Search results for: artificial neural networks; crop water stress index; canopy temperature

Authors: Shanshan Zhu, Mohammad Nasim

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Financial fraud continues to be a major threat to financial institutions across the world, causing colossal money losses and undermining public trust. Fraud prevention techniques, based on hard rules, have become ineffective due to evolving patterns of fraud in recent times. Against such a background, the present study probes into distinct methodologies that exploit emergent AI-driven techniques to further strengthen fraud detection. We would like to compare the performance of generative adversarial networks and graph neural networks with other popular techniques, like gradient boosting, random forests, and neural networks. To this end, we would recommend integrating all these state-of-the-art models into one robust, flexible, and smart system for real-time anomaly and fraud detection. To overcome the challenge, we designed synthetic data and then conducted pattern recognition and unsupervised and supervised learning analyses on the transaction data to identify which activities were fishy. With the use of actual financial statistics, we compare the performance of our model in accuracy, speed, and adaptability versus conventional models. The results of this study illustrate a strong signal and need to integrate state-of-the-art, AI-driven fraud detection solutions into frameworks that are highly relevant to the financial domain. It alerts one to the great urgency that banks and related financial institutions must rapidly implement these most advanced technologies to continue to have a high level of security.

Keywords: AI-driven fraud detection, financial security, machine learning, anomaly detection, real-time fraud detection

Procedia PDF Downloads 42

Authors: Anoop T. R., Otman Basir, Robert F. Hess, Eileen E. Birch, Brooke A. Koritala, Reed M. Jost, Becky Luu, David Stager, Ben Thompson

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Strabismus refers to a misalignment of the eyes. Early detection and treatment of strabismus in childhood can prevent the development of permanent vision loss due to abnormal development of visual brain areas. We developed a two-stage method for strabismus detection and classification based on photographs of the face. The first stage detects the presence or absence of strabismus, and the second stage classifies the type of strabismus. The first stage comprises face detection using Haar cascade, facial landmark estimation, face alignment, aligned face landmark detection, segmentation of the eye region, and detection of strabismus using VGG 16 convolution neural networks. Face alignment transforms the face to a canonical pose to ensure consistency in subsequent analysis. Using facial landmarks, the eye region is segmented from the aligned face and fed into a VGG 16 CNN model, which has been trained to classify strabismus. The CNN determines whether strabismus is present and classifies the type of strabismus (exotropia, esotropia, and vertical deviation). If stage 1 detects strabismus, the eye region image is fed into stage 2, which starts with the estimation of pupil center coordinates using mask R-CNN deep neural networks. Then, the distance between the pupil coordinates and eye landmarks is calculated along with the angle that the pupil coordinates make with the horizontal and vertical axis. The distance and angle information is used to characterize the degree and direction of the strabismic eye misalignment. This model was tested on 100 clinically labeled images of children with (n = 50) and without (n = 50) strabismus. The True Positive Rate (TPR) and False Positive Rate (FPR) of the first stage were 94% and 6% respectively. The classification stage has produced a TPR of 94.73%, 94.44%, and 100% for esotropia, exotropia, and vertical deviations, respectively. This method also had an FPR of 5.26%, 5.55%, and 0% for esotropia, exotropia, and vertical deviation, respectively. The addition of one more feature related to the location of corneal light reflections may reduce the FPR, which was primarily due to children with pseudo-strabismus (the appearance of strabismus due to a wide nasal bridge or skin folds on the nasal side of the eyes).

Keywords: strabismus, deep neural networks, face detection, facial landmarks, face alignment, segmentation, VGG 16, mask R-CNN, pupil coordinates, angle deviation, horizontal and vertical deviation

Procedia PDF Downloads 93

Authors: J. P. Reges, L. C. S. Mazza, E. J. Braga, J. A. Bessa, A. R. Alexandria

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This paper proposes the development of control and automation of irrigation system located sunflower harvest in the Teaching Unit, Research and Extension (UEPE), the Apodi Plateau in Limoeiro do Norte. The sunflower extraction, which in turn serves to get the produced oil from its seeds, animal feed, and is widely used in human food. Its nutritional potential is quite high what makes of foods produced from vegetal, very rich and healthy. The focus of research is to make the autonomous irrigation system sunflower crop from programmable logic control energized with alternative energy sources, solar photovoltaics. The application of automated irrigation system becomes interesting when it provides convenience and implements new forms of managements of the implementation of irrigated cropping systems. The intended use of automated addition to irrigation quality and consequently brings enormous improvement for production of small samples. Addition to applying the necessary and sufficient features of water management in irrigation systems, the system (PLC + actuators + Renewable Energy) will enable to manage the quantitative water required for each crop, and at the same time, insert the use of sources alternative energy. The entry of the automated collection will bring a new format, and in previous years, used the process of irrigation water wastage base and being the whole manual irrigation process.

