Search results for: deep gaining knowledge of

Authors: Rima Al-Awadhi, Abdul Jaleel Tharayil

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On a regular basis, KOC engages into various types of Projects. However, due to very nature and complexity involved, each project experience generates a lot of ‘learnings’ that need to be factored into while drafting a new contract and thus avoid repeating the same mistakes. But, many a time these learnings are localized and remain as tacit leading to scope re-work, larger cycle time, schedule overrun, adjustment orders and claims. Also, these experiences are not readily available to new employees leading to steep learning curve and longer time to competency. This is to share our experience in designing and implementing a process driven architecture for the ‘lessons learnt’ knowledge sharing framework in KOC. It high-lights the ‘lessons learnt’ sharing process adopted, integration with the organizational processes, governance framework, the challenges faced and learning from our experience in implementing a ‘lessons learnt’ framework.

Keywords: lessons learnt, knowledge transfer, knowledge sharing, successful practices, Lessons Learnt Workshop, governance framework

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Authors: Offer E. Edelstein, Iris Vered, Orly Sarid

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Background: Physical activity (PA) is considered as a major factor in bone density preservation and fracture prevention. Yet, gaps in understanding exist regarding how ethnocultural backgrounds might shape attitudes, intentions, and actual PA participation. Based on the theory of planned behavior (TPB) for predicting PA, the aims of the current study were: i) to compare attitudes, subjective norms, perceived control, intentions and knowledge, across two ethnocultural groups; ii) to evaluate the fit of the model across two ethnocultural groups of women: Israeli-born Jews and Ethiopian immigrants. Methods: Two hundred women (one hundred from each group), aged > 65, completed valid and reliable questionnaires assessing knowledge, TPB components, and actual PA. Results: The level of knowledge on osteoporosis was relatively low in both groups. Intention to participate in PA was the only variable that directly predicted actual PA. Intention to participate in PA served as a mediator among attitudes, subjective norms, perceived control, and actual PA. The TPB components mediated the link between knowledge and intention to participate in PA. Conclusion: It is important to understand and augment interventions that enhance PA, in the community, and with sensitivity concerning each ethnocultural group.

Keywords: attitudes, ethnocultural groups, knowledge, physical activity

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Authors: Karim Akkouche, Amine Nekmouche, Leyla Bouzid

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This study focuses on the development of a multifunctional Expert System (ES) called post-seismic damage inspection tool (PSDIT), a powerful tool which allows the evaluation, the processing, and the archiving of the collected data stock after earthquakes. PSDIT can be operated by two user types; an ordinary user (ingineer, expert, or architect) for the damage visual inspection and an administrative user for updating the knowledge and / or for adding or removing the ordinary user. The knowledge acquisition is driven by a hierarchical knowledge model, the Information from investigation reports and those acquired through feedback from expert / engineer questionnaires are part.

Keywords: .disaster, damaged structures, damage assessment, expert system

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Authors: Gabriel Sunday Ayayia

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Food waste has reached alarming levels worldwide, contributing to food insecurity, resource depletion, and environmental degradation. While numerous strategies exist to mitigate this issue, the role of traditional practices and indigenous knowledge remains underexplored. There is a need to investigate how these age-old practices can contribute to sustainable food waste reduction, particularly in the African context. This study explores the potential of traditional practices and indigenous knowledge in Africa to address this challenge sustainably. The study examines traditional African food management practices and indigenous knowledge related to food preservation and utilization; assess the impact of traditional practices on reducing food waste and its broader implications for sustainable development, and identify key factors influencing the continued use and effectiveness of traditional practices in contemporary African societies. Thus, the study argues that traditional practices and indigenous knowledge in Africa offer valuable insights and strategies for sustainable food waste reduction that can be adapted and integrated into global initiatives This research will employ a mixed-methods approach, combining qualitative and quantitative research techniques. Data collection will involve in-depth interviews, surveys, and participant observations in selected African communities. Moreover, a comprehensive review of literature on traditional food management practices and their impact on food waste reduction will be conducted. The significance of this study lies in its potential to bridge the gap between traditional knowledge and modern sustainability efforts. By uncovering the value of traditional practices in reducing food waste, this research can inform policies, interventions, and awareness campaigns aimed at achieving sustainable food systems worldwide.

