Federico Córdova, Chiristian Castro, Fernando Icaza, Juan Portoviejo

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In this article, the mathematical model is presented, and simulations were carried out using specialized software such as MATLAB before the construction of a 900-W wind turbine. The present study was conducted with the intention of taking advantage of the rotation of the blades of the wind generator after going through a process of amplification of speed by means of a system of gears to finally mechanically couple two electric generators of similar characteristics. This coupling allows generating a maximum voltage of 6 V in DC for each generator and putting in series the 12 V DC is achieved, which is later stored in batteries and used when the user requires it. Laboratory tests were made to verify the level of power generation produced based on the wind speed at the entrance of the blades.

Keywords: smart grids, wind turbine, modeling, renewable energy, robust control

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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: Vahid Mehrabi, Iman Sharifi, H. A. Talebi

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The automation of rehabilitation procedure by the implementation of robotic devices can overcome the limitation in conventional physiotherapy methods by increasing training sessions and duration of process. In this paper, the design of a simple rehabilitation robot for forearm-wrist therapy in stroke and spinal cord injuries is presented. Wrist’s biological joint motion is modeled by a gimbal-like mechanism which resembles the human arm anatomy. Presented device is an exoskeleton robot with rotation axes corresponding to human skeleton anatomy. The mechanical structure, actuator and sensor selection, system kinematics and comparison between our device range of motion and required active daily life values is illustrated.

Keywords: rehabilitation, robotic devices, physiotherapy, forearm-wrist

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Authors: Chien-Liang Chao, Yun-Sheng Lin

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Secondary metabolites are applied in the human life of the Chinese herbal medicine. Many drugs are originally extracted from natural products with combination of pharmaceutical and chemical studies. Crude extract of the leaves from Zamia furfureace L. has been shown to exhibit anticancer activities. The first chemical investigation of this plant was carried out by our group. In this study, four known compounds were isolated from Zamia furfureace L. with three lignins (Sesamin (1), Wodeshiol (2) and Paulownin (3)), and one dipeptide (Aurantiamide acetate (4)). The structures of these compounds were analyzed through the 1D-NMR(1H-NMR,13C-NMR)、2D-NMR(COSY、HMQC、HMBC、NOESY) spectroscopic analysis, and by comparison of variety of physical data (IR, mass spectrometry, ultraviolet, optical rotation). Among them, Aurantiamide acetate (4) exhibited weak cytotoxic activity against human gastric cancer cells.

Keywords: Zamia furfureace L., AGS, sesamin, Aurantiamide acetate, secondary metabolites

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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: Karim Akkouche

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Many methods have been developed for characterizing the behavior of bolted joints. However, in the presence of a certain model of stiffeners, no orientation was given in relation to their modeling. To this end, multitude of coarse errors can arise in the reproduction of the propagation of efforts and in representation of the modes of deformations. Considering these particularities, a numerical investigation was carried out in our laboratory. In this paper we will present a comparative study between three types of assemblies. A non-linear 3D modeling was chosen, given that it takes into consideration geometric and material non-linearity, using the Finite Element calculation code ABAQUS. Initially, we evaluated the influence of the presence of each stiffener on the "global" behavior of the assemblies, this by analyzing their Moment-Rotation curves, also by referring to the classification system proposed by NF EN 1993- 1.8 which is based on the resisting moment Mj-Rd and the initial stiffness Sj.int. In a second step, we evaluated the "local" behavior of their components by referring to the stress-strain curves.

Keywords: assembly, post-beam, end plate, nonlinearity

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Authors: Basman Elhadidi, Islam Elqatary, Osama Saaid, Hesham Othman

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An active slat is developed to increase the lift and delay the separation for a DU96-W180 airfoil. The active slat is a fixed slat that can be closed, fully opened or intermittently opened by a rotating vane depending on the need. Experimental results show that the active slat has reduced the mean pressure and increased the mean velocity on the suction side of the airfoil for all positive angles of attack, indicating an increase of lift. The experimental data and numerical simulations also show that the direction of actuator vane rotation can influence the mixing of the flow streams on the suction side and hence influence the aerodynamic performance.

Keywords: active slat, flow control, experimental investigation, aerodynamic performance

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Authors: Igor V. Kuzminov

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The proposed article is an analytical review of previously published articles in the series "Physics of Gravity" and "The picture of the world according to the second law of thermodynamics". The main topic is the physics of gravity. This article presents the proposed hypothesis on the physics of gravity in a brief form. A critique of existing views on the topic of gravity is also presented. Currently, the generally accepted and dominant theory in the field of gravity is the General Theory of Relativity. The proposed hypothesis is based on the concepts and laws of classical Newton physics. At the same time, a critique of the existing theory of gravity, based on postulates, conventions, and assumptions, is presented.

