Search results for: decision processing

Authors: Shohreh Ghasemi

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Introduction: Managing oral and maxillofacial trauma is a multifaceted challenge, as it can have life-threatening consequences and significant functional and aesthetic impact. Navigation techniques have been introduced to improve surgical precision to meet this challenge. A machine learning algorithm was also developed to support clinical decision-making regarding treating oral and maxillofacial trauma. Given these advances, this systematic meta-analysis aims to assess the efficacy of navigational techniques in treating oral and maxillofacial trauma and explore the impact of machine learning on their management. Methods: A detailed and comprehensive analysis of studies published between January 2010 and September 2021 was conducted through a systematic meta-analysis. This included performing a thorough search of Web of Science, Embase, and PubMed databases to identify studies evaluating the efficacy of navigational techniques and the impact of machine learning in managing oral and maxillofacial trauma. Studies that did not meet established entry criteria were excluded. In addition, the overall quality of studies included was evaluated using Cochrane risk of bias tool and the Newcastle-Ottawa scale. Results: Total of 12 studies, including 869 patients with oral and maxillofacial trauma, met the inclusion criteria. An analysis of studies revealed that navigation techniques effectively improve surgical accuracy and minimize the risk of complications. Additionally, machine learning algorithms have proven effective in predicting treatment outcomes and identifying patients at high risk for complications. Conclusion: The introduction of navigational technology has great potential to improve surgical precision in oral and maxillofacial trauma treatment. Furthermore, developing machine learning algorithms offers opportunities to improve clinical decision-making and patient outcomes. Still, further studies are necessary to corroborate these results and establish the optimal use of these technologies in managing oral and maxillofacial trauma

Keywords: trauma, machine learning, navigation, maxillofacial, management

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Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Eman AbdelSabour

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Sustainability is increasingly being considered as a critical aspect in shaping the urban environment. It works as an invention development basis for global urban growth. Currently, different models and structures impact the means of interpreting the criteria that would be included in defining a sustainable city. There is a collective need to improve the growth path to an extremely durable path by presenting different suggestions regarding multi-scale initiatives. The global rise in urbanization has led to increased demand and pressure for better urban planning choice and scenarios for a better sustainable urban alternative. The need for an assessment tool at the urban scale was prompted due to the trend of developing increasingly sustainable urban development (SUD). The neighborhood scale is being managed by a growing research committee since it seems to be a pertinent scale through which economic, environmental, and social impacts could be addressed. Although neighborhood design is a comparatively old practice, it is in the initial years of the 21st century when environmentalists and planners started developing sustainable assessment at the neighborhood level. Through this, urban reality can be considered at a larger scale whereby themes which are beyond the size of a single building can be addressed, while it still stays small enough that concrete measures could be analyzed. The neighborhood assessment tool has a crucial role in helping neighborhood sustainability to perform approach and fulfill objectives through a set of themes and criteria. These devices are also known as neighborhood assessment tool, district assessment tool, and sustainable community rating tool. The primary focus of research has been on sustainability from the economic and environmental aspect, whereas the social, cultural issue is rarely focused. Therefore, this research is based on Doha, Qatar, the current urban conditions of the neighborhoods is discussed in this study. The research problem focuses on the spatial features in relation to the socio-cultural aspects. This study is outlined in three parts; the first section comprises of review of the latest use of wellbeing assessment methods to enhance decision process of retrofitting physical features of the neighborhood. The second section discusses the urban settlement development, regulations and the process of decision-making rule. An analysis of urban development policy with reference to neighborhood development is also discussed in this section. Moreover, it includes a historical review of the urban growth of the neighborhoods as an atom of the city system present in Doha. Last part involves developing quantified indicators regarding subjective well-being through a participatory approach. Additionally, applying GIS will be utilized as a visualizing tool for the apparent Quality of Life (QoL) that need to develop in the neighborhood area as an assessment approach. Envisaging the present QoL situation in Doha neighborhoods is a process to improve current condition neighborhood function involves many days to day activities of the residents, due to which areas are considered dynamic.

Keywords: neighborhood, subjective wellbeing, decision support tools, Doha, retrofiring

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Authors: Launcelot McGrath, Xiaoxiao Liu, Colin Flanagan

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It is widely recognised that a comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Bio signal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. There are tremendous biological signals during anaesthesia, and not all of them are important, which to choose to observe is a significant decision. It is important that the anaesthesiologist understand both the signals themselves, and the limitations introduced by the processes of acquisition. In this article, we provide an all-sided overview of different types of biological signals as well as the mechanisms applied to acquire them.

