Search results for: thin film processing

Authors: Farid Arya, Trevor Hyde, Andrea Trevisi, Paolo Basso, Danilo Bardaro

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Heat transfer through multiple pane windows can be reduced by creating a vacuum pressure less than 0.1 Pa between the glass panes, with low emittance coatings on one or more of the internal surfaces. Fabrication of vacuum glazing (VG) requires the formation of a hermetic seal around the periphery of the glass panes together with an array of support pillars between the panes to prevent them from touching under atmospheric pressure. Atmospheric pressure and temperature differentials induce stress which can affect the integrity of the glazing. Several parameters define the stresses in VG including the glass thickness, pillar specifications, glazing dimensions and edge seal configuration. Inherent stresses in VG can result in fractures in the glass panes and failure of the edge seal. In this study, stress in VG with different glass thicknesses is theoretically studied using Finite Element Modelling (FEM). Based on the finding in this study, suggestions are made to address problems resulting from the use of thinner glass panes in the fabrication of VG. This can lead to the development of high performance, light and thin VG.

Keywords: vacuum glazing, stress, vacuum insulation, support pillars

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Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: opinion mining, opinion summarization, sentiment analysis, text mining

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Authors: Emanuela Paranhos Lima, Vitaly Félix Rodríguez Esquerre

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In this work, efficient directional coupler composed of dielectric waveguides and metallic film has been analyzed in details by simulations using finite element method (FEM). The structure consists of a step-index fiber with dielectric core, silica cladding, and a metal nanowire parallel to the core. The results show that an efficient conversion of optical dielectric modes to long range plasmonic is possible. Low insertion losses in conjunction with short coupling length and a broadband operation can be achieved under certain conditions. This kind of couplers has potential applications for the design of photonic integrated circuits for signal routing between dielectric/plasmonic waveguides, sensing, lithography, and optical storage systems. A high efficient focusing of light in a very small region can be obtained.

Keywords: directional coupler, finite element method, metallic nanowire, plasmonic, surface plasmon polariton, superfocusing

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Authors: Karolina Wieczorek, Sophie Wiliams

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Introduction: Patient data is often collected in Electronic Health Record Systems (EHR) for purposes such as providing care as well as reporting data. This information can be re-used to validate data models in clinical trials or in epidemiological studies. Manual validation of automated tools is vital to pick up errors in processing and to provide confidence in the output. Mentioning a disease in a discharge letter does not necessarily mean that a patient suffers from this disease. Many of them discuss a diagnostic process, different tests, or discuss whether a patient has a certain disease. The COVID-19 dataset in this study used natural language processing (NLP), an automated algorithm which extracts information related to COVID-19 symptoms, complications, and medications prescribed within the hospital. Free-text patient clinical patient notes are rich sources of information which contain patient data not captured in a structured form, hence the use of named entity recognition (NER) to capture additional information. Methods: Patient data (discharge summary letters) were exported and screened by an algorithm to pick up relevant terms related to COVID-19. Manual validation of automated tools is vital to pick up errors in processing and to provide confidence in the output. A list of 124 Systematized Nomenclature of Medicine (SNOMED) Clinical Terms has been provided in Excel with corresponding IDs. Two independent medical student researchers were provided with a dictionary of SNOMED list of terms to refer to when screening the notes. They worked on two separate datasets called "A” and "B”, respectively. Notes were screened to check if the correct term had been picked-up by the algorithm to ensure that negated terms were not picked up. Results: Its implementation in the hospital began on March 31, 2020, and the first EHR-derived extract was generated for use in an audit study on June 04, 2020. The dataset has contributed to large, priority clinical trials (including International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) by bulk upload to REDcap research databases) and local research and audit studies. Successful sharing of EHR-extracted datasets requires communicating the provenance and quality, including completeness and accuracy of this data. The results of the validation of the algorithm were the following: precision (0.907), recall (0.416), and F-score test (0.570). Percentage enhancement with NLP extracted terms compared to regular data extraction alone was low (0.3%) for relatively well-documented data such as previous medical history but higher (16.6%, 29.53%, 30.3%, 45.1%) for complications, presenting illness, chronic procedures, acute procedures respectively. Conclusions: This automated NLP algorithm is shown to be useful in facilitating patient data analysis and has the potential to be used in more large-scale clinical trials to assess potential study exclusion criteria for participants in the development of vaccines.

