Search results for: food distribution networks

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 96

Authors: Salami Abiodun Olusola, Bankole Faith Ayobami

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Agro-wastes are wastes produced from various agricultural activities and include manures, corncob, plant stalks, hulls, leaves, sugarcane bagasse, oil-palm spadix, and rice bran. In farming situation, the agro-waste is often useless and, thus, discarded. Huge quantities of waste resources generated from Nigerian agriculture could be converted to more useful forms of energy, which could contribute to the country’s primary energy needs and reduce problems associated with waste management. Accumulation of agro-wastes may cause health, safety, and environmental concern. However, biotechnological use of agro-waste could enhance food security through its bioconversion to useful renewable energy. Mushrooms are saprophytes which feed by secreting extracellular enzymes, digesting food externally, and absorb the nutrients in net-like hyphae. Therefore, mushrooms could be exploited for bioconversion of the cheap and numerous agro-wastes for providing nutritious food for animals, human and carbon recycling. The study investigated the bioconversion potentials of Pleurotus florida on agro-wastes using a simple and cost-effective biotechnological method. Four agro-wastes; corncobs, oil-palm spadix, corn straw, and sawdust, were composted and used as substrates while the biological efficiency (BE) and the nutritional composition of P. florida grown on the substrates were determined. Pleurotus florida contained 26.28-29.91% protein, 86.90-89.60% moisture, 0.48-0.91% fat, 19.64-22.82% fibre, 31.37-38.17% carbohydrate and 5.18-6.39% ash. The mineral contents ranged from 342-410 mg/100g Calcium, 1009-1133 mg/100g Phosphorus, 17-21 mg/100g Iron, 277-359 mg/100g Sodium, and 2088-2281 mg/100g Potassium. The highest yield and BE were obtained on corncobs (110 g, 55%), followed by oil-palm spadix (76.05 g, 38%), while the least BE was recorded on corn straw substrate (63.12 g, 31.56%). Utilization of the composted substrates yielded nutritional and edible mushrooms. The study presents biotechnological procedure for bioconversion of agro-wastes to edible and nutritious mushroom for efficient agro-wastes’ management, utilization, and recycling.

Keywords: agrowaste, bioconversion, biotechnology, utilization, recycling

Procedia PDF Downloads 79

Authors: Magdalena Maria Rost-Roszkowska, Alina Chachulska-Żymełka, Monika Tarnawska, Maria Augustyniak, Alina Kafel, Agnieszka Babczyńska

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The digestive system of insects is composed of three distinct regions: fore-, mid- and hingut. The middle region (the midgut) is treated as one of the barriers which protects the organism against any stressors which originate from external environment, e.g. toxic metals. Such factors can activate the cell death in epithelial cells to preserve the entire tissue/organs against the degeneration. Different mechanisms involved in homeostasis maintenance have been described, but the studies of animals under field conditions do not give the opportunity to conclude about potential ability of subsequent generation to inherit the tolerance mechanisms. It is possible only by a multigenerational strain of an animal led under laboratory conditions, exposed to a selected toxic factor, present also in polluted ecosystems. The main purpose of the project was to check if changes, which appear in the midgut epithelium after Cd treatment, can be fixed during the following generations of insects with the special emphasis on apoptosis. As the animal for these studies we chose 5th larval stage of the beet armyworm Spodoptera exigua Hübner (Lepidoptera: Noctuidae), which is one of pest of many vegetable crops. Animals were divided into some experimental groups: K, Cd, KCd, Cd1, Cd2, Cd3. A control group (K) fed a standard diet, and was conducted for XX generations, a cadmium group (Cd), fed on standard diet supplemented with cadmium (44 mg Cd per kg of dry weight of food) for XXX generations. A reference Cd group (KCd) has been initiated: control insects were fed with Cd supplemented diet (44 mg Cd per kg of dry weight of food). Experimental groups Cd1, Cd2, Cd3 developed from the control one: 5 mg Cd per kg of dry weight of food, 10 mg Cd per kg of dry weight of food, 20 mg Cd per kg of dry weight of food. We were interested in the activation of apoptosis during following generations in all experimental groups. Therefore, during the 1st year of the experiment, the measurements were done for 6 generations in all experimental group. The intensity and the course of apoptosis have been examined using transmission electron microscope (TEM), confocal microscope and flow cytometry. During apoptosis the cell started to shrink, extracellular spaces appeared between digestive and neighboring cells, the nucleus achieved a lobular shape. Eventually, the apoptotic cells was discharged into the midgut lumen. A quantitative analysis revealed that the number of apoptotic cells depends significantly on the generation, tissue and cadmium concentration in the insect rearing medium. In the following 6 generations, we observed that the percentage of apoptotic cells in the midguts from cadmium-exposed groups decreased gradually according to the following order of strains: Cd1, Cd2, Cd3 and KCd. At the same time, it was still higher than the percentage of apoptotic cells in the same tissues of the insects from the control and multigenerational cadmium strain. The results of our studies suggest that changes caused by cadmium treatment were preserved during 6-generational development of lepidopteran larvae. The study has been financed by the National Science Centre Poland, grant no 2016/21/B/NZ8/00831.

