Search results for: evolutionary cultural algorithm

Authors: Atinkut Atinafu Yilma, Eyob Messele Sefene

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Anomaly detection is one of the essential mechanisms to control and reduce production loss, especially in today's smart manufacturing. Quick anomaly detection aids in reducing the cost of production by minimizing the possibility of producing defective products. However, developing an anomaly detection model that can rapidly detect a production change is challenging. This paper proposes Gated Recurrent Unit (GRU) combined with Random Forest (RF) to detect anomalies in the production process in real-time quickly. The GRU is used as a feature detector, and RF as a classifier using the input features from GRU. The model was tested using various synthesis and real-world datasets against benchmark methods. The results show that the proposed GRU-RF outperforms the benchmark methods with the shortest time taken to detect anomalies in the production process. Based on the investigation from the study, this proposed model can eliminate or reduce unnecessary production costs and bring a competitive advantage to manufacturing industries.

Keywords: anomaly detection, multivariate time series data, smart manufacturing, gated recurrent unit network, random forest

Procedia PDF Downloads 119

Authors: Kaia Brose

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The Parthenon occupies a dominating position on top of the ancient Acropolis of Athens, giving the Greek world a clear view of the ancient city’s patron goddess, Athena. The technically refined nature of the fifth century B.C. temple, combined with the colossal statue of Athena Parthenos, has been considered a monumental tribute to Athenian hubris. The Parthenon specifically calls on Athena’s main personage as a goddess who creates and advances culture, a notion the Athenians specifically portray in her epithet as a parthenos or a virgin. This paper builds off of the modern research of R. F. Hooker, C. J. Herington and Susan Deacy, who discuss the evolution of Athena and distinguishes the significance of the Parthenos epithet attached to Athena on the Acropolis. The use of Parthenos suggests the Athenians intentionally portrayed a virginal Athena to reaffirm the presence of the patriarchy within the Greek world of the fifth century, specifically seen in the scene of Athena’s birth on the Parthenon. The suggestive subjugation of Athena to the patriarchy serves to establish a patriarchal presence within the Acropolis while simultaneously exposing the rise of Athenian hubris following the Persian War. Various elements of the Parthenon, from its pediments to the monumental statue inside, show Athena as a goddess reduced to mere womanhood in the presence of the patriarchy, a function that emphasizes the Athenian hubris displayed throughout the Parthenon.

Keywords: ancient history, greek mythology, women studies, religion

Procedia PDF Downloads 241

Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

Procedia PDF Downloads 199

Authors: Omer Cahana, Ofer Levi, Maya Herman

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

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

Procedia PDF Downloads 91

Authors: L. P. K. Liyanage, N. R. P. Withana, A. L. Sandika

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The purpose of this study was to determine tourist and community perception-based sustainable tourism indicators as well as Human Pressure Index (HPI) and Tourist Activity Index (TAI). Study was carried out in Sinharaja forest which is considered as one of the major eco-tourism destination in Sri Lanka. Data were gathered using a pre-tested semi-structured questionnaire as well as records from Forest department. Convenient sampling technique was applied. For the majority of issues, the responses were obtained on multi-point Likert-type scales. Visual portrayal was used for display analyzed data. The study revealed that the host community of the Kudawa gets many benefits from tourism. Also, tourism has caused negative impacts upon the environment and community. The study further revealed the need of proper waste management and involvement of local cultural events for the tourism business in the Kudawa conservation center. The TAI, which accounted to be 1.27 and monthly evolution of HPI revealed that congestion can be occurred in the Sinharaja rainforest during peak season. The results provide useful information to any party involved with tourism planning anywhere, since such attempts would be more effective once the people’s perceptions on these aspects are taken into account.