Keywords: automated, algorithm, NLP, COVID-19

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Authors: Chen Jiang, Eric Jordan, Maurice Gell, Balakrishnan Nair

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Nuclear fusion, one of the most promising options for reliably generating large amounts of carbon-free energy in the future, has seen a plethora of ground-breaking technological advances in recent years. An efficient and durable “breeding blanket”, needed to ensure a reactor’s self-sufficiency by maintaining the optimal coolant temperature as well as by minimizing radiation dosage behind the blanket, still remains a technological challenge for the various reactor designs for commercial fusion power plants. A relatively new dual-coolant lead-lithium (DCLL) breeder design has exhibited great potential for high-temperature (>700oC), high-thermal-efficiency (>40%) fusion reactor operation. However, the structural material, namely reduced activation ferritic-martensitic (RAFM) steel, is not chemically stable in contact with molten Pb-17%Li coolant. Thus, to utilize this new promising reactor design, the demand for effective corrosion-resistant coatings on RAFM steels represents a pressing need. Solution Spray Technologies LLC (SST) is developing a double-layer ceramic coating design to address the corrosion protection of RAFM steels, using a novel solution and solution/suspension plasma spray technology through a US Department of Energy-funded project. Plasma spray is a coating deposition method widely used in many energy applications. Novel derivatives of the conventional powder plasma spray process, known as the solution-precursor and solution/suspension-hybrid plasma spray process, are powerful methods to fabricate thin, dense ceramic coatings with complex compositions necessary for the corrosion protection in DCLL breeders. These processes can be used to produce ultra-fine molten splats and to allow fine adjustment of coating chemistry. Thin, dense ceramic coatings with chosen chemistry for superior chemical stability in molten Pb-Li, low activation properties, and good radiation tolerance, is ideal for corrosion-protection of RAFM steels. A key challenge is to accommodate its CTE mismatch with the RAFM substrate through the selection and incorporation of appropriate bond layers, thus allowing for enhanced coating durability and robustness. Systematic process optimization is being used to define the optimal plasma spray conditions for both the topcoat and bond-layer, and X-ray diffraction and SEM-EDS are applied to successfully validate the chemistry and phase composition of the coatings. The plasma-sprayed double-layer corrosion resistant coatings were also deposited onto simulated RAFM steel substrates, which are being tested separately under thermal cycling, high-temperature moist air oxidation as well as molten Pb-Li capsule corrosion conditions. Results from this testing on coated samples, and comparisons with bare RAFM reference samples will be presented and conclusions will be presented assessing the viability of the new ceramic coatings to be viable corrosion prevention systems for DCLL breeders in commercial nuclear fusion reactors.

Keywords: breeding blanket, corrosion protection, coating, plasma spray

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Authors: Mami Nishida

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This paper presented a study on the development and control of a small biped soft robot using shape memory alloys (SMAs). Author proposed a flexible flat plate (FFP) actuators consisting of a thin　polyethylene plate and SMAs. This actuator has a nail like an insect. This robot moves from the front to back and from left to right using two nails. The walking robot has two degrees of freedom and is controlled by switching the ON-OFF current signals to the SMA based FFPs. The resulting small biped soft robot weighs a mere 4.7 g (with a height of 67 mm). The small robot realizes biped walking by transferring the elastic potential energy (generated by deflections of the SMA based FFPs) to kinematic energy. Experimental results demonstrated the viability and utility of the small biped soft robot with the proposed SMA-based FFPs and the control strategy to achieve walking behavior.

Keywords: biped soft robot with nails, flexible flat plate (FFP) actuators, ON-OFF control strategy, shape memory alloys (SMA)

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Authors: S. Djaziri, F. Sket, A. Hynowska, S. Milenkovic

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The use of Fe-Al based intermetallics as an alternative to Cr/Ni based stainless steels is very promising for industrial applications that use critical raw materials parts under extreme conditions. However, the development of advanced Fe-Al based intermetallics with appropriate mechanical properties presents several challenges that involve appropriate processing and microstructure control. A processing strategy is being developed which aims at producing a net-shape porous Fe-based preform that is infiltrated with molten Al or Al-alloy. In the present work, porous Fe-based preforms produced by two different methods (selective laser melting (SLM) and Kochanek-process (KE)) are studied during infiltration with molten aluminum. In the objective to elucidate the mechanisms underlying the formation of Fe-Al intermetallic phases during infiltration, an in-house furnace has been designed for in situ observation of infiltration at synchrotron facilities combining x-ray radiography (XR) and x-ray diffraction (XRD) techniques. The feasibility of this approach has been demonstrated, and information about the melt flow front propagation has been obtained. In addition, reactive infiltration has been achieved where a bi-phased intermetallic layer has been identified to be formed between the solid Fe and liquid Al. In particular, a tongue-like Fe₂Al₅ phase adhering to the Fe and a needle-like Fe₄Al₁₃ phase adhering to the Al were observed. The growth of the intermetallic compound was found to be dependent on the temperature gradient present along the preform as well as on the reaction time which will be discussed in view of the different obtained results.

