Search results for: extra trees classifier

Authors: G. H. B. Dilhari, A. A. D. T. Saparamadu

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The coir is one of the by-products of the coconut and the coir industry can be identified as one of the traditional industries in Sri Lanka. Sri Lanka is one of the prominent countries for the coir production. Due to the labour insensitiveness, the labourers are the significant factor in the coir production process. The study has analyzed the determinants and its impact on income of the household level coir yarn labourers. The study was conducted in the Kumarakanda Grama Niladhari division, Galle, Sri Lanka. Simple random sampling was used to generate the sample of 100 household level coir yarn labourers and structured questionnaire, personal interviews and discussion were performed to gather the required data. The obtained data were statistically analyzed by using Statistical Package for Social Science (SPSS) software. Mann-Whitney U and Kruskal-Wallis test were carried out. The findings revealed that the household level coir yarn industry is dominated by the female workers and fewer amounts of workers have engaged this industry as the main occupation. In addition to that, elderly participation of the industry is greater than younger participation and most of them engaged as an extra income source. Level of education, the methods of engagement, satisfaction, labour’s children employment in the coir industry, support from the government, method of government support, working hours per day, employed as a main job, no of completed units per day, suffering any job related diseases and type of the diseases were related with income level of household level coir yarn labourers. The recommendations were formulated in respect to these problems including technological transformation for coir yarn production, strengthening of the raw material base and regulating the raw material supply, introduction of new technologies, markets and training programs, the establishment of the labourers association, the initiation of micro credit schemes, better consideration about the job oriented diseases.

Keywords: coir, coir yarn labourers, income, Galle

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Authors: AbdelRahman A. Faragalla, Mohamed H. Alqhtani, Mohamed M. M.Ahmed

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Saudi Arabia has currently been beset by a barrage of bizarre assemblages of subterranean termite fauna, inflicting heavy catastrophic havocs on human valued properties in various homes, storage facilities, warehouses, agricultural and horticultural crops including okra, sweet pepper, tomatoes, sorghum, date palm trees, citruses and many forest domains and green lush desert oases. The most pressing urgent priority is to use modern technologies to alleviate the painstaking obstacle of taxonomic identification of these injurious noxious pests that might lead to effective pest control in both infested agricultural commodities and field crops. Our study has indicated the use of DNA fingerprinting technologies, in order to generate basic information of the genetic similarity between 3 predominant families containing the most destructive termite species. The methodologies included extraction and DNA isolation from members of the major families and the use of randomly selected primers and PCR amplifications with the nucleotide sequences. GC content and annealing temperatures for all primers, PCR amplifications and agarose gel electrophoresis were also conducted in addition to the scoring and analysis of Random Amplification Polymorphic DNA-PCR (RAPDs). A phylogenetic analysis for different species using statistical computer program on the basis of RAPD-DNA results, represented as a dendrogram based on the average of band sharing ratio between different species. Our study aims to shed more light on this intriguing subject, which may lead to an expedited display of the kinship and relatedness of species in an ambitious undertaking to arrive at correct taxonomic classification of termite species, discover sibling species, so that a logistic rational pest management strategy could be delineated.

Keywords: DNA fingerprinting, Western Saudi Arabia, DNA primers, RAPD

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Authors: Funmilayo Sarah Eguakun, Peter Oluremi Adesoye

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One of the major problems being faced by human society is that the global temperature is believed to be rising due to human activity that releases carbon IV oxide (CO2) to the atmosphere. Carbon IV oxide is the most important greenhouse gas influencing global warming and possible climate change. With climate change becoming alarming, reducing CO2 in our atmosphere has become a primary goal of international efforts. Forest landsare major sink and could absorb large quantities of carbon if the trees are judiciously managed. The study aims at estimating the carbon sequestration capacity of Pinus caribaea (pine)and Tectona grandis (Teak) under the prevailing environmental conditions and exploring tree growth variables that influencesthe carbon sequestration capacity in Omo Forest Reserve, Ogun State, Nigeria. Improving forest management by manipulating growth characteristics that influences carbon sequestration could be an adaptive strategy of forestry to climate change. Random sampling was used to select Temporary Sample Plots (TSPs) in the study area from where complete enumeration of growth variables was carried out within the plots. The data collected were subjected to descriptive and correlational analyses. The results showed that average carbon stored by Pine and Teak are 994.4±188.3 Kg and 1350.7±180.6 Kg respectively. The difference in carbon stored in the species is significant enough to consider choice of species relevant in climate change adaptation strategy. Tree growth variables influence the capacity of the tree to sequester carbon. Height, diameter, volume, wood density and age are positively correlated to carbon sequestration. These tree growth variables could be manipulated by the forest manager as an adaptive strategy for climate change while plantations of high wood density speciescould be relevant for management strategy to increase carbon storage.

