Search results for: constrained clustering

Authors: Charles A. Khamala

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In 2003, constrained by an absent “rule of law culture” and negative economic growth, the new Kenyan government chose to pursue incremental judicial reforms rather than comprehensive constitutional reforms. President Mwai Kibaki’s first administration’s judicial reform strategy was two pronged. First, to implement unprecedented “radical surgery,” he appointed a new Chief Justice who instrumentally recommended that half the purportedly-corrupt judiciary should be removed by Presidential tribunals of inquiry. Second, the replacement High Court judges, initially, instrumentally-endorsed the “radical surgery’s” administrative decisions removing their corrupt predecessors. Meanwhile, retention of the welfare-reducing Constitution perpetuated declining public confidence in judicial institutions culminating in refusal by the dissatisfied opposition party to petition the disputed 2007 presidential election results, alleging biased and corrupt courts. Fatefully, widespread post-election violence ensued. Consequently, the international community prompted the second Kibaki administration to concede to a new Constitution. Suddenly, the High Court then adopted a non-instrumental interpretation to reject the 2003 “radical surgery.” This paper therefore critically analyzes whether the Kenyan court’s inconsistent interpretations–pertaining to the constitutionality of the 2003 “radical surgery” removing corruption from Kenya’s courts–was predicated on political expediency or human rights principles. If justice “must also seen to be done,” then pursuit of the CJ’s, Judicial Service Commission’s and president’s political or economic interests must be limited by respect for the suspected judges and magistrates’ due process rights. The separation of powers doctrine demands that the dismissed judges should have a right of appeal which entails impartial review by a special independent oversight mechanism. Instead, ignoring fundamental rights, Kenya’s new Supreme Court’s interpretation of another round of vetting under the new 2010 Constitution, ousts the High Court’s judicial review jurisdiction altogether, since removal of judicial corruption is “a constitutional imperative, akin to a national duty upon every judicial officer to pave way for judicial realignment and reformulation.”

Keywords: administrative decisions, corruption, fair hearing, judicial review, (non) instrumental

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Authors: Hoa Pham

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The literature on language teaching and second language acquisition has been largely driven by monolingual ideology with a common assumption that a second language (L2) is best taught and learned in the L2 only. The current study challenges this assumption by reporting learners' positive perceptions of tertiary level teachers' code switching practices in Vietnam. The findings of this study contribute to our understanding of code switching practices in language classrooms from a learners' perspective. Data were collected from student participants who were working towards a Bachelor degree in English within the English for Business Communication stream through the use of focus group interviews. The literature has documented that this method of interviewing has a number of distinct advantages over individual student interviews. For instance, group interactions generated by focus groups create a more natural environment than that of an individual interview because they include a range of communicative processes in which each individual may influence or be influenced by others - as they are in their real life. The process of interaction provides the opportunity to obtain the meanings and answers to a problem that are "socially constructed rather than individually created" leading to the capture of real-life data. The distinct feature of group interaction offered by this technique makes it a powerful means of obtaining deeper and richer data than those from individual interviews. The data generated through this study were analysed using a constant comparative approach. Overall, the students expressed positive views of this practice indicating that it is a useful teaching strategy. Teacher code switching was seen as a learning resource and a source supporting language output. This practice was perceived to promote student comprehension and to aid the learning of content and target language knowledge. This practice was also believed to scaffold the students' language production in different contexts. However, the students indicated their preference for teacher code switching to be constrained, as extensive use was believed to negatively impact on their L2 learning and trigger cognitive reliance on the L1 for L2 learning. The students also perceived that when the L1 was used to a great extent, their ability to develop as autonomous learners was negatively impacted. This study found that teacher code switching was supported in certain contexts by learners, thus suggesting that there is a need for the widespread assumption about the monolingual teaching approach to be re-considered.

Keywords: codeswitching, L1 use, L2 teaching, learners’ perception

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Authors: Novee Lor C. Leyso, Maylin C. Palatino

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Under-five malnutrition continues to persist in the Philippines, particularly in the island Province of Marinduque, with prevalence of some forms of malnutrition even worsening in recent years. Local spatial cluster detection provides a spatial perspective in understanding this phenomenon as key in analyzing patterns of geographic variation, identification of community-appropriate programs and interventions, and focused targeting on high-risk areas. Using data from a province-wide household-based census conducted in 2014–2016, this study aimed to determine and evaluate spatial clusters of under-five malnutrition, across the province and within each municipality at the individual level using household location. Malnutrition was defined as weight-for-age z-score that fall outside the 2 standard deviations from the median of the WHO reference population. The Kulldorff’s elliptical spatial scan statistic in binomial model was used to locate clusters with high-risk of malnutrition, while adjusting for age and membership to government conditional cash transfer program as proxy for socio-economic status. One large significant cluster of under-five malnutrition was found southwest of the province, in which living in these areas at least doubles the risk of malnutrition. Additionally, at least one significant cluster were identified within each municipality—mostly located along the coastal areas. All these indicate apparent geographical variations across and within municipalities in the province. There were also similarities and disparities in the patterns of risk of malnutrition in each cluster across municipalities, and even within municipality, suggesting underlying causes at work that warrants further investigation. Therefore, community-appropriate programs and interventions should be identified and should be focused on high-risk areas to maximize limited government resources. Further studies are also recommended to determine factors affecting variations in childhood malnutrition considering the evidence of spatial clustering found in this study.

