Search results for: corporate credit rating prediction

Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: D. G. Badagha, C. D. Modhera, S. A. Vasanwala

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There is a great challenge for civil engineering field to contribute in environment prevention by finding out alternatives of cement and natural aggregates. There is a problem of global warming due to cement utilization in concrete, so it is necessary to give sustainable solution to produce concrete containing waste. It is very difficult to produce designated grade of concrete containing different ingredient and water cement ratio including waste to achieve desired fresh and harden properties of concrete as per requirement and specifications. To achieve the desired grade of concrete, a number of trials have to be taken, and then after evaluating the different parameters at long time performance, the concrete can be finalized to use for different purposes. This research work is carried out to solve the problem of time, cost and serviceability in the field of construction. In this research work, artificial neural network introduced to fix proportion of concrete ingredient with 50% waste replacement for M20, M25, M30, M35, M40, M45, M50, M55 and M60 grades of concrete. By using the neural network, mix design of high performance concrete was finalized, and the main basic mechanical properties were predicted at 3 days, 7 days and 28 days. The predicted strength was compared with the actual experimental mix design and concrete cube strength after 3 days, 7 days and 28 days. This experimentally and neural network based mix design can be used practically in field to give cost effective, time saving, feasible and sustainable high performance concrete for different types of structures.

Keywords: artificial neural network, high performance concrete, rebound hammer, strength prediction

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Authors: Simran Gill, Samuel Bartels

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Turfgrass is an important component of urban and rural lawns and landscapes. However, broadleaf weeds such as dandelions (Taraxacum officinale) and white clovers (Trifolium repens) pose major challenges to the health and aesthetics of turfgrass fields. Chemical weed control methods, such as 2,4-D weedicides, have been widely deployed; however, their safety and environmental impacts are often debated. Alternative, environmentally friendly control methods have been considered, but experimental tests for their effectiveness have been limited. This study investigates the use and effectiveness of nitrogen and iron compounds as nutrient management methods of weed control. In a two-phase experiment, the first conducted on a blend of cool season turfgrasses in plastic containers, the blend included Perennial ryegrass (Lolium perenne), Kentucky bluegrass (Poa pratensis) and Creeping red fescue (Festuca rubra) grown under controlled conditions in the greenhouse, involved the application of different combinations of nitrogen (urea and ammonium sulphate) and iron (chelated iron and iron sulphate) compounds and their combinations (urea × chelated iron, urea × iron sulphate, ammonium sulphate × chelated iron, ammonium sulphate × iron sulphate) contrasted with chemical 2, 4-D weedicide and a control (no application) treatment. There were three replicates of each of the treatments, resulting in a total of 30 treatment combinations. The parameters assessed during weekly data collection included a visual quality rating of weeds (nominal scale of 0-9), number of leaves, longest leaf span, number of weeds, chlorophyll fluorescence of grass, the visual quality rating of grass (0-9), and the weight of dried grass clippings. The results drawn from the experiment conducted over the period of 12 weeks, with three applications each at an interval of every 4 weeks, stated that the combination of ammonium sulphate and iron sulphate appeared to be most effective in halting the growth and establishment of dandelions and clovers while it also improved turf health. The second phase of the experiment, which involved the ammonium sulphate × iron sulphate, weedicide, and control treatments, was conducted outdoors on already established perennial turf with weeds under natural field conditions. After 12 weeks of observation, the results were comparable among the treatments in terms of weed control, but the ammonium sulphate × iron sulphate treatment fared much better in terms of the improved visual quality of the turf and other quality ratings. Preliminary results from these experiments thus suggest that nutrient management based on nitrogen and iron compounds could be a useful environmentally friendly alternative for controlling broadleaf weeds and improving the health and quality of turfgrass.

Keywords: broadleaf weeds, nitrogen, iron, turfgrass

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Authors: J. Becker, R. Arnold

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The design of distribution logistics has a decisive impact on a company's logistics costs and performance. Hence, such solutions make an essential contribution to corporate success. This article describes a decision support system for analyzing the potential of distribution logistics in terms of logistics costs and performance. In contrast to previous procedures of business process re-engineering (BPR), this method maps distribution logistics holistically under variable distribution structures. Combined with qualitative measures the decision support system will contribute to a more efficient design of distribution logistics.

