Search results for: corporate credit rating prediction

Authors: Youn-Hwan Kim, Jae-Won Moon, Hae-Joong Kim

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This paper introduces the study on the method of accelerated life test to electrical rotating system. In recent years, as well as efficiency for motors and generators, there is a growing need for research on the life expectancy. It is considered impossible to calculate the acceleration coefficient by increasing the rotational load or temperature load as the acceleration stress in the motor system because the temperature of the copper exceeds the wire thermal class rating. In this paper, the accelerated life test methods of the electrical rotating system are classified according to the application. This paper describes the development of the test procedure for the highly accelerated life test (HALT) of the 100kW permanent magnet synchronous motor (PMSM) of electric vehicle. Finally, it explains how to select acceleration load for vibration, temperature, bearing load, etc. for accelerated life test.

Keywords: acceleration coefficient, electric vehicle motor, HALT, life expectancy, vibration

Procedia PDF Downloads 326

Authors: Selvam M., Vadthya Poornachandar, Surender Singh

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These Reclaimed Asphalt Pavement (RAP) aggregates are generally dumped in the open area after the demolition of Asphalt Pavements. The utilization of RAP aggregates in cement concrete pavements may provide several socio-economic-environmental benefits and could embrace the circular economy. The cross recycling of RAP aggregates in the concrete pavement could reduce the consumption of virgin aggregates and saves the fertile land. However, the structural, as well as functional properties of RAP-concrete could be significantly lower than the conventional Pavement Quality Control (PQC) pavements. This warrants judicious selection of RAP fraction (coarse and fine aggregates) along with the accurate proportion of the same for PQC highways. Also, the selection of the RAP fraction and its proportion shall not be solely based on the mechanical properties of RAP-concrete specimens but also governed by the structural and functional behavior of the pavement system. In this study, an effort has been made to predict the optimum RAP fraction and its corresponding proportion for cement concrete pavements by considering the low-volume and high-volume roads. Initially, the effect of inclusions of RAP on the fresh and mechanical properties of concrete pavement mixes is mapped through an extensive literature survey. Almost all the studies available to date are considered for this study. Generally, Indian Roads Congress (IRC) methods are the most widely used design method in India for the analysis of concrete pavements, and the same has been considered for this study. Subsequently, fatigue damage analysis is performed to evaluate the required safe thickness of pavement slab for different fractions of RAP (coarse RAP). Consequently, the performance of RAP-concrete is predicted by employing the AASHTO-1993 model for the following distresses conditions: faulting, cracking, and smoothness. The performance prediction and total cost analysis of RAP aggregates depict that the optimum proportions of coarse RAP aggregates in the PQC mix are 35% and 50% for high volume and low volume roads, respectively.

Keywords: concrete pavement, RAP aggregate, performance prediction, pavement design

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Zaide Montes Ortiz, Jorge Alberto Molina, Alejandro Reyes

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Insects are extremely successful organisms. A sophisticated olfactory system is in part responsible for their survival and reproduction. The detection of volatile organic compounds can positively or negatively affect many behaviors in insects. Compounds such as carbon dioxide (CO2), ammonium, indol, and lactic acid are essential for many species of mosquitoes like Anopheles gambiae in order to locate vertebrate hosts. For instance, in A. gambiae, the olfactory receptor AgOR2 is strongly activated by indol, which accounts for almost 30% of human sweat. On the other hand, in some insects of agricultural importance, the detection and identification of pheromone receptors (PRs) in lepidopteran species has become a promising field for integrated pest management. For example, with the disruption of the pheromone receptor, BmOR1, mediated by transcription activator-like effector nucleases (TALENs), the sensitivity to bombykol was completely removed affecting the pheromone-source searching behavior in male moths. Then, the detection and identification of olfactory receptors in the genomes of insects is fundamental to improve our understanding of the ecological interactions, and to provide alternatives in the integrated pests and vectors management. Hence, the objective of this study is to propose a bioinformatic workflow to enhance the detection and identification of potential olfactory receptors in genomes of relevant insects. Applying Hidden Markov models (Hmms) and different computational tools, potential candidates for pheromone receptors in Tuta absoluta were obtained, as well as potential carbon dioxide receptors in Rhodnius prolixus, the main vector of Chagas disease. This study showed the validity of a bioinformatic workflow with a potential to improve the identification of certain olfactory receptors in different orders of insects.