Keywords: general biosignals, anaesthesia, biological, electroencephalogram

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Authors: Aayushi Somani, Siba P. Samal

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Three-dimensional (3D) meshes are data structures, which store geometric information of an object or scene, generally in the form of vertices and edges. Current technology in laser scanning and other geometric data acquisition technologies acquire high resolution sampling which leads to high resolution meshes. While high resolution meshes give better quality rendering and hence is used often, the processing, as well as storage of 3D meshes, is currently resource-intensive. At the same time, web applications for data processing have become ubiquitous owing to their accessibility. For 3D meshes, the advancement of 3D web technologies, such as WebGL, WebVR, has enabled high fidelity rendering of huge meshes. However, there exists a gap in ability to stream huge meshes to a native client and browser application due to high network latency. Also, there is an inherent delay of loading WebGL pages due to large and complex models. The focus of our work is to identify the challenges faced when such meshes are streamed into and processed on hand-held devices, owing to its limited resources. One of the solutions that are conventionally used in the graphics community to alleviate resource limitations is mesh compression. Our approach deals with a two-step approach for random accessible progressive compression and its parallel implementation. The first step includes partition of the original mesh to multiple sub-meshes, and then we invoke data parallelism on these sub-meshes for its compression. Subsequent threaded decompression logic is implemented inside the Web Browser Engine with modification of WebGL implementation in Chromium open source engine. This concept can be used to completely revolutionize the way e-commerce and Virtual Reality technology works for consumer electronic devices. These objects can be compressed in the server and can be transmitted over the network. The progressive decompression can be performed on the client device and rendered. Multiple views currently used in e-commerce sites for viewing the same product from different angles can be replaced by a single progressive model for better UX and smoother user experience. Can also be used in WebVR for commonly and most widely used activities like virtual reality shopping, watching movies and playing games. Our experiments and comparison with existing techniques show encouraging results in terms of latency (compressed size is ~10-15% of the original mesh), processing time (20-22% increase over serial implementation) and quality of user experience in web browser.

Keywords: 3D compression, 3D mesh, 3D web, chromium, client-server architecture, e-commerce, level of details, parallelization, progressive compression, WebGL, WebVR

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Authors: Hiromi Kawai

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This study investigates the nature of EFL older children’s L2 perceptive and productive abilities using classroom data, in order to find a pedagogical solution to the teaching of L2 sounds at an early stage of learning in a formal school setting. It is still inconclusive whether older children with only EFL formal school instruction at the initial stage of L2 learning are able to attain native-like perception and production in English within the very limited amount of exposure to the target language available. Based on the notion of the lack of study of EFL Japanese children’s acquisition of English segments, the researcher uses a model of L1 speech processing which was developed for investigating L1 English children’s speech and literacy difficulties using a psycholinguistic framework. The model is composed of input channel, output channel, and lexical representation, and examines how a child receives information from spoken or written language, remembers and stores it within the lexical representations and how the child selects and produces spoken or written words. Concerning language universality and language specificity in the language acquisitional process, the aim of finding any sound errors in L1 English children seemed to conform to the author’s intention to find abilities of English sounds in older Japanese children at the novice level of English in an EFL setting. 104 students in Grade 5 (between the ages of 10 and 11 years old) of an elementary school in Tokyo participated in this study. Four tests to measure their perceptive ability and three oral repetition tests to measure their productive ability were conducted with/without reference to lexical representation. All the test items were analyzed to calculate item facility (IF) indices, and correlational analyses and Structural Equation Modeling (SEM) were conducted to examine the relationship between the receptive ability and the productive ability. IF analysis showed that (1) the participants were better at perceiving a segment than producing a segment, (2) they had difficulty in auditory discrimination of paired consonants when one of them does not exist in the Japanese inventory, (3) they had difficulty in both perceiving and producing English vowels, and (4) their L1 loan word knowledge had an influence on their ability to perceive and produce L2 sounds. The result of the Multiple Regression Modeling showed that the two production tests could predict the participants’ auditory ability of real words in English. The result of SEM showed that the hypothesis that perceptive ability affects productive ability was supported. Based on these findings, the author discusses the possible explicit method of teaching English segments to EFL older children in a formal school setting.

Keywords: EFL older children, english segments, perception, production, speech processing system

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Authors: Snigdha Sahai, Devaki Chikkavenkatappa Yellappa

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This study outlines approaches for testing and validating stochastic models used in epidemiology, focusing on the integration and functional testing of simulation code. It details methods for combining simple functions into comprehensive simulations, distinguishing between deterministic and stochastic components, and applying tests to ensure robustness. Techniques include isolating stochastic elements, utilizing large sample sizes for validation, and handling special cases. Practical examples are provided using R code to demonstrate integration testing, handling of incorrect inputs, and special cases. The study emphasizes the importance of both functional and defensive programming to enhance code reliability and user-friendliness.

Keywords: computational epidemiology, epidemiology, public health, infectious disease modeling, statistical analysis, health data analysis, disease transmission dynamics, predictive modeling in health, population health modeling, quantitative public health, random sampling simulations, randomized numerical analysis, simulation-based analysis, variance-based simulations, algorithmic disease simulation, computational public health strategies, epidemiological surveillance, disease pattern analysis, epidemic risk assessment, population-based health strategies, preventive healthcare models, infection dynamics in populations, contagion spread prediction models, survival analysis techniques, epidemiological data mining, host-pathogen interaction models, risk assessment algorithms for disease spread, decision-support systems in epidemiology, macro-level health impact simulations, socioeconomic determinants in disease spread, data-driven decision making in public health, quantitative impact assessment of health policies, biostatistical methods in population health, probability-driven health outcome predictions

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Authors: Irena Lucy Ishimora, Eva Maria Putri Salsabila

