Search results for: credit card fraud

Authors: Mojtaba Taheri, Saied Reza Ameli

Abstract:

In this research, we have calculated the effect of membership in international climate change treaties for 20 developed countries based on the human development index (HDI) and compared this effect with the process of pollutant reduction in the Environmental Kuznets Curve (EKC) theory. For this purpose, the data related to The real GDP per capita with 2010 constant prices is selected from the World Development Indicators (WDI) database. Ecological Footprint (ECOFP) is the amount of biologically productive land needed to meet human needs and absorb carbon dioxide emissions. It is measured in global hectares (gha), and the data retrieved from the Global Ecological Footprint (2021) database will be used, and we will proceed by examining step by step and performing several series of targeted statistical regressions. We will examine the effects of different control variables, including Energy Consumption Structure (ECS) will be counted as the share of fossil fuel consumption in total energy consumption and will be extracted from The United States Energy Information Administration (EIA) (2021) database. Energy Production (EP) refers to the total production of primary energy by all energy-producing enterprises in one country at a specific time. It is a comprehensive indicator that shows the capacity of energy production in the country, and the data for its 2021 version, like the Energy Consumption Structure, is obtained from (EIA). Financial development (FND) is defined as the ratio of private credit to GDP, and to some extent based on the stock market value, also as a ratio to GDP, and is taken from the (WDI) 2021 version. Trade Openness (TRD) is the sum of exports and imports of goods and services measured as a share of GDP, and we use the (WDI) data (2021) version. Urbanization (URB) is defined as the share of the urban population in the total population, and for this data, we used the (WDI) data source (2021) version. The descriptive statistics of all the investigated variables are presented in the results section. Related to the theories of sustainable development, Environmental Kuznets Curve (EKC) is more significant in the period of study. In this research, we use more than fourteen targeted statistical regressions to purify the net effects of each of the approaches and examine the results.

Keywords: climate change, globalization, environmental economics, sustainable development, international climate treaty

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Authors: Leigh Ann Wilson, Melanie Borrego

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The purpose of institutional learning outcomes (ILOs) is to assess what students across the university know and what they do not. The issue is gathering this information in a systematic and usable way. This presentation will explain how one institution has engineered this process for both student success and maximum faculty curriculum and course design input. At Brandman University, there are three levels of learning outcomes: course, program, and institutional. Institutional Learning Outcomes (ILOs) are mapped to specific courses. Faculty course developers write the signature assignments (SAs) in alignment with the Institutional Learning Outcomes for each course. These SAs use a specific rubric that is applied consistently by every section and every instructor. Each year, the 12-member General Education Team (GET), as a part of their work, conducts the calibration and assessment of the university-wide SAs and the related rubrics for one or two of the five ILOs. GET members, who are senior faculty and administrators who represent each of the university's schools, lead the calibration meetings. Specifically, calibration is a process designed to ensure the accuracy and reliability of evaluating signature assignments by working with peer faculty to interpret rubrics and compare scoring. These calibration meetings include the full time and adjunct faculty members who teach the course to ensure consensus on the application of the rubric. Each calibration session is chaired by a GET representative as well as the course custodian/contact where the ILO signature assignment resides. The overall calibration process GET follows includes multiple steps, such as: contacting and inviting relevant faculty members to participate; organizing and hosting calibration sessions; and reviewing and discussing at least 10 samples of student work from class sections during the previous academic year, for each applicable signature assignment. Conversely, the commitment for calibration teams consist of attending two virtual meetings lasting up to three hours in duration. The first meeting focuses on interpreting the rubric, and the second meeting involves comparing scores for sample work and sharing feedback about the rubric and assignment. Next, participants are expected to follow all directions provided and participate actively, and respond to scheduling requests and other emails within 72 hours. The virtual meetings are recorded for future institutional use. Adjunct faculty are paid a small stipend after participating in both calibration meetings. Full time faculty can use this work on their annual faculty report for "internal service" credit.

Keywords: assessment, assurance of learning, course design, institutional learning outcomes, rubrics, signature assignments

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Authors: Tefera Kebede Leyu

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The purpose of this study was to estimate the impact of LIVES project participation on the level of technical efficiency of farm households in three regions of Ethiopia. We used household-level data gathered by IRLI between February and April 2014 for the year 2013(retroactive). Data on 1,905 (754 intervention and 1, 151 control groups) sample households were analyzed using STATA software package version 14. Efforts were made to combine stochastic frontier modeling with impact evaluation methodology using the Heckman (1979) two-stage model to deal with possible selectivity bias arising from unobservable characteristics in the stochastic frontier model. Results indicate that farmers in the two groups are not efficient and operate below their potential frontiers i.e., there is a potential to increase crop productivity through efficiency improvements in both groups. In addition, the empirical results revealed selection bias in both groups of farmers confirming the justification for the use of selection bias corrected stochastic frontier model. It was also found that intervention farmers achieved higher technical efficiency scores than the control group of farmers. Furthermore, the selectivity bias-corrected model showed a different technical efficiency score for the intervention farmers while it more or less remained the same for that of control group farmers. However, the control group of farmers shows a higher dispersion as measured by the coefficient of variation compared to the intervention counterparts. Among the explanatory variables, the study found that farmer’s age (proxy to farm experience), land certification, frequency of visit to improved seed center, farmer’s education and row planting are important contributing factors for participation decisions and hence technical efficiency of farmers in the study areas. We recommend that policies targeting the design of development intervention programs in the agricultural sector focus more on providing farmers with on-farm visits by extension workers, provision of credit services, establishment of farmers’ training centers and adoption of modern farm technologies. Finally, we recommend further research to deal with this kind of methodological framework using a panel data set to test whether technical efficiency starts to increase or decrease with the length of time that farmers participate in development programs.

Keywords: impact evaluation, efficiency analysis and selection bias, stochastic frontier model, Heckman-two step

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Authors: Gabriela Borilova, Monika Petrakova, Petr Kralik

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Nowadays, European producers are increasingly interested in the production of halal meat products. Halal meat has been increasingly appearing in the EU's market network and meat products from European producers are being exported to Islamic countries. Halal criteria are mainly related to the origin of muscle used in production, and also to the way products are obtained and processed. Although the EU has legislatively addressed the question of food authenticity, the circumstances of previous years when products with undeclared horse or poultry meat content appeared on EU markets raised the question of the effectiveness of control mechanisms. Replacement of expensive or not-available types of meat for low-priced meat has been on a global scale for a long time. Likewise, halal products may be contaminated (falsified) by pork or food components obtained from pigs. These components include collagen, offal, pork fat, mechanically separated pork, emulsifier, blood, dried blood, dried blood plasma, gelatin, and others. These substances can influence sensory properties of the meat products - color, aroma, flavor, consistency and texture or they are added for preservation and stabilization. Food manufacturers sometimes access these substances mainly due to their dense availability and low prices. However, the use of these substances is not always declared on the product packaging. Verification of the presence of declared ingredients, including the detection of undeclared ingredients, are among the basic control procedures for determining the authenticity of food. Molecular biology methods, based on DNA analysis, offer rapid and sensitive testing. The PCR method and its modification can be successfully used to identify animal species in single- and multi-ingredient raw and processed foods and qPCR is the first choice for food analysis. Like all PCR-based methods, it is simple to implement and its greatest advantage is the absence of post-PCR visualization by electrophoresis. qPCR allows detection of trace amounts of nucleic acids, and by comparing an unknown sample with a calibration curve, it can also provide information on the absolute quantity of individual components in the sample. Our study addresses a problem that is related to the fact that the molecular biological approach of most of the work associated with the identification and quantification of animal species is based on the construction of specific primers amplifying the selected section of the mitochondrial genome. In addition, the sections amplified in conventional PCR are relatively long (hundreds of bp) and unsuitable for use in qPCR, because in DNA fragmentation, amplification of long target sequences is quite limited. Our study focuses on finding a suitable genomic DNA target and optimizing qPCR to reduce variability and distortion of results, which is necessary for the correct interpretation of quantification results. In halal products, the impact of falsification of meat products by the addition of components derived from pigs is all the greater that it is not just about the economic aspect but above all about the religious and social aspect. This work was supported by the Ministry of Agriculture of the Czech Republic (QJ1530107).