Keywords: Kudawa Conservation Center, Sinharaja World Heritage Rainforest, sustainability indicators, community perception

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Authors: Yamam Abuzinadah

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Mental health literacy has become a very influential topic around the world due to the increase of mental health issues that have been reported through national research and surveys. Mental health literacy refers to the awareness, attitudes, beliefs, knowledge and skills when dealing with mental illness. This research explores mental health literacy in the Arabic and the ways culture informs perceptions of mental health in general. Also, the impact of mental health literacy on: help-seeking attitudes, relationships and community interactions. The outcomes of this research will contribute to raising mental health awareness among the Arabic community, develop and enhance mental health service provision and explore new ideas in regards to elevating mental health literacy in the Arabic community. This research aims to explore attitudes, beliefs, perspective, values and perceptions toward mental health in general among the Arabic community. It will also aim to highlight the factors contributing to theses beliefs, perspective, value and perception and accordingly the role these factors play in regards to awareness, services access, recovery and care provided from the family and the community. This thesis will aim to reflect a detailed theorisation and exploration of: (1) The impact of cultural factors on mental health literacy ie. attitudes, beliefs, knowledge and skills. (2) The ways culture informs perceptions of mental health literacy. (3) The impact of mental health literacy on: help-seeking behaviors, and relationships and community interactions.

Keywords: Arab, mental health, literacy, awareness

Procedia PDF Downloads 432

Authors: Hao Mi, Ming Yang, Tian-yue Yang

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Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: remote monitoring, non-destructive testing, embedded Linux system, image processing

Procedia PDF Downloads 224

Authors: Kingsley Nnaemeka Ogbu, Constantine Mbajiorgu

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Vegetation has a marked effect on runoff and has become an important component in hydrologic model. The watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. Our approach is to develop a crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate Model (EPIC) model. This paper describes the development of a single crop growth model which has the capability of simulating all crops using unique parameter values for each crop. Simulated crop growth processes will reflect the vegetative seasonality of the natural watershed system. An existing model was employed for evaluating vegetative resistance by hydraulic and vegetative parameters incorporated into the WRM model. The improved WRM model will have the ability to evaluate the seasonal variation of the vegetative roughness coefficient with depth of flow and further enhance the hydrologic model’s capability for accurate hydrologic studies

Keywords: crop yield, roughness coefficient, PAR, WRM model

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Authors: Fuling Deng

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Today’s children are frequently exposed to multimodal texts that adopt diverse modes to communicate myriad meanings within different cultural contexts. To respond to the new textual landscape, scholars have considered new literacy theories which propose picturebooks as important educational resources. Picturebooks are multimodal, with their meaning conveyed through the synchronisation of multiple modes, including linguistic, visual, spatial, and gestural acting as access to multimodal literacy. Picturebooks have been popular reading materials in primary educational settings in China. However, often viewed as “easy” texts directed at the youngest readers, picturebooks remain on the margins of Chinese upper primary classrooms, where they are predominantly used for linguistic tasks, with little value placed on their multimodal affordances. Practices with picturebooks in the upper grades in Chinese primary schools also encounter many challenges associated with the curation of texts for use, designing curriculum, and assessment. To respond to these issues, a qualitative study was conducted with a community of Chinese primary teachers using multi-methods such as interviews, focus groups, and documents. The findings showed the impact of the teachers’ increased awareness of picturebooks' multimodal affordances on their pedagogical decisions in using picturebooks as educational resources in upper primary classrooms.

Keywords: picturebook education, multimodal literacy, teachers' response to contemporary picturebooks, community of practice

Procedia PDF Downloads 136

Authors: Iassen Vanev

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The two objectives of the research are to enlighten the significant role of Venice as an agent of “transporting” the features of the Italian Renaissance in Cyprus, as well as to assess the countercheck that the Cypriots might have exercised in opposition to the Venetian influence and to what extent they left they own mark on the Serenissima and influenced the Venetian Renaissance. This will be achieved in the backdrop of Venice’s interaction and dichotomy with the Ottoman Empire during the Venetian rule over the island (1474-1571). The research is carried out within the interdisciplinary domains of architecture, visual arts, and literature. Such studies have already been carried out extensively for other ex-Byzantine centers and consecutively Venetian possessions, like, for example Crete. Cyprus, however, has largely been left on the periphery of scholarly interest, and the author will strive to fill this gap as much as possible. The nature of much of the Venetian Renaissance Architecture on Cyprus was dictated by the Ottoman threat to the island. On the other hand, the vast majority of the Cypriot Renaissance literary production was done in Italy, a little before and after the Ottoman conquest of Cyprus in 1571. This conquest was an enormous crisis for both Venetians and Cypriots, and it led to migrations, mutual Venetian-Cypriot interaction in arts and literature, the formation of cultural landscapes, the shaping of Cypriot identity/ies, dialect and diasporas in Italy, etc., which makes the current research very important.

