Search results for: convolutional coding

Authors: Shanaka Gunasekara, Maleen Jayasuriya, Nalin Harischandra, Lilantha Samaranayake, Gamini Dissanayake

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One serious facet of the worsening Human-Elephant conflict (HEC) in nations such as Sri Lanka involves elephant-train collisions. Endangered Asian elephants are maimed or killed during such accidents, which also often result in orphaned or disabled elephants, contributing to the phenomenon of lone elephants. These lone elephants are found to be more likely to attack villages and showcase aggressive behaviour, which further exacerbates the overall HEC. Furthermore, Railway Services incur significant financial losses and disruptions to services annually due to such accidents. Most elephant-train collisions occur due to a lack of adequate reaction time. This is due to the significant stopping distance requirements of trains, as the full braking force needs to be avoided to minimise the risk of derailment. Thus, poor driver visibility at sharp turns, nighttime operation, and poor weather conditions are often contributing factors to this problem. Initial investigations also indicate that most collisions occur in localised “hotspots” where elephant pathways/corridors intersect with railway tracks that border grazing land and watering holes. Taking these factors into consideration, this work proposes the leveraging of recent developments in Convolutional Neural Network (CNN) technology to detect elephants using an RGB/infrared capable camera around known hotspots along the railway track. The CNN was trained using a curated dataset of elephants collected on field visits to elephant sanctuaries and wildlife parks in Sri Lanka. With this vision-based detection system at its core, a prototype unit of an early warning system was designed and tested. This weatherised and waterproofed unit consists of a Reolink security camera which provides a wide field of view and range, an Nvidia Jetson Xavier computing unit, a rechargeable battery, and a solar panel for self-sufficient functioning. The prototype unit was designed to be a low-cost, low-power and small footprint device that can be mounted on infrastructures such as poles or trees. If an elephant is detected, an early warning message is communicated to the train driver using the GSM network. A mobile app for this purpose was also designed to ensure that the warning is clearly communicated. A centralized control station manages and communicates all information through the train station network to ensure coordination among important stakeholders. Initial results indicate that detection accuracy is sufficient under varying lighting situations, provided comprehensive training datasets that represent a wide range of challenging conditions are available. The overall hardware prototype was shown to be robust and reliable. We envision a network of such units may help contribute to reducing the problem of elephant-train collisions and has the potential to act as an important surveillance mechanism in dealing with the broader issue of human-elephant conflicts.

Keywords: computer vision, deep learning, human-elephant conflict, wildlife early warning technology

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Delphine Ribes Lemay, Valentine Bernasconi, André Andrade, Lara DéFayes, Mathieu Salzmann, FréDéRic Kaplan, Nicolas Henchoz

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Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum für Gestaltung of Zurich holds one of the biggest poster collections of the world from which 52’000 were digitised. In the process of building a digital installation to valorize the collection, one objective was to develop an algorithm capable of predicting the next poster to show according to the ones already displayed. The work presented here describes the steps to build an algorithm able to automatically create sequences of posters reflecting associations performed by curator and professional designers. The exposed challenge finds similarities with the domain of song playlist algorithms. Recently, artificial intelligence techniques and more specifically, deep-learning algorithms have been used to facilitate their generations. Promising results were found thanks to Recurrent Neural Networks (RNN) trained on manually generated playlist and paired with clusters of extracted features from songs. We used the same principles to create the proposed algorithm but applied to a challenging medium, posters. First, a convolutional autoencoder was trained to extract features of the posters. The 52’000 digital posters were used as a training set. Poster features were then clustered. Next, an RNN learned to predict the next cluster according to the previous ones. RNN training set was composed of poster sequences extracted from a collection of books from the Gestaltung Museum of Zurich dedicated to displaying posters. Finally, within the predicted cluster, the poster with the best proximity compared to the previous poster is selected. The mean square distance between features of posters was used to compute the proximity. To validate the predictive model, we compared sequences of 15 posters produced by our model to randomly and manually generated sequences. Manual sequences were created by a professional graphic designer. We asked 21 participants working as professional graphic designers to sort the sequences from the one with the strongest graphic line to the one with the weakest and to motivate their answer with a short description. The sequences produced by the designer were ranked first 60%, second 25% and third 15% of the time. The sequences produced by our predictive model were ranked first 25%, second 45% and third 30% of the time. The sequences produced randomly were ranked first 15%, second 29%, and third 55% of the time. Compared to designer sequences, and as reported by participants, model and random sequences lacked thematic continuity. According to the results, the proposed model is able to generate better poster sequencing compared to random sampling. Eventually, our algorithm is sometimes able to outperform a professional designer. As a next step, the proposed algorithm should include a possibility to create sequences according to a selected theme. To conclude, this work shows the potentiality of artificial intelligence techniques to learn from existing content and provide a tool to curate large sets of data, with a permanent renewal of the presented content.

Keywords: Artificial Intelligence, Digital Humanities, serendipity, design research

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Authors: Mairo H. Ipadeola, Catherine James Atteng

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The term social inclusion refers to a process by which those disadvantaged in society can have access to fully participate in education like others. Student with special needs are expected to learn along with their peers within the some educational institutions which provide adequate access for all. There for, the study sort to understand the typical ways in which students with disabilities (SWD) were denied from fully participating as students in Kaduna Polytechnic. In doing this, two (2) objectives and research questions were raised. Firstly, to explore the attitudes of others towards students with disabilities in the institutions and secondly, to ascertain the extent of social participation and physical accessibility for students with disabilities (SWD) while in the institutions. Based on the objectives the paper postulated the research questions: what are the attitudes of management, teachers, and students towards students with special need in Kaduna Polytechnic and to what extent did the students with disabilities experience social participation and physical accessibility within Kaduna Polytechnic school environment? The study area was Kaduna Polytechnic. The study engaged the interview for the data collected which were transcribed and analyzed by thematic coding. The findings were categorized under themes, sub-themes, and codes. The findings revealed that the perception, behavior, and association experiences of students with disabilities within Kaduna Polytechnic were not encouraging. Their experiences were characterized by negative attitudes, feelings of rejection, neglect, and bullying. Data generated on social participation indicated that 71% of the respondents believed that learning, school activities, recreations, and student politics between SWD and the other student were in the direction of low / very low. All the respondents, particularly students with blindness and physical challenges faced difficulty with environmental and physical access above all within the school environment, classroom, walkways and ramps, Also, directions were none existent in most departments with physical access to classrooms, toilets, cafeterias, and school shops absent or very low (71% and 29% of the respondents). The conclusion was that the physical barriers limited the possibilities of social participation of SWD.The paper made some recommendations such as mass public enlightenment on radio and television to change the perception of society about people with disability. Also, the federal, state, and local governments enact building acts for fresh builders and adopted measures and time frames for existing public buildings to be made accessible for people with disabilities. All stakeholders should ensure that the five (5) percent budget set aside by State Universal Basic Education Board (SUBEB) and/or Tertiary Education Trust Fund (TETFUND) for the provision of specialized equipment and facilities for the student with special needs should be used prudently spent and monitored by the board.cm.

