Search results for: specific learning disability

Authors: M.Rüştü Karaman, İsmail İşeri, Kadir Saltalı, A.Reşit Brohi, Ayhan Horuz, Mümin Dizman

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Considerable relations has recently been given to the environmental hazardous caused by agricultural chemicals such as excess fertilizers. In this study, a neural network approach was investigated in the prediction of potential nitrate pollution across different land-use patterns by using a feedforward multilayered computer model of artificial neural network (ANN) with proper training. Periodical concentrations of some anions, especially nitrate (NO3-), and cations were also detected in drainage waters collected from the drain pipes placed in irrigated tomato field, unirrigated wheat field, fallow and pasture lands. The soil samples were collected from the irrigated tomato field and unirrigated wheat field on a grid system with 20 m x 20 m intervals. Site specific nitrate concentrations in the soil samples were measured for ANN based simulation of nitrate leaching potential from the land profiles. In the application of ANN model, a multi layered feedforward was evaluated, and data sets regarding with training, validation and testing containing the measured soil nitrate values were estimated based on spatial variability. As a result of the testing values, while the optimal structures of 2-15-1 was obtained (R2= 0.96, P < 0.01) for unirrigated field, the optimal structures of 2-10-1 was obtained (R2= 0.96, P < 0.01) for irrigated field. The results showed that the ANN model could be successfully used in prediction of the potential leaching levels of nitrate, based on different land use patterns. However, for the most suitable results, the model should be calibrated by training according to different NN structures depending on site specific soil parameters and varied agricultural managements.

Keywords: artificial intelligence, ANN, drainage water, nitrate pollution

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Yetunde M. Dairo, Johnny Collett, Helen Dawes

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Background and Aim: Engagement with physical activity (PA) research is poor among adults with intellectual disabilities (ID), particularly in those from residential homes. This study explored why, by asking managers of residential homes, adults with ID and their carers. Methods: Participants: A convenient sample of 23 individuals from two UK local authorities, including a group of ID residential home managers, adults with ID and their support staff. Procedures: A) Residential home managers (n=6) were asked questions about their willingness to allow their residents to participate in PA research; B) eleven adults with ID and their support workers (n=6) were asked questions about their willingness to accept 7-day accelerometer monitoring and/or the International Physical Activity Questionnaire-short version (IPAQ-s) as PA measures. The IPAQ-s was administered by the researcher and they were each provided with samples of accelerometers to try on. Results: A) Five out of six managers said that the burden of wearing the accelerometer for seven days would be too high for the people they support, the majority of whom might be unable to express their wishes. They also said they would be unwilling to act as proxy respondents for the same reason. Additionally, they cited time pressure, understaffing, and reluctance to spend time on the research paperwork as further reasons for non-participation. B) All 11 individuals with ID completed the IPAQ-s while only three accepted the accelerometer, one of whom was deemed inappropriate to wear it. Reasons for rejecting accelerometers included statements from participants of: ‘too expensive’, ‘too heavy’, ‘uncomfortable’, and two people said they would not want to wear it for more than one day. All adults with ID (11) and their support workers (6) provided information about their physical activity levels through the IPAQ-s. Conclusions: Care home managers are a barrier to research participation. However, adults with ID would be happy for the IPAQ-s as a PA measure, but less so for the 7-day accelerometer monitoring. In order to improve participation in this population, the choice of PA measure is considered important. Moreover, there is a need for studies exploring how best to engage ID residential home managers in PA research.

Keywords: intellectual disability, physical activity measurement, research engagement, research participation

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Authors: Meng Su

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High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

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Authors: Zahra F. Al-Khateeb, Shenel Shekerzade, Hasna Boumenar, Siân M. Henson, Jordi L. Tremoleda, A. T. Michael-Titus

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Traumatic brain injury (TBI) is the leading cause of disability and death in young adults and also increases the risk ofneurodegeneration. The mechanisms linking moderate to severe TBI to neurodegeneration are not known. It has been proposed that cellular senescence inductionpost-injury could amplify neuroinflammation and induce long-term changes. The impact of these processes after injury to an immature brain has not been characterised yet. We carried out a controlled cortical impact injury (CCI) in juvenile 1 month-old male CD1 mice. Animals were anesthetised and received a unilateral CCI injury. The sham group received anaesthesia and had a craniotomy. A naïve group had no intervention. The brain tissue was analysed at 5 days and 35 days post-injury using immunohistochemistry and markers for microglia, astrocytes, and senescence. Compared tonaïve animals, injured mice showed an increased microglial and astrocytic reaction early post-injury, as reflected in Iba1 and GFAP markers, respectively; the GFAP increase persisted in the later phase. The senescence analysis showed a significant increase inγH2AX-53BP1 nuclear foci, 8-oxoguanine, p19ARF, p16INK4a, and p53 expression in naïve vs. sham groups and naïve vs. CCI groups, at 5 dpi. At 35 days, the difference was no longer statistically significant in all markers. The injury induced a decrease p21 expression vs. the naïve group, at 35 dpi. These results indicate the induction of a complex senescence response after immature brain injury. Some changes occur early and may reflect the activation/proliferation of non-neuronal cells post-injury that had been hindered, whereas changes such as p21 downregulation may reflect a delayed response and pro-repair processes.

Keywords: cellular senescence, traumatic brain injury, brain injury, controlled cortical impact

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Authors: Nicole Merkle, Stefan Zander

