Search results for: relational processing

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: Amani Waheed, Adel Mishriky, Rasha Farouk, Essam Abdallah, Sarah Hussein

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Background: Occupational stress plays a crucial role in professional drivers' health. They are exposed to high workloads, low physical activity, high demand and low decisions as well as poor lifestyle factors including poor diet, sedentary work, and smoking. Dyslipidemia is a well-established modifiable cardiovascular risk factor. Occupational stress and other forms of chronic stress have been associated with raised levels of atherogenic lipids. Although stress management has some evidence in improving lipid profile, the association between occupational stress and dyslipidemia is not clear. Objectives: To assess the relational between occupational stress and lipid profile among professional drivers. Methodology: A cross-sectional study conducted at a large company in Ismailia City, Egypt, where, 131 professional drivers divided into 44 car drivers, 43 bus drivers, and 44 truck drivers were eligible after applying exclusion criteria. Occupational stress index (OSI), non-occupational risk factors of dyslipidemia were assessed using interview structured questionnaire. Blood pressure, body mass index (BMI) and lipid profile were measured. Results: The mean of total OSI score was 79.98 ± 6.14. The total OSI score is highest among truck drivers (82.16 ± 4.62), then bus drivers (80.26 ± 6.02) and lowest among car drivers (77.55 ± 6.79) with statistically significant. Eighty percent had Dyslipidemia. The duration of driving hours per day, exposure to passive smoking and increased BMI were the risk factors. No statistical significance between Total OSI score and dyslipidemia. Using, logistic regression analysis, occupational stress, duration of driving hours per day, and BMI were positive significant predictors for dyslipidemia. Conclusion: Professional drivers are exposed to occupational stress. A high proportion of drivers have dyslipidemia. Total OSI score doesn't have statistically significant relation with dyslipidemia.

Keywords: body mass index, dyslipidaemia, occupational stress, professional drivers

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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: Mohammad A. Bani-Khaled

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In this work we use the Discrete Proper Orthogonal Decomposition transform to characterize the properties of coupled dynamics in thin-walled beams by exploiting numerical simulations obtained from finite element simulations. The outcomes of the will improve our understanding of the linear and nonlinear coupled behavior of thin-walled beams structures. Thin-walled beams have widespread usage in modern engineering application in both large scale structures (aeronautical structures), as well as in nano-structures (nano-tubes). Therefore, detailed knowledge in regard to the properties of coupled vibrations and buckling in these structures are of great interest in the research community. Due to the geometric complexity in the overall structure and in particular in the cross-sections it is necessary to involve computational mechanics to numerically simulate the dynamics. In using numerical computational techniques, it is not necessary to over simplify a model in order to solve the equations of motions. Computational dynamics methods produce databases of controlled resolution in time and space. These numerical databases contain information on the properties of the coupled dynamics. In order to extract the system dynamic properties and strength of coupling among the various fields of the motion, processing techniques are required. Time- Proper Orthogonal Decomposition transform is a powerful tool for processing databases for the dynamics. It will be used to study the coupled dynamics of thin-walled basic structures. These structures are ideal to form a basis for a systematic study of coupled dynamics in structures of complex geometry.

Keywords: coupled dynamics, geometric complexity, proper orthogonal decomposition (POD), thin walled beams

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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

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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: Chong-Yang Qiao

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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

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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)

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

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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

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Authors: Jodie Bradnam, Mark Edwards, Bruce Watt

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Mobile technologies have emerged as tools to create and sustain social and romantic relationships. The integration of technologies in close relationships has been of particular research interest with findings supporting the positive role of mobile phones in nurturing feelings of closeness and connection. More recently, the use of text messaging to manage conflict has become a focus of research attention. Four hundred and eleven adults in committed romantic relationships completed a series of questionnaires measuring attachment orientation, relationship quality, texting frequencies, attitudes, and response expectations. Attachment orientation, relationship length, texting for connection and disconnection were significant predictors of relationship quality, specifically relationship intimacy. Text frequency varied as a function of attachment orientation, with high attachment anxiety associated with high texting frequencies and with low relationship quality. Sending text messages of love and support was related to higher intimacy and relationship satisfaction scores, while sending critical or impersonal texts was associated with significantly lower intimacy and relationship satisfaction scores. The use of texting to manage relational conflict was a stronger negative predictor of relationship satisfaction than was the use of texting to express love and affection. Consistent with research on face-to-face communication in couples, the expression of negative sentiments via text were related to lower relationship quality, and these negative sentiments had a stronger and more enduring impact on relationship quality than did the expression of positive sentiments. Attachment orientation, relationship length and relationship status emerged as variables of interest in understanding the use of mobile technologies in romantic relationships.

