Search results for: content classification

Authors: Arshia Aflaki, Hadis Karimipour, Anik Islam

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Generative Adversarial Attacks (GAAs) threaten critical sectors, ranging from fingerprint recognition to industrial control systems. Existing Deep Learning (DL) algorithms are not robust enough against this kind of cyber-attack. As one of the most critical industries in the world, the power grid is not an exception. In this study, a Deep Reinforcement Learning-based (DRL) framework assisting the DL model to improve the robustness of the model against generative adversarial attacks is proposed. Real-world smart grid stability data, as an IIoT dataset, test our method and improves the classification accuracy of a deep learning model from around 57 percent to 96 percent.

Keywords: generative adversarial attack, deep reinforcement learning, deep learning, IIoT, generative adversarial networks, power system

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Authors: M. Caporale

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French Curricula across the US have recently suffered low enrollment and have experienced difficulties with retention, thus resulting in fewer students minoring and majoring in French and enrolling in upper-level classes. Successful undergraduate programs offer French courses with a strong cultural and interdisciplinary or multidisciplinary component. The World Language Curriculum in liberal arts colleges in America needs to take into account the cultural aspects of the language and encourage students to think critically about the country or countries they are studying. Limiting the critical inquiry to language or literature narrowly defined provides and incomplete and stagnant picture of France and the Francophone world in today's global community. This essay discusses the creation and implementation of a hybrid interdisciplinary L1/L2 course titled "Topics in Francophone Cinema" (subtitle "Francophone Women on Screen and Behind the Camera"). Content-based interdisciplinary courses undoubtedly increase the profile of French and Francophone cultural Studies by introducing students of other disciplines to fundamental questions relating to the French and Francophone cultures (in this case, women's rights in the Francophone world). At the same time, this study determines that through targeted reading and writing assignments, sustained aural exposure to L2 through film,and student participation in a one-credit supplementary weekly practicum (creative film writing workshop), significant advances in L2 competence are achieved with students' oral and written production levels evolving from Advanced Low to Advanced-mid, as defined by the ACFL guidelines. Use of differentiated assessment methods for L1/L2 and student learning outcomes for both groups will also be addressed.

Keywords: interdisciplinary, Francophone cultural studies, language competency, content-based

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

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Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.

Keywords: data mining, defect prediction, missing requirements, software quality

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Authors: Azza EL-Medany, Jamila EL-Medany

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Lung fibrosis is a common side effect of the chemotherapeutic agent, bleomycin. Current evidence suggests that reactive oxygen species may play a key role in the development of lung fibrosis. The present work studied the effect of green tea extract on bleomycin–induced lung fibrosis in rats. Animals were divided into three groups: (1) Saline control group; (2) bleomycin group in which rats were injected with bleomycin (15mg/kg,i.p.) three times a week for four weeks; (3) bleomycin and green tea group in which green tea extract was given to rats (100mg/kg/day, p.o) a week prior to bleomycin and daily during bleomycin injections for 4 weeks until the end of the experiment. Bleomycin–induced pulmonary injury and lung fibrosis that was indicated by increased lung hydroxyproline content, elevated nitric oxide synthase, myeoloperoxidase (MPO), platelet activating factor (PAF), tumor necrosis factor α (TNF_α), transforming growth factor 1β (TGF1β) and angiotensin converting enzyme (ACE) activity in lung tissues. On the other hand, bleomycin induced a reduction in reduced glutathione concentration (GSH). Moreover, bleomycin resulted in a severe histological changes in lung tissues revealed as lymphocytes and neutrophils infiltration, increased collagen deposition and fibrosis. Co-administration of bleomycin and green tea extract reduced bleomycin–induced lung injury as evaluated by the significant reduction in hydroxyproline content, nitric oxide synthase activity, levels of MPO, PAF, TNF-α, and ACE in lung tissues. Furthermore, green tea extract ameliorated bleomycin– induced reduction in GSH concentration. Finally, histological evidence supported the ability of green tea extract to attenuate bleomycin–induced lung fibrosis and consolidation. Thus, the finding of the present study provides that green tea may serve as a novel target for potential therapeutic treatment of lung fibrosis.

Keywords: bleomycin, lung fibrosis, green tea, oxygen species

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Authors: Bin Liu

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Prokaryotic promoter, consisted of two short DNA sequences located at in -35 and -10 positions, is responsible for controlling the initiation and expression of gene expression. Different types of promoters have different functions, and their consensus sequences are similar. In addition, their consensus sequences may be different for the same type of promoter, which poses difficulties for promoter identification. Unfortunately, all existing computational methods treat promoter identification as a binary classification task and can only identify whether a query sequence belongs to a specific promoter type. It is desired to develop computational methods for effectively identifying promoters and their types. Here, a two-layer predictor is proposed to try to deal with the problem. The first layer is designed to predict whether a given sequence is a promoter and the second layer predicts the type of promoter that is judged as a promoter. Meanwhile, we also analyze the importance of feature and sequence conversation in two aspects: promoter identification and promoter type identification. To the best knowledge of ours, it is the first computational predictor to detect promoters and their types.

