Search results for: working models

Authors: A. G. Matveev

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The copyrights system is a combination of different elements. The number, content and the correlation of these elements are different for different legal orders. The models of copyrights systems display this system in terms of the interaction of economic and author's moral rights. Monistic and dualistic models are the most popular ones. The article deals with different points of view on the monism and dualism in copyright system. A specific model of the copyright in Switzerland in the XXth century is analyzed. The evolution of a French dualistic model of copyright is shown. The author believes that one should talk not about one, but rather about a number of dualism forms of copyright system.

Keywords: copyright, exclusive copyright, economic rights, author's moral rights, rights of personality, monistic model, dualistic model

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Authors: Yogendra Sisodia

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Researchers are becoming more interested in extracting useful information from legal documents thanks to the development of large-scale language models in natural language processing (NLP), and deep learning has accelerated the creation of powerful text mining models. Legal fields like contracts benefit greatly from semantic text search since it makes it quick and easy to find related clauses. After collecting sentence embeddings, it is relatively simple to locate sentences with a comparable meaning throughout the entire legal corpus. The author of this research investigated two pre-trained language models for this task: MiniLM and Roberta, and further fine-tuned them on Legal Contracts. The author used Multiple Negative Ranking Loss for the creation of sentence transformers. The fine-tuned language models and sentence transformers showed promising results.

Keywords: legal contracts, multiple negative ranking loss, natural language inference, sentence transformers, semantic textual similarity

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Authors: Herlandson C. Moura, Jorge H. Bidinotto, Eduardo M. Belo

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The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.

Keywords: adaptive control, digital Fly-By-Wire, oscillations suppression, PIO

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Authors: Riona Dighe

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This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country.

Keywords: artificial intelligence, NLP, sentiment analysis, gross national happiness

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Adel Bougamouza, Boualam Remini, Abd El Hadi Ammari, Feteh Sakhraoui

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The assessment of sediments being carried by a river is importance for planning and designing of various water resources projects. In this study, Artificial Neural Network Techniques are used to estimate the daily suspended sediment concentration for the corresponding daily discharge flow in the upstream of Foum El Gherza dam, Biskra, Algeria. The FFNN, GRNN, and RBNN models are established for estimating current suspended sediment values. Some statistics involving RMSE and R2 were used to evaluate the performance of applied models. The comparison of three AI models showed that the RBNN model performed better than the FFNN and GRNN models with R2 = 0.967 and RMSE= 5.313 mg/l. Therefore, the ANN model had capability to improve nonlinear relationships between discharge flow and suspended sediment with reasonable precision.

Keywords: artificial neural network, Oued Abiod watershed, feedforward network, generalized regression network, radial basis network, sediment concentration

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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: Ojoniyi Oluwafeyekikunmi Okiki

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Notwithstanding advances in disturbing wound care and control, infections remain a main motive of mortality, morbidity, and financial disruption in tens of millions of wound sufferers around the sector. Animal models have become popular gear for analyzing a big selection of outside worrying wound infections and trying out new antimicrobial techniques. This evaluation covers experimental infections in animal models of surgical wounds, pores and skin abrasions, burns, lacerations, excisional wounds, and open fractures. Animal modes of external stressful wound infections stated via extraordinary investigators vary in animal species used, microorganism traces, the quantity of microorganisms carried out, the dimensions of the wounds, and, for burn infections, the period of time the heated object or liquid is in contact with the skin. As antibiotic resistance continues to grow, new antimicrobial procedures are urgently needed. Those have to be examined using popular protocols for infections in external stressful wounds in animal models.

Keywords: surgical wounds, animals, wound infections, burns, wound models, colony-forming gadgets, lacerated wounds

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Authors: Zdzislaw Kaminski, Zbigniew Czyz, Krzysztof Skiba