Keywords: social inclusion, students with disability, social participation, environmental/physical access

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Authors: Abdoulie K. Ceesay

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Within the sequence of the whole genome, it is known that 99.9% of the human genome is similar, whilst our difference lies in just 0.1%. Among these minor dissimilarities, the most common type of genetic variations that occurs in a population is SNP, which arises due to nucleotide substitution in a protein sequence that leads to protein destabilization, alteration in dynamics, and other physio-chemical properties’ distortions. While causing variations, they are equally responsible for our difference in the way we respond to a treatment or a disease, including various cancer types. There are two types of SNPs; synonymous single nucleotide polymorphism (sSNP) and non-synonymous single nucleotide polymorphism (nsSNP). sSNP occur in the gene coding region without causing a change in the encoded amino acid, while nsSNP is deleterious due to its replacement of a nucleotide residue in the gene sequence that results in a change in the encoded amino acid. Predicting the effects of cancer related nsSNPs on protein stability, function, and dynamics is important due to the significance of phenotype-genotype association of cancer. In this thesis, Data of 5 oncogenes (ONGs) (AKT1, ALK, ERBB2, KRAS, BRAF) and 5 tumor suppressor genes (TSGs) (ESR1, CASP8, TET2, PALB2, PTEN) were retrieved from ClinVar. Five common in silico tools; Polyphen, Provean, Mutation Assessor, Suspect, and FATHMM, were used to predict and categorize nsSNPs as deleterious, benign, or neutral. To understand the impact of each variation on the phenotype, Maestro, PremPS, Cupsat, and mCSM-NA in silico structural prediction tools were used. This study comprises of in-depth analysis of 10 cancer gene variants downloaded from Clinvar. Various analysis of the genes was conducted to derive a meaningful conclusion from the data. Research done indicated that pathogenic variants are more common among ONGs. Our research also shows that pathogenic and destabilizing variants are more common among ONGs than TSGs. Moreover, our data indicated that ALK(409) and BRAF(86) has higher benign count among ONGs; whilst among TSGs, PALB2(1308) and PTEN(318) genes have higher benign counts. Looking at the individual cancer genes predisposition or frequencies of causing cancer according to our research data, KRAS(76%), BRAF(55%), and ERBB2(36%) among ONGs; and PTEN(29%) and ESR1(17%) among TSGs have higher tendencies of causing cancer. Obtained results can shed light to the future research in order to pave new frontiers in cancer therapies.

Keywords: tumor suppressor genes (TSGs), oncogenes (ONGs), non synonymous single nucleotide polymorphism (nsSNP), single nucleotide polymorphism (SNP)

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Authors: Favour Makuochukwu Orji, Yingkui Zhao

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A myriad of environmental issues face the Nigerian industrial region, resulting from; oil and gas production, mining, manufacturing and domestic wastes. Amidst these, much effort has been directed by stakeholders in the Nigerian oil producing regions, because of the impacts of the region on the wider Nigerian economy. Research to date has suggested that collaborative environmental management could be an effective approach in managing environmental issues; but little attention has been given to the roles and practices of stakeholders in effecting a collaborative environmental management framework for the Nigerian oil-producing region. This paper produces a framework to expand and deepen knowledge relating to stakeholders aspects of collaborative roles in managing environmental issues in the Nigeria oil-producing region. The knowledge is derived from analysis of stakeholders’ practices – studied through multiple case studies using document analysis. Selected documents of key stakeholders – Nigerian government agencies, multi-national oil companies and host communities, were analyzed. Open and selective coding was employed manually during document analysis of data collected from the offices and websites of the stakeholders. The findings showed that the stakeholders have a range of roles, practices, interests, drivers and barriers regarding their collaborative roles in managing environmental issues. While they have interests for efficient resource use, compliance to standards, sharing of responsibilities, generating of new solutions, and shared objectives; there is evidence of major barriers which includes resource allocation, disjointed policy and regulation, ineffective monitoring, diverse socio- economic interests, lack of stakeholders’ commitment and limited knowledge sharing. However, host communities hold deep concerns over the collaborative roles of stakeholders for economic interests, particularly, where government agencies and multi-national oil companies are involved. With these barriers and concerns, a genuine stakeholders’ collaboration is found to be limited, and as a result, optimal environmental management practices and policies have not been successfully implemented in the Nigeria oil-producing region. A framework is produced that describes practices that characterize collaborative environmental management might be employed to satisfy the stakeholders’ interests. The framework recommends critical factors, based on the findings, which may guide a collaborative environmental management in the oil producing regions. The recommendations are designed to re-define the practices of stakeholders in managing environmental issues in the oil producing regions, not as something wholly new, but as an approach essential for implementing a sustainable environmental policy. This research outcome may clarify areas for future research as well as to contribute to industry guidance in the area of collaborative environmental management.

Keywords: collaborative environmental management framework, case studies, document analysis, multinational oil companies, Nigerian oil producing regions, Nigerian government agencies, stakeholders analysis

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Authors: S. D. N. K. Bathige, G. I. Godahewa, Navaneethaiyer Umasuthan, Jehee Lee

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Kunitz-type serine protease inhibitors (KTIs) are identified in various organisms including animals, plants and microbes. These proteins shared single or multiple Kunitz inhibitory domains link together or associated with other types of domains. Characteristic Kunitz type domain composed of around 60 amino acid residues with six conserved cysteine residues to stabilize by three disulfide bridges. KTIs are involved in various physiological processes, such as ion channel blocking, blood coagulation, fibrinolysis and inflammation. In this study, two Kunitz-type domain containing protein was identified from rock bream database and designated as RbKunitz. The coding sequence of RbKunitz encoded for 507 amino acids with 56.2 kDa theoretical molecular mass and 5.7 isoelectric point (pI). There are several functional domains including MANEC superfamily domain, PKD superfamily domain, and LDLa domain were predicted in addition to the two characteristic Kunitz domain. Moreover, trypsin interaction sites were also identified in Kunitz domain. Homology analysis revealed that RbKunitz shared highest identity (77.6%) with Takifugu rubripes. Completely conserved 28 cysteine residues were recognized, when comparison of RbKunitz with other orthologs from different taxonomical groups. These structural evidences indicate the rigidity of RbKunitz folding structure to achieve the proper function. The phylogenetic tree was constructed using neighbor-joining method and exhibited that the KTIs from fish and non-fish has been evolved in separately. Rock bream was clustered with Takifugu rubripes. The SYBR Green qPCR was performed to quantify the RbKunitz transcripts in different tissues and challenged tissues. The mRNA transcripts of RbKunitz were detected in all tissues (muscle, spleen, head kidney, blood, heart, skin, liver, intestine, kidney and gills) analyzed and highest transcripts level was detected in gill tissues. Temporal transcription profile of RbKunitz in rock bream blood tissues was analyzed upon LPS (lipopolysaccharide), Poly I:C (Polyinosinic:polycytidylic acid) and Edwardsiella tarda challenge to understand the immune responses of this gene. Compare to the unchallenged control RbKunitz exhibited strong up-regulation at 24 h post injection (p.i.) after LPS and E. tarda injection. Comparatively robust expression of RbKunits was observed at 3 h p.i. upon Poly I:C challenge. Taken together all these data indicate that RbKunitz may involve into to immune responses upon pathogenic stress, in order to protect the rock bream.