Keywords: Binomial model, Kulldorff’s elliptical spatial scan statistic, Philippines, under-five malnutrition

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Authors: Lamia Emam

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The attractiveness of entrepreneurship education stems from its perceived value as a venue through which students can develop an entrepreneurial mindset, skill set, and practice, which may not necessarily lead to them starting a new business, but could, more importantly, be manifested as a life skill that could be applied to all types of organizations and career endeavors. This, in turn, raises important questions about what happens in our classrooms; our role as educators, the role of students, center of learning, and the instructional approach; all of which eventually contribute to achieving the desired EE outcomes. With application to an undergraduate entrepreneurship course -Entrepreneurship as Practice- the current paper aims to explore the effect of entrepreneurship education on the development of students’ general entrepreneurial tendencies. Towards that purpose, the researcher herein uses a pre-test and post-test quasi-experimental research design where the Durham University General Enterprising Tendency Test (GET2) is administered to the same group of students before and after course delivery. As designed and delivered, the Entrepreneurship as Practice module is a highly applied and experiential course where students are required to develop an idea for a start-up while practicing the entrepreneurship-related knowledge, mindset, and skills that are taught in class, both individually and in groups. The course is delivered using a combination of short lectures, readings, group discussions, case analysis, guest speakers, and, more importantly, actively engaging in a series of activities that are inspired by diverse methods for developing successful and innovative business ideas, including design thinking, lean-start up and business feasibility analysis. The instructional approach of the course particularly aims at developing the students' critical thinking, reflective, analytical, and creativity-based problem-solving skills that are needed to launch one’s own start-up. The analysis and interpretation of the experiment’s outcomes shall simultaneously incorporate the views of both the educator and students. As presented, the study responds to the rising call for the application of experimental designs in entrepreneurship in general and EE in particular. While doing so, the paper presents an educator’s perspective of EE to complement the dominant stream of research which is constrained to the students’ point of view. Finally, the study sheds light on EE in the MENA region, where the study is applied.

Keywords: entrepreneurship education, andragogy and heutagogy, scholarship of teaching and learning, experiment

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Authors: Gebrehiwot H., Giorgis Bahita

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Firms in Africa experience less financial market in comparison to other emerging and developed countries, thus lagging behind the rest of the world in terms of innovation and growth. Though there are different factors to be considered, underdeveloped financial systems take the lion's share in hindering firm innovation and growth in Africa. Insufficient capacity to innovate is one of the problems facing African businesses. Moreover, a critical challenge faced by firms in Africa is access to finance and the inability of financially constrained firms to grow. Only little is known about how different sources of finance affect firm innovation and growth in Africa, specifically the formal and informal finance effect on firm innovation and growth. This study's aim is to address this gap by using formal and informal finance for working capital and fixed capital and its role in firm innovation and firm growth using firm-level data from the World Bank enterprise survey 2006-2019 with a total of 5661 sample firms from 14 countries based on available data on the selected variables. Additionally, this study examines factors for accessing credit from a formal financial institution. The logit model is used to examine the effect of finance on a firm’s innovation and factors to access formal finance, while the Ordinary List Square (OLS) regression mode is used to investigate the effect of finance on firm growth. 2SLS instrumental variables are used to address the possible endogeneity problem in firm growth and finance-innovation relationships. A result from the logistic regression indicates that both formal and informal finance used for working capital and investment in fixed capital was found to have a significant positive association with product and process innovation. In the case of finance and growth, finding show that positive association of both formal and informal financing to working capital and new investment in fixed capital though the informal has positive relations to firm growth as measured by sale growth but no significant association as measured by employment growth. Formal finance shows more magnitude of effect on innovation and growth when firms use formal finance to finance investment in fixed capital, while informal finance show less compared to formal finance and this confirms previous studies as informal is mainly used for working capital in underdeveloped economies like Africa. The factors that determine credit access: Age, firm size, managerial experience, exporting, gender, and foreign ownership are found to have significant determinant factors in accessing credit from formal and informal sources among the selected sample countries.

Keywords: formal finance, informal finance, innovation, growth

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Authors: Maad Kamal Al-Anni

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This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the  signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness  for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).

Keywords: Intrusion Detection System (IDS), Mobile Adhoc Networks (MANET), Back Propagation Algorithm (BPA), Neural Networks (NN)

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Authors: Dipo Dunsin, Mohamed C. Ghanem, Karim Ouazzane

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Digital investigators often have a hard time spotting evidence in digital information. It has become hard to determine which source of proof relates to a specific investigation. A growing concern is that the various processes, technology, and specific procedures used in the digital investigation are not keeping up with criminal developments. Therefore, criminals are taking advantage of these weaknesses to commit further crimes. In digital forensics investigations, artificial intelligence is invaluable in identifying crime. It has been observed that an algorithm based on artificial intelligence (AI) is highly effective in detecting risks, preventing criminal activity, and forecasting illegal activity. Providing objective data and conducting an assessment is the goal of digital forensics and digital investigation, which will assist in developing a plausible theory that can be presented as evidence in court. Researchers and other authorities have used the available data as evidence in court to convict a person. This research paper aims at developing a multiagent framework for digital investigations using specific intelligent software agents (ISA). The agents communicate to address particular tasks jointly and keep the same objectives in mind during each task. The rules and knowledge contained within each agent are dependent on the investigation type. A criminal investigation is classified quickly and efficiently using the case-based reasoning (CBR) technique. The MADIK is implemented using the Java Agent Development Framework and implemented using Eclipse, Postgres repository, and a rule engine for agent reasoning. The proposed framework was tested using the Lone Wolf image files and datasets. Experiments were conducted using various sets of ISA and VMs. There was a significant reduction in the time taken for the Hash Set Agent to execute. As a result of loading the agents, 5 percent of the time was lost, as the File Path Agent prescribed deleting 1,510, while the Timeline Agent found multiple executable files. In comparison, the integrity check carried out on the Lone Wolf image file using a digital forensic tool kit took approximately 48 minutes (2,880 ms), whereas the MADIK framework accomplished this in 16 minutes (960 ms). The framework is integrated with Python, allowing for further integration of other digital forensic tools, such as AccessData Forensic Toolkit (FTK), Wireshark, Volatility, and Scapy.