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The constellation on Indonesian Legal System that varies shows a structural injustice – as a result of patriarchy – exists from the biggest range as a country to the smallest such as a family. Women in their lives, carry out excessive responsibilities in the community. However, the unequal positions between men and women in the society restrain women to fulfill their constructed role. Therefore, increasing the chance for women to become the victim of structural injustice. The lack of authority given to women and its effects can be seen through a case study of the Cirompang Indigenous Women’s involvement in natural resources management. The decision to make the Mount Halimun-Salak as a National Park and the expansion itself did not involve nor consider the existence of indigenous people (Kasepuhan Ciromopang) – especially the women’s experience regarding natural resources management – has been significantly impacting the fulfillment of the indigenous women’s rights. Moreover, the adat law that still reflects patriarchy, made matters worse because women are restricted from expressing their opinion. The writers explored the experience of Cirompang indigenous women through in-depth interviews with them and analyzed it with several theories such as ecofeminism, woman’s access to land and legal pluralism. This paper is important to show how the decision and expansion of the National Park reduced the rights of access to land, natural resources, expressing an opinion, and participating in development. Reflecting on the Cirompang Indigenous Women’s conditions on natural resources management, this paper aims to present the implications of the regulations that do not acknowledge Indigenous women’s experience and the proposed solutions. First, there should be an integration between the law regarding indigenous people and traditional rights in a regulation to align the understanding of indigenous people and their rights. Secondly, Indonesia as a country that’s rich with diversity should ratify the ILO Convention no 169 to reaffirm the protection of Indigenous people’s rights. Last, considering the position of indigenous women that still experienced unjustness in the community, the government and NGOs must collaborate to provide adequate assistance for them.

Keywords: Cirompang indigenous women, indigenous women’s rights, structural injustice, women access to land

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Authors: Ziwei Wang, Andrea Lucotti, Luigi Brambilla, Matteo Tommasini, Chiara Bertarelli

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Surface-enhanced Raman Spectroscopy (SERS) is a highly sensitive detection that provides abundant information on low concentration analytes from various researching areas. Based on localized surface plasmon resonance, metal nanostructures including gold, silver and copper have been investigated as SERS substrate during recent decades. There has been increasing more attention of exploring good performance, homogenous, repeatable SERS substrates. Here, we show that electrospinning, which is an inexpensive technique to fabricate large-scale, self-standing and repeatable membranes, can be effectively used for producing SERS substrates. Nanoparticles and nanorods are added to the feed electrospinning solution to collect functionalized polymer fibrous mats. We report stable electrospun membranes as SERS substrate using gold nanorods (AuNRs) and poly(vinyl alcohol). Particularly, a post-processing crosslinking step using glutaraldehyde under acetone environment was carried out to the electrospun membrane. It allows for using the membrane in any liquid environment, including water, which is of interest both for sensing of contaminant in wastewater, as well as for biosensing. This crosslinked AuNRs/PVA membrane has demonstrated excellent performance as SERS substrate for low concentration 10-6 M Rhodamine 6G (Rh6G) aqueous solution. This post-processing for fabricating SERS substrate is the first time reported and proved through Raman imaging of excellent stability and outstanding performance. Finally, SERS tests have been applied to several analytes, and the application of AuNRs/PVA membrane is broadened by removing the detected analyte by rinsing. Therefore, this crosslinked AuNRs/PVA membrane is re-usable.

Keywords: SERS spectroscopy, electrospinning, crosslinking, composite materials

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Authors: Yanqing Yi, Maria Cristina Lavagnolo, Alessandro Manzardo

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Construction and demolition waste (C&DW) is a major challenge in the European Union, emphasizing the urgent need for appropriate waste management processes. Selecting these solutions is challenging, as it requires identifying efficient C&DW management techniques that balance acceptable practices, regulatory compliance, resource conservation, economic viability, and environmental concerns. Techniques for analyzing many kinds of criteria allow for the use of multi-criteria analysis in life cycle assessment (LCA). Although LCA is commonly used to analyze environmental effects, the economic factor has not been fully integrated into the LCA approach in C&DW management. The life cycle costing (LCC) approach was designed to assess economic performance in the C&DW management process. The choice of an effective multi-criteria decision-making (MCDM) technique is critical for the C&DW system. This study seeks to propose a model that employs MCDM by considering LCA and LCC results, thereby augmenting both environmental and economic sustainability. A widely used compensatory MCDM technique, TOPSIS, has been chosen to identify the most effective C&DW management scheme by comparing and ranking various scenarios. Four waste management alternatives were examined in the Lombardy region of Italy, namely, (i) landfill; (ii) recycling for concrete production and road construction, incineration with energy recovery; (iii) recycling for road construction; (iv) recycling for concrete production and road construction. We determine that, with the implementation of various scenarios, the most suitable scenario emerges to be recycled for concrete production and road construction, with a score of 0.711/1; recycling for road construction, with a final score of 0.291/1, ranks second; recycling for concrete production and road construction, incineration with energy recovery scores 0.002/1, ranks third; and landfill (scores: 0/1) is the worst choice, indicating it has the highest environmental impact. Finally, suggestions were developed to improve the system's environmental performance.