Keywords: promoter, promoter type, random forest, sequence information

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Authors: Lee Yan Nian

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Safety investigation is different from an administrative investigation in that the former is conducted by an independent agency and the purpose of such investigation is to prevent accidents in the future and not to apportion blame or determine liability. Before October 2018, Taiwan railway occurrences were investigated by local supervisory authority. Characteristics of this kind of investigation are that enforcement actions, such as administrative penalty, are usually imposed on those persons or units involved in occurrence. On October 21, 2018, due to a Taiwan Railway accident, which caused 18 fatalities and injured another 267, establishing an agency to independently investigate this catastrophic railway accident was quickly decided. The Taiwan Transportation Safety Board (TTSB) was then established on August 1, 2019 to take charge of investigating major aviation, marine, railway and highway occurrences. The objective of this study is to assess the effectiveness of safety investigations conducted by the TTSB. In this study, the major railway occurrence investigation reports published by the TTSB are used for modeling and analysis. According to the classification of railway occurrences investigated by the TTSB, accident types of Taiwan railway occurrences can be categorized into: derailment, fire, Signal Passed at Danger and others. A Causal Factor Analysis System (CFAS) developed by the TTSB is used to identify the influencing causal factors and their causal relationships in the investigation reports. All terminologies used in the CFAS are equivalent to the Human Factors Analysis and Classification System (HFACS) terminologies, except for “Technical Events” which was added to classify causal factors resulting from mechanical failure. Accordingly, the Bayesian network structure of each occurrence category is established based on the identified causal factors in the CFAS. In the Bayesian networks, the prior probabilities of identified causal factors are obtained from the number of times in the investigation reports. Conditional Probability Table of each parent node is determined from domain experts’ experience and judgement. The resulting networks are quantitatively assessed under different scenarios to evaluate their forward predictions and backward diagnostic capabilities. Finally, the established Bayesian network of derailment is assessed using investigation reports of the same accident which was investigated by the TTSB and the local supervisory authority respectively. Based on the assessment results, findings of the administrative investigation is more closely tied to errors of front line personnel than to organizational related factors. Safety investigation can identify not only unsafe acts of individual but also in-depth causal factors of organizational influences. The results show that the proposed methodology can identify differences between safety investigation and administrative investigation. Therefore, effective intervention strategies in associated areas can be better addressed for safety improvement and future accident prevention through safety investigation.

Keywords: administrative investigation, bayesian network, causal factor analysis system, safety investigation

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Authors: E. Obreque-Slier, J. Laqui-Estaña, A. Peña-Neira, M. Medel-Marabolí

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Wine is an important source of polyphenols. Red wines show important concentrations of nonflavonoid (gallic acid, ellagic acid, caffeic acid and coumaric acid) and flavonoid compounds [(+)-catechin, (-)-epicatechin, (+)-gallocatechin and (-)-epigallocatechin]. However, a significant variability in the quantitative and qualitative distribution of chemical constituents in wine has to be expected depending on an array of important factors, such as the varietal differences of Vitis vinifera and cultural practices. It has observed that Carménère grapes present a differential composition and evolution of phenolic compounds when compared to other varieties and specifically with Cabernet Sauvignon grapes. Likewise, among the cultural practices, the aging in contact with oak wood is a high relevance factor. Then, the extraction of different polyphenolic compounds from oak wood into wine during its ageing process produces both qualitative and quantitative changes. Recently, many new techniques have been introduced in winemaking. One of these involves putting new pieces of wood (oak chips or inner staves) into inert containers. It offers some distinct and previously unavailable flavour advantages, as well as new options in wine handling. To our best knowledge, there is not information about the behaviour of Carménère wines (Chilean emblematic cultivar) in contact with oak wood. In addition, the effect of aging time and wood product (barrels, chips or staves) on the phenolic composition in Carménère wines has not been studied. This study aims at characterizing the condensed and hydrolyzable tannins from Carménère wines during the aging with staves, chips and barrels from French oak wood. The experimental design was completely randomized with two independent assays: aging time (0-12 month) and different formats of wood (barrel, chips and staves). The wines were characterized by spectrophotometric (total tannins and fractionation of proanthocyanidins into monomers, oligomers and polymers) and HPLC-DAD (ellagitannins) analysis. The wines in contact with different products of oak wood showed a similar content of total tannins during the study, while the control wine (without oak wood) presented a lower content of these compounds. In addition, it was observed that the polymeric proanthocyanidin fraction was the most abundant, while the monomeric fraction was the less abundant fraction in all treatments in two sample. However, significative differences in each fractions were observed between wines in contact from barrel, chips, and staves in two sample dates. Finally, the wine from barrels presented the highest content of the ellagitannins from the fourth to the last sample date. In conclusion, the use of alternative formats of oak wood affects the chemical composition of wines during aging, and these enological products are an interesting alternative to contribute with tannins to wine.