Keywords: adaptation, carbon sequestration, climate change, growth variables, wood density

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Authors: Mohammed Al Sulaimani, Hamad Al Manhi

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With the development of Remote Sensing technology, the resolution of optical Remote Sensing images has greatly improved, and images have become largely available. Numerous detectors have been developed for detecting different types of objects. In the past few years, Remote Sensing has benefited a lot from deep learning, particularly Deep Convolution Neural Networks (CNNs). Deep learning holds great promise to fulfill the challenging needs of Remote Sensing and solving various problems within different fields and applications. The use of Unmanned Aerial Systems in acquiring Aerial Photos has become highly used and preferred by most organizations to support their activities because of their high resolution and accuracy, which make the identification and detection of very small features much easier than Satellite Images. And this has opened an extreme era of Deep Learning in different applications not only in feature extraction and prediction but also in analysis. This work addresses the capacity of Machine Learning and Deep Learning in detecting and extracting Oil Leaks from Flowlines (Onshore) using High-Resolution Aerial Photos which have been acquired by UAS fixed with RGB Sensor to support early detection of these leaks and prevent the company from the leak’s losses and the most important thing environmental damage. Here, there are two different approaches and different methods of DL have been demonstrated. The first approach focuses on detecting the Oil Leaks from the RAW Aerial Photos (not processed) using a Deep Learning called Single Shoot Detector (SSD). The model draws bounding boxes around the leaks, and the results were extremely good. The second approach focuses on detecting the Oil Leaks from the Ortho-mosaiced Images (Georeferenced Images) by developing three Deep Learning Models using (MaskRCNN, U-Net and PSP-Net Classifier). Then, post-processing is performed to combine the results of these three Deep Learning Models to achieve a better detection result and improved accuracy. Although there is a relatively small amount of datasets available for training purposes, the Trained DL Models have shown good results in extracting the extent of the Oil Leaks and obtaining excellent and accurate detection.

Keywords: GIS, remote sensing, oil leak detection, machine learning, aerial photos, unmanned aerial systems

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Authors: Rosa Figueroa, Christopher Flores

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Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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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: King Kumire, Amos Kubeka

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The synergy of machine learning and astronomical technology advancement is giving rise to the new space age, which is pronounced by better habitability assessments. To initiate this discussion, it should be recorded for definition purposes that the symbiotic relationship between astronomy and improved computing has been code-named the Cis-Astro gateway concept. The cosmological fate of this phrase has been unashamedly plagiarized from the cis-lunar gateway template and its associated LaGrange points which act as an orbital bridge to the moon from our planet Earth. However, for this study, the scientific audience is invited to bridge toward the discovery of new habitable planets. It is imperative to state that cosmic probes of this magnitude can be utilized as the starting nodes of the astrobiological search for galactic life. This research can also assist by acting as the navigation system for future space telescope launches through the delimitation of target exoplanets. The findings and the associated platforms can be harnessed as building blocks for the modeling of climate change on planet earth. The notion that if the human genus exhausts the resources of the planet earth or there is a bug of some sort that makes the earth inhabitable for humans explains the need to find an alternative planet to inhabit. The scientific community, through interdisciplinary discussions of the International Astronautical Federation so far has the common position that engineers can reduce space mission costs by constructing a stable cis-lunar orbit infrastructure for refilling and carrying out other associated in-orbit servicing activities. Similarly, the Cis-Astro gateway can be envisaged as a budget optimization technique that models extra-solar bodies and can facilitate the scoping of future mission rendezvous. It should be registered as well that this broad and voluminous catalog of exoplanets shall be narrowed along the way using machine learning filters. The gist of this topic revolves around the indirect economic rationale of establishing a habitability scoping platform.

Keywords: machine-learning, habitability, exoplanets, supercomputing

Procedia PDF Downloads 90

Authors: King Kumire, Amos Kubeka

Abstract:

The synergy of machine learning and astronomical technology advancement is giving rise to the new space age, which is pronounced by better habitability assessments. To initiate this discussion, it should be recorded for definition purposes that the symbiotic relationship between astronomy and improved computing has been code-named the Cis-Astro gateway concept. The cosmological fate of this phrase has been unashamedly plagiarized from the cis-lunar gateway template and its associated LaGrange points which act as an orbital bridge to the moon from our planet Earth. However, for this study, the scientific audience is invited to bridge toward the discovery of new habitable planets. It is imperative to state that cosmic probes of this magnitude can be utilized as the starting nodes of the astrobiological search for galactic life. This research can also assist by acting as the navigation system for future space telescope launches through the delimitation of target exoplanets. The findings and the associated platforms can be harnessed as building blocks for the modeling of climate change on planet earth. The notion that if the human genus exhausts the resources of the planet earth or there is a bug of some sort that makes the earth inhabitable for humans explains the need to find an alternative planet to inhabit. The scientific community, through interdisciplinary discussions of the International Astronautical Federation so far, has the common position that engineers can reduce space mission costs by constructing a stable cis-lunar orbit infrastructure for refilling and carrying out other associated in-orbit servicing activities. Similarly, the Cis-Astro gateway can be envisaged as a budget optimization technique that models extra-solar bodies and can facilitate the scoping of future mission rendezvous. It should be registered as well that this broad and voluminous catalog of exoplanets shall be narrowed along the way using machine learning filters. The gist of this topic revolves around the indirect economic rationale of establishing a habitability scoping platform.

