Search results for: comprehensive model

Authors: Xinhuang Wu, Yousef Sardahi

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A hybrid and optimal multi-loop control structure combining linear and nonlinear control algorithms are introduced in this paper to regulate the position of a quadcopter unmanned aerial vehicle (UAV) driven by four brushless DC motors. To this end, a nonlinear mathematical model of the UAV is derived and then linearized around one of its operating points. Using the nonlinear version of the model, a sliding mode control is used to derive the control laws of the motor thrust forces required to drive the UAV to a certain position. The linear model is used to design two controllers, XG-controller and YG-controller, responsible for calculating the required roll and pitch to maneuver the vehicle to the desired X and Y position. Three attitude controllers are designed to calculate the desired angular rates of rotors, assuming that the Euler angles are minimal. After that, a many-objective optimization problem involving 20 design parameters and ten objective functions is formulated and solved by HypE (Hypervolume estimation algorithm), one of the widely used many-objective optimization algorithms approaches. Both stability and performance constraints are imposed on the optimization problem. The optimization results in terms of Pareto sets and fronts are obtained and show that some of the design objectives are competing. That is, when one objective goes down, the other goes up. Also, Numerical simulations conducted on the nonlinear UAV model show that the proposed optimization method is quite effective.

Keywords: optimal control, many-objective optimization, sliding mode control, linear control, cascade controllers, UAV, drones

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Authors: Lukáš Mazánek, Zdeňka Konečná

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The article is devoted to the area of personnel marketing. A comprehensive review of scientific literature and articles published predominantly in personnel-oriented journals was carried out, followed by a qualitative exploratory research with the aim to explore and explain the perception of personnel marketing. Due to the lack of research in this field in Czech Republic, we have focused on Czech HR managers, more specifically, on how they understand the tasks of personnel marketing, which tools they use and whether the companies they work for try to be a preferred employer. The answers from our respondents were used to help us determine what is important within this field. All of the respondents strive to be a preferred employer and try to achieve it by using an extensive range of marketing tools. The most frequently used tools are advertising, job fairs presentations, employee care and employer brand promotion.

Keywords: Czech Republic, personnel marketing, preferred employer, qualitative research study

Procedia PDF Downloads 341

Authors: Habib Akbarzadeh Bengar, Mohammad Asadi Kiadehi, Ali Rameeh

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The result of the past earthquakes has shown that insufficient amount of stirrups and brittle behavior of concrete lead to the shear and flexural failure in reinforced concrete (RC) members. In this paper, an analytical model proposed to predict the nonlinear behavior of RC and SFRC elements and frames. In this model, some important parameter such as shear effect, varying axial load, and longitudinal bar buckling are considered. The results of analytical model were verified with experimental tests. The results of verification have shown that the proposed analytical model can predict the nonlinear behavior of RC and SFRC members and also frames accurately. In addition, the results have shown that use of steel fibers increased bearing capacity and ductility of RC frame. Due to this enhancement in shear strength and ductility, insufficient amount of stirrups, which resulted in shear failure, can be offset with usage of the steel fibers. In addition to the steps taken, to analyze the effects of fibers percentages on the bearing capacity and ductility of frames parametric studies have been performed to investigate of these effects.

Keywords: nonlinear analysis, SFRC frame, shear failure, varying an axial load

Procedia PDF Downloads 221

Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Gundula Bartzke, Hans-Peter Piepho

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Rainfall is a critical component of climate governing vegetation growth and production, forage availability and quality for herbivores. However, reliable rainfall measurements are not always available, making it necessary to predict rainfall values for particular locations through time. Predicting rainfall in space and time can be a complex and challenging task, especially where the rain gauge network is sparse and measurements are not recorded consistently for all rain gauges, leading to many missing values. Here, we develop a flexible Bayesian model for predicting rainfall in space and time and apply it to Narok County, situated in southwestern Kenya, using data collected at 23 rain gauges from 1965 to 2015. Narok County encompasses the Maasai Mara ecosystem, the northern-most section of the Mara-Serengeti ecosystem, famous for its diverse and abundant large mammal populations and spectacular migration of enormous herds of wildebeest, zebra and Thomson's gazelle. The model incorporates geographical and meteorological predictor variables, including elevation, distance to Lake Victoria and minimum temperature. We assess the efficiency of the model by comparing it empirically with the established Gaussian process, Kriging, simple linear and Bayesian linear models. We use the model to predict total monthly rainfall and its standard error for all 5 * 5 km grid cells in Narok County. Using the Monte Carlo integration method, we estimate seasonal and annual rainfall and their standard errors for 29 sub-regions in Narok. Finally, we use the predicted rainfall to predict large herbivore biomass in the Maasai Mara ecosystem on a 5 * 5 km grid for both the wet and dry seasons. We show that herbivore biomass increases with rainfall in both seasons. The model can handle data from a sparse network of observations with many missing values and performs at least as well as or better than four established and widely used models, on the Narok data set. The model produces rainfall predictions consistent with expectation and in good agreement with the blended station and satellite rainfall values. The predictions are precise enough for most practical purposes. The model is very general and applicable to other variables besides rainfall.