Keywords: artificial intelligence, computer science, criminal investigation, digital forensics

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Authors: Vahid Ebrahimipour

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Word representation and context meaning of text-based documents play an essential role in knowledge modeling. Business procedures written in natural language are meant to store technical and engineering information, management decision and operation experience during the production system life cycle. Context meaning representation is highly dependent upon word sense, lexical relativity, and sematic features of the argument. This paper proposes a method for lexical semantic analysis and context meaning representation of maintenance activity in a mass production system. Our approach constructs a straightforward lexical semantic approach to analyze facilitates semantic and syntactic features of context structure of maintenance report to facilitate translation, interpretation, and conversion of human-readable interpretation into computer-readable representation and understandable with less heterogeneity and ambiguity. The methodology will enable users to obtain a representation format that maximizes shareability and accessibility for multi-purpose usage. It provides a contextualized structure to obtain a generic context model that can be utilized during the system life cycle. At first, it employs a co-occurrence-based clustering framework to recognize a group of highly frequent contextual features that correspond to a maintenance report text. Then the keywords are identified for syntactic and semantic extraction analysis. The analysis exercises causality-driven logic of keywords’ senses to divulge the structural and meaning dependency relationships between the words in a context. The output is a word contextualized representation of maintenance activity accommodating computer-based representation and inference using OWL/RDF.

Keywords: lexical semantic analysis, metadata modeling, contextual meaning extraction, ontology modeling, knowledge representation

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Authors: Hediyeh Talebi, Shokoofeh Ghiam, Changiz Eslahchi

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Traditionally, Alzheimer’s disease research has focused on genes with significant fold changes, potentially neglecting subtle but biologically important alterations. Our study introduces an integrative approach that highlights genes crucial to underlying biological processes, regardless of their fold change magnitude. Alzheimer's Single-cell RNA-seq data related to the peripheral blood mononuclear cells (PBMC) was extracted from the Gene Expression Omnibus (GEO). After quality control, normalization, scaling, batch effect correction, and clustering, differentially expressed genes (DEGs) were identified with adjusted p-values less than 0.05. These DEGs were categorized based on cell-type, resulting in four datasets, each corresponding to a distinct cell type. To distinguish between cells from healthy individuals and those with Alzheimer's, an adversarial autoencoder with a classifier was employed. This allowed for the separation of healthy and diseased samples. To identify the most influential genes in this classification, the weight matrices in the network, which includes the encoder and classifier components, were multiplied, and focused on the top 20 genes. The analysis revealed that while some of these genes exhibit a high fold change, others do not. These genes, which may be overlooked by previous methods due to their low fold change, were shown to be significant in our study. The findings highlight the critical role of genes with subtle alterations in diagnosing Alzheimer's disease, a facet frequently overlooked by conventional methods. These genes demonstrate remarkable discriminatory power, underscoring the need to integrate biological relevance with statistical measures in gene prioritization. This integrative approach enhances our understanding of the molecular mechanisms in Alzheimer’s disease and provides a promising direction for identifying potential therapeutic targets.

Keywords: alzheimer's disease, single-cell RNA-seq, neural networks, blood biomarkers

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Authors: Yu-Wen Huang, Yi-Cheng Chiang

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With urban development, lots of buildings are built around the city. The buildings always affect the urban wind environment. The accelerative situation of wind caused of buildings often makes pedestrians uncomfortable, even causes the accidents and dangers. Factors influencing pedestrian level wind including atmospheric boundary layer, wind direction, wind velocity, planting, building volume, geometric shape of the buildings and adjacent interference effects, etc. Planting has many functions including scraping and slowing urban heat island effect, creating a good visual landscape, increasing urban green area and improve pedestrian level wind. On the other hand, urban square is an important space element supporting the entrance to buildings, city landmarks, and activity collections, etc. The appropriateness of urban square environment usually dominates its success. This research focuses on the effect of tree-planting on the wind environment of urban square. This research studied the square belt of Taichung City Hall. Taichung City Hall is a cuboid building with a large mass opening. The square belt connects the front square, the central opening and the back square. There is often wind draft on the square belt. This phenomenon decreases the activities on the squares. This research applies tree-planting to improve the wind environment and evaluate the effects of two types of planting configuration. The Computational Fluid Dynamics (CFD) simulation analysis and extensive field measurements are applied to explore the improve efficiency of planting configuration on wind environment. This research compares efficiencies of different kinds of planting configuration, including the clustering array configuration and the dispersion, and evaluates the efficiencies by the SET*.

Keywords: micro-climate, wind environment, planting configuration, comfortableness, computational fluid dynamics (CFD)

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Authors: Andre Sanches Siqueira Campos

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Central banks can play a significant role in promoting regional economic and monetary integration by strengthening the payment and settlement systems. However, close coordination and cooperation require facilitating the implementation of reforms at domestic and cross-border levels in order to benchmark with international standards and commitments to the liberal order. This situation reflects the normative power of the regulatory globalization dimension of strong states, which may drive or constrain regional integration. In the MERCOSUR and SAARC regions, central banks have set financial initiatives that could facilitate South America and South Asia regions to move towards convergence integration and facilitate trade and investments connectivities. This is qualitative method research based on a combination of the Process-Tracing method with Qualitative Comparative Analysis (QCA). This research approaches multiple forms of data based on central banks, regional organisations, national governments, and financial institutions supported by existing literature. The aim of this research is to analyze the decision-making process of the Central Bank of Brazil (BCB) and the Reserve Bank of India (RBI) towards regional financial cooperation by identifying connectivity instruments that foster, gridlock, or redefine cooperation. The BCB and The RBI manage the monetary policy of the largest economies of those regions, which makes regional cooperation a relevant framework to understand how they provide an effective institutional arrangement for regional organisations to achieve some of their key policies and economic objectives. The preliminary conclusion is that both BCB and RBI demonstrate a reluctance to deepen regional cooperation because of the existing economic, political, and institutional asymmetries. Deepening regional cooperation is constrained by the interests of central banks in protecting their economies from risks of instability due to different degrees of development between countries in their regions and international financial crises that have impacted the international system in the 21st century. Reluctant regional integration also provides autonomy for national development and political ground for the contestation of Global Financial Governance by Brazil and India.