Keywords: exoplanets, habitability, machine-learning, supercomputing

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

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Authors: Arun Dhakal, Geoff Cockfield

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This paper examines the emission potential of different farming practices that the farmers have adopted in Dhanusha District of Nepal and scope of these practices in climate change mitigation. Which practice is more climate-smarter is the question that this aims to address through a life cycle assessment (LCA) of greenhouse gas (GHG) emissions. The LCA was performed to assess if there is difference in emission potential of broadly two farming systems (agroforestry–based and traditional agriculture) but specifically four farming systems. The required data for this was collected through household survey of randomly selected households of 200. The sources of emissions across the farming systems were paddy cultivation, livestock, chemical fertilizer, fossil fuels and biomass (fuel-wood and crop residue) burning. However, the amount of emission from these sources varied with farming system adopted. Emissions from biomass burning appeared to be the highest while the source ‘fossil fuel’ caused the lowest emission in all systems. The emissions decreased gradually from agriculture towards the highly integrated agroforestry-based farming system (HIS), indicating that integrating trees into farming system not only sequester more carbon but also help in reducing emissions from the system. The annual emissions for HIS, Medium integrated agroforestry-based farming system (MIS), LIS (less integrated agroforestry-based farming system and subsistence agricultural system (SAS) were 6.67 t ha-1, 8.62 t ha-1, 10.75 t ha-1 and 17.85 t ha-1 respectively. In one agroforestry cycle, the HIS, MIS and LIS released 64%, 52% and 40% less GHG emission than that of SAS. Within agroforestry-based farming systems, the HIS produced 25% and 50% less emissions than those of MIS and LIS respectively. Our finding suggests that a tree-based farming system is more climate-smarter than a traditional farming. If other two benefits (carbon sequestered within the farm and in the natural forest because of agroforestry) are to be considered, a considerable amount of emissions is reduced from a climate-smart farming. Some policy intervention is required to motivate farmers towards adopting such climate-friendly farming practices in developing countries.

Keywords: life cycle assessment, greenhouse gas, climate change, farming systems, Nepal

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Authors: SHWETA SRIKUMAR

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Winter stratospheric dynamics is a highly variable and spectacular field of research in middle atmosphere. It is well believed that the interaction of energetic planetary waves with mean flow causes the temperature to increase in the stratosphere and associated circulation reversal. This wave driven sudden disturbances in the polar stratosphere is defined as Sudden Stratospheric Warming. The main objective of the present work is to investigate the mid-winter major stratospheric warming events on equatorial dynamics over Peninsular India. To explore the effect of mid-winter stratospheric warming on Indian region (60oE -100oE), we have selected the winters 2003/04, 2005/06, 2008/09, 2012/13 and 2018/19. This study utilized the data from ERA-Interim Reanalysis, Outgoing Longwave Radiation (OLR) from NOAA and TRMM satellite data from NASA mission. It is observed that a sudden drop in OLR (averaged over Indian Region) occurs during the course of warming for the winters 2005/06, 2008/09 and 2018/19. But in winters 2003/04 and 2012/13, drop in OLR happens prior to the onset of major warming. Significant amplitude of planetary wave activity is observed in equatorial lower stratosphere which indicates the propagation of extra-tropical planetary waves from high latitude to equator. During the course of warming, a strong downward propagation of EP flux convergence is observed from polar to equator region. The polar westward wind reaches upto 20oN and the weak eastward wind dominates the equator during the winters 2003/04, 2005/06 and 2018/19. But in 2012/13 winter, polar westward wind reaches upto equator. The equatorial wind at 2008/09 is dominated by strong westward wind. Further detailed results will be presented in the conference.

Keywords: Equatorial dynamics, Outgoing Longwave Radiation, Sudden Stratospheric Warming, Planetary Waves

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Authors: Gholba Niranjan Dilip, Anil Kumar

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Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

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Authors: Jamal Alqinai

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A pragmatic approach to translation studies the rules and principles governing the use of language over and above the rules of syntax or morphology, and what makes some uses of language more appropriate than others in [communicative] situations. It attempts to explain translation as a procedure and product from the point of view of how, why and what is done by the source text author (ST) and what is to be done in the target text (TT) rendition. The latter will be subject to evaluation not as generated by the linguistics system but as conveyed and manipulated by participants in a communicative situation according to the referential and pragmatic standards employed. The failure of a purely lexical or structural translation stems from ignoring the relation between words as signs and the effect they have on their users. A more refined approach would also consider those processes that are sometimes labeled extra-linguistic or intuitive and which translators strive to reproduce unscathed in the translation process. We need to grasp the kind of actions an ST author performs on his readers by combining linguistic and non-linguistic elements against a backdrop of beliefs and cultural values. In other words, aside from considering the cohesive ties at the textual level, one needs to understand how the whole ST discourse hangs together logically in order to reproduce a coherent TT. The latter can only be achieved by an analysis of the pragmatic elements of presuppositions, implicatures and acts performed in the ST. Establishing cohesive ties within a text may require seeking reference outside the immediate text. The illocutionary functions manifested in one language/culture are relatively autonomous cultural/linguistic categories, but are imaginable by members of other cultures and, to some extent , are translatable though not, of course, without translation loss. Globalization and the spread of literacy worldwide may have created a universal empathy to comprehend the performative aspect of utterances when explained by approximate glosses or by paraphrase. Yet, it is often the multilayered and the culture-specific nature of illocutionary functions that de-universalize their possible interpretations. This paper addresses the pragmatic interpretation of culturally specific texts with examples adduced from a number of distinct settings to illustrate the influence of the pragmatic factors at stake.