Keywords: attachment, destructive conflict, intimacy, mobile communication, relationship quality, relationship satisfaction, texting

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Authors: Ruthanne Tobin

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The shift to a personalized learning (PL) curriculum in Canada represents an innovative approach to teaching and learning that is also evident in various initiatives across the 32-nation OECD. The premise behind PL is that empowering individual learners to have more input into how they access and construct knowledge, and express their understanding of it, will result in more meaningful school experiences and academic success. In this paper presentation, the author reports on a document analysis of the new curriculum in the province of British Columbia. Three theoretical frameworks are used to analyze the new curriculum. Framework 1 focuses on five dominant aspects (FDA) of PL at the classroom level. Framework 2 focuses on conceptualizing and enacting personalized learning (CEPL) within three spheres of influence. Framework 3 focuses on the integration of three types of knowledge (content, technological, and pedagogical). Analysis is ongoing, but preliminary findings suggest that the new curriculum addresses framework 1 quite well, which identifies five areas of personalized learning: 1) assessment for learning; 2) effective teaching and learning; 3) curriculum entitlement (choice); 4) school organization; and 5) “beyond the classroom walls” (learning in the community). Framework 2 appears to be less well developed in the new curriculum. This framework speaks to the dynamics of PL within three spheres of interaction: 1) nested agency, comprised of overarching constraints [and enablers] from policy makers, school administrators and community; 2) relational agency, which refers to a capacity for professionals to develop a network of expertise to serve shared goals; and 3) students’ personalized learning experience, which integrates differentiation with self-regulation strategies. Framework 3 appears to be well executed in the new PL curriculum, as it employs the theoretical model of technological, pedagogical content knowledge (TPACK) in which there are three interdependent bodies of knowledge. Notable within this framework is the emphasis on the pairing of technologies with excellent pedagogies to significantly assist students and teachers. This work will be of high relevance to educators interested in innovative school reform.

Keywords: curriculum reform, K-12 school change, innovations in education, personalized learning

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Authors: Catherine Mangan, Beth Anderson

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There has been a significant drive to use online or hybrid learning in Higher Education (HE) over recent years. HEs with a virtual presence offer their communities a range of benefits, including the potential for greater inclusivity, diversity, and collaboration; more flexible learning packages; and more engaging, dynamic content. Institutions can also experience significant challenges when seeking to extend learning spaces in this way, as can learners themselves. For example, staff members’ and learners’ digital literacy varies (as do their perceptions of technologies in use), and there can be confusion about optimal approaches to implementation. Furthermore, the speed with which HE institutions have needed to shift to fully online or hybrid models, owing to the COVID19 pandemic, has highlighted the significant barriers to successful implementation. HE environments have been shown to predict a range of organisational, academic, and experiential outcomes, both positive and negative. Much research has focused on the social aspect of virtual platforms, as well as the nature and effectiveness of the technologies themselves. There remains, however, a relative paucity of synthesised knowledge on the psychology of learners’ relationships with their institutions; specifically, how individual difference and interpersonal factors predict students’ ability and willingness to engage with novel virtual learning spaces. Accordingly, extending learning spaces remains challenging for institutions, and wholly remote courses, in particular, can experience high attrition rates. Focusing on the last five years, this pragmatic review summarises evidence from the psychological and pedagogical literature. In particular, the review highlights the importance of addressing the psychological and relational complexities of students’ shift from offline to online engagement. In doing so, it identifies considerations for HE institutions looking to deliver in this way.

Keywords: higher education, individual differences, interpersonal relationships, online learning, virtual environment

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Authors: Sarah Hebbouch

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Two decades ago, research on diasporic women’s participation within Sufi circles would have been inconceivable, not only because of a general lack of recognition of their contribution to Sufism but due to the intimacy of the rituals, often taking place in confined spaces, like zawiyas (Sufi lodges). Recent scholarly attention to female spiritual experience owes to a digital awareness and interest in exploring diasporic community reproduction of those experiences. Within a context where female disciples of a Sufi convent undergo a physical separation from the saint’s sanctuary -because of immigration from the homeland to the host country- technology becomes a social hub accounting for Sufis’ ritual commitment and preservation of cultural capital in the diaspora. This paper elucidates how female Sufi immigrants affiliating with the Boudchichi brotherhood (Morocco-based) maintain ‘a relational network’ and strong social online relationships with their female compatriots in Morocco through the use of online platforms. Sufi communities living in the diaspora find the internet an open interactive space that serves to kindle their distance of spiritual participation and corroborate their transnational belonging. The current paper explores the implications of the use of a digital baseline named “Tariqa Info,” the convent’s digital online platform, and how it mediates everyday ritual performance, the promotion of digital connection, and the communication of ideas and discourses. Such a platform serves the bolstering emotional bonds for transnational female disciples and inclusion within online communities in the homeland. Assisted by an ethnographic lens, this paper discusses the research findings of participatory field observation of Sufi women’s online communities, informed by the need to trace the many ostensible aspects of interconnectedness and divergences.