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Ambient Assisted Living (AAL) describes a technological and methodological stack of (e.g. formal model-theoretic semantics, rule-based reasoning and machine learning), different aspects regarding the behavior, activities and characteristics of humans. Hence, a semantic representation of the user environment and its relevant elements are required in order to allow assistive agents to recognize situations and deduce appropriate actions. Furthermore, the user and his/her characteristics (e.g. physical, cognitive, preferences) need to be represented with a high degree of expressiveness in order to allow software agents a precise evaluation of the users’ context models. The correct interpretation of these context models highly depends on temporal, spatial circumstances as well as individual user preferences. In most AAL approaches, model representations of real world situations represent the current state of a universe of discourse at a given point in time by neglecting transitions between a set of states. However, the AAL domain currently lacks sufficient approaches that contemplate on the dynamic adaptions of context-related representations. Semantic representations of relevant real-world excerpts (e.g. user activities) help cognitive, rule-based agents to reason and make decisions in order to help users in appropriate tasks and situations. Furthermore, rules and reasoning on semantic models are not sufficient for handling uncertainty and fuzzy situations. A certain situation can require different (re-)actions in order to achieve the best results with respect to the user and his/her needs. But what is the best result? To answer this question, we need to consider that every smart agent requires to achieve an objective, but this objective is mostly defined by domain experts who can also fail in their estimation of what is desired by the user and what not. Hence, a smart agent has to be able to learn from context history data and estimate or predict what is most likely in certain contexts. Furthermore, different agents with contrary objectives can cause collisions as their actions influence the user’s context and constituting conditions in unintended or uncontrolled ways. We present an approach for dynamically updating a semantic model with respect to the current user context that allows flexibility of the software agents and enhances their conformance in order to improve the user experience. The presented approach adapts rules by learning sensor evidence and user actions using probabilistic reasoning approaches, based on given expert knowledge. The semantic domain model consists basically of device-, service- and user profile representations. In this paper, we present how this semantic domain model can be used in order to compute the probability of matching rules and actions. We apply this probability estimation to compare the current domain model representation with the computed one in order to adapt the formal semantic representation. Our approach aims at minimizing the likelihood of unintended interferences in order to eliminate conflicts and unpredictable side-effects by updating pre-defined expert knowledge according to the most probable context representation. This enables agents to adapt to dynamic changes in the environment which enhances the provision of adequate assistance and affects positively the user satisfaction.

Keywords: ambient intelligence, machine learning, semantic web, software agents

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Authors: Rabih Joseph Nabhan

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This case study explores the impact of two major computer software programs Learn to Speak English and Learn English Spelling and Pronunciation, and some Internet search engines such as Google on mending the decoding and spelling deficiency of Simon X, a dyslexic student. The improvement in decoding and spelling may result in better reading comprehension and composition writing. Some computer programs and Internet materials can help regain the missing awareness and consequently restore his self-confidence and self-esteem. In addition, this study provides a systematic plan comprising a set of activities (four computer programs and Internet materials) which address the problem from the lowest to the highest levels of phoneme and phonological awareness. Four methods of data collection (accounts, observations, published tests, and interviews) create the triangulation to validly and reliably collect data before the plan, during the plan, and after the plan. The data collected are analyzed quantitatively and qualitatively. Sometimes the analysis is either quantitative or qualitative, and some other times a combination of both. Tables and figures are utilized to provide a clear and uncomplicated illustration of some data. The improvement in the decoding, spelling, reading comprehension, and composition writing skills that occurred is proved through the use of authentic materials performed by the student under study. Such materials are a comparison between two sample passages written by the learner before and after the plan, a genuine computer chat conversation, and the scores of the academic year that followed the execution of the plan. Based on these results, the researcher recommends further studies on other Lebanese dyslexic learners using the computer to mend their language problem in order to design and make a most reliable software program that can address this disability more efficiently and successfully.

Keywords: analysis, awareness, dyslexic, software

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Authors: Tianqi Wu, Min Wang

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Research investigating language acquisition from a constructionist perspective has demonstrated that language is learned as constructions at various linguistic levels, which is related to factors of frequency, semantic prototypicality, and form-meaning contingency. However, previous research on construction learning tended to focus on clause-level constructions such as verb argument constructions but few attempts were made to study morpheme-level constructions such as the progressive construction, which is regarded as a source of acquisition problems for English learners from diverse L1 backgrounds, especially for those whose L1 do not have an equivalent construction such as German and Chinese. To trace the developmental trajectory of Chinese EFL learners’ use of the progressive with respect to verb frequency, verb-progressive contingency, and verbal prototypicality and generality, a learner corpus consisting of three sub-corpora representing three different English proficiency levels was extracted from the Chinese Learners of English Corpora (CLEC). As the reference point, a native speakers’ corpus extracted from the Louvain Corpus of Native English Essays was also established. All the texts were annotated with C7 tagset by part-of-speech tagging software. After annotation all valid progressive hits were retrieved with AntConc 3.4.3 followed by a manual check. Frequency-related data showed that from the lowest to the highest proficiency level, (1) the type token ratio increased steadily from 23.5% to 35.6%, getting closer to 36.4% in the native speakers’ corpus, indicating a wider use of verbs in the progressive; (2) the normalized entropy value rose from 0.776 to 0.876, working towards the target score of 0.886 in native speakers’ corpus, revealing that upper-intermediate learners exhibited a more even distribution and more productive use of verbs in the progressive; (3) activity verbs (i.e., verbs with prototypical progressive meanings like running and singing) dropped from 59% to 34% but non-prototypical verbs such as state verbs (e.g., being and living) and achievement verbs (e.g., dying and finishing) were increasingly used in the progressive. Apart from raw frequency analyses, collostructional analyses were conducted to quantify verb-progressive contingency and to determine what verbs were distinctively associated with the progressive construction. Results were in line with raw frequency findings, which showed that contingency between the progressive and non-prototypical verbs represented by light verbs (e.g., going, doing, making, and coming) increased as English proficiency proceeded. These findings altogether suggested that beginning Chinese EFL learners were less productive in using the progressive construction: they were constrained by a small set of verbs which had concrete and typical progressive meanings (e.g., the activity verbs). But with English proficiency increasing, their use of the progressive began to spread to marginal members such as the light verbs.

Keywords: Construction learning, Corpus-based, Progressives, Prototype

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Noriyuki Suyama

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The progress and spread of digitalization have led to the provision of a variety of new services. The recent progress in digitization can be attributed to rapid developments in science and technology. First, the research and diffusion of artificial intelligence (AI) has made dramatic progress. Around 2000, the third wave of AI research, which had been underway for about 50 years, arrived. Specifically, machine learning and deep learning were made possible in AI, and the ability of AI to acquire knowledge, define the knowledge, and update its own knowledge in a quantitative manner made the use of big data practical even for commercial PCs. On the other hand, with the spread of social media, information exchange has become more common in our daily lives, and the lending and borrowing of goods and services, in other words, the sharing economy, has become widespread. The scope of this trend is not limited to any industry, and its momentum is growing as the SDGs take root. In addition, the Social Network Service (SNS), a part of social media, has brought about the evolution of the retail business. In the past few years, social network services (SNS) involving users or companies have especially flourished. The People's Republic of China (hereinafter referred to as "China") is a country that is stimulating enormous consumption through its own unique SNS, which is different from the SNS used in developed countries around the world. This paper focuses on the effectiveness and challenges of influencer marketing by focusing on the influence of influencers on users' behavior and satisfaction with Chinese SNSs. Specifically, Conducted was the quantitative survey of Tik Tok users living in China, with the aim of gaining new insights from the analysis and discussions. As a result, we found several important findings and knowledge.