Keywords: cadmium, cell death, digestive system, ultrastructure

Procedia PDF Downloads 216

Authors: Panaya Sudta, Wanchalerm Patanacharoenwong, Prachya Bumrungkun

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As With the evolution of smart grids, ensuring the reliability and efficiency of electrical systems in smart cities has become crucial. This paper proposes a distinct approach that combines advanced machine learning techniques to accurately predict electrical failures and address voltage reliability issues. This approach aims to improve the accuracy and efficiency of reliability evaluations in smart cities. The aim of this research is to develop a comprehensive predictive model that accurately predicts electrical failures and voltage reliability in smart cities. This model integrates RFM analysis, K-means clustering, and LSTM networks to achieve this objective. The research utilizes RFM analysis, traditionally used in customer value assessment, to categorize and analyze electrical components based on their failure recency, frequency, and monetary impact. K-means clustering is employed to segment electrical components into distinct groups with similar characteristics and failure patterns. LSTM networks are used to capture the temporal dependencies and patterns in customer data. This integration of RFM, K-means, and LSTM results in a robust predictive tool for electrical failures and voltage reliability. The proposed model has been tested and validated on diverse electrical utility datasets. The results show a significant improvement in prediction accuracy and reliability compared to traditional methods, achieving an accuracy of 92.78% and an F1-score of 0.83. This research contributes to the proactive maintenance and optimization of electrical infrastructures in smart cities. It also enhances overall energy management and sustainability. The integration of advanced machine learning techniques in the predictive model demonstrates the potential for transforming the landscape of electrical system management within smart cities. The research utilizes diverse electrical utility datasets to develop and validate the predictive model. RFM analysis, K-means clustering, and LSTM networks are applied to these datasets to analyze and predict electrical failures and voltage reliability. The research addresses the question of how accurately electrical failures and voltage reliability can be predicted in smart cities. It also investigates the effectiveness of integrating RFM analysis, K-means clustering, and LSTM networks in achieving this goal. The proposed approach presents a distinct, efficient, and effective solution for predicting and mitigating electrical failures and voltage issues in smart cities. It significantly improves prediction accuracy and reliability compared to traditional methods. This advancement contributes to the proactive maintenance and optimization of electrical infrastructures, overall energy management, and sustainability in smart cities.

Keywords: electrical state prediction, smart grids, data-driven method, long short-term memory, RFM, k-means, machine learning

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Authors: A. S. Lunev, A. A. Larenkov, O. E. Klementyeva, G. E. Kodina

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Background and aims: 68Ga-citrate is one of prospective radiopharmaceutical for PET-imaging of inflammation and infection. 68Ga-citrate is 67Ga-citrate analogue using since 1970s for SPECT-imaging. There's known rebinding reaction occurs past Ga-citrate injection and gallium (similar iron Fe3+) binds with blood transferrin. Then radiolabeled protein complex is delivered to pathological foci (inflammation/infection sites). But excessive gallium bindings with transferrin are cause of slow blood clearance, long accumulation time in foci (24-72 h) and exception of application possibility of the short-lived gallium-68 (T½ = 68 min). Injection of additional chemical agents (e.g. Fe3+ compounds) competing with radioactive gallium to the blood transferrin joining (blocking of its metal binding capacity) is one of the ways to solve formulated problem. This phenomenon can be used for correction of 68Ga-citrate pharmacokinetics for increasing of the blood clearance and accumulation in foci. The aim of real studying is research of effect of stable Fe-citrate on 68Ga-citrate tissue distribution. Materials and methods: 68Ga-citrate without/with extra injection of stable Fe-citrate (III) was injected nonlinear mice with inflammation models (aseptic soft tissue inflammation, lung infection, osteomyelitis). PET/X-RAY Genisys4 (Sofie Bioscience, USA) was used for non-invasive PET imaging (for 30, 60, 120 min past injection 68Ga-citrate) with subsequent reconstruction of imaging and their analysis (value of clearance, distribution volume). Scanning time is 10 min. Results and conclusions: I. v. injection of stable Fe-citrate blocks the metal-binding capability of transferrin serum and allows decreasing gallium-68 radioactivity in blood significantly and increasing accumulation in inflammation (3-5 time). It allows receiving more informative PET-images of inflammation early (for 30-60 min after injection). Pharmacokinetic parameters prove it. Noted there is no statistically significant difference between 68Ga-citrate accumulation for different inflammation model because PET imaging is indication of pathological processes and is not their identification.

Keywords: 68Ga-citrate, Fe-citrate, PET imaging, mice, inflammation, infection

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Authors: Rohit Semwal, Shivam Arora, Saurav, Sangita Roy

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This work proposes a supervised framework with deep convolutional neural networks (CNNs) for vision-based sign language recognition of static gestures. Our approach addresses the acquisition and segmentation of correct inputs for the CNN-based classifier. Microsoft Kinect™ sensor, despite complex environmental conditions, can track hands efficiently. Skin Colour based segmentation is applied on cropped images of hands in different poses, used to depict different sign language gestures. The segmented hand images are used as an input for our classifier. The CNN classifier proposed in the paper is able to classify the input images with a high degree of accuracy. The system was trained and tested on 39 static sign language gestures, including 26 letters of the alphabet and 13 commonly used words. This paper includes a problem definition for building the proposed system, which acts as a sign language translator between deaf/mute and the rest of the society. It is then followed by a focus on reviewing existing knowledge in the area and work done by other researchers. It also describes the working principles behind different components of CNNs in brief. The architecture and system design specifications of the proposed system are discussed in the subsequent sections of the paper to give the reader a clear picture of the system in terms of the capability required. The design then gives the top-level details of how the proposed system meets the requirements.