Keywords: digital connection, Sufi convent, social online relationship, transnational female disciples

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Authors: Bentayeb-Ait Lounis Saïda, Cheref Zahia, Cherifi Thizi, Ri Kahina Bahmed, Kahina Hallali Yasmine Abdellaoui, Kenza Adli

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Algeria is the leading consumer of dairy products in North Africa. This is a fact. However, the nutritional quality of the latter remains unknown. The aim of this study is to characterise the dairy products available on the Algerian market in order to assess whether they constitute a healthy and safe choice. To do this, it collected data on the labelling of 390 dairy products, including cheese, yoghurt, UHT milk and milk drinks, infant formula and dairy creams. We assessed their degree of processing according to the NOVA classification, as well as the proportion of imitation products. The study was carried out between March 2020 and August 2023. The results show that 88% are ultra-processed; 84% for 'cheese', 92% for dairy creams, 92% for 'yoghurt', 100% for infant formula, 92% for margarines and 36% for UHT milk/dairy drinks. As for imitation/analogue dairy products, the study revealed the following proportions: 100% for infant formula, 78% for butter/margarine, 18% for UHT milk/milk-based drinks, 54% for cheese, 2% for camembert and 75% for dairy cream. The harmful effects of consuming ultra-processed products on long-term health are increasingly documented in dozens of publications. The findings of this study sound the alarm about the health risks to which Algerian consumers are exposed. Various scientific, economic and industrial bodies need to be involved in order to safeguard consumer health in both the short and long term. Food awareness and education campaigns should be organised.

Keywords: dairy, UPF, NOVA, yoghurt, cheese

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Authors: Marvin Chibuzo Offiah, Susanne Rosenthal, Markus Borschbach

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Certain research projects in Computer Science often involve research on existing signal processing algorithms and developing improvements on them. Research budgets are usually limited, hence there is limited time for implementing the algorithms from scratch. It is therefore common practice, to use implementations provided by other researchers as a template. These are most commonly provided in a rapid development, i.e. 4th generation, programming language, usually Matlab. Rapid development is a common method in Computer Science research for quickly implementing and testing new developed algorithms, which is also a common task within agile project organization. The growing relevance of mobile devices in the computer market also gives rise to the need to demonstrate the successful executability and performance measurement of these algorithms on a mobile device operating system and processor, particularly on a smartphone. Open mobile systems such as Android, are most suitable for this task, which is to be performed most efficiently. Furthermore, efficiently implementing an interaction between the algorithm and a graphical user interface (GUI) that runs exclusively on the mobile device is necessary in cases where the project’s goal statement also includes such a task. This paper examines different proposed solutions for porting computer algorithms obtained through rapid development into a GUI-based smartphone Android app and evaluates their feasibilities. Accordingly, the feasible methods are tested and a short success report is given for each tested method.

Keywords: SMARTNAVI, Smartphone, App, Programming languages, Rapid Development, MATLAB, Octave, C/C++, Java, Android, NDK, SDK, Linux, Ubuntu, Emulation, GUI

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Authors: Lahari Ramya Pa, Sudhakar Ib, Madhu Vc, Madhusudhan Reddy Gd, Srinivasa Rao E.

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Selection of suitable armor materials for defense applications is very crucial with respect to increasing mobility of the systems as well as maintaining safety. Therefore, determining the material with the lowest possible areal density that resists the predefined threat successfully is required in armor design studies. A number of light metal and alloys are come in to forefront especially to substitute the armour grade steels. AA5083 aluminium alloy which fit in to the military standards imposed by USA army is foremost nonferrous alloy to consider for possible replacement of steel to increase the mobility of armour vehicles and enhance fuel economy. Growing need of AA5083 aluminium alloy paves a way to develop supplement aluminium alloys maintaining the military standards. It has been witnessed that AA 2xxx aluminium alloy, AA6xxx aluminium alloy and AA7xxx aluminium alloy are the potential material to supplement AA5083 aluminium alloy. Among those cited aluminium series alloys AA7xxx aluminium alloy (heat treatable) possesses high strength and can compete with armour grade steels. Earlier investigations revealed that layering of AA7xxx aluminium alloy can prevent spalling of rear portion of armour during ballistic impacts. Hence, present investigation deals with fabrication of hard layer (made of boron carbide) i.e. layer on AA 7075 aluminium alloy using friction stir processing with an intention of blunting the projectile in the initial impact and backing tough portion(AA7xxx aluminium alloy) to dissipate residual kinetic energy. An analytical approach has been adopted to unfold the ballistic performance of projectile. Penetration of projectile inside the armour has been resolved by considering by strain energy model analysis. Perforation shearing areas i.e. interface of projectile and armour is taken in to account for evaluation of penetration inside the armour. Fabricated surface composites (targets) were tested as per the military standard (JIS.0108.01) in a ballistic testing tunnel at Defence Metallurgical Research Laboratory (DMRL), Hyderabad in standardized testing conditions. Analytical results were well validated with experimental obtained one.