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This paper discusses the CFD simulation of a flow around a rotor of a Vertical Axis Wind Turbine. Numerical simulation, unlike experiments, enables us to validate project assumptions when it is designed and avoid a costly preparation of a model or a prototype for a bench test. CFD simulation enables us to compare characteristics of aerodynamic forces acting on rotor working surfaces and define operational parameters like torque or power generated by a turbine assembly. This research focused on the rotor with the blades capable of modifying their working surfaces, i.e. absorbing wind kinetic energy. The operation of this rotor is based on adjusting angular aperture α of the top and bottom parts of the blades mounted on an axis. If this angular aperture α increases, the working surface which absorbs wind kinetic energy also increases. The operation of turbines is characterized by parameters like the angular aperture of blades, power, torque, speed for a given wind speed. These parameters have an impact on the efficiency of assemblies. The distribution of forces acting on the working surfaces in our turbine changes according to the angular velocity of the rotor. Moreover, the resultant force from the force acting on an advancing blade and retreating blade should be as high as possible. This paper is part of the research to improve an efficiency of a rotor assembly. Therefore, using simulation, the courses of the above parameters were studied in three full rotations individually for each of the blades for three angular apertures of blade working surfaces, i.e. 30 °, 60 °, 90 °, at three wind speeds, i.e. 4 m / s, 6 m / s, 8 m / s and rotor speeds ranging from 100 to 500 rpm. Finally, there were created the characteristics of torque coefficients and power as a function of time for each blade separately and for the entire rotor. Accordingly, the correlation between the turbine rotor power as a function of wind speed for varied values of rotor rotational speed. By processing this data, the correlation between the power of the turbine rotor and its rotational speed for each of the angular aperture of the working surfaces was specified. Finally, the optimal values, i.e. of the highest output power for given wind speeds were read. The research results in receiving the basic characteristics of turbine rotor power as a function of wind speed for the three angular apertures of the blades. Given the nature of rotor operation, the growth in the output turbine can be estimated if angular aperture of the blades increases. The controlled adjustment of angle α enables a smooth adjustment of power generated by a turbine rotor. If wind speed is significant, this type of adjustment enables this output power to remain at the same level (by reducing angle α) with no risk of damaging a construction. This work has been financed by the Polish Ministry of Science and Higher Education.

Keywords: computational fluid dynamics, numerical analysis, renewable energy, wind turbine

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Authors: Debarati Bhaumik, Diptish Dey

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Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

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Authors: Nima Pirhadi, Shao Yong Bo, Xusheng Wan, Jianguo Lu, Jilei Hu

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Although gravels and gravelly soils are assumed to be non-liquefiable because of high conductivity and small modulus; however, the occurrence of this phenomenon in some historical earthquakes, especially recently earthquakes during 2008 Wenchuan, Mw= 7.9, 2014 Cephalonia, Greece, Mw= 6.1 and 2016, Kaikoura, New Zealand, Mw = 7.8, has been promoted the essential consideration to evaluate risk assessment and hazard analysis of seismic gravelly soil liquefaction. Due to the limitation in sampling and laboratory testing of this type of soil, in situ tests and site exploration of case histories are the most accepted procedures. Of all in situ tests, dynamic penetration test (DPT), Which is well known as the Chinese dynamic penetration test, and shear wave velocity (Vs) test, have been demonstrated high performance to evaluate seismic gravelly soil liquefaction. However, the lack of a sufficient number of case histories provides an essential limitation for developing new models. This study at first investigates recent earthquakes that caused liquefaction in gravelly soils to collect new data. Then, it adds these data to the available literature’s dataset to extend them and finally develops new models to assess seismic gravelly soil liquefaction. To validate the presented models, their results are compared to extra available models. The results show the reasonable performance of the proposed models and the critical effect of gravel content (GC)% on the assessment.

Keywords: liquefaction, gravel, dynamic penetration test, shear wave velocity

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Authors: S. M. Gupta, Vanita Aggarwal, Som Nath Sachdeva

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The work reported through this paper is an experimental work conducted on High Performance Concrete (HPC) with super plasticizer with the aim to develop some models suitable for prediction of compressive strength of HPC mixes. In this study, the effect of varying proportions of fly ash (0% to 50% at 10% increment) on compressive strength of high performance concrete has been evaluated. The mix designs studied were M30, M40 and M50 to compare the effect of fly ash addition on the properties of these concrete mixes. In all eighteen concrete mixes have been designed, three as conventional concretes for three grades under discussion and fifteen as HPC with fly ash with varying percentages of fly ash. The concrete mix designing has been done in accordance with Indian standard recommended guidelines i.e. IS: 10262. All the concrete mixes have been studied in terms of compressive strength at 7 days, 28 days, 90 days and 365 days. All the materials used have been kept same throughout the study to get a perfect comparison of values of results. The models for compressive strength prediction have been developed using Linear Regression method (LR), Artificial Neural Network (ANN) and Leave One Out Validation (LOOV) methods.