Keywords: sign language, CNN, HCI, segmentation

Procedia PDF Downloads 159

Authors: Bulent Topcuoglu

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Repeated amendments of organic matter and intensive use of fertilizers, metal-enriched chemicals and biocides may cause soil and environmental pollution in greenhouses. Specially, the impact of heavy metal pollution of soils on food metal content and underground water quality has become a public concern. Due to potential toxicity of heavy metals to human life and environment, determining the chemical form of heavy metals in greenhouse soils is an important approach of chemical characterization and can provide useful information on its mobility and bioavailability. A sequential extraction procedure was used to estimate the availability of heavy metals (Zn, Cd, Ni, Pb and Cr) in greenhouse soils of Antalya Aksu. Zn was predominantly associated with Fe-Mn oxide fraction, major portion of Cd associated with carbonate and organic matter fraction, a major portion of (>65 %) Ni and Cr were largely associated with Fe-Mn oxide and residual fractions and Pb was largely associated with organic matter and Fe-Mn oxide fractions. Results of the present study suggest that the mobility and bioavailability of metals probably increase in the following order: Cr < Pb < Ni < Cd < Zn. Among the elements studied, Zn and Cd appeared to be the most readily soluble and potentially bioavailable metals and these metals may carry a potential risk for metal transfer in food chain and contamination to ground water.

Keywords: metal speciation, metal mobility, greenhouse soils, biosystems engineering

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Authors: Jinyoung Choi, Tae-Ho Yoon, Sunmook Lee

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Degradation of standard oxygen transfer efficiency (SOTE) with time was observed for the assessment of lifetime of metal-based air-diffuser, which displaced a polymer composite-based air-diffuser in order to attain a longer lifetime in the actual field. The degradation of air-diffuser occurred due to the failure of the formation of small and uniform air bubbles since the patterns formed on the disc of air-diffuser deteriorated and/or changed from their initial shapes while they were continuously exposed to the air blowing condition during the operation in the field. Therefore, the lifetime assessment of metal-based air-diffuser was carried out through an accelerated degradation test by accelerating the air-blowing conditions in 200 L/min, 300 L/min, and 400 L/min and the lifetime of normal operating condition at 120 L/min was predicted. It was found that Weibull distribution was the most proper one for describing the lifetime distribution of metal-based air-diffuser in the present study. The shape and scale parameters indicated that the accelerated blowing conditions were all within the acceleration domain. The lifetime was predicted by adopting inverse power model for a stress-life relationship and estimated to be B10=94,004 hrs with CL=95%. Acknowledgement: This work was financially supported by the Ministry of Trade, Industry and Energy (Grant number: N0001475).

Keywords: accelerated degradation test, air-diffuser, lifetime assessment, SOTE

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Authors: Zhou Luchen, Wu Yubing, Burra Venkata Durga Kumar

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As the scale of the network becomes larger and more complex than before, the density of user devices is also increasing. The development of Unmanned Aerial Vehicle (UAV) networks is able to collect and transform data in an efficient way by using software-defined networks (SDN) technology. This paper proposed a three-layer distributed and dynamic cluster architecture to manage UAVs by using an AI-based resource allocation calculation algorithm to address the overloading network problem. Through separating services of each UAV, the UAV hierarchical cluster system performs the main function of reducing the network load and transferring user requests, with three sub-tasks including data collection, communication channel organization, and data relaying. In this cluster, a head node and a vice head node UAV are selected considering the Central Processing Unit (CPU), operational (RAM), and permanent (ROM) memory of devices, battery charge, and capacity. The vice head node acts as a backup that stores all the data in the head node. The k-means clustering algorithm is used in order to detect high load regions and form the UAV layered clusters. The whole process of detecting high load areas, forming and selecting UAV clusters, and moving the selected UAV cluster to that area is proposed as offloading traffic algorithm.

Keywords: k-means, resource allocation, SDN, UAV network, unmanned aerial vehicles

Procedia PDF Downloads 115

Authors: Zografou Konstantina, Anagnostellis Konstantinos, Brokaki Marina, Kaltsouni Eleftheria, Dimaki Maria, Kati Vassiliki

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Body size is crucial to ecology, influencing everything from individual reproductive success to the dynamics of communities and ecosystems. Understanding how temperature affects variations in body size is vital for both theoretical and practical purposes, as changes in size can modify trophic interactions by altering predator-prey size ratios and changing the distribution and transfer of biomass, which ultimately impacts food web stability and ecosystem functioning. Notably, a decrease in body size is frequently mentioned as the third "universal" response to climate warming, alongside shifts in distribution and changes in phenology. This trend is backed by ecological theories like the temperature-size rule (TSR) and Bergmann's rule, which have been observed in numerous species, indicating that many species are likely to shrink in size as temperatures rise. However, the thermal responses related to body size are still contradictory, and further exploration is needed. To tackle this challenge, we developed the MEIOSIS project, aimed at providing valuable insights into the relationship between the body size of species, species’ traits, environmental factors, and their response to climate change. We combined a digitized collection of butterflies from the Swiss Federal Institute of Technology in Zürich with our newly digitized butterfly collection from Goulandris Natural History Museum in Greece to analyse trends in time. For a total of 23868 images, the length of the right forewing was measured using ImageJ software. Each forewing was measured from the point at which the wing meets the thorax to the apex of the wing. The forewing length of museum specimens has been shown to have a strong correlation with wing surface area and has been utilized in prior studies as a proxy for overall body size. Temperature data corresponding to the years of collection were also incorporated into the datasets. A second dataset was generated when a custom computer vision tool was implemented for the automated morphological measuring of samples for the digitized collection in Zürich. Using the second dataset, we corrected manual measurements with ImageJ, and a final dataset containing 31922 samples was used for analysis. Setting time as a smoother variable, species identity as a random factor, and the length of right-wing size (a proxy for body size) as the response variable, we ran a global model for a maximum period of 110 years (1900 – 2010). Then, we investigated functional variability between different terrestrial biomes in a second model. Both models confirmed our initial hypothesis and resulted in a decreasing trend in body size over the years. We expect that this first output can be provided as basic data for the next challenge, i.e., to identify the ecological traits that influence species' temperature-size responses, enabling us to predict the direction and intensity of a species' reaction to rising temperatures more accurately.

