1,791 research outputs found

    Three-dimensional topology-based analysis segments volumetric and spatiotemporal fluorescence microscopy

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    Image analysis techniques provide objective and reproducible statistics for interpreting microscopy data. At higher dimensions, three-dimensional (3D) volumetric and spatiotemporal data highlight additional properties and behaviors beyond the static 2D focal plane. However, increased dimensionality carries increased complexity, and existing techniques for general segmentation of 3D data are either primitive, or highly specialized to specific biological structures. Borrowing from the principles of 2D topological data analysis (TDA), we formulate a 3D segmentation algorithm that implements persistent homology to identify variations in image intensity. From this, we derive two separate variants applicable to spatial and spatiotemporal data, respectively. We demonstrate that this analysis yields both sensitive and specific results on simulated data and can distinguish prominent biological structures in fluorescence microscopy images, regardless of their shape. Furthermore, we highlight the efficacy of temporal TDA in tracking cell lineage and the frequency of cell and organelle replication

    Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality

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    As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach. Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties. Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel

    Effect of Cyber Vulnerabilities on the Adoption of Self-Driving Vehicles ‚Äď A Review

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    One of the leading disruptive technologies in the upcoming technological revolution is Self-Driving vehicles. However, the absence of security is the greatest obstacle to adoption. This study looks at how cybersecurity impacts the adoption of driverless cars. The purpose of this paper is to perform a literature review supporting the in-depth analysis of cybersecurity and its impacts on the slower adoption rate of Self-Driving Vehicles. The study\u27s primary goal is to determine the connection between worries about cybersecurity and the rate of adoption of self-driving vehicles. Driverless vehicles are the most effective and cutting-edge technology in the transportation sector, yet there are barriers to their widespread adoption because of cybersecurity worries. As a result, this study will clarify the cybersecurity issues that contributed to the slower deployment of autonomous vehicles. The NIST Cybersecurity Framework serves as the study\u27s theoretical foundation. This paradigm consistently identifies the barriers to new technology adoption in cybersecurity

    Marketing digital y su influencia en las ventas en el hostel CheLagarto post pandemia, Miraflores, 2022

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    La finalidad de la presente investigaci√≥n ha sido determinar c√≥mo influye el marketing digital con las ventas en el hostel Che-Lagarto Post Pandemia, Miraflores, 2022. El tipo de investigaci√≥n fue b√°sica ‚Äď aplicada, de dise√Īo no experimental con un corte transversal, el m√©todo o ruta corresponde a un enfoque cuantitativo. La t√©cnica empleada para la recolecci√≥n de informaci√≥n fue la encuesta y el instrumento de recolecci√≥n ha sido el cuestionario. Se ha concluido que existe un alto grado de correlaci√≥n positiva entre el marketing digital y las ventas del hostel Che-Lagarto, con un Rho Spearman de 0,723 y se encuentra con un nivel de significancia de p<0,000. Por consiguiente se rechaza la hip√≥tesis nula (Ho) y se acepta la hip√≥tesis alterna (H1). As√≠ mismo, se ha podido validar que el marketing digital y las ventas se complementan brind√°ndole al hostel un conocimiento de c√≥mo ampliar sus ventas, captar y satisfacer a sus clientes y por consiguiente aumentar sus ventas. Por otro lado validamos tambi√©n la influencia de un adecuado flujo, feedback y fidelizaci√≥n de los clientes para el aumento de las ventas en el hoste

    Sentiment Analysis of Assamese Text Reviews: Supervised Machine Learning Approach with Combined n-gram and TF-IDF Feature

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    Sentiment analysis (SA) is a challenging application of natural language processing (NLP) in various Indian languages. However, there is limited research on sentiment categorization in Assamese texts. This paper investigates sentiment categorization on Assamese textual data using a dataset created by translating Bengali resources into Assamese using Google Translator. The study employs multiple supervised ML methods, including Decision Tree, K-nearest neighbour, Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine, combined with n-gram and Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction methods. The experimental results show that Multinomial Naive Bayes and Support Vector Machine have over 80% accuracy in analyzing sentiments in Assamese texts, while the Unigram model performs better than higher-order n-gram models in both datasets. The proposed model is shown to be an effective tool for sentiment classification in domain-independent Assamese text data

    Particle Swarm Optimization for Interference Mitigation of Wireless Body Area Network: A Systematic Review