Keywords: enological inputs, oak wood aging, polyphenols, red wine

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Authors: Jyoti Kumari, Pavuluri Srinivasa Rao

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Jackfruit (ArtocarpusheterophyllusL.) is an underutilized yet highly nutritious fruit with unique flavour, known for its therapeutic and culinary properties. Fresh jackfruit bulb has a very short shelf life due to high moisture and sugar content leading to microbial and enzymatic browning, hindering its consumer acceptability and marketability. An attempt has been made for the preservation of the ripe jackfruit bulbs, by the application of high pressure (HP) over a range of 200-500 MPa at ambient temperature for dwell times ranging from 5 to 20 min. The physicochemical properties of jackfruit bulbs such as the pH, TSS, and titrable acidity were not affected by the pressurization process. The ripening index of the fruit bulb also decreased following HP treatment. While the ascorbic acid and antioxidant activity of jackfruit bulb were well retained by high pressure processing (HPP), the total phenols and carotenoids showed a slight increase. The HPP significantly affected the colour and textural properties of jackfruit bulb. High pressure processing was highly effective in reducing the browning index of jackfruit bulbs in comparison to untreated bulbs. The firmness of the bulbs improved upon the pressure treatment with longer dwelling time. The polyphenol oxidase has been identified as the most prominent oxidative enzyme in the jackfruit bulb. The enzymatic activity of polyphenol oxidase and peroxidase were significantly reduced by up to 40% following treatment at 400 MPa/15 min. HPP of jackfruit bulbs at ambient temperatures is shown to be highly beneficial in improving the shelf stability, retaining its nutrient profile, color, and appearance while ensuring the maximum inactivation of the spoilage enzymes.

Keywords: antioxidant capacity, ascorbic acid, carotenoids, color, HPP-high pressure processing, jackfruit bulbs, polyphenol oxidase, peroxidase, total phenolic content

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Authors: Jiang Xu, Daoping Wang, Yinghong Pan

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The jointing-booting stage is a critical period of both vegetative growth and reproductive growth in rice. Therefore, the proteomic analysis of the mutant Osbri1, whose corresponding gene OsBRI1 encodes the putative BRs receptor OsBRI1, at jointing-booting stage is very important for understanding the effects of BRs on vegetative and reproductive growth. In this study, the proteomes of leaves from an allelic mutant of the DWARF 61 (D61, OsBRI1) gene, Fn189 (dwarf54, d54) and its wild-type variety T65 (Taichung 65) at jointing-booting stage were analysed by using a Q Exactive plus orbitrap mass spectrometer, and more than 3,100 proteins were identified in each sample. Ontology analysis showed that these proteins distribute in various space of the cells, such as the chloroplast, mitochondrion, and nucleus, they functioned as structural components and/or catalytic enzymes and involved in many physiological processes. Moreover, quantitative analysis displayed that 266 proteins were differentially expressed in two samples, among them, 77 proteins decreased and 189 increased more than two times in Fn189 compared with T65, the proteins whose content decreased in Fn189 including b5-like Heme/Steroid binding domain containing protein, putative retrotransposon protein, putative glutaminyl-tRNA synthetase, and higher content proteins such as mTERF, putative Oligopeptidase homologue, zinc knuckle protein, and so on. A former study founded that the transcription level of a mTERF was up-regulated in the leaves of maize seedling after EBR treatment. In our experiments, it was interesting that one mTERF protein increased, but another mTERF decreased in leaves of Fn189 at jointing-booting stage, which suggested that BRs may have differential regulation mechanisms on the expression of various mTERF proteins. The relationship between other differential proteins with BRs is still unclear, and the effects of BRs on rice protein contents and its regulation mechanisms still need further research.

Keywords: bri1 mutant, jointing-booting stage, proteomic analysis, rice

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Authors: Narupot Putwattana

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Changes in global situation such as environmental and economic crisis brought the new perspective for science education called integrative biology. STEM has been increasingly mentioned for several educational researches as the approach which combines the concept in Science (S), Technology (T), Engineering (E) and Mathematics (M) to apply in teaching and learning process so as to strengthen the 21st-century skills such as creativity and critical thinking. Recent studies demonstrated STEM as the pedagogy which described the engineering process along with the science classroom activities. So far, pedagogical contents for STEM explaining the content in biology have been scarce. A qualitative literature review was conducted so as to gather the articles based on electronic databases (google scholar). STEM education, engineering design, teaching and learning of biology were used as main keywords to find out researches involving with the application of STEM in biology teaching and learning process. All articles were analyzed to obtain appropriate teaching and learning model that unify the core concept of biology. The synthesized model comprised of engineering design, inquiry-based learning, biological prototype and biologically-inspired design (BID). STEM content and context integration were used as the theoretical framework to create the integrative biology instructional model for STEM education. Several disciplines contents such as biology, engineering, and technology were regarded for inquiry-based learning to build biological prototype. Direct and indirect integrations were used to provide the knowledge into the biology related STEM strategy. Meanwhile, engineering design and BID showed the occupational context for engineer and biologist. Technological and mathematical aspects were required to be inspected in terms of co-teaching method. Lastly, other variables such as critical thinking and problem-solving skills should be more considered in the further researches.