Keywords: bioinformatic workflow, insects, olfactory receptors, protein prediction

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Authors: Niroshan K. Walgama Wellalage, Tieling Zhang, Richard Dwight

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State-based Markov deterioration models (SMDM) sometimes fail to find accurate transition probability matrix (TPM) values, and hence lead to invalid future condition prediction or incorrect average deterioration rates mainly due to drawbacks of existing nonlinear optimization-based algorithms and/or subjective function types used for regression analysis. Furthermore, a set of separate functions for each condition state with age cannot be directly derived by using Markov model for a given bridge element group, which however is of interest to industrial partners. This paper presents a new approach for generating Homogeneous SMDM model output, namely, the Modified Weibull approach, which consists of a set of appropriate functions to describe the percentage condition prediction of bridge elements in each state. These functions are combined with Bayesian approach and Metropolis Hasting Algorithm (MHA) based Markov Chain Monte Carlo (MCMC) simulation technique for quantifying the uncertainty in model parameter estimates. In this study, factors contributing to rail bridge deterioration were identified. The inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered accordingly based on the real operational experience. Network level deterioration model for a typical bridge element group was developed using the proposed Modified Weibull approach. The condition state predictions obtained from this method were validated using statistical hypothesis tests with a test data set. Results show that the proposed model is able to not only predict the conditions in network-level accurately but also capture the model uncertainties with given confidence interval.

Keywords: bridge deterioration modelling, modified weibull approach, MCMC, metropolis-hasting algorithm, bayesian approach, Markov deterioration models

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Authors: E. Harrer, M. Mauerhofer, T. Werginz

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As a result of intensive competition, the building sector is suffering from a high degree of rivalry. Furthermore, there can be observed an unbalanced distribution of project risks. Clients are aimed to shift their own risks into the sphere of the constructors or planners. The consequence of this is that the number of conflicts between the involved parties is inordinately high or even increasing; an alternative approach to counter on that developments are cooperative project forms in the construction sector. This research compares conventional contract models and models with partnering agreements to examine the influence on project risks by an early integration of the involved parties. The goal is to show up deviations in different project stages from the design phase to the project transfer phase. These deviations are evaluated by a survey of experts from the three spheres: clients, contractors and planners. By rating the influence of the participants on specific risk factors it is possible to identify factors which are relevant for a smooth project execution.

Keywords: building projects, contract models, partnering, project risks

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Authors: Alese Boniface K., Adu Michael K., Owa Victor K.

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Security can be defined as the degree of resistance to, or protection from harm. It applies to any vulnerable and valuable assets, such as persons, dwellings, communities, nations or organizations. Cybercrime is any crime committed or facilitated via the Internet. It is any criminal activity involving computers and networks. It can range from fraud to unsolicited emails (spam). It includes the distant theft of government or corporate secrets through criminal trespass into remote systems around the globe. Nigeria like any other nations of the world is currently having their own share of the menace that has been used even as tools by terrorists. This paper is an attempt at presenting cyber security as an issue that requires a coordinated national response. It also acknowledges and advocates the key roles to be played by stakeholders and the importance of forging strong partnerships to prevent and tackle cybercrime in Nigeria.

Keywords: security, cybercrime, internet, government, stakeholders, partnerships

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Authors: Isaac Kayode Ogunlade

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Data acquisition (DAQ) is the process by which physical phenomena from the real world are transformed into an electrical signal(s) that are measured and converted into a digital format for processing, analysis, and storage by a computer. The DAQ is designed using PIC18F4550 microcontroller, communicating with Personal Computer (PC) through USB (Universal Serial Bus). The research deployed initial knowledge of data acquisition system and embedded system to develop a weather data acquisition device using LM35 sensor to measure weather parameters and the use of Artificial Intelligence(Artificial Neural Network - ANN)and statistical approach(Autoregressive Integrated Moving Average – ARIMA) to predict precipitation (rainfall). The device is placed by a standard device in the Department of Meteorology, Federal University of Technology, Akure (FUTA) to know the performance evaluation of the device. Both devices (standard and designed) were subjected to 180 days with the same atmospheric condition for data mining (temperature, relative humidity, and pressure). The acquired data is trained in MATLAB R2012b environment using ANN, and ARIMAto predict precipitation (rainfall). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correction Square (R2), and Mean Percentage Error (MPE) was deplored as standardize evaluation to know the performance of the models in the prediction of precipitation. The results from the working of the developed device show that the device has an efficiency of 96% and is also compatible with Personal Computer (PC) and laptops. The simulation result for acquired data shows that ANN models precipitation (rainfall) prediction for two months (May and June 2017) revealed a disparity error of 1.59%; while ARIMA is 2.63%, respectively. The device will be useful in research, practical laboratories, and industrial environments.