Keywords: food fraud, halal food, pork, qPCR

Procedia PDF Downloads 246

Authors: De-Shin Liu, Po-Chun Wen, Zhen-Wei Zhuang, Hsueh-Chih Liu, Pei-Chen Huang

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Wafer probing tests play an important role in semiconductor manufacturing procedures in accordance with the yield and reliability requirement of the wafer after the backend-of-the-line process. Accordingly, the stable physical and electrical contact between the probe and the tested wafer during wafer probing is regarded as an essential issue in identifying the known good die. The probe card can be integrated with multiple probe needles, which are classified as vertical, cantilever and micro-electro-mechanical systems type probe selections. Among all potential probe types, the vertical probe has several advantages as compared with other probe types, including maintainability, high probe density and feasibility for high-speed wafer testing. In the present study, the mechanical response of the wafer probing test with the vertical cobra probe on 720 μm thick silicon (Si) substrate with a 1.4 μm thick aluminum (Al) pad is investigated by the experiment and transient dynamic simulation approach. Because the deformation mechanism of the vertical cobra probe is determined by both bending and buckling mechanisms, the stable correlation between contact forces and overdrive (OD) length must be carefully verified. Moreover, the decent OD length with corresponding contact force contributed to piercing the native oxide layer of the Al pad and preventing the probing test-induced damage on the interconnect system. Accordingly, the scratch depth of the Al pad under various OD lengths is estimated by the atomic force microscope (AFM) and simulation work. In the wafer probing test configuration, the contact phenomenon between the probe needle and the tested object introduced large deformation and twisting of mesh gridding, causing the subsequent numerical divergence issue. For this reason, the arbitrary Lagrangian-Eulerian method is utilized in the present simulation work to conquer the aforementioned issue. The analytic results revealed a slight difference when the OD is considered as 40 μm, and the simulated is almost identical to the measured scratch depths of the Al pad under higher OD lengths up to 70 μm. This phenomenon can be attributed to the unstable contact of the probe at low OD length with the scratch depth below 30% of Al pad thickness, and the contact status will be being stable when the scratch depth over 30% of pad thickness. The splash of the Al pad is observed by the AFM, and the splashed Al debris accumulates on a specific side; this phenomenon is successfully simulated in the transient dynamic simulation. Thus, the preferred testing OD lengths are found as 45 μm to 70 μm, and the corresponding scratch depths on the Al pad are represented as 31.4% and 47.1% of Al pad thickness, respectively. The investigation approach demonstrated in this study contributed to analyzing the mechanical response of wafer probing test configuration under large strain conditions and assessed the geometric designs and material selections of probe needles to meet the requirement of high resolution and high-speed wafer-level probing test for thinned wafer application.

Keywords: wafer probing test, vertical probe, probe mark, mechanical response, FEA simulation

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Authors: Beverly Black

Abstract:

Background: Adolescent dating abuse (ADA) is widespread throughout the world and negatively impacts many adolescents. ADA is associated with lower self-esteem, poorer school performance, lower employment opportunities, higher rates of depression, absenteeism from school, substance abuse, bullying, smoking, suicide, pregnancy, eating disorders, and risky sexual behaviors, and experiencing domestic violence later in life. ADA prevention is sometimes addressed through school programming; yet, parental responses to ADA can also be an important vehicle for its prevention. In this exploratory study, the author examined how mothers, including abused mothers, responded to scenarios of ADA involving their children. Methods: Six focus groups were conducted between December, 2013 and June, 2014 with mothers (n=31) in the southern part of the United States. Three of the focus groups were comprised of mothers (n=17) who had been abused by their partners. Mothers were recruited from local community family agencies. Participants were provided a series of four scenarios about ADA and they were asked to explain how they would respond. Focus groups lasted approximately 45 minutes. All participants were given a gift card to a major retailer as a ‘thank you’. Using QSR-N10, two researchers’ analyzed the focus group data first using open and axial coding techniques to find overarching themes. Researchers triangulated the coded data to ensure accurate interpretations of the participants’ messages and used the scenario questions to structure the coded results. Results: Almost 30% of 699 comments coded as mothers’ recommendations for responding to ADA focused on the importance of providing advice to their children. Advice included breaking up, going to police, ignoring or avoiding the abusive partner, and setting boundaries in relationships. About 22% of comments focused on the need for educating teens about healthy and unhealthy relationships and seeking additional information. About 13% of the comments reflected the view that parents should confront abuser and/or abusers’ parents, and less than 2% noted the need to take their child to counseling. Mothers who had been abused offered similar responses as parents who had not experienced abuse. However, their responses were more likely to focus on sharing their own experience exercising caution in their responses, as they knew from their own experiences that authoritarian responses were ineffective. Over half of the comments indicated that parents would react stronger, quicker, and angrier if a girl was being abused by a boy than vice versa; parents expressed greater fear for their daughters than their sons involved in ADA. Conclusions. Results suggest that mothers have ideas about how to respond to ADA. Mothers who have been abused draw from their experiences and are aware that responding in an authoritarian manner may not be helpful. Because parental influence on teens is critical in their development, it is important for all parents to respond to ADA in a helpful manner to break the cycle of violence. Understanding responses to ADA can inform prevention programming to work with parents in responding to ADA.

Keywords: abused mothers' responses to dating abuse, adolescent dating abuse, mothers' responses to dating abuse, teen dating violence

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Authors: Abdul Ganiyu Iddrisu

Abstract:

Digitization is the process of transforming analog material into digital form, especially for storage and use in a computer. Significant development of information and communication technology (ICT) over the past years has encouraged many researchers to investigate its contribution to promoting economic growth, and reducing poverty. Yet compelling empirical evidence on the effects of digitization on economic growth remains weak, particularly in Africa. This is because extant studies that explicitly evaluate digitization and economic growth nexus are mostly reports and desk reviews. This points out an empirical knowledge gap in the literature. Hypothetically, digitization influences financial sector development which in turn influences economic growth. Digitization has changed the financial sector and its operating environment. Obstacles to access to financing, for instance, physical distance, minimum balance requirements, low-income flows among others can be circumvented. Savings have increased, micro-savers have opened bank accounts, and banks are now able to price short-term loans. This has the potential to develop the financial sector, however, empirical evidence on digitization-financial development nexus is dearth. On the other hand, a number of studies maintained that financial sector development greatly influences growth of economies. We therefore argue that financial sector development is one of the transmission mechanisms through which digitization affects economic growth. Employing macro-country-level data from African countries and using fixed effects, random effects and Hausman-Taylor estimation approaches, this paper contributes to the literature by analysing economic growth in Africa focusing on the role of digitization, and financial sector development. First, we assess how digitization influence financial sector development in Africa. From an economic policy perspective, it is important to identify digitization determinants of financial sector development so that action can be taken to reduce the economic shocks associated with financial sector distortions. This nexus is rarely examined empirically in the literature. Secondly, we examine the effect of domestic credit to private sector and stock market capitalization as a percentage of GDP as used to proxy for financial sector development on 2 economic growth. Digitization is represented by the volume of digital/ICT equipment imported and GDP growth is used to proxy economic growth. Finally, we examine the effect of digitization on economic growth in the light of financial sector development. The following key results were found; first, digitalization propels financial sector development in Africa. Second, financial sector development enhances economic growth. Finally, contrary to our expectation, the results also indicate that digitalization conditioned on financial sector development tends to reduce economic growth in Africa. However, results of the net effects suggest that digitalization, overall, improves economic growth in Africa. We, therefore, conclude that, digitalization in Africa does not only develop the financial sector but unconditionally contributes the growth of the continent’s economies.

Keywords: digitalization, economic growth, financial sector development, Africa

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Authors: Alejandra Sierra, Gloria Valadez, Adriana Dávalos, Mirliana Ramírez

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For years, the Pan American Health Organization/World Health Organization and other organizations have made efforts to the improve access and the quality of prenatal care as part of comprehensive programs for maternal and neonatal health, the standards of care have been renewed in order to migrate from a medical perspective to a holistic perspective. However, despite the efforts currently antenatal care models have not been verified by a scientific evaluation in order to determine their effectiveness. The teenage pregnancy is considered as a very important phenomenon since it has been strongly associated with inequalities, poverty and the lack of gender quality; therefore it is important to analyze the antenatal care that’s been given, including not only the clinical intervention but also the activities surrounding the advertising and the health education. In this study, the objective was to describe if the previously established activities (on the prenatal care models) are being performed in the care of pregnant teenagers attending prenatal care in health institutions in two cities in México and Chile during 2013. Methods: Observational and descriptive study, of a transversal cohort. 170 pregnant women (13-19 years) were included in prenatal care in two health institutions (100 women from León-Mexico and 70 from Chile-Coquimbo). Data collection: direct survey, perinatal clinical record card which was used as checklists: WHO antenatal care model WHO-2003, Official Mexican Standard NOM-007-SSA2-1993 and Personalized Service Manual on Reproductive Process- Chile Crece Contigo; for data analysis descriptive statistics were used. The project was approved by the relevant ethics committees. Results: Regarding the fulfillment of interventions focused on physical, gynecological exam, immunizations, monitoring signs and biochemical parameters in both groups was met by more than 84%; the activities of guidance and counseling pregnant teenagers in Leon compliance rates were below 50%, on the other hand, although pregnant women in Coquimbo had a higher percentage of compliance, no one reached 100%. The topics that less was oriented were: family planning, signs and symptoms of complications and labor. Conclusions: Although the coverage of the interventions indicated in the prenatal care models was high, there were still shortcomings in the fulfillment of activities to orientation, education and health promotion. Deficiencies in adherence to prenatal care guidelines could be due to different circumstances such as lack of registration or incomplete filling of medical records, lack of medical supplies or health personnel, absences of people at prenatal check-up appointments, among many others. Therefore, studies are required to evaluate the quality of prenatal care and the effectiveness of existing models, considering the role of the different actors (pregnant women, professionals and health institutions) involved in the functionality and quality of prenatal care models, in order to create strategies to design or improve the application of a complete process of promotion and prevention of maternal and child health as well as sexual and reproductive health in general.