Keywords: combined synchrotron radiography and diffraction, Fe-Al intermetallic compounds, in-situ molten Al infiltration, porous solid Fe preforms

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Authors: Albert W. K. Tan

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This research consolidates a comprehensive array of publications from peer-reviewed journals, case studies, and seminar reports focused on reverse logistics and network design. By synthesizing this secondary knowledge, our objective is to identify and articulate key decision factors crucial to reverse logistics network design for e-commerce. Through this exploration, we aim to present a refined mathematical model that offers valuable insights for companies seeking to optimize their reverse logistics operations. The primary goal of this research endeavor is to develop a comprehensive framework tailored to advising organizations and companies on crafting effective networks for their reverse logistics operations, thereby facilitating the achievement of their organizational goals. This involves a thorough examination of various network configurations, weighing their advantages and disadvantages to ensure alignment with specific business objectives. The key objectives of this research include: (i) Identifying pivotal factors pertinent to network design decisions within the realm of reverse logistics across diverse supply chains. (ii) Formulating a structured framework designed to offer informed recommendations for sound network design decisions applicable to relevant industries and scenarios. (iii) Propose a mathematical model to optimize its reverse logistics network. A conceptual framework for designing a reverse logistics network has been developed through a combination of insights from the literature review and information gathered from company websites. This framework encompasses four key stages in the selection of reverse logistics operations modes: (1) Collection, (2) Sorting and testing, (3) Processing, and (4) Storage. Key factors to consider in reverse logistics network design: I) Centralized vs. decentralized processing: Centralized processing, a long-standing practice in reverse logistics, has recently gained greater attention from manufacturing companies. In this system, all products within the reverse logistics pipeline are brought to a central facility for sorting, processing, and subsequent shipment to their next destinations. Centralization offers the advantage of efficiently managing the reverse logistics flow, potentially leading to increased revenues from returned items. Moreover, it aids in determining the most appropriate reverse channel for handling returns. On the contrary, a decentralized system is more suitable when products are returned directly from consumers to retailers. In this scenario, individual sales outlets serve as gatekeepers for processing returns. Considerations encompass the product lifecycle, product value and cost, return volume, and the geographic distribution of returns. II) In-house vs. third-party logistics providers: The decision between insourcing and outsourcing in reverse logistics network design is pivotal. In insourcing, a company handles the entire reverse logistics process, including material reuse. In contrast, outsourcing involves third-party providers taking on various aspects of reverse logistics. Companies may choose outsourcing due to resource constraints or lack of expertise, with the extent of outsourcing varying based on factors such as personnel skills and cost considerations. Based on the conceptual framework, the authors have constructed a mathematical model that optimizes reverse logistics network design decisions. The model will consider key factors identified in the framework, such as transportation costs, facility capacities, and lead times. The authors have employed mixed LP to find the optimal solutions that minimize costs while meeting organizational objectives.

Keywords: reverse logistics, supply chain management, optimization, e-commerce

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Authors: P. Mormile, M. Rippa, G. Bonanomi, F. Scala, Changrong Yan, L. Petti

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Photo-reflective films represent, in the panorama of agricultural films, a valid support for Spring and Summer cultivations, both in open field and under greenhouse. In fact, thanks to the high reflectivity of these films, thermal aggression, that causes serious problems to plants when traditional black mulch films are used, is avoided. Yellow or silver colored photo-reflective films protect plants from damages, assure the mulching effect, give a valid support to Integrated Pest Management and, according to recent trials, greatly contribute in saving water. This further advantage is determined by the high water condensation under the mulch film and this gives rise to reduction of irrigation. Water saving means also energy saving for electric system of water circulation. Trials performed at different geographic and ambient context confirm that the use of photo-reflective mulch films during the hot season allows to save water up to 30%.

Keywords: photo-selective mulches, saving water, water circulation, irrigation

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Authors: Alexander Wachtel, Michael T. Franzen, Walter F. Tichy

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Programming requires years of training. With natural language and end user development methods, programming could become available to everyone. It enables end users to program their own devices and extend the functionality of the existing system without any knowledge of programming languages. In this paper, we describe an Interactive Spreadsheet Processing Module (ISPM), a natural language interface to spreadsheets that allows users to address ranges within the spreadsheet based on inferred table schema. Using the ISPM, end users are able to search for values in the schema of the table and to address the data in spreadsheets implicitly. Furthermore, it enables them to select and sort the spreadsheet data by using natural language. ISPM uses a machine learning technique to automatically infer areas within a spreadsheet, including different kinds of headers and data ranges. Since ranges can be identified from natural language queries, the end users can query the data using natural language. During the evaluation 12 undergraduate students were asked to perform operations (sum, sort, group and select) using the system and also Excel without ISPM interface, and the time taken for task completion was compared across the two systems. Only for the selection task did users take less time in Excel (since they directly selected the cells using the mouse) than in ISPM, by using natural language for end user software engineering, to overcome the present bottleneck of professional developers.