Keywords: pragmatic, presupposition, implicature, cohesion

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Authors: Alma Ramić, Mirjana Skočibušić, Renata Odžak, Tomica Hrenar, Ines Primožič

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In an attempt to identify a new class of antimicrobial agents, the antimicrobial potential of Cinchona alkaloid derivatives was evaluated. The bark of the Cinchona trees is the source of a variety of alkaloids, among which the best known are quinine, quinidine, cinchonine and cinchonidine. They are very useful as organocatalysts in stereoselective synthesis. On the other hand, quinine is traditionally used in the treatment of malaria. Furthermore, Cinchona alkaloids possess various analgesic, anti-inflammatory and anti–arrhythmic properties as well. In this work we present the synthesis of twenty quaternary derivatives of pseudo−enantiomeric Cinchona alkaloid derivatives to evaluate their antibacterial activity. Quaternization of quinuclidine moiety was carried out with groups diverse in their size. The structures of compounds were systematically modified to obtain drug-like properties with proper physical and chemical properties and avoiding toxophore. All compounds were prepared in good yields and were characterized by standard analytical spectroscopy methods (1D and 2D NMR, IR, MS). The antibacterial activities of all compounds were evaluated against series of recent clinical isolates of antibiotic susceptible Gram-positive and resistant Gram-negative pathogens by determining their zone of inhibition and minimum inhibitory concentrations. All compounds showed good to strong broad-spectrum activity, equivalent or better in comparison with standard antibiotics used. Furthermore, seven compounds exhibited significant antibacterial efficiency against Gram-negative isolates. To visualize the results, principal component analysis was used as an additional classification tool. Cytotoxicity of compounds with different cell lines in human cell culture was determined. Based on these results, substituted quaternary Cinchona scaffold can be considered as promising new class of antimicrobials and further investigations should be performed. Supported by Croatian Science Foundation, Project No 3775 ADESIRE.

Keywords: antibacterial efficiency, cinchona alkaloids, cytotoxicity, pseudo‐enantiomers

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Authors: Renata Gagić-Serdar, Miroslava Markovic, Ljubinko Rakonjac, Aleksandar Lučić

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The paper present the results of the entomological experimental studies of the biological, ecological, and (bionomic) insect performances, such as seasonal adaptation of introduced monophagous false indigo and albizias weevil’s Acanthoscelides pallidipennis Motschulsky. and Bruchidius terrenus (Sharp), Coleoptera: Chrysomelidae: Bruchinae, to phenological phases of aggressive invasive host plant Amorpha fruticosa L. and Albizia julibrissin (Fabales: Fabaceae) on the territory of Republic of Serbia with special attention on assessing and monitoring of new formed and detected inter species relations between autochthons parasite wasps from fauna (Hymenoptera: Chalcidoidea) and herbaceous seed weevil beetle. During 15 years (2006-2021), on approximately 30 localities, data analyses were done for observed experimental host plants from samples with statistical significance. Status of genera from families Hymenoptera: Chalcidoidea.: Pteromalidae and Eulophidae, after intensive investigations, has been trophicly identified. Recorded seed pest species of A. fruticosa or A. julibrissin (Fabales: Fabaceae) was introduced in Serbia and planted as ornamental trees, they also were put undergo different kinds of laboratory and field research tests during this period in a goal of collecting data about lasting each of develop stage of their seed beetles. Field generations in different stages were also monitored by continuous infested seed collecting and its disection. Established host plant-seed predator linkage was observed in correlation with different environment parameters, especially water level fluctuations in bank corridor formation stands and riparian cultures.

Keywords: amorpha, albizia, chalcidoid wasp, invasiveness, weevils

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Authors: E.Tskitisvili, L. Jgenti, I. Eliava, T. Tskitishvili, N. Bagathuria, M. Gigolashvili

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The fight against dangerous nematode diseases that have world economic importance requires accurate data about the prevalence of these pests. In the point of view of the International Convention on Biological Diversity, the identification of the plant invasion causing dangerous pathogen in the early stages of invasion on new territory is the most important part of the program, which aims to monitor the Bio-Agro Coenosis and Bio-Control. Citrus nematode-specific belongs to the pathogen species, which can cause epiphytotics particularly for large areas and cause irreparable damage to citrus plantations. This paper provides a brief tour of the spread of citrus nematodes on the Black Sea coast (Adjara and Abkhazia). Also the bio-ecological monitoring data to detect the potential sources of invasion for evaluating the current conditions of the citrus nematodes prevalence. Through 2006-2010, the material was gained by structural monitoring system during the citrus vegetation period on tangerines, lemon and oranges from nine points of the study area. Mature forms of Tylenchulus semipenetrans Cobb, 1913 were observed in almost all of the samples of the root system, the peak of larvae was observed in late spring and outumn. 92 forms of nematode has been detected in the rhizosphere belonging to 8 Orders: Areolaimida, Dorylaimida, Enoplida, Mononchida, Tylenshida, Monshysterida, Rhabditida, Aphelenchida, 23 families and 40 genera. 75 forms are identified as species. It is estimated the number of nematodes fauna and ecological groups. To detect possible sources of invasion we obtained additional materials in 2013-2014 from citrus plantations planted in 2011, where is planted tangerine trees introduced from Spain and Japan. The fauna of rhizosphere is identified and Tylenchulus semipenetrans Cobb, 1913 is not detected.