Keywords: physics of gravity, gyroscopic forces of rotation of electrons, temperature dependence, quadratic dependence of gravitational forces on distance, inertia forces, theory of relativity

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Authors: Nour Eldin Afyouni, Hassan Assoum, Kamel Abed-Meraim, Anas Sakout

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The acoustic of an impinging jet holds significant importance in the engineering field. In HVAC systems, the jet impingement, in some cases, generates noise that destroys acoustic comfort. This paper presents an experimental study of a rectangular air jet impinging on a slotted plate to investigate the correlation between sound emission and turbulence dynamics. The experiment was conducted with an impact ratio L/H = 4 and a Reynolds number Re = 4700. The survey shows that coherent structures within the impinging jet are responsible for self-sustaining tone production. To achieve this, a specific experimental setup consisting of two simultaneous Stereoscopic Particle Image Velocimetry (S-PIV) measurements was developed to track vortical structures both before and after the plate, in addition to acoustic measurements. The results reveal a significant correlation between acoustic waves and the passage of coherent structures. Variations in the arrangement of vortical structures between the upstream and downstream sides of the plate were observed. This analysis of flow dynamics can enhance our understanding of slot noise.

Keywords: impinging jet, coherent structures, SPIV, aeroacoustics

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Authors: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

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Authors: Omer Cahana, Ofer Levi, Maya Herman

Abstract:

Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: magnetic resonance imaging, image reconstruction, pyramid network, deep learning

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Authors: Raquel Rossini, Edelvais Caldeira

Abstract:

The aim of this work is to investigate the motivation for the metaphorical uses of two verb combinations: come out and take over. Drawing from cognitive semantics theories, image schemas and metaphors, it was attempted to demonstrate that: a) the metaphorical senses of both 'come out' and 'take over' extend from both the verbs and the particles central (spatial) senses in such verb combinations; and b) the particles 'out' and 'over' also contribute to the whole meaning of the verb combinations. In order to do so, a random selection of 579 concordance lines for come out and 1,412 for take over was obtained from the Corpus of Contemporary American English – COCA. One of the main procedures adopted in the present work was the establishment of verb and particle central senses. As per the research questions addressed in this study, they are as follows: a) how does the identification of trajector and landmark help reveal patterns that contribute for the identification of the semantic network of these two verb combinations?; b) what is the relationship between the schematic structures attributed to the particles and the metaphorical uses found in empirical data?; and c) what conceptual metaphors underlie the mappings from the source to the target domains? The results demonstrated that not only the lexical verbs come and take, but also the particles out and over play an important whole in the different meanings of come out and take over. Besides, image schemas and conceptual metaphors were found to be helpful in order to establish the motivations for the metaphorical uses of these linguistic structures.

Keywords: cognitive linguistics, English syntax, multi-word verbs, prepositions

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Authors: Mara Getachew Zenebe, Luuk Fleskens, Abdu Obieda Ahmed

Abstract:

This paper seeks to advance our scientific understanding of optimizing flood utilization in regions impacted by climate change, with a focus on enhancing agricultural productivity through effective irrigation management. The study was conducted as part of a three-year (2021 to 2023) USAID-supported initiative aimed at promoting Economic Growth and Peace in the Gash Agricultural Scheme (GAS), situated in Sudan's water-stressed Eastern region. GAS is the country's largest flood-irrigated scheme, covering 100,800 hectares of cultivable land, with a potential to provide the food security needs of over a quarter of a million agro-pastoral community members. GAS relies on the Gash River, which sources its water from high-intensity rainfall events in the highlands of Ethiopia and Eritrea. However, climate change and variations in these highlands have led to increased variability in the Gash River's flow. The study conducted water balance analyses based on a ten-year dataset of the annual Gash River flow, irrigated area; as well as the evapotranspiration demand of the major sorghum crop. Data collection methods included field measurements, surveys, remote sensing, and CropWat modelling. The water balance assessment revealed that the existing three-year rotation-based irrigation system management, capping cultivated land at 33,000 hectares annually, is excessively risk-averse. While this system reduced conflicts among the agro-pastoral communities by consistently delivering on the land promised to be annually cultivated, it also increased GAS's vulnerability to flood damage due to several reasons. The irrigation efficiency over the past decade was approximately 30%, leaving significant unharnessed floodwater that caused damage to infrastructure and agricultural land. The three-year rotation resulted in inadequate infrastructural maintenance, given the destructive nature of floods. Additionally, it led to infrequent land tillage, allowing the encroachment of mesquite trees hindering major sorghum crop growth. Remote sensing data confirmed that mesquite trees have overtaken 70,000 hectares in the past two decades, rendering them unavailable for agriculture. The water balance analyses suggest shifting to a two-year rotation, covering approximately 50,000 hectares annually while maintaining risk aversion. This shift could boost GAS's annual sorghum production by two-thirds, exceeding 850,000 tons. The scheme's efficiency can be further enhanced through low-cost on-farm interventions. Currently, large irrigation plots that range from 420 to 756 hectares are irrigated with limited water distribution guidance, leading to uneven irrigation. As demonstrated through field trials, implementing internal longitudinal bunds and horizontal deflector bunds can increase adequately irrigated parts of the irrigation plots from 50% to 80% and thus nearly double the sorghum yield to 2 tons per hectare while reducing the irrigation duration from 30 days to a maximum of 17 days. Flow measurements in 2021 and 2022 confirmed that these changes sufficiently meet the sorghum crop's water requirements, even with a conservative 60% field application efficiency assumption. These insights and lessons from the GAS on enhancing agricultural resilience and sustainability in the face of climate change are relevant to flood-based livelihood systems globally.

Keywords: climate change, irrigation management and productivity, variable flood flows, water balance analysis

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