Search results for: financial market prediction

Authors: S. Naveen, V. Sivasubramanian

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A modeling theory is proposed to consider the vaporization of LNG during its contact with water following its release from an underwater source. The spillage of LNG underwater can lead to a decrease in the surface temperature of water and subsequent freezing. This can in turn affect the heat flux distribution from the released LNG onto the water surrounding it. The available models predict the rate of vaporization considering the surface of contact as a solid wall, and considering the entire phenomena as a solid-liquid operation. This assumption greatly under-predicted the overall heat transfer on LNG water interface. The vaporization flux would first decrease during the film boiling, followed by an increase during the transition boiling and a steady decrease during the nucleate boiling. A superheat theory is introduced to enhance the accuracy in the prediction of the heat transfer between LNG and water. The work suggests that considering the superheat theory can greatly enhance the prediction of LNG vaporization on underwater releases and also help improve the study of overall thermodynamics.

Keywords: evaporation rate, heat transfer, LNG vaporization, underwater LNG release

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Authors: Tayeb Chihi, Messaoud Fatmi

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We present calculations of the structural, elastic and electronic properties of nonferrous Ti, Zr, and Hf pure metals in both parent and martensite phases in bcc and hcp structures respectively. They are based on the generalized gradient approximation (GGA) within the density functional theory (DFT). The shear modulus, Young's modulus and Poisson's ratio for Ti, Zr, and Hf metals have were calculated and compared with the corresponding experimental values. Using elastic constants obtained from calculations GGA, the bulk modulus along the crystallographic axes of single crystals was calculated. This is in good agreement with experiment for Ti and Zr, whereas the hcp structure for Hf is a prediction. At zero temperature and zero pressure, the bcc crystal structure is found to be mechanically unstable for Ti, Zr, and Hf. In our calculations the hcp structures is correctly found to be stable at the equilibrium volume. In the electronic density of states (DOS), the smaller n(EF) is, the more stable the compound is. Therefore, in agreement with the results obtained from the total energy minimum.

Keywords: Ti, Zr, Hf, pure metals, transformation, energy

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Authors: Amir Hossein Ghaffari Nejad, Forouhar Ferdowsi, Reza Mashhadi

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In the interest-free banking system of Iran, the savings of society are in the form of bank deposits, and banks using the Islamic contracts, allocate the resources to applicants for obtaining facilities and credit. In the meantime, the central bank, with the aim of introducing monetary policy, determines the maximum interest rate on bank deposits in terms of macroeconomic requirements. But in recent years, the country's economic constraints with the stagflation and the consequence of the institutional weaknesses of the financial market of Iran have resulted in massive disturbances in the balance sheet of the banking system, resulting in a period of mismatch maturity in the banks' assets and liabilities and the implementation of a Ponzi game. This issue caused determination of the interest rate in long-term bank deposit contracts to be associated with non-observance of the maximum rate set by the central bank. The result of this condition was in the allocation of new sources of equipment to meet past commitments towards the old depositors and, as a result, a significant part of the supply of equipment was leaked out of the facilitating cycle and credit crunch emerged. The purpose of this study is to identify the most important factors affecting the occurrence of non-confirmatory financial banking behavior using data from 19 public and private banks of Iran. For this purpose, the causes of this non-confirmatory behavior of banks have been investigated using the panel vector autoregression method (PVAR) for the period of 2007-2015. Granger's causality test results suggest that the return of parallel markets for bank deposits, non-performing loans and the high share of the ratio of facilities to banks' deposits are all a cause of the formation of non-confirmatory behavior. Also, according to the results of impulse response functions and variance decomposition, NPL and the ratio of facilities to deposits have the highest long-term effect and also have a high contribution to explaining the changes in banks' non-confirmatory behavior in determining the interest rate on deposits.

