Search results for: sensory processing patterns

Authors: Liav Orgad

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This article challenges one of the most fundamental propositions in the democratic theory that the majority culture is protected merely by the forces of democracy and thus needs no special legal protection. By describing changes in the patterns of migration to Europe, in the face of the European society, and in the world as a whole, the Article demonstrates that the majority culture is no longer automatically protected by the forces of democracy. It claims that the changing reality is not adequately addressed by political theory and human rights law and advances the promotion of a new concept—'cultural majority rights'.

Keywords: European migration, European demography, democratic theory, majority rights, integration

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Authors: Georgios P. Georgiou

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Infants are able to discriminate a number of sound contrasts in most languages. However, this ability is not available in adults who might face difficulties in discriminating accurately second language sound contrasts as they filter second language speech through the phonological categories of their native language. For example, Spanish speakers often struggle to perceive the difference between the English /ε/ and /æ/ because both vowels do not exist in their native language; so they assimilate these vowels to the closest phonological category of their first language. The present study aims to uncover the perceptual patterns of Arabic adult speakers in regard to the vowels of their second language (Greek). Still, there is not any study that investigates the perception of Greek vowels by Arabic speakers and, thus, the present study would contribute to the enrichment of the literature with cross-linguistic research in new languages. To the purpose of the present study, 15 native speakers of Egyptian Arabic who permanently live in Cyprus and have adequate knowledge of Greek as a second language passed through vowel assimilation and vowel contrast discrimination tests (AXB) in their second language. The perceptual stimuli included non-sense words that contained vowels in both stressed and unstressed positions. The second language listeners’ patterns were analyzed through the Perceptual Assimilation Model which makes testable hypotheses about the assimilation of second language sounds to the speakers’ native phonological categories and the discrimination accuracy over second language sound contrasts. The results indicated that second language listeners assimilated pairs of Greek vowels in a single phonological category of their native language resulting in a Category Goodness difference assimilation type for the Greek stressed /i/-/e/ and the Greek stressed-unstressed /o/-/u/ vowel contrasts. On the contrary, the members of the Greek unstressed /i/-/e/ vowel contrast were assimilated to two different categories resulting in a Two Category assimilation type. Furthermore, they could discriminate the Greek stressed /i/-/e/ and the Greek stressed-unstressed /o/-/u/ contrasts only in a moderate degree while the Greek unstressed /i/-/e/ contrast could be discriminated in an excellent degree. Two main implications emerge from the results. First, there is a strong influence of the listeners’ native language on the perception of the second language vowels. In Egyptian Arabic, contiguous vowel categories such as [i]-[e] and [u]-[o] do not have phonemic difference but they are subject to allophonic variation; by contrast, the vowel contrasts /i/-/e/ and /o/-/u/ are phonemic in Greek. Second, the role of stress is significant for second language perception since stressed vs. unstressed vowel contrasts were perceived in a different manner by the Greek listeners.

Keywords: Arabic, bilingual, Greek, vowel perception

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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: Mohsen Solimani Babarsad, Payam Taheri

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Numerical results of vertical slot fishways with and without cylinders study are presented. The simulated results and the measured data in the fishways are compared to validate the application of the model. This investigation is made using FLUENT V.6.3, a Computational Fluid Dynamics solver. Advantages of using these types of numerical tools are the possibility of avoiding the St.-Venant equations’ limitations, and turbulence can be modeled by means of different models such as the k-ε model. In general, the present study has demonstrated that the CFD model could be useful for analysis and design of vertical slot fishways with cylinders.