Keywords: adolescent health, health systems, maternal health, primary health care

Procedia PDF Downloads 204

Authors: Abdul Ganiyu Iddrisu, Bei Chen

Abstract:

Digitization is the process of transforming analog material into digital form, especially for storage and use in a computer. Significant development of information and communication technology (ICT) over the past years has encouraged many researchers to investigate its contribution to promoting economic growth and reducing poverty. Yet the compelling empirical evidence on the effects of digitization on economic growth remains weak, particularly in Africa. This is because extant studies that explicitly evaluate digitization and economic growth nexus are mostly reports and desk reviews. This points out an empirical knowledge gap in the literature. Hypothetically, digitization influences financial sector development which in turn influences economic growth. Digitization has changed the financial sector and its operating environment. Obstacles to access to financing, for instance, physical distance, minimum balance requirements, and low-income flows, among others can be circumvented. Savings have increased, micro-savers have opened bank accounts, and banks are now able to price short-term loans. This has the potential to develop the financial sector. However, empirical evidence on the digitization-financial development nexus is dearth. On the other hand, a number of studies maintained that financial sector development greatly influences growth of economies. We, therefore, argue that financial sector development is one of the transmission mechanisms through which digitization affects economic growth. Employing macro-country-level data from African countries and using fixed effects, random effects and Hausman-Taylor estimation approaches, this paper contributes to the literature by analysing economic growth in Africa, focusing on the role of digitization and financial sector development. First, we assess how digitization influences financial sector development in Africa. From an economic policy perspective, it is important to identify digitization determinants of financial sector development so that action can be taken to reduce the economic shocks associated with financial sector distortions. This nexus is rarely examined empirically in the literature. Secondly, we examine the effect of domestic credit to the private sector and stock market capitalization as a percentage of GDP as used to proxy for financial sector development on economic growth. Digitization is represented by the volume of digital/ICT equipment imported and GDP growth is used to proxy economic growth. Finally, we examine the effect of digitization on economic growth in the light of financial sector development. The following key results were found; first, digitalization propels financial sector development in Africa. Second, financial sector development enhances economic growth. Finally, contrary to our expectation, the results also indicate that digitalization conditioned on financial sector development tends to reduce economic growth in Africa. However, results of the net effects suggest that digitalization, overall, improve economic growth in Africa. We, therefore, conclude that, digitalization in Africa does not only develop the financial sector but unconditionally contributes the growth of the continent’s economies.

Keywords: digitalization, financial sector development, Africa, economic growth

Procedia PDF Downloads 138

Authors: Teodorica G. Ani

Abstract:

Economic activity requires an encouraging regulatory environment and effective rules that are transparent and accessible to all. The World Bank has been publishing the annual Doing Business reports since 2004 to investigate the scope and manner of regulations that enhance business activity and those that constrain it. A streamlined business environment supporting the development of competitive small and medium enterprises (SMEs) may expand employment opportunities and improve the living conditions of low income households. Asia has emerged as one of the most attractive markets in the world. Economies in East Asia and the Pacific were among the most active in making it easier for local firms to do business. The study aimed to describe the ease of doing business and its effect to economic growth among selected economies in Asia for the year 2014. The study covered 29 economies in East Asia, Southeast Asia, South Asia and Middle Asia. Ease of doing business is measured by the Doing Business indicators (DBI) of the World Bank. The indicators cover ten aspects of the ease of doing business such as starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting investors, paying taxes, trading across borders, enforcing contracts and resolving insolvency. In the study, Gross Domestic Product (GDP) was used as the proxy variable for economic growth. Descriptive research was the research design used. Graphical analysis was used to describe the income and doing business among selected economies. In addition, multiple regression was used to determine the effect of doing business to economic growth. The study presented the income among selected economies. The graph showed China has the highest income while Maldives produces the lowest and that observation were supported by gathered literatures. The study also presented the status of the ten indicators of doing business among selected economies. The graphs showed varying trends on how easy to start a business, deal with construction permits and to register property. Starting a business is easiest in Singapore followed by Hong Kong. The study found out that the variations in ease of doing business is explained by starting a business, dealing with construction permits and registering property. Moreover, an explanation of the regression result implies that a day increase in the average number of days it takes to complete a procedure will decrease the value of GDP in general. The research proposed inputs to policy which may increase the awareness of local government units of different economies on the simplification of the policies of the different components used in measuring doing business.

Keywords: doing business, economic growth, gross domestic product, Asia

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Authors: Sumedha Weerasekara, Morgan Miles, Mark Morrison, Branka Krivokapic-Skoko

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This paper is aimed at exploring the interrelationships among entrepreneurial ecosystem factors and entrepreneurial cognition in regional and metropolitan ecosystems. Entrepreneurial ecosystem factors examined include: culture, infrastructure, access to finance, informal networks, support services, access to universities, and the depth and breadth of the talent pool. Using a multivariate approach we explore the impact of these ecosystem factors or elements on entrepreneurial cognition. In doing so, the existing body of knowledge from the literature on entrepreneurial ecosystem and cognition have been blended to explore the relationship between entrepreneurial ecosystem factors and cognition in a way not hitherto investigated. The concept of the entrepreneurial ecosystem has received increased attention as governments, universities and communities have started to recognize the potential of integrated policies, structures, programs and processes that foster entrepreneurship activities by supporting innovation, productivity and employment growth. The notion of entrepreneurial ecosystems has evolved and grown with the advancement of theoretical research and empirical studies. Importance of incorporating external factors like culture, political environment, and the economic environment within a single framework will enhance the capacity of examining the whole systems functionality to better understand the interaction of the entrepreneurial actors and factors within a single framework. The literature on clusters underplays the role of entrepreneurs and entrepreneurial management in creating and co-creating organizations, markets, and supporting ecosystems. Entrepreneurs are only one actor following a limited set of roles and dependent upon many other factors to thrive. As a consequence, entrepreneurs and relevant authorities should be aware of the other actors and factors with which they engage and rely, and make strategic choices to achieve both self and also collective objectives. The study uses stratified random sampling method to collect survey data from 12 different regions in regional and metropolitan regions of NSW, Australia. A questionnaire was administered online among 512 Small and medium enterprise owners operating their business in selected 12 regions in NSW, Australia. Data were analyzed using descriptive analyzing techniques and partial least squares - structural equation modeling. The findings show that even though there is a significant relationship between each and every entrepreneurial ecosystem factors, there is a weak relationship between most entrepreneurial ecosystem factors and entrepreneurial cognition. In the metropolitan context, the availability of finance and informal networks have the largest impact on entrepreneurial cognition while culture, infrastructure, and support services having the smallest impact and the talent pool and universities having a moderate impact on entrepreneurial cognition. Interestingly, in a regional context, culture, availability of finance, and the talent pool have the highest impact on entrepreneurial cognition, while informal networks having the smallest impact and the remaining factors – infrastructure, universities, and support services have a moderate impact on entrepreneurial cognition. These findings suggest the need for a location-specific strategy for supporting the development of entrepreneurial cognition.

