Search results for: transition regression model

Authors: Asror Nigmonov Asqar Ogli, Sitora Inoyatova Amonovna

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It has been a decade when the crowdfunding and P2P lending opportunities were created. Today, the market of these modern alternative investments is becoming increasingly complex to navigate. There are overwhelming amount of peer-to-peer lending platforms both in developed and emerging economies. This study looks into this market via the cross country empirical study. In this respect, it tests the effect of various macroeconomic factors on P2P loan lending. Based on the existing literature that largely lacks empirical investigations, it builds regression model that aims to explore the relationship between economy and P2P lending. Though the author found it extremely difficult to compare the findings with earlier studies, this paper had identified certain tendencies in the data and had certain policy implications. However, the paper could not find any significant effect of economic variables on P2P lending. The paper can be considered as a starting point in empirical investigation of P2P lending and highlights room further research based on limitations of the study.

Keywords: peer-to-peer lending, crowdfunding, marketplace lending, alternative finance, fintech

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Authors: Prateep Wajeetongratana

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The purpose of this research was to study the influence of accountants’ potential performance on their working process, a case study of Government Savings Banks in the northeast of Thailand. The independent variables included accounting knowledge, accounting skill, accounting value, accounting ethics, and accounting attitude, while the dependent variable included the success of the working process. A total of 155 accountants working for Government Savings Banks were selected by random sampling. A questionnaire was used as a tool for collecting data. Descriptive statistics in this research included percentage, mean, and multiple regression analyses. The findings revealed that the majority of accountants were female with an age between 35-40 years old. Most of the respondents had an undergraduate degree with ten years of experience. Moreover, the factors of accounting knowledge, accounting skill, accounting a value and accounting ethics and accounting attitude were rated at a high level. The findings from regression analysis of observation data revealed a causal relationship in that the observation data could explain at least 51 percent of the success in the accountants’ working process.

Keywords: influence, potential performance, success, working process

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Authors: Triveni Gogoi, Rima Chatterjee

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Shear wave velocity (Vs) estimation is an important approach in the seismic exploration and characterization of a hydrocarbon reservoir. There are varying methods for prediction of S-wave velocity, if recorded S-wave log is not available. But all the available methods for Vs prediction are empirical mathematical models. Shear wave velocity can be estimated using P-wave velocity by applying Castagna’s equation, which is the most common approach. The constants used in Castagna’s equation vary for different lithologies and geological set-ups. In this study, multiple regression analysis has been used for estimation of S-wave velocity. The EMERGE module from Hampson-Russel software has been used here for generation of S-wave log. Both single attribute and multi attributes analysis have been carried out for generation of synthetic S-wave log in Upper Assam basin. Upper Assam basin situated in North Eastern India is one of the most important petroleum provinces of India. The present study was carried out using four wells of the study area. Out of these wells, S-wave velocity was available for three wells. The main objective of the present study is a prediction of shear wave velocities for wells where S-wave velocity information is not available. The three wells having S-wave velocity were first used to test the reliability of the method and the generated S-wave log was compared with actual S-wave log. Single attribute analysis has been carried out for these three wells within the depth range 1700-2100m, which corresponds to Barail group of Oligocene age. The Barail Group is the main target zone in this study, which is the primary producing reservoir of the basin. A system generated list of attributes with varying degrees of correlation appeared and the attribute with the highest correlation was concerned for the single attribute analysis. Crossplot between the attributes shows the variation of points from line of best fit. The final result of the analysis was compared with the available S-wave log, which shows a good visual fit with a correlation of 72%. Next multi-attribute analysis has been carried out for the same data using all the wells within the same analysis window. A high correlation of 85% has been observed between the output log from the analysis and the recorded S-wave. The almost perfect fit between the synthetic S-wave and the recorded S-wave log validates the reliability of the method. For further authentication, the generated S-wave data from the wells have been tied to the seismic and correlated them. Synthetic share wave log has been generated for the well M2 where S-wave is not available and it shows a good correlation with the seismic. Neutron porosity, density, AI and P-wave velocity are proved to be the most significant variables in this statistical method for S-wave generation. Multilinear regression method thus can be considered as a reliable technique for generation of shear wave velocity log in this study.

Keywords: Castagna's equation, multi linear regression, multi attribute analysis, shear wave logs

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Authors: Saad Bin Nasir

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This research will examine the impact of corporate governance on firm performance in family and non-family owned firms in Pakistan. For the purpose of this research, corporate governance mechanisms which included are board size, board composition, leadership structure, board meetings are taken as independent variable and firm performance taken as dependent variable and it will be measured with return on asset and return on equity. Firm size and firm’s age will be taken as control variables. Secondary data will collect from audited annul reports of companies and panel data regression model will applied, to check the impact of corporate governance on firm performance.

Keywords: board size, board composition, Leadership Structure, board meetings, firm performance, family and non-family owned firms

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Authors: Esther M. Folarin, Evans Osabuohien, Ademola Onabote

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Agriculture constitutes an important part of development and poverty mitigation in lower-middle-income countries, like Nigeria. The level of agricultural productivity in the Nigerian economy in line with the level of demand necessary to meet the desired expectation of the Nigerian populace is threatening to meeting the standard of the United Nations (UN) Sustainable Development Goals (SDGs); This includes the SDG-2 (achieve food security through agricultural productivity). The overall objective of the study is to reveal the performance of the interaction variable in the model among other factors that help in the achievement of greater Nigerian agricultural productivity. The study makes use of Wave 4 (2018/2019) of the Living Standard Measurement Studies, Integrated Survey on Agriculture (LSMS-ISA). Qualitative analysis of the information was also used to provide complimentary answers to the quantitative analysis done in the study. The study employed human capital theory and Grossman’s theory of health Demand in explaining the relationships that exist between the variables within the model of the study. The study engages the Instrumental Variable Regression technique in achieving the broad objectives among other techniques for the other specific objectives. The estimation results show that there exists a positive relationship between female healthcare and the level of female agricultural productivity in Nigeria. In conclusion, the study emphasises the need for more provision and empowerment for greater female access to healthcare and educational attainment levels that aids higher female agricultural productivity and consequently an improvement in the total agricultural productivity of the Nigerian economy.