Keywords: customer satisfaction, social networking services, influencer marketing, Chinese consumers’ behavior

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Authors: Mahmoud Nabilu

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The present study explored the effect of using Mobile listening applications on developing listening skills by Iranian intermediate EFL learners. Fifty male intermediate English learners whose age range was between 15 and 20, participated in the study. The participants were placed in two groups on the basis of their scores on a placement test. Therefore, the participants of the study were homogenized in terms of general proficiency, and groups were assigned as one experimental group and one control group. The experimental group was instructed by the treatment which was using mobile applications to develop their listening skills while the control group received traditional methods. The research data were obtained from the 40-item multiple-choice tests as a pre-test and a post-test. The results of the t-test clearly revealed that the learners in the experimental group performed better in the post-test than the pre-test. This implies that using a mobile application for developing listening skills as a treatment was effective in helping the language learners perform better on post-test. However, a statistically significant difference was found between the post-tests scores of the two groups. The mean of the experimental group was greater compared to the control group. The participants were Iranian and from an Iranian Language Institute, so care should be taken while generalizing the results to the learners of other nationalities. However, in the researcher's view, the findings of this study have valuable implications for teachers and learners, methodologists and syllabus designers, linguists and MALL/CALL (mobile/computer-assisted language learning) experts. Using the result of the present paper is an aim of raising the consciousness of a better technique of developing listening skills in order to make language learning more efficient for the learners.

Keywords: Mobile listening applications, intermediate EFL learners, MALL, CALL

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Authors: Susmita Roy, Madhavan Nallani

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In recent years' polymersomes, self-assembled polymeric vesicles emerge from block copolymers, have been widely investigated due to their enhance stability and unique advantageous properties compared to their phospholipid counterpart, liposomes, dendrimers, and micelles. It provides a distinctive platform for advanced therapeutics and the creation of complex (bio) catalytically active systems for research in Nanomedicine and synthetic biology. Inspired by nature, where compartmentalization of biological components is all ubiquitous, we are interested in developing a platform technology of self-assembled multifunctional compartments with applications in areas from targeted drug/gene delivery, biosensing, pharmaceutical to cosmetics. Polymersome surfaces can be a proper choice of derivatization with a controlled amount of functional groups. To achieve site-specific targeting of polymersomes, biological recognition motives can be attached to the polymersomes surface by standard bioconjugation techniques, (like esterification, amidation, thiol-maleimide coupling, click-chemistry routes or other coupling methods). Herein, we are developing easy going, one-step bioconjugation strategies for site-specific surface functionalized biodegradable polymeric and/or polymer-lipid hybrid vesicles for targeted drug delivery. Biodegradable polymer, polycaprolactone-b-polyethylene glycol (PCL-PEG), polylactic acid-b-polyethylene glycol (PLA-PEG) and phospholipid, 1-palmitoyl-2- oleoyl-sn-glycero-3-phosphocholine (POPC) has been widely used for numerous vesicle formulations. Some of these drug-loaded formulations are being tested on mice for controlled release. These surface functionalized polymersomes are also appropriate for membrane protein reconstitution/insertion, antibodies conjugation and various bioconjugation with diverse targeted molecules for controlled drug delivery.

Keywords: drug delivery, membrane protein, polymersome, surface modification

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Authors: Carol S. Binnekade, Deon Scott, Christina C. Shuttleworth, Annelien A. Van Rooyen

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Higher education institutions are constantly challenged to deliver skilled graduates into the workplace. Employers expect graduates to have the required technical knowledge as well as various pervasive skills. This also applies to accountants who need to know the technical requirements of financial reporting and be able to communicate with individuals, teams and clients at a high level. Accountants need to develop effective business conversational skills and use these skills to communicate up, down and across organizations, taking into consideration cultural and gender diversity. In addition, they need to master business writing and presentation skills. However, providing students with these skills in a distance-learning environment where interaction between students and instructors is limited, is a challenge for academics. The study on which this paper reports, forms part of a larger body of research, which explored the perceptions of accounting practitioners of the communication skills (or lack thereof) of recently qualified accounting students. Feedback (qualitative and quantitative) was obtained from various accounting practitioners in South Africa. Taking into consideration that distance learners communicate mainly with their instructors via email communication and their assignments are submitted using various word processor software, the researchers were of the opinion that the accounting graduates would be capable of communicating effectively once they entered the workplace. However, the research findings, inter alia, suggested that the accounting graduates lacked communication skills and that training was needed to differentiate between business and social communication once they entered the workplace. Recommendations on how these communication challenges may be addressed by higher education institutions are provided.

Keywords: accounting practitioners, communication skills, distance education, pervasive skills

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Authors: Alvin Uchenna Ugwu

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The United Nation’s 2030 agenda and Global Action Programme (GAP) for implementation of the Sustainable Development Goals (SDGs), has mandated all sectors in the societies, including education, to develop strategies towards actualizing sustainability in all facets of the society, by the year 2030. Education is no doubt a key tool for social change. However, educational institutions in most African nations need a paradigmatic shift to strike a balance between policies (curricular) and practices, with regards to Education for Sustainable Development (ESD). The paradigm shift in this regard is described as whole-institution/school approach. The whole institution approaches advocate action-focused ESD. In other words, ESD policy and curriculum makers, formal and non-formal education institutions, need to ‘practice what they preach’. This paper is developed from an ongoing study carried out by the author and guided by two research questions: -What are the views of intermediate phase science and technology preservice teachers on the ESD content included in the science and technology modules? -What challenges or enable intermediate phase science and technology pre-service teachers to learn about ESD in science and technology modules? The study drew from the views and experiences of preservice science teachers, learning about ESD in a university’s college of education in South Africa. Using qualitative case study research design, the research data were generated via questionnaires and focus group discussions. Analysis of generated data indicates that universities and institutions of higher learning need to demonstrate practical involvement while implementing ESD in societies, rather than just standing as knowledge media. Findings of the study further suggest that natural sciences and technology courses in teacher education programmes and other institutions of higher learning, should be perceived as key transformative tools in shaping the consciousness of students towards integrating and fostering ESD in developing countries such as South Africa. Thus, this paper seeks to promote ‘Whole Institution Involvement’ in teacher education colleges in South Africa, as a measure of improving ESD in higher education settings. The paper suggests that in order to achieve ESD in higher education settings and beyond, policies and practices should be reexamined beyond rhetoric and buzzwords. The paper further argues that implementation of ESD is largely influenced by context, hence two different contexts should be examined empirically.