Keywords: Brazil, central banks, decision-making process, global financial governance, India, MERCOSUR, connectivity, payment system, regional cooperation, SAARC

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Authors: N. Casado-Sanz, B. Guirao

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The population aged 65 and over is projected to double in the coming decades. Due to this increase, driver population is expected to grow and in the near future, all countries will be faced with population aging of varying intensity and in unique time frames. This is the greatest challenge facing industrialized nations and due to this fact, the study of the relationships of dependency between population aging and road safety is becoming increasingly relevant. Although the deterioration of driving skills in the elderly has been analyzed in depth, to our knowledge few research studies have focused on the road infrastructure and the mobility of this particular group of users. In Spain, crosstown roads have one of the highest fatality rates. These rural routes have a higher percentage of elderly people who are more dependent on driving due to the absence or limitations of urban public transportation. Analysing road safety in these routes is very complex because of the variety of the features, the dispersion of the data and the complete lack of related literature. The objective of this paper is to identify key factors that cause traffic accidents. The individuals under study were the accidents with killed or seriously injured in Spanish crosstown roads during the period 2006-2015. Latent cluster analysis was applied as a preliminary tool for segmentation of accidents, considering population aging as the main input among other socioeconomic indicators. Subsequently, a linear regression analysis was carried out to estimate the degree of dependence between the accident rate and the variables that define each group. The results show that segmenting the data is very interesting and provides further information. Additionally, the results revealed the clear influence of the aging variable in the clusters obtained. Other variables related to infrastructure and mobility levels, such as the crosstown roads layout and the traffic intensity aimed to be one of the key factors in the causality of road accidents.

Keywords: cluster analysis, population ageing, rural roads, road safety

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Authors: Umut Yıldız, Berkin Kurtuluş, Yunus Emre Muslubaş

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Thrust vectoring, especially in military aviation, is a concept that sees much use to improve maneuverability in already agile aircraft. As this concept is fairly new and cost intensive to design and test, computational methods are useful in easing the preliminary design process. Computational Fluid Dynamics (CFD) can be utilized in many forms to simulate nozzle flow, and there exist various CFD studies in both 2D mechanical and 3D injection based thrust vectoring, and yet, 3D mechanical thrust vectoring analyses, at this point in time, are lacking variety. Additionally, the freely available test data is constrained to limited pitch angles and geometries. In this study, based on a test case provided by NASA, both steady and unsteady 3D CFD simulations are conducted to examine the aerodynamic performance of a mechanical thrust vectoring nozzle model and to validate the utilized numerical model. Steady analyses are performed to verify the flow characteristics of the nozzle at pitch angles of 0, 10 and 20 degrees, and the results are compared with experimental data. It is observed that the pressure data obtained on the inner surface of the nozzle at each specified pitch angle and under different flow conditions with pressure ratios of 1.5, 2 and 4, as well as at azimuthal angle of 0, 45, 90, 135, and 180 degrees exhibited a high level of agreement with the corresponding experimental results. To validate the CFD model, the insights from the steady analyses are utilized, followed by unsteady analyses covering a wide range of pitch angles from 0 to 20 degrees. Throughout the simulations, a mesh morphing method using a carefully calculated mathematical shape deformation model that simulates the vectored nozzle shape exactly at each point of its travel is employed to dynamically alter the divergent part of the nozzle over time within this pitch angle range. The mesh morphing based vectored nozzle shapes were compared with the drawings provided by NASA, ensuring a complete match was achieved. This computational approach allowed for the creation of a comprehensive database of results without the need to generate separate solution domains. The database contains results at every 0.01° increment of nozzle pitch angle. The unsteady analyses, generated using the morphing method, are found to be in excellent agreement with experimental data, further confirming the accuracy of the CFD model.

Keywords: thrust vectoring, computational fluid dynamics, 3d mesh morphing, mathematical shape deformation model

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Authors: Cristian Matti, Valerie Fowles, Eva Enyedi, Piotr Pogorzelski

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The system mapping process can be defined as a knowledge service where a team of facilitators, experts and practitioners facilitate a guided conversation, enable the exchange of information and support an iterative curation process. System mapping processes rely on science-based tools to introduce and simplify a variety of components and concepts of socio-technical systems through metaphors while facilitating an interactive dialogue process to enable the design of co-created maps. System maps work then as “artifacts” to provide information and focus the conversation into specific areas around the defined challenge and related decision-making process. Knowledge management facilitates the curation of that data gathered during the system mapping sessions through practices of documentation and subsequent knowledge co-production for which common practices from data science are applied to identify new patterns, hidden insights, recurrent loops and unexpected elements. This study presents empirical evidence on the application of these techniques to explore mechanisms by which visual tools provide guiding principles to portray system components, key variables and types of data through the lens of climate change. In addition, data science facilitates the structuring of elements that allow the analysis of layers of information through affinity and clustering analysis and, therefore, develop simple indicators for supporting the decision-making process. This paper addresses methodological and empirical elements on the horizontal learning process that integrate system mapping through visual tools, interpretation, cognitive transformation and analysis. The process is designed to introduce practitioners to simple iterative and inclusive processes that create actionable knowledge and enable a shared understanding of the system in which they are embedded.