Keywords: AA7075 aluminium alloy, friction stir processing, boron carbide, ballistic performance, target

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Authors: R. Tajau, R. Rohani, M. S. Alias, N. H. Mudri, K. A. Abdul Halim, M. H. Harun, N. Mat Isa, R. Che Ismail, S. Muhammad Faisal, M. Talib, M. R. Mohamed Zin

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Bio-based polymeric materials are increasingly used for a variety of applications, including surface coating, drug delivery systems, and tissue engineering. These polymeric materials are ideal for the aforementioned applications because they are derived from natural resources, non-toxic, low-cost, biocompatible, and biodegradable, and have promising thermal and mechanical properties. The nature of hydrocarbon chains, carbon double bonds, and ester bonds allows various sources of oil (edible), such as soy, sunflower, olive, and oil palm, to fine-tune their particular structures in the development of innovative materials. Palm oil can be the most eminent raw material used for manufacturing new and advanced natural polymeric materials involving radiation techniques, such as coating resins, nanoparticles, scaffold, nanotubes, nanocomposites, and lithography for different branches of the industry in countries where oil palm is abundant. The radiation technique is among the most versatile, cost-effective, simple, and effective methods. Crosslinking, reversible addition-fragmentation chain transfer (RAFT), polymerisation, grafting, and degradation are among the radiation mechanisms. Exposure to gamma, EB, UV, or laser irradiation, which are commonly used in the development of polymeric materials, is used in these mechanisms. Therefore, this review focuses on current radiation processing technologies for the development of various radiation-curable bio-based polymeric materials with a promising future in biomedical and industrial applications. The key focus of this review is on radiation curable palm oil-based products, which have been published frequently in recent studies.

Keywords: palm oil, radiation processing, surface coatings, VOC

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Authors: F. Bellali, M. Kharroubi, M. Loutfi, N.Bourhim

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In Morocco, fish processing industry is an important source income for a large amount of byproducts including skins, bones, heads, guts and scales. Those underutilized resources particularly scales contain a large amount of proteins and calcium. Scales from Sardina plichardus resulting from the transformation operation have the potential to be used as raw material for the collagen production. Taking into account this strong expectation of the regional fish industry, scales sardine upgrading is well justified. In addition, political and societal demands for sustainability and environment-friendly industrial production systems, coupled with the depletion of fish resources, drive this trend forward. Therefore, fish scale used as a potential source to isolate collagen has a wide large of applications in food, cosmetic and bio medical industry. The main aim of this study is to isolate and characterize the acid solubilize collagen from sardine fish scale, Sardina pilchardus. Experimental design methodology was adopted in collagen processing for extracting optimization. The first stage of this work is to investigate the optimization conditions of the sardine scale deproteinization on using response surface methodology (RSM). The second part focus on the demineralization with HCl solution or EDTA. Moreover, the last one is to establish the optimum condition for the isolation of collagen from fish scale by solvent extraction. The basic principle of RSM is to determinate model equations that describe interrelations between the independent variables and the dependent variables.

Keywords: Sardina pilchardus, scales, valorization, collagen extraction, response surface methodology

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Authors: Antonietta Maoloni, Federica Cardinali, Vesna Milanović, Andrea Osimani, Ilario Ferrocino, Maria Rita Corvaglia, Luca Cocolin, Lucia Aquilanti

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Table olives (Olea europaea L.) are among the most important fermented vegetables all over the world, while sea fennel (Crithmum maritimum L.) is an emerging food crop with interesting nutritional and sensory traits. Both of them are characterized by the presence of several bioactive compounds with potential beneficial health effects, thus representing two valuable substrates for the manufacture of innovative vegetable-based preserves. Given these premises, the present study was aimed at exploring the co-fermentation of table olives and sea fennel to produce new high-value preserves. Spanish style or Greek style processing method and the use of a multiple strain starter were explored. The preserves were evaluated for their microbial dynamics and key sensory traits. During the fermentation, a progressive pH reduction was observed. Mesophilic lactobacilli, mesophilic lactococci, and yeasts were the main microbial groups at the end of the fermentation, whereas Enterobacteriaceae decreased during fermentation. An evolution of the microbiota was revealed by metataxonomic analysis, with Lactiplantibacillus plantarum dominating in the late stage of fermentation, irrespective of processing method and use of the starter. Greek style preserves resulted in more crunchy and less fibrous than Spanish style one and were preferred by trained panelists.