Keywords: academic achievement, colour response card, feedback

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Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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Authors: Jungyeol Hong, Dongjoo Park

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The urban transit system is a critical part of a solution to the economic, energy, and environmental challenges. Furthermore, it ultimately contributes the improvement of people’s quality of lives. For taking these kinds of advantages, the city of Seoul has tried to construct an integrated transit system including both subway and buses. The effort led to the fact that approximately 6.9 million citizens use the integrated transit system every day for their trips. Diagnosing the current transit network is a significant task to provide more convenient and pleasant transit environment. Therefore, the critical objective of this study is to establish a methodological framework for the analysis of an integrated bus-subway network and to examine the relationship between subway ridership and parameters such as network topology measures, bus demand, and a variety of commercial business facilities. Regarding a statistical approach to estimate subway ridership at a station level, many previous studies relied on Ordinary Least Square regression, but there was lack of studies considering the endogeneity issues which might show in the subway ridership prediction model. This study focused on both discovering the impacts of integrated transit network topology measures and endogenous effect of bus demand on subway ridership. It could ultimately contribute to developing more accurate subway ridership estimation accounting for its statistical bias. The spatial scope of the study covers Seoul city in South Korea, and it includes 243 subway stations and 10,120 bus stops with the temporal scope set during twenty-four hours with one-hour interval time panels each. The subway and bus ridership information in detail was collected from the Seoul Smart Card data in 2015 and 2016. First, integrated subway-bus network topology measures which have characteristics regarding connectivity, centrality, transitivity, and reciprocity were estimated based on the complex network theory. The results of integrated transit network topology analysis were compared to subway-only network topology. Also, the non-recursive approach which is Three-Stage Least Square was applied to develop the daily subway ridership model as capturing the endogeneity between bus and subway demands. Independent variables included roadway geometry, commercial business characteristics, social-economic characteristics, safety index, transit facility attributes, and dummies for seasons and time zone. Consequently, it was found that network topology measures were significant size effect. Especially, centrality measures showed that the elasticity was a change of 4.88% for closeness centrality, 24.48% for betweenness centrality while the elasticity of bus ridership was 8.85%. Moreover, it was proved that bus demand and subway ridership were endogenous in a non-recursive manner as showing that predicted bus ridership and predicted subway ridership is statistically significant in OLS regression models. Therefore, it shows that three-stage least square model appears to be a plausible model for efficient subway ridership estimation. It is expected that the proposed approach provides a reliable guideline that can be used as part of the spectrum of tools for evaluating a city-wide integrated transit network.

Keywords: integrated transit system, network topology measures, three-stage least squared, endogeneity, subway ridership

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Authors: Antonella Falanga, Armando Cartenì

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Today, an unprecedented amount of data coming from several sources, including mobile devices, sensors, tracking systems, and online platforms, characterizes our lives. The term “big data” not only refers to the quantity of data but also to the variety and speed of data generation. These data hold valuable insights that, when extracted and analyzed, facilitate informed decision-making. The 4Vs of big data - velocity, volume, variety, and value - highlight essential aspects, showcasing the rapid generation, vast quantities, diverse sources, and potential value addition of these kinds of data. This surge of information has revolutionized many sectors, such as business for improving decision-making processes, healthcare for clinical record analysis and medical research, education for enhancing teaching methodologies, agriculture for optimizing crop management, finance for risk assessment and fraud detection, media and entertainment for personalized content recommendations, emergency for a real-time response during crisis/events, and also mobility for the urban planning and for the design/management of public and private transport services. Big data's pervasive impact enhances societal aspects, elevating the quality of life, service efficiency, and problem-solving capacities. However, during this transformative era, new challenges arise, including data quality, privacy, data security, cybersecurity, interoperability, the need for advanced infrastructures, and staff training. Within the transportation sector (the one investigated in this research), applications span planning, designing, and managing systems and mobility services. Among the most common big data applications within the transport sector are, for example, real-time traffic monitoring, bus/freight vehicle route optimization, vehicle maintenance, road safety and all the autonomous and connected vehicles applications. Benefits include a reduction in travel times, road accidents and pollutant emissions. Within these issues, the proper transport demand estimation is crucial for sustainable transportation planning. Evaluating the impact of sustainable mobility policies starts with a quantitative analysis of travel demand. Achieving transportation decarbonization goals hinges on precise estimations of demand for individual transport modes. Emerging technologies, offering substantial big data at lower costs than traditional methods, play a pivotal role in this context. Starting from these considerations, this study explores the usefulness impact of big data within transport demand estimation. This research focuses on leveraging (big) data collected during the COVID-19 pandemic to estimate the evolution of the mobility demand in Italy. Estimation results reveal in the post-COVID-19 era, more than 96 million national daily trips, about 2.6 trips per capita, with a mobile population of more than 37.6 million Italian travelers per day. Overall, this research allows us to conclude that big data better enhances rational decision-making for mobility demand estimation, which is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, cloud computing, decision-making, mobility demand, transportation

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Authors: Heikki Valmu, Eero Kupila, Raisa Vartia

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A major curricular reform was implemented in Metropolia UAS in 2014. The teaching was to be based on larger course entities and collaborative pedagogy. The most thorough reform was conducted in the department of electrical engineering and automation technology. It has been already shown that the reform has been extremely successful with respect to student progression and drop-out rate. The improvement of the results has been much more significant in this department compared to the other engineering departments making only minor pedagogical changes. In the beginning of the spring term of 2017, a thorough student feedback project was conducted in the department. The study consisted of thirty questions about the implementation of the curriculum, the student workload and other matters related to student satisfaction. The reply rate was more than 40%. The students were divided to four different categories: first year students [cat.1] and students of all the three different majors [categories 2-4]. These categories were found valid since all the students have the same course structure in the first two semesters after which they may freely select the major. All staff members are divided into four teams respectively. The curriculum consists of consecutive 15 credit (ECTS) courses each taught by a group of teachers (3-5). There are to be no end exams and continuous assessment is to be employed. In 2014 the different teacher groups were encouraged to employ innovatively different assessment methods within the given specs. One of these methods has been since used in categories 1 and 2. These students have to complete a number of compulsory tasks each week to pass the course and the actual grade is defined by a smaller number of tests throughout the course. The tasks vary from homework assignments, reports and laboratory exercises to larger projects and the actual smaller tests are usually organized during the regular lecture hours. The teachers of the other two majors have been pedagogically more conservative. The student progression has been better in categories 1 and 2 compared to categories 3 and 4. One of the main goals of this survey was to analyze the reasons for the difference and the assessment methods in detail besides the general student satisfaction. The results show that in the categories following more strictly the specified assessment model much more versatile assessment methods are used and the basic spirit of the new pedagogy is followed. Also, the student satisfaction is significantly better in categories 1 and 2. It may be clearly stated that continuous assessment and teacher cooperation improve the learning outcomes, student progression as well as student satisfaction. Too much academic freedom seems to lead to worse results [cat 3 and 4]. A standardized assessment model is launched for all students in autumn 2017. This model is different from the one used so far in categories 1 and 2 allowing more flexibility to teacher groups, but it will force all the teacher groups to follow the general rules in order to improve the results and the student satisfaction further.

Keywords: continuous assessment, course integration, curricular reform, student feedback

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Authors: Amit Gill, Lalith Wijerathne, Sebastian Poledna

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This research presents the details of the implementation of high performance computing (HPC) extension of agent-based economic models (ABEMs) to simulate hundreds of millions of heterogeneous agents. ABEMs offer an alternative approach to study the economy as a dynamic system of interacting heterogeneous agents, and are gaining popularity as an alternative to standard economic models. Over the last decade, ABEMs have been increasingly applied to study various problems related to monetary policy, bank regulations, etc. When it comes to predicting the effects of local economic disruptions, like major disasters, changes in policies, exogenous shocks, etc., on the economy of the country or the region, it is pertinent to study how the disruptions cascade through every single economic entity affecting its decisions and interactions, and eventually affect the economic macro parameters. However, such simulations with hundreds of millions of agents are hindered by the lack of HPC enhanced ABEMs. In order to address this, a scalable Distributed Memory Parallel (DMP) implementation of ABEMs has been developed using message passing interface (MPI). A balanced distribution of computational load among MPI-processes (i.e. CPU cores) of computer clusters while taking all the interactions among agents into account is a major challenge for scalable DMP implementations. Economic agents interact on several random graphs, some of which are centralized (e.g. credit networks, etc.) whereas others are dense with random links (e.g. consumption markets, etc.). The agents are partitioned into mutually-exclusive subsets based on a representative employer-employee interaction graph, while the remaining graphs are made available at a minimum communication cost. To minimize the number of communications among MPI processes, real-life solutions like the introduction of recruitment agencies, sales outlets, local banks, and local branches of government in each MPI-process, are adopted. Efficient communication among MPI-processes is achieved by combining MPI derived data types with the new features of the latest MPI functions. Most of the communications are overlapped with computations, thereby significantly reducing the communication overhead. The current implementation is capable of simulating a small open economy. As an example, a single time step of a 1:1 scale model of Austria (i.e. about 9 million inhabitants and 600,000 businesses) can be simulated in 15 seconds. The implementation is further being enhanced to simulate 1:1 model of Euro-zone (i.e. 322 million agents).