Keywords: indicators, knowledge management, system mapping, visual tools

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Authors: Yanping Liao, Zenan Wu, Ruigang Zhao

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Frequency diverse array (FDA) beamforming is a technology developed in recent years, and its antenna pattern has a unique angle-distance-dependent characteristic. However, the beam is always required to have strong concentration, high resolution and low sidelobe level to form the point-to-point interference in the concentrated set. In order to eliminate the angle-distance coupling of the traditional FDA and to make the beam energy more concentrated, this paper adopts a multi-carrier FDA structure based on proposed power exponential frequency offset to improve the array structure and frequency offset of the traditional FDA. The simulation results show that the beam pattern of the array can form a dot-shape beam with more concentrated energy, and its resolution and sidelobe level performance are improved. However, the covariance matrix of the signal in the traditional adaptive beamforming algorithm is estimated by the finite-time snapshot data. When the number of snapshots is limited, the algorithm has an underestimation problem, which leads to the estimation error of the covariance matrix to cause beam distortion, so that the output pattern cannot form a dot-shape beam. And it also has main lobe deviation and high sidelobe level problems in the case of limited snapshot. Aiming at these problems, an adaptive beamforming technique based on exponential correction for multi-carrier FDA is proposed to improve beamforming robustness. The steps are as follows: first, the beamforming of the multi-carrier FDA is formed under linear constrained minimum variance (LCMV) criteria. Then the eigenvalue decomposition of the covariance matrix is ​​performed to obtain the diagonal matrix composed of the interference subspace, the noise subspace and the corresponding eigenvalues. Finally, the correction index is introduced to exponentially correct the small eigenvalues ​​of the noise subspace, improve the divergence of small eigenvalues ​​in the noise subspace, and improve the performance of beamforming. The theoretical analysis and simulation results show that the proposed algorithm can make the multi-carrier FDA form a dot-shape beam at limited snapshots, reduce the sidelobe level, improve the robustness of beamforming, and have better performance.

Keywords: adaptive beamforming, correction index, limited snapshot, multi-carrier frequency diverse array, robust

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Authors: Adel Pasztor

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International doctoral mobility is a largely under-researched component of academic mobility and migration. This is in stark contrast to the case of student mobility where much research has been undertaken on Erasmus students; or the growing research on academic staff mobility which can be viewed as a key part of highly skilled migration. The aim of this paper is to remedy the situation by specifically focusing on international doctoral students studying at elite higher education institutions in the United Kingdom. In doing so, in-depth qualitative interviews with doctoral students and recent graduates were carried out in order to identify the signifiers of an internationally mobile doctoral student and unpack the decision-making processes leading onto the choice of higher education institution abroad. Overall, a diverse range of degree subjects from within the humanities and the social sciences were covered with a relatively large spread of nationalities which include the following countries: Italy, Germany, Hungary, Latvia, Bulgaria, Turkey, Lebanon, Israel, Australia, USA, China, and Chile. The interview questions were designed to probe the motivations, choices, educational trajectories and career plans of international doctoral students relative to their social class background, gender, nationality or funding. It was clear from the interviews that there were two main types of international doctoral students: those who ‘did not think anything else was ever a serious possibility’, contrasted with the other, more opportune type, to whom ‘it happened to be a PhD’. There were marked differences between the two types since initial access to university, mainly because educational decisions such as the doctorate do not happen in a vacuum, rather are built on the individual’s higher education aspirations and previous educational choices. The results were in line with existing literature suggesting that those with higher educated parents and from schools strongly supporting the choice process fared better as they were able to make well informed, well thought through as well as strategic decisions for their future involving the very best universities within the national boundaries. Being ‘at the right place’ often meant access to prestigious doctoral scholarships thus, the route of the PhD has been chosen even if it did not necessarily enhance career opportunities. At the same time, the initial higher education choices of those with limited capital were played out locally, although they did aim for the best universities within their geographically constrained landscape of choice. Here, the majority of students referred to some ‘turning points’ in their lives which lead them towards considering international doctoral opportunities but essentially their proactive, do-it-yourself attitude was behind the life-changing educational opportunities.

Keywords: choice, doctoral students, international mobility, PhD, UK

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Authors: Zimu Li, Zheng Wan

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The Suez Canal plays an important role in global shipping networks and is one of the most frequently used waterways in the world. The 2021 canal obstruction by ship Ever Given in March 2021, however, completed blocked the Suez Canal for a week and caused significant disruption to world trade. Therefore, it is very important to quantitatively analyze the impact of the accident on the robustness of the global shipping network. However, the current research on maritime transportation networks is usually limited to local or small-scale networks in a certain region. Based on the complex network theory, this study establishes a global shipping complex network covering 2713 nodes and 137830 edges by using the real trajectory data of the global marine transport ship automatic identification system in 2018. At the same time, two attack modes, deliberate (Suez Canal Blocking) and random, are defined to calculate the changes in network node degree, eccentricity, clustering coefficient, network density, network isolated nodes, betweenness centrality, and closeness centrality under the two attack modes, and quantitatively analyze the actual impact of Suez Canal Blocking on the robustness of global shipping network. The results of the network robustness analysis show that Suez Canal blocking was more destructive to the shipping network than random attacks of the same scale. The network connectivity and accessibility decreased significantly, and the decline decreased with the distance between the port and the canal, showing the phenomenon of distance attenuation. This study further analyzes the impact of the blocking of the Suez Canal on Chinese ports and finds that the blocking of the Suez Canal significantly interferes withChina's shipping network and seriously affects China's normal trade activities. Finally, the impact of the global supply chain is analyzed, and it is found that blocking the canal will seriously damage the normal operation of the global supply chain.