Keywords: non-conformity behavior, Ponzi Game, panel vector autoregression, nonperforming loans

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Authors: Sy, Melecio Jr

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The decision to adopt vertical integration (VI) is firm-specific, but there is a common practice among businesses in an industry to maximize the massive potential benefits of VI. This study aims to determine VI adoption in the restaurant industry in Davao City. Using a two-step sampling process, the study used a validated survey questionnaire among 264 restaurant owners and managers randomly selected and geographically classified. It is a quantitative study where the data were subjected to a structural equation model (SEM). The results revealed that VI is present but limited to procurement, production, restaurant services, and online marketing. Raw materials were outsourced while delivery to customers through third-party delivery services. VI slowly increased over ten years except for online marketing, which has grown significantly in a few years. The endogenous and exogenous variables were correlated and established the linear regression model. The SEM's best fit model revealed that strategic motives (SMOT) and market factors (MFAC) influenced VI adoption while MFAC is the best predictor. Favorable market factors may lead restaurants to adopt VI. It is, thus, recommended for restaurants to institutionalize strategic management, quantify the impact of double marginalization in future studies as a reason for VI and conduct this study during the new normal to see the influence of business efficiencies and firm economies on VI adoption.

Keywords: business efficiencies, business management, davao city, firm economies, market factors, philippines, strategic motives, structural equation model, supply chain, vertical integration adoption

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Authors: Tayo P. Ogundunmade, Adedayo A. Adepoju

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Terrorist attacks in liberal democracies bring about a few pessimistic results, for example, sabotaged public support in the governments they target, disturbing the peace of a protected environment underwritten by the state, and a limitation of individuals from adding to the advancement of the country, among others. Hence, seeking for techniques to understand the different factors involved in terrorism and how to deal with those factors in order to completely stop or reduce terrorist activities is the topmost priority of the government in every country. This research aim is to develop an efficient deep learning-based predictive model for the prediction of future terrorist activities in Nigeria, addressing low-quality prediction accuracy problems associated with the existing solution methods. The proposed predictive AI-based model as a counterterrorism tool will be useful by governments and law enforcement agencies to protect the lives of individuals in society and to improve the quality of life in general. A Heterogeneous Bayesian Neural Network (HETBNN) model was derived with Gaussian error normal distribution. Three primary transfer functions (HOTTFs), as well as two derived transfer functions (HETTFs) arising from the convolution of the HOTTFs, are namely; Symmetric Saturated Linear transfer function (SATLINS ), Hyperbolic Tangent transfer function (TANH), Hyperbolic Tangent sigmoid transfer function (TANSIG), Symmetric Saturated Linear and Hyperbolic Tangent transfer function (SATLINS-TANH) and Symmetric Saturated Linear and Hyperbolic Tangent Sigmoid transfer function (SATLINS-TANSIG). Data on the Terrorist activities in Nigeria gathered through questionnaires for the purpose of this study were used. Mean Square Error (MSE), Mean Absolute Error (MAE) and Test Error are the forecast prediction criteria. The results showed that the HETFs performed better in terms of prediction and factors associated with terrorist activities in Nigeria were determined. The proposed predictive deep learning-based model will be useful to governments and law enforcement agencies as an effective counterterrorism mechanism to understand the parameters of terrorism and to design strategies to deal with terrorism before an incident actually happens and potentially causes the loss of precious lives. The proposed predictive AI-based model will reduce the chances of terrorist activities and is particularly helpful for security agencies to predict future terrorist activities.

Keywords: activation functions, Bayesian neural network, mean square error, test error, terrorism

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Authors: Longqing Li

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The paper addresses the inefficiency of the classical model in measuring the Value-at-Risk (VaR) using a normal distribution or a Student’s t distribution. Specifically, the paper focuses on the one day ahead Value-at-Risk (VaR) of major stock market’s daily returns in US, UK, China and Hong Kong in the most recent ten years under 95% confidence level. To improve the predictable power and search for the best performing model, the paper proposes using two leading alternatives, Extreme Value Theory (EVT) and a family of GARCH models, and compares the relative performance. The main contribution could be summarized in two aspects. First, the paper extends the GARCH family model by incorporating EGARCH and TGARCH to shed light on the difference between each in estimating one day ahead Value-at-Risk (VaR). Second, to account for the non-normality in the distribution of financial markets, the paper applies Generalized Error Distribution (GED), instead of the normal distribution, to govern the innovation term. A dynamic back-testing procedure is employed to assess the performance of each model, a family of GARCH and the conditional EVT. The conclusion is that Exponential GARCH yields the best estimate in out-of-sample one day ahead Value-at-Risk (VaR) forecasting. Moreover, the discrepancy of performance between the GARCH and the conditional EVT is indistinguishable.