Keywords: traditional practices, indigenous knowledge, food waste reduction, sustainability

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Authors: Jana Soler Libran

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The primary aim of this paper is to analyze the relationship between power and knowledge suggested in Michel Foucault's theory. Taking into consideration the role of power in knowledge production, the goal is to evaluate to what extent genealogy can be presented as a practical method for epistemic resistance. To do so, the methodology used consists of a revision of Foucault’s literature concerning the topic discussed. In this sense, conceptual analysis is applied in order to understand the effect of the double dimension of power on knowledge production. In its negative dimension, power is conceived as an organ of repression, vetoing certain instances of knowledge considered deceitful. In opposition, in its positive dimension, power works as an organ of the production of truth by means of institutionalized discourses. This double declination of power leads to the first main findings of the present analysis: no truth or knowledge can lie outside power’s action, and power is constituted through accepted forms of knowledge. To second these statements, Foucaultian discourse formations are evaluated, presenting external exclusion procedures as paradigmatic practices to demonstrate how power creates and shapes the validity of certain epistemes. Thus, taking into consideration power’s mechanisms to produce and reproduce institutionalized truths, this paper accounts for the Foucaultian praxis of genealogy as a method to reveal power’s intention, instruments, and effects in the production of knowledge. In this sense, it is suggested to consider genealogy as a practice which, firstly, reveals what instances of knowledge are subjugated to power and, secondly, promotes aforementioned peripherical discourses as a form of epistemic resistance. In order to counterbalance these main theses, objections to Foucault’s work from Nancy Fraser, Linda Nicholson, Charles Taylor, Richard Rorty, Alvin Goldman, or Karen Barad are discussed. In essence, the understanding of the Foucaultian relationship between power and knowledge is essential to analyze how contemporary discourses are produced by both traditional institutions and new forms of institutionalized power, such as mass media or social networks. Therefore, Michel Foucault's practice of genealogy is relevant, not only for its philosophical contribution as a method to uncover the effects of power in knowledge production but also because it constitutes a valuable theoretical framework for political theory and sociological studies concerning the formation of societies and individuals in the contemporary world.

Keywords: epistemic resistance, Foucault’s genealogy, knowledge, power, truth

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Authors: Wen-Hsiang Lai

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Except for the internal aspects of entrepreneurship (i.e. motivation, opportunity perspective and alertness), there are external aspects that affecting entrepreneurship (i.e. the industrial cluster). By comparing the machinery companies located inside and outside the industrial district, this study aims to explore the cluster effects on the entrepreneurship of companies in Taiwan machinery clusters (TMC). In this study, three factors affecting the entrepreneurship in TMC are conducted as “competition”, “embedded-ness” and “specialized knowledge”. The “competition” in the industrial cluster is defined as the competitive advantages that companies gain in form of demand effects and diversified strategies; the “embedded-ness” refers to the quality of company relations (relational embedded-ness) and ranges (structural embedded-ness) with the industry components (universities, customers and complementary) that affecting knowledge transfer and knowledge generations; the “specialized knowledge” shares the internal knowledge within industrial clusters. This study finds that when comparing to the companies which are outside the cluster, the industrial cluster has positive influence on the entrepreneurship. Additionally, the factor of “relational embedded-ness” has significant impact on the entrepreneurship and affects the adaptation ability of companies in TMC. Finally, the factor of “competition” reveals partial influence on the entrepreneurship.