Keywords: architecture, arts, Cyprus, gender, identity, linguistics, Ottoman Empire, Venice

Procedia PDF Downloads 66

Authors: Nosakhare Enoma, Alphose Zingoni

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The requirements for various toroidal shell forms are increasing due to new applications, available storage space and the consideration of appearance. Because of the complexity of some of these structural forms, the finite element method is nowadays mainly used for their analysis, even for simple static studies. This paper presents an easy-to-use analytical algorithm for pressurized multi-segmented toroidal shells of revolution. The membrane solution, which acts as a particular solution of the bending-theory equations, is developed based on membrane theory of shells, and a general approach is formulated for quantifying discontinuity effects at the shell junctions using the well-known Geckeler’s approximation. On superimposing these effects, and applying the ensuing solution to the problem of the pressurized toroid with four segments, closed-form stress results are obtained for the entire toroid. A numerical example is carried out using the developed method. The analytical results obtained show excellent agreement with those from the finite element method, indicating that the proposed method can be also used for complementing and verifying FEM results, and providing insights on other related problems.

Keywords: bending theory of shells, membrane hypothesis, pressurized toroid, segmented toroidal vessel, shell analysis

Procedia PDF Downloads 320

Authors: Vesna Lukovic

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Online platforms as new business models play an important role in today’s economy and the functioning of the EU’s internal market. In the travel industry, algorithms used by online platforms for short-stay accommodation provide suggestions and price information to travelers. Those suggestions and recommendations are displayed in search results via recommendation (ranking) systems. There has been a growing consensus that the current legal framework was not sufficient to resolve problems arising from platform practices. In order to enhance the potential of the EU’s Single Market, smaller businesses should be protected, and their rights strengthened vis-à-vis large online platforms. The Regulation (EU) 2019/1150 of the European Parliament and of the Council on promoting fairness and transparency for business users of online intermediation services aims to level the playing field in that respect. This research looks at Airbnb through the lenses of this regulation. The research explores key determinants and finds that although regulation is an important step in the right direction, it is not enough. It does not entail sufficient clarity obligations that would make online platforms an intermediary service which both accommodation providers and travelers could use with ease.

Keywords: algorithm, online platforms, ranking, consumers, EU regulation

Procedia PDF Downloads 130

Authors: Felix Givois, Nicolas R. Gauger, Matthias Kabel

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The efficient digital material characterization is of great interest to many fields of application. It consists of the following three steps. First, a 3D reconstruction of 2D scans must be performed. Then, the resulting gray-value image of the material sample is enhanced by image processing methods. Finally, partial differential equations (PDE) are solved on the segmented image, and by averaging the resulting solutions fields, effective properties like stiffness or conductivity can be computed. Due to the high resolution of current CT images, the latter is typically performed with matrix-free solvers. Among them, a solver that uses the explicit formula of the Green-Eshelby operator in Fourier space has been proposed by Moulinec and Suquet. Its algorithmic, most complex part is the Fast Fourier Transformation (FFT). In our talk, we will discuss the potential quantum advantage that can be obtained by replacing the FFT with the Quantum Fourier Transformation (QFT). We will especially show that the data transfer for noisy intermediate-scale quantum (NISQ) devices can be improved by using appropriate boundary conditions for the PDE, which also allows using semi-classical versions of the QFT. In the end, we will compare the results of the QFT-based algorithm for simple geometries with the results of the FFT-based homogenization method.