Keywords: Value-at-Risk, Extreme Value Theory, conditional EVT, backtesting

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Authors: S. J. Matlan, M. Mukhlisin, M. R. Taha

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Characterization of the engineering behavior of unsaturated soil is dependent on the soil-water characteristic curve (SWCC), a graphical representation of the relationship between water content or degree of saturation and soil suction. A reasonable description of the SWCC is thus important for the accurate prediction of unsaturated soil parameters. The measurement procedures for determining the SWCC, however, are difficult, expensive, and time-consuming. During the past few decades, researchers have laid a major focus on developing empirical equations for predicting the SWCC, with a large number of empirical models suggested. One of the most crucial questions is how precisely existing equations can represent the SWCC. As different models have different ranges of capability, it is essential to evaluate the precision of the SWCC models used for each particular soil type for better SWCC estimation. It is expected that better estimation of SWCC would be achieved via a thorough statistical analysis of its distribution within a particular soil class. With this in view, a statistical analysis was conducted in order to evaluate the reliability of the SWCC prediction models against laboratory measurement. Optimization techniques were used to obtain the best-fit of the model parameters in four forms of SWCC equation, using laboratory data for relatively coarse-textured (i.e., sandy) soil. The four most prominent SWCCs were evaluated and computed for each sample. The result shows that the Brooks and Corey model is the most consistent in describing the SWCC for sand soil type. The Brooks and Corey model prediction also exhibit compatibility with samples ranging from low to high soil water content in which subjected to the samples that evaluated in this study.

Keywords: soil-water characteristic curve (SWCC), statistical analysis, unsaturated soil, geotechnical engineering

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Authors: Sara Hasani

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This research focuses on natural sudden onset disasters characterised as ‘occurring with little or no warning and often cause excessive injuries far surpassing the national response capacities’. Based on the panel analysis of the historic record of 4,252 natural onset disasters between 1980 to 2015, a predictive method was developed to predict the human impact of the disaster (fatality, injured, homeless) with less than 3% of errors. The geographical dispersion of the disasters includes every country where the data were available and cross-examined from various humanitarian sources. The records were then filtered into 4252 records of the disasters where the five predictive variables (disaster type, HDI, DRI, population, and population density) were clearly stated. The procedure was designed based on a combination of pattern recognition techniques and rule-based clustering for prediction and discrimination analysis to validate the results further. The result indicates that there is a relationship between the disaster human impact and the five socio-economic characteristics of the affected country mentioned above. As a result, a framework was put forward, which could predict the disaster’s human impact based on their severity rank in the early hours of disaster strike. The predictions in this model were outlined in two worst and best-case scenarios, which respectively inform the lower range and higher range of the prediction. A necessity to develop the predictive framework can be highlighted by noticing that despite the existing research in literature, a framework for predicting the human impact and estimating the needs at the time of the disaster is yet to be developed. This can further be used to allocate the resources at the response phase of the disaster where the data is scarce.

Keywords: disaster management, natural disaster, pattern recognition, prediction

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Authors: Matevž Breška, Iztok Peruš, Vlado Stankovski

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Systematic overview of existing Ground Motion Prediction Equations (GMPEs) has been published by Douglas. The number of earthquake recordings that have been used for fitting these equations has increased in the past decades. The current PF-L database contains 3550 recordings. Since the GMPEs frequently model the peak ground acceleration (PGA) the goal of the present study was to refit a selection of 44 of the existing equation models for PGA in light of the latest data. The algorithm Levenberg-Marquardt was used for fitting the coefficients of the equations and the results are evaluated both quantitatively by presenting the root mean squared error (RMSE) and qualitatively by drawing graphs of the five best fitted equations. The RMSE was found to be as low as 0.08 for the best equation models. The newly estimated coefficients vary from the values published in the original works.