Keywords: Citrus nematodes, infection, bioecological monitoring, epiphytotics

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Authors: Mervat Kassem, Nourhan Fanaki, F. Dabbous, Hamida Abou-Shleib, Y. R. Abdel-Fattah

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With increasing environmental awareness and emphasis on a sustainable society in harmony with the global environment, biosurfactants are gaining prominence and have already taken over for a number of important industrial uses. They are produced by living organisms, for examples Pseudomonas aeruginosa which produces rhamnolipids, Candida (formerly Torulopsis) bombicola, which produces high yields of sophorolipids from vegetable oils and sugars and Bacillus subtilis which produces a lipopeptide called surfactin. The main goal of this work was to optimize biosurfactants production by an environmental Gram positive isolate for large scale production with maximum yield and low cost. After molecular characterization, phylogenetic tree was constructed where it was found to be B. subtilis, which close matches to B. subtilis subsp. subtilis strain CICC 10260. For optimizing its biosurfactants production, sequential statistical design using Plackett-Burman and response surface methodology, was applied where 11 variables were screened. When analyzing the regression coefficients for the 11 variables, pH, glucose, glycerol, yeast extract, ammonium chloride and ammonium nitrate were found to have a positive effect on the biosurfactants production. Ammonium nitrate, pH and glucose were further studied as significant independent variables for Box-Behnken design and their optimal levels were estimated and were found to be 7.328 pH value, 3 g% glucose and 0.21g % ammonium nitrate yielding high biosurfactants concentration that reduced the surface tension of the culture medium from 72 to 18.16 mN/m. Next, kinetics of cell growth and biosurfactants production by the tested B. subtilis isolate, in bioreactor was compared with that of shake flask where the maximum growth and specific growth (µ) in the bioreactor was higher by about 25 and 53%, respectively, than in shake flask experiment, while the biosurfactants production kinetics was almost the same in both shake flask and bioreactor experiments.

Keywords: biosurfactants, B. subtilis, molecular identification, phylogenetic trees, Plackett-Burman design, Box-Behnken design, 16S rRNA

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Authors: Hema Lattupalli

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Introduction: A bony bridge extends from the lateral mass of the atlas to postero medial margin of vertebral artery groove, termed as a posterior bridge of atlas or posterior ponticulus. The foramen formed by the bridge is called as arcuate foramen or retroarticulare superior. Another bony bridge sometimes extends laterally from lateral mass to posterior root of transverse foramen forming and additional groove for vertebral artery, above and behind foramen transversarium called Lateral bridge or ponticulus lateralis. When both posterior and lateral are present together it is called as Posterolateral ponticuli. Aim and Objectives: The aim of the present study is to detect the presence of such Bridge or Ponticuli called as Lateral, Posterior and Posterolateral reported by earlier investigators in atlas vertebrae. Material and Methods: The study was done on 100 Atlas vertebrae from the Department of Anatomy Narayana Medical College Nellore, and also from SVIMS Tirupati was collected over a period of 2 years. The parameters that were studied include the presence of ponticuli, complete and incomplete and right and left side ponticuli. They were observed for all these parameters and the results were documented and photographed. Results: Ponticuli were observed in 25 (25%) of atlas vertebrae. Posterior ponticuli were found in 16 (16%), Lateral in 01 (01%) and Posterolateral in 08(08%) of the atlas vertebrae. Complete ponticuli were present in 09 (09%) and incomplete ponticuli in 16 (16%) of the atlas vertebrae. Bilateral ponticuli were seen in 10 (10%) and unilateral ponticuli were seen in 15 (15%) of the atlas vertebrae. Right side ponticuli were seen in 04 (04%) and Left side ponticuli in 05 (05%) of the atlas vertebrae respectively. Interpretation and Conclusion: In the present study posterior complete ponticuli were said to be more than the lateral complete ponticuli. The presence of Bilateral Incomplete Posterior ponticuli is higher and also Atlantic ponticuli. The present study is to say that knowledge of normal anatomy and variations in the atlas vertebra is very much essential to the neurosurgeons giving a message that utmost care is needed to perform surgeries related to craniovertebral regions. This is additional information to the Anatomists, Neurosurgeons and Radiologist. This adds an extra page to the literature.

Keywords: atlas vertebra, ponticuli, posterior arch, arcuate foramen

Procedia PDF Downloads 369

Authors: Samir Ali-arous, Mohamed Beddane, Khaled Djelouah

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This study investigated the insect diversity of olive (Olea europaea Linnaeus (Oleaceae)) groves grown in an arid climate in Algeria. In this context, several sampling methods were used within two orchards differently managed. Fifty arthropod species belonging to diverse orders and families were recorded. Hymenopteran species were quantitatively the most abundant, followed by species associated with Heteroptera, Aranea, Coleoptera and Homoptera orders. Regarding functional feeding groups, phytophagous species were dominant in the weeded and the unweeded orchard; however, higher abundance was recorded in the weeded site. Predators were ranked second, and pollinators were more frequent in the unweeded olive orchard. Two-factor Anova with repeated measures had revealed high significant effect of the weed management system, measures repetition and interaction with measurement repetition on arthropod’s abundances (P < 0.05). Likewise, generalized linear models showed that N/S ratio varied significantly between the two weed management approaches, in contrast, the remaining diversity indices including the Shannon index H’ had no significant correlation. Moreover, diversity parameters of arthropod’s communities in each agro-system highlighted multiples significant correlations (P <0.05). Rarefaction and extrapolation (R/E) sampling curves, evidenced that the survey and monitoring carried out in both sites had a optimum coverage of entomofauna present including scarce and transient species. Overall, calculated diversity and similarity indices were greater in the unweeded orchard than in the weeded orchard, demonstrating spontaneous flora's key role in entomofaunal diversity. Principal Component Analysis (PCA) has defined correlations between arthropod’s abundances and naturally occurring plants in olive orchards, including beneficials.