Keywords: biomimicry, engineering approach, STEM education, teaching and learning model

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Authors: Mohannad Mayyas, Farshid Pahlevani, Veena Sahajwalla

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Automotive shredder residue (ASR) is an industrial waste, generated during the recycling process of End-of-life vehicles. The large increasing production volumes of ASR and its hazardous content have raised concerns worldwide, leading some countries to impose more restrictions on ASR waste disposal and encouraging researchers to find efficient solutions for ASR processing. Although a great deal of research work has been carried out, all proposed solutions, to our knowledge, remain commercially and technically unproven. While the volume of waste materials continues to increase, the production of materials from new sustainable sources has become of great importance. Advanced ceramic materials such as nitrides, carbides and borides are widely used in a variety of applications. Among these ceramics, a great deal of attention has been recently paid to Titanium nitride (TiN) owing to its unique characteristics. In our study, we propose a new sustainable approach for ASR management where TiN nanoparticles with ideal particle size ranging from 200 to 315 nm can be synthesized as a by-product. In this approach, TiN is thermally synthesized by nitriding pressed mixture of automotive shredder residue (ASR) incorporated with titanium oxide (TiO2). Results indicated that TiO2 influences and catalyses degradation reactions of ASR and helps to achieve fast and full decomposition. In addition, the process resulted in titanium nitride (TiN) ceramic with several unique structures (porous nanostructured, polycrystalline, micro-spherical and nano-sized structures) that were simply obtained by tuning the ratio of TiO2 to ASR, and a product with appreciable TiN content of around 85% was achieved after only one hour nitridation at 1550 °C.

Keywords: automotive shredder residue, nano-ceramics, waste treatment, titanium nitride, thermal conversion

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Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

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Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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Authors: Arezoo Fallahi, Fateme Aslibigi, Parvaneh Taymoori, Babak Nematshahrbabaki

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Background and Aims: Cancer of the cervix, one of cancer-related death, is the second most common cancer in women worldwide. It develops over time but it is one of the most preventable types of cancer and there is the available proper screening program for its preventing. Since Pap smear test is vital to prevent and control of disease but women do not accomplish it regularly. Therefore, this study was aimed to explore women's needs referring to health care centers for doing Pap smear test. Material and methods: In this study, an inductive qualitative method with content analysis approach was used. This survey was done in varamin city (is located capital of Iran) in year 2014. Through the purposive sampling 15 women's view of point referring to health care centers of for doing Pap smear test was surveyed. Inclusion criteria were: 20-50 years old married women, having experience Pap smear test and attendance to participate in the Study. Recorded semi- structured interviews were typed and analyzed through of content analysis method. To obtain trustworthiness and rigor of the data, the criteria of credibility, dependability, confirmability and transferability was used. Results: During the data analysis, four main categories of “role of health care team”, “role of organizations”, “social support” and “policies and administration system” were developed. The participants emphasized on making motivational rules and coordination among organizations to do behaviors related to women health. Conclusion: The findings of study showed that doing Pap smear test are attributed to appropriate and intimate interactions with health professionals, family support, encouraging legislation and policies and coordination and notification of organizations. Therefore, designers and stockholders of policies and health system should more consider to growth and involve other organizations toward women's health.

Keywords: qualitative approach, pap smear test, women, health care centers

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Authors: Melanie Verhovnik

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Media shape the image of crime, peoples’ believes, attitudes and sometimes also behaviors. Media not only gives the impression that crime is increasing, it also suggest that very violent crime is more common than it actually is. It is also no wonder that humans are more afraid of being involved in a crime committed by strangers than committed by somebody they know – because this is the media construct. With the help of three case studies, the paper analyzes how media frames crime and criminals and gives valuable hints as to what better reporting could look like.

Keywords: court reporting, offenders in media, quantitative content analysis, victims in media

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Authors: Sreejani Barua, P. P. Srivastav

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Formulation of new functional food products for diabetes patients and obsessed people is a challenge for food industries till date. Starch is a certainly happening, ecological, reasonable and profusely obtainable polysaccharide in plant material. In the present scenario, there is a great interest in modifying starch functional properties without destroying its granular structure using different modification techniques. Resistant starch (RS) contains almost zero calories and can control blood glucose level to prevent diabetes. The current study focused on modification of mung bean starch which is a good source of legumes carbohydrate for the production of functional food. Heat moisture treatment (HMT) of mung starch was conducted at moisture content of 10-30%, temperature of 80-120 °C and time of 8-24 h.The content of resistant starch after modification was significantly increased from native starches containing RS 7.6%. The design combinations of HMT had been completed through Central Composite Rotatable Design (CCRD). The effects of HMT process variables on the yield of resistant starch was studied through Rapid Surface Methodology (RSM). The highest increase of resistant starch was found up to 34.39% when treated the native starch with 30% m.c at 120 °C temperature for 24 h.The functional properties of both native and modified mung bean starches showed that there was a reduction in the swelling power and swelling volume of HMT starches. However, the solubility of the HMT starches was higher than that of untreated native starch and also observed change in structural (scanning electron microscopy), X-Ray diffraction (XRD) pattern, blue value and thermal (differential scanning calorimetry) properties. Therefore, replacing native mung bean starch with heat-moisture treated mung bean starch leads to the development of new products with higher resistant starch levels and functional properties.