Keywords: life cycle assessment, life cycle costing, construction and demolition waste, multi-criteria decision making

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Authors: Michael Lousis

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The systematic identification of the most conspicuous and significant errors made by learners during three-years of testing of their progress in learning Arithmetic throughout the development of the Kassel Project in England and Greece was accomplished. How much retentive these errors were over three-years in the officially provided school instruction of Arithmetic in these countries has also been shown. The learners’ errors in Arithmetic stemmed from a sample, which was comprised of two hundred (200) English students and one hundred and fifty (150) Greek students. The sample was purposefully selected according to the students’ participation in each testing session in the development of the three-year project, in both domains simultaneously in Arithmetic and Algebra. Specific teaching practices have been invented and are presented in this study for subverting these learners’ errors, which were found out to be retentive to the level of the nationally provided mathematical education of each country. The invention and the development of these proposed teaching practices were founded on the rationality of the theoretical accounts concerning the explanation, prediction and control of the errors, on the conceptual metaphor and on an analysis, which tried to identify the required cognitive components and skills of the specific tasks, in terms of Psychology and Cognitive Science as applied to information-processing. The aim of the implementation of these instructional practices is not only the subversion of these errors but the achievement of the mathematical competence, as this was defined to be constituted of three elements: appropriate representations - appropriate meaning - appropriately developed schemata. However, praxis is of paramount importance, because there is no independent of science ‘real-truth’ and because praxis serves as quality control when it takes the form of a cognitive method.

Keywords: arithmetic, cognitive science, cognitive psychology, information-processing paradigm, Kassel project, level of the nationally provided mathematical education, praxis, remedial mathematical teaching practices, retentiveness of errors

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Authors: Michelangelo Sofo, Giuseppe Labianca

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In recent years, the scientific community's interest in the exploratory analysis of biomedical data has increased exponentially. Considering the field of research of nutritional biologists, the curative process, based on the analysis of clinical data, is a very delicate operation due to the fact that there are multiple solutions for the management of pathologies in the food sector (for example can recall intolerances and allergies, management of cholesterol metabolism, diabetic pathologies, arterial hypertension, up to obesity and breathing and sleep problems). In this regard, in this research work a system was created capable of evaluating various dietary regimes for specific patient pathologies. The system is founded on a mathematical-numerical model and has been created tailored for the real working needs of an expert in human nutrition using the human-centered design (ISO 9241-210), therefore it is in step with continuous scientific progress in the field and evolves through the experience of managed clinical cases (machine learning process). DietAdhoc® is a decision support system nutrition specialists for patients of both sexes (from 18 years of age) developed with an agile methodology. Its task consists in drawing up the biomedical and clinical profile of the specific patient by applying two algorithmic optimization approaches on nutritional data and a symbolic solution, obtained by transforming the relational database underlying the system into a deductive database. For all three solution approaches, particular emphasis has been given to the explainability of the suggested clinical decisions through flexible and customizable user interfaces. Furthermore, the system has multiple software modules based on time series and visual analytics techniques that allow to evaluate the complete picture of the situation and the evolution of the diet assigned for specific pathologies.

Keywords: medical decision support, physiological data extraction, data driven diagnosis, human centered AI, symbiotic AI paradigm

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Authors: Danielle Shackley, Yetunde Folajimi

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As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

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Authors: Liubov Lutsyshyn, Yaroslava Zhukova

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Over the past twenty years, there has been a drastic change in the mode of nutrition in many countries which has been reflected in the development of new products, production techniques, and has also led to the expansion of sales markets for food products. Studies have shown that solution of the food safety problems is almost impossible without the active and systematic activity of organizations directly involved in the production, storage and sale of food products, as well as without management of end-to-end traceability and exchange of information. The aim of this research is development of the integrated system of the quality management and safety assurance based on the principles of HACCP, traceability and system approach with creation of an algorithm for the identification and monitoring of parameters of technological process of manufacture of cooked sausage products. Methodology of implementation of the integrated system based on the principles of HACCP, traceability and system approach during the manufacturing of cooked sausage products for effective provision for the defined properties of the finished product has been developed. As a result of the research evaluation technique and criteria of performance of the implementation and operation of the system of the quality management and safety assurance based on the principles of HACCP have been developed and substantiated. In the paper regularities of influence of the application of HACCP principles, traceability and system approach on parameters of quality and safety of the finished product have been revealed. In the study regularities in identification of critical control points have been determined. The algorithm of functioning of the integrated system of the quality management and safety assurance has also been described and key requirements for the development of software allowing the prediction of properties of finished product, as well as the timely correction of the technological process and traceability of manufacturing flows have been defined. Based on the obtained results typical scheme of the integrated system of the quality management and safety assurance based on HACCP principles with the elements of end-to-end traceability and system approach for manufacture of cooked sausage products has been developed. As a result of the studies quantitative criteria for evaluation of performance of the system of the quality management and safety assurance have been developed. A set of guidance documents for the implementation and evaluation of the integrated system based on the HACCP principles in meat processing plants have also been developed. On the basis of the research the effectiveness of application of continuous monitoring of the manufacturing process during the control on the identified critical control points have been revealed. The optimal number of critical control points in relation to the manufacture of cooked sausage products has been substantiated. The main results of the research have been appraised during 2013-2014 under the conditions of seven enterprises of the meat processing industry and have been implemented at JSC «Kyiv meat processing plant».