Keywords: butterflies, shrinking body size, museum specimens, climate change

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Authors: Maftunakhon Latipova, Feruza Khaydarova

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Obesity is considered a chronic metabolic disease that occurs at any age. Regulation of body weight in the body is carried out through complex interaction of a complex of interrelated systems that control the body's energy system. Energy imbalance is the cause of obesity and overweight, in which the supply of energy from food exceeds the energy needs of the body. Obesity is closely related to impaired appetite regulation, and a hypothalamus is a key place for neural regulation of food consumption. The nucleus of the hypothalamus is connected and interdependent on receiving, integrating and sending hunger signals to regulate appetite. Purpose of the study: to identify markers of food behavior. Materials and methods: The screening was carried out to identify eating disorders in 200 men and women aged 18 to 35 years with overweight and obesity and to check the effects of Orexin A and Neuropeptide Y markers. A questionnaire and questionnaires were conducted with over 200 people aged 18 to 35 years. Questionnaires were for eating disorders and hidden depression (on the Zang scale). Anthropometry is measured by OT, OB, BMI, Weight, and Height. Based on the results of the collected data, 3 groups were divided: People with obesity, People with overweight, Control Group of Healthy People. Results: Of the 200 analysed persons, 86% had eating disorders. Of these, 60% of eating disorders were associated with childhood. According to the Zang test result: Normal condition was about 37%, mild depressive disorder 20%, moderate depressive disorder 25% and 18% of people suffered from severe depressive disorder without knowing it. One group of people with obesity had eating disorders and moderate and severe depressive disorder, and group 2 was overweight with mild depressive disorder. According to laboratory data, the first group had the lowest concentration of Orexin A and Neuropeptide U in blood serum. Conclusions: Being overweight and obese are the first signal of many diseases, and prevention and detection of these disorders will prevent various diseases, including type 2 diabetes. Obesity etiology is associated with eating disorders and signal transmission of the orexinorghetic system of the hypothalamus.

Keywords: obesity, endocrinology, hypothalamus, overweight

Procedia PDF Downloads 77

Authors: Nebri Rachid, Berrouane Fatima, Doumandji Salah Eddine

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During six months, from November 2013 to May 2014, census of Nematocera insects was conducted on four livestock: cattle, sheep, equine and cameline. The census, that took place in a station located in Mitidja plain, Algeria, revealed thirteen Nematocera species that had been observed and identified: Scatopse notata, Chironomus Sp., Sciara bicolor, Psychoda phalaenoïdes, Culex pipiens, Orthocladius Sp., Psycoda alternata, Trichocera regelationis, Culicoïdes Sp., Contarinia Sp., Ectaetia Sp., Tipula Sp., and Culicoïdes coprosus. A factorial correspondence analysis has been performed to study the distribution of the different species captured in colored traps that were placed in the four farms. The results showed the presence of three collections of Nematocera relating to the breeding type where the highest availability is in favor of the equine and the cattle. The analysis of the comparative diversity of Nematocera specimens revealed an indifferent taxonomic structure compared with the hosts. However, in terms of individuals, the supremacy is to the equine’s advantage. On the ecological arrival scale, Psycoda alternata, is undeniably the most predominant on the equines as well as on the cattle.

Keywords: Algeria, availability, biodiversity, census, livestock, nematocera

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Authors: Ganesh Nandakumaran, Mehmet Karaata

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A wave is a distributed execution, often made up of a broadcast phase followed by a feedback phase, requiring the participation of all the system processes before a particular event called decision is taken. Wave algorithms with one initiator such as the 1-wave algorithm have been shown to be very efficient for broadcasting messages in tree networks. Extensions of this algorithm broadcasting a sequence of waves using a single initiator have been implemented in algorithms such as the m-wave algorithm. However as the network size increases, having a single initiator adversely affects the message delivery times to nodes further away from the initiator. As a remedy, broadcast waves can be allowed to be initiated by multiple initiator nodes distributed across the network to reduce the completion time of broadcasts. These waves initiated by one or more initiator processes form a collection of waves covering the entire network. Solutions to global-snapshots, distributed broadcast and various synchronization problems can be solved efficiently using waves with multiple concurrent initiators. In this paper, we propose the first stabilizing multi-wave sequence algorithm implementing waves started by multiple initiator processes such that every process in the network receives at least one sequence of broadcasts. Due to being stabilizing, the proposed algorithm can withstand transient faults and do not require initialization. We view a fault as a transient fault if it perturbs the configuration of the system but not its program.

Keywords: distributed computing, multi-node broadcast, propagation of information with feedback and cleaning (PFC), stabilization, wave algorithms

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Authors: Siming Xie

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In social networks, the term homophily refers to the tendency of agents with similar characteristics to link with one another and is so robustly observed across many contexts and dimensions. The starting point of my research is the observation that the “type” of agents is not a single exogenous variable. Agents, despite their differences in race, religion, and other hard to alter characteristics, may share interests and engage in activities that cut across those predetermined lines. This research aims to capture the interactions of homophily effects in a model where agents have two-dimension characteristics (i.e., race and personal hobbies such as basketball, which one either likes or dislikes) and with biases in meeting opportunities and in favor of same-type friendships. A novel feature of my model is providing a matching process with biased meeting probability on different dimensions, which could help to understand the structuring process in multidimensional networks without missing layer interdependencies. The main contribution of this study is providing a welfare based matching process for agents with multi-dimensional characteristics. In particular, this research shows that the biases in meeting opportunities on one dimension would lead to the emergence of homophily on the other dimension. The objective of this research is to determine the pattern of homophily in network formations, which will shed light on our understanding of segregation and its remedies. By constructing a two-dimension matching process, this study explores a method to describe agents’ homophilous behavior in a social network with multidimension and construct a game in which the minorities and majorities play different strategies in a society. It also shows that the optimal strategy is determined by the relative group size, where society would suffer more from social segregation if the two racial groups have a similar size. The research also has political implications—cultivating the same characteristics among agents helps diminishing social segregation, but only if the minority group is small enough. This research includes both theoretical models and empirical analysis. Providing the friendship formation model, the author first uses MATLAB to perform iteration calculations, then derives corresponding mathematical proof on previous results, and last shows that the model is consistent with empirical evidence from high school friendships. The anonymous data comes from The National Longitudinal Study of Adolescent Health (Add Health).