Keywords: global shipping networks, ship AIS trajectory data, main channel, complex network, eigenvalue change

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Authors: Nsikan Affiah, Kayode Osungbade, Williams Uzoma

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Malaria remains one of the major killers of humans worldwide, threatening the lives of more than one-third of the world’s population. Some people refers it to; a disease of poverty because it contributes towards national poverty through its impact on foreign direct investment, tourism, labour productivity, and trade. At the micro level, it may cause poverty through spending on health care, income losses, and premature deaths. Unfortunately, malaria is a disease that affects both low-income household and its high-income counterpart, but low-income households are still at greater risk because significant part of the available monthly income is dedicated to various preventive and treatment measures. The objective of this study is to estimate direct and indirect cost of malaria treatment in households in a section of South-South Region (Akwa Ibom State) of Nigeria. A cross-sectional study of Six Hundred and Forty (640) heads of households or any adult representative of households in three local government areas of Akwa Ibom State, Nigeria from May 1-31, 2015 were ascertained through interviewer-administered questionnaire adapted from Nigerian Malaria Indicator Survey Report. The clustering technique was used to select 640 households with the help of Primary Health Care (PHC) house numbering system. Using exchange rate of 197 Naira/USD, result shows that direct cost of malaria treatment was 8,894.44 USD while the indirect cost of malaria treatment was 11,012.81 USD. Total cost of treatment made up of 44.7% direct cost and 55.3% indirect cost, with average direct cost of malaria treatment per household estimated at 20.6 USD and the average indirect cost of treatment per household estimated at 25.1 USD. Average total cost for each episode (888) of malaria was estimated at 22.4 USD. While at household level, the average total cost was estimated at 45.5 USD. From the average total cost, low-income households would spend 36% of monthly household income on treating malaria and the impact could be said to be catastrophic, compared to high-income households where only 1.2% of monthly household income is spent on malaria treatment. It could be concluded that the cost of malaria treatment is well beyond the means of households and given the reality of repeated bouts of malaria and its contribution to the impoverishment of households, there is a need for urgent action.

Keywords: direct cost, indirect cost, low income households, malaria

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Authors: Faheem Shahzad Baloch, Muhammad Azhar Nadeem, Muhammad Amjad Nawaz, Ephrem Habyarimana, Gonul Comertpay, Tolga Karakoy, Rustu Hatipoglu, Mehmet Zahit Yeken, Vahdettin Ciftci

Abstract:

Turkey is considered a bridge between Europe, Asia, and Africa and possibly played an important role in the distribution of many crops including common bean. Hundreds of common bean landraces can be found in Turkey, particularly in farmers’ fields, and they consistently contribute to the overall production. To investigate the existing genetic diversity and hybridization events between the Andean and Mesoamerican gene pools in the Turkish common bean, 188 common bean accessions (182 landraces and 6 modern cultivars as controls) were collected from 19 different Turkish geographic regions. These accessions were characterized using phenotypic data (growth habit and seed weight), geographic provenance, 12557 high-quality whole-genome DArTseq markers, and 3767 novel DArTseq loci were also identified. The clustering algorithms resolved the Turkish common bean landrace germplasm into the two recognized gene pools, the Mesoamerican and Andean gene pools. Hybridization events were observed in both gene pools (14.36% of the accessions) but mostly in the Mesoamerican (7.97% of the accessions), and was low relative to previous European studies. The lower level of hybridization witnessed the existence of Turkish common bean germplasm in its original form as compared to Europe. Mesoamerican gene pool reflected a higher level of diversity, while the Andean gene pool was predominant (56.91% of the accessions), but genetically less diverse and phenotypically more pure, reflecting farmers greater preference for the Andean gene pool. We also found some genetically distinct landraces and overall, a meaningful level of genetic variability which can be used by the scientific community in breeding efforts to develop superior common bean strains.

Keywords: bean germplasm, DArTseq markers, genotyping by sequencing, Turkey, whole genome diversity

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Authors: Miao Li, Mengyuan Xu

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As a low-carbon travel mode, bicycling has drawn increasing political interest in the contemporary Chinese urban context, and the public sharing bikes have become the most popular ways of bike usage in China now. This research aims to explore the spatial-temporal relationship between sharing bike usage and different characteristics of the urban street built environment. In the research, street segments were used as the analytic unit of the street built environment defined by street intersections. The sharing bike usage data in the research include a total of 2.64 million samples that are the entire sharing bike distribution data recorded in two days in 2018 within a neighborhood of 185.4 hectares in the city of Wuhan, China. And these data are assigned to the 97 urban street segments in this area based on their geographic location. The built environment variables used in this research are categorized into three sections: 1) street design characteristics, such as street width, street greenery, types of bicycle lanes; 2) condition of other public transportation, such as the availability of metro station; 3) Street function characteristics that are described by the categories and density of the point of interest (POI) along the segments. Spatial Lag Models (SLM) were used in order to reveal the relationships of specific urban streets built environment characteristics and the likelihood of sharing bicycling usage in whole and different periods a day. The results show: 1) there is spatial autocorrelation among sharing bicycling usage of urban streets in case area in general, non-working day, working day and each period of a day, which presents a clustering pattern in the street space; 2) a statistically strong association between bike sharing usage and several different built environment characteristics such as POI density, types of bicycle lanes and street width; 3) the pattern that bike sharing usage is influenced by built environment characteristics depends on the period within a day. These findings could be useful for policymakers and urban designers to better understand the factors affecting bike sharing system and thus propose guidance and strategy for urban street planning and design in order to promote the use of sharing bikes.