Keywords: lactic acid bacteria, Lactiplantibacillus plantarum, metataxonomy, panel test, rock samphire

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Authors: Shuangxin Wu

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Superalloy ultra-thin-walled components occupy a considerable proportion of aero engines and play an increasingly important role in structural weight reduction and performance improvement. To solve problems such as high deformation resistance and poor formability at room temperature, the introduction of pulse current in the processing process can improve the plasticity of metal materials, but the influence mechanism of pulse current on the forming limit of superalloy ultra-thin sheet is not clear, which is of great significance for determining the material processing window and improving the micro-forming process. The effect of pulse current on the microstructure evolution of superalloy thin plates was observed by optical microscopy (OM) and X-ray diffraction topography (XRT) by applying pulse current to GH3039 with a thickness of 0.2mm under plane strain and uniaxial tensile states. Compared with the specimen without pulse current applied at the same temperature, the internal void volume fraction is significantly reduced, reflecting the non-thermal effect of pulse current on the growth of micro-pores. ED (electrically deforming) specimens have larger and deeper dimples, but the elongation is not significantly improved because the pulse current promotes the void coalescence process, resulting in material fracture. The electro-plastic phenomenon is more obvious in the plane strain state, which is closely related to the effect of stress triaxial degree on the void evolution under pulsed current.

Keywords: pulse current, superalloy, ductile fracture, void damage

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Authors: Kasidis Chomrat, Nopasit Chakpitak, Anukul Tamprasirt, Annop Thananchana

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Internet of Things devices or IoT and Edge Computing has become one of the biggest things happening in innovations and one of the most discussed of the potential to improve and disrupt traditional business and industry alike. With rises of new hang cliff challenges like COVID-19 pandemic that posed a danger to workforce and business process of the system. Along with drastically changing landscape in business that left ruined aftermath of global COVID-19 pandemic, looming with the threat of global energy crisis, global warming, more heating global politic that posed a threat to become new Cold War. How emerging technology like edge computing and usage of specialized design visual processing units will be great opportunities for business. The literature reviewed on how the internet of things and disruptive wave will affect business, which explains is how all these new events is an effect on the current business and how would the business need to be adapting to change in the market and world, and example test benchmarking for consumer marketed of newer devices like the internet of things devices equipped with new edge computing devices will be increase efficiency and reducing posing a risk from a current and looming crisis. Throughout the whole paper, we will explain the technologies that lead the present technologies and the current situation why these technologies will be innovations that change the traditional practice through brief introductions to the technologies such as cloud computing, edge computing, Internet of Things and how it will be leading into future.

Keywords: internet of things, edge computing, machine learning, pattern recognition, image classification

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Authors: Ocheme B. Ocheme, Omobolanle O. Oloyede, S. James, Eleojo V. Akpa

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The effect of fermentation time on some functional properties of Moringa (Moringa oleifera) seed flour was examined. Fermentation, an effective processing method used to improve nutritional quality of plant foods, tends to affect the characteristics of food components and their behaviour in food systems just like other processing methods. Hence the need for this study. Moringa seeds were fermented naturally by soaking in potable water and allowing it to stand for 12, 24, 48 and 72 hours. At the end of fermentation, the seeds were oven dried at 600C for 12 hours and then milled into flour. Flour obtained from unfermented seeds served as control: hence a total of five flour samples. The functional properties were analyzed using standard methods. Fermentation significantly (p<0.05) increased the water holding capacity of Moringa seed flour from 0.86g/g - 2.31g/g. The highest value was observed after 48 hours of fermentation The same trend was observed for oil absorption capacity with values between 0.87 and 1.91g/g. Flour from unfermented Moringa seeds had a bulk density of 0.60g/cm3 which was significantly (p<0.05) higher than the bulk densities of flours from seeds fermented for 12, 24 and 48. Fermentation significantly (p<0.05) decreased the dispersibility of Moringa seed flours from 36% to 21, 24, 29 and 20% after 12, 24, 48 and 72 hours of fermentation respectively. The flours’ emulsifying capacities increased significantly (p<0.05) with increasing fermentation time with values between 50 – 68%. The flour obtained from seeds fermented for 12 hours had a significantly (p<0.05) higher foaming capacity of 16% while the flour obtained from seeds fermented for 0, 24 and 72 hours had the least foaming capacities of 9%. Flours from seeds fermented for 12 and 48 hours had better functional properties than flours from seeds fermented for 24 and 72 hours.