Keywords: homophily, multidimension, social networks, friendships

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Authors: Ali Mohamadi Sani

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The aim of this study was to determine the antimicrobial effect of Helichrysum arenarium L. essential oil in "in-vitro" condition on the growth of seven microbial species including Bacillus subtilis, Escherichia coli, Staphylococcus aureus, Saccharomyces cereviciae, Candida albicans, Aspergillus flavus and Aspergillus parasiticus using microdilution method. The minimum inhibitory concentration (MIC) and minimum bactericidal or fungicidal concentration (MBC, MFC) were determined for the essential oil at ten concentrations. Finally, the sensitivity of tested microbes to the essential oil of H. arenarium was investigated. Results showed that Bacillus subtilis (MIC=781.25 and MBC=6250 µg/ml) was more resistance than two other bacterial species. Among the tested yeasts, Saccharomyces cereviciae (MIC=97.65 and MFC=781.25 µg/ml) was more sensitive than Candida albicans, while among the fungal species, growth of Aspergillus parasiticus inhibited at lower concentration of oil than the Aspergillus flavus. The extracted essential oil exhibited the same MIC value in the liquid medium against all fungal strains (48.82 µg/ml), while different activity against A. flavus and A. parasiticus was observed in this medium with MFC values of 6250 and 390.625µg/ml, respectively. The results of the present study indicated that Helichrysum arenarium L essential oil had significant (P<0.05) antimicrobial activity; therefore, it can be used as a natural preservation to increase the shelf life of food products.

Keywords: Helichrysum arenarium, antimicrobial, essential oil, MIC

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Authors: Yousif Mohamed Y. Abdallah, Eltayeb Wagallah

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Image enhancement allows the observer to see details in images that may not be immediately observable in the original image. Image enhancement is the transformation or mapping of one image to another. The enhancement of certain features in images is accompanied by undesirable effects. To achieve maximum image quality after denoising, a new, low order, local adaptive Gaussian scale mixture model and median filter were presented, which accomplishes nonlinearities from scattering a new nonlinear approach for contrast enhancement of bones in bone scan images using both gamma correction and negative transform methods. The usual assumption of a distribution of gamma and Poisson statistics only lead to overestimation of the noise variance in regions of low intensity but to underestimation in regions of high intensity and therefore to non-optional results. The contrast enhancement results were obtained and evaluated using MatLab program in nuclear medicine images of the bones. The optimal number of bins, in particular the number of gray-levels, is chosen automatically using entropy and average distance between the histogram of the original gray-level distribution and the contrast enhancement function’s curve.

Keywords: bone scan, nuclear medicine, Matlab, image processing technique

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Authors: Napasri Suwanajote

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The purposes of this research were to analyse and evaluate successful factors in OTOP production process for the developing of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The research has been designed as a qualitative study to gather information from 30 OTOP producers in Bangkontee District, Samudsongkram Province. They were all interviewed on 3 main parts. Part 1 was about the production process including 1) production 2) product development 3) the community strength 4) marketing possibility and 5) product quality. Part 2 evaluated appropriate successful factors including 1) the analysis of the successful factors 2) evaluate the strategy based on Sufficiency Economic Philosophy and 3) the model of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The results showed that the production did not affect the environment with potential in continuing standard quality production. They used the raw materials in the country. On the aspect of product and community strength in the past 1 year, it was found that there was no appropriate packaging showing product identity according to global market standard. They needed the training on packaging especially for food and drink products. On the aspect of product quality and product specification, it was found that the products were certified by the local OTOP standard. There should be a responsible organization to help the uncertified producers pass the standard. However, there was a problem on food contamination which was hazardous to the consumers. The producers should cooperate with the government sector or educational institutes involving with food processing to reach FDA standard. The results from small group discussion showed that the community expected high education and better standard living. Some problems reported by the community included informal debt and drugs in the community. There were 8 steps in developing the model of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality.

Keywords: production process, OTOP, sufficiency economic philosophy, marketing management

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Authors: Yasir Ali Arfat

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Composite films based on fish protein isolate (FPI) and fish skin gelatin (FSG) blend incorporated with 50 and 100% (w/w, protein) basil leaf essential oil (BEO) in the absence and presence of 3% (w/w, protein) ZnO nanoparticles (ZnONP) were prepared and characterised. Tensile strength (TS) decreased, whilst elongation at break (EAB) increased as BEO level increased (p < 0.05). However, ZnONP addition resulted in higher TS but lower EAB (p < 0.05). The lowest water vapour permeability (WVP) was observed for the film incorporated with 100% BEO and 3% ZnONP (p < 0.05). BEO and ZnONP incorporation decreased transparency of FPI/FSG films (p < 0.05). FTIR spectra indicated that films added with BEO exhibited higher hydrophobicity. Both BEO and ZnONP had a marked impact on thermal stability of the films. Microstructural study revealed that presence of ZnONP prevented bilayer formation of film containing 100% BEO. FPI/FSG films incorporated with 100% BEO, especially in combination with ZnONP, exhibited strong antibacterial activity against food pathogenic and spoilage bacteria and thus could be used as an active food packaging material to ensure safety and to extend the shelf-life of packaged foods.