Keywords: entrepreneurship, industrial cluster, industrial district, economies of agglomerations, Taiwan Machinery Cluster (TMC)

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Authors: Kamalendu Pal

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This paper describes the main features of a knowledge-based system evaluation method. System evaluation is placed in the context of a hybrid legal decision-support system, Advisory Support for Home Settlement in Divorce (ASHSD). Legal knowledge for ASHSD is represented in two forms, as rules and previously decided cases. Besides distinguishing the two different forms of knowledge representation, the paper outlines the actual use of these forms in a computational framework that is designed to generate a plausible solution for a given case, by using rule-based reasoning (RBR) and case-based reasoning (CBR) in an integrated environment. The nature of suitability assessment of a solution has been considered as a multiple criteria decision making process in ASHAD evaluation. The evaluation was performed by a combination of discussions and questionnaires with different user groups. The answers to questionnaires used in this evaluations method have been measured as a combination of linguistic variables, fuzzy numbers, and by using defuzzification process. The results show that the designed evaluation method creates suitable mechanism in order to improve the performance of the knowledge-based system.

Keywords: case-based reasoning, fuzzy number, legal decision-support system, linguistic variable, rule-based reasoning, system evaluation

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Authors: Nahla S. Abdel-Azim, Khaled A. Shams, Elsayed A. Omer, Mahmoud M. Sakr

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Medicinal plants play a significant role in the health care system in Egypt. Knowledge developed over the years by people is mostly unrecorded and orally passes on from one generation to the next. This knowledge is facing the danger of becoming extinct. Therefore there is an urgent need to document the medicinal and aromatic plants associated with traditional knowledge. The Egyptian Encyclopedia of wild medicinal plants (EEWMP) is the first attempt to collect most of the basic elements of the medicinal plant resources of Egypt and their traditional uses. It includes scientific data on about 500 medicinal plants in the form of monographs. Each monograph contains all available information and scientific data on the selected species including the following: names, description, distribution, parts used, habitat, conservational status, active or major chemical constituents, folk medicinal uses and heritage resources, pharmacological and biological activities, authentication, pharmaceutical products, and cultivation. The DNA bar-coding is also included (when available). A brief Arabic summary is given for every monograph. This work revealed the diversity in plant parts used in the treatment of different ailments. In addition, the traditional knowledge gathered can be considered a good starting point for effective in situ and ex-situ conservation of endangered plant species.

Keywords: encyclopedia, medicinal plant, traditional medicine, wild flora

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Authors: Elison Eduardo Jardim Bierhals, Claudineia Brazil, Francisco Pereira, Elton Rossini

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The use of alternative energy sources to meet energy demand reduces environmental damage. To diversify an energy matrix and to minimize global warming, a solar energy is gaining space, being an important source of renewable energy, and its potential depends on the climatic conditions of the region. Brazil presents a great solar potential for a generation of electric energy, so the knowledge of solar radiation and its characteristics are fundamental for the study of energy use. Due to the above reasons, this article aims to verify the climatic variability corresponding to the variations in solar radiation anomalies, in the face of climate change scenarios. The data used in this research are part of the Intercomparison of Interconnected Models, Phase 5 (CMIP5), which contributed to the preparation of the fifth IPCC-AR5 report. The solar radiation data were extracted from The Australian Community Climate and Earth System Simulator (ACCESS) model using the RCP 4.5 and RCP 8.5 scenarios that represent an intermediate structure and a pessimistic framework, the latter being the most worrisome in all cases. In order to allow the use of solar radiation as a source of energy in a given location and/or region, it is important, first, to determine its availability, thus justifying the importance of the study. The results pointed out, for the 75-year period (2026-2100), based on a pessimistic scenario, indicate a drop in solar radiation of the approximately 12% in the eastern region of Rio Grande do Sul. Factors that influence the pessimistic prospects of this scenario should be better observed by the responsible authorities, since they can affect the possibility to produce electricity from solar radiation.