Keywords: natural language processing, natural language interfaces, human computer interaction, end user development, dialog systems, data recognition, spreadsheet

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Authors: Laura Rueda-Gensini, Jader Rodríguez, Juan C. Cruz, Carolina Munoz-Camargo

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There is an emerging interest in botanicals and plant extracts for medicinal practices due to their widely reported health benefits. A large variety of phytochemicals found in plants have been correlated with antioxidant, immunomodulatory, and analgesic properties, which makes plant-derived products promising candidates for modulating the progression and treatment of numerous diseases. Non-centrifugal cane sugar (NCS), in particular, has been known for its high antioxidant and nutritional value, but composition-wise variability due to changing environmental and processing conditions have considerably limited its use in the nutraceutical and biomedical fields. This work is therefore aimed at assessing the effect of thermal exposure during NCS production over its bioactive composition and, in turn, its therapeutic value. Accordingly, two modified dehydration methods are proposed that employ: (i) vacuum-aided evaporation, which reduces the necessary temperatures to dehydrate the sample, and (ii) window refractance evaporation, which reduces thermal exposure time. The biochemical composition of NCS produced under these two methods was compared to traditionally-produced NCS by estimating their total polyphenolic and protein content with Folin-Ciocalteu and Bradford assays, as well as identifying the major phenolic compounds in each sample via HPLC-coupled mass spectrometry. Their antioxidant activities were also compared as measured by their scavenging potential of ABTS and DPPH radicals. Results show that the two modified production methods enhance polyphenolic and protein yield in resulting NCS samples when compared to traditional production methods. In particular, reducing employed temperatures with vacuum-aided evaporation demonstrated to be superior at preserving polyphenolic compounds, as evidenced both in the total and individual polyphenol concentrations. However, antioxidant activities were not significantly different between these. Although additional studies should be performed to determine if the observed compositional differences affect other therapeutic activities (e.g., anti-inflammatory, analgesic, and immunoprotective), these results suggest that reducing thermal exposure holds great promise for the production of natural products with enhanced nutritional value.

Keywords: non-centrifugal cane sugar, polyphenolic compounds, thermal processing, antioxidant activity

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Authors: B. Elshafei, X. Mao

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The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.

Keywords: data fusion, Gaussian process regression, signal denoise, temporal extrapolation

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Authors: Aly Mohamed El Husseiny, Azza Ezz Abdelkader

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There is a critical thin line between freedom of choice and randomness. The distance between imagination and perception and between perception and execution varies depending on numerous factors. While in developed areas residents have the opportunity and abilities to build flexible homes, residents in developing areas create their own dwellings in informal settlements, even though none of them is comfortable at home in the long run. This paper explores three factors: What residents really need, what they do with limited flexibility, and what they do when there are no limits, as in the case of informal settlements. This paper studies alteration to residential buildings and how they connect to the changes in people’s lifecycle in all past cases. This study also examines all approaches to flexibility, focusing on a social approach. The results of this study are based on three practical studies: Interviews with residents in an informal settlement (Eshash Mahfouz in Minya in Egypt), a civil study of buildings in a middle-class district, and a survey of residents from many countries, including Egypt, and interviews with a number of them to determine residents’ needs and the extent of renovations they made or would like to make to their homes.

Keywords: flexibility, housing, freedom of choice, social, changes, residents

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Authors: Tannistha Dasgupta

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This study explores the aspects of second language (L2) spoken fluency that are related to L2 linguistic knowledge and processing skill. It draws on Levelt’s ‘blueprint’ of the L2 speaker which discusses the cognitive issues underlying the act of speaking. However, L2 speaking assessments have largely neglected the underlying mechanism involved in language production; emphasis is given on the relationship between subjective ratings of L2 speech sample and objectively measured aspects of fluency. Hence, in this study, the relation between L2 linguistic knowledge and processing skill i.e. Cognitive Fluency (CF), and objectively measurable aspects of L2 spoken fluency i.e. Utterance Fluency (UF) is examined. The participants of the study are L2 learners of English, studying at high school level in Hyderabad, India. 50 participants with intermediate level of proficiency in English performed several lexical retrieval tasks and attention-shifting tasks to measure CF, and 8 oral tasks to measure UF. Each aspect of UF (speed, pause, and repair) were measured against the scores of CF to find out those aspects of UF which are reliable indicators of CF. Quantitative analysis of the data shows that among the three aspects of UF; speed is the best predictor of CF, and pause is weakly related to CF. The study suggests that including the speed aspect of UF could make L2 fluency assessment more reliable, valid, and objective. Thus, incorporating the assessment of psycholinguistic mechanisms into L2 spoken fluency testing, could result in fairer evaluation.