Keywords: agricultural productivity, education, female, healthcare, investment

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Authors: Julio Kauss, Miguel Cadillo, David Mauricio

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E-commerce is a business model that allows companies to optimize the processes of buying, selling, transferring goods and exchanging services through computer networks or the Internet. In Peru, the electronic commerce is used infrequently. This situation is due, in part to the fact that there is no model that allows companies to implement an e-commerce solution, which means that most SMEs do not have adequate knowledge to adapt to electronic commerce. In this work, a reference model is proposed for the implementation of an e-commerce solution in Peruvian SMEs in the retail sector. It consists of five phases: Business Analysis, Business Modeling, Implementation, Post Implementation and Results. The present model was validated in a SME of the Peruvian retail sector through the implementation of an electronic commerce platform, through which the company increased its sales through the delivery channel by 10% in the first month of deployment. This result showed that the model is easy to implement, is economical and agile. In addition, it allowed the company to increase its business offer, adapt to e-commerce and improve customer loyalty.

Keywords: e-commerce, retail, SMEs, reference model

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Authors: Omoruyi Gold Idemudia, Alexander P. Sadimenko

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The synthesis of N-heterocycles with novel properties, having broad spectrum biological activities that may become alternative medicinal drugs, have been attracting a lot of research attention due to the emergence of medicinal drug’s limitations such as disease resistance and their toxicity effects among others. Acylpyrazolones have been employed as pharmaceuticals as well as analytical reagent and their application as coordination complexes with transition metal ions have been well established. By way of a condensation reaction with amines acylpyrazolone ketones form a more chelating and superior group of compounds known as azomethines. 4-propyl-3-methyl-1-phenyl-2-pyrazolin-5-one was reacted with phenylhydrazine to get a new phenylhydrazone which was further reacted with aqueous solutions of palladium and platinum salts, in an effort towards the discovery of transition metal based synthetic drugs. The compounds were characterized by means of analytical, spectroscopic, thermogravimetric analysis TGA, as well as x-ray crystallography. 4-propyl-3-methyl-1-phenyl-2-pyrazolin-5-one phenylhydrazone crystallizes in a triclinic crystal system with a P-1 (No. 2) space group based on x-ray crystallography. The bidentate ON ligand formed a square planar geometry on coordinating with metal ions based on FTIR, electronic and NMR spectra as well as magnetic moments. Reported compounds showed antibacterial activities against the nominated bacterial isolates using the disc diffusion technique at 20 mg/ml in triplicates. The metal complexes exhibited a better antibacterial activity with platinum complex having an MIC value of 0.63 mg/ml. Similarly, ligand and complexes also showed antioxidant scavenging properties against 2, 2-diphenyl-1-picrylhydrazyl DPPH radical at 0.5mg/ml relative to ascorbic acid (standard drug).

Keywords: acylpyrazolone, antibacterial studies, metal complexes, phenylhydrazone, spectroscopy

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Authors: Muhammad Arif Jalil, Somchat Sonasang, Preecha Yupapin

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This study hypothesizes that the universe is at the center of the universe among the white and black holes, which are the entangled pairs. The coupling between them is in terms of spacetime forming the universe and things. The birth of things is based on exchange energy between the white and black sides. That is, the transition from the white side to the black side is called wave-matter, where it has a speed faster than light with positive gravity. The transition from the black to the white side has a speed faster than light with negative gravity called a wave-particle. In the part where the speed is equal to light, the particle rest mass is formed. Things can appear to take shape here. Thus, the gravity is zero because it is the center. The gravitational force belongs to the Earth itself because it is in a position that is twisted towards the white hole. Therefore, it is negative. The coupling of black-white holes occurs directly on both sides. The mass is formed at the saturation and will create universes and other things. Therefore, it can be hundreds of thousands of universes on both sides of the B and white holes before reaching the saturation point of multi-universes. This work will use the DJV circuit that the research team made as an entangled or two-level system circuit that has been experimentally demonstrated. Therefore, this principle has the possibility for interpretation. This work explains the emergence of multiple universes and can be applied as a practical guideline for searching for universes in the future. Moreover, the results indicate that the DJV circuit can create the elementary particles according to Feynman's diagram with rest mass conditions, which will be discussed for fission and fusion applications.