Keywords: big data, sharing bike usage, spatial statistics, urban street built environment

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Authors: Chukwuka Justus Iwegbu

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Background: A major concern facing the world is providing senior citizens, individuals with disabilities, and other vulnerable groups with high-quality care. This issue is more serious in Nigeria's North Eastern area, where the burden of disease and disability is heavy, and access to care is constrained. This study aims to fill this gap by exploring the roles, challenges and support needs of caregivers, care home management, funding and administration in challenged communities in North Eastern Nigeria. The study will also provide a comprehensive understanding of the current situation and identify opportunities for improving the quality of care and support for caregivers and care recipients in these communities. Methods: A mixed-methods design, including both quantitative and qualitative data collection methods, will be used, and it will be guided by the stress process model of caregiving. The qualitative stage approach will comprise a survey, In-depth interviews, observations, and focus group discussion and the quantitative analysis will be used in order to comprehend the variations between caregiver's roles and care home management. A review of relevant documents, such as care home policies and funding reports, would be used to gather quantitative data on the administrative and financial aspects of care. The data collected will be analyzed using both descriptive statistics and thematic analysis. A sample size of around 200-300 participants, including caregivers, care recipients, care home managers and administrators, policymakers and health care providers, would be recruited. Findings: The study revealed that caregivers in challenged communities in North Eastern Nigeria face significant challenges, including lack of training and support, limited access to funding and resources, and high levels of burnout. Care home management and administration were also found to be inadequate, with a lack of clear policies and procedures and limited oversight and accountability. Conclusion: There is a need for increased investment in training and support for caregivers, as well as a need for improved care home management and administration in challenged communities in North Eastern Nigeria. It also highlights the importance of involving community members in decision-making and planning processes related to care homes and services. The study would contribute to the existing body of knowledge by providing a detailed understanding of the challenges faced by caregivers, care home managers and administrators.

Keywords: caregivers, care home management, funding, administration, challenge communities, North Eastern Nigeria

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Authors: Asif Zaman, Moinul Islam Zaber, Amin Ahsan Ali

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In recent years of industrialization, the South Asian countries are being affected by air pollution due to a severe increase in fine particulate matter 2.5 (PM₂.₅). Among them, Bangladesh is one of the most polluting countries. In this paper, statistical analyses were conducted on the time series of PM₂.₅ from various districts in Bangladesh, mostly around Dhaka city. Research has been conducted on the dynamic interactions and relationships between PM₂.₅ concentrations in different zones. The study is conducted toward understanding the characteristics of PM₂.₅, such as spatial-temporal characterization, correlation of other contributors behind air pollution such as human activities, driving factors and environmental casualties. Clustering on the data gave an insight on the districts groups based on their AQI frequency as representative districts. Seasonality analysis on hourly and monthly frequency found higher concentration of fine particles in nighttime and winter season, respectively. Cross correlation analysis discovered a phenomenon of correlations among cities based on time-lagged series of air particle readings and visualization framework is developed for observing interaction in PM₂.₅ concentrations between cities. Significant time-lagged correlations were discovered between the PM₂.₅ time series in different city groups throughout the country by cross correlation analysis. Additionally, seasonal heatmaps depict that the pooled series correlations are less significant in warmer months, and among cities of greater geographic distance as well as time lag magnitude and direction of the best shifted correlated particulate matter time series among districts change seasonally. The geographic map visualization demonstrates spatial behaviour of air pollution among districts around Dhaka city and the significant effect of wind direction as the vital actor on correlated shifted time series. The visualization framework has multipurpose usage from gathering insight of general and seasonal air quality of Bangladesh to determining the pathway of regional transportation of air pollution.

Keywords: air quality, particles, cross correlation, seasonality

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Authors: Sa’adu Mafara Abubakar, Muhammad Nuraddeen Danjuma, Adewole Tomiwa Adetunji, Richard Mundembe, Salisu Mohammed, Francis Bayo Lewu, Joseph I. Kiok

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Pearl millet is the most drought tolerant of all domesticated cereals, is cultivated extensively to feed millions of people who mainly live in hash agroclimatic zones. It serves as a major source of food for more than 40 million smallholder farmers living in the marginal agricultural lands of Northern Nigeria. Pearl millet grain is more nutritious than other cereals like maize, is also a principal source of energy, protein, vitamins, and minerals for millions of poorest people in the regions where it is cultivated. Pearl millet has recorded relatively little research attention compared with other crops and no sufficient work has analyzed its genetic diversity in north-western Nigeria. Therefore, this study was undertaken with the objectives to analyze the genetic diversity of pearl millet accessions using SSR marker and to analyze the extent of evolutionary relationship among pearl millet accessions at the molecular level. The result of the present study confirmed diversity among accessions of pearl millet in the study area. Simple Sequence Repeats (SSR) markers were used for genetic analysis and evolutionary relationship of the accessions of pearl millet. To analyze the level of genetic diversity, 8 polymorphic SSR markers were used to screen 69 accessions collected based on three maturity periods. SSR markers result reveal relationships among the accessions in terms of genetic similarities, evolutionary and ancestral origin, it also reveals a total of 53 alleles recorded with 8 microsatellites and an average of 6.875 per microsatellite, the range was from 3 to 9 alleles in PSMP2248 and PSMP2080 respectively. Moreover, both the factorial analysis and the dendrogram of phylogeny tree grouping patterns and cluster analysis were almost in agreement with each other that diversity is not clustering according to geographical patterns but, according to similarity, the result showed maximum similarity among clusters with few numbers of accessions. It has been recommended that other molecular markers should be tested in the same study area.