Keywords: agent-based economic model, high performance computing, MPI-communication, MPI-process

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Authors: Angelika Kris D. Dalangin, Cesar B. Quicoy

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Rice farming is a major source of livelihood and employment in the Philippines, but it requires a substantial amount of capital. Capital may come from income (farm, non-farm, and off-farm), savings and credit. However, rice farmers suffer from lack of capital due to high costs of inputs and low productivity. Capital insufficiency, coupled with low productivity, hindered them to meet their basic household and production needs. Hence, they resorted to borrowing money, mostly from informal lenders who charge very high interest rates. As another source of capital, savings can help rice farmers meet their basic needs for both the household and the farm. However, information is inadequate whether the farmers save or not, as well as, why they do not depend on savings to augment their lack of capital. Thus, it is worth analyzing how rice farmers saved. The study revealed, using the actual savings which is the difference between the household income and expenditure, that about three-fourths (72%) of the total number of farmers interviewed are savers. However, when they were asked whether they are savers or not, more than half of them considered themselves as non-savers. This gap shows that there are many farmers who think that they do not have savings at all; hence they continue to borrow money and do not depend on savings to augment their lack of capital. The study also identified the forms of savings, saving motives, and savings utilization among rice farmers. Results revealed that, for the past 12 months, most of the farmers saved cash at home for liquidity purposes while others deposited cash in banks and/or saved their money in the form of livestock. Among the most important reasons of farmers for saving are for daily household expenses, for building a house, for emergency purposes, for retirement, and for their next production. Furthermore, the study assessed the factors affecting the rice farmers’ savings behaviour using logistic regression. Results showed that the factors found to be significant were presence of non-farm income, per capita net farm income, and per capita household expense. The presence of non-farm income and per capita net farm income positively affects the farmers’ savings behaviour. On the other hand, per capita household expenses have negative effect. The effect, however, of per capita net farm income and household expenses is very negligible because of the very small chance that the farmer is a saver. Generally, income and expenditure were proved to be significant factors that affect the savings behaviour of the rice farmers. However, most farmers could not save regularly due to low farm income and high household and farm expenditures. Thus, it is highly recommended that government should develop programs or implement policies that will create more jobs for the farmers and their family members. In addition, programs and policies should be implemented to increase farm productivity and income.

Keywords: agricultural economics, agricultural finance, binary logistic regression, logit, Philippines, Quezon, rice farmers, savings, savings behaviour

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Authors: Rachel Barker

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The exponential velocity in the truly knowledge-intensive world today has increasingly bombarded organizations with unfathomable challenges. Hence organizations are introduced to strange lexicons of descriptors belonging to a new paradigm of who, what and how knowledge at individual and organizational levels should be managed. Although organizational knowledge has been recognized as a valuable intangible resource that holds the key to competitive advantage, little progress has been made in understanding how knowledge sharing at individual level could benefit knowledge use at collective level to ensure added value. The research problem is that a lack of research exists to measure knowledge sharing through a multi-layered structure of ideas with at its foundation, philosophical assumptions to support presuppositions and commitment which requires actual findings from measured variables to confirm observed and expected events. The purpose of this paper is to address this problem by presenting a theoretical approach to measure knowledge sharing in emerging knowledge organizations. The research question is that despite the competitive necessity of becoming a knowledge-based organization, leaders have found it difficult to transform their organizations due to a lack of knowledge on who, what and how it should be done. The main premise of this research is based on the challenge for knowledge leaders to develop an organizational culture conducive to the sharing of knowledge and where learning becomes the norm. The theoretical constructs were derived and based on the three components of the knowledge management theory, namely technical, communication and human components where it is suggested that this knowledge infrastructure could ensure effective management. While it is realised that it might be a little problematic to implement and measure all relevant concepts, this paper presents effect of eight critical success factors (CSFs) namely: organizational strategy, organizational culture, systems and infrastructure, intellectual capital, knowledge integration, organizational learning, motivation/performance measures and innovation. These CSFs have been identified based on a comprehensive literature review of existing research and tested in a new framework adapted from four perspectives of the balanced score card (BSC). Based on these CSFs and their items, an instrument was designed and tested among managers and employees of a purposefully selected engineering company in South Africa who relies on knowledge sharing to ensure their competitive advantage. Rigorous pretesting through personal interviews with executives and a number of academics took place to validate the instrument and to improve the quality of items and correct wording of issues. Through analysis of surveys collected, this research empirically models and uncovers key aspects of these dimensions based on the CSFs. Reliability of the instrument was calculated by Cronbach’s a for the two sections of the instrument on organizational and individual levels.The construct validity was confirmed by using factor analysis. The impact of the results was tested using structural equation modelling and proved to be a basis for implementing and understanding the competitive predisposition of the organization as it enters the process of knowledge management. In addition, they realised the importance to consolidate their knowledge assets to create value that is sustainable over time.

Keywords: innovation, intellectual capital, knowledge sharing, performance measures

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Authors: Shaukat Ali Shahee, Usha Ananthakumar

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A data set exhibits class imbalance problem when one class has very few examples compared to the other class, and this is also referred to as between class imbalance. The traditional classifiers fail to classify the minority class examples correctly due to its bias towards the majority class. Apart from between-class imbalance, imbalance within classes where classes are composed of a different number of sub-clusters with these sub-clusters containing different number of examples also deteriorates the performance of the classifier. Previously, many methods have been proposed for handling imbalanced dataset problem. These methods can be classified into four categories: data preprocessing, algorithmic based, cost-based methods and ensemble of classifier. Data preprocessing techniques have shown great potential as they attempt to improve data distribution rather than the classifier. Data preprocessing technique handles class imbalance either by increasing the minority class examples or by decreasing the majority class examples. Decreasing the majority class examples lead to loss of information and also when minority class has an absolute rarity, removing the majority class examples is generally not recommended. Existing methods available for handling class imbalance do not address both between-class imbalance and within-class imbalance simultaneously. In this paper, we propose a method that handles between class imbalance and within class imbalance simultaneously for binary classification problem. Removing between class imbalance and within class imbalance simultaneously eliminates the biases of the classifier towards bigger sub-clusters by minimizing the error domination of bigger sub-clusters in total error. The proposed method uses model-based clustering to find the presence of sub-clusters or sub-concepts in the dataset. The number of examples oversampled among the sub-clusters is determined based on the complexity of sub-clusters. The method also takes into consideration the scatter of the data in the feature space and also adaptively copes up with unseen test data using Lowner-John ellipsoid for increasing the accuracy of the classifier. In this study, neural network is being used as this is one such classifier where the total error is minimized and removing the between-class imbalance and within class imbalance simultaneously help the classifier in giving equal weight to all the sub-clusters irrespective of the classes. The proposed method is validated on 9 publicly available data sets and compared with three existing oversampling techniques that rely on the spatial location of minority class examples in the euclidean feature space. The experimental results show the proposed method to be statistically significantly superior to other methods in terms of various accuracy measures. Thus the proposed method can serve as a good alternative to handle various problem domains like credit scoring, customer churn prediction, financial distress, etc., that typically involve imbalanced data sets.