Keywords: Ground Motion Prediction Equations, Levenberg-Marquardt algorithm, refitting PF-L database, peak ground acceleration

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Authors: Huynh Viet Khai, Mitsuyasu Yabe

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The study investigated the economic value of biodiversity attributes that could provide policy-makers reliable information to estimate welfare losses due to biodiversity reductions and analyse the trade-off between biodiversity and economics. In order to obtain the non-market benefits of biodiversity conservation, an indirect utility function and willingness to pay for biodiversity attributes were applied using the approach of choice modelling with the analysis of conditional logit model. The study found that Mekong Delta residents accepted their willingness to pay for VND 913 monthly for a one percent increase in healthy vegetation, VND 360 for an additional mammal species and VND 2,440 to avoid the welfare losses of 100 local farmers.

Keywords: choice modelling, genetic resources, wetland conservation, marginal willingness to pay

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Authors: Hadi Molaei, Yasavoli Davoud, Keshavarz, Mozhde Poordana

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The present study aimed was to prediction the academic performance based on academic motivation, self-efficacy and Resiliency in the students. The present study was descriptive and correlational. Population of the study consisted of all students in Arak schools in year 1393-94. For this purpose, the number of 304 schools students in Arak was selected using multi-stage cluster sampling. They all questionnaires, self-efficacy, Resiliency and academic motivation Questionnaire completed. Data were analyzed using Pearson correlation and multiple regressions. Pearson correlation showed academic motivation, self-efficacy, and Resiliency with academic performance had a positive and significant relationship. In addition, multiple regression analysis showed that the academic motivation, self-efficacy and Resiliency were predicted academic performance. Based on the findings could be conclude that in order to increase the academic performance and further progress of students must provide the ground to strengthen academic motivation, self-efficacy and Resiliency act on them.

Keywords: academic motivation, self-efficacy, resiliency, academic performance

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Authors: Hoang Thi Quynh, Shima Kazuto

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Organic farming is gaining interest in Vietnam. However, organic fertilizer production is not sufficiently regulated, resulting in unknown quality. This study investigated characteristics of so-called organic fertilizers in the Vietnam’s market and their mineralization in soil-plant system. We collected 15 commercial products (11 domestic and 4 imported) which labelled 'organic fertilizer' in the market to analyze nutrients composition. A 20 day-incubation experiment was carried on with 80 g sandy-textured soil, amended with the fertilizer at a rate of 109.4 mgN.kg⁻¹soil in 150 mL glass bottle at 25℃. We categorized them according to nutrients content and mineralization rate, and then selected 8 samples for cultivation experiment. The experiment was conducted by growing Komatsuna (Brassica campestris) in sandy-textured soil using an automatic watering apparatus in a greenhouse. The fertilizers were applied to the top one-third of the soil stratum at a rate of 200 mgN.kg⁻¹ soil. Our study also analyzed material flow of coffee husk compost in Central Highland of Vietnam. Total N, P, K, Ca, Mg and C: N ratio varied greatly cross the domestic products, whereas they were quite similar among the imported materials. The proportion of inorganic-N to T-N of domestic products was higher than 25% in 8 of 11 samples. These indicate that N concentration increased dramatically in most domestic products compared with their raw materials. Additionally, most domestic products contained less P, and their proportions of Truog-P to T-P were greatly different. These imply that some manufactures were interested in adjusting P concentration, but some ones were not. Furthermore, the compost was made by mixing with chemical substances to increase nutrients content (N, P), and also added construction surplus soil to gain weight before packing product to sell in the market as 'organic fertilizer'. There was a negative correlation between C:N ratio and mineralization rate of the fertilizers. There was a significant difference in N efficiency among the fertilizer treatments. N efficiency of most domestic products was higher than chemical fertilizer and imported organic fertilizers. These results suggest regulations on organic fertilizers production needed to support organic farming that is based on internationally accepted standards in Vietnam.