Keywords: slot Fish-way, CFD, k-ε model, St.-Venant equations’

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Authors: Nazli Uren, Ayse Okur

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Tactile comfort of a textile product is an essential property and a major concern when it comes to customer perceptions and preferences. The subjective nature of comfort and the difficulties regarding the simulation of human hand sensory feelings make it hard to establish a well-accepted link between tactile comfort and objective evaluations. On the other hand, shear behavior of a fabric is a mechanical parameter which can be measured by various objective test methods. The principal aim of this study is to determine the tactile comfort of commercially available denim fabrics by subjective measurements, create a tactile score database for denim fabrics and investigate the relations between tactile comfort and shear behavior. In-plane shear behaviors of 17 different commercially available denim fabrics with a variety of raw material and weave structure were measured by a custom design shear frame and conventional bias extension method in two corresponding diagonal directions. Tactile comfort of denim fabrics was determined via subjective customer evaluations as well. Aforesaid relations were statistically investigated and introduced as regression equations. The analyses regarding the relations between tactile comfort and shear behavior showed that there are considerably high correlation coefficients. The suggested regression equations were likewise found out to be statistically significant. Accordingly, it was concluded that the tactile comfort of denim fabrics can be estimated with a high precision, based on the results of in-plane shear behavior measurements.

Keywords: denim fabrics, in-plane shear behavior, linear regression estimation, tactile comfort

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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: Jeansou Moun

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This study argues that constructivism and structuralism, which have been the two important schools of mathematical philosophy since the mid-19th century, can and should be synthesized into structure-constructivism. In fact, the philosophy of mathematics is divided into more than ten schools depending on the point of view. However, the biggest trend is Platonism which claims that mathematical objects are "abstract entities" that exists independently of the human mind and material objects. Its opposite is constructivism. According to the latter, mathematical objects are products of the construction of the human mind. However, whether the basis of the construction is a logical device, a symbolic system, or an empirical perception, it is subdivided into logicism, formalism, and intuitionism. However, these three schools themselves are further subdivided into various variants, and among them, structuralism, which emerged in the mid-20th century, is receiving the most attention. On the other hand, structuralism which emphasizes structure instead of individual objects, is divided into non-eliminative structuralism, which supports the a priori of structure, and non-eliminative structuralism, which rejects any abstract entity. In this context, it is believed that the structure itself is not an a priori entity but a result of the construction of the cognitive subject and that no object has ever been given to us in its full meaning from the outset. In other words, concepts are progressively structured through a dialectical cycle between sensory perception, imagination (abstraction), concepts, judgments, and reasoning. Symbols are needed for formal operation. However, without concrete manipulation, the formal operation cannot have any meaning. However, when formal structurization is achieved, the reality (object) itself is also newly structured. This is the "structure-constructivism".

Keywords: philosophy of mathematics, platonism, logicism, formalism, constructivism, structuralism, structure-constructivism

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Authors: Farnaz Farhangi

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Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance.

Keywords: artificial neural network (ANN), double seasonal ARIMA, forecasting, hybrid model

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Authors: Evgeny Pyshkin, Maxim Mozgovoy, Vladislav Volkov

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The paper follows a discourse on computer-assisted language learning. We examine problems of foreign language teaching and learning and introduce a metamodel that can be used to define learning models of language grammar structures in order to support teacher/student interaction. Special attention is paid to the concept of a virtual language lab. Our approach to language education assumes to encourage learners to experiment with a language and to learn by discovering patterns of grammatically correct structures created and managed by a language expert.

Keywords: computer-assisted instruction, language learning, natural language grammar models, HCI

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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: Salamatu Shaibu, Jan Hernning Sommer

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Integrating biodiversity conservation practices in mining landscapes ensures the continual provision of various ecosystem services to the dependent communities whilst serving as ecological insurance for corporate mining when purchasing reclamation security bonds. Climate hazards such as long dry seasons, erratic rainfall patterns, and extreme weather events contribute to biodiversity loss in addition to the impact due to mining. Both corporate mining and mine-fringe communities perceive the effect of climate on biodiversity from the context of the benefits they accrue, which motivate their conservation practices. In this study, pragmatic approaches including semi-structured interviews, field visual observation, and review were used to collect data on corporate mining employees and households of fringing communities in the southwestern mining hub. The perceived changes in the local climatic conditions and the consequences on environmental management practices that promote biodiversity conservation were examined. Using a thematic content analysis tool, the result shows that best practices such as concurrent land rehabilitation, reclamation ponds, artificial wetlands, land clearance, and topsoil management are directly affected by prolonging long dry seasons and erratic rainfall patterns. Excessive dust and noise generation directly affect both floral and faunal diversity coupled with excessive fire outbreaks in rehabilitated lands and nearby forest reserves. Proposed adaptive measures include engaging national conservation authorities to promote reforestation projects around forest reserves. National government to desist from using permit for mining concessions in forest reserves, engaging local communities through educational campaigns to control forest encroachment and burning, promoting community-based resource management to promote community ownership, and provision of stricter environmental legislation to compel corporate, artisanal, and small scale mining companies to promote biodiversity conservation.