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    Wireless body area networks (WBAN) has now become an important technology in supporting services in the health sector and several other fields. Various surveys and research have been carried out massively on the use of swarm intelligent (SI) algorithms in various fields in the last ten years, but the use of SI in wireless body area networks (WBAN) in the last five years has not seen any significant progress. The aim of this research is to clarify and convince as well as to propose a answer to this problem, we have identified opportunities and topic trends using the particle swarm optimization (PSO) procedure as one of the swarm intelligence for optimizing wireless body area network interference mitigation performance. In this research, we analyzes primary studies collected using predefined exploration strings on online databases with the help of Publish or Perish and by the preferred reporting items for systematic reviews and meta-analysis (PRISMA) way. Articles were carefully selected for further analysis. It was found that very few researchers included optimization methods for swarm intelligence, especially PSO, in mitigating wireless body area network interference, whether for intra, inter, or cross-WBAN interference. This paper contributes to identifying the gap in using PSO for WBAN interference and also offers opportunities for using PSO both standalone and hybrid with other methods to further research on mitigating WBAN interference

    IXIA BreakingPoint, All-in-one Applications and Network Security Testing Platform

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    Tato bakal√°Ňôsk√° pr√°ce se zab√Ĺv√° zprovoznńõn√≠m testŇĮ na speci√°ln√≠m hardware pomoc√≠ kter√Ĺch je moŇĺn√© simulovat legitimn√≠ provoz, √ļtok odepŇôen√≠m sluŇĺby, exploity, malware, fuzzing a ovńõŇôit bezpeńćnost infrastruktury organizace. ZaŇô√≠zen√≠ vyuŇĺ√≠v√° BreakingPoint software a dok√°Ňĺe simulovat legitimn√≠ i Ň°kodliv√Ĺ provoz aby ovńõŇôilo s√≠tńõ v realistick√Ĺch podm√≠nk√°ch.This bachelor thesis is focused on running tests on special hardware, which can be used to simulate legitimate traffic, denial of service attacks, exploits, malware, fuzzing, and validating an organization‚Äėssecurity infrastructure. The device uses BreakingPoint software and can simulate legitimate and malicious traffic to validate networks in realistic conditions.460 - Katedra informatikydobŇô

    Penerapan internet of things pada smart parking system untuk kebutuhan pengembangan smart city

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    Perkembangan zaman membuat kemajuan teknologi semakin pesat sehingga membuat manusia tidak terlepas dengan teknologi. Internet of Things (IoT) merupakan salah satu penerapan teknologi untuk memenuhi kebutuhan manusia dengan pemanfaatan konektivitas internet. Kendaraan sebagai kebutuhan sekunder manusia membutuhkan tempat parkir yang dapat memberikan rasa aman dan nyaman. Penelitian ini bertujuan untuk mengatasi permasalahan pengguna dalam mencari tempat parkir dengan menciptakan smart parking system. Studi kasus dilakukan di sebuah mall. Penelitian diawali dengan identifikasi permasalahan, kemudian merancang solusi, pengumpulan data yang relevan, serta perancangan dan pengkodean sistem. Smart parking system yang dirancang menggunakan sensor light dependent resistor (LDR) serta kamera automatic number plate recognition (ANPR)  dan lampu liquid-crystal display (LCD) sebagai alat yang dapat dengan mudah memberikan informasi yang kemudian ditransfer ke pengontrol. Kendaraan yang masuk ke dalam sistem harus teridentifikasi dengan memanfaatkan quick response (QR) Code. Melalui smart parking system, pengemudi akan mendapatkan kemudahan menemukan tempat parkir kosong dengan lokasi terdekat, metode pembayaran yang mudah, serta keamanan yang terjamin karena terhubung dengan aplikasi pengguna

    SHARE : A Framework for Personalized and Healthy Recipe Recommendations

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    This paper presents a personalized recommendation system that suggests recipes to users based on their health history and similar users' preferences. Specifically, the system utilizes collaborative filtering to determine other users with similar dietary preferences and exploits this information to identify suitable recipes for an individual. The system is able to handle a wide range of health constraints, preferences, and specific diet plans, such as low-carb or vegetarian. We demonstrate the usability of the system through a series of experiments on a large real-world data set of recipes. The results indicate that our system is able to provide highly personalized and accurate recommendations.publishedVersionPeer reviewe
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