Keywords: most likelihood amplitude estimation (MLQAE), numerical homogenization, quantum Fourier transformation (QFT), NISQ devises

Procedia PDF Downloads 78

Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

Procedia PDF Downloads 73

Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

Procedia PDF Downloads 134

Authors: M. Iruleswari, A. Jeyapaul Murugan

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Most fundamental part used in many Digital Signal Processing (DSP) application is a Finite Impulse Response (FIR) filter because of its linear phase, stability and regular structure. Designing a high-speed and hardware efficient FIR filter is a very challenging task as the complexity increases with the filter order. In most applications the higher order filters are required but the memory usage of the filter increases exponentially with the order of the filter. Using multipliers occupy a large chip area and need high computation time. Multiplier-less memory-based techniques have gained popularity over past two decades due to their high throughput processing capability and reduced dynamic power consumption. This paper describes the design and implementation of highly efficient Look-Up Table (LUT) based circuit for the implementation of FIR filter using Distributed arithmetic algorithm. It is a multiplier less FIR filter. The LUT can be subdivided into a number of LUT to reduce the memory usage of the LUT for higher order filter. Analysis on the performance of various filter orders with different address length is done using Xilinx 14.5 synthesis tool. The proposed design provides less latency, less memory usage and high throughput.

Keywords: finite impulse response, distributed arithmetic, field programmable gate array, look-up table

Procedia PDF Downloads 457

Authors: Alexander A. Stakheev, Larisa V. Samokhvalova, Sergey K. Zavriev

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Fusarium fungi are among the most devastating plant pathogens distributed all over the world. Significant reduction of grain yield and quality caused by Fusarium leads to multi-billion dollar annual losses to the world agricultural production. These organisms can also cause infections in immunocompromised persons and produce the wide range of mycotoxins, such as trichothecenes, fumonisins, and zearalenone, which are hazardous to human and animal health. Identification of Fusarium fungi based on the morphology of spores and spore-forming structures, colony color and appearance on specific culture media is often very complicated due to the high similarity of these features for closely related species. Modern Fusarium taxonomy increasingly uses data of crossing experiments (biological species concept) and genetic polymorphism analysis (phylogenetic species concept). A number of novel Fusarium sibling species has been established using DNA barcoding techniques. Species recognition is best made with the combined phylogeny of intron-rich protein coding genes and ribosomal DNA sequences. However, the internal transcribed spacer of (ITS), which is considered to be universal DNA barcode for Fungi, is not suitable for genus Fusarium, because of its insufficient variability between closely related species and the presence of non-orthologous copies in the genome. Nowadays, the translation elongation factor 1 alpha (TEF1α) gene is the “gold standard” of Fusarium taxonomy, but the search for novel informative markers is still needed. In this study, we used two novel DNA markers, frataxin (FXN) and heat shock protein 90 (HSP90) to discover phylogenetic relationships between Fusarium species. Multilocus phylogenetic analysis based on partial sequences of TEF1α, FXN, HSP90, as well as intergenic spacer of ribosomal DNA (IGS), beta-tubulin (β-TUB) and phosphate permease (PHO) genes has been conducted for 120 isolates of 19 Fusarium species from different climatic zones of Russia and neighboring countries using maximum likelihood (ML) and maximum parsimony (MP) algorithms. Our analyses revealed that FXN and HSP90 genes could be considered as informative phylogenetic markers, suitable for evolutionary and taxonomic studies of Fusarium genus. It has been shown that PHO gene possesses more variable (22 %) and parsimony informative (19 %) characters than other markers, including TEF1α (12 % and 9 %, correspondingly) when used for elucidating phylogenetic relationships between F. avenaceum and its closest relatives – F. tricinctum, F. acuminatum, F. torulosum. Application of novel DNA barcodes confirmed the fact that F. arthrosporioides do not represent a separate species but only a subspecies of F. avenaceum. Phylogeny based on partial PHO and FXN sequences revealed the presence of separate cluster of four F. avenaceum strains which were closer to F. torulosum than to major F. avenaceum clade. The strain F-846 from Moldova, morphologically identified as F. poae, formed a separate lineage in all the constructed dendrograms, and could potentially be considered as a separate species, but more information is needed to confirm this conclusion. Variable sites in PHO sequences were used for the first-time development of specific qPCR-based diagnostic assays for F. acuminatum and F. torulosum. This work was supported by Russian Foundation for Basic Research (grant № 15-29-02527).