Keywords: decision support system, distribution logistics, potential analyses, supply chain management

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Authors: Harish Krishnamurthy, Anna Lafontant, Ren Yi

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Unidentified Flying Objects (UFOs) have been an interesting topic for most enthusiasts and hence people all over the United States report such findings online at the National UFO Report Center (NUFORC). Some of these reports are a hoax and among those that seem legitimate, our task is not to establish that these events confirm that they indeed are events related to flying objects from aliens in outer space. Rather, we intend to identify if the report was a hoax as was identified by the UFO database team with their existing curation criterion. However, the database provides a wealth of information that can be exploited to provide various analyses and insights such as social reporting, identifying real-time spatial events and much more. We perform analysis to localize these time-series geospatial events and correlate with known real-time events. This paper does not confirm any legitimacy of alien activity, but rather attempts to gather information from likely legitimate reports of UFOs by studying the online reports. These events happen in geospatial clusters and also are time-based. We look at cluster density and data visualization to search the space of various cluster realizations to decide best probable clusters that provide us information about the proximity of such activity. A random forest classifier is also presented that is used to identify true events and hoax events, using the best possible features available such as region, week, time-period and duration. Lastly, we show the performance of the scheme on various days and correlate with real-time events where one of the UFO reports strongly correlates to a missile test conducted in the United States.

Keywords: time-series clustering, feature extraction, hoax prediction, geospatial events

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Authors: Opemiposi Adegbulu

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Conflicts of interest is an elusive, diverse and engaging subject, a cross-cutting problem of governance; all levels of governance, ranging from local to global, public to corporate or financial sectors. In all these areas, its mismanagement could lead to the distortion of decision-making processes, corrosion of trust and the weakening of administration. According to Professor Peters, an expert in the area, conflict of interest, a problem at the root of many scandals has “become a pervasive ethical concern in our professional, organisational, and political life”. Conflicts of interest corrode trust, and like in the public sector, trust is mandatory for the market, consumers/clients, shareholders and other stakeholders in the private sector. However, conflicts of interest in the private sector are distinct and must be treated in like manner when regulatory efforts are made to address them. The research looks at identifying conflicts of interest in the private sector and differentiating them from those in the public sector. The public interest is submitted as a criterion which allows for such differentiation. This is significant because it would for the use of tailor-made or sector-specific approaches to addressing this complex issue. This is conducted through extensive review of literature and theories on the definition of conflicts of interest. This study will employ theoretical, doctrinal and comparative methods. The nature of conflicts of interest in the private sector will be explored, through an analysis of the public sector where the notion of conflicts of interest appears more clearly identified, reasons, why they are of business ethics concern, will be advanced, and then, once again, looking at public sector solutions and other solutions, the study will identify ways of mitigating and managing conflicts in the private sector. An exploration of public sector conflicts of interest and solutions will be carried out because the typologies of conflicts of interest in both sectors appear very similar at the core and thus, lessons can be learnt with regards to the management of these issues in the private sector. Conflicts of interest corrode trust, and like in the public sector, trust is mandatory for the market, consumers/clients, shareholders and other stakeholders in the private sector. This research will then focus on some specific challenges to understanding and identifying conflicts of interest in the private sector; origin, diverging theories, the psychological barrier to the definition, similarities with public sector conflicts of interest due to the notions of corrosion of trust, ‘being in a particular kind of situation,’ etc. The notion of public interest will be submitted as a key element at the heart of the distinction between public sector and private sector conflicts of interests. It will then be proposed that the appreciation of the notion of conflicts of interest differ according to sector, country to country, based on the public interest test, using the United Kingdom (UK), the United States of America (US), France and the Philippines as illustrations.

Keywords: conflicts of interest, corporate governance, global governance, public interest

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Authors: Mohammed N. Al Kindi, Mazin Al Khabouri, Khalsa Al Lamki, Tommasso Pappuci, Giovani Romeo, Nadia Al Wardy

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Hereditary hearing loss is a heterogeneous group of complex disorders with an overall incidence of one in every five hundred newborns presented as syndromic and non-syndromic forms. Cadherin-related 23 (CDH23) is one of the listed deafness causative genes. CDH23 is found to be expressed in the stereocilia of hair cells and the retina photoreceptor cells. Defective CDH23 has been associated mostly with prelingual severe-to-profound sensorineural hearing loss (SNHL) in either syndromic (USH1D) or non-syndromic SNHL (DFNB12). An Omani family diagnosed clinically with severe-profound sensorineural hearing loss was genetically analysed by whole exome sequencing technique. A novel homozygous missense variant, c.A7451C (p.D2484A), in exon 53 of CDH23 was detected. One hundred and thirty control samples were analysed where all were negative for the detected variant. The variant was analysed in silico for pathogenicity verification using several mutation prediction software. The variant proved to be a pathogenic mutation and is reported for the first time in Oman and worldwide. It is concluded that in silico mutation prediction analysis might be used as a useful molecular diagnostics tool benefiting both genetic counseling and mutation verification. The aspartic acid 2484 alanine missense substitution might be the main disease-causing mutation that damages CDH23 function and could be used as a genetic hearing loss marker for this particular Omani family.