Keywords: Kunitz-type, rock bream, immune response, serine protease inhibitor

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Authors: M. Patterson, R. Bond, K. Cowan, M. Mulvenna, C. Reid, F. McMahon, P. McGowan, H. Cormican

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This paper outlines the design of a simulator to allow for the optimisation of clinical workflows through a pathology laboratory and to improve the laboratory’s efficiency in the processing, testing, and analysis of specimens. Often pathologists have difficulty in pinpointing and anticipating issues in the clinical workflow until tests are running late or in error. It can be difficult to pinpoint the cause and even more difficult to predict any issues which may arise. For example, they often have no indication of how many samples are going to be delivered to the laboratory that day or at a given hour. If we could model scenarios using past information and known variables, it would be possible for pathology laboratories to initiate resource preparations, e.g. the printing of specimen labels or to activate a sufficient number of technicians. This would expedite the clinical workload, clinical processes and improve the overall efficiency of the laboratory. The simulator design visualises the workflow of the laboratory, i.e. the clinical tests being ordered, the specimens arriving, current tests being performed, results being validated and reports being issued. The simulator depicts the movement of specimens through this process, as well as the number of specimens at each stage. This movement is visualised using an animated flow diagram that is updated in real time. A traffic light colour-coding system will be used to indicate the level of flow through each stage (green for normal flow, orange for slow flow, and red for critical flow). This would allow pathologists to clearly see where there are issues and bottlenecks in the process. Graphs would also be used to indicate the status of specimens at each stage of the process. For example, a graph could show the percentage of specimen tests that are on time, potentially late, running late and in error. Clicking on potentially late samples will display more detailed information about those samples, the tests that still need to be performed on them and their urgency level. This would allow any issues to be resolved quickly. In the case of potentially late samples, this could help to ensure that critically needed results are delivered on time. The simulator will be created as a single-page web application. Various web technologies will be used to create the flow diagram showing the workflow of the laboratory. JavaScript will be used to program the logic, animate the movement of samples through each of the stages and to generate the status graphs in real time. This live information will be extracted from an Oracle database. As well as being used in a real laboratory situation, the simulator could also be used for training purposes. ‘Bots’ would be used to control the flow of specimens through each step of the process. Like existing software agents technology, these bots would be configurable in order to simulate different situations, which may arise in a laboratory such as an emerging epidemic. The bots could then be turned on and off to allow trainees to complete the tasks required at that step of the process, for example validating test results.

Keywords: laboratory-process, optimization, pathology, computer simulation, workflow

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Authors: Camilla Simoncelli

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The connection between language and cognition has been one of the main interests of linguistics from several years. According to the Sapir-Whorf Linguistic Relativity Theory, the way we perceive reality depends on the language we speak which in turn has a central role in the human cognition. This paper is in line with this research work with the aim of analyzing how language structures reflect on our cognitive abilities even in the description of space, which is generally considered as a human natural and universal domain. The main objective is to identify the differences in the encoding of spatial inclusion relationships in French and Italian speakers to make evidence that a significant variation exists at various levels even in two similar systems. Starting from the constitution a corpora, the first step of the study has been to establish the relevant complex prepositions marking an inclusion relation in French and Italian: au centre de, au cœur de, au milieu de, au sein de, à l'intérieur de and the opposition entre/parmi in French; al centro di, al cuore di, nel mezzo di, in seno a, all'interno di and the fra/tra contrast in Italian. These prepositions had been classified on the base of the type of Noun following them (e.g. mass nouns, concrete nouns, abstract nouns, body-parts noun, etc.) following the Collostructional Analysis of lexemes with the purpose of analyzing the preferred construction of each preposition comparing the relations construed. Comparing the Italian and the French results it has been possible to define the degree of representativeness of each target Noun for the chosen preposition studied. Lexicostatistics and Statistical Association Measures showed the values of attraction or repulsion between lexemes and a given preposition, highlighting which words are over-represented or under-represented in a specific context compared to the expected results. For instance, a Noun as Dibattiti has a negative value for the Italian Al cuore di (-1,91), but it has a strong positive representativeness for the corresponding French Au cœur de (+677,76). The value, positive or negative, is the result of a hypergeometric distribution law which displays the current use of some relevant nouns in relations of spatial inclusion by French and Italian speakers. Differences on the kind of location conceptualization denote syntactic and semantic constraints based on spatial features as well as on linguistic peculiarity, too. The aim of this paper is to demonstrate that the domain of spatial relations is basic to human experience and is linked to universally shared perceptual mechanisms which create mental representations depending on the language use. Therefore, linguistic coding strongly correlates with the way spatial distinctions are conceptualized for non-verbal tasks even in close language systems, like Italian and French.

Keywords: cognitive semantics, cross-linguistic variations, locational terms, non-verbal spatial representations

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Authors: Sinead Impey, Gaye Stephens, Lucy Hederman, Declan O'Sullivan

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Continued paper use in the clinical area post-Electronic Health Record (EHR) adoption is regularly linked to hardware and software usability challenges. Although paper is used as a workaround to circumvent challenges, including limited availability of a computer, this perspective does not consider the important role paper, such as the nurses’ handover sheet, play in practice. The purpose of this study is to confirm the hypothesis that paper use post-EHR adoption continues as paper provides both a cognitive tool (that assists with workflow) and a compensation tool (to circumvent usability challenges). Distinguishing the different motivations for continued paper-use could assist future evaluations of electronic record systems. Methods: Qualitative data were collected from three clinical care environments (ICU, general ward and specialist day-care) who used an electronic record for at least 12 months. Data were collected through semi-structured interviews with 22 nurses. Data were transcribed, themes extracted using an inductive bottom-up coding approach and a thematic index constructed. Findings: All nurses interviewed continued to use paper post-EHR adoption. While two distinct motivations for paper use post-EHR adoption were confirmed by the data - paper as a cognitive tool and paper as a compensation tool - further finding was that there was an overlap between the two uses. That is, paper used as a compensation tool could also be adapted to function as a cognitive aid due to its nature (easy to access and annotate) or vice versa. Rather than present paper persistence as having two distinctive motivations, it is more useful to describe it as presenting on a continuum with compensation tool and cognitive tool at either pole. Paper as a cognitive tool referred to pages such as nurses’ handover sheet. These did not form part of the patient’s record, although information could be transcribed from one to the other. Findings suggest that although the patient record was digitised, handover sheets did not fall within this remit. These personal pages continued to be useful post-EHR adoption for capturing personal notes or patient information and so continued to be incorporated into the nurses’ work. Comparatively, the paper used as a compensation tool, such as pre-printed care plans which were stored in the patient's record, appears to have been instigated in reaction to usability challenges. In these instances, it is expected that paper use could reduce or cease when the underlying problem is addressed. There is a danger that as paper affords nurses a temporary information platform that is mobile, easy to access and annotate, its use could become embedded in clinical practice. Conclusion: Paper presents a utility to nursing, either as a cognitive or compensation tool or combination of both. By fully understanding its utility and nuances, organisations can avoid evaluating all incidences of paper use (post-EHR adoption) as arising from usability challenges. Instead, suitable remedies for paper-persistence can be targeted at the root cause.

Keywords: cognitive tool, compensation tool, electronic record, handover sheet, nurse, paper persistence

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Authors: A. F. Huseynov, N. T. Mardanov, J. Y. Nakhchivanski

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This practical research is aimed to improve the management quality and efficiency of public administration agencies providing e-services. The monitoring system developed will provide continuous review of the websites compliance with the selected indicators, their evaluation based on the selected indicators and ranking of services according to the quality criteria. The responsible departments in the government agencies were surveyed; the questionnaire includes issues of management and feedback, e-services provided, and the application of information systems. By analyzing the main affecting factors and barriers, the recommendations will be given that lead to the relevant decisions to strengthen the state agencies competencies for the management and the provision of their services. Component 1. E-services monitoring system. Three separate monitoring activities are proposed to be executed in parallel: Continuous tracing of e-government sites using built-in web-monitoring program; this program generates several quantitative values which are basically related to the technical characteristics and the performance of websites. The expert assessment of e-government sites in accordance with the two general criteria. Criterion 1. Technical quality of the site. Criterion 2. Usability/accessibility (load, see, use). Each high-level criterion is in turn subdivided into several sub-criteria, such as: the fonts and the color of the background (Is it readable?), W3C coding standards, availability of the Robots.txt and the site map, the search engine, the feedback/contact and the security mechanisms. The on-line survey of the users/citizens – a small group of questions embedded in the e-service websites. The questionnaires comprise of the information concerning navigation, users’ experience with the website (whether it was positive or negative), etc. Automated monitoring of web-sites by its own could not capture the whole evaluation process, and should therefore be seen as a complement to expert’s manual web evaluations. All of the separate results were integrated to provide the complete evaluation picture. Component 2. Assessment of the agencies/departments efficiency in providing e-government services. - the relevant indicators to evaluate the efficiency and the effectiveness of e-services were identified; - the survey was conducted in all the governmental organizations (ministries, committees and agencies) that provide electronic services for the citizens or the businesses; - the quantitative and qualitative measures are covering the following sections of activities: e-governance, e-services, the feedback from the users, the information systems at the agencies’ disposal. Main results: 1. The software program and the set of indicators for internet sites evaluation has been developed and the results of pilot monitoring have been presented. 2. The evaluation of the (internal) efficiency of the e-government agencies based on the survey results with the practical recommendations related to the human potential, the information systems used and e-services provided.