Keywords: multi-universes, feynman diagram, fission, fusion

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Authors: Oscar Norman Nekesa, Yoshitaka Kajita

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Tom Mboya Street is a vital urban corridor within the spectrum of Nairobi city, it experiences high volumes of pedestrian and vehicular traffic. Despite past intervention measures to lessen this catastrophe, rates have remained high. This highlights significant safety concerns that need urgent attention. This study investigates the correlation and pedestrian accident predictability with significant independent variables using multiple linear regression to model to develop effective mobility management strategies for accident mitigation. The methodology involves collecting and analyzing data on pedestrian accidents and various related independent variables. Data sources include the National Transport and Safety Authority (NTSA), Kenya National Bureau of Statistics, and Nairobi City County records, covering five years. This study aims to investigate that traffic volumes (pedestrian and vehicle), Vehicular speed, human factors, illegal parking, policy issues, urban-land use, built environment, traffic signals conditions, inadequate lighting, and insufficient traffic control measures significantly have predictability with the rate of pedestrian accidents. Explanatory variables related to road design and geometry are significant in predictor models for the Tom Mboya Road link but less influential in junction along the 5 km stretch road models. The most impactful variable across all models was vehicular traffic flow. The study recommends infrastructural improvements, enhanced enforcement, and public awareness campaigns to reduce accidents and improve urban mobility. These insights can inform policy-making and urban planning to enhance pedestrian safety along the dense packed Tom Mboya Street and similar urban settings. The findings will inform evidence-based interventions to enhance pedestrian safety and improve urban mobility.

Keywords: multiple linear regression, urban mobility, traffic management, Nairobi, Tom Mboya street, infrastructure conditions., pedestrian safety, correlation and prediction

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Authors: Patrick Strefford, Michael Davies, Masahiko Iguchi

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The Sustainable Development Goals (SDGs) were adopted in 2015 to end poverty, protect the planet, and ensure prosperity for all. Sustainable tourism is firmly positioned in these Goals, since tourism has significant potential to contribute to inclusive and sustainable economic growth, as well as to promote sustainable use of natural capital. Recognizing this, the new quasi-democratic government of Myanmar has embraced Sustainable Tourism as a core component of its economic reforms and opening up of the country. However, it is also highly likely that the Democrats within the government also support Sustainable Tourism as a potential contributor to the democratization of the country. This paper outlines how the government of Myanmar has understood the concept of Sustainable Tourism, and how it intends to implement and facilitate Sustainable Tourism. This paper, therefore, focuses primarily on the institutional frameworks that have been put in place, a specific one being the Inlay Lake Destination Management Plan, which is one of the four priority destinations identified by the government. The plan aims to improve local infrastructure, manage the local environment and develop local human resources. Importantly, the Plan also includes the establishment of a Destination Management Organization (DMO) to implement and manage Inlay Lake as a Sustainable Tourism destination. This research aims to investigate, for example, the equality in both input to the DMO and benefits accrued to the various stakeholders. How such equality can be ensured and how this can be reliably quantified will be crucial to ultimately evaluating the success of any such plans to implement Sustainable Tourism in Myanmar in the coming years. However, this research paper concludes that while the establishment of the DMO is a positive development, there are considerable institutional, economic and cultural factors that severely limit the ability of the DMO to function as an agent of sustainable tourism implementation.

Keywords: sustainable tourism, myanmar, country in transition, destination management organizations

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Authors: Do-Jin Jang, Sung-Ah Kim

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In designing a kinetic façade, it is hard for the designer to make digital models due to its complex geometry with motion. This paper aims to present a methodology of converting a point cloud of a physical model into a single digital model with a certain topology and motion. The method uses a Microsoft Kinect sensor, and color markers were defined and applied to three paper folding-inspired designs. Although the resulted digital model cannot represent the whole folding range of the physical model, the method supports the designer to conduct a performance-oriented design process with the rough physical model in the reduced folding range.

Keywords: design media, kinetic facades, tangible user interface, 3D scanning

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Authors: Asharaf Abdul Salam

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Arabian Gulf countries have norms and rules different from others: having an exodus of male immigrant labor contract holders of age 20-60 years as a floating population. Such a demographic scenario camouflages population ageing. However, it is observed on examining vigilantly, not only in the native population but also in the general population. This research on population ageing in the Arabian Gulf examines ageing scenario and concerns through analyses of international databases (US Census Bureau and United Nations) and national level databases (Censuses and Surveys) apart from a review of published research. Transitions in demography and epidemiology lead to gains in life expectancy and thereby reductions in fertility, leading to ageing of the population in the region. Even after bringing adult immigrants, indices and age pyramids show an increasing ageing trend in the total population, demonstrating an ageing workforce. Besides, the exclusive native population analysis reveals a trend of expansive pyramids (pre-transitional stage) turning to constrictive (transition stage) and cylindrical (post-transition stage) shapes. Age-based indices such as the index of ageing, age dependency ratio, and median age confirm this trend. While the feminine nature of ageing is vivid, gains in life expectancy and causes of death in old age, indicating co-morbidity compression, are concerns to ageing. Preparations are in demand to cope with ageing from different dimensions, as explained in the United Nations Plans of Action. A strategy of strengthening informal care with supportive semi-formal and supplementary formal care networks would alleviate this crisis associated with population ageing.

Keywords: total versus native population, indices of ageing, age pyramids, feminine nature, comorbidity compression, strategic interventions

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Authors: Yake Zhang, Peng Xu

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The development of building energy models (BEM) required for architectural design and analysis is a time-consuming and complex process, demanding a deep understanding and proficient use of simulation software. To streamline the generation of complex building energy models, this study proposes an automated method for generating building energy models using a large language model and the BEM library aimed at improving the efficiency of model generation. This method leverages a large language model to parse user-specified requirements for target building models, extracting key features such as building location, window-to-wall ratio, and thermal performance of the building envelope. The BEM library is utilized to retrieve energy models that match the target building’s characteristics, serving as reference information for the large language model to enhance the accuracy and relevance of the generated model, allowing for the creation of a building energy model that adapts to the user’s modeling requirements. This study enables the automatic creation of building energy models based on natural language inputs, reducing the professional expertise required for model development while significantly decreasing the time and complexity of manual configuration. In summary, this study provides an efficient and intelligent solution for building energy analysis and simulation, demonstrating the potential of a large language model in the field of building simulation and performance modeling.