Keywords: bionanocomposite, fish protein isolate, fish skin gelatin, basil essential oil, ZnO nanoparticles, antimicrobial packaging

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Authors: Maria Isabel Garcia-Planas, Maria Victoria Garcia-Camba

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Recent advances in cognitive neuroscience have allowed a step forward in perceiving the processes involved in learning from the point of view of the acquisition of new information or the modification of existing mental content. The evoked potentials technique reveals how basic brain processes interact to achieve adequate and flexible behaviours. The objective of this work, using evoked potentials, is to study if it is possible to distinguish if a patient suffers a specific type of learning disorder to decide the possible therapies to follow. The methodology used, is the analysis of the dynamics of different areas of the brain during a cognitive activity to find the relationships between the different areas analyzed in order to better understand the functioning of neural networks. Also, the latest advances in neuroscience have revealed the existence of different brain activity in the learning process that can be highlighted through the use of non-invasive, innocuous, low-cost and easy-access techniques such as, among others, the evoked potentials that can help to detect early possible neuro-developmental difficulties for their subsequent assessment and cure. From the study of the amplitudes and latencies of the evoked potentials, it is possible to detect brain alterations in the learning process specifically in dyscalculia, to achieve specific corrective measures for the application of personalized psycho pedagogical plans that allow obtaining an optimal integral development of the affected people.

Keywords: dyscalculia, neurodevelopment, evoked potentials, Learning disabilities, neural networks

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Authors: Retius Chifurira

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Logistic regression model is the most used regression model to predict meteorological drought probabilities. When the dependent variable is extreme, the logistic model fails to adequately capture drought probabilities. In order to adequately predict drought probabilities, we use the generalized linear model (GLM) with the quantile function of the generalized extreme value distribution (GEVD) as the link function. The method maximum likelihood estimation is used to estimate the parameters of the generalized extreme value (GEV) regression model. We compare the performance of the logistic and the GEV regression models in predicting drought probabilities for Zimbabwe. The performance of the regression models are assessed using the goodness-of-fit tests, namely; relative root mean square error (RRMSE) and relative mean absolute error (RMAE). Results show that the GEV regression model performs better than the logistic model, thereby providing a good alternative candidate for predicting drought probabilities. This paper provides the first application of GLM derived from extreme value theory to predict drought probabilities for a drought-prone country such as Zimbabwe.

Keywords: generalized extreme value distribution, general linear model, mean annual rainfall, meteorological drought probabilities

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Authors: Prayas Sharma

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This paper proposed a generalized class of estimators, an exponential class of estimators based on the adaption of Sharma and Singh (2015) and Solanki and Singh (2013), and a simple difference estimator for estimating unknown population mean in the case of Poisson distributed population in simple random sampling without replacement. The expressions for mean square errors of the proposed classes of estimators are derived from the first order of approximation. It is shown that the adapted version of Solanki and Singh (2013), the exponential class of estimator, is always more efficient than the usual estimator, ratio, product, exponential ratio, and exponential product type estimators and equally efficient to simple difference estimator. Moreover, the adapted version of Sharma and Singh's (2015) estimator is always more efficient than all the estimators available in the literature. In addition, theoretical findings are supported by an empirical study to show the superiority of the constructed estimators over others with an application to earthquake data of Turkey.

Keywords: auxiliary attribute, point bi-serial, mean square error, simple random sampling, Poisson distribution

Procedia PDF Downloads 158

Authors: Young-Ok Yoon, Su-Yeon Lee, Seong-Ho Ha, Gil-Yong Yeom, Bong-Hwan Kim, Hyun-Kyu Lim, Shae K. Kim

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Al-7Si-0.4Mg alloy (designated A356) is widely used in the automotive and aerospace industries as structural components due to an excellent combination of castability and mechanical properties. Grain refinement has a significant effect on the mechanical properties of castings, mainly since the distribution of secondary phase is changed. As a grain refiner, the Al-Ti-B master alloys containing TiAl3 and TiB2 particles have been widely used in Al foundries. The Mg loss and Mg based inclusion formation by the strong affinity of Mg to oxygen in the melting process of Mg contained alloys have been an issue. This can be significantly improved only by Mg+Al2Ca master alloy as an alloying element instead of pure Mg. Moreover, the eutectic Si modification and grain refinement is simultaneously obtained because Al2Ca behaves as Ca, a typical Si modifier. The present study is focused on the combined effects of Mg+Al2Ca and Al-Ti-B master alloys on the grain refiment of Al-7Si-0.4Mg alloy and their proper ratio for the optimum effect. The aim of this study, therefore, is to investigate the change of the microstructure in Al-7Si-0.4Mg alloy with different ratios of Ti and Al2Ca (detected Ca content) and their effects on the tensile property. The distribution and morphology of the secondary phases by the grain refinement will be discussed.

Keywords: Al-7Si-0.4Mg alloy, Al2Ca, Al-Ti-B alloy, grain refinement

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Authors: Muhammad Umair Arshad, Saima Ishtiaq, Ali Imran

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Different polysaccharides contribute towards the management of glycemic and satiety and consequently manage the metabolic syndrome. In the present study, we compared the postprandial glycemic and satiety responses of different dietary polysaccharides when added to milk (2% Milk Fat). The objective of this study was to evaluate different polysaccharides against postprandial glucose, appetite responses, and food intake at subsequent meals. In a repeated measures crossover design, 30 females (18–30 years) consumed 250 ml milk with 2% M.F. (control), or milk with carrageenan (2.5 g), guar gum (2.5 g) and alginate (2.5 g), followed by an ad libitum pizza meal after 120 min. Alginate and guar gum addition resulted in lower caloric intake at subsequent pizza meal. The post-treatment (0–120 min) glucose and average appetite were suppressed by alginate and guar gum (p < 0.0001), with a more pronounced effect of guar gum. However, alginate resulted in lower blood glucose (p < 0.0001) compared with control and carrageenan during post-treatment. Alginate and guar gum, added milk, and other beverages would be beneficial in the short-term regulation of postprandial glycemia and satiety.