Keywords: Mung bean starch, heat moisture treatment, functional properties, resistant starch

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Samuel Pimpong

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In most liberal and constitutional democracies, the media serves as a dominant power in the construction of the fundamental building blocks for the consolidation of democratic governance. However, the extent to which the media can enhance democratic consolidation in a country depends to a large extent on the independence of the media, the robustness of legislative frameworks and the safety of journalists in discharging their duties without fear or favor. This study sought to examine pertinent issues, practices and challenges facing the media in Ghana’s Fourth Republic and attempts to make recommendations regarding the way forward. The work adopted a qualitative study approach. A total of sixteen (16) participants were purposively selected for face-to-face interviews. The study hinges on the democratic participant media theory and the development media theory. Primary data was analyzed via thematic analysis procedure. The study revealed that although Ghana has repealed its criminal libel laws, nonetheless other statutory Acts, such as the Electronic Communications Act 2008 (ACT 775) and the Criminal and other offences Act 1960 (Act 29), among others continue to stifle freedom of expression. On the other hand, press freedom is being abused by the use of fake content publication. Further, the study revealed that the absence of a comprehensive regulatory structure impedes the activities carried out by the media. Consequently, the study recommends a regulatory structure to oversee media activities and content, as the National Media Commission (NMC) lacks the authority to do so. In this direction, the study recommends a limitation on the role of the National Communications Authority (NCA) to administer broadcasting signals and transfer its licensing and sanctioning powers to the NMC in order to create one sole and completely independent media regulatory authority that deals with all media related issues.

Keywords: media, constitutional democracy, democratic consolidation, fourth republic

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Authors: Aleksandras Velykis, Antanas Satkus

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Clay loam and clay soils are typical for northern Lithuania. These soils are susceptible to physical degradation in the case of intensive use of heavy machinery for field operations. However, clayey soils having poor physical properties by origin require more intensive tillage to maintain proper physical condition for grown crops. Therefore not only choice of suitable tillage system is very important for these soils in the region, but also additional search of other measures is essential for good soil physical state maintenance. Research objective: To evaluate the long-term effects of different intensity tillage as well as its combinations with supplementary agronomic practices on improvement of soil physical conditions and environmental sustainability. The experiment examined the influence of deep and shallow ploughing, ploughless tillage, combinations of ploughless tillage with incorporation of lime sludge and cover crop for green manure and application of the same cover crop for mulch without autumn tillage under spring and winter crop growing conditions on clay loam (27% clay, 50% silt, 23% sand) Endocalcaric Endogleyic Cambisol. Methods: The indicators characterizing the impact of investigated measures were determined using the following methods and devices: Soil dry bulk density – by Eijkelkamp cylinder (100 cm3), soil water content – by weighing, soil structure – by Retsch sieve shaker, aggregate stability – by Eijkelkamp wet sieving apparatus, soil mineral nitrogen – in 1 N KCL extract using colorimetric method. Results: Clay loam soil physical state (dry bulk density, structure, aggregate stability, water content) depends on tillage system and its combination with additional practices used. Application of cover crop winter mulch without tillage in autumn, ploughless tillage and shallow ploughing causes the compaction of bottom (15-25 cm) topsoil layer. However, due to ploughless tillage the soil dry bulk density in subsoil (25-35 cm) layer is less compared to deep ploughing. Soil structure in the upper (0-15 cm) topsoil layer and in the seedbed (0-5 cm), prepared for spring crops is usually worse when applying the ploughless tillage or cover crop mulch without autumn tillage. Application of lime sludge under ploughless tillage conditions helped to avoid the compaction and structure worsening in upper topsoil layer, as well as increase aggregate stability. Application of reduced tillage increased soil water content at upper topsoil layer directly after spring crop sowing. However, due to reduced tillage the water content in all topsoil markedly decreased when droughty periods lasted for a long time. Combination of reduced tillage with cover crop for green manure and winter mulch is significant for preserving the environment. Such application of cover crops reduces the leaching of mineral nitrogen into the deeper soil layers and environmental pollution. This work was supported by the National Science Program ‘The effect of long-term, different-intensity management of resources on the soils of different genesis and on other components of the agro-ecosystems’ [grant number SIT-9/2015] funded by the Research Council of Lithuania.