Keywords: artificial intelligence, building energy modelling, building simulation, large language model

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Authors: Nurulnadwan Aziz, Ariffin Abdul Mutalib, Siti Mahfuzah Sarif

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This paper reports an ongoing project regarding the development of Conceptual Design Model of Assistive Courseware for Low Vision (AC4LV) learners. Having developed the intended model, it has to be validated prior to producing it as guidance for the developers to develop an AC4LV. This study requires two phases of validation process which are through expert review and prototyping method. This paper presents a part of the validation process which is findings from experts review on Conceptual Design Model of AC4LV which has been carried out through a questionnaire. Results from 12 international and local experts from various respectable fields in Human-Computer Interaction (HCI) were discussed and justified. In a nutshell, reviewed Conceptual Design Model of AC4LV was formed. Future works of this study are to validate the reviewed model through prototyping method prior to testing it to the targeted users.

Keywords: assistive courseware, conceptual design model, expert review, low vision learners

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Authors: Vishal Das, Tianyi Mao, Zhicheng Geng, Carmen Flores, Diego Pelloso, Fang Wang

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Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets.

Keywords: sell-in and sell-out forecasting, demand planning, DeepAR, retail, ensemble machine learning, time-series

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Authors: Khadeeja Munawar, Shamsul Haque

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We examined the plausibility of transition theory suggesting that memories of transitional events, which give rise to a significant and persistent change in the fabric of daily life, are organized around the historically defined autobiographical periods (H-DAPs). 141 Pakistani older adults retrieved 10 autobiographical memories (AMs) each to 10 cue words. As the history of Pakistan is dominated by various political and nationwide transitional events, it was expected that the participants would recall memories with H-DAPs references. The content analysis revealed that 0.7% of memories had H-DAP references and 0.4% memories mentioned major transitional events such as War/Natural Disaster. There was a vivid reminiscence bump between 10 - 20 years of age in lifespan distribution of AMs. There were 67.9% social-focused AMs. Significantly more self-focused memories were reported by individuals who endorsed themselves as conservatives. Only a few H-DAPs were reported, although the history of Pakistan was dominated by numerous political, historical and nationwide transitional events. Memories within and outside of the bump period were mostly positive. The participants rarely used historical/political or nationwide significant events or periods to date the memories elicited. The intense and nationwide (as well as region-wise) significant historical/political events spawned across decades in the lives of participants of the present study but these events did not produce H-DAPs. The findings contradicted the previous studies on H-DAPs and transition theory. The dominance of social-focused AMs in the present study is in line with the past studies comparing the memories of collectivist and individualist cultures (i.e., European Americans vs. Asian, African and Latin-American cultures). The past empirical evidence shows that conservative values and beliefs are adopted as a coping strategy to feel secure in the face of danger when future is dominated with uncertainty and to connect to likeminded others. In the present study, conservative political ideology is somehow assisting the participants in living a stable life midst of their complex social worlds. The reminiscence bump, as well as dominance of positive memories within and outside the bump period, are in line with the narrative/identity account which states that the events and experiences during adolescence and early adulthood assimilate into a person’s lifelong narratives. Hence these events are used as identity markers and are more easily recalled later in life. Also, according to socioemotional theory and the positivity effect, the participants evaluated past events more positively as they grow up and the intensity of negative emotions decreased with time.

Keywords: autobiographical memory, historically defined autobiographical periods, narrative/identity account, Pakistan, reminiscence bump, SMS framework, transition theory

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Authors: Beatriz Muñiz Cano, J. Ripoll Sau, D. Pacile, P. M. Sheverdyaeva, P. Moras, J. Camarero, R. Miranda, M. Garnica, M. A. Valbuena

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Graphene, in its pristine state, is a semiconductor with a zero band gap and massless Dirac fermions carriers, which conducts electrons like a metal. Nevertheless, the absence of a bandgap makes it impossible to control the material’s electrons, something that is essential to perform on-off switching operations in transistors. Therefore, it is necessary to generate a finite gap in the energy dispersion at the Dirac point. Intense research has been developed to engineer band gaps while preserving the exceptional properties of graphene, and different strategies have been proposed, among them, quantum confinement of 1D nanoribbons or the introduction of super periodic potential in graphene. Besides, in the context of developing new 2D materials and Van der Waals heterostructures, with new exciting emerging properties, as 2D transition metal chalcogenides monolayers, it is fundamental to know any possible interaction between chalcogenide atoms and graphene-supporting substrates. In this work, we report on a combined Scanning Tunneling Microscopy (STM), Low Energy Electron Diffraction (LEED), and Angle-Resolved Photoemission Spectroscopy (ARPES) study on a new superstructure when Te is evaporated (and intercalated) onto graphene over Ir(111). This new superstructure leads to the electronic doping of the Dirac cone while the linear dispersion of massless Dirac fermions is preserved. Very interestingly, our ARPES measurements evidence a large band gap (~400 meV) at the Dirac point of graphene Dirac cones below but close to the Fermi level. We have also observed signatures of the Dirac point binding energy being tuned (upwards or downwards) as a function of Te coverage.