Keywords: climate change, energy, IPCC, solar radiation

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Authors: Bernice Badal

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This article addresses a persistent challenge related to teacher agency in knowledge acquisition in professional development (PD) workshops in contexts of educational change, given that scholarship identifies a need for more teacher involvement and amplification of teacher's voices. Theoretical concepts are drawn from Bandura’s Social cognitive theory, incorporating the triadic causation model of agency to examine the reciprocal nature of the context, teacher characteristics, and systemic influences that shape how knowledge is transmitted and acquired in PD workshops. This qualitative study, using a mix of classroom observations and interviews, explored the political, contextual, and personal characteristics of teacher agency in PD through an analysis of data extracted from a PhD study. The narratives of six teachers from three township schools are examined to show how PD efforts in South Africa have failed to take account of the holistic development of teacher agency in knowledge dissemination and how this shapes teacher self-efficacy beliefs about being able to masterfully apply the tenets of the reform. Agency, teacher voice, and contextual considerations were used as markers of the quality of the training provided to understand how knowledge is acquired and meaning is made. The findings suggest that systemic influences of institutionally imposed PD offer partial understandings of the reform, which is offered in traditional formats that do not consider teacher empowerment in knowledge production and the development of teacher agency. Common in all the participants’ responses is the need for more information and training on the prescribed approach for teaching English as a second language; however, this paper holds the view that more information may not solve teachers’ dilemmas. Accordingly, it recommends a restructuring of the programme with facilitators being more cognisant of teacher agency for the development of transformative teachers. The findings of the study contribute to the field of teacher knowledge, teacher training, and professional development in the context of educational reforms.

Keywords: teacher professional development, teacher voice, teacher agency, educational reforms, teacher knowledge

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Authors: Wann-Yun Shieh, Chin-Man Wang, Ya-Cheng Shieh

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This study aims to develop an objective scoring system to evaluate hand-eye coordination and hand dexterity for Parkinson’s disease. This system contains three boards, and each of them is implemented with the sensors to sense a user’s finger operations. The operations include the peg test, the block test, and the blind block test. A user has to use the vision, hearing, and tactile abilities to finish these operations, and the board will record the results automatically. These results can help the physicians to evaluate a user’s reaction, coordination, dexterity function. The results will be collected to a cloud database for further analysis and statistics. A researcher can use this system to obtain systematic, graphic reports for an individual or a group of users. Particularly, a deep learning model is developed to learn the features of the data from different users. This model will help the physicians to assess the Parkinson’s disease symptoms by a more intellective algorithm.

Keywords: deep learning, hand-eye coordination, reaction, hand dexterity

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Authors: Teboho Moja, Frans Swanepoel, Okunade Samuel Kehinde

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Knowledge production is key to development worldwide. However, some countries are producers of knowledge used globally, whilst others are mainly consumers of that knowledge. There is a correlation between knowledge production and funding levels for research. Countries in Africa recognize the need to provide research funding at levels that would enhance knowledge production but struggle in balancing many competing needs. African countries have committed to funding research at the level of 1% of their GDP on research and development (R&D), which is the recommended percentage to be earmarked for education; however, many countries have not been able to fulfill this. In 2015-2016 Science Granting Councils in 15 out of 54 African states came together to form a Science Granting Council Initiative to strengthen the research capacity in their countries and to support research and evidence-based policies that will contribute to the continent’s economic and social development. The members of the SGCI work collaboratively; however, there is a challenge in addressing research problems that cut across national boundaries as many governments are more interested in prioritizing national issues given their limited resources. This article focuses on the governance structures of those science granting councils to understand and explore reasons for the continuing underfunding of research, the impact of research, and the perceived direct benefit of research outcomes in informing policy and in benefitting the broader society.

Keywords: research, Science Granting Council, funding, European Research Council, African Research Council