Keywords: inorganic N, mineralization, N efficiency, so-called organic fertilizers, Vietnam’s market

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Authors: Ajay Kumar Rana

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Predicting human behaviour, discerning monozygotic twins or left over remnant tissues/fluids of a single human source remains a big challenge in forensic science. Recent advances in the field of DNA methylations which are broadly chemical hallmarks in response to environmental factors can certainly help to identify and discriminate various single-source DNA samples collected from the crime scenes. In this review, cytosine methylation of DNA has been methodologically discussed with its broad applications in many challenging forensic issues like body fluid identification, race/ethnicity identification, monozygotic twins dilemma, addiction or behavioural prediction, age prediction, or even authenticity of the human DNA. With the advent of next-generation sequencing techniques, blooming of DNA methylation datasets and together with standard molecular protocols, the prospect of investigating and solving the above issues and extracting the exact nature of the truth for reconstructing the crime scene events would be undoubtedly helpful in defending and solving the critical crime cases.

Keywords: DNA methylation, differentially methylated regions, human identification, forensics

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Authors: Edgard Barki, Tânia Modesto Veludo-de-Oliveira, Felipe Zambaldi

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The social enterprise sector can be characterized as organizations that aim to solve social problems with financial sustainability and using market mechanisms. This sector has shown an increasing interest worldwide. Despite the growth and relevance of the sector, there is still a gap regarding the assessment of the social impact resulting from the initiatives of the organizations in this field. A number of metrics have been designed worldwide to evaluate the impact of social enterprises (e.g., IRIS, GIIRS, BACO), as well as some ad hoc studies that have been carried out, mainly in the microcredit sector, but there is still a gap to be filled in the development of research in social impact evaluation. Therefore, this research seeks to evaluate the social impact of two social enterprises (Terra Nova and Vivenda) in the area of housing in Brazil. To evaluate these impacts and their dimensions, we conducted an exploratory research, through three focus groups, thirty in-depth interviews and a survey with beneficiaries of both organizations. The results allowed us to evaluate how the two organizations were able to create a deep social impact in the populations served. Terra Nova has a more collective perspective, with a clear benefit of social inclusion and improvement of the community’s infrastructure, while Vivenda has a more individualized perspective, improving self-esteem, sociability and family coexistence.

Keywords: Brazil, housing, social enterprise, social impact evaluation

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Authors: Lamis Kattan, Joanne Haddad

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This paper examines the impact of gender focused labor legislation on women's labor force participation and economic empowerment. We rely on historical legislative acts passed by state legislatures and exploit whether or not states passed regulatory laws regulating overall and industry specific employment and work conditions for women, night work laws and labor laws requiring provision of seats for working women. We exploit the fact that not all states enacted these laws as well as the variation in the timing of enactment of such laws. Our results show that women in comparison to men in treated states are more likely to be in the labor force post introduction of night work laws in comparison to control states. We also document the effect of industry-specific labor policies on women's likelihood to be employed in the affected industry and in higher-wage occupations within the industry of interest. Policy implications of our findings endorse the adoption of labor laws in favor of women to advocate their empowerment through a higher involvement in the labor market and financial independence.

Keywords: female employment, labor laws, marriage, fertility

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Authors: Yusuf Abdullahi

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The contribution of entrepreneurship in the economic development cannot be over-emphasized neither it should be underestimated for any reason as this is already a known fact that it plays an important role as a change agent and as a prime mover of economy. In developed or underdeveloped economy, the essence of entrepreneurship is highly significant as this exist in both private as well as public sectors of any economy. The roles of entrepreneurship are worldly acclaimed but yet as laudable as these roles there can be no significant success by any entrepreneur except with availability of finance. Nonetheless, mere availability of finance also cannot guarantee the success of an enterprise but there must be in place appropriate financial strategies for the funding/investment needs of an enterprise. Thus, little has been said when it comes to strategies needed to access various sources of finance. This paper, therefore, establishes appropriate strategies to obtain funds sources of finance by both small and as well as medium enterprises.