Keywords: Cdh23, d2484a, in silico, Oman

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

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In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: Joselito Medina-Marin, Maria G. Serna-Diaz, Juan C. Seck-Tuoh-Mora, Norberto Hernandez-Romero, Irving Barragán-Vite

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Wind speed forecasting is an important issue for planning wind power generation facilities. The accuracy in the wind speed prediction allows a good performance of wind turbines for electricity generation. A model based on artificial neural networks is presented in this work. A dataset with atmospheric information about air temperature, atmospheric pressure, wind direction, and wind speed in Pachuca, Hidalgo, México, was used to train the artificial neural network. The data was downloaded from the web page of the National Meteorological Service of the Mexican government. The records were gathered for three months, with time intervals of ten minutes. This dataset was used to develop an iterative algorithm to create 1,110 ANNs, with different configurations, starting from one to three hidden layers and every hidden layer with a number of neurons from 1 to 10. Each ANN was trained with the Levenberg-Marquardt backpropagation algorithm, which is used to learn the relationship between input and output values. The model with the best performance contains three hidden layers and 9, 6, and 5 neurons, respectively; and the coefficient of determination obtained was r²=0.9414, and the Root Mean Squared Error is 1.0559. In summary, the ANN approach is suitable to predict the wind speed in Pachuca City because the r² value denotes a good fitting of gathered records, and the obtained ANN model can be used in the planning of wind power generation grids.

Keywords: wind power generation, artificial neural networks, wind speed, coefficient of determination

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Authors: Renuka H. Deshmukh, Snehal Nifadkar, Anil P. Dongre

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The research study aims to analyze the financial performance of the companies associated with CDM projects implemented in India from 2001 to 2014 by calculating net profit with and without CDM revenue. Further the study also highlights the Year-wise and sector-wise lending to CDM projects in India as well as in the state of Maharashtra. The study further aims to examine the year-wise trend of Certified Emission Reductions (CER) issued by the CDM projects implemented in Maharashtra from 2001-2014. The study as well analyses the responses of selected corporate with respect to the challenges in implementing and obtaining finance from commercial banks.

Keywords: adaptation costs, internal rate of return, mitigation, vulnerability, CER

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Authors: Prof. Ammal Mokhtar Metwally, Samia M. Sami, Nihad A. Ibrahim, Fatma A. Shaaban, Iman I. Salama

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Objectives: Reducing neonatal mortality by 2030 is still a challenging goal in developing countries. low birth weight (LBW) is a significant contributor to this, especially where weighing newborns is not possible routinely. The present study aimed to determine a simple, easy, reliable anthropometric measure(s) that can predict LBW) and neonatal mortality. Methods: A prospective cohort study of 570 babies born in districts of El Menia governorate, Egypt (where most deliveries occurred at home) was examined at birth. Newborn weight, length, head, chest, mid-arm, and thigh circumferences were measured. Follow up of the examined neonates took place during their first four weeks of life to report any mortalities. The most predictable anthropometric measures were determined using the statistical package of SPSS, and multiple Logistic regression analysis was performed.: Results: Head and chest circumferences with cut-off points < 33 cm and ≤ 31.5 cm, respectively, were the significant predictors for LBW. They carried the best combination of having the highest sensitivity (89.8 % & 86.4 %) and least false negative predictive value (1.4 % & 1.7 %). Chest circumference with a cut-off point ≤ 31.5 cm was the significant predictor for neonatal mortality with 83.3 % sensitivity and 0.43 % false negative predictive value. Conclusion: Using chest circumference with a cut-off point ≤ 31.5 cm is recommended as a single simple anthropometric measurement for the prediction of both LBW and neonatal mortality. The predicted measure could act as a substitute for weighting newborns in communities where scales to weigh them are not routinely available.

Keywords: low birth weight, neonatal mortality, anthropometric measures, practical screening

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Authors: Maharjan Shree Kumar

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Climate change impacts are observed in all livelihood sectors primarily in agriculture and forestry. Multiple factors have influenced the climate vulnerabilities and adaptations in agricultural at the household level. This study focused on the factors affecting adaptation in agriculture in Madi and Deukhuri valleys of Central and Western Nepal. The systematic random sampling technique was applied to select 154 households in Madi and 150 households in Deukhuri. The main purpose of the study was to analyze the socio-economic factors that either influence or restrain the farmers’ adaptation to climate change at the household level by applying the linear probability model. Based on the analysis, it is revealed that crop diversity, education, training and total land holding (acre) were positively significant for adaptation choices the study sites. Rest of the variables were not significant though indicated positive as expected except age, occupation, ethnicity, family size, and access to credit.