Keywords: high performance concrete, fly ash, concrete mixes, compressive strength, strength prediction models, linear regression, ANN

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Authors: Duncan Oteki, Andebut Yeneneh, Daba Gedafa, Nabil Suleiman

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Most agencies lack the equipment required to measure the dynamic modulus (|E*|) of asphalt mixtures, necessitating the need to use predictive models. This study compared measured |E*| values for nine North Dakota asphalt mixes using the original Witczak, modified Witczak, and Hirsch models. The influence of temperature on the |E*| models was investigated, and Pavement ME simulations were conducted using measured |E*| and predictions from the most accurate |E*| model. The results revealed that the original Witczak model yielded the lowest Se/Sy and highest R² values, indicating the lowest bias and highest accuracy, while the poorest overall performance was exhibited by the Hirsch model. Using predicted |E*| as inputs in the Pavement ME generated conservative distress predictions compared to using measured |E*|. The original Witczak model was recommended for predicting |E*| for low-reliability pavements in North Dakota.

Keywords: asphalt mixture, binder, dynamic modulus, MEPDG, pavement ME, performance, prediction

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Authors: Andrey Khalov

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The rapid proliferation of unstructured data in IT infrastructure management demands innovative approaches for extracting actionable knowledge. This paper presents a framework for ontology-based knowledge extraction that combines relational graph neural networks (R-GNN) with large language models (LLMs). The proposed method leverages the DOLCE framework as the foundational ontology, extending it with concepts from ITSMO for domain-specific applications in IT service management and outsourcing. A key component of this research is the use of transformer-based models, such as DeBERTa-v3-large, for automatic entity and relationship extraction from unstructured texts. Furthermore, the paper explores how transfer learning techniques can be applied to fine-tune large language models (LLaMA) for using to generate synthetic datasets to improve precision in BERT-based entity recognition and ontology alignment. The resulting IT Ontology (ITO) serves as a comprehensive knowledge base that integrates domain-specific insights from ITIL processes, enabling more efficient decision-making. Experimental results demonstrate significant improvements in knowledge extraction and relationship mapping, offering a cutting-edge solution for enhancing cognitive computing in IT service environments.

Keywords: ontology mapping, R-GNN, knowledge extraction, large language models, NER, knowlege graph

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Authors: Dennis Kreutzer, Sarah Müller-Abdelrazeq, Ingrid Isenhardt

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Resource scarcity, energy transition and the planned climate neutrality pose enormous challenges for manufacturing companies. In order to achieve these goals and a holistic sustainable development, the European Union has listed the circular economy as part of the Circular Economy Action Plan. In addition to a reduction in resource consumption, reduced emissions of greenhouse gases and a reduced volume of waste, the principles of the circular economy also offer enormous economic potential for companies, such as the generation of new circular business models. However, many manufacturing companies, especially small and medium-sized enterprises, do not have the necessary capacity to plan their transformation. They need support and strategies on the path to circular transformation, because this change affects not only production but also the entire company. Maturity models offer an approach, as they enable companies to determine the current status of their transformation processes. In addition, companies can use the models to identify transformation strategies and thus promote the transformation process. While maturity models are established in other areas, e.g. IT or project management, only a few circular economy maturity models can be found in the scientific literature. The aim of this paper is to analyse the identified maturity models of the circular economy through a systematic literature review (SLR) and, besides other aspects, to check their completeness as well as their quality. Since the terms "maturity model" and "readiness model" are often used to assess the transformation process, this paper considers both types of models to provide a more comprehensive result. For this purpose, circular economy maturity models at the company (micro) level were identified from the literature, compared, and analysed with regard to their theoretical and methodological structure. A specific focus was placed, on the one hand, on the analysis of the business units considered in the respective models and, on the other hand, on the underlying metrics and indicators in order to determine the individual maturity level of the entire company. The results of the literature review show, for instance, a significant difference in the holism of their assessment framework. Only a few models include the entire company with supporting areas outside the value-creating core process, e.g. strategy and vision. Additionally, there are large differences in the number and type of indicators as well as their metrics. For example, most models often use subjective indicators and very few objective indicators in their surveys. It was also found that there are rarely well-founded thresholds between the levels. Based on the generated results, concrete ideas and proposals for a research agenda in the field of circular economy maturity models are made.