Keywords: cooked sausage products, HACCP, quality management, safety assurance

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Authors: Garima Sarkar

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The main purpose of the present paper is to investigate the development and progress achieved by women in the decision-making sphere and to access the level of their political-participation in Parliamentary Elections of Malaysia and their status in overall Malaysian political domain. The paper also focuses on the role and status of women in the major political parties of the state both the parties in power as well as the parties in opposition. The primary objective of the study is to focus on the major hindrances and social malpractices faced by women and also Muslim women’s access to justice in Malaysia. It also demonstrates the linkages between national policy initiatives and the advancement of women in various areas, such as economics, health, employment, politics, power-sharing, social development and law and most importantly evaluating their status in the dominant religion of the nation. In Malaysia, women’s political participation is being challenged from every nook and corner of the society. A high percentage of women are getting educated, forming a significant labor force in present day Malaysia, who can be employed in the manufacturing sector, retail trade, hotels and restaurant, agriculture etc. Women today consist of almost half of the population and exceed boys in the tertiary sector by a ratio of 80:20. Despite these achievements, however, women’s labor force engagement remains confined to ‘ traditional women’s occupations’, such as those of primary school teachers, data entry clerks and organizing polls during elections and motivating other less enlightened women to cast their votes. In the political arena, the past few General Elections of Malaysia clearly exhibited a slight change in the number of women Members of Parliament from 10.6% (20 out of 193 Parliamentary seats in 1999) to 10.5% (23 out of 219 Parliamentary seats in 2004). Amidst the political posturing for the recent General Election in 2013 of Malaysia, women’s political participation remains a prime concern in Malaysia. It is evident that while much of the attention of women revolves around charitable assistance, they are much less likely to be portrayed as active participants in electoral politics and governance. According to the electoral roll for the third quarter of 2012, 6,578,916 women are registered as voters. They represent 50.2% of the total number of the registered voters. However, this parity in terms of voter registration is not reflected in the number of elected representatives at the Parliamentary level. Only 10.4% of sitting Members of Parliament are women. The women’s participation in the legislature and executive branches are important since their presence brings the spotlight squarely on issues that have been historically neglected and overlooked. In the recent 2013 General Elections in Malaysia out of 35 full ministerial position only two, or 5.7% have been filled by women. In each of the 2009, 2010, and in the present 2013 Cabinet members, there have only been two women ministers, with this number reduced to one briefly when the Prime Minister appointed himself placeholder in the Ministry of Women, Family and Community Development. In the recent past, in its Election Manifesto, Barisan Nasional made a pledge of ‘increasing the number of women participating in national decision-making processes’. Even after such pledges, the Malaysian leadership has failed to mirror the strong presence of women in leadership positions of public life which primarily includes politics, the judiciary and in business. There has been a strong urge to political parties by various gender-sensitive groups to nominate more women as candidates for contesting elections at the Parliamentary as well as at the State level. The democratization process will never be truly democratic without a proper gender agenda and representation. Although Malaysia signed the Beijing Platform for Action document in 1995, the state has a long way to go in enhancing the participation of women in every segment of Malaysian political, economic and cultural. There has been a small percentage of women representation in decision-making bodies compared to the 30% targeted by the Beijing Platform for Action. Thus, democratization in terms of representation of women in leadership positions and decision-making positions or bodies is essential since it’s a move towards a qualitative transformation of women in shaping national decision-making processes. The democratization process has to ensure women’s full participation and their goals of development and their full participation has to be included in the process of formulating and shaping the developmental goals.

Keywords: women, gender equality, Islam, democratization, political representation, Parliament

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Authors: Zulkifli Zainal Abidin, Ahmad Shahril Mohd Ghani

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Agent-based systems technology has been addressed as a new paradigm for conceptualizing, designing, and implementing software systems. Agents are sophisticated systems that act autonomously across open and distributed environments in solving problems. Nevertheless, it is impractical to rely on a single agent to do all computing processes in solving complex problems. An increasing number of applications lately require multiple agents to work together. A multi-agent system (MAS) is a loosely coupled network of agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver. However, the network of MAS still requires a main system to govern or oversees the operation of the agents in order to achieve a unified goal. We had developed a fleet management system (FMS) in order to manage the fleet of agents, plan route for the agents, perform real-time data processing and analysis, and issue sets of general and specific instructions to the agents. This FMS should be able to perform real-time data processing, communicate with the autonomous surface vehicle (ASV) agents and generate bathymetric map according to the data received from each ASV unit. The first algorithm is developed to communicate with the ASV via radio communication using standard National Marine Electronics Association (NMEA) protocol sentences. Next, the second algorithm will take care of the path planning, formation and pattern generation is tested using various sample data. Lastly, the bathymetry map generation algorithm will make use of data collected by the agents to create bathymetry map in real-time. The outcome of this research is expected can be applied on various other multi-agent systems.

Keywords: autonomous surface vehicle, fleet management system, multi agent system, bathymetry