Keywords: data acquisition system, design device, weather development, predict precipitation and (FUTA) standard device

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Authors: Manju Kanu, Subrata Sinha, Surabhi Johari

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Viral proteins of Ebola viruses belong to one of the best studied viruses; however no effective prevention against EBOV has been developed. Epitope-based vaccines provide a new strategy for prophylactic and therapeutic application of pathogen-specific immunity. A critical requirement of this strategy is the identification and selection of T-cell epitopes that act as vaccine targets. This study describes current methodologies for the selection process, with Ebola virus as a model system. Hence great challenge in the field of ebola virus research is to design universal vaccine. A combination of publicly available bioinformatics algorithms and computational tools are used to screen and select antigen sequences as potential T-cell epitopes of supertypes Human Leukocyte Antigen (HLA) alleles. MUSCLE and MOTIF tools were used to find out most conserved peptide sequences of viral proteins. Immunoinformatics tools were used for prediction of immunogenic peptides of viral proteins in zaire strains of Ebola virus. Putative epitopes for viral proteins (VP) were predicted from conserved peptide sequences of VP. Three tools NetCTL 1.2, BIMAS and Syfpeithi were used to predict the Class I putative epitopes while three tools, ProPred, IEDB-SMM-align and NetMHCII 2.2 were used to predict the Class II putative epitopes. B cell epitopes were predicted by BCPREDS 1.0. Immunogenic peptides were identified and selected manually by putative epitopes predicted from online tools individually for both MHC classes. Finally sequences of predicted peptides for both MHC classes were looked for common region which was selected as common immunogenic peptide. The immunogenic peptides were found for viral proteins of Ebola virus: epitopes FLESGAVKY, SSLAKHGEY. These predicted peptides could be promising candidates to be used as target for vaccine design.

Keywords: epitope, b cell, immunogenicity, ebola

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Authors: Gerhard D. Muller, Nadine C. Sonnenberg, Suné Donoghue

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This study delves into the intricacies of male and female consumers’ perceptions of Corporate Social Responsibility (CSR) in the South African clothing retail sector, a sector experiencing increasing consumption, yet facing significant environmental and social challenges. The aim is to discern between male and female consumers’ perceptions of clothing retailers’ CSR initiatives based on the Triple Bottom Line (TBL) framework, which evaluates organizational sustainability across social, environmental, and economic domains. Methodologically, the study is embedded in a quantitative research paradigm adopting a cross-sectional survey design. A purposive sampling strategy was used to recruit male and female respondents from a diverse South African demographic background. A structured questionnaire was developed and included established consumer CSR perception scales that were adapted for the purposes of this study. The questionnaire was distributed via online platforms. The data collected from the online survey, were split by gender to allow for comparison between male and female consumers’ perceptions of clothing retailers’ CSR initiatives. Exploratory Factor Analysis (EFA) was conducted on each of the datasets. The EFA for females revealed a five-factor solution, whereas the male EFA presented a six-factor solution, with the notable addition of an Economic Performance dimension. Results indicate subtle differences in the gender groups’ CSR perceptions. While both genders seem to value clothing retailers’ focus on quality services, females seem to have more pronounced perceptions surrounding clothing retailers’ contributions to social and environmental causes. Males, on the other hand, seem to be more discerning in their perceptions surrounding clothing retailers’ support of social and environmental causes. Ethical stakeholder relationships emerged as a shared concern across genders. Still, males presented a distinct factor, Economic Performance, highlighting a gendered divergence in the weighting of economic success and financial performance in CSR evaluation. The implications of these results are multifaceted. Theoretically, the study enriches the discourse on CSR by integrating gender insights into the TBL framework, offering a greater understanding of consumers’ CSR perceptions in the South African clothing retail context. Practically, it provides actionable insights for clothing retailers, suggesting that CSR initiatives should be gender-sensitive and communicate the TBL's elements effectively to resonate with the pertinent concerns of each segment. Additionally, the findings advocate for a contextualized approach to CSR in emerging markets that aligns with local cultural and social differences.

Keywords: consumer perceptions, corporate Social responsibility, gender differentiation, triple bottom line

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Authors: Andre S. de Lima, Alfredo R. de Faria, Jose J. R. Faria

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Accurate prediction of thermal stresses is particularly important for laminated composite structures, as large temperature changes may occur during fabrication and field application. The normal transverse deformation plays an important role in the prediction of such stresses, especially for problems involving thick laminated plates subjected to uniform temperature loads. Bearing this in mind, the present study aims to investigate the thermo-mechanical behavior of laminated composite structures using a new beam element based on global-local superposition, accounting for through-the-thickness effects. The element formulation is based on a global-local superposition in the thickness direction, utilizing a cubic global displacement field in combination with a linear layerwise local displacement distribution, which assures zig-zag behavior of the stresses and displacements. By enforcing interlaminar stress (normal and shear) and displacement continuity, as well as free conditions at the upper and lower surfaces, the number of degrees of freedom in the model is maintained independently of the number of layers. Moreover, the proposed formulation allows for the determination of transverse shear and normal stresses directly from the constitutive equations, without the need of post-processing. Numerical results obtained with the beam element were compared to analytical solutions, as well as results obtained with commercial finite elements, rendering satisfactory results for a range of length-to-thickness ratios. The results confirm the need for an element with through-the-thickness capabilities and indicate that the present formulation is a promising alternative to such analysis.