Keywords: entrepreneurship, Nigeria, financial strategies, economic development

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Authors: Victoria Adikpe

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The ills of the Nigerian public sector have been identified at various fora to include lack of financial accountability and poor reporting of government performance. With the enthronement of democracy, citizens’ expectations from the government are drifting from the mere provision of public services to efficiency and accountability. One of the major challenges to achieving accountability in Nigeria is the capability of the cash basis of accounting to meet the reporting requirements of policies and programmes of the government. This paper discussed the growing trend in the debate about the adoption of private sector financial management processes in the public sector as part of the public sector reform programmes. The paper does not claim the ultimate superiority of accrual over cash accounting but shows how it will help to further strengthen the quality of government accounting and reporting.

Keywords: cash accounting, accrual accounting, accountability, reporting

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Authors: Flora Babongo, Valerie Chavez

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Inferring causality from observational data is one of the fundamental subjects, especially in quantitative finance. So far most of the papers analyze additive noise models with either linearity, nonlinearity or Gaussian noise. We fill in the gap by providing a nonlinear and non-gaussian causal multiplicative noise model that aims to distinguish the cause from the effect using a two steps method based on Bayesian additive models for location, scale and shape (BAMLSS) and on causal additive models (CAM). We have tested our method on simulated and real data and we reached an accuracy of 0.86 on average. As real data, we considered the causality between financial indices such as S&P 500, Nasdaq, CAC 40 and Nikkei, and companies' log-returns. Our results can be useful in inferring causality when the data is heteroskedastic or non-injective.

Keywords: causal inference, DAGs, BAMLSS, financial index

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Authors: Milad Abbasi

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Carbon fiber-reinforced polymer (CFRP) composite structures are increasingly being utilized in the automotive industry due to their lightweight and specific energy absorption capabilities. Although it is impossible to predict composite mechanical properties directly using theoretical methods, various research has been conducted so far in the literature for accurate simulation of CFRP structures' energy-absorbing behavior. In this research, axial compression experiments were carried out on hand lay-up unidirectional CFRP composite tubes. The fabrication method allowed the authors to extract the material properties of the CFRPs using ASTM D3039, D3410, and D3518 standards. A neural network machine learning algorithm was then utilized to build a robust prediction model to forecast the axial compressive properties of CFRP tubes while reducing high-cost experimental efforts. The predicted results have been compared with the experimental outcomes in terms of load-carrying capacity and energy absorption capability. The results showed high accuracy and precision in the prediction of the energy-absorption capacity of the CFRP tubes. This research also demonstrates the effectiveness and challenges of machine learning techniques in the robust simulation of composites' energy-absorption behavior. Interestingly, the proposed method considerably condensed numerical and experimental efforts in the simulation and calibration of CFRP composite tubes subjected to compressive loading.

Keywords: CFRP composite tubes, energy absorption, crushing behavior, machine learning, neural network

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Authors: Arta Jashari, Enver Kutllovci

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Abstract— The purpose of this study is to examine the relationship of recruitment and selection practice and performance in medium service enterprises in Kosovo. A total of 110 managers from public and private sector was analyzed. Our empirical results show that enterprises in Kosovo use recruitment and selection practice and they know how important is to have the right people with skills and knowledge accordingly with the job requirements. The outcome of Pearson correlation analysis provides evidence that recruitment and selection practice, positively and significantly influence the financial performance. Also, our results show a significant relationship between the education of managers and the use of the recruitment and selection practice. From our results we can conclude and suggest that with a good recruiting and selection, the organization will fill with a group of potentially qualified candidates who will be able to fulfill the enterprises objective.