Keywords: satiety, glycemic control, milk polysaccharides, food intake

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Authors: Fawad Ali, Muhammad Ateeq, Ikhtiar Khan

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Ground water, a major source of drinking water supply in Peshawar has been severely contaminated by leather tanning industry. Effluents from the tanneries contain high concentration of chromium besides several other chemical species. Release of untreated effluents from the tanning industry has severely damaged surface and ground water, agriculture soil as well as vegetables and crops. Chromium is a well-known carcinogenic and mutagenic agent. Once in the human food chain, it causes multiple problems to the exposed population including various types of cancer, skin dermatitis, and DNA damage. In order to assess the extent of chromium and other heavy metals contamination, water samples were analyzed for heavy metals using Graphite Furnace Atomic Absorption Spectrometer (GFAAS, Analyst 700, Perkin Elmer). Total concentration of chromium was above the permissible limit (0.048 mg/l) in 85% of the groundwater (drinking water) samples. The concentration of cobalt, manganese, cadmium, nickel, lead, zinc and iron was also determined in the ground water, surface water, agriculture soil, and vegetables samples from the affected area.

Keywords: heavy metals, soil, groundwater, tannery effluents, food chain

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Authors: Giselle Maggie-Fer Castañeda Lozano

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The main objective of this research was to explore the role of neural network architectures in simulating behavioral phenomena as a potential explanation for selective associations, specifically related to biological constraints on learning. Biological constraints on learning refer to the limitations observed in conditioning procedures, where learning is expected to occur. The study involved simulations of five different experiments exploring various phenomena and sources of biological constraints in learning. These simulations included the interaction between response and reinforcer, stimulus and reinforcer, specificity of stimulus-reinforcer associations, species differences, neuroanatomical constraints, and learning in uncontrolled conditions. The overall results demonstrated that by manipulating neural network architectures, conditions can be created to model and explain diverse biological constraints frequently reported in comparative psychology literature as learning typicities. Additionally, the simulations offer predictive content worthy of experimental testing in the pursuit of new discoveries regarding the specificity of learning. The implications and limitations of these findings are discussed. Finally, it is suggested that this research could inaugurate a line of inquiry involving the use of neural networks to study biological factors in behavior, fostering the development of more ethical and precise research practices.

Keywords: comparative psychology, connectionism, conditioning, experimental analysis of behavior, neural networks

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Authors: Anthony Chidi Ezika, Gbolahan Joseph Adekoya, Emmanuel Rotimi Sadiku, Yskandar Hamam, Suprakas Sinha Ray

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High-energy-density polymer/ceramic composites require a high breakdown strength and dielectric constant. Interface polarization and electric percolation are responsible for the high dielectric constant. In order to create composite dielectrics, high conductivity ceramic particles are combined with polymers to increase the dielectric constant. In this study, bonding and the non-uniform distribution of charges in the ceramic/ceramic interface zone are investigated using density functional theory (DFT) modeling. This non-uniform distribution of charges is intended to improve the ceramic/ceramic interface's dipole polarization (dielectric response). The interfacial chemical bond formation can also improve the structural stability of the hybrid filler and, consequently, of the composite films. To comprehend the electron-transfer process, the density of state and electron localization function of the PPy with hybrid fillers are also studied. The polymer nanocomposite is anticipated to provide a suitable dielectric response for energy storage applications.

Keywords: energy storage, V₂C/ ZnS hybrid, polypyrrole, MXene, nanocomposite, dielectric

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Authors: Flóra Pomázi, Sándor Baranya, Zoltán Szalai

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A harmonized monitoring of the suspended sediment transport along such a large river as the world’s most international river, the Danube River, is a rather challenging task. The traditional monitoring method in Hungary is obsolete but using indirect measurement devices and techniques like optical backscatter sensors (OBS), laser diffraction or acoustic backscatter sensors (ABS) could provide a fast and efficient alternative option of direct methods. However, these methods are strongly sensitive to the particle characteristics (i.e. particle shape, particle size and mineral composition). The current method does not provide sufficient information about particle size distribution, mineral analysis is rarely done, and the shape of the suspended sediment particles have not been examined yet. The aims of the study are (1) to determine the particle characterisation of suspended sediment in the Danube River using advanced particle characterisation methods as laser diffraction and automated imaging, and (2) to perform a sensitivity analysis of the indirect methods in order to determine the impact of suspended particle characteristics. The particle size distribution is determined by laser diffraction. The particle shape and mineral composition analysis is done by the Morphologi G3ID image analyser. The investigated indirect measurement devices are the LISST-Portable|XR, the LISST-ABS (Sequoia Inc.) and the Rio Grande 1200 kHz ADCP (Teledyne Marine). The major findings of this study are (1) the statistical shape of the suspended sediment particle - this is the first research in this context, (2) the actualised particle size distribution – that can be compared to historical information, so that the morphological changes can be tracked, (3) the actual mineral composition of the suspended sediment in the Danube River, and (4) the reliability of the tested indirect methods has been increased – based on the results of the sensitivity analysis and the previous findings.