Keywords: maturity model, circular economy, transformation, metric, assessment

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Authors: Jonathan H. Westover, Maureen S. Andrade, Angela Schill, Jeff Peterson, Samuel Choi

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The purpose of this research is to understand employee perspectives on their work characteristics and conditions, particularly related to the COVID-19 pandemic and the future of work. Working conditions impact job satisfaction. People tend to measure job satisfaction by comparing aspects of the job they have with those they want. Job satisfaction is related to the value that one places on specific aspects of a job, such as autonomy, pay and benefits, challenge, growth, or meaningful work, and the degree to which such elements are present. The value one places on these various job characteristics may differ based on gender, age, personality, occupation, context, or other factors. This study will examine various job characteristics and working conditions with an emphasis on COVID-19 to determine how managers and leaders and better support and develop their employees.

Keywords: COVID-19, employee perspectives nature of work, future of work

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Authors: Snezhana G. Gocheva-Ilieva, Maya P. Stoimenova

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Ambient air pollution with fine particulate matter (PM10) is a systematic permanent problem in many countries around the world. The accumulation of a large number of measurements of both the PM10 concentrations and the accompanying atmospheric factors allow for their statistical modeling to detect dependencies and forecast future pollution. This study applies the classification and regression trees (CART) method for building and analyzing PM10 models. In the empirical study, average daily air data for the city of Pleven, Bulgaria for a period of 5 years are used. Predictors in the models are seven meteorological variables, time variables, as well as lagged PM10 variables and some lagged meteorological variables, delayed by 1 or 2 days with respect to the initial time series, respectively. The degree of influence of the predictors in the models is determined. The selected best CART models are used to forecast future PM10 concentrations for two days ahead after the last date in the modeling procedure and show very accurate results.

Keywords: cross-validation, decision tree, lagged variables, short-term forecasting

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Authors: Jessica Lopez Espejel, Mahaman Sanoussi Yahaya Alassan, Walid Dahhane, El Hassane Ettifouri

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Pretrained transformer-based models have shown high performance in natural language generation tasks. However, a new wave of interest has surged: automatic programming language code generation. This task consists of translating natural language instructions to a source code. Despite the fact that well-known pre-trained models on language generation have achieved good performance in learning programming languages, effort is still needed in automatic code generation. In this paper, we introduce JaCoText, a model based on Transformer neural network. It aims to generate java source code from natural language text. JaCoText leverages the advantages of both natural language and code generation models. More specifically, we study some findings from state of the art and use them to (1) initialize our model from powerful pre-trained models, (2) explore additional pretraining on our java dataset, (3) lead experiments combining the unimodal and bimodal data in training, and (4) scale the input and output length during the fine-tuning of the model. Conducted experiments on CONCODE dataset show that JaCoText achieves new state-of-the-art results.

Keywords: java code generation, natural language processing, sequence-to-sequence models, transformer neural networks

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

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Authors: Misbah Khan, Jian Wen, Muhammad Asif Shakoori

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The Organic Rankine Cycle (ORC) plays an important role in converting low-temperature heat sources into electrical power by using refrigerants as working fluids. The thermophysical properties of working fluids are essential for designing ORC. HFO-1336mzz(Z) (cis-1,1,1,4,4,4-hexafluoro-2-butene) considered as working fluid and have almost 99% low GWP and relatively same thermophysical properties used as a replacement of HFC-245fa (1,1,1,3,3-pentafluoro-propane). The environmental, safety, healthy and thermophysical properties of HFO-1336mzz(Z) are needed to use it in a practical system. In this paper, Molecular dynamics simulations were used to investigate the Homogeneous condensation, thermophysical and structural properties of HFO-1336mzz(Z) and HFC-245fa. The effect of various temperatures and pressures on thermophysical properties and condensation was extensively investigated. The liquid densities and isobaric heat capacities of this refrigerant was simulated at 273.15K to 353.15K temperatures and pressure0.5-4.0MPa. The simulation outcomes were compared with experimental data to validate our simulation method. The mean square displacement for different temperatures was investigated for dynamical analysis. The variations in potential energies and condensation rate were simulated to get insight into the condensation process. The radial distribution function was simulated at the micro level for structural analysis and revealed that the phase transition of HFO-1336mzz(Z) did not affect the intramolecular structure.

Keywords: homogenous condensation, refrigerants, molecular dynamics simulations, organic rankine cycle

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Authors: Chuang-Chun Chiou

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In this study, a systematic framework of graceful ageing has been used to explore the possible human resource utilization models for graceful ageing purpose. This framework is based on the Chinese culture. We call ‘Nine-old’ target. They are ageing gracefully with feeding, accomplishment, usefulness, learning, entertainment, care, protection, dignity, and termination. This study is focused on two areas: accomplishment and usefulness. We exam the current practices of initiatives and laws of promoting labor participation. That is to focus on how to increase Labor Force Participation Rate of the middle aged as well as the elderly and try to promote the elderly to achieve graceful ageing. Then we present the possible models that support graceful ageing.