Keywords: clay loam, endocalcaric endogleyic cambisol, mineral nitrogen, physical state

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Authors: Asteria Nsamba

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Social networking applications and tools have become compelling platforms for generating and sharing knowledge across the world. Social networking applications and tools refer to a variety of social media platforms which include Facebook, Twitter WhatsApp, blogs and Wikis. The most popular of these platforms are Facebook, with 2.41 billion active users on a monthly basis, followed by WhatsApp with 1.6 billion users and Twitter with 330 million users. These communication platforms have not only impacted social lives but have also impacted students’ learning, across different delivery modes in higher education: distance, conventional and blended learning modes. With this amount of interest in these platforms, knowledge sharing has gained importance within the context in which it is required. In open distance learning (ODL) contexts, social networking platforms can offer students and teachers the platform on which to create and share knowledge, and form learning collaborations. Thus, they can serve as support mechanisms to increase interactions and reduce isolation and loneliness inherent in ODL. Despite this potential and opportunity, research indicates that many ODL teachers are not inclined to using social media tools in learning. Although it is unclear why these tools are uncommon in these environments, concerns raised in the literature have indicated that many teachers have not mastered the art of teaching with technology. Using technological, pedagogical content knowledge (TPCK) and product quality theory, and Bloom’s Taxonomy as lenses, this paper is aimed at; firstly, assessing the quality of three social media applications: Facebook, Twitter and WhatsApp, in order to determine the extent to which they are suitable platforms for teaching and learning, in terms of content generation, information sharing and learning collaborations. Secondly, the paper demonstrates the application of teaching, learning and assessment using Bloom’s Taxonomy.

Keywords: distance education, quality, social networking tools, TPACK

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Authors: Beenish Urooj, Ubaid Ullah, Sidra Riasat

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National Testing Service-Pakistan (NTS) is an agency in Pakistan that conducts student success appraisal examinations. In this research paper, we must present a security model for the NTS organization. The security model will depict certain security countermeasures for a better defense against certain types of breaches and system malware. We will provide a security roadmap, which will help the company to execute its further goals to maintain security standards and policies. We also covered multiple aspects in securing the environment of the organization. We introduced the processes, architecture, data classification, auditing approaches, survey responses, data handling, and also training and awareness of risk for the company. The primary contribution is the Risk Survey, based on the maturity model meant to assess and examine employee training and knowledge of risks in the company's activities.

Keywords: NTS, risk assessment, threat factors, security, services

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Authors: Ljubica Dokić, Ivana Nikolić, Dragana Šoronja–Simović, Zita Šereš, Biljana Pajin, Nils Juul, Nikola Maravić

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The possibility of application the dietary fibers in production of crackers was observed in this work, as well as their influence on rheological and textural properties on the dough for crackers and influence on sensory properties of obtained crackers. Three different dietary fibers, oat, potato and pea fibers, replaced 10% of wheat flour. Long fermentation process and baking test method were used for crackers production. The changes of dough for crackers were observed by rheological methods of determination the viscoelastic dough properties and by textural measurements. Sensory quality of obtained crackers was described using quantity descriptive method (QDA) by trained members of descriptive panel. Additional analysis of crackers surface was performed by videometer. Based on rheological determination, viscoelastic properties of dough for crackers were reduced by application of dietary fibers. Manipulation of dough with 10% of potato fiber was disabled, thus the recipe modification included increase in water content at 35%. Dough compliance to constant stress for samples with dietary fibers decreased, due to more rigid and stiffer dough consistency compared to control sample. Also, hardness of dough for these samples increased and dough extensibility decreased. Sensory properties of final products, crackers, were reduced compared to control sample. Application of dietary fibers affected mostly hardness, structure and crispness of the crackers. Observed crackers were low marked for flavor and taste, due to influence of fibers specific aroma. The sample with 10% of potato fibers and increased water content was the most adaptable to applied stresses and to production process. Also this sample was close to control sample without dietary fibers by evaluation of sensory properties and by results of videometer method.

Keywords: crackers, dietary fibers, rheology, sensory properties

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Authors: Mehmet Sinan Erguven

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Modern art is relatively new but a popular area in Turkish art society. Modern art museums are mainly located in big cities like Istanbul and Ankara where cultural life is more dynamic. Odunpazarı Modern Museum (OMM) will open its doors on September 2019 and be the only modern art museum located in a small town in Turkey. OMM executives declare the mission of the museum as; art must go beyond the metropolises of the world, give a new lease of life to cities that make a difference with their cultural texture, and reach a greater audience through that expansion. So OMM will not only serve as a museum but a landmark for regenerating the city brand of Eskişehir like the Guggenheim in Bilbao. OMM is located in the Odunpazarı area, the heart of Eskişehir. Named after the historical timber market it once hosted, Odunpazarı is a nominated site for the UNESCO Intangible Cultural Heritage List, and is Eskişehir’s first area of settlement. This study focuses on the complex nature of opening a modern art museum in a small town. The management and marketing dynamics of OMM are discussed in the study. Content analysis technique is used on local and national news to display the perception differences before and after the opening of OMM. In depth interviews with the executives of the museum are conducted in order to enlighten the insights of opening a modern art museum in a small town. Early findings of the content analysis point out that, the comments on the national press are mostly positive. On the other hand, different views occur on the local press. The location OMM is constructed and grandness of the museum building are criticized by some of the local newspapers. OMM’s potential as a tourist attraction is agreed by most of the media. OMM executives stated the most challenging task as reaching the different target audiences on international, national and local levels. These early findings will be improved and compared shortly before and after the opening of the museum.