Keywords: automation, control, sunflower, irrigation, programming, renewable energy

Procedia PDF Downloads 399

Authors: Çiğdem Bakır, Mecit Yüzkat

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With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower, and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software we used in our study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: preprocessing of the data used, feature detection, and classification. We tried to determine the success of our study with different accuracy metrics and the results. We presented it comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: decision tree, water quality, water pollution, machine learning

Procedia PDF Downloads 83

Authors: Tsu-Hsu Yen

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The interfacial gas adsorption presents a frequent challenge and opportunity for micro-/nano-fluidic operation. In this study, we investigate the wettability, gas accumulation, and nanobubble formation on various homogeneous surface conditions by using MD simulation, including a series of 3D and quasi-2D argon-water-solid systems simulation. To precisely determine the wettability on various substrates, several indicators were calculated. Among these wettability indicators, the water PMF (potential of mean force) has the most correlation tendency with interfacial water molecular orientation than depletion layer width and droplet contact angle. The results reveal that the aggregation of argon molecules on substrates not only depending on the level of hydrophobicity but also determined by the competition between gas-solid and water-solid interaction as well as water molecular structure near the surface. In addition, the surface nanobubble is always observed coexisted with the gas enrichment layer. The water structure adjacent to water-gas and water-solid interfaces also plays an important factor in gas out-flux and gas aggregation, respectively. The quasi-2D simulation shows that only a slight difference in the curved argon-water interface from the plane interface which suggests no noticeable obstructing effect on gas outflux from the gas-water interfacial water networks.

Keywords: gas aggregation, interfacial nanobubble, molecular dynamics simulation, wettability

Procedia PDF Downloads 115

Authors: Kamal Khan Abro, Nasreen Memon, Attaullah Ansari, Mahpara Pirzada, Saima Pathan

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Jacobabad district has a hot desert climate with very hot summers and insignificant winters. The highest recorded temperature is 53.8 °C (127.0 °F), and the lowest recorded temperature is −4.9 °C (25.0 °F). Rainfall is short and mostly occurs in the monsoon season (July–September). Agriculture point of view Jacobabad district is very important district of Sindh Pakistan in which many types of crop and vegetables are cultivated annually such as Wheat, Rice, and Brassica, Cabbage, Spinach, Chili etc. which are badly attacked by many crops pest. Insects are very tiny, sensitive and most attractive mortal and most important collection of animal wildlife they play important role in biological control agent, biodiversity & agroecosystem. The brassica crop extremely infested by many different types of pest such as Aphids, Whitefly, Jassids, Thrips, Mealybug, scale insect pink worm, bollworm and borers Mealy bug, scale insect etc. These pests destroy many crops. The present study was carried out from Jacobabad district from January 2017 to April 2017.

Keywords: prevelance, green peach aphid, Jacobabad, Sindh Pakistan

Procedia PDF Downloads 291

Authors: Bernabeo R. Alberto

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With the tremendous improvements in the processing power of computers, the possibilities of artificial intelligence will increasingly be used in aviation and make autonomous flights, preventive maintenance, ATM (Air Traffic Management) optimization, pilots, cabin crew, ground staff, and airport staff training possible in a cost-saving, less time-consuming and less polluting way. Through the use of artificial intelligence, we foresee an interviewing scenario where the interviewee will interact with the artificial intelligence tool to contextualize the character and the necessary information in a way that aligns reasonably with the character and the scenario. We are creating simulated scenarios connected with either an aviation incident or accident to enhance also the training of future accident/incident investigators integrating artificial intelligence and augmented reality tools. The project's goal is to improve the learning and teaching scenario through academic and professional expertise in aviation and in the artificial intelligence field. Thus, we intend to contribute to the needed high innovation capacity, skills, and training development and management of artificial intelligence, supported by appropriate regulations and attention to ethical problems.

Keywords: artificial intelligence, aviation accident, aviation incident, risk, safety