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Authors: Tomola Obamuyi

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The banking system in Nigeria adopted innovative banking, with the aim of enhancing financial inclusion, and making financial services readily and cheaply available to majority of the people, and to contribute to the efficiency of the financial system. Some of the innovative services include: Automatic Teller Machines (ATMs), National Electronic Fund Transfer (NEFT), Point of Sale (PoS), internet (Web) banking, Mobile Money payment (MMO), Real-Time Gross Settlement (RTGS), agent banking, among others. The introduction of these payment systems is expected to increase bank efficiency and customers' satisfaction, culminating in better performance for the commercial banks. However, opinions differ on the possible effects of the various innovative payment systems on the performance of commercial banks in the country. Thus, this study empirically determines how commercial banks use innovation to gain competitive advantage in the specific context of Nigeria's finance and business. The study also analyses the effects of financial innovation on the performance of commercial banks, when different periods of analysis are considered. The study employed secondary data from 2009 to 2018, the period that witnessed aggressive innovation in the financial sector of the country. The Vector Autoregression (VAR) estimation technique forecasts the relative variance of each random innovation to the variables in the VAR, examine the effect of standard deviation shock to one of the innovations on current and future values of the impulse response and determine the causal relationship between the variables (VAR granger causality test). The study also employed the Multi-Criteria Decision Making (MCDM) to rank the innovations and the performance criteria of Return on Assets (ROA) and Return on Equity (ROE). The entropy method of MCDM was used to determine which of the performance criteria better reflect the contributions of the various innovations in the banking sector. On the other hand, the Range of Values (ROV) method was used to rank the contributions of the seven innovations to performance. The analysis was done based on medium term (five years) and long run (ten years) of innovations in the sector. The impulse response function derived from the VAR system indicated that the response of ROA to the values of cheques transaction, values of NEFT transactions, values of POS transactions was positive and significant in the periods of analysis. The paper also confirmed with entropy and range of value that, in the long run, both the CHEQUE and MMO performed best while NEFT was next in performance. The paper concluded that commercial banks would enhance their performance by continuously improving on the services provided through Cheques, National Electronic Fund Transfer and Point of Sale since these instruments have long run effects on their performance. This will increase the confidence of the populace and encourage more usage/patronage of these services. The banking sector will in turn experience better performance which will improve the economy of the country. Keywords: Bank performance, financial innovation, multi-criteria decision making, vector autoregression,

Keywords: Bank performance, financial innovation, multi-criteria decision making, vector autoregression

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Authors: Takatoshi Ishii, Koji Kimita, Keiichi Muramatsu, Yoshiki Shimomura

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To increase the quality of learning, teacher and learner need mutual effort for realization of educational value. For this purpose, we need to manage the co-creational education among teacher and learners. In this research, we try to find a feature of co-creational education. To be more precise, we analyzed learners’ reports by natural language processing, and extract some features that describe the state of the co-creational education.

Keywords: co-creational education, e-portfolios, ICT integration, latent dirichlet allocation

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Authors: Anna Jastrzebska, K. S. Suresh, T. Kitashima, Y. Yamabe-Mitarai, Z. Pakiela

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Near α titanium alloys are important materials for aerospace applications, especially in high temperature applications such as jet engine. Mechanical properties of Ti alloys strongly depends on their processing route, then it is very important to understand micro-structure change by different processing. In our previous study, Nb was found to improve oxidation resistance of Ti alloys. In this study, micro-structure evolution of Ti-6Al-4Nb (wt %) alloy was investigated after plain strain compression test in hot working temperatures in the α and β phase region. High-resolution EBSD was successfully used for precise phase and texture characterization of this alloy. 1.1 kg of Ti-6Al-4Nb ingot was prepared using cold crucible levitation melting. The ingot was subsequently homogenized in 1050 deg.C for 1h followed by cooling in the air. Plate like specimens measuring 10×20×50 mm3 were cut from an ingot by electrical discharge machining (EDM). The plain strain compression test using an anvil with 10 x 35 mm in size was performed with 3 different strain rates: 0.1s-1, 1s-1and 10s-1 in 700 deg.C and 1050 deg.C to obtain 75% of deformation. The micro-structure was investigated by scanning electron microscopy (SEM) equipped with electron backscatter diffraction (EBSD) detector. The α/β phase ratio and phase morphology as well as the crystallographic texture, subgrain size, misorientation angles and misorientation gradients corresponding to each phase were determined over the middle and the edge of sample areas. The deformation mechanism in each working temperature was discussed. The evolution of texture changes with strain rate was investigated. The micro-structure obtained by plain strain compression test was heterogeneous with a wide range of grain sizes. This is because deformation and dynamic recrystallization occurred during deformation at temperature in the α and β phase. It was strongly influenced by strain rate.

Keywords: EBSD, plain strain compression test, Ti alloys

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Authors: Naina Mahajan, Bikram Pal Kaur

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The purpose of traffic accident analysis is to find the possible causes of an accident. Road accidents cannot be totally prevented but by suitable traffic engineering and management the accident rate can be reduced to a certain extent. This paper discusses the classification techniques C4.5 and ID3 using the WEKA Data mining tool. These techniques use on the NH (National highway) dataset. With the C4.5 and ID3 technique it gives best results and high accuracy with less computation time and error rate.

Keywords: C4.5, ID3, NH(National highway), WEKA data mining tool

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Authors: Abdul Khalique Baloch, Ali Okatan

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The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.