Keywords: fermentation, flour, functional properties, Moringa

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Authors: Domenica L. DelPrete

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Viewed as a type of rapport-talk, troubles telling is a common conversational practice among female friends who wish to establish connection, show empathy, or share a disconcerting experience. This study shows how troubles talk between a mother and her adolescent daughter has a different interactional outcome. Specifically, it reveals how discursive interaction with an adolescent daughter becomes increasingly volatile when the mother steps out of the role of nurturer and into the role of troubles teller. Naturally occurring interactions between a mother and her 15-year-old daughter were videotaped in their family home over a two-week period. The data were primarily analyzed from an interactional sociolinguistic perspective, using conversation analytic techniques for transcriptions and discursive analysis. The following questions guided this research: (1) How are troubles telling discursively accomplished in the everyday talk of a mother and her adolescent daughter? and (2) What topic prompts the mother to engage in troubles talk? The data show that the mother engages her daughter in troubles to talk on issues related to body image and physical appearance and does so by (1) repeated questioning, (2) not accepting the daughter’s response as adequate, and (3) proffering self-deprecation. Findings reveal that engaging an adolescent daughter in a conversational practice reserved for female friendship groups creates a negative connection and relational disharmony. Since 'telling one’s troubles' assumes an egalitarian relationship between individuals, mother’s trouble telling creates a peer-like interaction that the adolescent daughter repeatedly resists. This study also proposes a discursive consciousness raising, which hopes to enhance communication between mothers and daughters by revealing the signals that show an adolescent daughter’s unwillingness to participate in troubles talk. Being in tune to these cues may prompt mothers to hesitate before pursuing a topic that will not garner the positive interactional outcome they seek.

Keywords: discursive interaction, maternal roles, mother-daughter interaction, troubles telling

Procedia PDF Downloads 131

Authors: Rupali, Anil Kumar Jaiswal

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Cloud computing can be defined as one of the prominent technologies that lets a user change, configure and access the services online. it can be said that this is a prototype of computing that helps in saving cost and time of a user practically the use of cloud computing can be found in various fields like education, health, banking etc.  Cloud computing is an internet dependent technology thus it is the major responsibility of Cloud Service Providers(CSPs) to care of data stored by user at data centers. Scheduling in cloud computing environment plays a vital role as to achieve maximum utilization and user satisfaction cloud providers need to schedule resources effectively.  Job scheduling for cloud computing is analyzed in the following work. To complete, recreate the task calculation, and conveyed scheduling methods CloudSim3.0.3 is utilized. This research work discusses the job scheduling for circulated processing condition also by exploring on this issue we find it works with minimum time and less cost. In this work two load balancing techniques have been employed: ‘Throttled stack adjustment policy’ and ‘Active VM load balancing policy’ with two brokerage services ‘Advanced Response Time’ and ‘Reconfigure Dynamically’ to evaluate the VM_Cost, DC_Cost, Response Time, and Data Processing Time. The proposed techniques are compared with Round Robin scheduling policy.

Keywords: physical machines, virtual machines, support for repetition, self-healing, highly scalable programming model

Procedia PDF Downloads 168

Authors: Vadim Kulikov

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The classical Stroop effect is the phenomenon that it takes more time to name the ink color of a printed word if the word denotes a conflicting color than if it denotes the same color. Over the last 80 years, there have been many variations of the experiment revealing various mechanisms behind semantic, attentional, behavioral and perceptual processing. The Stroop task is known to exhibit asymmetry. Reading the words out loud is hardly dependent on the ink color, but naming the ink color is significantly influenced by the incongruent words. This asymmetry is reversed, if instead of naming the color, one has to point at a corresponding color patch. Another debated aspects are the notions of automaticity and how much of the effect is due to semantic and how much due to response stage interference. Is automaticity a continuous or an all-or-none phenomenon? There are many models and theories in the literature tackling these questions which will be discussed in the presentation. None of them, however, seems to capture all the findings at once. A computational model is proposed which is based on the philosophical idea developed by the author that the mind operates as a collection of different information processing modalities such as different sensory and descriptive modalities, which produce emergent phenomena through mutual interaction and coherence. This is the framework theory where ‘framework’ attempts to generalize the concepts of modality, perspective and ‘point of view’. The architecture of this computational model consists of blocks of neurons, each block corresponding to one framework. In the simplest case there are four: visual color processing, text reading, speech production and attention selection modalities. In experiments where button pressing or pointing is required, a corresponding block is added. In the beginning, the weights of the neural connections are mostly set to zero. The network is trained using Hebbian learning to establish connections (corresponding to ‘coherence’ in framework theory) between these different modalities. The amount of data fed into the network is supposed to mimic the amount of practice a human encounters, in particular it is assumed that converting written text into spoken words is a more practiced skill than converting visually perceived colors to spoken color-names. After the training, the network performs the Stroop task. The RT’s are measured in a canonical way, as these are continuous time recurrent neural networks (CTRNN). The above-described aspects of the Stroop phenomenon along with many others are replicated. The model is similar to some existing connectionist models but as will be discussed in the presentation, has many advantages: it predicts more data, the architecture is simpler and biologically more plausible.