Procedia PDF Downloads 22

Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores

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This work introduces the use of EMGs (electromyography) from muscle sensors to develop an Artificial Neural Network (ANN) for pattern recognition to control a small unmanned aerial vehicle. The objective of this endeavor exhibits interfacing drone applications beyond manual control directly. MyoWare Muscle sensor contains three EMG electrodes (dual and single type) used to collect signals from the posterior (extensor) and anterior (flexor) forearm and the bicep. Collection of raw voltages from each sensor were connected to an Arduino Uno and a data processing algorithm was developed with the purpose of interpreting the voltage signals given when performing flexing, resting, and motion of the arm. Each sensor collected eight values over a two-second period for the duration of one minute, per assessment. During each two-second interval, the movements were alternating between a resting reference class and an active motion class, resulting in controlling the motion of the drone with left and right movements. This paper further investigated adding up to three sensors to differentiate between hand gestures to control the principal motions of the drone (left, right, up, and land). The hand gestures chosen to execute these movements were: a resting position, a thumbs up, a hand swipe right motion, and a flexing position. The MATLAB software was utilized to collect, process, and analyze the signals from the sensors. The protocol (machine learning tool) was used to classify the hand gestures. To generate the input vector to the ANN, the mean, root means squared, and standard deviation was processed for every two-second interval of the hand gestures. The neuromuscular information was then trained using an artificial neural network with one hidden layer of 10 neurons to categorize the four targets, one for each hand gesture. Once the machine learning training was completed, the resulting network interpreted the processed inputs and returned the probabilities of each class. Based on the resultant probability of the application process, once an output was greater or equal to 80% of matching a specific target class, the drone would perform the motion expected. Afterward, each movement was sent from the computer to the drone through a Wi-Fi network connection. These procedures have been successfully tested and integrated into trial flights, where the drone has responded successfully in real-time to predefined command inputs with the machine learning algorithm through the MyoWare sensor interface. The full paper will describe in detail the database of the hand gestures, the details of the ANN architecture, and confusion matrices results.

Keywords: artificial neural network, biosensors, electromyography, machine learning, MyoWare muscle sensors, Arduino

Procedia PDF Downloads 174

Authors: Hygreeva Kiran Namburi, Anna Hojna, Edita Lecianova, Fencl Zdenek

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Irradiation Assisted Stress Corrosion Cracking [IASCC] is one of the most significant environmental degradation in the internal components made from Austenitic stainless steel. This mechanism is still not fully understood and there are no suitable criteria for prediction of the damage during operation. In this work, core basket material 08Ch18N10T austenitic stainless steel acquired from decommissioned NPP Nord / Greifswald Unit 1, VVER 440-230 type, operated for 15 years and irradiated at 5.2 dpa is studied. This material was tensile tested at two different test temperatures and strain rates in air and at the elevated temperature under the water environment. SEM observations of the fracture surface documented ductile fracture of the samples tested in air, but areas of IASCC tested in water. This paper emphasizes on the microscopic examination results from the mechanically tested samples to determine the underlying IASCC physical damage process. TEM observations of thin foils made from the gauge sections that are closer to the fractured surface of the specimen aimed to find variances in interaction of dislocations and grain boundaries owing to different test conditions.

Keywords: irradiation assisted stress corrosion cracking, core basket material, SEM observations of the fracture surface, microscopic examination results

Procedia PDF Downloads 349

Authors: Remy Kulagowski, Tobias Sturm, Audrey Leopold, Aurelie Metay, Josephine Peigne, Alexis Thoumazeau, Alain Brauman, Bruno Fogliani, Florent Tivet

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A major challenge for agriculture is to enhance productivity while limiting the impact on the environment. Conservation agriculture (CA) is one strategy whereby both sustainability and productivity can be achieved by preserving and improving the soil quality. Soils provide and regulate a large number of ecosystem services (ES) such as agricultural productivity and climate change adaptation and mitigation. The aim of this study is to assess the impacts of contrasted CA crop management on soil functions for maize (Zea mays L.) cultivation in an eight years field experiment (2010-2018). The study included two CA practices: direct seeding in dead mulch (DM) and living mulch (LM), and conventional plough-based tillage (CT) practices on a fluvisol in New Caledonia (French Archipelago in the South Pacific). In 2018, soil quality of the cropping systems were evaluated with the Biofunctool® set of indicators, that consists in twelve integrative, in-field, and low-tech indicators assessing the biological, physical and chemical properties of soils. Main soil functions were evaluated including (i) carbon transformation, (ii) structure maintenance, and (iii) nutrient cycling in the ten first soil centimeters. The results showed significant higher score for soil structure maintenance (e.g., aggregate stability, water infiltration) and carbon transformation function (e.g., soil respiration, labile carbon) under CA in DM and LM when compared with CT. Score of carbon transformation index was higher in DM compared with LM. However, no significant effect of cropping systems was observed on nutrient cycling (i.e., nitrogen and phosphorus). In conclusion, the aggregated synthetic scores of soil multi-functions evaluated with Biofunctool® demonstrate that CA cropping systems lead to a better soil functioning. Further analysis of the results with agronomic performance of the soil-crop systems would allow to better understand the links between soil functioning and production ES of CA.