Keywords: adaptation, agriculture, climate, factors, Nepal

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Authors: Mohamed Sarrab, Hadj Bourdoucen

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Mobile applications are verified to check the correctness or evaluated to check the performance with respect to specific security properties such as availability, integrity, and confidentiality. Where they are made available to the end users of the mobile application is achievable only to a limited degree using software engineering static verification techniques. The more sensitive the information, such as credit card data, personal medical information or personal emails being processed by mobile application, the more important it is to ensure the confidentiality of this information. Monitoring non-trusted mobile application during execution in an environment where sensitive information is present is difficult and unnerving. The paper addresses the issue of monitoring and controlling the flow of confidential information during non-trusted mobile application execution. The approach concentrates on providing a dynamic and usable information security solution by interacting with the mobile users during the run-time of mobile application in response to information flow events.

Keywords: mobile application, run-time verification, usable security, direct information flow

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Authors: RuiJin Zhang, HengJiang Cai, JinSong Gui

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It takes Patinopecten yessoensis larvae more than 20 days from spawning to settlement. Due to the natural environmental factors such as current, Patinopecten yessoensis larvae are transported to a distance more than hundreds of kilometers, leading to a high instability of their spatial and temporal distribution and great difficulties in the natural spat collection. Therefore predicting the distribution is of great significance to improve the operating efficiency of the collecting. Hydrodynamic model of Northern China Yellow Sea was established and the motions equations of physical oceanography and verified by the tidal harmonic constants and the measured data velocities of Dalian Bay. According to the passivity drift characteristics of the larvae, combined with the hydrodynamic model and the particle tracking model, the spatial and temporal distribution prediction model was established and the spatial and temporal distribution of the larvae under the influence of flow and wind were simulated. It can be concluded from the model results: ocean currents have greatest impacts on the passive drift path and diffusion of Patinopecten yessoensis larvae; the impact of wind is also important, which changed the direction and speed of the drift. Patinopecten yessoensis larvae were generated in the sea along Zhangzi Island and Guanglu-Dachangshan Island, but after two months, with the impact of wind and currents, the larvae appeared in the west of Dalian and the southern of Lvshun, and even in Bohai Bay. The model results are consistent with the relevant literature on qualitative analysis, and this conclusion explains where the larvae come from in the perspective of numerical simulation.

Keywords: numerical simulation, Patinopecten yessoensis larvae, predicting model, spatial and temporal distribution

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Authors: Birce Boga Bakirli

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In this paper, we investigate ideas on human capital development and management in industries. We structured a model to be able to gather the data from the interviews conducted with worker, specialists and owners of companies. Different aspects of the situation are found in these interviews, and we used the information to model the benefit of the business owners and workers perspectives. These are modelled as a bi-level programming problem. Several instances of the generic cases are solved. The results show the importance of education within and out of the company for workers, and it returns for the company.

Keywords: bi-level programming, corporate strategy, cost tradeoffs, human capital, mixed integer programming, Stackelberg game, supplier relations, strategic planning

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Authors: A. Nicknam, N. Eftekhari, A. Mazarei, M. Ganjvar

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This article presents an alternative collapse capacity intensity measure in the three elements form which is influenced by the spectral ordinates at periods longer than that of the first mode period at near and far source sites. A parameter, denoted by β, is defined by which the spectral ordinate effects, up to the effective period (2T_1), on the intensity measure are taken into account. The methodology permits to meet the hazard-levelled target extreme event in the probabilistic and deterministic forms. A MATLAB code is developed involving OpenSees to calculate the collapse capacities of the 8 archetype RC structures having 2 to 20 stories for regression process. The incremental dynamic analysis (IDA) method is used to calculate the structure’s collapse values accounting for the element stiffness and strength deterioration. The general near field set presented by FEMA is used in a series of performing nonlinear analyses. 8 linear relationships are developed for the 8structutres leading to the correlation coefficient up to 0.93. A collapse capacity near field prediction equation is developed taking into account the results of regression processes obtained from the 8 structures. The proposed prediction equation is validated against a set of actual near field records leading to a good agreement. Implementation of the proposed equation to the four archetype RC structures demonstrated different collapse capacities at near field site compared to those of FEMA. The reasons of differences are believed to be due to accounting for the spectral shape effects.