Keywords: Algeria, olive, insects, diversity, wild plants

Procedia PDF Downloads 75

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

Procedia PDF Downloads 162

Authors: Jeferson Brito Fernades, Breno Padovezi Rocha, Roger Augusto Rodrigues, Heraldo Luiz Giacheti

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The geotechnical projects demand the appropriate knowledge of soil characteristics and parameters. The determination of geotechnical soil parameters can be done by means of laboratory or in situ tests. In countries with tropical weather, like Brazil, unsaturated soils are very usual. In these soils, the soil suction has been recognized as an important stress state variable, which commands the geo-mechanical behavior. Triaxial and direct shear tests on saturated soils samples allow determine only the minimal soil shear strength, in other words, no suction contribution. This paper briefly describes the triaxial test with controlled suction as well as discusses the influence of suction on the shear strength parameters of a lateritic tropical sandy soil from a Brazilian research site. In this site, a sample pit was excavated to retrieve disturbed and undisturbed soil blocks. The samples extracted from these blocks were tested in laboratory to represent the soil from 1.5, 3.0 and 5.0 m depth. The stress curves and shear strength envelopes determined by triaxial tests varying suction and confining pressure are presented and discussed. The water retention characteristics on this soil complement this analysis. In situ CPT tests were also carried out at this site in different seasons of the year. In this case, the soil suction profile was determined by means of the soil water retention. This extra information allowed assessing how soil suction also affected the CPT data and the shear strength parameters estimative via correlation. The major conclusions of this paper are: the undisturbed soil samples contracted before shearing and the soil shear strength increased hyperbolically with suction; and it was possible to assess how soil suction also influenced CPT test data based on the water content soil profile as well as the water retention curve. This study contributed with a better understanding of the shear strength parameters and the soil variability of a typical unsaturated tropical soil.

Keywords: site characterization, triaxial test, CPT, suction, variability

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Authors: H. Jung, N. Kim, B. Kang, J. Choe

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History matching is a crucial procedure for predicting reservoir performances and making future decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it is important to have reliable initial models for successful history matching of highly heterogeneous reservoirs such as channel reservoirs. In this paper, we proposed a novel scheme for regenerating geological models using support vector machine (SVM) and principal component analysis (PCA). First, we perform PCA for figuring out main geological characteristics of models. Through the procedure, permeability values of each model are transformed to new parameters by principal components, which have eigenvalues of large magnitude. Secondly, the parameters are projected into two-dimensional plane by multi-dimensional scaling (MDS) based on Euclidean distances. Finally, we train an SVM classifier using 20% models which show the most similar or dissimilar well oil production rates (WOPR) with the true values (10% for each). Then, the other 80% models are classified by trained SVM. We select models on side of low WOPR errors. One hundred channel reservoir models are initially generated by single normal equation simulation. By repeating the classification process, we can select models which have similar geological trend with the true reservoir model. The average field of the selected models is utilized as a probability map for regeneration. Newly generated models can preserve correct channel features and exclude wrong geological properties maintaining suitable uncertainty ranges. History matching with the initial models cannot provide trustworthy results. It fails to find out correct geological features of the true model. However, history matching with the regenerated ensemble offers reliable characterization results by figuring out proper channel trend. Furthermore, it gives dependable prediction of future performances with reduced uncertainties. We propose a novel classification scheme which integrates PCA, MDS, and SVM for regenerating reservoir models. The scheme can easily sort out reliable models which have similar channel trend with the reference in lowered dimension space.

Keywords: history matching, principal component analysis, reservoir modelling, support vector machine

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Authors: Mojo Mengistu Gelasso

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The tropical forest is considered the most important forest ecosystem for mitigating climate change by sequestering a high amount of carbon. The potential carbon stock of the forest can be influenced by many factors. Therefore, studying these factors is crucial for understanding the determinants that affect the potential for woody carbon storage in the forest. This study was conducted to evaluate the potential for woody carbon stock and how it varies based on plant community types, as well as along altitudinal, slope, and aspect gradients in the Munessa dry Afromontane forest. Vegetation data was collected using systematic sampling. Five line transects were established at 100 m intervals along the altitudinal gradient between two consecutive transect lines. On each transect, 10 quadrats (20 x 20 m), separated by 200 m, were established. The woody carbon was estimated using an appropriate allometric equation formulated for tropical forests. The data was analyzed using one-way ANOVA in R software. The results showed that the total woody carbon stock of the Munessa forest was 210.43 ton/ha. The analysis of variance revealed that woody carbon density varied significantly based on environmental factors, while community types had no significant effect. The highest mean carbon stock was found at middle altitudes (2367-2533 m.a.s.l), lower slopes (0-13%), and west-facing aspects. The Podocarpus falcatus-Croton macrostachyus community type also contributed a higher woody carbon stock, as larger tree size classes and older trees dominated it. Overall, the potential for woody carbon sequestration in this study was strongly associated with environmental variables. Additionally, the uneven distribution of species with larger diameter at breast height (DBH) in the study area might be linked to anthropogenic factors, as the current forest growth indicates characteristics of a secondary forest. Therefore, our study suggests that the development and implementation of a sustainable forest management plan is necessary to increase the carbon sequestration potential of this forest and mitigate climate change.