Keywords: conservation agriculture, cropping systems, ecosystem services, soil functions

Procedia PDF Downloads 157

Authors: Muhammad Ashfaq, Muhammad Saleem Haider, Muhammad Ali, Muhammad Sajjad, Amna Ali, Urooj Mubashar

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Phenotypic diversity was confirmed by measuring different morphological traits i.e. seed traits (seed length, seed width, seed thickness, seed length-width ratio, 1000 grain weight) and root-shoot traits (shoot length, root length, shoot fresh weight, root fresh weight, root-shoot ratio, root numbers and root thickness). Variance and association study of desirable traits determine the genotypic differences among the rice germplasm. All the traits showed significant differences among the genotypes. The traits were studied in Randomized complete block design (RCBD) at different water levels. Some traits showed positive correlation with each other and beneficial for increasing the yield and production of the crop. Seed thickness has positive correlation with seed length and seed width (r= 0.104**, r=0.246**). On the other hand, various root shoot traits showed positive highly significant association at different water levels i.e. root length, fresh root weight, root thickness, shoot thickness and root numbers. Our main focus to study the performance/correlation of root shoots traits under stress condition. Fresh root weight, shoot thickness and root numbers showed positive significant association with shoot length, root length, fresh root and shoot weight (r=0.2530**, r=0.2891**, r=0.4626**, r=0.4515**, r=0.5781**, r=0.7164**, r=0.0603**, r= 0.5570**, r=0.5824**). Long root length genotypes favors and suitable for drought stress conditions and screening of diverse genotypes for the further development of new plant material that performing well under different environmental conditions. After screening genetic diversity of potential rice, lines were studied to check the polymorphism by using some SSR markers. DNA was extracted, and PCR analyses were done to study PIC values and allelic diversity of the genotypes. The main objective of this study is to screen out the genotypes on the basis of various genotypic and phenotypic traits.

Keywords: rice, morphological traits, association, germplasm, genetic diversity, water levels, variation

Procedia PDF Downloads 321

Authors: Fatemeh Benakashani, Hossein Tavakoli, Elias Soltani

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To ensure the sustainability of crop cultivation and rural economies, it is imperative that we focus on cultivating resilient crops capable of withstanding salt stress. However, the effective management of weeds in salt-affected soils remains a significant challenge. This study investigates the impact of living mulches, specifically Berseem clover (Trifolium alexandrinum) and Barley (Hordeum vulgare), on weed control, as well as the quality and yield of Black cumin (Nigella sativa) in salt-affected soil. In our research, we employed a two-fold mowing strategy for the living mulches: once following crop establishment and once before the flowering stage. Notably, the weed-free plots demonstrated Black cumin's seed yield, and oil content (31.1% to 34.3%), consistent with previous studies, highlighting its potential for the reclamation and utilization of salt-affected lands. However, Black cumin exhibited limited competitiveness against prevalent weeds in the field, such as Amaranthus retroflexus, Chenopodium album, and Portulaca oleracea, which significantly diminished both the 1000 grain mass in plots where weeds were present. Interestingly, the introduction of living mulches led to improvements in seed yield and seed oil content when compared to both weed-free and weed-infested plots. Notably, Berseem clover exhibited greater biomass than Barley, indicating its heightened competitiveness against weeds. Nevertheless, it's worth noting that in the long term, Berseem clover also competed with the main crop, thereby limiting overall productivity. Consequently, we recommend relocating the Berseem clover living mulch following the establishment of Black cumin as a strategy for weed management in Black cumin fields situated in salt-affected soils.

Keywords: weed management, competition, clover, barley, medicinal plant

Procedia PDF Downloads 65

Authors: Yu-Lin Shen, Ming-Kuen Chang

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In addition to a considerable amount of machinery and equipment, intricacies of the transmission pipeline exist in Petrochemical plants. Long term corrosion may lead to pipeline thinning and rupture, causing serious safety concerns. With the advances in non-destructive testing technology, more rapid and long-range ultrasonic detection techniques are often used for pipeline inspection, EMAT without coupling to detect, it is a non-contact ultrasonic, suitable for detecting elevated temperature or roughened e surface of line. In this study, we prepared artificial defects in pipeline for Electromagnetic Acoustic Transducer testing (EMAT) to survey the relationship between the defect location, sizing and the EMAT signal. It was found that the signal amplitude of EMAT exhibited greater signal attenuation with larger defect depth and length. In addition, with bigger flat hole diameter, greater amplitude attenuation was obtained. In summary, signal amplitude attenuation of EMAT was affected by the defect depth, defect length and the hole diameter and size.