Keywords: education for sustainable development, higher education institutions, pre-service science teachers, qualitative case study research, whole institution involvement

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Authors: Muhammad Anjum Aqueel, Muhammad Jaffar Hussain, Abu Bakar Muhammad Raza

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Diaeretiella rapae (M’Intosh) attack most of the aphid species. However, it is specialized in feeding on crucifer aphid, Brevicoryne brassicae. Biological potential of parasitoid is its density-dependency due to sharing of limited resources in few cases. The present study was carried out to check the biotic potential of D. rapae at its different densities (1, 2, 4, 8 and 10 pairs) on fixed number of B. brassicae (100 in number) as a host. The present study was performed under laboratory conditions (25 ± 2 ºC temperature and 65-70 % R.H.). Different biological parameters for parasitoid (e.g. percent parasitism, adult emergence, adult longevity and per pair parasitism) were evaluated to check its biotic potential. The present findings showed that maximum parasitism (43.09 % ± 0.63) was observed in highest density (10 pairs) and minimum parasitism (16.59 % ± 1.28) in lowest density (1 pair) of the parasitoid. Maximum adult emergence (80.31 % ± 1.33) was observed in highest density (10 pairs) and minimum parasitism (45.99 % ± 1.27) in lowest density (1 pair) of the parasitoid. In the case of adult longevity, highest (8.2 days ± 0.38) and lowest (6 days ± 0.32) longevity were observed in lowest (1 pair) and highest (10 pairs) densities of parasitoids respectively. However, per pair parasitism rate decreased with the increase in parasitoid densities due to intra-specific competition, developed between the parasitoids for parasitism. The positive but close relationship was observed between percent parasitism and adult emergence. The increase in parasitoid densities increased the percent parasitism and adult emergence of the parasitoid. So, we conclude that an inter-specific competition negatively affected the efficacy of parasitoids and may reduce the fitness of the emerging parasitoid.

Keywords: Diaeretiella rapae, Parasitoid densities, Percent parasitism, adult emergence

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Authors: Sergio A. Navarro Tuch, Martin Rogelio Bustamante Bello, Leopoldo Julian Lechuga Lopez

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In recent years, Mexico’s population has seen a rise of different physiological and mental negative states. Two main consequences of this problematic are deficient work performance and high levels of stress generating and important impact on a person’s physical, mental and emotional health. Several approaches, such as the use of audiovisual stimulus to induce emotions and modify a person’s emotional state, can be applied in an effort to decreases these negative effects. With the use of different non-invasive physiological sensors such as EEG, luminosity and face recognition we gather information of the subject’s current emotional state. In a controlled environment, a subject is shown a series of selected images from the International Affective Picture System (IAPS) in order to induce a specific set of emotions and obtain information from the sensors. The raw data obtained is statistically analyzed in order to filter only the specific groups of information that relate to a subject’s emotions and current values of the physical variables in the controlled environment such as, luminosity, RGB light color, temperature, oxygen level and noise. Finally, a neural network based control algorithm is given the data obtained in order to feedback the system and automate the modification of the environment variables and audiovisual content shown in an effort that these changes can positively alter the subject’s emotional state. During the research, it was found that the light color was directly related to the type of impact generated by the audiovisual content on the subject’s emotional state. Red illumination increased the impact of violent images and green illumination along with relaxing images decreased the subject’s levels of anxiety. Specific differences between men and women were found as to which type of images generated a greater impact in either gender. The population sample was mainly constituted by college students whose data analysis showed a decreased sensibility to violence towards humans. Despite the early stage of the control algorithm, the results obtained from the population sample give us a better insight into the possibilities of emotional domotics and the applications that can be created towards the improvement of performance in people’s lives. The objective of this research is to create a positive impact with the application of technology to everyday activities; nonetheless, an ethical problem arises since this can also be applied to control a person’s emotions and shift their decision making.

Keywords: data analysis, emotional domotics, performance improvement, neural network

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Authors: Zineb Kassab, Nassima El Miri, A. Aboulkas, Abdellatif Barakat, Mounir El Achaby

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Recently, more attention has been given to biopolymers with a focus on sustainable development and environmental preservation. Following this tendency, the attempt has been made to replace polymers derived from petroleum with superior biodegradable polymers (biopolymers). In this context, biopolymers are considered potential replacements for conventional plastic materials. However, some of their properties must be improved for better competitiveness, especially regarding their mechanical, thermal and barrier properties. Bio-nanocomposite technology using nanofillers has already been proven as an effective way to produce new materials with specific properties and high performances. With the emergence of nanostructured bio-composite materials, incorporating elongated rod-like cellulose nanocrystals (CNC) has attracted more and more attention in the field of nanotechnology. This study is aimed to develop bio-composite films of biopolymer matrices [Carboxymethyle cellulose (CMC), Starch (ST), Chitosan (CS) and Polyvinyl alcohol (PVA)] reinforced with cellulose nanocrystals (CNC) using the solution casting method. The CNC were extracted at a nanometric scale from lignocellulosic fibers via sulfuric acid hydrolysis and then characterized using X-ray diffraction (XRD), thermogravimetric analysis (TGA), confocal microscopy, infrared spectroscopy (IR), atomic force and transmission electron microscopies (AFM and TEM) techniques. The as extracted CNC were used as a reinforcing phase to produce a variety of bio-composite films at different CNC loading (0.5-10 wt %) with specific properties. The rheological properties of film-forming solutions (FFS) of bio-composites were studied, and their relation to the casting process was evaluated. Then, the structural, optical transparency, water vapor permeability, thermal stability and mechanical properties of all prepared bio-composite films were evaluated and studied in this report. The high performances of these bio-composite films are expected to have potential in biomaterials or packaging applications.