Keywords: biodiversity conservation, climate hazards, corporate mining, mining landscapes

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Authors: Kamal Donko

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In the frontier zone of Benin Republic, land seems to be a fundamental political resource as it is used as a tool for socio-political mobilization, blackmail, inclusion and exclusion, conquest and political control. This paper seeks to examine the complex and intriguing interlinks between land, identity and politics in central Benin. It aims to investigate what roles territorialisation and land ownership are playing in the electioneering process in central Benin. It employs ethnographic multi-sited approach to data collections including observations, interviews and focused group discussions. Research findings reveal a complex and intriguing relationship between land ownership and politics in central Benin. Land is found to be playing a key role in the electioneering process in the region. The study has also discovered many emerging socio-spatial patterns of controlling and maintaining political power in the zone which are tied to land politics. These include identity reconstruction and integration mechanism through intermarriages, socio-political initiatives and construction of infrastructure of sovereignty. It was also found that ‘Diaspora organizations’ and identity issues; strategic creation of administrative units; alliance building strategy; gerrymandering local political field, etc. These emerging socio-spatial patterns of territorialisation for maintaining political power affect migrant and native communities’ relationships. It was also found that ‘Diaspora organizations’ and identity issues; strategic creation of administrative units; alliance building strategy; gerrymandering local political field, etc. are currently affecting migrant’s and natives’ relationships. The study argues that territorialisation is not only about national boundaries and the demarcation between different nation states, but more importantly, it serves as a powerful tool of domination and political control at the grass root level. Furthermore, this study seems to provide another perspective from which the political situation in Africa can be studied. Investigating how the dynamics of land ownership is influencing politics at the grass root or micro level, this study is fundamental to understanding spatial issues in the frontier zone.

Keywords: land, migration, politics, territorialisation

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Authors: Neti Yuliana, Srisetyani, Siti Nurdjanah, Dewi Sartika, Yoan Martiansari, Putri Nabila

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Naturally sweet potato flour usually requires a modification process to improve its inherent property for expanding its application in food system. The study was aimed to modify sweet potato flour (SPF), to increase its utilization for composite noodle production, trough fermentation of sweet potato slices before its flouring process. Fermentation were prepared with five different starters: pickle brine, Lactobacillus plantarum, Leuconostoc mesenteroides, mixed of Lactobacillus plantarum, Leuconostoc mesenteroides , and mixed of Lactobacillus plantarum, Leuconostoc mesenteroides, and Sacharomyces cerevisiae. Samples were withdrawn every 0, 24, 48, 72 and 96 hours. The fermented flours were characterized for swelling power, solubility, paste transmittance, pH, sensory properties (acidic aroma and whiteness), and the amount of broken composite noodle strips. The results indicated that there was no significant effect of different starters on fermented SPF characteristic and on the amount of broken noodle strip, while length of fermentation significantly affected. Longer fermentation, reaching 48-72 h, increased swelling power, pH, acidic aroma and whiteness of flour and reduced solubility, paste transmittance, and the amount of broken noodle strip. The results suggested that fermentation within 48-72 h period of time could provide great composite SPF for noodle.