Keywords: angle resolved photoemission spectroscopy, ARPES, graphene, spintronics, spin-orbitronics, 2D materials, transition metal dichalcogenides, TMDCs, TMDs, LEED, STM, quantum materials

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Authors: Meng-Huan Wu, Wei-Er Wang, Tsu-Nai Wang, Wei-Jian Hsu, Vincent Chin-Hung Chen

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Objective: Studies have reported a strong link between asthma and bipolar disorder. We conducted a 17-year community-based large cohort study to examine the relationship between asthma, early smoking initiation, and bipolar disorder during adolescence and early adulthood. Methods: A total of 162,766 participants aged 11–16 years were categorized into asthma and non-asthma groups at baseline and compared within the observation period. Covariates during late childhood or adolescence included parental education, cigarette smoking by family members of participants, and participant’s gender, age, alcohol consumption, smoking, and exercise habits. Data for urbanicity, prednisone use, allergic comorbidity, and Charlson comorbidity index were acquired from the National Health Insurance Research Database. The Cox proportional-hazards model was used to evaluate the association between asthma and bipolar disorder. Results: Our findings revealed that asthma increased the risk of bipolar disorder after adjustment for key confounders in the Cox proportional hazard regression model (adjusted HR: 1.31, 95% CI: 1.12-1.53). Hospitalizations or visits to the emergency department for asthma exhibited a dose–response effect on bipolar disorder (adjusted HR: 1.59, 95% CI: 1.22-2.06). Patients with asthma with onset before 20 years of age who smoked during late childhood or adolescence had the greatest risk for bipolar disorder (adjusted HR: 3.10, 95% CI: 1.29-7.44). Conclusions: Patients newly diagnosed with asthma had a 1.3 times higher risk of developing bipolar disorder. Smoking during late childhood or adolescence increases the risk of developing bipolar disorder in patients with asthma.

Keywords: adolescence, asthma, smoking, bipolar disorder, early adulthood

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Authors: Ehsan Mehryaar

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The rate of penetration is (RoP) one of the vital factors in the cost and time of tunnel boring projects; therefore, predicting it can lead to a substantial increase in the efficiency of the project. RoP is heavily dependent geological properties of the project site and TBM properties. In this study, 151-point data from Queen’s water tunnel is collected, which includes unconfined compression strength, peak slope index, angle with weak planes, and distance between planes of weaknesses. Since the size of the data is small, it was observed that it is imbalanced. To solve that problem synthetic minority oversampling technique is utilized. The model based on the model tree is proposed, where each leaf consists of a support vector machine model. Proposed model performance is then compared to existing empirical equations in the literature.

Keywords: Model tree, SMOTE, rate of penetration, TBM(tunnel boring machine), SVM

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Authors: Vidar Hepsø

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The energy and oil companies on the Norwegian Continental shelf come from a situation where each asset control and manage their energy supply (island mode) and move towards a situation where the assets need to collaborate and coordinate energy use with others due to increased cost and scarcity of electric energy sharing the energy that is provided. Currently, several areas are electrified either with an onshore grid cable or are receiving intermittent energy from offshore wind-parks. While the onshore grid in Norway is well regulated, the offshore grid is still in the making, with several oil and gas electrification projects and offshore wind development just started. The paper will describe the shift in the mindset that comes with operating this new offshore grid. This transition process heralds an increase in collaboration across boundaries and integration of energy management across companies, businesses, technical disciplines, and engagement with stakeholders in the larger society. This transition will be described as a function of the new challenges with increased complexity of the energy mix (wind, oil/gas, hydrogen and others) coupled with increased technical and organization complexity in energy management. Organizational complexity denotes an increasing integration across boundaries, whether these boundaries are company, vendors, professional disciplines, regulatory regimes/bodies, businesses, and across numerous societal stakeholders. New practices must be developed, made legitimate and institutionalized across these boundaries. Only parts of this complexity can be mitigated technically, e.g.: by use of batteries, mixing energy systems and simulation/ forecasting tools. Many challenges must be mitigated with legitimated societal and institutionalized governance practices on many levels. Offshore electrification supports Norway’s 2030 climate targets but is also controversial since it is exploiting the larger society’s energy resources. This means that new systems and practices must also be transparent, not only for the industry and the authorities, but must also be acceptable and just for the larger society. The paper report from ongoing work in Norway, participant observation and interviews in projects and people working with offshore grid development in Norway. One case presented is the development of an offshore floating windfarm connected to two offshore installations and the second case is an offshore grid development initiative providing six installations electric energy via an onshore cable. The development of the offshore grid is analyzed using a capability platform framework, that describes the technical, competence, work process and governance capabilities that are under development in Norway. A capability platform is a ‘stack’ with the following layers: intelligent infrastructure, information and collaboration, knowledge sharing & analytics and finally business operations. The need for better collaboration and energy forecasting tools/capabilities in this stack will be given a special attention in the two use cases that are presented.

Keywords: capability platform, electrification, carbon footprint, control rooms, energy forecsting, operational model

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Authors: Golamhassan Ghahramani Aghdam, Pedram Bavili Tabrizi

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The aim of this research is to investigate the relationship between accounting information and the value of the companies accepted in Tehran Exchange Market. The dependent variable in this research is the value of a company that is measured by price coefficients, and the independent variables are balance sheet information, profit and loss information, cash flow state information, and profit quality characteristics. The profit quality characteristic index is to be related and to be on-time. This research is an application research, and the research population includes all companies that are active in Tehran exchange market. The number of 194 companies was selected by the systematic method as the statistics sample in the period of 2018-2019. The multi-variable linear regression model was used for the hypotheses test. The results show that there is no relationship between accounting information and companies’ value (stock value) that can be due to the lack of efficiency of the investment market and the inability to use the accounting information by investment market activists.

Keywords: accounting information, company value, profit quality characteristics, price coefficient

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Authors: Adriana Garcia G., Alberto A. Escobar P., Amira D. Calvo L., Gabriel Lizama U., Alejandro Zepeda P., Fernando Martínez B., Susana Rincón A.