Keywords: connectionism, Hebbian learning, artificial neural networks, philosophy of mind, Stroop

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Authors: Song Zhang, Hui Wang, Johannes Henrich Schleifenbaum

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Due to the increasing demand for quality assurance and reliability for additive manufacturing, the development of an advanced in-situ monitoring system is required to monitor the process anomalies as input for further process control. Optical-based monitoring systems, such as CMOS cameras and NIR cameras, are proved as effective ways to monitor the geometrical distortion and exceptional thermal distribution. Therefore, many studies and applications are focusing on the availability of the optical-based monitoring system for detecting varied types of defects. However, the capability of the monitoring setup is not quantified. In this study, a quantification model to evaluate the capability of the monitoring setups for the LPBF machine based on acquired monitoring data of a designed test artifact is presented, while the design of the relevant test artifacts is discussed. The monitoring setup is evaluated based on its hardware properties, location of the integration, and light condition. Methodology of data processing to quantify the capacity for each aspect is discussed. The minimal capability of the detectable size of the monitoring set up in the application is estimated by quantifying its resolution and accuracy. The quantification model is validated using a CCD camera-based monitoring system for LPBF machines in the laboratory with different setups. The result shows the model to quantify the monitoring system's performance, which makes the evaluation of monitoring systems with the same concept but different setups possible for the LPBF process and provides the direction to improve the setups.

Keywords: data processing, in-situ monitoring, LPBF process, optical system, quantization model, test artifact

Procedia PDF Downloads 197

Authors: Tingwei Shu, Dong Zhou, Chengjun Guo

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Semantic communication is an emerging form of communication that realize intelligent communication by extracting semantic information of data at the source and transmitting it, and recovering the data at the receiving end. It can effectively solve the problem of data transmission under the situation of large data volume, low SNR and restricted bandwidth. With the development of Deep Learning, semantic communication further matures and is gradually applied in the fields of the Internet of Things, Uumanned Air Vehicle cluster communication, remote sensing scenarios, etc. We propose an improved semantic communication system for the situation where the data volume is huge and the spectrum resources are limited during the transmission of remote sensing images. At the transmitting, we need to extract the semantic information of remote sensing images, but there are some problems. The traditional semantic communication system based on Convolutional Neural Network cannot take into account the global semantic information and local semantic information of the image, which results in less-than-ideal image recovery at the receiving end. Therefore, we adopt the improved vision-Transformer-based structure as the semantic encoder instead of the mainstream one using CNN to extract the image semantic features. In this paper, we first perform pre-processing operations on remote sensing images to improve the resolution of the images in order to obtain images with more semantic information. We use wavelet transform to decompose the image into high-frequency and low-frequency components, perform bilinear interpolation on the high-frequency components and bicubic interpolation on the low-frequency components, and finally perform wavelet inverse transform to obtain the preprocessed image. We adopt the improved Vision-Transformer structure as the semantic coder to extract and transmit the semantic information of remote sensing images. The Vision-Transformer structure can better train the huge data volume and extract better image semantic features, and adopt the multi-layer self-attention mechanism to better capture the correlation between semantic features and reduce redundant features. Secondly, to improve the coding efficiency, we reduce the quadratic complexity of the self-attentive mechanism itself to linear so as to improve the image data processing speed of the model. We conducted experimental simulations on the RSOD dataset and compared the designed system with a semantic communication system based on CNN and image coding methods such as BGP and JPEG to verify that the method can effectively alleviate the problem of excessive data volume and improve the performance of image data communication.