Keywords: human resource utilization model, labor participation, graceful ageing, employment

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Authors: Abdulrahman Sumayli, Saad M. AlShahrani

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For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

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Authors: L. Grinis

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Turbulent flow in complex geometries receives considerable attention due to its importance in many engineering applications. It has been the subject of interest for many researchers. Some of these interests include the design of storm water channels. The design of these channels requires testing through physical models. The main practical limitation of physical models is the so called “scale effect”, that is, the fact that in many cases only primary physical mechanisms can be correctly represented, while secondary mechanisms are often distorted. These observations form the basis of our study, which centered on problems associated with the design of storm water channels near the Dead Sea, in Israel. To help reach a final design decision we used different physical models. Our research showed good coincidence with the results of laboratory tests and theoretical calculations, and allowed us to study different effects of fluid flow in an open channel. We determined that problems of this nature cannot be solved only by means of theoretical calculation and computer simulation. This study demonstrates the use of physical models to help resolve very complicated problems of fluid flow through baffles and similar structures. The study applies these models and observations to different construction and multiphase water flows, among them, those that include sand and stone particles, a significant attempt to bring to the testing laboratory a closer association with reality.

Keywords: open channel, physical modeling, baffles, turbulent flow

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Authors: L. J. de Bessa Neto, V. S. Filho, J. V. Ferreira Nunes, G. C. Bergamo

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There are many situations in which human activities have significant effects on the environment. Damage to the ozone layer is one of them. The objective of this work is to use the Least Squares Method, considering the linear, exponential, logarithmic, power and polynomial models of the second degree, to analyze through the coefficient of determination (R²), which model best fits the behavior of the chlorodifluoromethane (HCFC-142b) in parts per trillion between 1992 and 2018, as well as estimates of future concentrations between 5 and 10 periods, i.e. the concentration of this pollutant in the years 2023 and 2028 in each of the adjustments. A total of 809 observations of the concentration of HCFC-142b in one of the monitoring stations of gases precursors of the deterioration of the ozone layer during the period of time studied were selected and, using these data, the statistical software Excel was used for make the scatter plots of each of the adjustment models. With the development of the present study, it was observed that the logarithmic fit was the model that best fit the data set, since besides having a significant R² its adjusted curve was compatible with the natural trend curve of the phenomenon.

Keywords: chlorodifluoromethane (HCFC-142b), ozone, least squares method, regression models

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Authors: Byeongcheol Kang, Junyi Kim, Hyungsik Jung, Hyungjun Yang, Jaewoo An, Jonggeun Choe

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It is important to figure out reservoir properties for better production management. Due to the limited information, there are geological uncertainties on very heterogeneous or channel reservoir. One of the solutions is to generate multiple equi-probable realizations using geostatistical methods. However, some models have wrong properties, which need to be excluded for simulation efficiency and reliability. We propose a novel method of model selection scheme, based on distance-based clustering for reliable application of production optimization algorithm. Distance is defined as a degree of dissimilarity between the data. We calculate Hausdorff distance to classify the models based on their similarity. Hausdorff distance is useful for shape matching of the reservoir models. We use multi-dimensional scaling (MDS) to describe the models on two dimensional space and group them by K-means clustering. Rather than simulating all models, we choose one representative model from each cluster and find out the best model, which has the similar production rates with the true values. From the process, we can select good reservoir models near the best model with high confidence. We make 100 channel reservoir models using single normal equation simulation (SNESIM). Since oil and gas prefer to flow through the sand facies, it is critical to characterize pattern and connectivity of the channels in the reservoir. After calculating Hausdorff distances and projecting the models by MDS, we can see that the models assemble depending on their channel patterns. These channel distributions affect operation controls of each production well so that the model selection scheme improves management optimization process. We use one of useful global search algorithms, particle swarm optimization (PSO), for our production optimization. PSO is good to find global optimum of objective function, but it takes too much time due to its usage of many particles and iterations. In addition, if we use multiple reservoir models, the simulation time for PSO will be soared. By using the proposed method, we can select good and reliable models that already matches production data. Considering geological uncertainty of the reservoir, we can get well-optimized production controls for maximum net present value. The proposed method shows one of novel solutions to select good cases among the various probabilities. The model selection schemes can be applied to not only production optimization but also history matching or other ensemble-based methods for efficient simulations.