Keywords: EMAT, artificial defect, NDT, ultrasonic testing

Procedia PDF Downloads 350

Authors: Ákos Tótin

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In crop production the most modern hybrids are available for us, therefore the yield and yield stability is determined by the agro-technology. The purpose of the experiment is to adapt the modern agrotechnology to the new type of hybrids. The long-term experiment was set up in 2015-2016 on chernozem soil in the Hajdúság (eastern Hungary). The plots were set up in 75 thousand ha-1 plant density. We examined some mainly use hybrids of Hungary. The conducted studies are: germination dynamic, growing dynamic and the effect of annual weather for the yield. We use three different sowing date as early, average and late, and measure how many plant germinated during the germination process. In the experiment, we observed the germination dynamics in 6 hybrid in 4 replication. In each replication, we counted the germinated plants in 2m long 2 row wide area. Data will be shown in the average of the 6 hybrid and 4 replication. Growing dynamics were measured from the 10cm (4-6 leaf) plant highness. We measured 10 plants’ height in two weeks replication. The yield was measured buy a special plot harvester - the Sampo Rosenlew 2010 – what measured the weight of the harvested plot and also took a sample from it. We determined the water content of the samples for the water release dynamics. After it, we calculated the yield (t/ha) of each plot at 14% of moisture content to compare them. We evaluated the data using Microsoft Excel 2015. The annual weather in each crop year define the maize germination dynamics because the amount of heat is determinative for the plants. In cooler crop year the weather is prolonged the germination. At the 2015 crop year the weather was cold in the beginning what prolonged the first sowing germination. But the second and third sowing germinated faster. In the 2016 crop year the weather was much favorable for plants so the first sowing germinated faster than in the previous year. After it the weather cooled down, therefore the second and third sowing germinated slower than the last year. The statistical data analysis program determined that there is a significant difference between the early and late sowing date growing dynamics. In 2015 the first sowing date had the highest amount of yield. The second biggest yield was in the average sowing time. The late sowing date has lowest amount of yield.

Keywords: germination, maize, sowing date, yield

Procedia PDF Downloads 231

Authors: Hichem Sassi

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Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

Procedia PDF Downloads 64

Authors: Ahmad Javid Zia, Abdulkerim Ilgun, Suleyman Kamil Akin, Mustafa Altin

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Curing conditions that help concrete, which is one of the most widely used building materials in construction sector, gain strength today is one the important issues. In this study the varying concrete strength depending on water temperature at curing stage is investigated through tests at laboratory. At laboratory the curing conditions has been determined according to both TS EN 12390-2 and regular construction site while performing the experiments on specimens. Five samples have been taken from concrete and cured under five different curing conditions and the compressive strength results of concrete specimens have been compared. One of these five curing conditions has been prepared accordance with TS EN 12390-2, the sample cured at 20 ± 2 ˚C and accepted as reference samples. Two of the remaining sample groups have been cured in 5 ± 2 ˚C and 15 ± 2 ˚C and the other two have been cured outside of the laboratory. One group of the samples which have been cured outside has been watered twice a day and the other group has not been watered at all. The experiments have been carried out on 150x150x150 mm cube samples of C20 (200 kg/cm2) and C25 (250 kg/cm2). 7 and 28 days compressive strength of specimens have been measured and compared.

Keywords: concrete curing, curing conditions, water temperature, concrete compressive strength

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Authors: Risti Suryantari, Flaviana

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The aim of this research is to design calibration method of Thermochromic Liquid Crystal for temperature distribution measurement based on mathematical morphology of hue image A glass of water is placed on the surface of sample TLC R25C5W at certain temperature. We use scanner for image acquisition. The true images in RGB format is converted to HSV (hue, saturation, value) by taking of hue without saturation and value. Then the hue images is processed based on mathematical morphology using Matlab2013a software to get better images. There are differences on the final images after processing at each temperature variation based on visualization observation and the statistic value. The value of maximum and mean increase with rising temperature. It could be parameter to identify the temperature of the human body surface like hand or foot surface.

Keywords: thermochromic liquid crystal, TLC, mathematical morphology, hue image

Procedia PDF Downloads 472

Authors: Farid Shekari

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Salicylic Acid (SA) is a plant hormone that improves some physiological responses of plants under stress conditions. Seeds of two desi type chickpea cultivars, viz., Kaka and Pirooz, primed with 250, 500, 750, and 1000 μM of SA and a group of seeds without any treating (as control) were evaluated under rain fed conditions. Seed priming in both cultivars led to higher efficiency compare to non-primed treatments. In general, seed priming with 500 and 750 μM of SA had appropriate effects; however the cultivars responses were different in this regard. Kaka showed better performance both in primed and non-primed seed than Pirooz. Results of this study revealed that not only yield quantity but also yield quality, as seed protein amounts, could positively affect by SA treatments. It seems that SA by enhancing of soluble sugars and proline amounts enhanced total water potential (ψ) and RWC. The increment in RWC led to rose of chlorophyll content of plants chlorophyll stability. In general, SA increased water use efficiency, both in biologic and seed yield base, and drought tolerance of chickpea plants. HI was a little decreased in SA treatments and it shows that SA more effective in biomass production than seed yield.