Keywords: starters, fermented flour, sweet potato, composite noodle

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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: Gerald Steyn

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Sub-Saharan Africa is the world's fastest urbanizing region, but approximately 60 to 70 percent of urban African households are poor and living in slums. Although influential global institutions such as the World Bank propagate a new approach to housing and land policies, sustainable African urban concepts have yet to be applied significantly or even convincingly conceptualized. Most African city planners, urban designers, architects, policymakers, and developers have been trained in Western curriculums and continue to practice and plan according to such formal paradigms. Only a few activists promote Post-Colonial Afrocentric urbanism, recognizing the imperative of foregrounding the needs of low-income people. There is a vast body of authoritative literature on analyzing poverty and slums in sub-Saharan Africa and on promoting the need for land and city planning reform. However, of the latter, only a few venture beyond advising and sometimes outlining policy changes. The current study moves beyond a purely theoretical discourse into the realm of practice by designing replicable diagrammatic concepts at different urban scales. The guiding philosophy was that land-use concepts and urban requirements favoring low-income households must be fully integrated into the larger conurbation. Information was derived from intensive research over two decades, involving literature surveys and observations during regular travels into East and Southern Africa. Appropriate existing urban patterns, particularly vernacular and informal, were subsequently analyzed and reimagined as precedents to inform and underpin the represented design concepts. Five interrelated concepts are proposed, ranging in scale from (1) regional to (2) cities and (3) urban villages to (4) neighborhoods and (5) streets. Each concept is described, first in terms of its context and associated issues of concern, followed by a discussion of the patterns available to inform a possible solution, and finally, an explanation and graphic illustration of the proposal. Since each of the five concepts is unfolded from existing informal and vernacular practices studied in situ, the approach is entirely bottom-up. Contrary to an idealized vision of the African city, this study proposes actual concepts for critical assessment by peers in the tradition of architectural research in design.

Keywords: african urban concepts, post-colonial afrocentric urbanism, sub-saharan africa, sustainable african urban design

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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: Sorcha Bennett, Joe Sullivan

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Triple-Level Cell (TLC) NAND Flash memory at, and below, 20nm (nanometer) is still largely unexplored by researchers, and with the ever more commonplace existence of Flash in consumer and enterprise applications there is a need for such gaps in knowledge to be filled. At the time of writing, there was little published data or literature on TLC, and more specifically reliability testing, with a further emphasis on both endurance and retention. This paper will give an introduction to NAND Flash memory, followed by an overview of the relevant current research on the reliability of Flash memory, along with the planned future work which will provide results to help characterise the reliability of TLC memory.

Keywords: endurance, patterns, raw flash, reliability, retention, TLC NAND flash memory, trade-off

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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: Zarine Avetisyan

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Paper presents the discussion of scholars concerning speech impact, peculiarities of its realization, speech strategies, and techniques. Departing from the viewpoints of many prominent linguists, the paper suggests manipulative argumentation be viewed as a most pervasive speech strategy with a certain set of techniques which are to be found in modern American political discourse. The precedence of their occurrence allows us to regard them as pragmatic patterns of speech impact realization in effective public speaking.

Keywords: speech impact, manipulative argumentation, political discourse, technique

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Authors: Doo Byong Bae, Jae Jun Yoo, Il Gyu Park, Choi Seowon, Oh Chang Kook

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There are several wind load provisions to evaluate the wind response on tank structures such as API, Euro-code, etc. the assessment of wind action applying these provisions is made by performing the finite element analysis using both linear bifurcation analysis and geometrically nonlinear analysis. By comparing the pressure patterns obtained from the analysis with the results of wind tunnel test, most appropriate wind load criteria will be recommended.

Keywords: wind load, finite element analysis, linear bifurcation analysis, geometrically nonlinear analysis

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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: Olga Colbert