Keywords: advanced particle characterisation, automated imaging, indirect methods, laser diffraction, mineral composition, suspended sediment

Procedia PDF Downloads 148

Authors: Natalia Oli, Abhinav Vaidya, Katja Pahkala, Gabriele Eiben, Alexandra Krettek

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The nutritional transition towards a high fat and energy dense diet, decreasing physical activity level, and poor cardiovascular health knowledge contributes to a rising burden of cardiovascular diseases in Nepal. Dietary and physical activity behaviors are formed early in life and influenced by family, particularly by mothers in the social context of Nepal. The purpose of this study was to explore knowledge, attitude and behavior of mothers regarding diet and physical activity of their pre-school children. Cross-sectional study was conducted in the semi-urban area of Duwakot and Jhaukhel communities near the capital Kathmandu. Between August and November 2014, nine trained enumerators interviewed all mothers having children aged 2 to 7 years in their homes. Questionnaire contained information about mothers’ socio-demographic characteristics; their knowledge, attitude, and behavior regarding diet and physical activity as well as their children’s diet and physical activity. Knowledge, attitude and behavior responses were scored. SPSS version 22.0 was used for data analyses. Out of the 1,052 eligible mothers, 962 consented to participate in the study. The mean age was 28.9 ± 4.5 years. The majority of them (73%) were housewives. Mothers with higher education and income had higher knowledge, attitude, and behavior scores (All p < 0.001) whereas housewives and farmers had low knowledge score (p < 0.001). They, along with laborers, also exhibited lower attitude (p<0.001) and behavior scores (p < 0.001). Children’s diet score increased with mothers’ level of education (p <0.001) and income (p=0.041). Their physical activity score, however, declined with increasing level of their mothers’ education (p < 0.001) and income (p < 0.001). Children’s overall behavior score correlated poorly with mothers’ knowledge (r = 0.009, p=0.003), attitude (r =0.012, p=0.001), and behavior (r = 0.007, p= 0.008). Such poor correlation can be due to existence of the barriers among mothers. Mothers reported such barriers as expensive healthy food, difficulty to give up favorite food, taste preference of others family members and lack of knowledge on healthy food. Barriers for physical activity were lack of leisure time, lack of parks and playgrounds, being busy by caring for children and old people, feeling lazy and embarrassed in front of others. Additionally, among the facilitators for healthy lifestyle, mentioned by mothers, were better information, family eating healthy food and supporting physical activity, advice of medical personnel regarding healthy lifestyle and own ill health. The study demonstrated poor correlation of mothers’ knowledge and attitude with children’s behavior regarding diet and physical activity. Hence improving mothers’ knowledge or attitude may not be enough to improve dietary and physical activity habits of their children. Barriers and facilitators that affect mothers’ practices towards their children should also be addressed due to future intervention.

Keywords: attitude, behavior, diet, knowledge, mothers, physical activity

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Authors: Kirolos Gerges Yakoub Gerges

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Self-sustaining agricultural machines act in stochastic surroundings and therefore, should be capable of perceive the surroundings in real time. This notion can be done using image sensors blended with superior device learning, mainly Deep mastering. Deep convolutional neural networks excel in labeling and perceiving colour pix and since the fee of RGB-cameras is low, the hardware cost of accurate notion relies upon heavily on memory and computation power. This paper investigates the opportunity of designing lightweight convolutional neural networks for semantic segmentation (pixel clever class) with reduced hardware requirements, to allow for embedded usage in self-reliant agricultural machines. The usage of compression techniques, a lightweight convolutional neural community is designed to carry out actual-time semantic segmentation on an embedded platform. The community is skilled on two big datasets, ImageNet and Pascal Context, to apprehend as much as four hundred man or woman instructions. The 400 training are remapped into agricultural superclasses (e.g. human, animal, sky, road, area, shelterbelt and impediment) and the capacity to provide correct actual-time perception of agricultural environment is studied. The network is carried out to the case of self-sufficient grass mowing the usage of the NVIDIA Tegra X1 embedded platform. Feeding case-unique pics to the community consequences in a fully segmented map of the superclasses within the picture. As the network remains being designed and optimized, handiest a qualitative analysis of the technique is entire on the abstract submission deadline. intending this cut-off date, the finalized layout is quantitatively evaluated on 20 annotated grass mowing pictures. Light-weight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show aggressive performance on the subject of accuracy and speed. It’s miles viable to offer value-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: centrifuge pump, hydraulic energy, agricultural applications, irrigationaxial flux machines, axial flux applications, coreless machines, PM machinesautonomous agricultural machines, deep learning, safety, visual perception

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Authors: Gabriel K. Nzulu, Hans Högberg, Per Eklund, Lars Hultman, Martin Magnuson

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X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDX) are applied to investigate the properties of rock samples from a drill hole from the Kubi Gold Project of the Asante Gold Corporation near Dunwka-on-Offin in the Central Region of Ghana. The distribution of these minerals in the rocks were observed in the drill hole sections. X-ray diffraction indicates that the samples contain garnet, pyrite, periclase, and quartz as the main indicator minerals. SEM revealed morphologies of these minerals. From EDX and XPS, Fe, Mg, Al, S, O, Hg, Ti, Mn, Na, Ag, Au, Cu, Si, and K are identified as the pathfinder elements in the area that either form alloys with gold or inherent elements in the sediments. This finding can be ascribed to primary geochemical distribution, which developed from crystallization of magma and hydrothermal liquids as well as the movement of metasomatic elements and the precipitous rate of chemical weathering of lateralization in secondary processes. The results indicate that Au mineralization in the Kubi Mine area is controlled by garnet, pyrite, goethite, and kaolinite that grades up to the surface (oxides) with hematite and limonite alterations.

Keywords: gold, minerals, pathfinder element, spectroscopy, X-ray

Procedia PDF Downloads 113