Keywords: management, marketing, Odunpazarı modern museum, small town

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Authors: Oluwafemi Babatunde Oduntan, Isaac A. Bamgboye

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The solid waste disposal and its management from pineapple juice processing constitute environmental contamination affecting public health. The use of this by-product called pomace has potentials to reduce cost of aquafeed. Pineapple pomace collected after juice extraction was dried and milled. The interactive effects of feeding rate (1.28, 1.44 and 1.60kg/min), screw speed (305, 355 and 405rpm), moisture content (16, 19 and 22%), temperatures (60, 80, 100 and 120°C), cutting speed (1300, 1400 and 1500rpm), pomace inclusion ratio (5, 10, 15, 20%) and open surface die (50, 75 and 100%) on the extrudate physical properties (bulk density, unit density, expansion ratio, durability and floatability) were investigated using optimal custom design (OCD) matrix and response surface methodology. The predicted values were found to be in good agreement with the experimental values for, expansion ratio, durability and floatability (R2 = 0.7970; 0.9264; 0.9098 respectively) with the exceptions of unit density and bulk density (R2 = 0.1639; 0.2768 respectively). All the extrudates showed relatively high floatability, durability. The inclusion of pineapple pomace produced less expanded and more compact textured extrudates. Results indicated that increased in the value of pineapple pomace, screw speed, feeding rate decreased unit density, bulk density, expansion ratio, durability and floatability of the extrudate. However, increasing moisture content of feed mash resulted in increase unit density and bulk density. Addition of extrusion temperature and cutting speed increased the floatability and durability of extrudate. The proportion of pineapple pomace in aquafeed extruded product was observed to have significantly lower effect on the selected responses.

Keywords: aquafeed, extrusion, physical properties, pineapple pomace, waste

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Authors: Christine F. Boos, Fernando M. Azevedo

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Electroencephalogram (EEG) is a record of the electrical activity of the brain that has many applications, such as monitoring alertness, coma and brain death; locating damaged areas of the brain after head injury, stroke and tumor; monitoring anesthesia depth; researching physiology and sleep disorders; researching epilepsy and localizing the seizure focus. Epilepsy is a chronic condition, or a group of diseases of high prevalence, still poorly explained by science and whose diagnosis is still predominantly clinical. The EEG recording is considered an important test for epilepsy investigation and its visual analysis is very often applied for clinical confirmation of epilepsy diagnosis. Moreover, this EEG analysis can also be used to help define the types of epileptic syndrome, determine epileptiform zone, assist in the planning of drug treatment and provide additional information about the feasibility of surgical intervention. In the context of diagnosis confirmation the analysis is made using long term EEG recordings with at least 24 hours long and acquired by a minimum of 24 electrodes in which the neurophysiologists perform a thorough visual evaluation of EEG screens in search of specific electrographic patterns called epileptiform discharges. Considering that the EEG screens usually display 10 seconds of the recording, the neurophysiologist has to evaluate 360 screens per hour of EEG or a minimum of 8,640 screens per long term EEG recording. Analyzing thousands of EEG screens in search patterns that have a maximum duration of 200 ms is a very time consuming, complex and exhaustive task. Because of this, over the years several studies have proposed automated methodologies that could facilitate the neurophysiologists’ task of identifying epileptiform discharges and a large number of methodologies used neural networks for the pattern classification. One of the differences between all of these methodologies is the type of input stimuli presented to the networks, i.e., how the EEG signal is introduced in the network. Five types of input stimuli have been commonly found in literature: raw EEG signal, morphological descriptors (i.e. parameters related to the signal’s morphology), Fast Fourier Transform (FFT) spectrum, Short-Time Fourier Transform (STFT) spectrograms and Wavelet Transform features. This study evaluates the application of these five types of input stimuli and compares the classification results of neural networks that were implemented using each of these inputs. The performance of using raw signal varied between 43 and 84% efficiency. The results of FFT spectrum and STFT spectrograms were quite similar with average efficiency being 73 and 77%, respectively. The efficiency of Wavelet Transform features varied between 57 and 81% while the descriptors presented efficiency values between 62 and 93%. After simulations we could observe that the best results were achieved when either morphological descriptors or Wavelet features were used as input stimuli.

Keywords: Artificial neural network, electroencephalogram signal, pattern recognition, signal processing

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Authors: Samira-Sadat JamaliDinan, Haidar Almohri, Mohammad-Reza Nazem-Zadeh

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Lateralization of temporal lobe epilepsy (TLE) is very important for positive surgical outcomes. We propose a machine learning framework to ultimately identify the epileptogenic hemisphere for temporal lobe epilepsy (TLE) cases using magnetoencephalography (MEG) coherence source imaging (CSI) and diffusion tensor imaging (DTI). Unlike most studies that use classification algorithms, we propose an effective clustering approach to distinguish between normal and TLE cases. We apply the famous Minkowski weighted K-Means (MWK-Means) technique as the clustering framework. To overcome the problem of poor initialization of K-Means, we use particle swarm optimization (PSO) to effectively select the initial centroids of clusters prior to applying MWK-Means. We demonstrate that compared to K-means and MWK-means independently, this approach is able to improve the result of a benchmark data set.