Keywords: classification, imbalanced dataset, Lowner-John ellipsoid, model based clustering, oversampling

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Authors: W. A. Nalaka Wijesooriya

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The increasing paddy production, stabilization of domestic rice consumption and the increasing dynamism of rice processing and domestic markets call for a rethinking of the general direction of the rice milling industry in Sri Lanka. The main purpose of the study was to explore levels of concentration in rice milling industry in Polonnaruwa and Hambanthota which are the major hubs of the country for rice milling. Concentration indices reveal that the rice milling industry in Polonnaruwa operates weak oligopsony and is highly competitive in Hambanthota. According to the actual quantity of paddy milling per day, 47 % is less than 8Mt/Day, while 34 % is 8-20 Mt/day, and the rest (19%) is greater than 20 Mt/day. In Hambanthota, nearly 50% of the mills belong to the range of 8-20 Mt/day. Lack of experience of the milling industry, poor knowledge on milling technology, lack of capital and finding an output market are the major entry barriers to the industry. Major problems faced by all the rice millers are the lack of a uniform electricity supply and low quality paddy. Many of the millers emphasized that the rice ceiling price is a constraint to produce quality rice. More than 80% of the millers in Polonnaruwa which is the major parboiling rice producing area have mechanical dryers. Nearly 22% millers have modern machineries like color sorters, water jet polishers. Major paddy purchasing method of large scale millers in Polonnaruwa is through brokers. In Hambanthota major channel is miller purchasing from paddy farmers. Millers in both districts have major rice selling markets in Colombo and suburbs. Huge variation can be observed in the amount of pledge (for paddy storage) loans. There is a strong relationship among the storage ability, credit affordability and the scale of operation of rice millers. The inter annual price fluctuation ranged 30%-35%. Analysis of market margins by using series of secondary data shows that farmers’ share on rice consumer price is stable or slightly increases in both districts. In Hambanthota a greater share goes to the farmer. Only four mills which have obtained the Good Manufacturing Practices (GMP) certification from Sri Lanka Standards Institution can be found. All those millers are small quantity rice exporters. Priority should be given for the Small and medium scale millers in distribution of storage paddy of PMB during the off season. The industry needs a proper rice grading system, and it is recommended to introduce a ceiling price based on graded rice according to the standards. Both husk and rice bran were underutilized. Encouraging investment for establishing rice oil manufacturing plant in Polonnaruwa area is highly recommended. The current taxation procedure needs to be restructured in order to ensure the sustainability of the industry.

Keywords: conduct, performance, structure (SCP), rice millers

Procedia PDF Downloads 327

Authors: Amit Das

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Background and significance of the study: Generally investors and market forecasters use financial statement for investigation while it awakens contribute to investing. The main vicinity of financial accounting and reporting practices recommends a few basic financial performance indicators, namely, return on capital employed, return on assets and earnings per share, which is associated considerably with share prices. It is principally true in case of Indian pharmaceutical companies also. Share investing is intriguing a financial risk in addition to investors look for those financial evaluations which have noteworthy shock on share price. A crucial intention of financial statement analysis and reporting is to offer information which is helpful predominantly to exterior clients in creating credit as well as investment choices. Sound financial performance attracts the investors automatically and it will increase the share price of the respective companies. Keeping in view of this, this research work investigates the impact of financial performance indicators on share price of pharmaceutical companies in India which is listed in the Bombay Stock Exchange. Methodology: This research work is based on secondary data collected from moneycontrol database on September 28, 2015 of top 101 pharmaceutical companies in India. Since this study selects four financial performance indicators purposively and availability in the database, that is, earnings per share, return on capital employed, return on assets and net profits as independent variables and one dependent variable, share price of 101 pharmaceutical companies. While analysing the data, correlation statistics, multiple regression technique and appropriate test of significance have been used. Major findings: Correlation statistics show that four financial performance indicators of 101 pharmaceutical companies are associated positively and negatively with its share price and it is very much significant that more than 80 companies’ financial performances are related positively. Multiple correlation test results indicate that financial performance indicators are highly related with share prices of the selected pharmaceutical companies. Furthermore, multiple regression test results illustrate that when financial performances are good, share prices have been increased steadily in the Bombay stock exchange and all results are statistically significant. It is more important to note that sensitivity indices were changed slightly through financial performance indicators of selected pharmaceutical companies in India. Concluding statements: The share prices of pharmaceutical companies depend on the sound financial performances. It is very clear that share prices are changed with the movement of two important financial performance indicators, that is, earnings per share and return on assets. Since 101 pharmaceutical companies are listed in the Bombay stock exchange and Sensex are changed with this, it is obvious that Government of India has to take important decisions regarding production and exports of pharmaceutical products so that financial performance of all the pharmaceutical companies are improved and its share price are increased positively.

Keywords: financial performance indicators, share prices, pharmaceutical companies, India

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Authors: Maria Reznakova, Michala Strnadova, Lukas Reznak

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The global financial crisis that erupted in 2008 is associated mainly with the debt crisis. It quickly spread globally through financial markets, international banks and trade links, and affected many economic sectors. Measured by the index of the year-on-year change in GDP and industrial production, the consequences of the global financial crisis manifested themselves with some delay also in the Czech economy. This can be considered a result of the overwhelming export orientation of Czech industrial enterprises. These events offer an important opportunity to study how financial and macroeconomic instability affects corporate performance. Corporate performance factors have long been given considerable attention. It is therefore reasonable to ask whether the findings published in the past are also valid in the times of economic instability and subsequent recession. The decisive factor in effective corporate performance measurement is the existence of an appropriate system of indicators that are able to assess progress in achieving corporate goals. Performance measures may be based on non-financial as well as on financial information. In this paper, financial indicators are used in combination with other characteristics, such as the firm size and ownership structure. Financial performance is evaluated based on traditional performance indicators, namely, return on equity and return on assets, supplemented with indebtedness and current liquidity indices. As investments are a very important factor in corporate performance, their trends and importance were also investigated by looking at the ratio of investments to previous year’s sales and the rate of reinvested earnings. In addition to traditional financial performance indicators, the Economic Value Added was also used. Data used in the research were obtained from a questionnaire survey administered in industrial enterprises in the Czech Republic and from AMADEUS (Analyse Major Database from European Sources), from which accounting data of companies were obtained. Respondents were members of the companies’ senior management. Research results unequivocally confirmed that corporate performance dropped significantly in the 2010-2012 period, which can be considered a result of the global financial crisis and a subsequent economic recession. It was reflected mainly in the decreasing values of profitability indicators and the Economic Value Added. Although the total year-on-year indebtedness declined, intercompany indebtedness increased. This can be considered a result of impeded access of companies to bank loans due to the credit crunch. Comparison of the results obtained with the conclusions of previous research on a similar topic showed that the assumption that firms under foreign control achieved higher performance during the period investigated was not confirmed.

Keywords: corporate performance, foreign control, intercompany indebtedness, ratio of investment

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Authors: Sunil Sangwan, Narayan Chandra Nayak

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Over the last few decades, Indian banking system has achieved remarkable growth in its credit volume. However, one of the most disturbing facts about this growth is the uneven distribution of financial services across regions. Having witnessed limited success from all the earlier efforts towards financial inclusion targeting the rural poor and the underprivileged, provision of microfinance, of late, has emerged as a supplementary mechanism. There are two prominent modes of microfinance distribution in India namely Bank-SHG linkage (SBLP) and private Microfinance Institutions (MFIs). Ironically, such efforts also seem to have failed to achieve the desired targets as the microfinance services have witnessed skewed distribution across the states of the country. This study attempts to make a comparative analysis of the geographical skew of the SBLP and MFI in India and examine the factors influencing their regional distribution. The results indicate that microfinance services are largely concentrated in the southern region, accounting for about 50% of all microfinance clients and 49% of all microfinance loan portfolios. This is distantly followed by an eastern region where client outreach is close to 25% only. The north-eastern, northern, central, and western regions lag far behind in microfinance sectors, accounting for only 4%, 4%, 10%, and 7 % client outreach respectively. The penetration of SHGs is equally skewed, with the southern region accounting for 46% of client outreach and 70% of loan portfolios followed by an eastern region with 21% of client outreach and 13% of the loan portfolio. Contrarily, north-eastern, northern, central, western and eastern regions account for 5%, 5%, 10%, and 13% of client outreach and 3%, 3%, 7%, and 4% of loan portfolios respectively. The study examines the impact of literacy rate, rural poverty, population density, primary sector share, non-farm activities, loan default behavior and bank penetration on the microfinance penetration. The study is limited to 17 major states of the country over the period 2008-2014. The results of the GMM estimation indicate the significant positive impact of literacy rate, non-farm activities and population density on microfinance penetration across the states, while the rise in loan default seems to deter it. Rural poverty shows the significant negative impact on the spread of SBLP, while it has a positive impact on MFI penetration, hence indicating the policy of exclusion being adhered to by the formal financial system especially towards the poor. However, MFIs seem to be working as substitute mechanisms to banks to fill the gap. The findings of the study are a pointer towards enhancing financial literacy, non-farm activities, rural bank penetration and containing loan default for achieving greater microfinance prevalence.