Keywords: composite beam element, global-local superposition, laminated composite structures, thermal stresses

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Authors: Reza E. Sedgh, Rajesh P. Dhakal

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Shear walls have been used extensively as the main lateral force resisting systems in multi-storey buildings. The recent development in performance based design urges practicing engineers to conduct nonlinear static or dynamic analysis to evaluate seismic performance of multi-storey shear wall buildings by employing distinct analytical models suggested in the literature. For practical purpose, application of macroscopic models to simulate the global and local nonlinear behavior of structural walls outweighs the microscopic models. The skill level, computational time and limited access to RC specialized finite element packages prevents the general application of this method in performance based design or assessment of multi-storey shear wall buildings in design offices. Hence, this paper organized to verify capability of nonlinear shell element in commercially available package (Sap2000) in simulating results of some specimens under monotonic and cyclic loads with very oversimplified available cyclic material laws in the analytical tool. The selection of constitutive models, the determination of related parameters of the constituent material and appropriate nonlinear shear model are presented in detail. Adoption of proposed simple model demonstrated that the predicted results follow the overall trend of experimental force-displacement curve. Although, prediction of ultimate strength and the overall shape of hysteresis model agreed to some extent with experiment, the ultimate displacement(significant strength degradation point) prediction remains challenging in some cases.

Keywords: analytical model, nonlinear shell element, structural wall, shear behavior

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Authors: Lidia Sanchis-Marco, Antonio Rubia, Pedro Serrano

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This paper studies the informational content of pricing errors in the term structure of sovereign CDS spreads. The residuals from a non-arbitrage model are employed to construct a Price discrepancy estimate, or noise measure. The noise estimate is understood as an indicator of market distress and reflects frictions such as illiquidity. Empirically, the noise measure is computed for an extensive panel of CDS spreads. Our results reveal an important fraction of systematic risk is not priced in default swap contracts. When projecting the noise measure onto a set of financial variables, the panel-data estimates show that greater price discrepancies are systematically related to a higher level of offsetting transactions of CDS contracts. This evidence suggests that arbitrage capital flows exit the marketplace during time of distress, and this consistent with a market segmentation among investors and arbitrageurs where professional arbitrageurs are particularly ineffective at bringing prices to their fundamental values during turbulent periods. Our empirical findings are robust for the most common CDS pricing models employed in the industry.

Keywords: credit default swaps, noise measure, illiquidity, capital arbitrage

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Authors: Bulus Kpame, Mukhtar Mohammed Alhaji, Garba Jibril

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In any team sport, the most important variables that should be used to measure performance are physical condition, and technical and tactical performance. In a complex game like football, it is extremely difficult to measure the relative importance of each of these variables. However, physical fitness itself has been shown to consist of several components, like endurance, strength, flexibility, agility, coordination and speed. Each of these components has been shown to consist of several subcomponents. This paper attempts to describe a test battery to assess and evaluate physical performance in football players. This battery comprises a functional, structured training session of about 2.5hrs. it consists of quality rating of the warm-up procedure, tests of flexibility, football skills, power, speed, and endurance. Acceptable values for performance in each of the tests are also presented under each test. It is hoped that this battery of tests will be helpful to the coach in determining the effect of a specific training program. It would also be helpful to train physician and trainer, to monitor progress during rehabilitation after sustaining any injury.

Keywords: assessment, evaluation, performance, programs

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Authors: Zhou Yuwei

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This study used the multidimensional rating scale for disruptive behaviour in preschool children (parent version) to assess changes in the disruptive behaviour (tantrums, disobedience, aggression, and low level of concern for others) of 200 young children in Nanjing, Jiangsu Province, China, before and after living at home during the new crown epidemic, and five additional teachers of young children were selected to conduct interviews on the performance and changes in their disruptive behaviour at school. The following conclusions were drawn from the questionnaires and interviews: (1) 49% of the children showed a decrease in disruptive behaviour compared to the pre-epidemic period; (2) boys were more disruptive than girls due to individual factors; (3) children with a decrease in disruptive behaviour were more likely to have democratic and authoritative parenting styles due to parental education and upbringing; and the higher the level of parental education, the greater the decrease in disruptive behaviour. (4) For parents who worked outside the home during the epidemic and who did not work, disruptive behaviour scores were higher for their children. Meanwhile, disruptive behaviour was more pronounced the longer the child used electronic devices. The longer the parent-child interaction, the less disruptive behaviour was evident.