Keywords: munessa forest, woody carbon stock, environmental factors, climate mitigation

Procedia PDF Downloads 81

Authors: Mengistu Gelasso Mojo

Abstract:

The tropical forest is considered the most important forest ecosystem for mitigating climate change by sequestering a high amount of carbon. The potential carbon stock of the forest can be influenced by many factors. Therefore, studying these factors is crucial for understanding the determinants that affect the potential for woody carbon storage in the forest. This study was conducted to evaluate the potential for woody carbon stock and how it varies based on plant community types, as well as along altitudinal, slope, and aspect gradients in the Munessa dry Afromontane forest. Vegetation data was collected using systematic sampling. Five line transects were established at 100 m intervals along the altitudinal gradient between two consecutive transect lines. On each transect, 10 quadrats (20 x 20 m), separated by 200 m, were established. The woody carbon was estimated using an appropriate allometric equation formulated for tropical forests. The data was analyzed using one-way ANOVA in R software. The results showed that the total woody carbon stock of the Munessa forest was 210.43 ton/ha. The analysis of variance revealed that woody carbon density varied significantly based on environmental factors, while community types had no significant effect. The highest mean carbon stock was found at middle altitudes (2367-2533 m.a.s.l), lower slopes (0-13%), and west-facing aspects. The Podocarpus falcatus-Croton macrostachyus community type also contributed a higher woody carbon stock, as larger tree size classes and older trees dominated it. Overall, the potential for woody carbon sequestration in this study was strongly associated with environmental variables. Additionally, the uneven distribution of species with larger diameter at breast height (DBH) in the study area might be linked to anthropogenic factors, as the current forest growth indicates characteristics of a secondary forest. Therefore, our study suggests that the development and implementation of a sustainable forest management plan is necessary to increase the carbon sequestration potential of this forest and mitigate climate change.

Keywords: munessa forest, woody carbon stock, environmental factors, climate mitigation

Procedia PDF Downloads 89

Authors: Manutchanok Jongprasithporn, Rawiphorn Srivilai, Paweena Pongsopha

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Fall is the most serious accident leading to increased unintentional injuries and mortality. Falls are not only the cause of suffering and functional impairments to the individuals, but also the cause of increasing medical cost and days away from work. The early detection of falls could be an advantage to reduce fall-related injuries and consequences of falls. Smartphones, embedded accelerometer, have become a common device in everyday life due to decreasing technology cost. This paper explores a physical activity monitoring and fall detection application in smartphones which is a non-invasive biomedical device to determine physical activities and fall event. The combination of application and sensors could perform as a biomedical sensor to monitor physical activities and recognize a fall. We have chosen Android-based smartphone in this study since android operating system is an open-source and no cost. Moreover, android phone users become a majority of Thai’s smartphone users. We developed Thai 3 Axis (TH3AX) as a physical activities and fall detection application which included command, manual, results in Thai language. The smartphone was attached to right hip of 10 young, healthy adult subjects (5 males, 5 females; aged< 35y) to collect accelerometer and gyroscope data during performing physical activities (e.g., walking, running, sitting, and lying down) and falling to determine threshold for each activity. Dependent variables are including accelerometer data (acceleration, peak acceleration, average resultant acceleration, and time between peak acceleration). A repeated measures ANOVA was performed to test whether there are any differences between DVs’ means. Statistical analyses were considered significant at p<0.05. After finding threshold, the results were used as training data for a predictive model of activity recognition. In the future, accuracies of activity recognition will be performed to assess the overall performance of the classifier. Moreover, to help improve the quality of life, our system will be implemented with patients and elderly people who need intensive care in hospitals and nursing homes in Thailand.

Keywords: activity recognition, accelerometer, fall, gyroscope, smartphone

Procedia PDF Downloads 692

Authors: Abraham Finkelman

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Computerized Surgical Guides have been proven to be a predictable way to perform dental implants, with a relatively high accuracy in comparison to a treatment plan. When using the CSG Bone supported, it allows us to make the necessary changes of the hard tissue prior to the implant placement and after the implant placement. The CSG gives us an accurate position for the drilling, and during the implant placement it allows us to alter the vertical position of the implant altering the final position of the abutment and avoiding any risk of any damage to the adjacent anatomical structures. Any Changes required to the bone level can be done prior to the fixation of the CSG using a reduction guide, which incur extra surgical fees and the need of a second surgical guide. Any changes of the bone level after the implant placement are at the risk of damaging the implant neck surface. The technique consists of a universal system that allows us to remove the excess bone around the implant sockets prior to the implant placement which then enables us to place the implant in the vertical position with accuracy as planned with the CSG. The systems consist of a hollow pin of different sizes and diameters. Depending on the implant system that we are using. Length sizes are from 6mm-16mm and a diameter of 2.6mm-4.8mm. Upon the completion of the drilling, the pin is then inserted into the implant socket-using the insertion tool. Once the insertion tool has unscrewed the pin, we can continue with the bone reduction. The bone reduction can be done using conventional methods upon the removal of all the excess bone around the pin. The insertion tool is then screwed into the pin and the pin is then removed. We now, have the new bone level at the crest of the implant socket which is our mark for the vertical position of the implant. In some cases, when we are locating the implant very close to anatomical structures, any form of deviation to the vertical position of the implant during the surgery, can cause damage to such anatomical structures, creating irreversible damages such as paresthesia or dysesthesia of the mandibular nerve. If we are planning for immediate loading and we have done our temporary restauration in base of our computerized plan, deviation in the vertical position of the implant will affect the position of the abutment, affecting the accuracy of the temporary prosthesis, extending the working time till we adapt the prosthesis to the new position.