Keywords: biopolymer composites, cellulose nanocrystals, food packaging, lignocellulosic fibers

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Authors: Vladimir Messerle, Alfred Mosse, Alexandr Ustimenko, Oleg Lavrichshev

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Hygiene and sanitary study of typical medical-biological waste made in Kazakhstan, Russia, Belarus and other countries show that their risk to the environment is much higher than that of most chemical wastes. For example, toxicity of solid waste (SW) containing cytotoxic drugs and antibiotics is comparable to toxicity of radioactive waste of high and medium level activity. This report presents the results of the thermodynamic analysis of thermal processing of SW and experiments at the developed plasma unit for SW processing. Thermodynamic calculations showed that the maximum yield of the synthesis gas at plasma gasification of SW in air and steam mediums is achieved at a temperature of 1600K. At the air plasma gasification of SW high-calorific synthesis gas with a concentration of 82.4% (СO – 31.7%, H2 – 50.7%) can be obtained, and at the steam plasma gasification – with a concentration of 94.5% (СO – 33.6%, H2 – 60.9%). Specific heat of combustion of the synthesis gas produced by air gasification amounts to 14267 kJ/kg, while by steam gasification - 19414 kJ/kg. At the optimal temperature (1600 K), the specific power consumption for air gasification of SW constitutes 1.92 kWh/kg, while for steam gasification - 2.44 kWh/kg. Experimental study was carried out in a plasma reactor. This is device of periodic action. The arc plasma torch of 70 kW electric power is used for SW processing. Consumption of SW was 30 kg/h. Flow of plasma-forming air was 12 kg/h. Under the influence of air plasma flame weight average temperature in the chamber reaches 1800 K. Gaseous products are taken out of the reactor into the flue gas cooling unit, and the condensed products accumulate in the slag formation zone. The cooled gaseous products enter the gas purification unit, after which via gas sampling system is supplied to the analyzer. Ventilation system provides a negative pressure in the reactor up to 10 mm of water column. Condensed products of SW processing are removed from the reactor after its stopping. By the results of experiments on SW plasma gasification the reactor operating conditions were determined, the exhaust gas analysis was performed and the residual carbon content in the slag was determined. Gas analysis showed the following composition of the gas at the exit of gas purification unit, (vol.%): СO – 26.5, H2 – 44.6, N2–28.9. The total concentration of the syngas was 71.1%, which agreed well with the thermodynamic calculations. The discrepancy between experiment and calculation by the yield of the target syngas did not exceed 16%. Specific power consumption for SW gasification in the plasma reactor according to the results of experiments amounted to 2.25 kWh/kg of working substance. No harmful impurities were found in both gas and condensed products of SW plasma gasification. Comparison of experimental results and calculations showed good agreement. Acknowledgement—This work was supported by Ministry of Education and Science of the Republic of Kazakhstan and Ministry of Education and Science of the Russian Federation (Agreement on grant No. 14.607.21.0118, project RFMEF160715X0118).

Keywords: coal, efficiency, ignition, numerical modeling, plasma-fuel system, plasma generator

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Authors: M. Taheri Tehrani, H. Ajorloo

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In this paper, an intelligent multi-agent framework is developed for each router in which agents have two vital functionalities, traffic shaping and buffer allocation and are positioned in the ports of the routers. With traffic shaping functionality agents shape the traffic forward by dynamic and real time allocation of the rate of generation of tokens in a Token Bucket algorithm and with buffer allocation functionality agents share their buffer capacity between each other based on their need and the conditions of the network. This dynamic and intelligent framework gives this opportunity to some ports to work better under burst and more busy conditions. These agents work intelligently based on Reinforcement Learning (RL) algorithm and will consider effective parameters in their decision process. As RL have limitation considering much parameter in its decision process due to the volume of calculations, we utilize our novel method which invokes Principle Component Analysis (PCA) on the RL and gives a high dimensional ability to this algorithm to consider as much as needed parameters in its decision process. This implementation when is compared to our previous work where traffic shaping was done without any sharing and dynamic allocation of buffer size for each port, the lower packet drop in the whole network specifically in the source routers can be seen. These methods are implemented in our previous proposed intelligent simulation environment to be able to compare better the performance metrics. The results obtained from this simulation environment show an efficient and dynamic utilization of resources in terms of bandwidth and buffer capacities pre allocated to each port.

Keywords: principal component analysis, reinforcement learning, buffer allocation, multi- agent systems

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Authors: Despoina Mouratidis, Katia Lida Kermanidis

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Machine translation (MT) is a very important task in Natural Language Processing (NLP). MT evaluation is crucial in MT development, as it constitutes the means to assess the success of an MT system, and also helps improve its performance. Several methods have been proposed for the evaluation of (MT) systems. Some of the most popular ones in automatic MT evaluation are score-based, such as the BLEU score, and others are based on lexical similarity or syntactic similarity between the MT outputs and the reference involving higher-level information like part of speech tagging (POS). This paper presents a language-independent machine learning framework for classifying pairwise translations. This framework uses vector representations of two machine-produced translations, one from a statistical machine translation model (SMT) and one from a neural machine translation model (NMT). The vector representations consist of automatically extracted word embeddings and string-like language-independent features. These vector representations used as an input to a multi-layer neural network (NN) that models the similarity between each MT output and the reference, as well as between the two MT outputs. To evaluate the proposed approach, a professional translation and a "ground-truth" annotation are used. The parallel corpora used are English-Greek (EN-GR) and English-Italian (EN-IT), in the educational domain and of informal genres (video lecture subtitles, course forum text, etc.) that are difficult to be reliably translated. They have tested three basic deep learning (DL) architectures to this schema: (i) fully-connected dense, (ii) Convolutional Neural Network (CNN), and (iii) Long Short-Term Memory (LSTM). Experiments show that all tested architectures achieved better results when compared against those of some of the well-known basic approaches, such as Random Forest (RF) and Support Vector Machine (SVM). Better accuracy results are obtained when LSTM layers are used in our schema. In terms of a balance between the results, better accuracy results are obtained when dense layers are used. The reason for this is that the model correctly classifies more sentences of the minority class (SMT). For a more integrated analysis of the accuracy results, a qualitative linguistic analysis is carried out. In this context, problems have been identified about some figures of speech, as the metaphors, or about certain linguistic phenomena, such as per etymology: paronyms. It is quite interesting to find out why all the classifiers led to worse accuracy results in Italian as compared to Greek, taking into account that the linguistic features employed are language independent.