Keywords: DNA barcode, fusarium, identification, phylogenetics, taxonomy

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Authors: Aisami A., Z. A. Adamu, Lawan Bulama

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Due to growing issues of crude oil pollution in both marine and terrestrial environments, Billions to Trillions of US Dollars were spent over the years for the treatment of this spill. There is an urgent need for effective bioremediation strategies. This current study focuses on the isolation and characterization of a crude oil-degrading bacterium from hydrocarbon-contaminated soil samples. Soil samples were collected from an oil spill site and subjected to enrichment culture techniques in a mineral salt medium supplemented with crude oil as the singular carbon source. The isolates were screened for their crude oil-degrading capabilities using gravimetric analysis. The most efficient isolation was identified through 16S rRNA gene sequencing. Cultural and physical conditions such pH, temperature salinity and crude oil concentrations were optimized. The isolates showed significant crude oil degradation efficiency, reducing oil concentration (2.5%) by 75% within 15 days of incubation. The strain was identified as Providencia sp. through molecular characterization, the sequence was deposited at the NCBI Genbank with accession number MN880494. The bacterium exhibited optimal growth at 32.5°C, pH 7.0 to 7.5, and in the presence of 1.5% (w/v) NaCl. The isolated Providencia sp. shows encouraging potential for bioremediation of crude oil-contaminated environments. This study successfully isolated and characterized a crude oil-degrading Providencia sp., highlighting its potential in bioremediation.

Keywords: crude oil degradation, providencia sp., bioremediation, hydrocarbon utilization, environmental pollution.

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Authors: Rungrot Trongsakul

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This paper was aimed to study the model of education cooperation during Thailand and neighbor countries, especially the countries which the territory-cohesion border with Thailand used “Soft Power” to enhance the good relationship. This research employed qualitative method, analyzed and synthesized the content of cooperation projects, policies, laws, relevant theories, relevant research papers and documents and used SWOT analysis. The research findings revealed that Thailand’s education cooperation projects with neighbor countries had two characteristics: 1) education cooperation projects/programs were a part in economic cooperation projects, and 2) there were directly education cooperation projects. The suggested education cooperation model was based on the concept of “Soft Power”, thus the determination of action plans or projects as key factors of public and private organizations should be based on sincere participation among people, communities and relevant organizations of the neighbor countries. Adoption of education-cultural exchange, learning and sharing process is a key to strengthen good relationship of the countries’ cooperation. The roles of education in this included sharing and acceptance of culture and local wisdom, human resource development, knowledge management, integration and networking building could enhance relationship between agents of related organizations of Thailand and neighbors countries.

Keywords: education, soft-power, relationship, cooperation, Thailand neighboring countries

Procedia PDF Downloads 359

Authors: Aida Humaira

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Aromatic plants hold an honored place in Indonesian culture, where they are deeply intertwined with everyday customs, rituals, and ceremonies. From the fragrant herbs and spices used in cooking to the aromatic incense burned in temples and homes, aromatic plants play multifaceted roles in enhancing well-being and fostering spiritual connections. These plants are valued not only for their pleasant aromas but also for their medicinal properties and symbolic meanings. This article aims to summarize the role of aromatic plants in Indonesian traditional culture, medicine, spirituality, and how it shifted to a modern version of aromatherapy. Traditional Indonesian medicine, known as Jamu, relies heavily on aromatic plants for their therapeutic benefits. Herbalists and traditional healers use a wide array of aromatic herbs, roots, barks, and resins to treat various ailments, ranging from digestive disorders and respiratory infections to skin conditions and reproductive issues. In conclusion, aromatic plants represent a cultural treasure with multifaceted uses and significance deeply rooted in Indonesia’s tradition. From their medicinal properties to their spiritual symbolism, these plants embody the interconnection of culture, nature, and well-being. Further research and collaboration are needed to document and preserve traditional knowledge surrounding Indonesian aromatic plants and ensure their continued recognition and sustainable utilization in the face of modernization and environmental challenges.