Keywords: pearl millet, genetic diversity, simple sequence repeat (SSR)

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Authors: Violet Justine Mtonga, Cecilia Diaz

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this paper explores the prevalence of loyalty programs in the UK gambling industry and their association with unintended consequences and harm amongst program members. The use of loyalty programs within the UK gambling industry has risen significantly with over 40 million cards in circulation. Some research suggests that as of 2013-2014, nearly 95% of UK consumers have at least one loyalty card with 78% being members of two or more programs, and the average household possesses ‘22 loyalty programs’, nearly half of which tend to be used actively. The core design of loyalty programs is to create a relational ‘win-win’ approach where value is jointly created between the parties involved through repetitive engagement. However, main concern about the diffusion of gambling organisations’ loyalty programs amongst consumers, might be the use by the organisations within the gambling industry to over influence customer engagement and potentially cause unintended harm. To help understand the complex phenomena of the diffusions and adaptation of the use of loyalty programs in the gambling industry, and the potential unintended outcomes, this study is theoretically underpinned by the social exchange theory of relationships entrenched in the processes of social exchanges of resources, rewards, and costs for long-term interactions and mutual benefits. Qualitative data were collected via in-depth interviews from 14 customers and 12 employees within the UK land-based gambling firms. Data were analysed using a combination of thematic and clustering analysis to help reveal and discover the emerging themes regarding the use of loyalty cards for gambling companies and exploration of subgroups within the sample. The study’s results indicate that there are different unintended consequences and harm of loyalty program engagement and usage such as maladaptive gambling behaviours, risk of compulsiveness, and loyalty programs promoting gambling from home. Furthermore, there is a strong indication of a rite of passage among loyalty program members. There is also strong evidence to support other unfavorable behaviors such as amplified gambling habits and risk-taking practices. Additionally, in pursuit of rewards, loyalty program incentives effectuate overconsumption and heighten expenditure. Overall, the primary findings of this study show that loyalty programs in the gambling industry should be designed with an ethical perspective and practice.

Keywords: gambling, loyalty programs, social exchange theory, unintended harm

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Authors: Hsin Lee, Hsuan Lee

Abstract:

Legal artificial intelligence (LegalAI) has been increasing applications within legal systems, propelled by advancements in natural language processing (NLP). Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. Most existing language models have difficulty understanding the long-distance dependencies between different structures. Another unique challenge is that while the Judiciary of Taiwan has released legal judgments from various levels of courts over the years, there remains a significant obstacle in the lack of labeled datasets. This deficiency makes it difficult to train models with strong generalization capabilities, as well as accurately evaluate model performance. To date, models in Taiwan have yet to be specifically trained on judgment data. Given these challenges, this research proposes a Longformer-based pre-trained language model explicitly devised for retrieving similar judgments in Taiwanese legal documents. This model is trained on a self-constructed dataset, which this research has independently labeled to measure judgment similarities, thereby addressing a void left by the lack of an existing labeled dataset for Taiwanese judgments. This research adopts strategies such as early stopping and gradient clipping to prevent overfitting and manage gradient explosion, respectively, thereby enhancing the model's performance. The model in this research is evaluated using both the dataset and the Average Entropy of Offense-charged Clustering (AEOC) metric, which utilizes the notion of similar case scenarios within the same type of legal cases. Our experimental results illustrate our model's significant advancements in handling similarity comparisons within extensive legal judgments. By enabling more efficient retrieval and analysis of legal case documents, our model holds the potential to facilitate legal research, aid legal decision-making, and contribute to the further development of LegalAI in Taiwan.

Keywords: legal artificial intelligence, computation and language, language model, Taiwanese legal cases

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Authors: Tianqi Wu, Min Wang

Abstract:

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

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

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: Elisavet Anastasiadou

Abstract:

Global citizenship refers to an economic, social, political, and cultural interconnectedness, and it is inextricably intertwined with social justice, respect for human rights, peace, and a sense of responsibility to act on a local and global level. It aims to be transformative, enhance critical thinking and participation with pedagogical approaches based on social justice and democracy. The purpose of this study is to explore how Global Citizenship Education (GCE) is presented and implemented in two educational contexts, specifically in the curricula and pedagogical practices of primary education in Finland and Scotland. The impact of GCE is recognized as means for further development by institution such as and Finnish and Scottish curricula acknowledge the significance of GCE, emphasizing the student's ability to act and succeed in diverse and global communities. This comparative study should provide a good basis for further developing teaching practices based on informed understanding of how GCE is constrained or enabled from two different perspectives, extend the methodological applications of Practice Architectures and provide critical insights into GCE as a theoretical notion adopted by national and international educational policy. The study is directly connected with global citizenship aiming at future and societal change. The empirical work employs a multiple case study approach, including interviews and analysis of existing documents (textbook, curriculum). The data consists of the Finnish and Scottish curriculum. A systematic analysis of the curriculum in relation to GCE will offer insights into how the aims of GCE are presented and framed within the two contexts. This will be achieved using the theory of Practice Architectures. Curricula are official policy documentations (texts) that frame and envisage pedagogical practices. Practices, according to the theory of practice architectures, consist of sayings, doings, and relatings. Hence, even if the text analysis includes the semantic space (sayings) that are prefigured by the cultural-discursive arrangements and the relating prefigured by the socio-political arrangements, they will inevitably reveal information on the (doings) prefigured by the material-economic arrangements, as they hang together in practices. The results will assist educators in making changes to their teaching and enhance their self-conscious understanding of the history-making significance of their practices. It will also have a potential reform and focus on educationally relevant to such issues. Thus, the study will be able to open the ground for interventions and further research while it will consider the societal demands of a world in change.

Keywords: citizenhsip, curriculum, democracy, practices

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Authors: Stefan Papastefanou

Abstract:

Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

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