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Different studies have recently been focused on the use of antioxidants such as polyphenols because of to its anticarcinogenic capacity. However, these compounds are highly sensible to environmental factors such as light and heat, so lose its long-term stability, besides possess an astringent and bitter taste. Nevertheless, the polyphenols can be protected by microcapsule formulation. In this sense, a rich source of polyphenols is sorghum, besides presenting a high starch content. Due to the above, the aim of this work was to obtain modified starches from sorghum by extrusion to encapsulate polyphenols the sorghum by spray drying. Polyphenols were extracted by ethanol solution from sorghum (Pajarero/red) and determined by the method of Folin-Ciocalteu, obtaining GAE at 30 mg/g. Moreover, was extracted starch of sorghum (Sinaloense/white) through wet milling (yield 32 %). The hydrolyzed starch was modified with three treatments: acetic anhydride (2.5g/100g), sodium tripolyphosphate (4g/100g), and sodium tripolyphosphate/ acetic anhydride (2g/1.25g by each 100 g) by extrusion. Processing conditions of extrusion were as follows: barrel temperatures were of 60, 130 and 170 °C at the feeding, transition, and high-pressure extrusion zones, respectively. Analysis of Fourier Transform Infrared spectroscopy (FTIR), showed bands exhibited of acetyl groups (1735 cm-1) and phosphates (1170 cm-1, 910 cm-1 and 525 cm-1), indicating the respective modification of starch. Besides, all modified starches not developed viscosity, which is a characteristic required for use in the encapsulation of polyphenols using the spray drying technique. As result of the modification starch, was obtained a water solubility index (WSI) from 33.8 to 44.8 %, and crystallinity from 8 to 11 %, indicating the destruction of the starch granule. Afterwards, microencapsulation of polyphenols was developed by spray drying, with a blend of 10 g of modified starch, 60 ml polyphenol extract and 30 ml of distilled water. Drying conditions were as follows: inlet air temperature 150 °C ± 1, outlet air temperature 80°C ± 5. As result of the microencapsulation: were obtained yields of 56.8 to 77.4 % and an efficiency of encapsulation from 84.6 to 91.4 %. The FTIR analysis showed evidence of microcapsules loaded with polyphenols in bands 1042 cm-1, 1038 cm-1 and 1148 cm-1. Analysis Differential scanning calorimetry (DSC) showed transition temperatures from 144.1 to 173.9 °C. For the order hand, analysis of Scanning Electron Microscopy (SEM), were observed rounded surfaces with concavities, typical feature of microcapsules produced by spray drying, how result of rapid evaporation of water. Finally, the modified starches were obtained by extrusion with good characteristics for use as cover materials by spray drying, where the phosphorylated starch was the best treatment in this work, according to the encapsulation yield, efficiency, and transition temperature.

Keywords: encapsulation, extrusion, modified starch, polyphenols, spray drying

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Authors: Sina Saadati, Mohammadreza Razzazi

Abstract:

In this article, we have tried to present an agent-based model of human muscle. A suitable model of muscle is necessary for the analysis of mankind's movements. It can be used by clinical researchers who study the influence of motion sicknesses, like Parkinson's disease. It is also useful in the development of a prosthesis that receives the electromyography signals and generates force as a reaction. Since we have focused on computational efficiency in this research, the model can compute the calculations very fast. As far as it concerns prostheses, the model can be known as a charge-efficient method. In this paper, we are about to illustrate an agent-based model. Then, we will use it to simulate the human gait cycle. This method can also be done reversely in the analysis of gait in motion sicknesses.

Keywords: agent-based modeling and simulation, human muscle, gait cycle, motion sickness

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Authors: Irina P. Starodubtseva, Aleksandr N. Pavlenko

Abstract:

Quenching is a term generally accepted for the process of rapid cooling of a solid that is overheated above the thermodynamic limit of the liquid superheat. The main objective of many previous studies on quenching is to find a way to reduce the total time of the transient process. Computational experiments were performed to simulate quenching by a falling liquid nitrogen film of an extremely overheated vertical copper plate with a structured capillary-porous coating. The coating was produced by directed plasma spraying. Due to the complexities in physical pattern of quenching from chaotic processes to phase transition, the mechanism of heat transfer during quenching is still not sufficiently understood. To our best knowledge, no information exists on when and how the first stable liquid-solid contact occurs and how the local contact area begins to expand. Here we have more models and hypotheses than authentically established facts. The peculiarities of the quench front dynamics and heat transfer in the transient process are studied. The created numerical model determines the quench front velocity and the temperature fields in the heater, varying in space and time. The dynamic pattern of the running quench front obtained numerically satisfactorily correlates with the pattern observed in experiments. Capillary-porous coatings with straight and reverse orientation of crests are investigated. The results show that the cooling rate is influenced by thermal properties of the coating as well as the structure and geometry of the protrusions. The presence of capillary-porous coating significantly affects the dynamics of quenching and reduces the total quenching time more than threefold. This effect is due to the fact that the initialization of a quench front on a plate with a capillary-porous coating occurs at a temperature significantly higher than the thermodynamic limit of the liquid superheat, when a stable solid-liquid contact is thermodynamically impossible. Waves present on the liquid-vapor interface and protrusions on the complex micro-structured surface cause destabilization of the vapor film and the appearance of local liquid-solid micro-contacts even though the average integral surface temperature is much higher than the liquid superheat limit. The reliability of the results is confirmed by direct comparison with experimental data on the quench front velocity, the quench front geometry, and the surface temperature change over time. Knowledge of the quench front velocity and total time of transition process is required for solving practically important problems of nuclear reactors safety.