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The present work examines the representation of dreaming in Charles Dickens’ novels, particularly Oliver Twist. Dickens showed great interest in the science of dreaming and had ample knowledge of the latest dream theories in the Victorian era, as can be seen in his personal correspondence, most notably in his famous letter to Dr. Thomas Stone on 2/2/1851. This essay places Dickens’ personal writings side by side with his novels to elucidate whether the scientific paradigm about dreaming included in the novel is consistent with the current (in Dickens’ time) scientific knowledge, or whether it is anachronistic or visionary (ahead of his time). Oliver Twist is particularly useful because it contains entire passages pondering on the nature of dreaming, enumerating types of common dreams, and taking a stand on the interference of sensory perception during the dreaming state. The author is particularly intrigued by Dickens’ assumption of the commonality and universality of lucid dreaming as revealed in these passages. This essay places popular Victorian dream theories, such as those contained in Robert Macnish’s The Philosophy of Sleep, side by side with recent dream theory, particularly psychophysiologist Stephen LaBerge’s numerous articles and books on the topic of lucid dreaming to see if Dickens deviated in any way from the reigning paradigm of the Victorian era in his representation of dreaming in his novels. While Dickens puts to great narrative use many of the characteristics of dreaming described by leading Victorian theorists, the author of this study argues, however, that Dickens’ most visionary statements derive from his acute observations of his own dreaming experiences.

Keywords: consciousness, Dickens, dreaming, lucid dreaming, Victorian

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Authors: Siddhartha Khare, Nitish Kr Boro, Omm Animesh Mishra

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Recent food price hikes may signal the end of an era of predictable global grain crop plenty due to climate change, population expansion, and dietary changes. Food consumption will treble in 20 years, requiring enormous production expenditures. Climate and the atmosphere changed owing to rainfall and seasonal cycles in the past decade. India's tropical agricultural relies on evapotranspiration and monsoons. In places with limited resources, the global environmental change affects agricultural productivity and farmers' capacity to adjust to changing moisture patterns. Motivated by these difficulties, satellite remote sensing might be combined with near-surface imaging data (smartphones, UAVs, and PhenoCams) to enable phenological monitoring and fast evaluations of field-level consequences of extreme weather events on smallholder agriculture output. To accomplish this technique, we must digitally map all communities agricultural boundaries and crop kinds. With the improvement of satellite remote sensing technologies, a geo-referenced database may be created for rural Indian agriculture fields. Using AI, we can design digital agricultural solutions for individual farms. Main objective is to Geo-enable each farm along with their seasonal crop information by combining Artificial Intelligence (AI) with satellite and near-surface data and then prepare long term crop monitoring through in-depth field analysis and scanning of fields with satellite derived vegetation indices. We developed an AI based algorithm to understand the timelapse based growth of vegetation using PhenoCam or Smartphone based images. We developed an android platform where user can collect images of their fields based on the android application. These images will be sent to our local server, and then further AI based processing will be done at our server. We are creating digital boundaries of individual farms and connecting these farms with our smart phone application to collect information about farmers and their crops in each season. We are extracting satellite-based information for each farm from Google earth engine APIs and merging this data with our data of tested crops from our app according to their farm’s locations and create a database which will provide the data of quality of crops from their location.

Keywords: artificial intelligence, satellite remote sensing, crop monitoring, android and web application

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Authors: Ceyda Dadali, Yeşim Elmaci

Abstract:

Coffee was introduced as an economic crop during the fifteenth century; nowadays it is the most important food commodity ranking second after crude oil. Desirable sensory properties make coffee one of the most often consumed and most popular beverages in the world. The coffee preparation method has a significant effect on flavor and composition of coffee brews. Three different extraction methodologies namely decoction, infusion and pressure methods have been used for coffee brew preparation. Each of these methods is related to specific granulation (coffee grind) of coffee powder, water-coffee ratio temperature and brewing time. Coffee is a mixture of 1500 chemical compounds. Chemical composition of coffee highly depends on brewing methods, coffee bean species and roasting time-temperature. Coffee contains a wide number of very important bioactive compounds, such as diterpenes: cafestol and kahweol, alkaloids: caffeine, theobromine and trigonelline, melanoidins, phenolic compounds. The phenolic compounds of coffee include chlorogenic acids (quinyl esters of hidroxycinnamic acids), caffeic, ferulic, p-coumaric acid. In coffee caffeoylquinic acids, feruloylquinic acids and di-caffeoylquinic acids are three main groups of chlorogenic acids constitues 6% -10% of dry weight of coffee. The bioavailability of chlorogenic acids in coffee depends on the absorption and metabolization to biomarkers in individuals. Also, the interaction of coffee polyphenols with other compounds such as dietary proteins affects the biomarkers. Since bioactive composition of coffee depends on brewing methods effect of coffee brewing method on bioactive compounds of coffee will be discussed in this study.