Keywords: attention-shifting, cognitive fluency, lexical retrieval, utterance fluency

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Authors: Andrei Nosov

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This paper discusses the development directions of dialogue systems within the digitalisation of the railway industry, where technologies based on conversational AI are already potentially applied or will be applied. Conversational AI is one of the popular natural language processing (NLP) tasks, as it has great prospects for real-world applications today. At the same time, it is a challenging task as it involves many areas of NLP based on complex computations and deep insights from linguistics and psychology. In this review, we focus on dialogue systems and their implementation in the railway domain. We comprehensively review the state-of-the-art research results on dialogue systems and analyse them from three perspectives: type of problem to be solved, type of model, and type of system. In particular, from the perspective of the type of tasks to be solved, we discuss characteristics and applications. This will help to understand how to prioritise tasks. In terms of the type of models, we give an overview that will allow researchers to become familiar with how to apply them in dialogue systems. By analysing the types of dialogue systems, we propose an unconventional approach in contrast to colleagues who traditionally contrast goal-oriented dialogue systems with open-domain systems. Our view focuses on considering retrieval and generative approaches. Furthermore, the work comprehensively presents evaluation methods and datasets for dialogue systems in the railway domain to pave the way for future research. Finally, some possible directions for future research are identified based on recent research results.

Keywords: digitalisation, railway, dialogue systems, conversational AI, natural language processing, natural language understanding, natural language generation

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Authors: S. Naveen, V. Sivasubramanian

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A modeling theory is proposed to consider the vaporization of LNG during its contact with water following its release from an underwater source. The spillage of LNG underwater can lead to a decrease in the surface temperature of water and subsequent freezing. This can in turn affect the heat flux distribution from the released LNG onto the water surrounding it. The available models predict the rate of vaporization considering the surface of contact as a solid wall, and considering the entire phenomena as a solid-liquid operation. This assumption greatly under-predicted the overall heat transfer on LNG water interface. The vaporization flux would first decrease during the film boiling, followed by an increase during the transition boiling and a steady decrease during the nucleate boiling. A superheat theory is introduced to enhance the accuracy in the prediction of the heat transfer between LNG and water. The work suggests that considering the superheat theory can greatly enhance the prediction of LNG vaporization on underwater releases and also help improve the study of overall thermodynamics.

Keywords: evaporation rate, heat transfer, LNG vaporization, underwater LNG release

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

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Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: convolution neural network, deep learning, malaria, thin blood smears

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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Authors: Xu Jiaqiao

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Text sentiment analysis is an important branch of natural language processing. This technology is widely used in public opinion analysis and web surfing recommendations. At present, the mainstream sentiment analysis methods include three parts: sentiment analysis based on a sentiment dictionary, based on traditional machine learning, and based on deep learning. This paper mainly analyzes and compares the advantages and disadvantages of the SVM method of traditional machine learning and the Long Short-term Memory (LSTM) method of deep learning in the field of Chinese sentiment analysis, using Chinese comments on Sina Microblog as the data set. Firstly, this paper classifies and adds labels to the original comment dataset obtained by the web crawler, and then uses Jieba word segmentation to classify the original dataset and remove stop words. After that, this paper extracts text feature vectors and builds document word vectors to facilitate the training of the model. Finally, SVM and LSTM models are trained respectively. After accuracy calculation, it can be obtained that the accuracy of the LSTM model is 85.80%, while the accuracy of SVM is 91.07%. But at the same time, LSTM operation only needs 2.57 seconds, SVM model needs 6.06 seconds. Therefore, this paper concludes that: compared with the SVM model, the LSTM model is worse in accuracy but faster in processing speed.