Keywords: capillary-porous coating, heat transfer, Leidenfrost phenomenon, numerical simulation, quenching

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Authors: Angga S. Fajar, Y. Takahashi, J. Kiyono, S. Sawada

Abstract:

The structural system concept of RC pier accompanied with multi sliding friction damping mechanism was developed based on numerical analysis approach. However in the implementation, to make design for such kind of this structural system consumes a lot of effort in case high of complexity. During making design, the special behaviors of this structural system should be considered including flexible small deformation, sufficient elastic deformation capacity, sufficient lateral force resistance, and sufficient energy dissipation. The confinement distribution of friction devices has significant influence to its. Optimization and prediction with multi function regression of this structural system expected capable of providing easier and simpler design method. The confinement distribution of friction devices is optimized with SNOPT in Opensees, while some design variables of the structure are predicted using multi function regression of ANN. Based on the optimization and prediction this structural system is able to be designed easily and simply.

Keywords: RC Pier, multi sliding friction device, optimal design, flexible small deformation

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Authors: Evy Rombaut, Marie-Anne Guerry

Abstract:

Predicting voluntary turnover of employees is an important topic of study, both in academia and industry. Researchers try to uncover determinants for a broader understanding and possible prevention of turnover. In the current study, we use a data set based approach to reveal determinants for turnover, differing for blue and white collar workers. Our data set based approach made it possible to study actual turnover for more than 500000 employees in 15692 Belgian corporations. We use logistic regression to calculate individual turnover probabilities and test the goodness of our model with the AUC (area under the ROC-curve) method. The results of the study confirm the relationship of known determinants to employee turnover such as age, seniority, pay and work distance. In addition, the study unravels unknown and verifies known differences between blue and white collar workers. It shows opposite relationships to turnover for gender, marital status, the number of children, nationality, and pay.

Keywords: employee turnover, blue collar, white collar, dataset analysis

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Authors: L. K. Nivedha, V. Maruthapandian, R. Kothandaraman

Abstract:

Rechargeable zinc-air batteries (ZAB) are gaining significant research attention owing to their high energy density and copious zinc resources worldwide. However, the unsolved obstacles such as dendrites, passivation, depth of discharge and the lack of an efficient cathode catalyst restrict their practical application1. By and large, non-noble transition metal-based catalysts are well-reputed materials for catalysing oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) with greater stability in alkaline medium2. Herein, we report the synthesis and application of Mn₃O4-NiFeLDH/Carbon composite as a cathode catalyst for rechargeable ZAB. The synergetic effects of the mixed transition metals (Mn/Ni/Fe) have aided in catalysing ORR and OER in alkaline electrolyte with a shallow potential gap of 0.7 V. The composite, by its distinctive physicochemical characteristics, shows an excellent OER activity with a current density of 1.5 mA cm⁻² at a potential of 1.6 V and a superior ORR activity with an onset potential of 0.8 V when compared with their counterparts. Nevertheless, the catalyst prefers a two-electron pathway for the electrochemical reduction of oxygen which results in a limiting current density of 2.5 mA cm⁻². The bifunctional activity of the Mn₃O₄-NiFeLDH/Carbon composite was utilized in developing rechargeable ZAB. The fully fabricated ZAB delivers an open circuit voltage of 1.4 V, a peak power density of 70 mW cm⁻², and a specific capacity of 800 mAh g⁻¹ at a current density of 20 mA cm⁻² with an average discharge voltage of 1 V and the cell is operable upto 50 mA cm-2. Rechargeable ZAB demonstrated over 110 h at 10 mA cm⁻². Further, the cause for the diminished charge-discharge performance experienced beyond the 100th cycle was investigated, and carbon corrosion was testified using Infrared spectroscopy.

Keywords: rechargeable zinc-air battery, oxygen evolution reaction, bifunctional catalyst, alkaline medium

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Authors: Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, Alan F. Blumberg

Abstract:

Storm surge is an abnormal water level caused by a storm. Accurate prediction of a storm surge is a challenging problem. Researchers developed various ensemble modeling techniques to combine several individual forecasts to produce an overall presumably better forecast. There exist some simple ensemble modeling techniques in literature. For instance, Model Output Statistics (MOS), and running mean-bias removal are widely used techniques in storm surge prediction domain. However, these methods have some drawbacks. For instance, MOS is based on multiple linear regression and it needs a long period of training data. To overcome the shortcomings of these simple methods, researchers propose some advanced methods. For instance, ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast. This application creates a better forecast of sea level using a combination of several instances of the Bayesian Model Averaging (BMA). An ensemble dressing method is based on identifying best member forecast and using it for prediction. Our contribution in this paper can be summarized as follows. First, we investigate whether the ensemble models perform better than any single forecast. Therefore, we need to identify the single best forecast. We present a methodology based on a simple Bayesian selection method to select the best single forecast. Second, we present several new and simple ways to construct ensemble models. We use correlation and standard deviation as weights in combining different forecast models. Third, we use these ensembles and compare with several existing models in literature to forecast storm surge level. We then investigate whether developing a complex ensemble model is indeed needed. To achieve this goal, we use a simple average (one of the simplest and widely used ensemble model) as benchmark. Predicting the peak level of Surge during a storm as well as the precise time at which this peak level takes place is crucial, thus we develop a statistical platform to compare the performance of various ensemble methods. This statistical analysis is based on root mean square error of the ensemble forecast during the testing period and on the magnitude and timing of the forecasted peak surge compared to the actual time and peak. In this work, we analyze four hurricanes: hurricanes Irene and Lee in 2011, hurricane Sandy in 2012, and hurricane Joaquin in 2015. Since hurricane Irene developed at the end of August 2011 and hurricane Lee started just after Irene at the beginning of September 2011, in this study we consider them as a single contiguous hurricane event. The data set used for this study is generated by the New York Harbor Observing and Prediction System (NYHOPS). We find that even the simplest possible way of creating an ensemble produces results superior to any single forecast. We also show that the ensemble models we propose generally have better performance compared to the simple average ensemble technique.