Keywords: semantic communication, transformer, wavelet transform, data processing

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Authors: A. O. Glazkov, A. S. Krylova, G. G. Nadareishvili, A. S. Terenchenko, S. I. Yudin

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The topic of the presented materials concerns the design of the exhaust system for a certain internal combustion engine. The exhaust system can be divided into two parts. The first is the engine exhaust manifold, turbocharger, and catalytic converters, which are called “hot part.” The second part is the gas exhaust system, which contains elements exclusively for reducing exhaust noise (mufflers, resonators), the accepted designation of which is the "cold part." The design of the exhaust system from the point of view of acoustics, that is, reducing the exhaust noise to a predetermined level, consists of working on the second part. Modern computer technology and software make it possible to design "cold part" with high accuracy in a given frequency range but with the condition of accurately specifying the input parameters, namely, the amplitude spectrum of the input noise and the acoustic impedance of the noise source in the form of an engine with a "hot part". Getting this data is a difficult problem: high temperatures, high exhaust gas velocities (turbulent flows), and high sound pressure levels (non-linearity mode) do not allow the calculated results to be applied with sufficient accuracy. The aim of this work is to obtain the most reliable acoustic output parameters of an engine with a "hot part" based on a complex of computational and experimental studies. The presented methodology includes several parts. The first part is a finite element simulation of the "cold part" of the exhaust system (taking into account the acoustic impedance of radiation of outlet pipe into open space) with the result in the form of the input impedance of "cold part". The second part is a finite element simulation of the "hot part" of the exhaust system (taking into account acoustic characteristics of catalytic units and geometry of turbocharger) with the result in the form of the input impedance of the "hot part". The next third part of the technique consists of the mathematical processing of the results according to the proposed formula for the convergence of the mathematical series of summation of multiple reflections of the acoustic signal "cold part" - "hot part". This is followed by conducting a set of tests on an engine stand with two high-temperature pressure sensors measuring pulsations in the nozzle between "hot part" and "cold part" of the exhaust system and subsequent processing of test results according to a well-known technique in order to separate the "incident" and "reflected" waves. The final stage consists of the mathematical processing of all calculated and experimental data to obtain a result in the form of a spectrum of the amplitude of the engine noise and its acoustic impedance.

Keywords: acoustic impedance, engine exhaust system, FEM model, test stand

Procedia PDF Downloads 59

Authors: Boris Ershov

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Depression is the most common mood disorder experienced by patients who have sustained a traumatic brain injury (TBI) and is associated with poorer cognitive functional outcomes. However, in some cases, similar cognitive impairments can also be observed in depression. There is not enough information about the features of the cognitive deficit in patients with TBI in relation to patients with depression. TBI patients without depressive symptoms (TBInD, n25), TBI patients with depressive symptoms (TBID, n31), and 28 patients with bipolar II disorder (BP) were included in the study. There were no significant differences in participants in respect to age, handedness and educational level. The patients clinical status was determined by using Montgomery–Asberg Depression Rating Scale (MADRS). All participants completed a cognitive battery (The Brief Assessment of Cognition in Affective Disorders (BAC-A)). Additionally, the Rey–Osterrieth Complex Figure (ROCF) was used to assess visuospatial construction abilities and visual memory, as well as planning and organizational skills. Compared to BP, TBInD and TBID showed a significant impairments in visuomotor abilities, verbal and visual memory. There were no significant differences between BP and TBID groups in working memory, speed of information processing, problem solving. Interference effect (cognitive inhibition) was significantly greater in TBInD and TBID compared to BP. Memory bias towards mood-related information in BP and TBID was greater in comparison with TBInD. These results suggest that depressive symptoms are associated with impairments some executive functions in combination at decrease of speed of information processing.

Keywords: bipolar II disorder, depression, neurocognitive deficits, traumatic brain injury

Procedia PDF Downloads 347

Authors: Sahar S. Tabrizi, Dogan Ibrahim

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Cloud Computing is a convenient model for on-demand networks that uses shared pools of virtual configurable computing resources, such as servers, networks, storage devices, applications, etc. The cloud serves as an environment for companies and organizations to use infrastructure resources without making any purchases and they can access such resources wherever and whenever they need. Cloud computing is useful to overcome a number of problems in various Information Technology (IT) domains such as Geographical Information Systems (GIS), Scientific Research, e-Governance Systems, Decision Support Systems, ERP, Web Application Development, Mobile Technology, etc. Companies can use Cloud Computing services to store large amounts of data that can be accessed from anywhere on Earth and also at any time. Such services are rented by the client companies where the actual rent depends upon the amount of data stored on the cloud and also the amount of processing power used in a given time period. The resources offered by the cloud service companies are flexible in the sense that the user companies can increase or decrease their storage requirements or the processing power requirements at any time, thus minimizing the overall rental cost of the service they receive. In addition, the Cloud Computing service providers offer fast processors and applications software that can be shared by their clients. This is especially important for small companies with limited budgets which cannot afford to purchase their own expensive hardware and software. This paper is an overview of the Cloud Computing, giving its types, principles, advantages, and disadvantages. In addition, the paper gives some example engineering applications of Cloud Computing and makes suggestions for possible future applications in the field of engineering.

Keywords: cloud computing, cloud systems, cloud services, IaaS, PaaS, SaaS

Procedia PDF Downloads 233