Keywords: sentiment analysis, support vector machine, long short-term memory, Chinese microblog comments

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Authors: Cemre Polat, Mustafa Soyler, Bulent Yaniktepe, Coskun Ozalp

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In this study, it was aimed to control the flow actively by giving an oscillating rotational motion to a vertically placed cylinder, and flow characteristics were determined. In the study, firstly, the flow structure around the flat cylinder was investigated with dye experiments, and then the cylinders with different oscillation angles (θ = 60°, θ = 120°, and θ = 180°) and different rotation speeds (15 rpm and 30 rpm) the flow structure around it was examined. Thus, the effectiveness of oscillation and rotation speed in flow control has been investigated. In the dye experiments, the dye/water mixture obtained by mixing Rhodamine 6G in powder form with water, which shines under laser light and allows detailed observation of the flow structure, was used. During the experiments, the dye was injected into the flow with the help of a thin needle at a distance that would not affect the flow from the front of the cylinder. In dye experiments, 100 frames per second were taken with a Canon brand EOS M50 (24MP) digital mirrorless camera at a resolution of 1280 * 720 pixels. Then, the images taken were analyzed, and the pictures representing the flow structure for each experiment were obtained. As a result of the study, it was observed that no separation points were formed at 180° swing angle at 15 rpm speed, 120° and 180° swing angle at 30 rpm, and the flow was controlled according to the fixed cylinder.

Keywords: active flow control, cylinder, flow visualization rotationally oscillating

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Authors: Zunaira Zaman, David Bohannon

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This study explores the application of machine learning (ML) for detecting security vulnerabilities in source code. The research aims to assist organizations with large application portfolios and limited security testing capabilities in prioritizing security activities. ML-based approaches offer benefits such as increased confidence scores, false positives and negatives tuning, and automated feedback. The initial approach using natural language processing techniques to extract features achieved 86% accuracy during the training phase but suffered from overfitting and performed poorly on unseen datasets during testing. To address these issues, the study proposes using the abstract syntax tree (AST) for Java and C++ codebases to capture code semantics and structure and generate path-context representations for each function. The Code2Vec model architecture is used to learn distributed representations of source code snippets for training a machine-learning classifier for vulnerability prediction. The study evaluates the performance of the proposed methodology using two datasets and compares the results with existing approaches. The Devign dataset yielded 60% accuracy in predicting vulnerable code snippets and helped resist overfitting, while the Juliet Test Suite predicted specific vulnerabilities such as OS-Command Injection, Cryptographic, and Cross-Site Scripting vulnerabilities. The Code2Vec model achieved 75% accuracy and a 98% recall rate in predicting OS-Command Injection vulnerabilities. The study concludes that even partial AST representations of source code can be useful for vulnerability prediction. The approach has the potential for automated intelligent analysis of source code, including vulnerability prediction on unseen source code. State-of-the-art models using natural language processing techniques and CNN models with ensemble modelling techniques did not generalize well on unseen data and faced overfitting issues. However, predicting vulnerabilities in source code using machine learning poses challenges such as high dimensionality and complexity of source code, imbalanced datasets, and identifying specific types of vulnerabilities. Future work will address these challenges and expand the scope of the research.

Keywords: code embeddings, neural networks, natural language processing, OS command injection, software security, code properties

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Authors: Mounir Tichamakdj, Khaled Sandjak, Boualem Tiliouine

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The design of flexible pavements is currently carried out using a multilayer elastic theory. However, for thin-surface pavements subject to light or medium traffic volumes, the importance of the non-linear stress-strain behavior of unbound granular materials requires the use of more sophisticated numerical models for the structural design of these pavements. The simplified analysis of the nonlinear behavior of granular materials in pavement design will be developed in this study. To achieve this objective, an equivalent linear model derived from a volumetric shear stress model is used to simulate the nonlinear elastic behavior of two unlinked local granular materials often used in pavements. This model is included here to adequately incorporate material non-linearity due to stress dependence and stiffness of the granular layers in the flexible pavement analysis. The sensitivity of the pavement design criteria to the likely variations in asphalt layer thickness and the mineralogical nature of unbound granular materials commonly used in pavement structures are also evaluated.

Keywords: granular materials, linear equivalent model, non-linear behavior, pavement design, shear volumetric strain model

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Latha Nataraj, Todd Henry, Micheal Wallock, Asha Hall, Christine Hatter, Babak Anasori, Yury Gogotsi

Abstract:

Multifunctional composites with enhanced strength and toughness for superior damage tolerance are essential for advanced aerospace and military applications. Detection of structural changes prior to visible damage may be achieved by incorporating fillers with tunable properties such as two-dimensional (2D) nanomaterials with high aspect ratios and more surface-active sites. While 2D graphene with large surface areas, good mechanical properties, and high electrical conductivity seems ideal as a filler, the single-atomic thickness can lead to bending and rolling during processing, requiring post-processing to bond to polymer matrices. Lately, an emerging family of 2D transition metal carbides and nitrides, MXenes, has attracted much attention since their discovery in 2011. Metallic electronic conductivity and good mechanical properties, even with increased polymer content, coupled with hydrophilicity make MXenes a good candidate as a filler material in polymer composites and exceptional as multifunctional damage indicators in composites. Here, we systematically study MXene-based (Ti₃C₂) coated on glass fibers for fiber reinforced polymer composite for self-sensing using microscopy and micromechanical testing. Further testing is in progress through the investigation of local variations in optical, acoustic, and thermal properties within the damage sites in response to strain caused by mechanical loading.