Keywords: chlorophyll, harvest index, proline, seed protein, soluble sugar, water use efficiency, yield component

Procedia PDF Downloads 423

Authors: Jehnming Lin

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In the laser cleavage of glass, the laser is mostly adopted as a heat source to generate a thermal stress state on the substrates. The crack propagation of the soda-lime glass in the laser thermal cleavage with the straight-turning paths was investigated in this study experimentally and numerically. The crack propagation was visualized by a high speed camera with the off-line examination on the micro-crack propagation. The temperature and stress distributions induced by the laser heat source were calculated by ANSYS software based on the finite element method (FEM). With the cutting paths in various turning directions, the experimental and numerical results were in comparison and verified. The fracture modes due to the normal and shear stresses were verified at the turning point of the laser cleavage path. It shows a significant variation of the stress profiles along the straight-turning paths and causes a change on the fracture modes.

Keywords: laser cleavage, glass, fracture, stress analysis

Procedia PDF Downloads 229

Authors: Leila Ramezani, Mohammad Saeid Mossadegh

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Different uses of land by humans alter the physico chemical characteristics of the soil and affect the soil microhabitat. The objective of this study was to evaluate the influence of tillage in three different human land uses on microarthropods biodiversity in Khuzestan province, southwest of Iran. Three microhabitats including a permanent grassland with old Date-Palms around and no till system, and two wheat fields, one with conservative agricultural practices and low till system and the other with conventional agricultural practices (deep tillage), were compared for the biodiversity of the two main groups of soil microarthropods (Oribatida and Collembola). Soil samples were collected from the top to a depth of 15 cm bimonthly during a period of two years. Significant differences in the biodiversity index of microarthropods were observed between the different tillage systems (F = 36.748, P =0.000). Indeed, analysis of species diversity showed that the diversity index at the conservative field with low till (2.58 ± 0.01) was higher (p < 0.05) than the conventional tilled field (2.45 ± 0.08) and the diversity of natural grassland was the highest (2.79 ± 0.19, p < 0.05). Indeed, the index of biodiversity and population abundance differed significantly in different seasons (p < 0.00).

Keywords: biodiversity, Collembola, microarthropods, Oribatida

Procedia PDF Downloads 175

Authors: Hamid Merzouk, Dajhida Talantikite, Amel Tounsi

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Thin films of ZnS have been deposited by chemical route into acidic medium. The deposition time fixed at 5 hours, and the bath temperature varied from 80° C to 95°C with an interval of 5°C. The X-ray diffraction (XRD), UV/ visible spectrophotometry, Fourier Transform Infrared spectroscopy (FTIR) have been used to study the effect of temperature on the structural and optical properties of ZnS thin films. The XRD spectrum of the ZnS layer obtained shows an increase of peaks intensity of ZnS with increasing bath temperature. The study of optical properties exhibit good transmittance (60–80% in the visible region), and the band gap energy of the ZnS thin film decrease from 3.71 eV to 3.64 eV while the refractive index (n) increase with increasing temperature bath. The FTIR analyze confirm our studies and show characteristics bands of vibration of Zn-S.

Keywords: ZnS thin films, XRD spectra, optical gap, XRD

Procedia PDF Downloads 155

Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

Procedia PDF Downloads 88

Authors: Eleni Maria Pavlopoulou, Vasiliki N. Georgiannou, Filippos C. Chortis

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The response of M31 sand stabilized with colloidal silica (CS) aqueous gel is investigated in the laboratory. CS is introduced in the water regime, forming a hydrosol. The low viscosity hydrosol thickens in a controllable manner to form a stable, non-toxic gel; the gel fills the pore space, retains the pore water, and supports the grain structure. The role of colloidal silica on subsequent sand behavior is examined with the aid of direct shear, triaxial, and normal compression tests. Under the examined loading modes, while the strength of the treated sand is enhanced, its stiffness may reduce, and its compressibility increase. However, in most geotechnical problems, the loading conditions are complex, involving changes in both stress magnitude and direction. Rotation of principal stresses (σ1, σ2, σ3) in varying amounts expressed as angle α, (from α=0° to 90°) in concurrence with increasing shear stress loading is commonly encountered in soil structures such as foundations, embankments, underwater slopes. To assess the influence of anisotropy on the response of sands before and after their stabilization, hollow cylinder tests were performed. The behavior of stabilized sand is compared with the characteristic sand behavior, i.e., a reduction in peak stress ratio associated with a softer stress-strain response with the increasing angle a. The influence of the magnitude of the intermediate principal stress (σ2) on the mechanical response of treated and untreated sand is also examined.