Keywords: machine learning, machine translation evaluation, neural network architecture, pairwise classification

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Authors: Ashley Hobson, Ashley Efaw

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Professionalism is an essential skill for clinicians, particularly for Occupational Therapy Providers (OTPs). The World Federation of Occupational Therapy (WFOT) Guiding Principles for Ethical Occupational Therapy and American Occupational Therapy Association (AOTA) Code of Ethics establishes expectations for professionalism among OTPs, emphasizing its importance in the field. However, the teaching and assessment of professionalism vary across OTP programs. The flexibility provided by the country standards allows programs to determine their own approaches to meeting these standards, resulting in inconsistency. Educators in both academic and fieldwork settings face challenges in objectively assessing and providing feedback on student professionalism. Although they observe instances of unprofessional behavior, there is no standardized assessment measure to evaluate professionalism in OTP students. While most students are committed to learning and applying professionalism skills, they enter OTP programs with varying levels of proficiency in this area. Consequently, they lack a uniform understanding of professionalism and lack an objective means to self-assess their current skills and identify areas for growth. It is crucial to explicitly teach professionalism, have students to self-assess their professionalism skills, and have OTP educators assess student professionalism. This approach is necessary for fostering students' professionalism journeys. Traditionally, there has been no objective way for students to self-assess their professionalism or for educators to provide objective assessments and feedback. To establish a uniform approach to professionalism, the authors incorporated professionalism content into our curriculum. Utilizing an operational definition of professionalism, the authors integrated professionalism into didactic, fieldwork, and capstone courses. The complexity of the content and the professionalism skills expected of students increase each year to ensure students graduate with the skills to practice in accordance with the WFOT Guiding Principles for Ethical Occupational Therapy Practice and AOTA Code of Ethics. Two professionalism assessments were developed based on the expectations outlined in the both documents. The Professionalism Self-Assessment allows students to evaluate their professionalism, reflect on their performance, and set goals. The Professionalism Assessment for Educators is a modified version of the same tool designed for educators. The purpose of this workshop is to provide educators with a framework and tools for assessing student professionalism. The authors discuss how to integrate professionalism content into OTP curriculum and utilize professionalism assessments to provide constructive feedback and equitable learning opportunities for OTP students in academic, fieldwork, and capstone settings. By adopting these strategies, educators can enhance the development of professionalism among OTP students, ensuring they are well-prepared to meet the demands of the profession.

Keywords: professionalism, assessments, student learning, student preparedness, ethical practice

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Authors: Fadila Hadjeris

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The study aims at investigating the functions of English-Arabic code switching in English as a foreign language classroom oral discourse and the extent to which they can contribute to the flow of classroom interaction. It also seeks to understand the views, beliefs, and perceptions of teachers and learners towards this practice. We hypothesized that code switching is a communicative strategy which facilitates classroom interaction. Due to this fact, both teachers and learners support its use. The study draws on a key body of literature in bilingualism, second language acquisition, and classroom discourse in an attempt to provide a framework for considering the research questions. It employs a combination of qualitative and quantitative research methods which include classroom observations and questionnaires. The analysis of the recordings shows that teachers’ code switching to Arabic is not only used for academic and classroom management reasons. Rather, the data display instances in which code switching is used for social reasons. The analysis of the questionnaires indicates that teachers and pupils have different attitudes towards this phenomenon. Teachers reported their deliberate switching during EFL teaching, yet the majority was against this practice. According to them, the use of the mother has detrimental effects on the acquisition and the practice of the target language. In contrast, pupils showed their preference to their teachers’ code switching because it enhances and facilitates their understanding. These findings support the fact that the shift to pupils’ mother tongue is a strategy which aids and facilitates the teaching and the learning of the target language. This, in turn, necessitates recommendations which are suggested to teachers and course designers.

Keywords: bilingualism, codeswitching, classroom interaction, classroom discourse, EFL learning/ teaching, SLA

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Authors: Nursel Bektas

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The purpose of this study is to investigate the roles of teaching assistants (TAs) working in reception classes through practitioners’ perspectives. The study has two major purposes; firstly to explore the general roles of TAs, and secondly to identify their roles in supporting children for mathematics. A small-scale case study approach was adopted for this study. The research was carried out in two reception classes within a primary school in London. The qualitative data were gathered through observations and semi-structured interviews with four reception class practitioners, comprising two teachers and two TAs. The results show that TAs consider their role to be more like a teacher, whereas classroom teachers do not corroborate this and they generally believe that the role of TAs depends on their personal characteristics and skills. In regard to the general role of TAs, the study suggests that reception class TAs are deployed both at the classroom level to provide academic support for children’s learning and development, and at the school level they are deployed as support staff such as Midday Meal Supervisor or assistants. In terms of the pedagogical roles of TAs, it was found that TAs have a strong teaching role in literacy development, with notable autonomy if conducting their own phonics sessions without teacher direction, but a negligible influence in numeracy/ math’s. In addition, the results show that the TA role is perceived to be quite limited in planning and assessment processes. Linked to their limited roles in such processes, all participants agree that all the responsibility regarding the children’s learning and development, planning and assessment lies with the teacher. Therefore, data suggest that TAs’ roles in these areas depend on TAs’ their own initiatives.

Keywords: early years education, reception classes, roles, teaching assistants

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Authors: Natalia Caldeira De Carvalho, Tassia Batista Pessato, Luis Gustavo R. Fernandes, Ricardo L. Zollner, Flavia Maria Netto

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Protein hydrolysates are ingredients of enteral diets and hypoallergenic formulas. Enzymatic hydrolysis is the most commonly used method for reducing the antigenicity of milk protein. The antigenicity and physicochemical characteristics of the protein hydrolysates depend on the reaction parameters. Among them, pH has been pointed out as of the major importance. Hydrolysis reaction in laboratory scale is commonly carried out under controlled pH (pH-stat). However, from the industrial point of view, controlling pH during hydrolysis reaction may be infeasible. This study evaluated the impact of pH control on the physicochemical properties and antigenicity of the hydrolysates of whey proteins with Alcalase. Whey protein isolate (WPI) solutions containing 3 and 7 % protein (w/v) were hydrolyzed with Alcalase 50 and 100 U g-1 protein at 60°C for 180 min. The reactions were carried out under controlled and uncontrolled pH conditions. Hydrolyses performed under controlled pH (pH-stat) were initially adjusted and maintained at pH 8.5. Hydrolyses carried out without pH control were initially adjusted to pH 8.5. Degree of hydrolysis (DH) was determined by OPA method, peptides profile was evaluated by HPLC-RP, and molecular mass distribution by SDS-PAGE/Tricine. The residual α-lactalbumin (α-La) and β-lactoglobulin (β-Lg) concentrations were determined using commercial ELISA kits. The specific IgE and IgG binding capacity of hydrolysates was evaluated by ELISA technique, using polyclonal antibodies obtained by immunization of female BALB/c mice with α-La, β-Lg and BSA. In hydrolysis under uncontrolled pH, the pH dropped from 8.5 to 7.0 during the first 15 min, remaining constant throughout the process. No significant difference was observed between the DH of the hydrolysates obtained under controlled and uncontrolled pH conditions. Although all hydrolysates showed hydrophilic character and low molecular mass peptides, hydrolysates obtained with and without pH control exhibited different chromatographic profiles. Hydrolysis under uncontrolled pH released, predominantly, peptides between 3.5 and 6.5 kDa, while hydrolysis under controlled pH released peptides smaller than 3.5 kDa. Hydrolysis with Alcalase under all conditions studied decreased by 99.9% the α-La and β-Lg concentrations in the hydrolysates detected by commercial kits. In general, β-Lg concentrations detected in the hydrolysates obtained under uncontrolled pH were significantly higher (p<0.05) than those detected in hydrolysates produced with pH control. The anti-α-La and anti-β-Lg IgE and IgG responses to all hydrolysates decreased significantly compared to WPI. Levels of specific IgE and IgG to the hydrolysates were below 25 and 12 ng ml-1, respectively. Despite the differences in peptide composition and α-La and β-Lg concentrations, no significant difference was found between IgE and IgG binding capacity of hydrolysates obtained with or without pH control. These results highlight the impact of pH on the hydrolysates characteristics and their concentrations of antigenic protein. Divergence between the antigen detection by commercial ELISA kits and specific IgE and IgG binding response was found in this study. This result shows that lower protein detection does not imply in lower protein antigenicity. Thus, the use of commercial kits for allergen contamination analysis should be cautious.