Keywords: disruptive behaviour, home life, children, COVID-19

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Authors: Mohamed Abo El Nasr, Mohamed Shoeib, Abdelhamid Abdelkhalik, Amro Serag

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Background: Early assessment of severity of chest trauma, either blunt or penetrating is of critical importance in prediction of patient outcome. Different trauma scoring systems are widely available and are based on anatomical or physiological parameters to expect patient morbidity or mortality. Up till now, there is no ideal, universally accepted trauma score that could be applied in all trauma centers and is suitable for assessment of severity of chest trauma patients. Aim: Our aim was to compare various trauma scoring systems regarding their predictability of morbidity and mortality in chest trauma patients. Patients and Methods: This study was a prospective study including 400 patients with chest trauma who were managed at Tanta University Emergency Hospital, Egypt during a period of 2 years (March 2014 until March 2016). The patients were divided into 2 groups according to the mode of trauma: blunt or penetrating. The collected data included age, sex, hemodynamic status on admission, intrathoracic injuries, and associated extra-thoracic injuries. The patients outcome including mortality, need of thoracotomy, need for ICU admission, need for mechanical ventilation, length of hospital stay and the development of acute respiratory distress syndrome were also recorded. The relevant data were used to calculate the following trauma scores: 1. Anatomical scores including abbreviated injury scale (AIS), Injury severity score (ISS), New injury severity score (NISS) and Chest wall injury scale (CWIS). 2. Physiological scores including revised trauma score (RTS), Acute physiology and chronic health evaluation II (APACHE II) score. 3. Combined score including Trauma and injury severity score (TRISS ) and 4. Chest-Specific score Thoracic trauma severity score (TTSS). All these scores were analyzed statistically to detect their sensitivity, specificity and compared regarding their predictive power of mortality and morbidity in blunt and penetrating chest trauma patients. Results: The incidence of mortality was 3.75% (15/400). Eleven patients (11/230) died in blunt chest trauma group, while (4/170) patients died in penetrating trauma group. The mortality rate increased more than three folds to reach 13% (13/100) in patients with severe chest trauma (ISS of >16). The physiological scores APACHE II and RTS had the highest predictive value for mortality in both blunt and penetrating chest injuries. The physiological score APACHE II followed by the combined score TRISS were more predictive for intensive care admission in penetrating injuries while RTS was more predictive in blunt trauma. Also, RTS had a higher predictive value for expectation of need for mechanical ventilation followed by the combined score TRISS. APACHE II score was more predictive for the need of thoracotomy in penetrating injuries and the Chest-Specific score TTSS was higher in blunt injuries. The anatomical score ISS and TTSS score were more predictive for prolonged hospital stay in penetrating and blunt injuries respectively. Conclusion: Trauma scores including physiological parameters have a higher predictive power for mortality in both blunt and penetrating chest trauma. They are more suitable for assessment of injury severity and prediction of patients outcome.

Keywords: chest trauma, trauma scores, blunt injuries, penetrating injuries

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Authors: Oladipo Joseph Ajayi, Yakubu Muhammed, Raufu Olusola Sanusi

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This study determined the perceived effect of livelihood diversification on welfare of rural household in Niger state, Nigeria. Multi-stage sampling technique was adopted for sampling the respondents. Data used for the study were obtained from primary source. Structured questionnaire with interview schedule was administered to 180 randomly selected rural farmers in the study area. Descriptive statistics analysis and z-test statistics were used to analyse the data collected. The study revealed the mean age of the household to be 43 years, mean years of schooling was 8.5, mean household size was 6 people, mean farming experience of 17.5 years and mean farm size of 1.8 hectares. The effect of livelihood diversification revealed that livelihood diversification had positive and significant effect on food security (65.6%) and income generation (66.8%) in the study area. The major constraints to diversification in the study area were poor infrastructure, unavailability of credit and climatic risk and uncertainty. The study, therefore, recommended that rural household should be sensitised to diversify their income source into non-farm activities.

Keywords: income, livelihood diversification , rural household, welfare

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Authors: Zouari Ezzeddine, Ghraieb Ikram

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From the results of the academic literature which evokes the limitations of previous studies, this article shows the reasons for multidimensionality Prediction of financial bubbles. A new framework for modeling study predicting financial bubbles by linking a set of variable presented on several dimensions dictating its multidimensional character. It takes into account the preferences of financial actors. A multicriteria anticipation of the appearance of bubbles in international financial markets helps to fight against a possible crisis.

Keywords: classical measures, predictions, financial bubbles, multidimensional, artificial neural networks