Keywords: temporal lobe epilepsy, machine learning, clustering, magnetoencephalography

Procedia PDF Downloads 147

Authors: Sheldon McCall, Miao Yu, Liyun Gong, Shigang Yue, Stefanos Kollias

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Falls are a significant health concern for older adults globally, and prompt identification is critical to providing necessary healthcare support. Our study proposes a new fall detection method using computer vision based on modern deep learning techniques. Our approach involves training a trans- former model on a large 2D pose dataset for general action recognition, followed by transfer learning. Specifically, we freeze the first few layers of the trained transformer model and train only the last two layers for fall detection. Our experimental results demonstrate that our proposed method outperforms both classical machine learning and deep learning approaches in fall/non-fall classification. Overall, our study suggests that our proposed methodology could be a valuable tool for identifying falls.

Keywords: healthcare, fall detection, transformer, transfer learning

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Authors: Nandini Nalika, Suhel Parvez

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Nanotechnology has emerged to play a vital role in developing all through the industrial world with an immense production of nanomaterials including nanoparticles (NPs). Many toxicological studies have confirmed that due to unique small size and physico-chemical properties of NPs (1-100nm), they can be potentially hazardous. Metallic NPs of small size have been shown to induce higher levels of cellular oxidative stress and can easily pass through the Blood Brain Barrier (BBB) and significantly accumulate in brain. With the wide applications of titanium dioxide nanoparticles (TNPs) in day-to-day life in form of cosmetics, paints, sterilisation and so on, there is growing concern regarding the deleterious effects of TNPs on central nervous system and mitochondria appear to be important cellular organelles targeted to the pro-oxidative effects of NPs and an important source that contribute significantly for the production of reactive oxygen species after some toxicity or an injury. The aim of our study was to elucidate the effect of TNPs in anatase form with different concentrations (5-50 µg/ml) following with various oxidative stress markers in isolated brain mitochondria as an in vitro model. Oxidative stress was determined by measuring the different oxidative stress markers like lipid peroxidation as well as the protein carbonyl content which was found to be significantly increased. Reduced glutathione content and major glutathione metabolizing enzymes were also modulated signifying the role of glutathione redox cycle in the pathophysiology of TNPs. The study also includes the mitochondrial enzymes (Complex 1, Complex II, complex IV, Complex V ) and the enzymes showed toxicity in a relatively short time due to the effect of TNPs. The study provide a range of concentration that were toxic to the neuronal cells and data pointing to a general toxicity in brain mitochondria by TNPs, therefore, it is in need to consider the proper utilization of NPs in the environment.

Keywords: mitochondria, nanoparticles, brain, in vitro

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Authors: Abdul Salam Turkmani

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Clay of the northern Palmyra region is one of the most important raw materials used in the Syrian ceramics industry. This study is focused on the evaluation of various laboratory analyses such as chemical analysis (XRF), mineral X-ray diffraction analysis (XRD), differential thermal analysis (DTA), and semi-industrial tests carried out on samples collected on two representative locations of the upper Oligocene in AlMkamen valley (MK) and lower Miocene in AlZukara valley (ZR) of the northern part of Palmyra, Syria. Chemical results classify the (MK) and (ZR) clays as semi-plastic red clay slightly carbonate and (eliminate probable) illite-chlorite clays with a very fine particle size distribution. Content of SiO₂ between 46.28-57.66%, Al2O3 13.81-25.2%, Fe₂O₃ 3.47-11.58%, CaO 1.15-7.19%, Na₂O+K₂O varied between 3.34-3.71%. Based on clay chemical composition and iron and carbonate content, these deposits can be considered as red firing clays. Their mineralogical composition is mainly represented by illite, kaolinite and quartz, and accessories minerals such as calcite, feldspar, phillipsite, and goethite. The results of the DTA test confirm the presence of gypsum and quartz phases in (MK) clay. Ceramic testing shows good green and dry bending strength values, which varied between 9-14 kg/cm², at 1160°C to 1180°C. Water absorption moves from 14.6 % at 1120°C to 2.2% at 1180°C to 1.6% at 1200°C. Breaking load after firing changes from 400 to 590 kg/cm². At 1200°C (MK), clay reaches perfect vitrification. After firing, the color of the clay changes from orange-hazel to red-brown at 1180°C. Technological results confirmed the suitability of the studied clays to produce floor and wall ceramic tiles. Using one of the two types of clay into the ceramic body or both types together gave satisfactory industrial results.

Keywords: ceramic, clay, industry , Palmyra

Procedia PDF Downloads 191