Keywords: allergy, enzymatic hydrolysis, milk protein, pH conditions, physicochemical characteristics

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Authors: David Maman, David E. Rothem, Merav Ben Natan, Yaron Berkovich

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Introduction: Hip fractures are a major health concern due to their impact on disability, independence, and mortality. Timely rehabilitation is crucial for improving patient outcomes and reducing healthcare costs, yet delays in rehabilitation, often due to challenges in discharge processes, can lead to adverse events and increased healthcare burdens. Aim: The study aimed to investigate two primary aspects related to hip fracture older adults patients: firstly, identifying subgroups more prone to delayed discharge for further rehabilitation; and secondly, exploring the consequences of this delay on short-term outcomes and the incidence of adverse events. Methods: Conducting a retrospective analysis, we examined the medical records of 474 patients aged 65 and older, hospitalized for hip fractures between 2018 and 2022 in a major hospital in the north-central region of Israel. All patients were eligible for further rehabilitation, including options for in-patient or home-based care. Results: Of the studied patients, 61.4% experienced delayed discharge, with an average waiting period of 3.5 days. Factors such as older age, prolonged hospital stay, and the need for in-patient rehabilitation were associated with a higher likelihood of delayed discharge. Those promptly discharged demonstrated lower rates of infections, falls, and mortality. Furthermore, delayed discharge to further rehabilitation correlated with elevated hospitalization costs. Notably, no significant differences were observed in re-hospitalization or repeat surgery rates. Conclusion: This study underscores the pressing need for efficient strategies to ensure timely rehabilitation, particularly for older adults. Implementing such strategies can optimize outcomes, mitigate adverse events, and contribute to a reduction in healthcare costs.

Keywords: hip fracture rehabilitation, delayed discharge, older adults, healthcare coordination, adverse events

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Authors: Ezra N. S. Lockhart

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The number of children diagnosed with autism spectrum disorder (ASD) has increased approximately 173% during the last decade making ASD the fastest growing developmental disability in the United States. This rise in prevalence rates indeed has an effect on schools. ASD is overwhelmingly the most reported primary special education eligibility category for students accessing special education, at a national average of 61.3%. ASD is regarded as an urgent public health concern at an estimated annual per capita cost of $3.2 million. Furthermore, considering that ASD is a lifelong disorder estimated lifetime per capita cost reach $35 billion. The resources available to special education programs are insufficient to meet the educational needs of the 6.4 million students receiving special educational services. This is especially true given that there has been and continues to be a chronic shortage of fully certified special education teachers for decades. Reports indicate that 81.1% of students with special needs spend 40% or more in general education classrooms. Regardless of whether support is implemented in the special education or general education classroom the resource demand is obvious. Schools are actively seeking to implement low-cost alternatives and budget saving measures in response to this demand. In public school settings, programs such as Applied Behavior Analysis are challenging to implement and fund at $40,000 per student per year. As an alternative, computer-based interventions are inexpensive, less time-consuming to implement, and require minimal teacher or paraprofessional training to administer. Affordability, pricing schemes, availability, and compatibility of computer-based interventions that support social and emotional skill development in individuals with ASD are discussed.

Keywords: affordability, autism spectrum disorder, computer-based intervention, emotional skills, social skills

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Authors: Jane Mullen

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Little research has been undertaken on adult dyslexia and the impact it has on those who have professional careers. There are many complexities behind the career decisions people make, but for teachers who are dyslexic, it can be even more complex. Dyslexia particularly impacts on written and verbal communication, as well as planning and organisation skills which are essential skills for a teacher. As the teachers are aware of their areas of weakness many, make the conscious decision not to disclose their disability at work. In England, the reduction to three attempts to pass the compulsory English and Maths tests prior to undertaking teacher training may mean that dyslexics are now excluded from trying to enter the profession. Together with the fact that dyslexic teachers often chose to remain ‘hidden’ the situation appears to be counter to the inclusive rhetoric that dominates the current educational discourse. This paper is based on in-depth narrative research that has been undertaken with a small group of teachers who are dyslexic in England and firstly explores the strategies and resources that the teachers have found useful. The narratives of the teachers are full of difficulties as well as diversity, consequently, the paper secondly examines how life experiences have impacted on the way the teachers see their dyslexia and how it affects them professionally. Using a narrative methodology enables the teachers to tell their ‘stories’ of how they feel their dyslexia impacts on their lives professionally. The first interview centred around a limited number of semi structured questions about family background, educational experiences, career development, management roles and professional disclosure. The second interview focused on the complexities of being a teacher who is dyslexic and to ‘unlock’ some of their work based narratives visual elicitation was used. Photographs of work-based strategies, issues or concerns were sent to the researcher and these were used as the basis for discussion in the second interview. The paper concludes by discussing possible reasonable adjustments and professional development that might benefit teachers who are dyslexic.