Keywords: collapse capacity, fragility analysis, spectral shape effects, IDA method

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Authors: Husham Bayazed

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Immune responses following surgical trauma play a pivotal role in predicting postoperative outcomes from healing and recovery to postoperative complications. Postoperative complications, including infections and protracted recovery, occur in a significant number of about 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on the healthcare system in any community. The accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain major clinical provocations. Recent Findings: Recent studies are focusing on immune dysregulation mechanisms that occur in response to surgical trauma as a key determinant of postoperative complications. Antecedent studies mainly were plunging into the detection of inflammatory plasma markers, which facilitate in providing important clues regarding their pathogenesis. However, recent Single-cell technologies, such as mass cytometry or single-cell RNA sequencing, have markedly enhanced our ability to understand the immunological basis of postoperative immunological trauma complications and to identify their prognostic biological signatures. Summary: The advent of proteomic technologies has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers and providing patients and surgeons with information to improve surgical outcomes. However, more studies are required to accurately predict the risk of postoperative complications in individual patients.

Keywords: immune dysregulation, postoperative complications, surgical trauma, flow cytometry

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Authors: Amao Funmilayo Lanrewaju

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The study examined the socio-economic characteristics of the residents in selected urban fringes of Ibadan; identified and examined the housing and neighbourhood characteristics and evaluated housing quality in the study area. It analysed the relationship between the socio-economic characteristics of the residents, housing and neighbourhood characteristics as well as housing quality in the study area. This was with a view to providing information that would enhance the housing quality in urban fringes of Ibadan. Primary and secondary data were used for the study. A survey of eleven purposively selected communities from Oluyole and Egbeda local government areas in the urban fringes was conducted through a questionnaire administration and expert rating by five independent assessors (Qualified Architects) using penalty scoring within similar time-frames. The study employed a random sampling method to select a sample size of 480 houses representing 5% of the sampling frame of 9600 houses. Respondent in the first house was selected randomly and subsequently every 20th house in the streets involved was systematically selected for questionnaire administration, usually a household-head per building. The structured questionnaire elicited information on socio-economic characteristics of the residents, housing and neighbourhood characteristics, factors affecting housing quality and housing quality in the study area. Secondary data obtained for the study included the land-use plan of Ibadan from previous publications, housing demographics, population figures from relevant institutions and other published materials. The data collected were analysed using descriptive and inferential statistics such as frequency distribution, Cross tabulation, Correlation Analysis, Analysis of Variance (ANOVA) and Relative Importance Index (RII). The result of the survey revealed that respondents from the Yoruba ethnic group constituted the majority, comprising 439 (91.5%) of the 480 respondents from the two local government areas selected. It also revealed that the type of tenure status of majority of the respondents in the two local government areas was self-ownership (234, 48.8%), while 44.0% of the respondents acquired their houses through personal savings. Cross tabulation indicated that majority (67.1%, 322 out of 480) of the respondents were low-income earners. The study showed that both housing and neighbourhood services were not adequately provided across neighbourhoods in the study area. Correlation analysis indicated a significant relationship between respondents’ socio–economic status and their general housing quality (r=0.46; p-value of 0.01< 0.05). The ANOVA indicated that the relationship between socio-economic characteristics of the residents, housing and neighbourhood characteristics in the study area was significant (F=18.289, p=0.00; the coefficient of determination R2= 0.192). The findings from the study however revealed that there was no significant difference in the results obtained from users based evaluation and expert rating. The study concluded that housing quality in the urban fringes of Ibadan is generally poor and the socio-economic status of the residents significantly influenced the housing quality.

Keywords: housing quality, urban fringes, economic status, poverty

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Authors: Marc Toutain, Pascale Leconte, Antoine Gauthier