Keywords: bone reduction, computer aided navigation, dental implant placement, surgical guides

Procedia PDF Downloads 332

Authors: S. C. Lin, W. F. Hung, W. W. Cheng

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Teacher empowerment was regarded as investing teachers with the right to participate in the determination of school goals and policies and to exercise professional judgment about what and how to teach. Professional commitment was considered as a person’s belief in and acceptance of the values of his or her chosen occupation or line of work, and a willingness to maintain membership in that occupation. An effective school has been defined as one in which students’ progress further than might be expected from consideration of its intake. An effective school thus adds extra value to its students' outcomes, in comparison with other schools serving similar intakes. A number of literature from various countries explored that teacher empowerment and professional commitment significantly influenced school effectiveness. However, there lacked more empirical studies to examine the relationships among them. Hence, this study was to explore the relationships among teacher empowerment, professional commitment and school effectiveness in junior high schools in Taiwan. Samples were seven hundred and five junior high school teachers selected from Taichung City, Changhua County and Nantou County. Questionnaire was applied to collect data. Data were analyzed by using descriptive statistics, t-test, one-way ANOVA, Pearson’s product-moment correlation, and multiple regression analysis. The findings of this study were as follows: First, the overall performances of teachers’ perceptions of teacher empowerment, teacher professional commitment and school effectiveness were above average. Second, the teachers’ perceptions of teacher empowerment were significant different in gender, designated duty, and school size. Third, the teachers’ perceptions of teacher professional commitment were significant different in gender, designated duty, and school size. Fourth, the teachers’ perceptions of school effectiveness were significant different in designated duty. Fifth, teacher empowerment was mid-positively correlation by teacher professional commitment. Sixth, there was mid-positively correlation between teacher empowerment and school effectiveness. Seventh, there was mid-positively correlation between teacher professional commitment and school effectiveness. Eighth, Teacher empowerment and professional commitment could significantly predict school effectiveness. Based on the findings of this study, the study proposed some suggestions for educational authorities, schools, teachers, and future studies as well.

Keywords: junior high school teacher, teacher empowerment, teacher professional commitment, school effectiveness

Procedia PDF Downloads 462

Authors: Konstantinos Tolidis

Abstract:

The mountains are amongst the most fragile environments in the world. The world’s mountain areas cover 24% of the Earth’s land surface and are home to 12% of the global population. A further 14% of the global population is estimated to live in the vicinity of their surrounding areas. As urbanization continues to increase in the world, the mountains are also key centers for recreation and tourism; their attraction is often heightened by their remarkably high levels of biodiversity. Due to the fact that the features in mountain areas vary spatially (development degree, human geography, socio-economic reality, relations of dependency and interaction with other areas-regions), the spatial planning on these areas consists of a crucial process for preserving the natural, cultural and human environment and consists of one of the major processes of an integrated spatial policy. This research has been focused on the spatial decision problem of land use allocation optimization which is an ordinary planning problem on the mountain areas. It is a matter of fact that such decisions must be made not only on what to do, how much to do, but also on where to do, adding a whole extra class of decision variables to the problem when combined with the consideration of spatial optimization. The utility of optimization as a normative tool for spatial problem is widely recognized. However, it is very difficult for planners to quantify the weights of the objectives especially when these are related to mountain areas. Furthermore, the land use allocation optimization problems at mountain areas must be addressed not only by taking into account the general development objectives but also the spatial objectives (e.g. compactness, compatibility and accessibility, etc). Therefore, the main research’s objective was to approach the land use allocation problem by utilizing a hybrid meta-heuristic optimization technique tailored to the mountain areas’ spatial characteristics. The results indicates that the proposed methodological approach is very promising and useful for both generating land use alternatives for further consideration in land use allocation decision-making and supporting spatial management plans at mountain areas.

Keywords: multiobjective land use allocation, mountain areas, spatial planning, spatial decision making, meta-heuristic methods

Procedia PDF Downloads 347

Authors: Luvindran Sugumaran, Mohd Nashrul Mohd Zubir, Kazi Md Salim Newaz, Tuan Zaharinie Tuan Zahari, Suazlan Mt Aznam, Aiman Mohd Halil

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Due to the rapid advancement of integrated circuits and the increasing trend towards miniaturizing electronic devices, the amount of heat produced by electronic devices has consistently exceeded the maximum limit for heat dissipation. Currently, the two-phase cooling technique based on phase change pool boiling heat transfer has received a lot of attention because of its potential to fully utilize the latent heat of the fluid and produce a highly effective heat dissipation capacity while keeping the equipment's operating temperature within an acceptable range. There are numerous strategies available for the alteration of heating surfaces, but to find the best, simplest, and most dependable one remains a challenge. Lately, surface texturing via laser ablation has been used in a variety of investigations, demonstrating its significant potential for enhancing the pool boiling heat transfer performance. In this research, the nucleate pool boiling heat transfer performance of laser-textured copper surfaces of different patterns was investigated. The bare copper surface serves as a reference to compare the performance of laser-structured surfaces. It was observed that the heat transfer coefficients were increased with the increase of surface area ratio and the ratio of the peak-to-valley height of the microstructure. Laser machined grain structure produced extra nucleation sites, which ultimately caused the improved pool boiling performance. Due to an increase in nucleation site density and surface area, the enhanced nucleate boiling served as the primary heat transfer mechanism. The pool boiling performance of the laser-textured copper surfaces is superior to the bare copper surface in all aspects.

Keywords: heat transfer coefficient, laser texturing, micro structured surface, pool boiling

Procedia PDF Downloads 92

Authors: Bin Feng, Zhulin Zong

Abstract:

Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.

Keywords: radar system, fault diagnosis, deep learning, radar fault

Procedia PDF Downloads 92