Keywords: e-government, web-sites monitoring, survey, internal efficiency

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Authors: Xinyue Jiao, Yu-Ren Lin

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Argumentation is an essential aspect of scientific thinking which has been widely concerned in recent reform of science education. The purpose of the present studies was to explore the influences of two variables termed ‘the argumentation strategy’ and ‘the kind of science concept’ on student’s web-based argumentation. The first variable was divided into either monological (which refers to individual’s internal discourse and inner chain reasoning) or dialectical (which refers to dialogue interaction between/among people). The other one was also divided into either descriptive (i.e., macro-level concept, such as phenomenon can be observed and tested directly) or theoretical (i.e., micro-level concept which is abstract, and cannot be tested directly in nature). The present study applied the quasi-experimental design in which 138 7th grade students were invited and then assigned to either monological group (N=70) or dialectical group (N=68) randomly. An argumentation learning program called ‘the PWAL’ was developed to improve their scientific argumentation abilities, such as arguing from multiple perspectives and based on scientific evidence. There were two versions of PWAL created. For the individual version, students can propose argument only through knowledge recall and self-reflecting process. On the other hand, the students were allowed to construct arguments through peers’ communication in the collaborative version. The PWAL involved three descriptive science concept-based topics (unit 1, 3 and 5) and three theoretical concept-based topics (unit 2, 4 and 6). Three kinds of scaffoldings were embedded into the PWAL: a) argument template, which was used for constructing evidence-based argument; b) the model of the Toulmin’s TAP, which shows the structure and elements of a sound argument; c) the discussion block, which enabled the students to review what had been proposed during the argumentation. Both quantitative and qualitative data were collected and analyzed. An analytical framework for coding students’ arguments proposed in the PWAL was constructed. The results showed that the argumentation approach has a significant effect on argumentation only in theoretical topics (f(1, 136)=48.2, p < .001, η2=2.62). The post-hoc analysis showed the students in the collaborative group perform significantly better than the students in the individual group (mean difference=2.27). However, there is no significant difference between the two groups regarding their argumentation in descriptive topics. Secondly, the students made significant progress in the PWAL from the earlier descriptive or theoretical topic to the later one. The results enabled us to conclude that the PWAL was effective for students’ argumentation. And the students’ peers’ interaction was essential for students to argue scientifically especially for the theoretical topic. The follow-up qualitative analysis showed student tended to generate arguments through critical dialogue interactions in the theoretical topic which promoted them to use more critiques and to evaluate and co-construct each other’s arguments. More explanations regarding the students’ web-based argumentation and the suggestions for the development of web-based science learning were proposed in our discussions.

Keywords: argumentation, collaborative learning, scientific concepts, web-based learning

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Authors: Omar Faruqe Hamim, M. Shamsul Hoque, Rich C. McIlroy, Katherine L. Plant, Neville A. Stanton

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Road safety has become an alarming issue, especially in low-middle income developing countries. The traditional approaches lack the out of the box thinking, making engineers confined to applying usual techniques in making roads safer. A socio-technical approach has recently been introduced in improving road intersections through designing with intent. This Design With Intent (DWI) approach aims to give practitioners a more nuanced approach to design and behavior, working with people, people’s understanding, and the complexities of everyday human experience. It's a collection of design patterns —and a design and research approach— for exploring the interactions between design and people’s behavior across products, services, and environments, both digital and physical. Through this approach, it can be seen that how designing with people in behavior change can be applied to social and environmental problems, as well as commercially. It has a total of 101 cards across eight different lenses, such as architectural, error-proofing, interaction, ludic, perceptual, cognitive, Machiavellian, and security lens each having its own distinct characteristics of extracting ideas from the participant of this approach. For this research purpose, a three-legged accident blackspot intersection of a national highway has been chosen to perform the DWI workshop. Participants from varying fields such as civil engineering, naval architecture and marine engineering, urban and regional planning, and sociology actively participated for a day long workshop. While going through the workshops, the participants were given a preamble of the accident scenario and a brief overview of DWI approach. Design cards of varying lenses were distributed among 10 participants and given an hour and a half for brainstorming and generating ideas to improve the safety of the selected intersection. After the brainstorming session, the participants spontaneously went through roundtable discussions regarding the ideas they have come up with. According to consensus of the forum, ideas were accepted or rejected. These generated ideas were then synthesized and agglomerated to bring about an improvement scheme for the intersection selected in our study. To summarize the improvement ideas from DWI approach, color coding of traffic lanes for separate vehicles, channelizing the existing bare intersection, providing advance warning traffic signs, cautionary signs and educational signs motivating road users to drive safe, using textured surfaces at approach with rumble strips before the approach of intersection were the most significant one. The motive of this approach is to bring about new ideas from the road users and not just depend on traditional schemes to increase the efficiency, safety of roads as well and to ensure the compliance of road users since these features are being generated from the minds of users themselves.

Keywords: design with intent, road safety, human experience, behavior

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Authors: Carol Okigbo

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Mathematical problem solving has long been accorded an important place in mathematics curricula at every education level in both advanced and emerging economies. Its classroom approaches have varied, such as teaching for problem-solving, teaching about problem-solving, and teaching mathematics through problem-solving. It requires engaging in tasks for which the solution methods are not eminent, making sense of problems and persevering in solving them by exhibiting processes, strategies, appropriate attitude, and adequate exposure. Teachers play important roles in helping students acquire competency in problem-solving; thus, they are expected to be good problem-solvers and have proper conceptions of problem-solving. Studies show that teachers’ conceptions influence their decisions about what to teach and how to teach. Therefore, how teachers view their roles in teaching problem-solving will depend on their pedagogical conceptions of problem-solving. If teaching problem-solving is a major component of secondary school mathematics instruction, as recommended by researchers and mathematics educators, then it is necessary to establish teachers’ conceptions, what they do, and how they approach problem-solving. This study is designed to determine secondary school teachers’ conceptions regarding mathematical problem solving, its current situation, how teachers’ conceptions relate to their demographics, as well as the interaction patterns in the mathematics classroom. There have been many studies of mathematics problem solving, some of which addressed teachers’ conceptions using single-method approaches, thereby presenting only limited views of this important phenomenon. To address the problem more holistically, this study adopted an integrated mixed methods approach which involved a quantitative survey, qualitative analysis of open-ended responses, and ethnographic observations of teachers in class. Data for the analysis came from a random sample of 327 secondary school mathematics teachers in two Nigerian states - Anambra State and Enugu State who completed a 45-item questionnaire. Ten of the items elicited demographic information, 11 items were open-ended questions, and 25 items were Likert-type questions. Of the 327 teachers who responded to the questionnaires, 37 were randomly selected and observed in their classes. Data analysis using ANOVA, t-tests, chi-square tests, and open coding showed that the teachers had different conceptions about problem-solving, which fall into three main themes: practice on exercises and word application problems, a process of solving mathematical problems, and a way of teaching mathematics. Teachers reported that no period is set aside for problem-solving; typically, teachers solve problems on the board, teach problem-solving strategies, and allow students time to struggle with problems on their own. The result shows a significant difference between male and female teachers’ conception of problems solving, a significant relationship among teachers’ conceptions and academic qualifications, and teachers who have spent ten years or more teaching mathematics were significantly different from the group with seven to nine years of experience in terms of their conceptions of problem-solving.