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Authors: Ismatilla Mardanov

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There are three types of countries, the first of which is willing to attract foreign direct investment (FDI) in enormous amounts and do whatever it takes to make this happen. Therefore, FDI pours into such countries. In the second cluster of countries, even if the country is suffering tremendously from the shortage of investments, the governments are hesitant to attract investments because they are at the hands of local oligarchs/cartels. Therefore, FDI inflows are moderate to low in such countries. The third type is countries whose companies prefer investing in the most efficient locations globally and are hesitant to invest in the homeland. Sorting countries into such clusters, the present study examines the essential institutions and economic factors that make these countries different. Past literature has discussed various determinants of FDI in all kinds of countries. However, it did not classify countries based on government motivation, institutional setup, and economic factors. A specific approach to each target country is vital for corporate foreign direct investment risk analysis and decisions. The research questions are 1. What specific institutional and economic factors paint the pictures of the three clusters; 2. What specific institutional and economic factors are determinants of FDI; 3. Which of the determinants are endogenous and exogenous variables? 4. How can institutions and economic and political variables impact corporate investment decisions Hypothesis 1: In the first type, country institutions and economic factors will be favorable for FDI. Hypothesis 2: In the second type, even if country economic factors favor FDI, institutions will not. Hypothesis 3: In the third type, even if country institutions favorFDI, economic factors will not favor domestic investments. Therefore, FDI outflows occur in large amounts. Methods: Data come from open sources of the World Bank, the Fraser Institute, the Heritage Foundation, and other reliable sources. The dependent variable is FDI inflows. The independent variables are institutions (economic and political freedom indices) and economic factors (natural, material, and labor resources, government consumption, infrastructure, minimum wage, education, unemployment, tax rates, consumer price index, inflation, and others), the endogeneity or exogeneity of which are tested in the instrumental variable estimation. Political rights and civil liberties are used as instrumental variables. Results indicate that in the first type, both country institutions and economic factors, specifically labor and logistics/infrastructure/energy intensity, are favorable for potential investors. In the second category of countries, the risk of loss of assets is very high due to governmentshijacked by local oligarchs/cartels/special interest groups. In the third category of countries, the local economic factors are unfavorable for domestic investment even if the institutions are well acceptable. Cluster analysis and instrumental variable estimation were used to reveal cause-effect patterns in each of the clusters.

Keywords: foreign direct investment, economy, institutions, instrumental variable estimation

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Authors: Thomas Yeboah

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In recent times, micro-finance programmes targeting women have become the central means of donor poverty alleviation strategies. In view of the renewed focus on post-Millennium Development Goals (MDGs) poverty reduction strategies, there is the likelihood that funding might increase in the next coming decades to support different initiatives by donor agencies. In this paper, we critically examine the role of microfinance in shaping gender relations and empowerment outcomes of women. It is widely argued that providing and reaching out to women with credit methodologies serves as a means of increasing women’s bargaining power and challenging existing gender subordination thereby releasing them from power structures which dominate their lives. This paper cautions this view and instead show that the mainstream argument surrounding microfinance and gender empowerment is much complex than what the popular rhetoric preaches. Drawing on empirical cases on microfinance literature, we argue that lack of systematic strategy to incorporate men and the wider socio-cultural dynamics within which women’s lives are embedded radically constraints the empowerment potential of microcredit programmes and in some context may lead to unintended consequences for women.

Keywords: microfinance, empowerment, women, men, gender relations

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Authors: Ilknur Çevik Tekin, Serdar Öge

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As a result of globalization and changing consumer preferences, the number of shopping malls has increased significantly in recent years. Consumers prefer shopping malls to both do comfortable shopping in a short time and benefit from the social facilities there. Employees, who are obliged to behave to the consumers in the way the company wants them to do, often spend intensive emotional effort because companies buy the emotions the employees must display to customers in order to ensure customer satisfaction. The emotions the employees constantly try to contain may lead to the phenomenon of burn-out in time. This study was conducted to reveal the relationship between the emotional labor and burn-out levels of shopping mall employees, who work in shopping malls and are supposed to reflect the corporate culture.

Keywords: emotional labor, burnout, shopping mall employees

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Authors: Fatemeh Faramarzi, Hosein Mahjoob

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Building dams on rivers for utilization of water resources causes problems in hydrodynamic equilibrium and results in leaving all or part of the sediments carried by water in dam reservoir. This phenomenon has also significant impacts on water and sediment flow regime and in the long term can cause morphological changes in the environment surrounding the river, reducing the useful life of the reservoir which threatens sustainable development through inefficient management of water resources. In the past, empirical methods were used to predict the sedimentation amount in dam reservoirs and to estimate its efficient lifetime. But recently the mathematical and computational models are widely used in sedimentation studies in dam reservoirs as a suitable tool. These models usually solve the equations using finite element method. This study compares the results from tow software packages, GSTARS4 & HEC-6, in the prediction of the sedimentation amount in Dez dam, southern Iran. The model provides a one-dimensional, steady-state simulation of sediment deposition and erosion by solving the equations of momentum, flow and sediment continuity and sediment transport. GSTARS4 (Generalized Sediment Transport Model for Alluvial River Simulation) which is based on a one-dimensional mathematical model that simulates bed changes in both longitudinal and transverse directions by using flow tubes in a quasi-two-dimensional scheme to calibrate a period of 47 years and forecast the next 47 years of sedimentation in Dez Dam, Southern Iran. This dam is among the highest dams all over the world (with its 203 m height), and irrigates more than 125000 square hectares of downstream lands and plays a major role in flood control in the region. The input data including geometry, hydraulic and sedimentary data, starts from 1955 to 2003 on a daily basis. To predict future river discharge, in this research, the time series data were assumed to be repeated after 47 years. Finally, the obtained result was very satisfactory in the delta region so that the output from GSTARS4 was almost identical to the hydrographic profile in 2003. In the Dez dam due to the long (65 km) and a large tank, the vertical currents are dominant causing the calculations by the above-mentioned method to be inaccurate. To solve this problem, we used the empirical reduction method to calculate the sedimentation in the downstream area which led to very good answers. Thus, we demonstrated that by combining these two methods a very suitable model for sedimentation in Dez dam for the study period can be obtained. The present study demonstrated successfully that the outputs of both methods are the same.