Keywords: non-stationary covariance function, gaussian process, ungulate biomass, MCMC, maasai mara ecosystem

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Authors: Tugba Bayoglu

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Aerodynamic parameter estimation is very crucial in missile design phase, since accurate high fidelity aerodynamic model is required for designing high performance and robust control system, developing high fidelity flight simulations and verification of computational and wind tunnel test results. However, in literature, there is not enough missile aerodynamic parameter identification study for three main reasons: (1) most air to air missiles cannot fly with constant speed, (2) missile flight test number and flight duration are much less than that of fixed wing aircraft, (3) variation of the missile aerodynamic parameters with respect to Mach number is higher than that of fixed wing aircraft. In addition to these challenges, identification of aerodynamic parameters for high wind angles by using classical estimation techniques brings another difficulty in the estimation process. The reason for this, most of the estimation techniques require employing polynomials or splines to model the behavior of the aerodynamics. However, for the missiles with a large variation of aerodynamic parameters with respect to flight variables, the order of the proposed model increases, which brings computational burden and complexity. Therefore, in this study, it is aimed to solve nonlinear aerodynamic parameter identification problem for a supersonic air to air missile by using Artificial Neural Networks. The method proposed will be tested by using simulated data which will be generated with a six degree of freedom missile model, involving a nonlinear aerodynamic database. The data will be corrupted by adding noise to the measurement model. Then, by using the flight variables and measurements, the parameters will be estimated. Finally, the prediction accuracy will be investigated.

Keywords: air to air missile, artificial neural networks, open loop simulation, parameter identification

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Authors: Zohreh Khojasteh-Ghamari, Takashi Irohara

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One of the efficient ways in supply chain risk management is avoiding the interruption in Supply Chain (SC) before it occurs. Although the majority of the organizations focus on their first-tier suppliers to avoid risk in the SC, studies show that in only 60 percent of the disruption cases the reason is first tier suppliers. In the 40 percent of the SC disruptions, the reason is downstream SC, which is the second tier and lower. Due to the increasing complexity and interrelation of modern supply chains, the SC elements have become difficult to trace. Moreover, studies show that there is a vital need to better understand the integration of risk and visibility, especially in the context of multiple objectives. In this study, we propose a multi-objective programming model to avoid disruption in SC. The objective of this study is evaluating the effect of downstream SCV on managing supply chain risk. We propose a multi-objective mathematical programming model with the objective functions of minimizing the total cost and maximizing the downstream supply chain visibility (SCV). The decision variable is supplier selection. We assume there are several manufacturers and several candidate suppliers. For each manufacturer, our model proposes the best suppliers with the lowest cost and maximum visibility in downstream supply chain. We examine the applicability of the model by numerical examples. We also define several scenarios for datasets and observe the tendency. The results show that minimum visibility in downstream SC is needed to have a safe SC network.

Keywords: downstream supply chain, optimization, supply chain risk, supply chain visibility

Procedia PDF Downloads 245

Authors: Hamed Saremi, Suzan Taghavy, Seyed Mohammad Hanif Sanjari, Mostafa Kahali

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One of the challenges in manufacturing and service companies to provide a product or service is recognized Brand to consumers in target markets. They provide most of their processes under the same capacity. But the constant threat of devastating internal and external resources to prevent a rise Brands and more companies are recognizing the stages are bankrupt. This paper has tried to identify and analyze effective indicators of brand equity and focuses on indicators and presents a model of intelligent create a model to prevent possible damage. In this study, the identified indicators of brand equity are based on literature study and according to expert opinions, set of indicators By techniques DEMATEL Then to used Multi-Step Adaptive Neural-Fuzzy Inference system (ANFIS) to design a multi-stage intelligent system for assessment of brand equity.