Keywords: conceptions, education, mathematics, problem solving, teacher

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Authors: Ipsita Biswas, Arnab Sarkar, Ashikur Rahaman, Gopeswar Mukherjee, Subhrangsu Chatterjee, Shamee Bhattacharjee, Deba Prasad Mandal

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One of the major reasons for the high mortality rate of lung cancer is the substantial delays in disease detection at late metastatic stages. It is of utmost importance to understand the detailed molecular signaling and detect the molecular markers that can be used for the early diagnosis of cancer. Several studies explored the emerging roles of long noncoding RNAs (lncRNAs) in various cancers as well as lung cancer. A long non-coding RNA LINC00273 was recently discovered to promote cancer cell migration and invasion, and its positive correlation with the pathological stages of metastasis may prove it to be a potential target for inhibiting cancer cell metastasis. Comparing real-time expression of LINC00273 in various human clinical cancer tissue samples with normal tissue samples revealed significantly higher expression in cancer tissues. This long intergenic noncoding RNA was found to be highly expressed in human liver tumor-initiating cells, human gastric adenocarcinoma AGS cell line, as well as human non-small cell lung cancer A549 cell line. SiRNA and shRNA-induced knockdown of LINC00273 in both in vitro and in vivo nude mice significantly subsided AGS and A549 cancer cell migration and invasion. LINC00273 knockdown also reduced TGF-β induced SNAIL, SLUG, VIMENTIN, ZEB1 expression, and metastasis in A549 cells. Plenty of reports have suggested the role of microRNAs of the miR200 family in reversing epithelial to mesenchymal transition (EMT) by inhibiting ZEB transcription factors. In this study, hsa-miR-200a-3p was predicted via IntaRNA-Freiburg RNA tools to be a potential target of LINC00273 with a negative free binding energy of −8.793 kcal/mol, and this interaction was verified as a confirmed target of LINC00273 by RNA pulldown, real-time PCR and luciferase assay. Mechanistically, LINC00273 accelerated TGF-β induced EMT by sponging hsa-miR-200a-3p which in turn liberated ZEB1 and promoted prometastatic functions in A549 cells in vitro as verified by real-time PCR and western blotting. The similar expression patterns of these EMT regulatory pathway molecules, viz. LINC00273, hsa-miR-200a-3p, ZEB1 and TGF-β, were also detected in various clinical samples like breast cancer tissues, oral cancer tissues, lung cancer tissues, etc. Overall, this LINC00273 mediated EMT regulatory signaling can serve as a potential therapeutic target for the prevention of lung cancer metastasis.

Keywords: epithelial to mesenchymal transition, long noncoding RNA, microRNA, non-small-cell lung carcinoma

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Authors: Yumei Han, Naiqing Song, Xiaoping Yang, Yuping Han

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This study explores the conceptualization of key competencies that children are expected to develop in three year preschools (age 3-6) and the assessment practices of such key competencies in China. Assessment of children development has been put into the central place of early childhood education quality evaluation system in China. In the context of students key competencies development centered education reform in China, defining and selecting key competencies of children in preschools are of great significance in that they would lay a solid foundation for children’s lifelong learning path, and they would lead to curriculum and instruction reform, teacher development reform as well as quality evaluation reform in the early childhood education area. Based on sense making theory and framework, this study adopted multiple stakeholders’ (early childhood educators, parents, evaluation administrators, scholars in the early childhood education field) perspectives and grass root voices to conceptualize and operationalize key competencies for children in preschools in Southwest China. On the ground of children development theories, Chinese and international literature related to children development and key competencies, and key competencies frameworks by UNESCO, OECD and other nations, the authors designed a two-phase sequential mixed method study to address three main questions: (a) How is early childhood key competency defined or labeled from literature and from different stakeholders’ views? (b) Based on the definitions explicated in the literature and the surveys on different stakeholders, what domains and components are regarded to constitute the key competency framework of children in three-year preschools in China? (c) How have early childhood key competencies been assessed and measured, and how such assessment and measurement contribute to enhancing early childhood development quality? On the first phase, a series of focus group surveys were conducted among different types of stakeholders around the research questions. Moreover, on the second phase, based on the coding of the participants’ answers, together with literature synthesis findings, a questionnaire survey was designed and conducted to select most commonly expected components of preschool children’s key competencies. Semi-structured open questions were also included in the questionnaire for the participants to add on competencies beyond the checklist. Rudimentary findings show agreeable concerns on the significance and necessity of conceptualization and assessment of key competencies for children in preschools, and a key competencies framework composed of 7 domains and 25 indicators was constructed. Meanwhile, the findings also show issues in the current assessment practices of children’s competencies, such as lack of effective assessment tools, lack of teacher capacity in applying the tools to evaluating children and advancing children development accordingly. Finally, the authors put forth suggestions and implications for China and international communities in terms of restructuring early childhood key competencies framework, and promoting child development centered reform in early childhood education quality evaluation and development.

Keywords: assessment, conceptualization, early childhood education quality in China, key competencies

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Authors: Mary Deyo, Zmara Harrison

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This study reports the outcomes of an 8-week experiential learning program for 6 adults with Intellectual and Developmental Disabilities (IDD) at a day habilitation program. The intervention foci for this program include executive function, language learning in the domains of expressive, receptive, and pragmatic language, and quality of life. The interprofessional collaboration aimed at supporting adults with IDD to reach person-centered, functional goals across skill domains is critical. This study is a significant addition to the speech-language pathology literature in that it examines a therapy method that potentially meets this need while targeting domains within the speech-language pathology scope of practice. Communication therapy was provided during highly valued and meaningful hands-on learning experiences, referred to as the Garden Club, which incorporated all aspects of planting and caring for a garden as well as related journaling, sensory, cooking, art, and technology-based activities. Direct care staff and an undergraduate research assistant were trained by SLP to be impactful language guides during their interactions with participants in the Garden Club. SLP also provided direct therapy and modeling during Garden Club. Research methods used in this study included a mixed methods analysis of a literature review, a quasi-experimental implementation of communication therapy in the context of experiential learning activities, Quality of Life participant surveys, quantitative pre- post- data collection and linear mixed model analysis, qualitative data collection with qualitative content analysis and coding for themes. Outcomes indicated overall positive changes in expressive vocabulary, following multi-step directions, sequencing, problem-solving, planning, skills for building and maintaining meaningful social relationships, and participant perception of the Garden Project’s impact on their own quality of life. Implementation of this project also highlighted supports and barriers that must be taken into consideration when planning similar projects. Overall findings support the use of experiential learning projects in day habilitation programs for adults with IDD, as well as additional research to deepen understanding of best practices, supports, and barriers for implementation of experiential learning with this population. This research provides an important contribution to research in the fields of speech-language pathology and other professions serving adults with IDD by describing an interprofessional experiential learning program with positive outcomes for executive function, language learning, and quality of life.