Keywords: anisotropy, colloidal silica, laboratory tests, sands, soil stabilization

Procedia PDF Downloads 135

Authors: Ethel E. Phiri, Lionel Hartzenberg, Percy Chimwamuromba, Emmanuel Nepolo, Jens Kossmann, James R. Lloyd

Abstract:

Orphan crops are underresearched and underutilized food plant species that have not been categorized as major food crops, but have the potential to be economically and agronomically significant. They have been documented to have the ability to tolerate extreme environmental conditions. However, limited research has been conducted to uncover their potential as food crop species. The New Partnership for Africa’s Development (NEPAD) has classified Marama bean, Tylosema esculentum, as an orphan crop. The plant is one of the 101 African orphan crops that must have their genomes sequenced, assembled, and annotated in the foreseeable future. Marama bean is a perennial leguminous plant that primarily grows in poor, arid soils in southern Africa. The plants produce large tubers that can weigh as much as 200kg. While the foliage provides fodder, the tuber is carbohydrate rich and is a staple food source for rural communities in Namibia. Also, the edible seeds are protein- and oil-rich. Marama Bean plants respond rapidly to increased temperatures and severe water scarcity without extreme consequences. Advances in molecular biology and biotechnology have made it possible to effectively transfer technologies between model- and major crops to orphan crops. In this research, the aim was to assemble the first transcriptomic analysis of Marama Bean RNA-sequence data. Many model plant species have had their genomes sequenced and their transcriptomes assembled. Therefore the availability of transcriptome data for a non-model crop plant species will allow for gene identification and comparisons between various species. The data has been sequenced using the Ilumina Hiseq 2500 sequencing platform. Data analysis is underway. In essence, this research will eventually evaluate the potential use of Marama Bean as a crop species to improve its value in agronomy. data for a non-model crop plant species will allow for gene identification and comparisons between various species. The data has been sequenced using the Ilumina Hiseq 2500 sequencing platform. Data analysis is underway. In essence, this researc will eventually evaluate the potential use of Marama bean as a crop species to improve its value in agronomy.

Keywords: 101 African orphan crops, RNA-Seq, Tylosema esculentum, underutilised crop plants

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Authors: Innocent Kafodya, Guijun Xian

Abstract:

This paper presents effects of distilled water, seawater and sustained bending strains of 30% and 50% ultimate strain at room temperature, on the durability of unidirectional pultruded carbon fiber reinforced polymer (CFRP) plates. In this study, dynamic mechanical analyzer (DMA) was used to investigate the synergic effects of the immersions and bending strains on the visco-elastic properties of (CFRP) such as storage modulus, tan delta and glass transition temperature. The study reveals that the storage modulus and glass transition temperature increase while tan delta peak decreases in the initial stage of both immersions due to the progression of curing. The storage modulus and Tg subsequently decrease and tan delta increases due to the matrix plasticization. The blister induced damages in the unstrained seawater samples enhance water uptake and cause more serious degradation of Tg and storage modulus than in water immersion. Increasing sustained bending decreases Tg and storage modulus in a long run for both immersions due to resin matrix cracking and debonding. The combined effects of immersions and strains are not clearly reflected due to the statistical effects of DMA sample sizes and competing processes of molecular reorientation and postcuring.

Keywords: pultruded CFRP plate, bending strain, glass transition temperature, storage modulus, tan delta

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Authors: Tamrat Balcha Balla

Abstract:

Background: Tablets are still the most preferred means of drug delivery. The search for new and improved direct compression tablet excipients is an area of research focus. Taro Boloso-I is a variety of Colocasia esculenta (L. Schott) yielding 67% more than the other varieties (Godare) in Ethiopia. This study aimed to enhance the flowability while keeping the compressibility and compactibility of the pregelatinized Taro Boloso-I starch. Methods: Central composite design was used for the optimization of two factors which were the temperature and duration of pregelatinization against 5 responses. The responses were angle of repose, Hausner ratio, Kawakita compressibility index, mean yield pressure and tablet breaking force. Results and Discussions: An increase in both temperature and time resulted in decrease in the angle of repose. The increase in temperature was shown to decrease the Hausner ratio and to decrease the Kawakita compressibility index. The mean yield pressure was observed to increase with increasing levels of both temperature and time. The pregelatinized (optimized) Taro Boloso-I starch could show desired flow property and compressibility. Conclusions: Pregelatinized Taro Boloso - I starch could be regarded as a potential direct compression excipient in terms of flowability, compressibility and compactibility.