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Introduction: Eating Disorders (ED), such as anorexia, bulimia, and binge eating, are psychiatric illnesses that mostly affect young people. The main symptoms concern eating (restriction, excessive food intake) and weight control behaviors (laxatives, vomiting). Psychological comorbidities (depression, executive function disorders, etc.) and problematic behaviors toward physical activity (PA) are commonly associated with ED. Acquaintances on ED risk factors are still lacking, and more community sample studies are needed to improve prevention and early detection. To our knowledge, studies are needed to specifically investigate the link between ED risk level, PA, and psychological risk factors in a community sample of adolescents. The aim of this study is to assess the relation between ED risk level, exercise (type, frequency, and motivations for engaging in exercise), and psychological factors based on the Jacobi risk factors model. We suppose that a high risk of ED will be associated with the practice of high caloric cost PA, motivations oriented to weight and shape control, and psychological disturbances. Method: An online survey destined for students has been sent to several middle schools and colleges in northwest France. This survey combined several questionnaires, the Eating Attitude Test-26 assessing ED risk; the Exercise Motivation Inventory–2 assessing motivations toward PA; the Hospital Anxiety and Depression Scale assessing anxiety and depression, the Contour Drawing Rating Scale; and the Body Esteem Scale assessing body dissatisfaction, Rosenberg Self-esteem Scale assessing self-esteem, the Exercise Dependence Scale-Revised assessing PA dependence, the Multidimensional Assessment of Interoceptive Awareness assessing interoceptive awareness and the Frost Multidimensional Perfectionism Scale assessing perfectionism. Machine learning analysis will be performed in order to constitute groups with a tree-based model clustering method, extract risk profile(s) with a bootstrap method comparison, and predict ED risk with a prediction method based on a decision tree-based model. Expected results: 1044 complete records have already been collected, and the survey will be closed at the end of May 2022. Records will be analyzed with a clustering method and a bootstrap method in order to reveal risk profile(s). Furthermore, a predictive tree decision method will be done to extract an accurate predictive model of ED risk. This analysis will confirm typical main risk factors and will give more data on presumed strong risk factors such as exercise motivations and interoceptive deficit. Furthermore, it will enlighten particular risk profiles with a strong level of proof and greatly contribute to improving the early detection of ED and contribute to a better understanding of ED risk factors.

Keywords: eating disorders, risk factors, physical activity, machine learning

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Authors: Iqra Ali

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This study uses panel Vector Auto Regression (VAR) to analyses the dynamic link between banking and insurance activities based on the asset size of the insurance industry for 73 countries between 1980 and 2014. Assets in the insurance industry and banking activities usually have a Granger causal link, according to panel Granger-causality tests. Impulse response analyses for the entire sample show that the size of insurance assets responds favorably to a shock to the liquid liabilities and deposits of the financial system but negatively to a shock to deposit money bank assets and private credit offered by commercial banks, other financial institutions, and deposit banks. While the findings for middle- and low-income nations varied significantly, the observations for high-income countries are essentially the same. Furthermore, we find that there is a substantial interplay between banking and insurance activity in civil law nations as opposed to common law ones.

Keywords: vector autoregression, banking, insurance, Granger-causality

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Authors: Geng Xiangren, Liu Lei, Gui Ye-Wei, Tang Wei, Wang An-ling

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The malfunction of thermal protection system (TPS) caused by aerodynamic heating is a latent trouble to aircraft structure safety. Accurately predicting the structure temperature field is quite important for the TPS design of hypersonic vehicle. Since Thornton’s work in 1988, the coupled method of aerodynamic heating and heat transfer has developed rapidly. However, little attention has been paid to the influence of structural deformation on aerodynamic heating and structural temperature field. In the flight, especially the long-endurance flight, the structural deformation, caused by the aerodynamic heating and temperature rise, has a direct impact on the aerodynamic heating and structural temperature field. Thus, the coupled interaction cannot be neglected. In this paper, based on the method of static aero-thermo-elasticity, considering the influence of aero-thermo-elasticity deformation, the aerodynamic heating and heat transfer coupled results of hypersonic vehicle wing model were calculated. The results show that, for the low-curvature region, such as fuselage or center-section wing, structure deformation has little effect on temperature field. However, for the stagnation region with high curvature, the coupled effect is not negligible. Thus, it is quite important for the structure temperature prediction to take into account the effect of elastic deformation. This work has laid a solid foundation for improving the prediction accuracy of the temperature distribution of aircraft structures and the evaluation capacity of structural performance.

Keywords: aerothermoelasticity, elastic deformation, structural temperature, multi-field coupling

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Authors: Laura Josabeth Oros-Avilés, Josefina León-León

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In recent decades, the concern of the monetary authorities has increased because of the instability of the financial sector caused by the crash of speculative bubbles. In fact, the crash of "housing bubble" in U.S. (2007-2008) led the latest global crisis. The aim of paper is to analyze the features and causes of the financial and banking crisis from an historical view. In particular, in this research, a comparative study of some analytical approaches about economic and financial history is discussed. In addition, the role of monetary policy of central banks in managing financial crises, from its origins to today, is analyzed. According to the studied approaches, two types of factors that cause the financial instability were identified: subjective and objectives. In the research, these factors are deeply discussed, in order to noting the agreements and disagreement between the authors. Specially, it is worth noting that all of them recognized that the credit boom and the financial deregulation are the main causes of financial crises.