Keywords: bank penetration, literacy rate, microfinance, primary sector share, rural non-farm activities, rural poverty

Procedia PDF Downloads 229

Authors: Omar Romero-Hernandez, Federico Castillo, Armando Sanchez, Sergio Romero, Andrea Romero, Michael Mitchell

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Environmental policies for resource conservation in rural areas include subsidies on services and social programs to cover living expenses. Government's expectation is that rural communities who benefit from social programs, such as payment for ecosystem services, are provided with an incentive to conserve natural resources and preserve natural sinks for greenhouse gases. At the same time, global climate change has affected the lives of people worldwide. The capability to adapt to global warming depends on the available resources and the standard of living, putting rural communities at a disadvantage. This paper explores whether rural entrepreneurship can represent a solution to resource conservation and global warming adaptation in rural communities. The research focuses on a sample of two coffee communities in Oaxaca, Mexico. Researchers used geospatial information contained in aerial photographs of the geographical areas of interest. Households were identified in the photos via the roofs of households and georeferenced via coordinates. From the household population, a random selection of roofs was performed and received a visit. A total of 112 surveys were completed, including questions of socio-demographics, perception to climate change and adaptation activities. The population includes two groups of study: entrepreneurs and non-entrepreneurs. Data was sorted, filtered, and validated. Analysis includes descriptive statistics for exploratory purposes and a multi-regression analysis. Outcomes from the surveys indicate that coffee farmers, who demonstrate entrepreneurship skills and hire employees, are more eager to adapt to climate change despite the extreme adverse socioeconomic conditions of the region. We show that farmers with entrepreneurial tendencies are more creative in using innovative farm practices such as the planting of shade trees, the use of live fencing, instead of wires, and watershed protection techniques, among others. This result counters the notion that small farmers are at the mercy of climate change and have no possibility of being able to adapt to a changing climate. The study also points to roadblocks that farmers face when coping with climate change. Among those roadblocks are a lack of extension services, access to credit, and reliable internet, all of which reduces access to vital information needed in today’s constantly changing world. Results indicate that, under some circumstances, funding and supporting entrepreneurship programs may provide more benefit than traditional social programs.

Keywords: entrepreneurship, global warming, rural communities, climate change adaptation

Procedia PDF Downloads 239

Authors: Jungyeol Hong, Dongjoo Park

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The primary purpose of this study is to develop a methodological framework to predict daily subway ridership at a station-level and to examine the association between subway ridership and bus demand incorporating commercial business facility in the vicinity of each subway station. The socio-economic characteristics, land-use, and built environment as factors may have an impact on subway ridership. However, it should be considered not only the endogenous relationship between bus and subway demand but also the characteristics of commercial business within a subway station’s sphere of influence, and integrated transit network topology. Regarding a statistical approach to estimate subway ridership at a station level, therefore it should be considered endogeneity and heteroscedastic issues which might have in the subway ridership prediction model. This study focused on both discovering the impacts of bus demand, commercial business characteristics, and network topology on subway ridership and developing more precise subway ridership estimation accounting for its statistical bias. The spatial scope of the study covers entire Seoul city in South Korea and includes 243 stations with the temporal scope set at twenty-four hours with one-hour interval time panels each. The data for subway and bus ridership was collected Seoul Smart Card data from 2015 and 2016. Three-Stage Least Square(3SLS) approach was applied to develop daily subway ridership model as capturing the endogeneity and heteroscedasticity between bus and subway demand. Independent variables incorporating in the modeling process were commercial business characteristics, social-economic characteristics, safety index, transit facility attributes, and dummies for seasons and time zone. As a result, it was found that bus ridership and subway ridership were endogenous each other and they had a significantly positive sign of coefficients which means one transit mode could increase another transportation mode’s ridership. In other words, two transit modes of subway and bus have a mutual relationship instead of the competitive relationship. The commercial business characteristics are the most critical dimension among the independent variables. The variables of commercial business facility rate in the paper containing six types; medical, educational, recreational, financial, food service, and shopping. From the model result, a higher rate in medical, financial buildings, shopping, and food service facility lead to increment of subway ridership at a station, while recreational and educational facility shows lower subway ridership. The complex network theory was applied for estimating integrated network topology measures that cover the entire Seoul transit network system, and a framework for seeking an impact on subway ridership. The centrality measures were found to be significant and showed a positive sign indicating higher centrality led to more subway ridership at a station level. The results of model accuracy tests by out of samples provided that 3SLS model has less mean square error rather than OLS and showed the methodological approach for the 3SLS model was plausible to estimate more accurate subway ridership. Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT (2017R1C1B2010175).

Keywords: subway ridership, bus ridership, commercial business characteristic, endogeneity, network topology

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Authors: Camilo Cerda Sarabia, Fernanda Bravo Cornejo, Diego Santibanez Oyarce, Hugo Osses Prado, Esteban Gómez Terán, Belén Diaz Diaz, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Antimicrobial resistance (AMR) represents a significant and rapidly escalating global health threat. Projections estimate that by 2050, AMR infections could claim up to 10 million lives annually. Respiratory infections, in particular, pose a severe risk not only to individual patients but also to the broader public health system. Despite the alarming rise in resistant respiratory infections, AMR within the lung microbiome (microbial community) remains underexplored and poorly characterized. The lungs, as a complex and dynamic microbial environment, host diverse communities of microorganisms whose interactions and resistance mechanisms are not fully understood. Unlike studies that focus on individual genomes, analyzing the entire microbiome provides a comprehensive perspective on microbial interactions, resistance gene transfer, and community dynamics, which are crucial for understanding AMR. However, this holistic approach introduces significant computational challenges and exposes the limitations of traditional analytical methods such as the difficulty of identifying the AMR. Machine learning has emerged as a powerful tool to overcome these challenges, offering the ability to analyze complex genomic data and uncover novel insights into AMR that might be overlooked by conventional approaches. This study investigates microbial resistance within the lung microbiome using unsupervised machine learning approaches to uncover resistance patterns and potential clinical associations. it downloaded and selected lung microbiome data from HumanMetagenomeDB based on metadata characteristics such as relevant clinical information, patient demographics, environmental factors, and sample collection methods. The metadata was further complemented by details on antibiotic usage, disease status, and other relevant descriptions. The sequencing data underwent stringent quality control, followed by a functional profiling focus on identifying resistance genes through specialized databases like Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. Subsequent analyses employed unsupervised machine learning techniques to unravel the structure and diversity of resistomes in the microbial community. Some of the methods employed were clustering methods such as K-Means and Hierarchical Clustering enabled the identification of sample groups based on their resistance gene profiles. The work was implemented in python, leveraging a range of libraries such as biopython for biological sequence manipulation, NumPy for numerical operations, Scikit-learn for machine learning, Matplotlib for data visualization and Pandas for data manipulation. The findings from this study provide insights into the distribution and dynamics of antimicrobial resistance within the lung microbiome. By leveraging unsupervised machine learning, we identified novel resistance patterns and potential drivers within the microbial community.

Keywords: antibiotic resistance, microbial community, unsupervised machine learning., sequences of AMR gene

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Authors: German Ricci

Abstract:

The research aims to analyse the symbolic power of the International Monetary Fund (IMF) in its relationship with a borrowing country, drawing upon Pierre Bourdieu’s Field Theory. This theory of power, typical of constructivist structuralism, has been minor used in international relations. Thus, selecting this perspective offers a new understanding of how the IMF's power operates and is structured. The IMF makes periodic economic reviews in which the staff evaluates the Government's performance. It also offers “last instance” loans when private external credit is not accessible. This relationship generates great expectations in financial agents because the IMF’s statements indicate the capacity of the Nation-State to meet its payment obligations (or not). Therefore, it is argued that the IMF is a legitimate actor for financial agents concerned about a government facing an economic crisis both for the effects of its immediate economic contribution through loans and the promotion of adjustment programs, helpful to guarantee the payment of the external debt. This legitimacy implies a symbolic power relationship in addition to the already known economic power relationship. Obtaining the IMF's consent implies that the government partially puts its political-economic decisions into play since the monetary policy must be agreed upon with the Fund. This has consequences at the local level. First, it implies that the debtor state must establish a daily relationship with the Fund. This everyday interaction with the Fund influences how officials and policymakers internalize the meaning of political management. On the other hand, if the Government has access to the IMF's seal of approval, the State will be again in a position to re-enter the financial market and go back into debt to face external debt. This means that private creditors increase the chances of collecting the debt and, again, grant credits. Thus, it is argued that the borrowing country submits to the relationship with the IMF in search of the latter's economic and symbolic capital. Access to this symbolic capital has objective and subjective repercussions at the national level that might tend to reproduce the relevance of the financial market and legitimizes the IMF’s intervention during economic crises. The paper has Argentina as its case study, given its historical relationship with the IMF and the relevance of the current indebtedness period, which remains largely unexplored. Argentina’s economy is characterized by recurrent financial crises, and it is the country to which the Fund has lent the most in its entire history. It surpasses more than three times the second, Egypt. In addition, Argentina is currently the country that owes the most to the Fund after receiving the largest loan ever granted by the IMF in 2018, and a new agreement in 2022. While the historical strong association with the Fund culminated in the most acute economic and social crisis in the country’s contemporary history, producing an unprecedented political and institutional crisis in 2001, Argentina still recognized the IMF as the only way out during economic crises.