Keywords: experiential learning, adults, intellectual and developmental disabilities, expressive language, receptive language, pragmatic language, executive function, communication therapy, day habilitation, interprofessionalism, quality of life

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Authors: Joseph Falzon, Margaret Nelson

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Background: Regeneration and re-use of abandoned historic buildings present a continuous challenge for policy makers and stakeholders in the tourism and leisure industry. Obsolete historic buildings provide great potential for tourism and leisure accommodation, presenting unique heritage experiences to travellers and host communities. Contemporary demands in the hospitality industry continuously require higher standards, some of which are in conflict with heritage conservation principles. Objective: The aim of this research paper is to critically discuss regeneration policies with stakeholders of the tourism and leisure industry and to examine current practices in policy development and the resultant impact of policies on the Maltese tourism and leisure industry. Research Design: Six semi-structured interviews with stakeholders involved in the tourism and leisure industry participated in the research. A number of measures were taken to reduce bias and thus improve trustworthiness. Clear statements of the purpose of the research study were provided at the start of each interview to reduce expectancy bias. The interviews were semi-structured to minimise interviewer bias. Interviewees were allowed to expand and elaborate as necessary, with only necessary probing questions, to allow free expression of opinion and practices. Interview guide was submitted to participants at least two weeks before the interview to allow participants to prepare for the interview and prevent recall bias during the interview as much as possible. Interview questions and probes contained both positive and negative aspects to prevent interviewer bias. Policy documents were available during the interview to prevent recall bias. Interview recordings were transcribed ‘intelligent’ verbatim. Analysis was carried out using thematic analysis with the coding frame developed independently by two researchers. All phases of the study were governed by research ethics. Findings: Findings were grouped in main themes: financing of regeneration, governance, legislation and policies. Other key issues included value of historic buildings and approaches for regeneration. Whist regeneration of historic buildings was noted, participants discussed a number of barriers that hindered regeneration. Stakeholders identified gaps in policies and gaps at policy implementation stages. European Union funding policies facilitated regeneration initiatives but funding criteria based on economic deliverables presented the intangible heritage gap. Stakeholders identified niche markets for heritage tourism accommodation. Lack of research-based policies was also identified. Conclusion: Potential of regeneration is hindered by inadequate legal framework that supports contemporary needs of the tourism industry. Policies should be developed by active stakeholder participation. Adequate funding schemes have to support the tangible and intangible components of the built heritage.

Keywords: governance, historic buildings, policy, tourism

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Laila Albughayl

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The purpose of this qualitative case study was to explore Saudi females’ leadership journeys as they navigate the labyrinth of leadership in higher education. To gain a better understanding of how these leaders overcame challenges and accessed support as they progressed through the labyrinth to top positions in Saudi higher education. The significance of this research derived from the premise that leaders need to acquire essential leadership competencies such as knowledge, skills, and practices to effectively lead through economic transformation, growing globalism, and rapidly developing technology in an increasingly diverse world. In addition, understanding Saudi women’s challenges in the labyrinth will encourage policymakers to improve the situation under which these women work. The metaphor ‘labyrinth’ for Eagly and Carli (2007) encapsulates the winding paths, dead ends, and maze-like pathways that are full of challenges and supports that women traverse to access and maintain leadership positions was used. In this study, ‘labyrinth’ was used as the conceptual framework to explore women leaders’ challenges and opportunities in leadership in Saudi higher education. A proposed model for efficient navigation of the labyrinth of leadership was used. This model focused on knowledge, skills, and behaviours (KSB) as the analytical framework for examining responses to the research questions. This research was conducted using an interpretivist qualitative approach. A case study was the methodology used. Semi-structured interviews were the main data collection method. Purposive sampling was used to select ten Saudi leaders in three public universities. In coding, the 6-step framework of thematic analysis for Braun and Clarke was used to identify, analyze, and report themes within the data. NVivo software was also used as a tool to assist with managing and organizing the data. The resultant findings showed that the challenges identified by participants in navigating the labyrinth of leadership in Saudi higher education replicated some of those identified in the literature. The onset findings also revealed that the organizational barriers in Saudi higher education came as the top hindrance to women’s advancement in the labyrinth of leadership, followed by societal barriers. The findings also showed that women’s paths in the labyrinth of leadership in higher education were still convoluted and tedious compared to their male counterparts. In addition, the findings revealed that Saudi women leaders use significant strategies to access leadership posts and effectively navigate the labyrinth; this was not indicated in the literature. In addition, the resultant findings revealed that there are keys that assisted Saudi female leaders in effectively navigating the labyrinth of leadership. For example, the findings indicated that spirituality (religion) was a powerful key that enabled Saudi women leaders to pursue and persist in their leadership paths. Based on participants' experiences, a compass for effective navigation of the labyrinth of leadership in higher education was created for current and aspirant Saudi women leaders to follow. Finally, the findings had several significant implications for practice, policy, theory, and future research.

Keywords: women, leadership, labyrinth, higher education

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Authors: Olatokunbo Yakeem

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Human trafficking is a crime against gross violations of human rights. Trafficking in persons is a severe socio-economic dilemma that affects the national and international dimensions. Human trafficking or modern-day-slavery emanated from slavery, and it has been in existence before the 6ᵗʰ century. Today, no country is exempted from dehumanizing human beings, and as a result, it has been an international issue. The United Nations (UN) presented the International Protocol to fight human trafficking worldwide, which brought about the international definition of human trafficking. The protocol is to prevent, suppress, and punish trafficking in persons, especially women and children. The trafficking protocol has a link with transnational organised crime rather than migration. Over a hundred and fifty countries nationwide have enacted their criminal and panel code trafficking legislation from the UN trafficking protocol. Sex trafficking is the most common type of exploitation of women and children. Other forms of this crime involve exploiting vulnerable victims through forced labour, child involvement in warfare, domestic servitude, debt bondage, and organ removal for transplantation. Trafficking of women and children into sexual exploitation represents the highest form of human trafficking than other types of exploitation. Trafficking of women and children can either happen internally or across the border. It affects all kinds of people, regardless of their race, social class, culture, religion, and education levels. However, it is more of a gender-based issue against females. Furthermore, human trafficking can lead to life-threatening infections, mental disorders, lifetime trauma, and even the victim's death. The study's significance is to explore why the root causes of women and children trafficking in Nigeria are based around poverty, entrusting children in the hands of relatives and friends, corruption, globalization, weak legislation, and ignorance. The importance of this study is to establish how the national, regional, and international organisations are using the 3P’s Protection, Prevention, and Prosecution) to tackle human trafficking. The methodology approach for this study will be a qualitative paradigm. The rationale behind this selection is that the qualitative method will identify the phenomenon and interpret the findings comprehensively. The data collection will take the form of semi-structured in-depth interviews through telephone and email. The researcher will use a descriptive thematic analysis to analyse the data by using complete coding. In summary, this study aims to recommend to the Nigerian federal government to include human trafficking as a subject in their educational curriculum for early intervention to prevent children from been coerced by criminal gangs. And the research aims to find the root causes of women and children trafficking. Also, to look into the effectiveness of the strategies in place to eradicate human trafficking globally. In the same vein, the research objective is to investigate how the anti-trafficking bodies such as law enforcement and NGOs collaborate to tackle the upsurge in human trafficking.