Keywords: starch, compression, pregelatinization, Taro Boloso-I

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Authors: Mohamed Korany, Azza Gazy, Essam Khamis, Marwa Adel, Miranda Fawzy

Abstract:

Background: Two new, simple and specific methods; First, a Zero-crossing first-derivative technique and second, a chemometric-assisted spectrophotometric artificial neural network (ANN) were developed and validated in accordance with ICH guidelines. Both methods were used for the simultaneous estimation of the two closely related antioxidant nutraceuticals ; Coenzyme Q10 (Q) ; also known as Ubidecarenone or Ubiquinone-10, and Vitamin E (E); alpha-tocopherol acetate, in their pharmaceutical binary mixture. Results: For first method: By applying the first derivative, both Q and E were alternatively determined; each at the zero-crossing of the other. The D1 amplitudes of Q and E, at 285 nm and 235 nm respectively, were recorded and correlated to their concentrations. The calibration curve is linear over the concentration range of 10-60 and 5.6-70 μg mL-1 for Q and E, respectively. For second method: ANN (as a multivariate calibration method) was developed and applied for the simultaneous determination of both analytes. A training set (or a concentration set) of 90 different synthetic mixtures containing Q and E, in wide concentration ranges between 0-100 µg/mL and 0-556 µg/mL respectively, were prepared in ethanol. The absorption spectra of the training sets were recorded in the spectral region of 230–300 nm. A Gradient Descend Back Propagation ANN chemometric calibration was computed by relating the concentration sets (x-block) to their corresponding absorption data (y-block). Another set of 45 synthetic mixtures of the two drugs, in defined range, was used to validate the proposed network. Neither chemical separation, preparation stage nor mathematical graphical treatment were required. Conclusions: The proposed methods were successfully applied for the assay of Q and E in laboratory prepared mixtures and combined pharmaceutical tablet with excellent recoveries. The ANN method was superior over the derivative technique as the former determined both drugs in the non-linear experimental conditions. It also offers rapidity, high accuracy, effort and money saving. Moreover, no need for an analyst for its application. Although the ANN technique needed a large training set, it is the method of choice in the routine analysis of Q and E tablet. No interference was observed from common pharmaceutical additives. The results of the two methods were compared together

Keywords: coenzyme Q10, vitamin E, chemometry, quantitative analysis, first derivative spectrophotometry, artificial neural network

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Authors: Mojtaba Esmaeilzad Limoudehi, Ebrahim Amiri

Abstract:

Water scarcity is now one of the leading challenges for human societies. In this regard, recognizing the relationship between soil, water, plant growth, and plant response to stress is very significant. In this paper, considering the importance of drought stress and the role of choosing suitable cultivars in resistance against drought, a split-plot experiment using early, intermediate, and late-maturing cultivars was carried out in Katul filed, Golestan province during two cultivation years of 2015 and 2016. The main factor was irrigation intervals at four levels, including 7 days, 14 days, 21 days, and 28 days. The subfactor was the subplot of six maize cultivars (two early maturing cultivars, two medium maturing cultivars, and two late-maturing cultivars). The results of variance analysis have revealed that irrigation interval and cultivars treatment have significant effects on the number of grain in each corn, number of rows in each corn, number of grain per row, the weight of 1000 grains, grain yield, and biomass yield. Although, the interaction of these two factors on the mentioned attributes was meaningful. The best grain yield was achieved at 7 days irrigation interval and late maturing maize cultivars treatment, which was equal to 12301 kg/ha.

Keywords: corn, growth period, optimization, stress

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Authors: Evan Lowhorn, Rocio Alba-Flores

Abstract:

The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.

Keywords: classification, computer vision, convolutional neural networks, drone control

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Authors: Dalila Oulhaci, Mohammed Zahaf

Abstract:

The watering technique is essential to maintain a moist perimeter around the roots of the crop. This is the case with topical watering, where the soil around the root system can be kept permanently moist between the two extremes of water content. Moreover, one of the oldest methods used since Roman times throughout North Africa and the Near East was based on the repeated pouring of water into porous earthen vessels buried in the ground. In this context, these two techniques have been combined by replacing the earthen vase with plastic bottles filled with sand which release water through their perforated walls into the surrounding soil. The objective of this work is to first determine the purifying power of the activated sludge treatment plant of Toggourt and then that of the bottled Sawdust filter. For the station, the BOD purification rate was (96.5%), the COD purification rate was (87%) and suspended solids (90%). For the bottle, the BOD removal rate was (35%), and COD removal rate was (12.58%). This work falls within the framework of water saving, sustainable development and environmental protection, and also within the framework of agriculture.

Keywords: wasterwater, sawdust, purification, irrigation, touggourt (Algeria)

Procedia PDF Downloads 86