Keywords: Human Resource, Kosovo, Recruitment and Selection, Performance

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Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

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Authors: Raphaël Weuts, Mykyta Petik, Anton Vedder

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Artificial Intelligence has the potential to create and already creates enormous value in healthcare. Prescriptive systems might be able to make the use of healthcare capacity more efficient. Such systems might entail interpretations that exclude the effect of confounders that brings risks with it. Those risks might be mitigated by regulation that prevents systems entailing such risks to come to market. One modality of regulation is that of legislation, and the European AI Act is an example of such a regulatory instrument that might mitigate these risks. To assess the risk mitigation potential of the AI Act for those risks, this research focusses on a case study of a hypothetical application of medical device software that entails the aforementioned risks. The AI Act refers to the harmonised norms for already existing legislation, here being the European medical device regulation. The issue at hand is a causal link between a confounder and the value the algorithm optimises for by proxy. The research identifies where the AI Act already looks at confounders (i.a. feedback loops in systems that continue to learn after being placed on the market). The research identifies where the current proposal by parliament leaves legal uncertainty on the necessity to check for confounders that do not influence the input of the system, when the system does not continue to learn after being placed on the market. The authors propose an amendment to article 15 of the AI Act that would require high-risk systems to be developed in such a way as to mitigate risks from those aforementioned confounders.

Keywords: AI Act, healthcare, confounders, risks

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Authors: Emad A. Mohammed

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The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

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Authors: Semra Turkan

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In this paper, we have examined the factors that affect the productivity of Turkey’s Top 500 Industrial Enterprises in 2014. The labor productivity of enterprises is taken as an indicator of productivity of industrial enterprises. When the relationships between some financial ratios and labor productivity, it is seen that there is a nonparametric relationship between labor productivity and return on sales. In addition, the distribution of labor productivity of enterprises is right-skewed. If the dependent distribution is skewed, the quantile regression is more suitable for this data. Hence, the nonparametric relationship between labor productivity and return on sales by quantile smoothing splines.

Keywords: quantile regression, smoothing spline, labor productivity, financial ratios

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Authors: Petra F. A. Dilling, Sinan Caykoylu

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The aim of this study was to shed light on the understanding of the factors determining a company’s disclosure on climate change reporting. The underlying study examines the effect of gender diversity and the mediating effect of female representation in management and on the board of directors and the existence of a dedicated sustainability board committee. To test the study’s objectives, the authors use a global sample of the largest companies and their reporting for the year 2020. The results suggest that corporate female participation has a significant influence on the quality of climate change reporting. In addition, having a dedicated sustainability board committee also significantly impacts the non-financial disclosure of climate change information.

Keywords: climate change, non-financial reporting, governance, board diversity, sustainability, CSR

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Authors: Ved Kulkarni, Karthik Kini

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This experiment aims to understand how the media affects the power markets in the mainland United States and study the duration of reaction time between news updates and actual price movements. it have taken into account electric utility companies trading in the NYSE and excluded companies that are more politically involved and move with higher sensitivity to Politics. The scrapper checks for any news related to keywords, which are predefined and stored for each specific company. Based on this, the classifier will allocate the effect into five categories: positive, negative, highly optimistic, highly negative, or neutral. The effect on the respective price movement will be studied to understand the response time. Based on the response time observed, neural networks would be trained to understand and react to changing market conditions, achieving the best strategy in every market. The stock trader would be day trading in the first phase and making option strategy predictions based on the black holes model. The expected result is to create an AI-based system that adjusts trading strategies within the market response time to each price movement.

Keywords: data mining, language processing, artificial neural networks, sentiment analysis

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Authors: Thidathit Prachanukool, Chaiyaporn Yuksen, Welawat Tienpratarn, Sorravit Savatmongkorngul, Panvilai Tangkulpanich, Chetsadakon Jenpanitpong, Yuranan Phootothum, Malivan Phontabtim, Promphet Nuanprom

Abstract:

Background: Ruptured appendicitis has a high morbidity and mortality and requires immediate surgery. The Alvarado Score is used as a tool to predict the risk of acute appendicitis, but there is no such score for predicting rupture. This study aimed to developed the prediction score to determine the likelihood of ruptured appendicitis in an Asian population. Methods: This study was diagnostic, retrospectively cross-sectional and exploratory model at the Emergency Medicine Department in Ramathibodi Hospital between March 2016 and March 2018. The inclusion criteria were age >15 years and an available pathology report after appendectomy. Clinical factors included gender, age>60 years, right lower quadrant pain, migratory pain, nausea and/or vomiting, diarrhea, anorexia, fever>37.3°C, rebound tenderness, guarding, white blood cell count, polymorphonuclear white blood cells (PMN)>75%, and the pain duration before presentation. The predictive model and prediction score for ruptured appendicitis was developed by multivariable logistic regression analysis. Result: During the study period, 480 patients met the inclusion criteria; of these, 77 (16%) had ruptured appendicitis. Five independent factors were predictive of rupture, age>60 years, fever>37.3°C, guarding, PMN>75%, and duration of pain>24 hours to presentation. A score > 6 increased the likelihood ratio of ruptured appendicitis by 3.88 times. Conclusion: Using the Ramathibodi Welawat Ruptured Appendicitis Score. (RAMA WeRA Score) developed in this study, a score of > 6 was associated with ruptured appendicitis.

Keywords: predictive model, risk score, ruptured appendicitis, emergency room

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Authors: Salam Alrekabi, A. B. Cundy, Mohammed Haloob Al-Majidi

Abstract:

Novel multiscale hybrid reinforced cementitious composites based on carbon nanotubes (MHRCC-CNT), and carbon nanofibers (MHRCC-CNF) are new types of cement-based material fabricated with micro steel fibers and nanofilaments, featuring superior strain hardening, ductility, and energy absorption. This study focused on established models to predict the compressive strength, and direct and splitting tensile strengths of the produced cementitious composites. The analysis was carried out based on the experimental data presented by the previous author’s study, regression analysis, and the established models that available in the literature. The obtained models showed small differences in the predictions and target values with experimental verification indicated that the estimation of the mechanical properties could be achieved with good accuracy.

Keywords: multiscale hybrid reinforced cementitious composites, carbon nanotubes, carbon nanofibers, mechanical strength prediction

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Authors: Ayesha Sanjana Kawser Parsha

Abstract:

S-acylation is an irreversible bond in which cysteine residues are linked to fatty acids palmitate (74%) or stearate (22%), either at the COOH or NH2 terminal, via a thioester linkage. There are several experimental methods that can be used to identify the S-palmitoylation site; however, since they require a lot of time, computational methods are becoming increasingly necessary. There aren't many predictors, however, that can locate S- palmitoylation sites in Arabidopsis Thaliana with sufficient accuracy. This research is based on the importance of building a better prediction tool. To identify the type of machine learning algorithm that predicts this site more accurately for the experimental dataset, several prediction tools were examined in this research, including the GPS PALM 6.0, pCysMod, GPS LIPID 1.0, CSS PALM 4.0, and NBA PALM. These analyses were conducted by constructing the receiver operating characteristics plot and the area under the curve score. An AI-driven deep learning-based prediction tool has been developed utilizing the analysis and three sequence-based input data, such as the amino acid composition, binary encoding profile, and autocorrelation features. The model was developed using five layers, two activation functions, associated parameters, and hyperparameters. The model was built using various combinations of features, and after training and validation, it performed better when all the features were present while using the experimental dataset for 8 and 10-fold cross-validations. While testing the model with unseen and new data, such as the GPS PALM 6.0 plant and pCysMod mouse, the model performed better, and the area under the curve score was near 1. It can be demonstrated that this model outperforms the prior tools in predicting the S- palmitoylation site in the experimental data set by comparing the area under curve score of 10-fold cross-validation of the new model with the established tools' area under curve score with their respective training sets. The objective of this study is to develop a prediction tool for Arabidopsis Thaliana that is more accurate than current tools, as measured by the area under the curve score. Plant food production and immunological treatment targets can both be managed by utilizing this method to forecast S- palmitoylation sites.

Keywords: S- palmitoylation, ROC PLOT, area under the curve, cross- validation score

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Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

Abstract:

Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

Procedia PDF Downloads 69