Keywords: children, Nigeria, trafficking, women

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Authors: Meifang Chen

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Background: As China increased its engagement in global health affairs and research, global Health (GH) emerged as a new discipline in China after 2010. Duke Kunshan University (DKU), as a member of the Chinese Consortium of Universities for Global Health, is the first university that experiments “Western-style” liberal arts and sciences (LAS) education pedagogy in GH undergraduate and postgraduate programs in China since 2014. The COVID-19 pandemic has brought significant disruption to education across the world. At the peak of the pandemic, 45 countries in the Europe and Central Asia regions closed their schools, affecting 185 million students. DKU, as many other universities and schools, was unprepared for this sudden abruptness and were forced to build emergency remote learning systems almost immediately. This qualitative study aims to gain a deeper understanding of 1) how Chinese students and parents embrace GH training in the liberal arts and sciences education context, and 2) how the COVID pandemic influences the students’ learning experience as well as affects students and parents’ perceptions of GH-related study and career development in China. Methods: students and parents at DKU were invited and recruited for open-ended, semi-structured interviews during Sept 2021-Mar 2022. Open coding procedures and thematic content analysis were conducted using Nvivo 12 software. Results: A total of 18 students and 36 parents were interviewed. Both students and parents were fond of delivering GH education using the liberal arts and sciences pedagogy. Strengths of LAS included focusing on whole person development, allowing personal enrichment, tailoring curriculum to individual’s interest, providing well-rounded knowledge through interdisciplinary learning, and increasing self-study capacity and adaptability. Limitations of LAS included less time to dive deep into disciplines. There was a significant improvement in independence, creativity, problem solving, and team coordinating capabilities among the students. The impact of the COVID pandemic on GH learning experience included less domestic and abroad fieldwork opportunities, less in-person interactions (especially with foreign students and faculty), less timely support, less lab experience, and coordination challenges due to time-zone difference. The COVID pandemic increased the public’s awareness of the importance of GH and acceptance of GH as a career path. More job and postgraduate program opportunities were expected in near future. However, some parents expressed concerns about GH-related employment opportunities in China. Conclusion: The application of the liberal arts and science education pedagogy in GH training were well-received by the Chinese students and parents. Although global pandemic like COVID disrupted GH learning in many ways, most Chinese students and parents held optimistic attitudes toward GH study and career development.

Keywords: COVID, global health, liberal arts and sciences pedagogy, China

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Authors: S. C. Lin, W. W. Cheng, M. S. Wu

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This study attempted to investigate the connection between teacher professional development program and students’ Learning. This study took Readers’ Theater Teaching Program (RTTP) for professional development as an example to inquiry how participants apply their new knowledge and skills learned from RTTP to their teaching practice and how the impact influence students learning. The goals of the RTTP included: 1) to enhance teachers RT content knowledge; 2) to implement RT instruction in teachers’ classrooms in response to their professional development. 2) to improve students’ ability of reading fluency in professional development teachers’ classrooms. This study was a two-year project. The researchers applied mixed methods to conduct this study including qualitative inquiry and one-group pretest-posttest experimental design. In the first year, this study focused on designing and implementing RTTP and evaluating participants’ satisfaction of RTTP, what they learned and how they applied it to design their English reading curriculum. In the second year, the study adopted quasi-experimental design approach and evaluated how participants RT instruction influenced their students’ learning, including English knowledge, skill, and attitudes. The participants in this study composed two junior high school English teachers and their students. Data were collected from a number of different sources including teaching observation, semi-structured interviews, teaching diary, teachers’ professional development portfolio, Pre/post RT content knowledge tests, teacher survey, and students’ reading fluency tests. To analyze the data, both qualitative and quantitative data analysis were used. Qualitative data analysis included three stages: organizing data, coding data, and analyzing and interpreting data. Quantitative data analysis included descriptive analysis. The results indicated that average percentage of correct on pre-tests in RT content knowledge assessment was 40.75% with two teachers ranging in prior knowledge from 35% to 46% in specific RT content. Post-test RT content scores ranged from 70% to 82% correct with an average score of 76.50%. That gives teachers an average gain of 35.75% in overall content knowledge as measured by these pre/post exams. Teachers’ pre-test scores were lowest in script writing and highest in performing. Script writing was also the content area that showed the highest gains in content knowledge. Moreover, participants hold a positive attitude toward RTTP. They recommended that the approach of professional learning community, which was applied in RTTP was benefit to their professional development. Participants also applied the new skills and knowledge which they learned from RTTP to their practices. The evidences from this study indicated that RT English instruction significantly influenced students’ reading fluency and classroom climate. The result indicated that all of the experimental group students had a big progress in reading fluency after RT instruction. The study also found out several obstacles. Suggestions were also made.

Keywords: teacher’s professional development, program evaluation, readers’ theater, english reading instruction, english reading fluency

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Authors: Roni Ramon-Gonen, Amir Dori, Shahar Shelly

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Background and objectives: Healthcare professionals produce abundant textual information in their daily clinical practice. The extraction of insights from all the gathered information, mainly unstructured and lacking in normalization, is one of the major challenges in computational medicine. In this respect, text mining assembles different techniques to derive valuable insights from unstructured textual data, so it has led to being especially relevant in Medicine. Neurological patient’s history allows the clinician to define the patient’s symptoms and along with the result of the nerve conduction study (NCS) and electromyography (EMG) test, assists in formulating a differential diagnosis. Past medical history (PMH) helps to direct the latter. In this study, we aimed to identify relevant PMH, understand which PMHs are common among patients in the referral cohort and documented by the medical staff, and examine the differences by sex and age in a large cohort based on textual format notes. Methods: We retrospectively identified all patients with abnormal NCS between May 2016 to February 2022. Age, gender, and all NCS attributes reports were recorded, including the summary text. All patients’ histories were extracted from the text report by a query. Basic text cleansing and data preparation were performed, as well as lemmatization. Very popular words (like ‘left’ and ‘right’) were deleted. Several words were replaced with their abbreviations. A bag of words approach was used to perform the analyses. Different visualizations which are common in text analysis, were created to easily grasp the results. Results: We identified 5282 unique patients. Three thousand and five (57%) patients had documented PMH. Of which 60.4% (n=1817) were males. The total median age was 62 years (range 0.12 – 97.2 years), and the majority of patients (83%) presented after the age of forty years. The top two documented medical histories were diabetes mellitus (DM) and surgery. DM was observed in 16.3% of the patients, and surgery at 15.4%. Other frequent patient histories (among the top 20) were fracture, cancer (ca), motor vehicle accident (MVA), leg, lumbar, discopathy, back and carpal tunnel release (CTR). When separating the data by sex, we can see that DM and MVA are more frequent among males, while cancer and CTR are less frequent. On the other hand, the top medical history in females was surgery and, after that, DM. Other frequent histories among females are breast cancer, fractures, and CTR. In the younger population (ages 18 to 26), the frequent PMH were surgery, fractures, trauma, and MVA. Discussion: By applying text mining approaches to unstructured data, we were able to better understand which medical histories are more relevant in these circumstances and, in addition, gain additional insights regarding sex and age differences. These insights might help to collect epidemiological demographical data as well as raise new hypotheses. One limitation of this work is that each clinician might use different words or abbreviations to describe the same condition, and therefore using a coding system can be beneficial.