Keywords: aromatic plants, indonesia, Jamu, traditional medicine

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Authors: Olivia Kurucz

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Gamification is one of the outstanding new methodological possibilities of urban public participation processes to make the most informed decision possible for the future steps of urban development. This paper examines four Hungarian experimental projects in which gamified tools were applied during the public participation progresses by the Urban Future Laboratory (UFLab) research workshop of Budapest University of Technology and Economics (BUTE). The recently implemented future planning projects (in the cities of Pécel, Kistarcsa, Budapest, and Salgótarján) were initiated by various motives, but the multi-stakeholder dialogues were facilitated through physical gamified tools in all cases. Based on the urban gamification hypothesis, the use of gamified tools supported certain steps of participatory processes in several aspects: it helped to increase the attractiveness of public events, to create a more informal atmosphere, to ensure equal conditions for actors, to recall a design mindset, to bridge contrasting social or cultural differences, to fix opinions and to assist dialogue between city actors, designers, and residents. This statement is confirmed by assessing the applied tools, analyzing the case studies, and comparing them to perceive their effects and interrelations.

Keywords: experimental projects, future planning, gamification, gamified tools, Hungary, public participation, UFLab, urban gamification

Procedia PDF Downloads 138

Authors: T. Wanyama, B. Far

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Automatic irrigation systems usually conveniently protect landscape investment. While conventional irrigation systems are known to be inefficient, automated ones have the potential to optimize water usage. In fact, there is a new generation of irrigation systems that are smart in the sense that they monitor the weather, soil conditions, evaporation and plant water use, and automatically adjust the irrigation schedule. In this paper, we present an agent based smart irrigation system. The agents are built using a mix of commercial off the shelf software, including MATLAB, Microsoft Excel and KEPServer Ex5 OPC server, and custom written code. The Irrigation Scheduler Agent uses fuzzy logic to integrate the information that affect the irrigation schedule. In addition, the Multi-Agent system uses Open Platform Connectivity (OPC) technology to share data. OPC technology enables the Irrigation Scheduler Agent to communicate over the Internet, making the system scalable to a municipal or regional agent based water monitoring, management, and optimization system. Finally, this paper presents simulation and pilot installation test result that show the operational effectiveness of our system.

Keywords: community water usage, fuzzy logic, irrigation, multi-agent system

Procedia PDF Downloads 298

Authors: Qintao Shen, Lei Luo, Jun Ma, Jie Yu, Qingbo Wu, Yongqi Ma, Zhengji Liu

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Control Flow Integrity (CFI) is one of the most promising technique to defend Code-Reuse Attacks (CRAs). Traditional CFI Systems and recent Context-Sensitive CFI use coarse control flow graphs (CFGs) to analyze whether the control flow hijack occurs, left vast space for attackers at indirect call-sites. Coarse CFGs make it difficult to decide which target to execute at indirect control-flow transfers, and weaken the existing CFI systems actually. It is an unsolved problem to extract CFGs precisely and perfectly from binaries now. In this paper, we present an algorithm to get a more precise CFG from binaries. Parameters are analyzed at indirect call-sites and functions firstly. By comparing counts of parameters prepared before call-sites and consumed by functions, targets of indirect calls are reduced. Then the control flow would be more constrained at indirect call-sites in runtime. Combined with CCFI, we implement our policy. Experimental results on some popular programs show that our approach is efficient. Further analysis show that it can mitigate COOP and other advanced attacks.