Keywords: distance-based clustering, geological uncertainty, particle swarm optimization (PSO), production optimization

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Authors: Yuzhi Cai

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In this paper, we discuss a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. Together with a combining forecasts technique, we then predict USD to GBP currency exchange rates. Combined forecasts contain all the information captured by the fitted QAR models at different quantile levels and are therefore better than those obtained from individual models. Our results show that an unequally weighted combining method performs better than other forecasting methodology. We found that a median AR model can perform well in point forecasting when the predictive density functions are symmetric. However, in practice, using the median AR model alone may involve the loss of information about the data captured by other QAR models. We recommend that combined forecasts should be used whenever possible.

Keywords: combining forecasts, MCMC, predictive density functions, quantile forecasting, quantile modelling

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Authors: Nidhi Bansal, Upasna Agarwal

Abstract:

Innovation is a key driver for economic growth and well-being of developed as well as emerging economies like India. Very few studies examined the relationship between IWB and work-family enrichment. Therefore, the present study examines the relationship between work-family enrichment (WFE) and innovative workplace behavior (IWB) and whether it is mediated by positive emotions. Social exchange theory and broaden and build theory explain the proposed relationships. Data were collected from 250 full time dual working parents in different Indian organizations through a survey questionnaire. Snowball technique was used for approaching respondents. Mediation analysis was assessed through PROCESS macro (Hayes, 2012) in SPSS. With correlational analysis, it was explored that all three variables were significantly and positively related. Analysis suggests that work-family enrichment is significantly related to innovative workplace behavior and this relationship is partially mediated by positive emotions. A cross-sectional design, use of self-reported questions and data collected only from dual working parents are few limitations of the study. This is one of the few studies to examine the innovative workplace behavior in response to work-family enrichment and first attempt to examine the mediation effect of emotions between these two variables.

Keywords: dual working parents, emotions, innovative workplace behavior, work-family enrichment

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Authors: Rajneekant Bachan, Dhanush Vijay

Abstract:

The visualization of operating systems has refined the Turing machine, and current trends suggest that the emulation of 32 bit architectures will soon emerge. After years of technical research into Web services, we demonstrate the synthesis of gigabit switches, which embodies the robust principles of theory. Loam, our new algorithm for forward-error correction, is the solution to all of these challenges.

Keywords: software engineering, architectures, introspective models, operating systems

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Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

Abstract:

Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

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Authors: Laura Petrosyan

Abstract:

The high and persistent working results are possible when people are interested in the results of the work. Motivation of working may be present as a psychological complicated phenomena, which determines person's behavior in working process. Researchers point out that working motivation is displayed in three correlated conditions. These are interest in outcomes of work, satisfaction with the work, and the third, is the level of devotion of employee. Solution of the problem of effective staff management depends on the development of workers' skills. Despite, above mentioned problem could be solved by the process of finding methods to induce the employees to the effective work. Motivation of the managerial activity aroused not only during the working process, but also before it starts. During education the future manager obtains many professional skills. However, the experience shows, that only professional skills are not enough for the effective work. Presently, one of the global educational problems is the development of motivation in professions. In psychological literature the fact is mentioned, that the motivation can be inside and outside. Outside motivation is active only at short time. Instead, inside motivation can be active during all process of the professional development. Hence, the motivation of managerial activity might be developed during the education. The future manager choose the profession being under some impression of personal qualities. Detection of future manager’s motivation will influence on the development of syllabuses. Moreover, use of the psychological methods could be evolved for preparing motivated managers. Conducted research has been done in the Public Administration Academy of the RA. The aim of research was to discover students' motivation of profession. 102 master students took part in the research from Public Administration Academy. In the research were used the following methods: method of identifying a person's motivation to succeed (T. Elers) and method of studying students’ motivation (T.E. Ilyin). First of the methods designed to explore a person's motivational orientation to get success represented by Hackhausen. The method gives the opportunity to reveal the level of motivation to success. In the second method separated three scales: i) Knowledge achievements, ii) Knowledge of the profession, iii) Get a diploma. The data obtained from these tests gave quantitative data. Aanalyses of our survey results exposes that within master students the high level have the average rates of knowledge achievements. The average rates of knowledge of the profession and geting a diploma not in high level. Furthermore, there are almost equal to each other. In the educational process The student acquiring skills not synthesize with the wield profession. Results show that specialists really view about profession not formulated yet.

Keywords: managerial activity, motivation, psychological complicated phenomena, working process, education the future manager

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