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: Haruna Maama

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Environmental, social and governance (ESG) reporting is gaining considerable attention despite being voluntary. Meanwhile, it consumes resources to provide ESG reporting, raising a question of its value relevance. The study examined the impact of ESG reporting on the market value of listed firms in SSA. The annual and integrated reports of 276 listed sub-Sahara Africa (SSA) firms. The integrated reporting scores of the firm were analysed using a content analysis method. A multiple regression estimation technique using a GMM approach was employed for the analysis. The results revealed that ESG has a positive relationship with firms’ market value, suggesting that investors are interested in the ESG information disclosure of firms in SSA. This suggests that extensive ESG disclosures are attempts by firms to obtain the approval of powerful social, political and environmental stakeholders, especially institutional investors. Furthermore, the market value analysis evidence is consistent with signalling theory, which postulates that firms provide integrated reports as a signal to influence the behaviour of stakeholders. This finding reflects the value placed on investors' social, environmental and governance disclosures, which affirms the views that conventional investors would care about the social, environmental and governance issues of their potential or existing investee firms. Overall, the evidence is consistent with the prediction of signalling theory. In the context of this theory, integrated reporting is seen as part of firms' overall competitive strategy to influence investors' behaviour. The findings of this study make unique contributions to knowledge and practice in corporate reporting.

Keywords: environmental accounting, ESG accounting, signalling theory, sustainability reporting, sub-saharan Africa

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Authors: Dereje Regasa

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The global food system is at a crossroads, which requires prompt action to minimize the effects of the crises. Agroecology is gaining prominence due to its contributions to sustainable food systems. To support efforts in mitigating the crises, the Food and Agriculture Organization (FAO) established alternative approaches for sustainable agri-food systems. Agroecological elements and principles were developed to guide and support measures that countries need to achieve the Sustainable Development Goals (SDGs). The SDGs require the systemic integration of practices for a smart intensification or adaptation of traditional or industrial agriculture. As one of the countries working towards SDGs, the agricultural practices in Ethiopia need to be guided by these agroecological elements and principles. Aiming at the identification of challenging aspects of a sustainable agri-food system and the characterization of an enabling environment for agroecology, as well as exploring to what extent the existing policies and strategies support the agroecological transition process, five policy and strategy documents were reviewed. These documents are the Rural Development Policy and Strategy, the Environment Policy, the Biodiversity Policy, and the Soil Strategy of the Ministry of Agriculture (MoA). Using the Agroecology Criteria Tool (ACT), the contents were reviewed, focusing on agroecological requirements and the inclusion of sustainable practices. ACT is designed to support a self-assessment of elements supporting agroecology. For each element, binary values were assigned based on the inclusion of the minimum requirements index and then validated through discussion with the document owners. The results showed that the documents were well below the requirements for an agroecological transition of the agri-food system. The Rural Development Policy and Strategy only suffice to 83% in Human and Social Value. It does not support the transition concerning the other elements. The Biodiversity Policy and Soil Strategy suffice regarding the inclusion of Co-creation and Sharing of knowledge (100%), while the remaining elements were not considered sufficiently. In contrast, the Environment Policy supports the transition with three elements accounting for 100%. These are Resilience, Recycling, and Human and Social Care. However, when the four documents were combined, elements such as Synergies, Diversity, Efficiency, Human and Social value, Responsible governance, and Co-creation and Sharing of knowledge were identified as fully supportive (100%). This showed that the policies and strategies complemented one another to a certain extent. However, the evaluation results call for improvements concerning elements like Culture and food traditions, Circular and solidarity economy, Resilience, Recycling, and Regulation and balance since the majority of the elements were not sufficiently observed. Consequently, guidance for the smart intensification of local practices is needed, as well as traditional knowledge enriched with advanced technologies. Ethiopian agricultural and environmental policies and strategies should provide sufficient support and guidance for the intensification of sustainable practices and should provide a framework for an agroecological transition towards a sustainable agri-food system.

Keywords: agroecology, diversity, recycling, sustainable food system, transition

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Authors: Abolaji Mayowa Akinyele

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The purpose of this study was to investigate student’s awareness, Knowledge and understanding of environmental issues for sustainable development. Findings revealed that; despite the positive attitude shown by students towards environmental education, a relatively low level of understanding of environmental concept was recorded in school settings regardless of efforts by government and other environmental agencies at creating awareness about environmental related issues. This brought about the investigation of students environmental education knowledge in high school settings. About 205 Students were randomly selected for data collection using validated instruments titled student’s knowledge and attitude questionnaire as well as student’s response to questions (interview) concerning global warming. T-test statistics, chi-square and simple percentage were the major statistical tools employed in data analysis. This study revealed that environment based-education (school curriculum) as well as efforts by government/environmental agencies (mass media) plays a major role in promoting students understanding, of environmental concepts, awareness of major environmental issues and positive attitude towards natural environment.