Keywords: asset prices, banking crises, financial bubble, financial instability, monetary policy

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Authors: Christina Volioti, Sotiris Manitsaris, Eleni Katsouli, Vasiliki Tsekouropoulou, Leontios J. Hadjileontiadis

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The piano sonatas of Beethoven represent part of the Intangible Cultural Heritage. The aims of this research were to further explore this intangibility by placing emphasis on defining emotional normative ratings for the “Waldstein” (Op. 53) and “Tempest” (Op. 31) Sonatas of Beethoven. To this end, a musicological analysis was conducted on these particular sonatas and referential patterns in these works of Beethoven were defined. Appropriate interactive questionnaires were designed in order to create a statistical normative rating that describes the emotional status when an individual listens to these musical excerpts. Based on these ratings, it is possible for emotional annotations for these same referential patterns to be created and integrated into the music score.

Keywords: emotional annotations, intangible cultural heritage, musicological analysis, normative ratings

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Authors: Feddaoui Amina

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Through this study we will try to shed light on the theoretical framework proposed for understanding creative accounting as a financial numbers game and one of the most important techniques of accounts manipulation, its main actors and its practices. We will discover the role of the modified Jones model (1995) in detecting creative accounting practices using discretionary accruals. Finally we will try to confirm the importance and the need to address this type of practices using corporate governance as a main control system and an important defense line to reduce these dangerous accounts manipulation.

Keywords: financial numbers game, creative accounting, modified Jones model, accounts manipulation

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Authors: Nadish Anand, Richard D. Gould

Abstract:

A simplistic model is introduced for determining the thermal characteristics of a Low-rise Residential (LRR) building and then predicts the energy usage by its Heating Ventilation & Air Conditioning (HVAC) system according to changes in weather conditions which are reflected in the Ambient Temperature (Outside Air Temperature). The LRR buildings are treated as a simple lump for solving the heat transfer problem and the model is derived using the lumped capacitance model of transient conduction heat transfer from bodies. Since most contemporary HVAC systems have a thermostat control which will have an offset temperature and user defined set point temperatures which define when the HVAC system will switch on and off. The aim is to predict without any error the Body Temperature (i.e. the Inside Air Temperature) which will estimate the switching on and off of the HVAC system. To validate the mathematical model derived from lumped capacitance we have used EnergyPlus simulation engine, which simulates Buildings with considerable accuracy. We have predicted through the low order model the Inside Air Temperature of a single house kept in three different climate zones (Detroit, Raleigh & Austin) and different orientations for summer and winter seasons. The prediction error from the model for the same day as that of model parameter calculation has showed an error of < 10% in winter for almost all the orientations and climate zones. Whereas the prediction error is only <10% for all the orientations in the summer season for climate zone at higher latitudes (Raleigh & Detroit). Possible factors responsible for the large variations are also noted in the work, paving way for future research.

Keywords: building energy, energy consumption, energy+, HVAC, low order model, lumped capacitance

Procedia PDF Downloads 266

Authors: Mohammad Mahdi Mozaffari, Mehdi Ajalli, Delaram Jafargholi

Abstract:

The main purpose of this study is to investigate the impact of the system of ERP (Enterprise Resource Planning) and SCM (Supply Chain Management) on the competitive advantage and performance of Mamot Company. The methods for collecting information in this study are library studies and field research. A questionnaire was used to collect the data needed to determine the relationship between the variables of the research. This questionnaire contains 38 questions. The direction of the current research is applied. The statistical population of this study consists of managers and experts who are familiar with the SCM system and ERP. Number of statistical society is 210. The sampling method is simple in this research. The sample size is 136 people. Also, among the distributed questionnaires, Reliability of the Cronbach's Alpha Cronbach's Questionnaire is evaluated and its value is more than 70%. Therefore, it confirms reliability. And formal validity has been used to determine the validity of the questionnaire, and the validity of the questionnaire is confirmed by the fact that the score of the impact is greater than 1.5. In the present study, one variable analysis was used for central indicators, dispersion and deviation from symmetry, and a general picture of the society was obtained. Also, two variables were analyzed to test the hypotheses; measure the correlation coefficient between variables using structural equations, SPSS software was used. Finally, multivariate analysis was used with statistical techniques related to the SPLS structural equations to determine the effects of independent variables on the dependent variables of the research to determine the structural relationships between the variables. The results of the test of research hypotheses indicate that: 1. Supply chain management practices have a positive impact on the competitive advantage of the Mammoth industrial complex. 2. Supply chain management practices have a positive impact on the performance of the Mammoth industrial complex. 3. Planning system Organizational resources have a positive impact on the performance of the Mammoth industrial complex. 4. The system of enterprise resource planning has a positive impact on Mamot's competitive advantage. 5.The competitive advantage has a positive impact on the performance of the Mammoth industrial complex 6.The system of enterprise resource planning Mamot Industrial Complex Supply Chain Management has a positive impact. The above results indicate that the system of enterprise resource planning and supply chain management has an impact on the competitive advantage and corporate performance of Mamot Company.