Keywords: deep learning, computer vision, image processing, rotten fruit detection, fruits quality criteria, vegetables quality criteria

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Authors: Eliane Abdo, Olivier Colot

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SMEs are always considered as a national priority due to their contribution to job creation, innovation and growth. Once the start-up phase is crossed with encouraging results, the company enters the phase of growth. In order to improve its competitiveness, maintain and increase its market share, the company is in the necessity even the obligation to develop its tangible and intangible investments. SMEs are generally closed companies with special and critical financial situation, limited resources and difficulty to access the capital markets; their shareholders are always living in a conflict between their independence and their need to increase capital that leads to the entry of new shareholder. The capital structure was always considered the core of research in corporate finance; moreover, the financial crisis and its repercussions on the credit’s availability, especially for SMEs make SME financing a hot topic. On the other hand, financial theories do not provide answers to capital structure’s questions; they offer tools and mode of financing that are more accessible to larger companies. Yet, SME’s capital structure can’t be independent of their governance structure. The classic financial theory supposes independence between the investment decision and the financing decision. Thus, investment determines the volume of funding, but not the split between internal or external funds. In this context, we find interesting to study the hypothesis that SMEs respond positively to the financial theories applied to large firms and to check if they are constrained by conventional solutions used by large companies. In this context, this research focuses on the analysis of the resource’s structure of SME in parallel with their investments’ structure, in order to highlight a link between their assets and liabilities structure. We founded our conceptual model based on two main theoretical frameworks: the Pecking order theory, and the Trade Off theory taking into consideration the SME’s characteristics. Our data were generated from DIANE database. Five hypotheses were tested via a panel regression to understand the type of dependence between the financing methods of 3,244 French SMEs and the development of their investment over a period of 10 years (2007-2016). The results show dependence between equity and internal financing in case of intangible investments development. Moreover, this type of business is constraint to financial debts since the guarantees provided are not sufficient to meet the banks' requirements. However, for tangible investments development, SMEs count sequentially on internal financing, bank borrowing, and new shares issuance or hybrid financing. This is compliant to the Pecking Order Theory. We, therefore, conclude that unlisted SMEs incur more financial debts to finance their tangible investments more than their intangible. However, they always prefer internal financing as a first choice. This seems to be confirmed by the assumption that the profitability of the company is negatively related to the increase of the financial debt. Thus, the Pecking Order Theory predictions seem to be the most plausible. Consequently, SMEs primarily rely on self-financing and then go, into debt as a priority to finance their financial deficit.

Keywords: capital structure, investments, life cycle, pecking order theory, trade off theory

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Authors: Ahcene Boucied

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In this paper, a case study is conducted to analyze the effectiveness of web pages designed in Barbados for the tourism and hospitality industry. The assessment is made from two perspectives: to understand how the Barbados’ tourism industry is using the web, and to identify the effect of information technology on economic issues. In return, this is used: (a) to provide interested parties with accurate information and marketing insight necessary for decision making for electronic commerce/e-commerce, and (b) to demonstrate pragmatic difficulties in searching and designing web pages.

Keywords: segmentation, tourism stakeholders, destination marketing, case study

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Authors: F. Bellali, M. Kharroubi, Y. Rady, N. Bourhim

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In Morocco, fish processing industry is an important source income for a large amount of by-products including skins, bones, heads, guts, and scales. Those underutilized resources particularly scales contain a large amount of proteins and calcium. Sardina plichardus scales from resulting from the transformation operation have the potential to be used as raw material for the collagen production. Taking into account this strong expectation of the regional fish industry, scales sardine upgrading is well justified. In addition, political and societal demands for sustainability and environment-friendly industrial production systems, coupled with the depletion of fish resources, drive this trend forward. Therefore, fish scale used as a potential source to isolate collagen has a wide large of applications in food, cosmetic, and biomedical industry. The main aim of this study is to isolate and characterize the acid solubilize collagen from sardine fish scale, Sardina pilchardus. Experimental design methodology was adopted in collagen processing for extracting optimization. The first stage of this work is to investigate the optimization conditions of the sardine scale deproteinization on using response surface methodology (RSM). The second part focus on the demineralization with HCl solution or EDTA. And the last one is to establish the optimum condition for the isolation of collagen from fish scale by solvent extraction. The advancement from lab scale to pilot scale is a critical stage in the technological development. In this study, the optimal condition for the deproteinization which was validated at laboratory scale was employed in the pilot scale procedure. The deproteinization of fish scale was then demonstrated on a pilot scale (2Kg scales, 20l NaOH), resulting in protein content (0,2mg/ml) and hydroxyproline content (2,11mg/l). These results indicated that the pilot-scale showed similar performances to those of lab-scale one.

Keywords: deproteinization, pilot scale, scale, sardine pilchardus

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Kamal Kasmaoui, Farid Makhlouf

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This article seeks to assess the role of the level of interpersonal trust in a country in the remittance landscape. Using historical data from the 2010-2014 wave of the World Value Survey (WVS) for interpersonal trust, our findings underline the substitution role played by the interpersonal trust with remittances. More accurately, remittances tend to drop when the rate of interpersonal trust in the country of origin is high. Overall, a rise in trust is likely to underpin social cohesion, limiting, therefore, the need for remittances. These results are still fairly solid and unambiguous after controlling for confounding factors and possible reverse causality.

Keywords: interpersonal trust, social capital, remittances, 2SLS

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Authors: Chahrazed Zouaoui

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In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial.