Keywords: environmental issues, sustainable development, students attitude, students knowledge

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Authors: Surjeet Bakshi, Surabhi Sharma

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Objective: To assess the impact of educational sessions by reputed regional faculties on knowledge of primary care physicians on evidence based diabetes management methods and practice. Study Design: Retrospective pre-post intervention study. Methodology: Nine cities in Kerala from August to October, 2012 were selected for the study. 125 MBBS doctors participated in the study. 11 regional faculties provided six educational sessions throughout the period. Validated questionnaires were used to evaluate the knowledge of the participants on evidence based diabetes management methods before and after the intervention. Results: The mean score on pre-test was 8 and the mean score on post-test was 9. A paired t-test was conducted on participant’s pre- and post test score and the results were statistically significant (p<0.001). Conclusion: Even though the general attitude to and level of knowledge of diabetes management is good among the primary care physicians in India, there do exist some knowledge gaps which might influence their future practices when it comes to counselling and information on diabetes management methods. In the present study, the performance and awareness level of the participants have expressively improved among primary care physicians. There is a significant improvement in the test score and the training conducted. It seems that if such study programmes are included in the students study programme, it will give higher score in the knowledge and attitude towards diabetes management.

Keywords: diabetes, management, primary care physicians, evidence base, improvement score, knowledge

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, ECG, ResNet, sliding window

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Authors: Muntadher Sallal, Gareth Owenson, Mo Adda, Safa Shubbar

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Bitcoin is a digital currency based on a peer-to-peer network to propagate and verify transactions. Bitcoin is gaining wider adoption than any previous crypto-currency. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a delay overhead in information propagation, which makes the system vulnerable to double-spend attacks. Aiming at alleviating the propagation delay problem, this paper introduces proximity-aware extensions to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol, that are based on how clusters are formulated and how nodes can define their membership, is to improve the information propagation delay in the Bitcoin network. In MNBC protocol, physical internet connectivity increases, as well as the number of hops between nodes, decreases through assigning nodes to be responsible for maintaining clusters based on physical internet proximity. We show, through simulations, that the proposed protocol defines better clustering structures that optimize the performance of the transaction propagation over the Bitcoin protocol. The evaluation of partition attacks in the MNBC protocol, as well as the Bitcoin network, was done in this paper. Evaluation results prove that even though the Bitcoin network is more resistant against the partitioning attack than the MNBC protocol, more resources are needed to be spent to split the network in the MNBC protocol, especially with a higher number of nodes.

Keywords: Bitcoin network, propagation delay, clustering, scalability

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Authors: Maryam Ehsanpour, Maryam Malekpour, Rastin Afkhami

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Today, industry is one of the indices of growth and development of countries and is a suitable applicable criterion to compare the countries. Bandar Abbas has a high industrial centralization in term of geographical redundancy of industries in comparison with other rural and urban places of Hormozgan province. Most important and major industries of the province are located in Bandar abbas eighth refinery, power plant, zinc melting company, Almahdi Aluminium, Hormozgan steel, south steel, which are the most important of these industries. So, it is necessary to study pollution from these industries and their destructive effects on environment of region. In respect of these things, general purpose of this research is codling and presenting managing solution of Almahdi Aluminium factory in them of measuring of air (atmosphere) parameters. For gaining this purpose it is necessary to determine measure of heavy metals suspension in the air (atmosphere) in the neighborhood of industries and also in residential regions close to them as partial purposes. So, for achieving the purposes above, operation of sampling from the air in two hot and cold seasons of the year (2010-2011) was performed, after field reviews to recognize the sources of effluence and to choose place of sampling stations. Sampling and preparation way to read was based on EPA and NIOSH. Also, decreasing process was included Fe>Al>Cd>Pb>Ni respectively, in term of results gaining from sampling of ingredients existing in the air (atmosphere). Also Ni and Fe elements in samples of air were higher than permissive measure in both of cold and hot season. Average of these two metals was 34% and 33% in cold season and 44% and 34% micrograms/m3 in hot season. Finally, suitable managing solutions to improve existing situation is presented in term for all results.