Keywords: contex-sensitive, CFI, binary analysis, code reuse attack

Procedia PDF Downloads 323

Authors: Omer Cahana, Maya Herman, Ofer Levi

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Magnetic resonance imaging (MRI) is a long medical scan that stems from a long acquisition time. This length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach, such as compress sensing (CS) or parallel imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. In order to achieve that, two properties have to exist: i) the signal must be sparse under a known transform domain, ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm needs to be applied to recover the signal. While the rapid advance in the deep learning (DL) field, which has demonstrated tremendous successes in various computer vision task’s, the field of MRI reconstruction is still in an early stage. In this paper, we present an extension of the state-of-the-art model in MRI reconstruction -VarNet. We utilize VarNet by using dilated convolution in different scales, which extends the receptive field to capture more contextual information. Moreover, we simplified the sensitivity map estimation (SME), for it holds many unnecessary layers for this task. Those improvements have shown significant decreases in computation costs as well as higher accuracy.

Keywords: MRI, deep learning, variational network, computer vision, compress sensing

Procedia PDF Downloads 162

Authors: Filip Jaroš

Abstract:

The Swiss scholar Adolf Portmann (1897-1982) was an outstanding figure in the history of biology and the philosophy of the life sciences. Portmann’s biological theory is primarily focused on the problem of animal form (Gestalt), and it poses a significant counterpart to neo-Darwinian theories about the explanatory primacy of a genetic level over the outer appearance of animals. Besides that, Portmann’s morphological studies related to species-specific ontogeny and the influence of environmental surroundings can be classified as the antecedents of contemporary synthetic approaches such as “eco-evo-devo, “extended synthesis or biosemiotics. The most influential of Portmann’s concepts up to the present is his thesis of a social womb (Soziale Mutterschos): human children are born physiologically premature in comparison with other primates, and they find a second womb in a social environment nurturing their healthy development. It is during the first year of extra-uterine life when a specific human nature is formed, characterized by the strong tie between an individual and a broader historical, cultural whole. In my paper, I will closely analyze: a) the historical coordinates of Portmann’s philosophy of the life sciences (e.g., the philosophical anthropology of A. Gehlen, H. Plessner, and their concept of humans as beings “open to the world”), b) the relation of Portmann’s concept of the social womb to contemporary theories of infant birth evolution.

Keywords: adolf portmann, extended synthesis, philosophical anthropology, social womb

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Authors: Sankalp Devanand, Prateek Agasimani, Shamith V. S., Rohith Neeraje

Abstract:

Machine translation has witnessed significant advancements in recent years, but the translation of languages with distinct linguistic characteristics, such as English and Sanskrit, remains a challenging task. This research presents the development of a dedicated English-to-Sanskrit machine translation model, aiming to bridge the linguistic and cultural gap between these two languages. Using a variety of natural language processing (NLP) approaches, including FastText embeddings, this research proposes a thorough method to improve word meaning retrieval. Data preparation, part-of-speech tagging, dictionary searches, and transliteration are all included in the methodology. The study also addresses the implementation of an interpreter pattern and uses a word similarity task to assess the quality of word embeddings. The experimental outcomes show how the suggested approach may be used to enhance word meaning retrieval tasks with greater efficacy, accuracy, and adaptability. Evaluation of the model's performance is conducted through rigorous testing, comparing its output against existing machine translation systems. The assessment includes quantitative metrics such as BLEU scores, METEOR scores, Jaccard Similarity, etc.

Keywords: machine translation, English to Sanskrit, natural language processing, word meaning retrieval, fastText embeddings

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Authors: Peter Shi

Abstract:

Algorithmic trading is a rapidly expanding domain within quantitative finance, constituting a substantial portion of trading volumes in the US financial market. The demand for rigorous and robust mathematical theories underpinning these trading algorithms is ever-growing. In this study, the author establishes a new stock market model that integrates the Efficient Market Hypothesis and the statistical arbitrage. The model, for the first time, finds probabilistic relations between the rational price and the market price in terms of the conditional expectation. The theory consequently leads to a mathematical justification of the old market adage: buy-low and sell-high. The thresholds for “low” and “high” are precisely derived using a max-min operation on Bayes’s error. This explicit connection harmonizes the Efficient Market Hypothesis and Statistical Arbitrage, demonstrating their compatibility in explaining market dynamics. The amalgamation represents a pioneering contribution to quantitative finance. The study culminates in comprehensive numerical tests using historical market data, affirming that the “buy-low” and “sell-high” algorithm derived from this theory significantly outperforms the general market over the long term in four out of six distinct market environments.