Keywords: enterprise resource planning, supply chain management, competitive advantage, Mamot company performance

Procedia PDF Downloads 98

Authors: Said Alshukri, Mazhar Hussain Malik

Abstract:

Green hydrogen is hydrogen produced using renewable energy sources. In the last few years, Oman aimed to reduce its dependency on fossil fuels. Recently, the hydrogen economy has become a global trend, and many countries have started to investigate the feasibility of implementing this sector. Oman created an alliance to establish the policy and rules for this sector. With motivation coming from both global and local interest in green hydrogen, this paper investigates the potential of producing hydrogen from wind and solar energies in three different locations in Oman, namely Duqm, Salalah, and Sohar. By using machine learning-based software “WEKA” and local metrological data, the project was designed to figure out which location has the highest wind and solar energy potential. First, various supervised models were tested to obtain their prediction accuracy, and it was found that the Random Forest (RF) model has the best prediction performance. The RF model was applied to 2021 metrological data for each location, and the results indicated that Duqm has the highest wind and solar energy potential. The system of one wind turbine in Duqm can produce 8335 MWh/year, which could be utilized in the water electrolysis process to produce 88847 kg of hydrogen mass, while a solar system consisting of 2820 solar cells is estimated to produce 1666.223 MWh/ year which is capable of producing 177591 kg of hydrogen mass.

Keywords: green hydrogen, machine learning, wind and solar energies, WEKA, supervised models, random forest

Procedia PDF Downloads 79

Authors: Rudra P. Pradhan

Abstract:

This paper examines the Granger causal nexus between financial development and energy consumption in the group of 35 Financial Action Task Force (FATF) Countries over the period 1988-2012. The study uses two financial development indicators such as private sector credit and stock market capitalization and seven energy consumption indicators such as coal, oil, gas, electricity, hydro-electrical, nuclear and biomass. Using panel cointegration tests, the study finds that financial development and energy consumption are cointegrated, indicating the presence of a long-run relationship between the two. Using a panel vector error correction model (VECM), the study detects both bidirectional and unidirectional causality between financial development and energy consumption. The variation of this causality is due to the use of different proxies for both financial development and energy consumption. The policy implication of this study is that economic policies should recognize the differences in the financial development-energy consumption nexus in order to maintain sustainable development in the selected 35 FATF countries.

Keywords: energy consumption, financial development, FATF countries, Panel VECM

Procedia PDF Downloads 265

Authors: R. O. Osaseri, A. R. Usiobaifo

Abstract:

The power transformer which is responsible for the voltage transformation is of great relevance in the power system and oil-immerse transformer is widely used all over the world. A prompt and proper maintenance of the transformer is of utmost importance. The dissolved gasses content in power transformer, oil is of enormous importance in detecting incipient fault of the transformer. There is a need for accurate prediction of the incipient fault in transformer oil in order to facilitate the prompt maintenance and reducing the cost and error minimization. Study on fault prediction and diagnostic has been the center of many researchers and many previous works have been reported on the use of artificial intelligence to predict incipient failure of transformer faults. In this study machine learning technique was employed by using gradient decent algorithms and Support Vector Machine (SVM) in predicting incipient fault diagnosis of transformer. The method focuses on creating a system that improves its performance on previous result and historical data. The system design approach is basically in two phases; training and testing phase. The gradient decent algorithm is trained with a training dataset while the learned algorithm is applied to a set of new data. This two dataset is used to prove the accuracy of the proposed model. In this study a transformer fault diagnostic model based on Support Vector Machine (SVM) and gradient decent algorithms has been presented with a satisfactory diagnostic capability with high percentage in predicting incipient failure of transformer faults than existing diagnostic methods.

Keywords: diagnostic model, gradient decent, machine learning, support vector machine (SVM), transformer fault

Procedia PDF Downloads 322

Authors: Saba Gachpaz, Hamid Reza Heidari

Abstract:

The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

Procedia PDF Downloads 84