Keywords: Bayesian learning, ensemble model, statistical analysis, storm surge prediction

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Authors: Timine Suoware, Sylvester Edelugo, Charles Amgbari

Abstract:

Natural fibre composites (NFC) are becoming very attractive especially for automotive interior and non-structural building applications because they are biodegradable, low cost, lightweight and environmentally friendly. NFC are known to release high combustible products during exposure to heat atmosphere and this behaviour has raised concerns to end users. To improve on their fire response, flame retardants (FR) such as aluminium tri-hydroxide (ATH) and ammonium polyphosphate (APP) are incorporated during processing to delay the start and spread of fire. In this paper, APP was modified with Gum Arabic powder (GAP) and synergized with carbon black (CB) to form new FR species. Four FR species at 0, 12, 15 and 18% loading ratio were added to oil palm fibre polyester composite (OPFC) panels as follows; OPFC12%APP-GAP, OPFC15%APP-GAP/CB, OPFC18%ATH/APP-GAP and OPFC18%ATH/APPGAP/CB. The panels were produced using hand lay-up compression moulding and cured at room temperature. Specimens were cut from the panels and these were tested for ignition time (Tig), peak heat released rate (HRRp), average heat release rate (HRRavg), peak mass loss rate (MLRp), residual mass (Rm) and average smoke production rate (SPRavg) using cone calorimeter apparatus as well as the available flame energy (ɸ) in driving the flame using radiant panel flame spread apparatus. From the ignitability data obtained at 50 kW/m2 heat flux (HF), it shows that the hybrid FR modified with APP that is OPFC18%ATH/APP-GAP exhibited superior flame retardancy and the improvement was based on comparison with those without FR which stood at Tig = 20 s, HRRp = 86.6 kW/m2, HRRavg = 55.8 kW/m2, MLRp =0.131 g/s, Rm = 54.6% and SPRavg = 0.05 m2/s representing respectively 17.6%, 67.4%, 62.8%, 50.9%, 565% and 62.5% improvements less than those without FR (OPFC0%). In terms of flame spread, the least flame energy (ɸ) of 0.49 kW2/s3 for OPFC18%ATH/APP-GAP caused early flame regression. This was less than 39.6 kW2/s3 compared to those without FR (OPFC0%). It can be concluded that hybrid FR modified with APP could be useful in the automotive and building industries to delay the start and spread of fire.

Keywords: flame retardant, flame regression, oil palm fibre, composite panel

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Authors: Swati Bhomia, C. M. Kishtawal, Neeru Jaiswal

Abstract:

Traditionally, monsoon forecasts have encountered many difficulties that stem from numerous issues such as lack of adequate upper air observations, mesoscale nature of convection, proper resolution, radiative interactions, planetary boundary layer physics, mesoscale air-sea fluxes, representation of orography, etc. Uncertainties in any of these areas lead to large systematic errors. Global circulation models (GCMs), which are developed independently at different institutes, each of which carries somewhat different representation of the above processes, can be combined to reduce the collective local biases in space, time, and for different variables from different models. This is the basic concept behind the multi-model superensemble and comprises of a training and a forecast phase. The training phase learns from the recent past performances of models and is used to determine statistical weights from a least square minimization via a simple multiple regression. These weights are then used in the forecast phase. The superensemble forecasts carry the highest skill compared to simple ensemble mean, bias corrected ensemble mean and the best model out of the participating member models. This approach is a powerful post-processing method for the estimation of weather forecast parameters reducing the direct model output errors. Although it can be applied successfully to the continuous parameters like temperature, humidity, wind speed, mean sea level pressure etc., in this paper, this approach is applied to rainfall, a parameter quite difficult to handle with standard post-processing methods, due to its high temporal and spatial variability. The present study aims at the development of advanced superensemble schemes comprising of 1-5 day daily precipitation forecasts from five state-of-the-art global circulation models (GCMs), i.e., European Centre for Medium Range Weather Forecasts (Europe), National Center for Environmental Prediction (USA), China Meteorological Administration (China), Canadian Meteorological Centre (Canada) and U.K. Meteorological Office (U.K.) obtained from THORPEX Interactive Grand Global Ensemble (TIGGE), which is one of the most complete data set available. The novel approaches include the dynamical model selection approach in which the selection of the superior models from the participating member models at each grid and for each forecast step in the training period is carried out. Multi-model superensemble based on the training using similar conditions is also discussed in the present study, which is based on the assumption that training with the similar type of conditions may provide the better forecasts in spite of the sequential training which is being used in the conventional multi-model ensemble (MME) approaches. Further, a variety of methods that incorporate a 'neighborhood' around each grid point which is available in literature to allow for spatial error or uncertainty, have also been experimented with the above mentioned approaches. The comparison of these schemes with respect to the observations verifies that the newly developed approaches provide more unified and skillful prediction of the summer monsoon (viz. June to September) rainfall compared to the conventional multi-model approach and the member models.

Keywords: multi-model superensemble, dynamical model selection, similarity criteria, neighborhood technique, rainfall prediction

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