Keywords: abnormal studies, healthcare analytics, medical history, nerve conduction studies, text mining, textual analysis

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Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

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Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

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Authors: Eric Morettin

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Cybersecurity and digital literacy are pressing issues among Canadian citizens, yet formal education does not provide today’s students with the necessary knowledge and skills needed to adapt to these challenging issues within the physical and digital labor-market. Canada’s current education systems do not highlight the importance of these respective fields, aside from using technology for learning management systems and alternative methods of assignment completion. Educators are not properly trained to integrate technology into the compulsory courses within public education, to better prepare their learners in these topics and Canada’s digital economy. ICTC addresses these gaps in education and training through cross-Canadian educational programming in digital literacy and competency, cybersecurity and coding which is bridged with Canada’s provincially regulated K-12 curriculum guidelines. After analyzing Canada’s provincial education, it is apparent that there are gaps in learning related to technology, as well as inconsistent educational outcomes that do not adequately represent the current Canadian and global economies. Presently only New Brunswick, Nova Scotia, Ontario, and British Columbia offer curriculum guidelines for cybersecurity, computer programming, and digital literacy. The remaining provinces do not address these skills in their curriculum guidelines. Moreover, certain courses across some provinces not being updated since the 1990’s. The three territories respectfully take curriculum strands from other provinces and use them as their foundation in education. Yukon uses all British Columbia curriculum. Northwest Territories and Nunavut respectfully use a hybrid of Alberta and Saskatchewan curriculum as their foundation of learning. Education that is provincially regulated does not allow for consistency across the country’s educational outcomes and what Canada’s students will achieve – especially when curriculum outcomes have not been updated to reflect present day society. Through this, ICTC has aligned Canada’s provincially regulated curriculum and created opportunities for focused education in the realm of technology to better serve Canada’s present learners and teachers; while addressing inequalities and applicability within curriculum strands and outcomes across the country. As a result, lessons, units, and formal assessment strategies, have been created to benefit students and teachers in this interdisciplinary, cross-curricular, practice - as well as meeting their compulsory education requirements and developing skills and literacy in cyber education. Teachers can access these lessons and units through ICTC’s website, as well as receive professional development regarding the assessment and implementation of these offerings from ICTC’s education coordinators, whose combines experience exceeds 50 years of teaching in public, private, international, and Indigenous schools. We encourage you to take this opportunity that will benefit students and educators, and will bridge the learning and curriculum gaps in Canadian education to better reflect the ever-changing public, social, and career landscape that all citizens are a part of. Students are the future, and we at ICTC strive to ensure their futures are bright and prosperous.

Keywords: cybersecurity, education, curriculum, teachers

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Authors: Lydia Tan, Philippe Lemey, Lieselot Houspie, Marco Viveen, Darren Martin, Frank Coenjaerts

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Introduction: Human respiratory syncytial virus (hRSV) is the leading cause of severe respiratory tract infections in infants under the age of two. Genomic substitutions and related evolutionary dynamics of hRSV are of great influence on virus transmission behavior. The evolutionary patterns formed are due to a precarious interplay between the host immune response and RSV, thereby selecting the most viable and less immunogenic strains. Studying genomic profiles can teach us which genes and consequent proteins play an important role in RSV survival and transmission dynamics. Study design: In this study, genetic diversity and evolutionary rate analysis were conducted on 36 RSV subgroup B whole genome sequences and 37 subgroup A genome sequences. Clinical RSV isolates were obtained from nasopharyngeal aspirates and swabs of children between 2 weeks and 5 years old of age. These strains, collected during epidemic seasons from 2001 to 2011 in the Netherlands and Belgium by either conventional or 454-sequencing. Sequences were analyzed for genetic diversity, recombination events, synonymous/non-synonymous substitution ratios, epistasis, and translational consequences of mutations were mapped to known 3D protein structures. We used Bayesian statistical inference to estimate the rate of RSV genome evolution and the rate of variability across the genome. Results: The A and B profiles were described in detail and compared to each other. Overall, the majority of the whole RSV genome is highly conserved among all strains. The attachment protein G was the most variable protein and its gene had, similar to the non-coding regions in RSV, more elevated (two-fold) substitution rates than other genes. In addition, the G gene has been identified as the major target for diversifying selection. Overall, less gene and protein variability was found within RSV-B compared to RSV-A and most protein variation between the subgroups was found in the F, G, SH and M2-2 proteins. For the F protein mutations and correlated amino acid changes are largely located in the F2 ligand-binding domain. The small hydrophobic phosphoprotein and nucleoprotein are the most conserved proteins. The evolutionary rates were similar in both subgroups (A: 6.47E-04, B: 7.76E-04 substitution/site/yr), but estimates of the time to the most recent common ancestor were much lower for RSV-B (B: 19, A: 46.8 yrs), indicating that there is more turnover in this subgroup. Conclusion: This study provides a detailed description of whole RSV genome mutations, the effect on translation products and the first estimate of the RSV genome evolution tempo. The immunogenic G protein seems to require high substitution rates in order to select less immunogenic strains and other conserved proteins are most likely essential to preserve RSV viability. The resulting G gene variability makes its protein a less interesting target for RSV intervention methods. The more conserved RSV F protein with less antigenic epitope shedding is, therefore, more suitable for developing therapeutic strategies or vaccines.

Keywords: drug target selection, epidemiology, respiratory syncytial virus, RSV

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Authors: Fernanda Mary R. G. Da Silva, Adriana C. F. Mozzambani, Marcelo F. Mello

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Introduction: Sexual assault against children and adolescents is a public health problem with serious consequences on their quality of life, especially for those who develop post-traumatic stress disorder (PTSD). The broad literature in this research area points to greater losses in verbal learning, explicit memory, speed of information processing, attention and executive functioning in PTSD. Objective: To compare the neuropsychological functions of adolescents from 14 to 17 years of age, victims of sexual violence with PTSD with those of healthy controls. Methodology: Application of a neuropsychological battery composed of the following subtests: WASI vocabulary and matrix reasoning; Digit subtests (WISC-IV); verbal auditory learning test RAVLT; Spatial Span subtest of the WMS - III scale; abbreviated version of the Wisconsin test; concentrated attention test - D2; prospective memory subtest of the NEUPSILIN scale; five-digit test - FDT and the Stroop test (Trenerry version) in adolescents with a history of sexual violence in the previous six months, referred to the Prove (Violence Care and Research Program of the Federal University of São Paulo), for further treatment. Results: The results showed a deficit in the word coding process in the RAVLT test, with impairment in A3 (p = 0.004) and A4 (p = 0.016) measures, which compromises the verbal learning process (p = 0.010) and the verbal recognition memory (p = 0.012), seeming to present a worse performance in the acquisition of verbal information that depends on the support of the attentional system. A worse performance was found in list B (p = 0.047), a lower priming effect p = 0.026, that is, lower evocation index of the initial words presented and less perseveration (p = 0.002), repeated words. Therefore, there seems to be a failure in the creation of strategies that help the mnemonic process of retention of the verbal information necessary for learning. Sustained attention was found to be impaired, with greater loss of setting in the Wisconsin test (p = 0.023), a lower rate of correct responses in stage C of the Stroop test (p = 0.023) and, consequently, a higher index of erroneous responses in C of the Stroop test (p = 0.023), besides more type II errors in the D2 test (p = 0.008). A higher incidence of total errors was observed in the reading stage of the FDT test p = 0.002, which suggests fatigue in the execution of the task. Performance is compromised in executive functions in the cognitive flexibility ability, suggesting a higher index of total errors in the alternating step of the FDT test (p = 0.009), as well as a greater number of persevering errors in the Wisconsin test (p = 0.004). Conclusion: The data from this study suggest that sexual violence and PTSD cause significant impairment in the neuropsychological functions of adolescents, evidencing risk to quality of life in stages that are fundamental for the development of learning and cognition.

Keywords: adolescents, neuropsychological functions, PTSD, sexual violence

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Authors: Stefan K. Behfar

Abstract:

The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: Chandu Rathnayake, Isuri Anuradha

Abstract:

Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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