Keywords: bioactive compounds of coffee, biomarkers, coffee brew, effect of brewing

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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

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Authors: Sajid Maqsood, Aysha Al Rashedi, Aisha Abushelaibi, Kusaimah Manheem

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Impact of different natural antioxidants on lipid oxidation, microbial load and sensorial quality in ground camel meat (leg region) during 9 days of refrigerated storage were investigated. Control camel meat showed higher lipid oxidation products (Peroxide value and Thiobarbituric acid reactive substances (TBARS)) during the storage period. Upon addition of different natural antioxidants PV and TBARS were retarded, especially in samples added with tannic acid (TA), catechin (CT) and gallic acid (GA) (p<0.05). Haem iron content decreased with increasing storage period and was found to be lower in samples added with caffeic acid (CA) and gallic acid (GA) at the end of storage period (p<0.05). Furthermore, lower mesophilic bacterial count (MBC) and psychrophilic bacterial counts (PBC) were observed in TA and CT treated samples compared to control and other samples (p<0.05). Camel meat treated with TA and CT also received higher likeness scores for colour, odor and overall appearance compared to control samples (p<0.05). Therefore, adding different natural antioxidants especially TA and CT showed retarding effect on lipid oxidation and microbial growth and were also effective in maintaining sensory attributes (color and odor) of ground camel meat during storage at 4°C. Hence, TA and CT could be considered as the potential natural antioxidant for preserving the quality of the camel meat displayed at refrigerated shelves.

Keywords: natural antioxidants, lipid oxidation, quality, camel meat

Procedia PDF Downloads 434

Authors: Lucas Barbosa Da Silva, Jun Okamoto Jr.

Abstract:

Precision Agriculture has long being benefited from crop fields’ aerial imagery. This important tool has allowed identifying patterns in crop fields, generating useful information to the production management. Reflectance intensity data in different ranges from the electromagnetic spectrum may indicate presence or absence of nutrients in the soil of an area. Different relations between the different light bands may generate even more detailed information. The knowledge of the nutrients content in the soil or in the crop during its growth is a valuable asset to the farmer that seeks to optimize its yield. However, small farmers in Brazil often lack the resources to access this kind information, and, even when they do, it is not presented in a comprehensive and/or objective way. So, the challenges of implementing this technology ranges from the sampling of the imagery, using aerial platforms, building of a mosaic with the images to cover the entire crop field, extracting the reflectance information from it and analyzing its relationship with the parameters of interest, to the display of the results in a manner that the farmer may take the necessary decisions more objectively. In this work, it’s proposed an analysis of soil nutrient contents based on image processing of satellite imagery and comparing its outtakes with commercial laboratory’s chemical analysis. Also, sources of satellite imagery are compared, to assess the feasibility of using Google Earth data in this application, and the impacts of doing so, versus the application of imagery from satellites like Landsat-8 and Pleiades. Furthermore, an algorithm for building mosaics is implemented using Google Earth imagery and finally, the possibility of using unmanned aerial vehicles is analyzed. From the data obtained, some soil parameters are estimated, namely, the content of Potassium, Phosphorus, Boron, Manganese, among others. The suitability of Google Earth Imagery for this application is verified within a reasonable margin, when compared to Pleiades Satellite imagery and to the current commercial model. It is also verified that the mosaic construction method has little or no influence on the estimation results. Variability maps are created over the covered area and the impacts of the image resolution and sample time frame are discussed, allowing easy assessments of the results. The final results show that easy and cheaper remote sensing and analysis methods are possible and feasible alternatives for the small farmer, with little access to technological and/or financial resources, to make more accurate decisions about soil nutrient management.

Keywords: remote sensing, precision agriculture, mosaic, soil, nutrient content, satellite imagery, aerial imagery

Procedia PDF Downloads 175