Keywords: dyslexia, life history, narrative, professional, professional development, strategies, teachers

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Authors: Ekta Yadav, Sukanta Bhattacharya, Brijesh Yadav, Ariel Hus, Jagjit Yadav

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Background and Significance: Respiratory exposure to toxicants (chemicals or particulates) causes disruption of lung homeostasis leading to lung toxicity/injury manifested as pulmonary inflammation, edema, and/or other effects depending on the type and extent of exposure. This emphasizes the need for investigating toxicant type-specific mechanisms to understand therapeutic targets. Aquaporins, aka water channels, are known to play a role in lung homeostasis. Particularly, the two major lung aquaporins AQP5 and AQP1 expressed in alveolar epithelial and vasculature endothelia respectively allow for movement of the fluid between the alveolar air space and the associated vasculature. In view of this, the current study is focused on understanding the regulation of lung aquaporins and other targets during inhalation exposure to toxic chemicals (Cigarette smoke chemicals) versus toxic particles (Carbon nanoparticles) or co-exposures to understand their relevance as markers of injury and intervention. Methodologies: C57BL/6 mice (5-7 weeks old) were used in this study following an approved protocol by the University of Cincinnati Institutional Animal Care and Use Committee (IACUC). The mice were exposed via oropharyngeal aspiration to multiwall carbon nanotube (MWCNT) particles suspension once (33 ugs/mouse) followed by housing for four weeks or to Cigarette smoke Extract (CSE) using a daily dose of 30µl/mouse for four weeks, or to co-exposure using the combined regime. Control groups received vehicles following the same dosing schedule. Lung toxicity/injury was assessed in terms of homeostasis changes in the lung tissue and lumen. Exposed lungs were analyzed for transcriptional expression of specific targets (AQPs, surfactant protein A, Mucin 5b) in relation to tissue homeostasis. Total RNA from lungs extracted using TRIreagent kit was analyzed using qRT-PCR based on gene-specific primers. Total protein in bronchoalveolar lavage (BAL) fluid was determined by the DC protein estimation kit (BioRad). GraphPad Prism 5.0 (La Jolla, CA, USA) was used for all analyses. Major findings: CNT exposure alone or as co-exposure with CSE increased the total protein content in the BAL fluid (lung lumen rinse), implying compromised membrane integrity and cellular infiltration in the lung alveoli. In contrast, CSE showed no significant effect. AQP1, required for water transport across membranes of endothelial cells in lungs, was significantly upregulated in CNT exposure but downregulated in CSE exposure and showed an intermediate level of expression for the co-exposure group. Both CNT and CSE exposures had significant downregulating effects on Muc5b, and SP-A expression and the co-exposure showed either no significant effect (Muc5b) or significant downregulating effect (SP-A), suggesting an increased propensity for infection in the exposed lungs. Conclusions: The current study based on the lung toxicity mouse model showed that both toxicant types, particles (CNT) versus chemicals (CSE), cause similar downregulation of lung innate defense targets (SP-A, Muc5b) and mostly a summative effect when presented as co-exposure. However, the two toxicant types show differential induction of aquaporin-1 coinciding with the corresponding differential damage to alveolar integrity (vascular permeability). Interestingly, this implies the potential of AQP1 as a differential marker of toxicant type-specific lung injury.

Keywords: aquaporin, gene expression, lung injury, toxicant exposure

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Authors: Saikiran Subbagari, Avinash Malladhi

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Anti Money Laundering (AML) regulations are designed to prevent money laundering and terrorist financing activities worldwide. Financial institutions around the world are legally obligated to identify, assess and mitigate the risks associated with money laundering and report any suspicious transactions to governing authorities. With increasing volumes of data to analyze, financial institutions seek to automate their AML processes. In the rise of financial crimes, optical character recognition (OCR), in combination with machine learning (ML) algorithms, serves as a crucial tool for automating AML processes by extracting the data from documents and identifying suspicious transactions. In this paper, we examine the utilization of OCR for AML and delve into various OCR techniques employed in AML processes. These techniques encompass template-based, feature-based, neural network-based, natural language processing (NLP), hidden markov models (HMMs), conditional random fields (CRFs), binarizations, pattern matching and stroke width transform (SWT). We evaluate each technique, discussing their strengths and constraints. Also, we emphasize on how OCR can improve the accuracy of customer identity verification by comparing the extracted text with the office of foreign assets control (OFAC) watchlist. We will also discuss how OCR helps to overcome language barriers in AML compliance. We also address the implementation challenges that OCR-based AML systems may face and offer recommendations for financial institutions based on the data from previous research studies, which illustrate the effectiveness of OCR-based AML.

Keywords: anti-money laundering, compliance, financial crimes, fraud detection, machine learning, optical character recognition

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Authors: Henry Memczak, Marc Hovestaedt, Bernhard Ay, Sandra Saenger, Thorsten Wolff, Frank F. Bier

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The influenza viruses cause flu epidemics every year and serious pandemics in larger time intervals. The only cost-effective protection against influenza is vaccination. Due to rapid mutation continuously new subtypes appear, what requires annual reimmunization. For a correct vaccination recommendation, the circulating influenza strains had to be detected promptly and exactly and characterized due to their antigenic properties. During the flu season 2016/17, a wrong vaccination recommendation has been given because of the great time interval between identification of the relevant influenza vaccine strains and outbreak of the flu epidemic during the following winter. Due to such recurring incidents of vaccine mismatches, there is a great need to speed up the process chain from identifying the right vaccine strains to their administration. The monitoring of subtypes as part of this process chain is carried out by national reference laboratories within the WHO Global Influenza Surveillance and Response System (GISRS). To this end, thousands of viruses from patient samples (e.g., throat smears) are isolated and analyzed each year. Currently, this analysis involves complex and time-intensive (several weeks) animal experiments to produce specific hyperimmune sera in ferrets, which are necessary for the determination of the antigen profiles of circulating virus strains. These tests also bear difficulties in standardization and reproducibility, which restricts the significance of the results. To replace this test a peptide-based assay for influenza virus subtyping from corresponding virus samples was developed. The differentiation of the viruses takes place by a set of specifically designed peptidic recognition molecules which interact differently with the different influenza virus subtypes. The differentiation of influenza subtypes is performed by pattern recognition guided by machine learning algorithms, without any animal experiments. Synthetic peptides are immobilized in multiplex format on various platforms (e.g., 96-well microtiter plate, microarray). Afterwards, the viruses are incubated and analyzed comparing different signaling mechanisms and a variety of assay conditions. Differentiation of a range of influenza subtypes, including H1N1, H3N2, H5N1, as well as fine differentiation of single strains within these subtypes is possible using the peptide-based subtyping platform. Thereby, the platform could be capable of replacing the current antigenic characterization of influenza strains using ferret hyperimmune sera.

Keywords: antigenic characterization, influenza-binding peptides, influenza subtyping, influenza surveillance

Procedia PDF Downloads 157