Keywords: damage sensing, fiber composites, MXene, self-sensing

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Authors: S. Gao, S. Srirattayawong

Abstract:

In this study, a computational fluid dynamics (CFD) model has been developed for studying the effect of surface roughness profile on the EHL problem. The cylinders contact geometry, meshing and calculation of the conservation of mass and momentum equations are carried out by using the commercial software packages ICEMCFD and ANSYS Fluent. The user defined functions (UDFs) for density, viscosity and elastic deformation of the cylinders as the functions of pressure and temperature have been defined for the CFD model. Three different surface roughness profiles are created and incorporated into the CFD model. It is found that the developed CFD model can predict the characteristics of fluid flow and heat transfer in the EHL problem, including the leading parameters such as the pressure distribution, minimal film thickness, viscosity, and density changes. The obtained results show that the pressure profile at the center of the contact area directly relates to the roughness amplitude. The rough surface with kurtosis value over 3 influences the fluctuated shape of pressure distribution higher than other cases.

Keywords: CFD, EHL, kurtosis, surface roughness

Procedia PDF Downloads 320

Authors: Chong-Yang Qiao

Abstract:

Mobile augmented reality (MAR) tracking targets from the surroundings and aids operators for interactive data and procedures visualization, potential equipment and system understandably. Operators remotely communicate and coordinate with each other for the continuous tasks, information and data exchange between control room and work-site. In the routine work, distributed control system (DCS) monitoring and work-site manipulation require operators interact in real-time manners. The critical question is the improvement of user experience in cooperative works through applying Augmented Reality in the traditional industrial field. The purpose of this exploratory study is to find the cognitive model for the multiple task performance by MAR. In particular, the focus will be on the comparison between different tasks and environment factors which influence information processing. Three experiments use interface and interaction design, the content of start-up, maintenance and stop embedded in the mobile application. With the evaluation criteria of time demands and human errors, and analysis of the mental process and the behavior action during the multiple tasks, heuristic evaluation was used to find the operators performance with different situation factors, and record the information processing in recognition, interpretation, judgment and reasoning. The research will find the functional properties of MAR and constrain the development of the cognitive model. Conclusions can be drawn that suggest MAR is easy to use and useful for operators in the remote collaborative works.

Keywords: mobile augmented reality, remote collaboration, user experience, cognition model

Procedia PDF Downloads 197

Authors: D. F. Carvalho, A. O. Uscamayta, J. C. Guerrero, H. F. Oliveira, P. M. Azevedo-Marques

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The creation of vector contours slices (ROIs) on body silhouettes in oncologic patients is an important step during the radiotherapy planning in clinic and hospitals to ensure the accuracy of oncologic treatment. The radiotherapy planning of patients is performed by complex softwares focused on analysis of tumor regions, protection of organs at risk (OARs) and calculation of radiation doses for anomalies (tumors). These softwares are supplied for a few manufacturers and run over sophisticated workstations with vector processing presenting a cost of approximately twenty thousand dollars. The Brazilian project SIPRAD (Radiotherapy Planning System) presents a proposal adapted to the emerging countries reality that generally does not have the monetary conditions to acquire some radiotherapy planning workstations, resulting in waiting queues for new patients treatment. The SIPRAD project is composed by a set of integrated and interoperabilities softwares that are able to execute all stages of radiotherapy planning on simple personal computers (PCs) in replace to the workstations. The goal of this work is to present an image processing technique, computationally feasible, that is able to perform an automatic contour delineation in patient body silhouettes (SIPRAD-Body). The SIPRAD-Body technique is performed in tomography slices under grayscale images, extending their use with a greedy algorithm in three dimensions. SIPRAD-Body creates an irregular polyhedron with the Canny Edge adapted algorithm without the use of preprocessing filters, as contrast and brightness. In addition, comparing the technique SIPRAD-Body with existing current solutions is reached a contours similarity at least 78%. For this comparison is used four criteria: contour area, contour length, difference between the mass centers and Jaccard index technique. SIPRAD-Body was tested in a set of oncologic exams provided by the Clinical Hospital of the University of Sao Paulo (HCRP-USP). The exams were applied in patients with different conditions of ethnology, ages, tumor severities and body regions. Even in case of services that have already workstations, it is possible to have SIPRAD working together PCs because of the interoperability of communication between both systems through the DICOM protocol that provides an increase of workflow. Therefore, the conclusion is that SIPRAD-Body technique is feasible because of its degree of similarity in both new radiotherapy planning services and existing services.