Keywords: efficient market hypothesis, behavioral finance, Bayes' decision, algorithmic trading, risk control, stock market

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Authors: Vladimir Simovic, Matija Varga, Tonco Marusic

Abstract:

This research paper shows matrix technology models and examples of information systems in education (in the Republic of Croatia and in the Germany) in support of business, education (when learning and teaching) and e-learning. Here we researched and described the aims and objectives of the main process in education and technology, with main matrix classes of data. In this paper, we have example of matrix technology with detailed description of processes related to specific data classes in the processes of education and an example module that is support for the process: ‘Filling in the directory and the diary of work’ and ‘evaluation’. Also, on the lower level of the processes, we researched and described all activities which take place within the lower process in education. We researched and described the characteristics and functioning of modules: ‘Fill the directory and the diary of work’ and ‘evaluation’. For the analysis of the affinity between the aforementioned processes and/or sub-process we used our application model created in Visual Basic, which was based on the algorithm for analyzing the affinity between the observed processes and/or sub-processes.

Keywords: designing, education management, information systems, matrix technology, process affinity

Procedia PDF Downloads 439

Authors: Guillaume Morin-Duponchelle, Ahmed Nait Chabane, Benoit Zerr, Pierre Schoesetters

Abstract:

Industrial inspection can be achieved through robotic systems, allowing visual and chemical servoing. A popular scheme for visual servo-controlled robotic is the image-based servoing sys-tems. In this paper, an approach of visual and chemical servoing of a hexapod robot using a visual and chemical Jacobian matrix are proposed. The basic idea behind the visual Jacobian matrix is modeling the differential relationship between the camera system and the robotic control system to detect and track accurately points of interest in confined environments. This approach allows the robot to easily detect and navigates to the QR code or seeks a gas source localization using surge cast algorithm. To track the QR code target, a visual servoing based on Jacobian matrix is used. For chemical servoing, three gas sensors are embedded on the hexapod. A Jacobian matrix applied to the gas concentration measurements allows estimating the direction of the main gas source. The effectiveness of the proposed scheme is first demonstrated on simulation. Finally, a hexapod prototype is designed and built and the experimental validation of the approach is presented and discussed.

Keywords: chemical servoing, hexapod robot, Jacobian matrix, visual servoing, navigation

Procedia PDF Downloads 125

Authors: Italo Luigi de Paoli

Abstract:

The use of synthetic soil for the vegetables cultivation in conditions of limited water consumption The separate collection of urban organic waste and green waste for the countries of the European Union averages 100 kg / inhabitant x year with an annual growth of about 10%. The production of quality compost averages 38% - 40% of the production of organic waste material. Most of the compost produced is used as an organic soil improver in those nutrient-poor soils in order to improve its quality. This study seeks to enhance the production of quality compost by creating a synthetic soil, where the percentages of compost on average oscillate between 50% and 60% in which, with appropriate precautions, different species of horticultural can be grown in conditions of high environmental safety without the use of pesticides and with a consumption of water used for irrigation limited to the actual evaporation of the plants. The project started in 2018 and is still ongoing, confirms its validity through a series of different horticultural productions, especially if this technology is applied where the availability of land suitable for the cultivation of vegetables is limited and where the use of water for irrigation represents a cultural criticality. Furthermore, the creation of "open field" crops, together with their automation, represents a further possibility in the concrete development of such technologies, giving the final product organoleptic characteristics equal if not superior to what the market offers today for human nutrition.

Keywords: water scarcity, compost, vegetable foods, syntetic soil

Procedia PDF Downloads 173