Keywords: Dez Dam, prediction, sedimentation, water resources, computational models, finite element method, GSTARS4, HEC-6

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Authors: Chongyang Ye, Rong Liu

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Elastic compression stockings (ECSs) have been widely applied in prophylaxis and treatment of chronic venous insufficiency of lower extremities. The medical function of ECS is to improve venous return and increase muscular pumping action to facilitate blood circulation, which is largely determined by the complex interaction between the ECS and lower limb tissues. Understanding the mechanical transmission of ECS along the skin surface, deeper tissues, and vascular system is essential to assess the effectiveness of the ECSs. In this study, a three-dimensional (3D) finite element (FE) model of the leg-ECS system integrated with a 3D fluid-solid interaction (FSI) model of the leg-vein system was constructed to analyze the biomechanical properties of veins and soft tissues under different ECS compression. The Magnetic Resonance Imaging (MRI) of the human leg was divided into three regions, including soft tissues, bones (tibia and fibula) and veins (peroneal vein, great saphenous vein, and small saphenous vein). The ECSs with pressure ranges from 15 to 26 mmHg (Classes I and II) were adopted in the developed FE-FSI model. The soft tissue was assumed as a Neo-Hookean hyperelastic model with the fixed bones, and the ECSs were regarded as an orthotropic elastic shell. The interfacial pressure and stress transmission were simulated by the FE model, and venous hemodynamics properties were simulated by the FSI model. The experimental validation indicated that the simulated interfacial pressure distributions were in accordance with the pressure measurement results. The developed model can be used to predict interfacial pressure, stress transmission, and venous hemodynamics exerted by ECSs and optimize the structure and materials properties of ECSs design, thus improving the efficiency of compression therapy.

Keywords: elastic compression stockings, fluid-solid interaction, tissue and vein properties, prediction

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Authors: Ildikó Balatoni, Nikolett Kosztin

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In this study the importance of maintaining the mental and physical health of employees was examined from the perspective of the employers. To this end companies in Hajdú-Bihar county of Hungary that are within in the TOP 100 based on their net revenue were interviewed. Economic sectors that were represented the most in this survey were processing, services, trade, agriculture, and construction. We examined whether or not companies provided any benefits to their employees concerning health awareness. Among respondents those who offered various services of medical specialists and/or discounted gym or swim passes in addition to compulsory medical examinations were hard to find, however more employers organize health and sports days. Nevertheless, a significant albeit very shallow positive correlation were found between the number of offered benefits vs. total gross income and vs. number of employees (r2=0.2555, p<0.001 and r2=0.1196 and p<0.05, respectively). In conclusion, while workplace health promotion is necessary it requires a change in employers’attitudes.

Keywords: corporate health promotion, employees, employers, health

Procedia PDF Downloads 128

Authors: Ana Neves, John Leander, Ignacio Gonzalez, Raid Karoumi

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Systematic monitoring and inspection are needed to assess the present state of a structure and predict its future condition. If an irregularity is noticed, repair actions may take place and the adequate intervention will most probably reduce the future costs with maintenance, minimize downtime and increase safety by avoiding the failure of the structure as a whole or of one of its structural parts. For this to be possible decisions must be made at the right time, which implies using systems that can detect abnormalities in their early stage. In this sense, Structural Health Monitoring (SHM) is seen as an effective tool for improving the safety and reliability of infrastructures. This paper explores the decision-making problem in SHM regarding the maintenance of civil engineering structures. The aim is to assess the present condition of a bridge based exclusively on measurements using the suggested method in this paper, such that action is taken coherently with the information made available by the monitoring system. Artificial Neural Networks are trained and their ability to predict structural behavior is evaluated in the light of a case study where acceleration measurements are acquired from a bridge located in Stockholm, Sweden. This relatively old bridge is presently still in operation despite experiencing obvious problems already reported in previous inspections. The prediction errors provide a measure of the accuracy of the algorithm and are subjected to further investigation, which comprises concepts like clustering analysis and statistical hypothesis testing. These enable to interpret the obtained prediction errors, draw conclusions about the state of the structure and thus support decision making regarding its maintenance.