Keywords: radiotherapy, image processing, DICOM RT, Treatment Planning System (TPS)

Procedia PDF Downloads 296

Authors: Jimmy Jiun-Ming Su, Yuan-Min Lin

Abstract:

Bioprinting has been applied to produce 3D cellular constructs for tissue engineering. Microextrusion printing is the most common used method. However, printing low viscosity bioink is a challenge for this method. Herein, we developed a new 3D printing system to fabricate cell-laden hydrogels via a DLP-based projector. The bioprinter is assembled from affordable equipment including a stepper motor, screw, LED-based DLP projector, open source computer hardware and software. The system can use low viscosity and photo-polymerized bioink to fabricate 3D tissue mimics in a layer-by-layer manner. In this study, we used gelatin methylacrylate (GelMA) as bioink for stem cell encapsulation. In order to reinforce the printed construct, surface modified hydroxyapatite has been added in the bioink. We demonstrated the silanization of hydroxyapatite could improve the crosslinking between the interface of hydroxyapatite and GelMA. The results showed that the incorporation of silanized hydroxyapatite into the bioink had an enhancing effect on the mechanical properties of printed hydrogel, in addition, the hydrogel had low cytotoxicity and promoted the differentiation of embedded human bone marrow stem cells (hBMSCs) and retinal pigment epithelium (RPE) cells. Moreover, this bioprinting system has the ability to generate microchannels inside the engineered tissues to facilitate diffusion of nutrients. We believe this 3D bioprinting system has potential to fabricate various tissues for clinical applications and regenerative medicine in the future.

Keywords: bioprinting, cell encapsulation, digital light processing, GelMA hydrogel

Procedia PDF Downloads 181

Authors: K. Arroudj, M. Lanez, M. N. Oudjit

Abstract:

The introduction of siliceous mineral additions in cement production allows, in addition to the ecological and economic gain, improvement of concrete performance. This improvement is mainly due to the fixing of Portlandite, released during the hydration of cement, by fine siliceous, forming denser calcium silicate hydrates and therefore a more compact cementitious matrix. This research is part of the valuation of the Dune Sand (DS) in the cement industry in Algeria. The high silica content of DS motivated us to study its effect, at ground state, on the properties of mortars in fresh and hardened state. For this purpose, cement pastes and mortars based on ground dune sand (fine quartz) has been analyzed with a replacement to cement of 15%, 20% and 25%. This substitution has reduced the amount of heat of hydration and avoids any risk of initial cracking. In addition, the grinding of the dune sand provides amorphous thin populations adsorbed at the surface of the crystal particles of quartz. Which gives to ground quartz pozzolanic character. This character results an improvement of mechanical strength of mortar (66 MPa in the presence of 25% of ground quartz).

Keywords: mineralogical structure, pozzolanic reactivity, Quartz, mechanical strength

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Authors: Besma Abdeltif, Chebabhi Imen

Abstract:

Introduction: it is important to define the genital pathologies encountered in Algeria in postpartum dairy cows. The objective was to identify the different pathologies of reproduction in cows found at the communal abattoir of Souk Ahras. Materials and Methods: Our study was carried at the communal slaughterhouse of Souk-Ahras on 63 genital tracts were examined macroscopically after slaughter. Results: The results obtained reveal a high frequency of pregnant females (14.28%), most of the gestations were at their beginning. Uterine anomalies ranked first in the genital lesions of the cow (20.37%). The frequencies of these abnormalities are in ascending order: aplasia of the horns = 1.85%, traumatic cervical = 1.85%, cervical tumors = 1.85%, chronic endometritis = 3.70% and Acute endometritis = 11.11%. The ovarian cyst is the most common lesion, with a frequency of 3.70%, followed by smooth ovaries (1.85%). These are single, thin-walled cysts more present on the right ovary than the left ovary. Salpingitis is the only tubal lesion found on 5.55% of the non-pregnant genital tract. Neoformation is the only vaginal lesion identified in this work (1.85%). Conclusion: Our result, in general, conforms to the data of the literature.

Keywords: genital tract, cow, slaughterhouse, pathology

Procedia PDF Downloads 106