Keywords: Almahdi aluminium factory, Bandar Abbas, scarce metals, atmosphere

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Authors: Fajana Sola Isaac

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In the last two decades, we have witnessed an increased interest in exchange rate pass-through (ERPT) in developing economies and emerging markets. This is perhaps due to the acknowledged significance of the pattern of exchange rate pass-through as a key instrument in monetary policy design, principally in retort to a shock in exchange rate in literature. This paper analyzed Exchange Rate Pass-Through by A Model of Oil Prices and Asymmetric Exchange Rate Fluctuations in Selected African Countries. The study adopted A Non-Linear Autoregressive Distributed Lag approach using yearly data on Algeria, Burundi, Nigeria and South Africa from 1986 to 2022. The paper found asymmetry in exchange rate pass-through in net oil-importing and net oil-exporting countries in the short run during the period under review. An ERPT exhibited a complete pass-through in the short run in the case of net oil-importing countries but an incomplete pass-through in the case of the net oil-exporting countries that were examined. An extended result revealed a significant impact of oil price shock on exchange rate pass-through to domestic price in the long run only for net oil importing countries. The Wald restriction test also confirms the evidence of asymmetric with the role of oil price acting as an accelerator to exchange rate pass-through to domestic price in the countries examined. The study found the outcome to be very useful for gaining expansive knowledge on the external shock impact on ERPT and could be of critical value for national monetary policy decisions on inflation targeting, especially for countries examined and other developing net oil importers and exporters.

Keywords: pass through, exchange rate, ARDL, monetary policy

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: Yi-Ying Lin, Su-Guo, Chia-Hao, Ming-Szu Hong

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Objectives: Smoking is a common addictive behavior in teenagers. Long-term smoking is hazardous to health, causes family and social expenditure, and is an important topic that should not be overlooked by academia or the government. The aims of this study are to examine the effectiveness of these courses in terms of teenagers’ knowledge and attitudes towards the hazards of smoking and the effectiveness of their self-efficacy in rejecting smoking. Methods: This study adopted a pre-test post-test design and selected 7th, 8th, 10th, and 11th graders from two junior high schools. Total of 1073 valid questionnaires were collected. The self-completed questionnaire included background information, smoking status of relatives staying with the subject, attitudes of parents towards child smoking, knowledge and attitudes towards smoking, and anti-smoking self-efficacy. Results and clinical applications: Subjects in the experimental group underwent course interventions, which are 'smoking prevention courses,' in the semester. After course intervention, it was found that the intervention showed significant efficacy in terms of knowledge and self-efficacy in rejecting smoking in senior high school students but no efficacy in junior high school. We recommend that this course can be used in normal senior high schools. With regards to junior high schools, smoking prevention courses should be designed to be gamified, or combined with activities with both anti-smoking messages and entertainment at the same time, so that knowledge, attitudes, and self-efficacy can be subconsciously cultivated.

Keywords: adolescent students, smoking knowledge, attitudes, anti-smoking self-efficiency, smoking prevention intervention

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Authors: Sandra Mendes

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Social Work has encountered over time with multivariate requests in the field of its action, provisioning frameworks of knowledge and praxis. Over the years, we have observed a transformation of society and, consequently, of the public who deals with the social work practitioners. Both, training and profession have had need to adapt and readapt the ways of doing, bailing up theories to action, while action unfolds emancipation of new theories. The theoretical questioning of this subject lies on classical authors from social sciences, and contemporary authors of Social Work. In fact, both enhance, in the design of social work, an integration and social cohesion function, creating a culture of action and theory, attributing to its method a relevant function, which shall be promoter of social changes in various dimensions of both individual and collective life, as well as scientific knowledge. On the other hand, it is assumed that Social Work, through its professionalism and through the academy, is now closer to distinguish itself from other Social Sciences as an autonomous scientific field, being, however, in the center of power struggles. This paper seeks to fill the gap in social work literature about the study of the scientific field of this area of knowledge.