Keywords: artificial neural networks, clustering analysis, model-free damage detection, statistical hypothesis testing, structural health monitoring

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Authors: Chandu Rathnayake, Isuri Anuradha

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

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

Procedia PDF Downloads 73

Authors: Ning Li, Jiacheng Zhang, Zheng Yang, Sylvia Kang

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Introduction: Polycystic ovary syndrome (PCOS) is a common physiological symptom in women of reproductive age. Women with PCOS may have infrequent or prolonged menstrual periods and excess male hormone (androgen) levels. Mira analyzes the cycle profiles and the luteinizing hormone (LH) changes in urine, closely related to the fertility level of healthy women and PCOS women. From the difference between the two groups, Mira helps to understand the physiological state of PCOS women and their hormonal changes in the menstrual cycle. Methods: In this study, data from 1496 cycles and information from 342 women belonging to two groups (181 PCOS and 161 Healthy) were collected and analyzed. Women test their luteinizing hormone (LH) in urine daily with Mira fertility test wand and Mira analyzer, from the day after the menstruation to the starting day of the next menstruation. All the collected data meets Mira’s user agreement and users’ identification was removed. The cycle length, LH peak, and other cycle information of the PCOS group were compared with the Healthy group. Results: The average cycle length of PCOS women is 41 days and of the Healthy women is 33 days. 91.4% of cycle length is within 40 days for the Healthy group, while it decreases to 71.9% for the PCOS group. This means PCOS women have a longer menstrual cycle and more variation during the cycle. With more variation, the ovulation prediction becomes more difficult for the PCOS group. The deviation between the LH surge day and the predicted ovulation day, calculated by the starting day of the next menstruation minus 14 days, is greater in the PCOS group compared with the Healthy group. Also, 46.96% of PCOS women have an irregular cycle, and only 19.25% of healthy women show an irregular cycle. Conclusion: PCOS women have longer menstrual cycles and more variation during the menstrual cycles. The traditional ovulation prediction is not suitable for PCOS women.

Keywords: menstrual cycle, PCOS, urinary luteinizing hormone, Mira

Procedia PDF Downloads 180

Authors: Tatiana V. Korsakova

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The cognitive methodology in management is considered: Cognitive structuring - the formation of ideas about the functioning of a developing organization; Cognitive modeling - heuristic construction of existing actions (zone of successful actions); and Cognitive construct - the formation of filters for converting external information into specific events of managerial reality. The major findings of the study are the identification of areas of successful actions in the organization, harmonization of criteria for evaluating the effectiveness of company management, and the frame-description that indicates the connection of environmental elements with the elements of the organization. It is stated the development of specific events of managerial reality in the direction of the further development of the organization depends on the personal cognitive construct of the development-subjects when it is used in the zone of successful actions.

Keywords: cognitive construct, focus of applicability, knowledge corporate culture, zones of successful actions

Procedia PDF Downloads 296

Authors: Kim Rod

Abstract:

People whose chronic pain limits their independence are especially likely to become anxious and depressed. Mindfulness training has shown promise for stress-related disorders. Methods: Chronic pain patients who complained of anxiety and depression and who scored higher than moderate in Hamilton Depression Rating Scale (HDRS) and Hospital Anxiety and Depression Scale (HADS) as well as moderate in Quality of Life Scale (QOLS) were observed for eight weeks, three days a week for an hour of Mindfulness Meditation training with an hour daily home Mindfulness Meditation practice. Pain was evaluated on study entry and completion, and patients were given the Patients’ Global Impression of Change (PGIC) to score at the end of the training program. Results: Forty-seven patients (47) completed the Mindfulness Meditation Training program. Over the year-long observation, patients demonstrated noticeable improvement in depression, anxiety, pain, and global impression of change. Conclusion: Chronic pain patients who suffer with anxiety and depression may benefit from incorporating Mindfulness Meditation into their treatment plans.

Keywords: mindfulness, meditation, depression, anxiety, chronic pain

Procedia PDF Downloads 445

Authors: Markus Stralla

Abstract:

This study employs a GMM framework to examine the impact of potential regulatory intervention regarding the occupations of supervisory board members in cooperative banking. To achieve insights, the study proceeds in two different ways. First, it investigates the changes in board structure prior and following to the German Act to Strengthen Financial Market and Insurance Supervision (FinVAG). Second, the study estimates the influence of Ph.D.Share, professional concentration and supervisory power on bank-risk changes in consideration of the implementation of FinVAG. Therefore, the study is based on a sample of 246 German cooperative banks from 2006-2011 while applying four different measures of bank risk, namely credit-, equity-, liquidity-risk, and Z-Score, with the former three also being addressed in FinVAG. Results indicate that the implementation of FinVAG results in (most likely unintentional) structural changes, especially at the expense of farmers, and affects all risk measures and relations between risk measures and supervisory board characteristics in a risk-reducing and therefore intended way. To disentangle the complex relationship between board characteristics and risk measures, the study utilizes two-step system GMM estimator to account for unobserved heterogeneity and simultaneity in order to reduce endogeneity problems. The findings may be especially relevant for stakeholders, regulators, supervisors and managers.

Keywords: bank governance, bank risk-taking, board of directors, regulation

Procedia PDF Downloads 428