Keywords: IMF, fields theory, symbolic power, Argentina, Bourdieu

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Authors: Emily Wood, James K. Westphal, Itamar Lerner

Abstract:

Background: Polysomnography (PSG) recordings are regularly used in research and clinical settings to study sleep and sleep-related disorders. Typical PSG studies are conducted in professional laboratories and performed by qualified researchers. However, the number of sleep labs worldwide is disproportionate to the increasing number of individuals with sleep disorders like sleep apnea and insomnia. Consequently, there is a growing need to supply cheaper yet reliable means to measure sleep, preferably autonomously by subjects in their own home. Over the last decade, a variety of devices for self-monitoring of sleep became available in the market; however, very few have been directly validated against PSG to demonstrate their ability to perform reliable automatic sleep scoring. Two popular mobile EEG-based systems that have published validation results, the DREEM 3 headband and the Z-Machine, have never been directly compared one to the other by independent researchers. The current study aimed to compare the performance of DREEM 3 and the Z-Machine to help investigators and clinicians decide which of these devices may be more suitable for their studies. Methods: 26 participants have completed the study for credit or monetary compensation. Exclusion criteria included any history of sleep, neurological or psychiatric disorders. Eligible participants arrived at the lab in the afternoon and received the two devices. They then spent two consecutive nights monitoring their sleep at home. Participants were also asked to keep a sleep log, indicating the time they fell asleep, woke up, and the number of awakenings occurring during the night. Data from both devices, including detailed sleep hypnograms in 30-second epochs (differentiating Wake, combined N1/N2, N3; and Rapid Eye Movement sleep), were extracted and aligned upon retrieval. For analysis, the number of awakenings each night was defined as four or more consecutive wake epochs between sleep onset and termination. Total sleep time (TST) and the number of awakenings were compared to subjects’ sleep logs to measure consistency with the subjective reports. In addition, the sleep scores from each device were compared epoch-by-epoch to calculate the agreement between the two devices using Cohen’s Kappa. All analysis was performed using Matlab 2021b and SPSS 27. Results/Conclusion: Subjects consistently reported longer times spent asleep than the time reported by each device (M= 448 minutes for sleep logs compared to M= 406 and M= 345 minutes for the DREEM and Z-Machine, respectively; both ps<0.05). Linear correlations between the sleep log and each device were higher for the DREEM than the Z-Machine for both TST and the number of awakenings, and, likewise, the mean absolute bias between the sleep logs and each device was higher for the Z-Machine for both TST (p<0.001) and awakenings (p<0.04). There was some indication that these effects were stronger for the second night compared to the first night. Epoch-by-epoch comparisons showed that the main discrepancies between the devices were for detecting N2 and REM sleep, while N3 had a high agreement. Overall, the DREEM headband seems superior for reliably scoring sleep at home.

Keywords: DREEM, EEG, seep monitoring, Z-machine

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Authors: Jin Sheng

Abstract:

This paper addresses a promising area of insurance sector to develop in Asia. Captive insurance, which provides risk-mitigation services for its parent company, has great potentials to develop in energy, infrastructure, agriculture, logistics, catastrophe, and alternative risk transfer (ART), and will greatly affect the framework of insurance industry. However, the Asian captive insurance market only takes a small proportion in the global market. The recent supply chain interruption case of Hanjin Shipping indicates the significance of risk management for an Asian company’s sustainability and resilience. China has substantial needs and great potentials to develop captive insurance, on account of the currency volatility, enterprises’ credit risks, and legal and operational risks of the Belt and Road initiative. Up to date, Mainland Chinese enterprises only have four offshore captives incorporated by CNOOC, Sinopec, Lenovo and CGN Power), three onshore captive insurance companies incorporated by CNPC, China Railway, and COSCO, as well as one industrial captive insurance organization - China Ship-owners Mutual Assurance Association. Its captive market grows slowly with one or two captive insurers licensed yearly after September 2011. As an international financial center, Hong Kong has comparative advantages in taxation, professionals, market access and well-established financial infrastructure to develop a functional captive insurance market. For example, Hong Kong’s income tax for an insurance company is 16.5%; while China's income tax for an insurance company is 25% plus business tax of 5%. Furthermore, restrictions on market entry and operations of China’s onshore captives make establishing offshore captives in international or regional captive insurance centers such as Singapore, Hong Kong, and other overseas jurisdictions to become attractive options. Thus, there are abundant business opportunities in this area. Using methodology of comparative studies and case analysis, this paper discusses the incorporation, regulatory issues, taxation and prospect of captive insurance market in Hong Kong, China and Singapore. Hong Kong and Singapore are both international financial centers with prominent advantages in tax concessions, technology, implementation, professional services, and well-functioning legal system. Singapore, as the domicile of 71 active captives, has been the largest captive insurance hub in Asia, as well as an established reinsurance hub. Hong Kong is an emerging captive insurance hub with 5 to 10 newly licensed captives each year, according to the Hong Kong Financial Services Development Council. It is predicted that Hong Kong will become a domicile for 50 captive insurers by 2025. This paper also compares the formation of a captive in Singapore with other jurisdictions such as Bermuda and Vermont.

Keywords: Alternative Risk Transfer (ART), captive insurance company, offshore captives, risk management, reinsurance, self-insurance fund

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Authors: Benjamin Arazi

Abstract:

Blockchain is the technology behind cryptocurrencies like Bitcoin. It revolutionizes the meaning of trust in the sense of offering total reliability without relying on any central entity that controls or supervises the system. The Wall Street Journal states: “Blockchain Marks the Next Step in the Internet’s Evolution”. Blockchain was listed as #1 in Linkedin – The Learning Blog “most in-demand hard skills needed in 2020”. As stated there: “Blockchain’s novel way to store, validate, authorize, and move data across the internet has evolved to securely store and send any digital asset”. GSMA, a leading Telco organization of mobile communications operators, declared that “Blockchain has the potential to be for value what the Internet has been for information”. Motivated by these seminal observations, this paper presents the foundations of a Blockchain-based “Internet of Assets” academic program that joins under one roof leading application areas that are characterized by the transfer of assets over communication lines. Two such areas, which are pillars of our economy, are Fintech – Financial Technology and mobile communications services. The next application in line is Healthcare. These challenges are met based on available extensive professional literature. Blockchain-based assets communication is based on extending the principle of Bitcoin, starting with the basic question: If digital money that travels across the universe can ‘prove its own validity’, can this principle be applied to digital content. A groundbreaking positive answer here led to the concept of “smart contract” and consequently to DLT - Distributed Ledger Technology, where the word ‘distributed’ relates to the non-existence of reliable central entities or trusted third parties. The terms Blockchain and DLT are frequently used interchangeably in various application areas. The World Bank Group compiled comprehensive reports, analyzing the contribution of DLT/Blockchain to Fintech. The European Central Bank and Bank of Japan are engaged in Project Stella, “Balancing confidentiality and auditability in a distributed ledger environment”. 130 DLT/Blockchain focused Fintech startups are now operating in Switzerland. Blockchain impact on mobile communications services is treated in detail by leading organizations. The TM Forum is a global industry association in the telecom industry, with over 850 member companies, mainly mobile operators, that generate US$2 trillion in revenue and serve five billion customers across 180 countries. From their perspective: “Blockchain is considered one of the digital economy’s most disruptive technologies”. Samples of Blockchain contributions to Fintech (taken from a World Bank document): Decentralization and disintermediation; Greater transparency and easier auditability; Automation & programmability; Immutability & verifiability; Gains in speed and efficiency; Cost reductions; Enhanced cyber security resilience. Samples of Blockchain contributions to the Telco industry. Establishing identity verification; Record of transactions for easy cost settlement; Automatic triggering of roaming contract which enables near-instantaneous charging and reduction in roaming fraud; Decentralized roaming agreements; Settling accounts per costs incurred in accordance with agreement tariffs. This clearly demonstrates an academic education structure where fundamental technologies are studied in classes together with these two application areas. Advanced courses, treating specific implementations then follow separately. All are under the roof of “Internet of Assets”.

Keywords: blockchain, education, financial technology, mobile telecommunications services

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