Keywords: field theory, knowledge, science, social work

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Authors: A. Rabin, G. Mazor, I. Ladizhenski, R. Shneck, Z.

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The hardness and hardenability of AISI D2 cold work tool steel with conventional quenching (CQ), deep cryogenic quenching (DCQ) and rapid deep cryogenic quenching heat treatments caused by temporary porous coating based on magnesium sulfate was investigated. Each of the cooling processes was examined from the perspective of the full process efficiency, heat flux in the austenite-martensite transformation range followed by characterization of the temporary porous layer made of magnesium sulfate using confocal laser scanning microscopy (CLSM), surface and core hardness and hardenability using Vickr’s hardness technique. The results show that the cooling rate (CR) at the austenite-martensite transformation range have a high influence on the hardness of the studied steel.

Keywords: AISI D2, controllable cooling, magnesium sulfate coating, rapid cryogenic heat treatment, temporary porous layer

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Authors: Jame O. Ezema, Odenigbo Veronica

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The study covers the topic Information Communication and Technology (ICTs) knowledge as a way of enhancing Literacy and Lifelong learning in Nigeria. This work delved into defining of ICTs. Types of ICTs and media technologies were also mentioned. It further explained how ICTs can be strengthened and the uses of ICTs in education was duly emphasized. The paper also enumerated some side effects of ICTs on learners while the role of ICTs in enhancing literacy was explained. The study carried out strategies to use ICTs meaningfully in Literacy Programs and also emphasized the word lifelong learning in Nigeria. Some recommendations were made towards acquiring ICTs knowledge, so as to enhance Literacy and Lifelong learning in Nigeria.

Keywords: literacy, distance-learning, life-long learning for sustainable development, e-learning

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: Mihai-Ionut Firescu, Mark A. P. Carson

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We present a case of inferior vena cava agenesis (IVCA) associated with bilateral deep venous thrombosis (DVT) in a patient with Beckwith-Wiedemann syndrome (BWS). In adult patients with BWS presenting with bilateral lower limb oedema, specific aetiological factors should be considered. These include cardiomyopathy and intraabdominal tumours. Congenital malformations of the IVC, through causing relative venous stasis, can lead to lower limb oedema either directly or indirectly by favouring lower limb venous thromboembolism; however, they are yet to be reported as an associated feature of BWS. Given its life-threatening potential, the prompt initiation of treatment for bilateral DVT is paramount. In BWS patients, however, this can prove more complicated. Due to overgrowth, the above-average birth weight can continue throughout childhood. In this case, the patient’s weight reached 170 kg, impacting on anticoagulation choice, as direct oral anticoagulants have a limited evidence base in patients with a body mass above 120 kg. Furthermore, the presence of IVCA leads to a long-term increased venous thrombosis risk. Therefore, patients with IVCA and bilateral DVT warrant specialist consideration and may benefit from multidisciplinary team management, with hematology and vascular surgery input. Conclusion: Here, we showcased a rare cause for bilateral lower limb oedema, respectively bilateral deep venous thrombosis complicating IVCA in a patient with Beckwith-Wiedemann syndrome. The importance of this case lies in its novelty, as the association between IVC agenesis and BWS has not yet been described. Furthermore, the treatment of DVT in such situations requires special consideration, taking into account the patient’s weight and the presence of a significant, predisposing vascular abnormality.

Keywords: Beckwith-Wiedemann syndrome, bilateral deep venous thrombosis, inferior vena cava agenesis, venous thromboembolism

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Authors: Ethan James

Abstract:

Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

Procedia PDF Downloads 170