Keywords: mining, GIS, geo-clustering, neighborhood

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Authors: Alejandro Molina-Villegas, Silvia Fernández-Sabido, Eduardo Mendoza-Vargas, Fátima Miranda-Pestaña

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The automatic detection of indigenous languages ​​in digital texts is essential to promote their inclusion in digital media. Underrepresented languages, such as Maya, are often excluded from language detection tools like Google’s language-detection library, LANGDETECT. This study addresses these limitations by developing a hybrid language detection solution that accurately distinguishes Maya (YUA) from Spanish (ES). Two strategies are employed: the first focuses on creating a profile for the Maya language within the LANGDETECT library, while the second involves training a Naive Bayes classification model with two categories, YUA and ES. The process includes comprehensive data preprocessing steps, such as cleaning, normalization, tokenization, and n-gram counting, applied to text samples collected from various sources, including articles from La Jornada Maya, a major newspaper in Mexico and the only media outlet that includes a Maya section. After the training phase, a portion of the data is used to create the YUA profile within LANGDETECT, which achieves an accuracy rate above 95% in identifying the Maya language during testing. Additionally, the Naive Bayes classifier, trained and tested on the same database, achieves an accuracy close to 98% in distinguishing between Maya and Spanish, with further validation through F1 score, recall, and logarithmic scoring, without signs of overfitting. This strategy, which combines the LANGDETECT profile with a Naive Bayes model, highlights an adaptable framework that can be extended to other underrepresented languages in future research. This fills a gap in Natural Language Processing and supports the preservation and revitalization of these languages.

Keywords: indigenous languages, language detection, Maya language, Naive Bayes classifier, natural language processing, low-resource languages

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Authors: Alison Ryan, Himani Chhabra, Mohammed Dungarwalla, Judith Jones

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Background: Impacted and ectopic teeth present in 1-2% of orthodontic patients and often require joint surgical and orthodontic management. The authors present two patients undergoing orthodontic treatment, where the impacted teeth, in a hopeless position, spontaneously erupted during the period of cessation of general anaesthetic lists during the COVID-19 pandemic. Patient information: A healthy 11-year-old boy was referred to the Department of Oral and Maxillofacial Surgery for the management of a mesioangular impacted LR7. The patient was seen by the joint oral surgery/orthodontic team, who planned for the removal of the LR7 under general anaesthetic. A healthy 13-year-old boy was referred to the same Department and team for surgical extraction of unerupted and buccally impacted UL3 and UR3 under general anaesthetic. Management and outcome: The majority of elective dental-alveolar work ceased as a result of the global pandemic. On resumption of activity, the first patient was reviewed in July 2021. The LR7 had spontaneously erupted in a favourable position, and following MDT review, a decision was made to forgo any further surgical intervention. The second patient was reviewed in July 2021. The UL3 had clinically erupted, and there was radiographic evidence of favourable movement of UR3. Due to the nature of the patient’s malocclusion, the decision was made to proceed with the extractions as previously planned. Key Learning Points: Severely impacted teeth do have a prospect of spontaneous eruption or alignment without clinical intervention, and current literature states the initial location, axial inclination, degree of root formation, and relation of the impacted tooth to adjacent teeth roots may influence spontaneous eruption. There is potential to introduce a period of observation to account for this possibility in the developing dentition, with the aim of reducing the unnecessary need for surgical intervention. This could help prevent episodes of general anaesthetic and allocate theatre space more appropriately.

Keywords: spontaneous eruption, impaction, observation, hopeless position, surgical, orthodontic, change in treatment plan

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Authors: Biao Sun, Tejas Bhatelia, Vishnu Pareek, Ranjeet Utikar, Moses Tadé

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Additive manufacturing (AM), commonly known as 3D printing, with the continuing advances in parallel processing and computational modeling, has created a paradigm shift (with significant radical thinking) in the design and operation of chemical processing plants, especially LNG plants. With the rising energy demands, environmental pressures, and economic challenges, there is a continuing industrial need for disruptive technologies such as AM, which possess capabilities that can drastically reduce the cost of manufacturing and operations of chemical processing plants in the future. However, the continuing challenge for 3D printing is its lack of adaptability in re-designing the process plant equipment coupled with the non-existent theory or models that could assist in selecting the optimal candidates out of the countless potential fabrications that are possible using AM. One of the most common packings used in the LNG process is structured packing in the packed column (which is a unit operation) in the process. In this work, we present an example of an optimum strategy for the application of AM to this important unit operation. Packed columns use a packing material through which the gas phase passes and comes into contact with the liquid phase flowing over the packing, typically performing the necessary mass transfer to enrich the products, etc. Structured packing consists of stacks of corrugated sheets, typically inclined between 40-70° from the plane. Computational Fluid Dynamics (CFD) was used to test and model various geometries to study the governing hydrodynamic characteristics. The results demonstrate that the costly iterative experimental process can be minimized. Furthermore, they also improve the understanding of the fundamental physics of the system at the multiscale level. SpiroPak, patented by Curtin University, represents an innovative structured packing solution currently at a technology readiness level (TRL) of 5~6. This packing exhibits remarkable characteristics, offering a substantial increase in surface area while significantly enhancing hydrodynamic and mass transfer performance. Recent studies have revealed that SpiroPak can reduce pressure drop by 50~70% compared to commonly used commercial packings, and it can achieve 20~50% greater mass transfer efficiency (particularly in CO2 absorption applications). The implementation of SpiroPak has the potential to reduce the overall size of columns and decrease power consumption, resulting in cost savings for both capital expenditure (CAPEX) and operational expenditure (OPEX) when applied to retrofitting existing systems or incorporated into new processes. Furthermore, pilot to large-scale tests is currently underway to further advance and refine this technology.

Keywords: Additive Manufacturing (AM), 3D printing, Computational Fluid Dynamics (CFD, structured packing (SpiroPak)

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