Keywords: brand, cosmetic product, ANFIS, DEMATEL

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Authors: M. A. Tayfun, M. A. Alkhalidi

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The exact theoretical expression describing the probability distribution of nonlinear sea-surface elevations derived from the second-order narrowband model has a cumbersome form that requires numerical computations, not well-disposed to theoretical or practical applications. Here, the same narrowband model is re-examined to develop a simpler closed-form approximation suitable for theoretical and practical applications. The salient features of the approximate form are explored, and its relative validity is verified with comparisons to other readily available approximations, and oceanic data.

Keywords: ocean waves, probability distributions, second-order nonlinearities, skewness coefficient, wave steepness

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Authors: Wipassorn Vinicchayakul, Pichaya Supanakoon, Sathaporn Promwong

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A human’s hand localization is revised by using radar cross section (RCS) measurements with a minimum root mean square (RMS) error matching algorithm on a touchless keypad mock-up model. RCS and frequency transfer function measurements are carried out in an indoor environment on the frequency ranged from 3.0 to 11.0 GHz to cover federal communications commission (FCC) standards. The touchless keypad model is tested in two different distances between the hand and the keypad. The initial distance of 19.50 cm is identical to the heights of transmitting (Tx) and receiving (Rx) antennas, while the second distance is 29.50 cm from the keypad. Moreover, the effects of Rx angles relative to the hand of human factor are considered. The RCS input parameters are compared with power loss parameters at each frequency. From the results, the performance of the RCS input parameters with the second distance, 29.50 cm at 3 GHz is better than the others.

Keywords: radar cross section, fingerprint-based localization, minimum root mean square (RMS) error matching algorithm, touchless keypad model

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Authors: Dawit Abay Tesfamariam

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Ethiopia’s Climate- Resilient Green Economy strategy focuses mainly on generating and utilization of Renewable Energy (RE). The data collected in 2016 by Ethiopian Electric Power (EEP) indicates that the intermittent RE sources on the grid from solar and wind energy were only 8 % of the total energy produced. On the other hand, the EEP electricity generation plan in 2030 indicates that 36 % of the energy generation share will be covered by solar and wind sources. Thus, a case study was initiated to model and compute the balance and consumption of electricity in three different scenarios: 2016, 2025, and 2030 using the Energy PLAN Model (EPM). Initially, the model was validated using the 2016 annual power-generated data to conduct the EPM analysis for two predictive scenarios. The EPM simulation analysis using EPM for 2016 showed that there was no significant excess power generated. Hence, the model’s results are in line with the actual 2016 output. Thus, the EPM was applied to analyze the role of energy storage in RE in Ethiopian grid systems. The results of the EPM simulation analysis showed there will be excess production of 402 /7963 MW average and maximum, respectively, in 2025. The excess power was dominant in all months except in the three rainy months of the year (June, July, and August). Consequently, based on the validated outcomes of EPM indicates, there is a good reason to think about other alternatives for the utilization of excess energy and storage of RE. Thus, from the scenarios and model results obtained, it is realistic to infer that; if the excess power is utilized with a storage mechanism that can stabilize the grid system; as a result, the extra RE generated can be exported to support the economy. Therefore, researchers must continue to upgrade the current and upcoming energy storage system to synchronize with RE potentials that can be generated from RE.

Keywords: renewable energy, storage, wind, energyplan

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Authors: Tatiana G. Smirnova, Stan G. Benjamin

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Rapid Update Cycle (RUC) land surface model (LSM) was a land-surface component in several generations of operational weather prediction models at the National Center for Environment Prediction (NCEP) at the National Oceanic and Atmospheric Administration (NOAA). It was designed for short-range weather predictions with an emphasis on severe weather and originally was intentionally simple to avoid uncertainties from poorly known parameters. Nevertheless, the RUC LSM, when coupled with the hourly-assimilating atmospheric model, can produce a realistic evolution of time-varying soil moisture and temperature, as well as the evolution of snow cover on the ground surface. This result is possible only if the soil/vegetation/snow component of the coupled weather prediction model has sufficient skill to avoid long-term drift. RUC LSM was first implemented in the operational NCEP Rapid Update Cycle (RUC) weather model in 1998 and later in the Weather Research Forecasting Model (WRF)-based Rapid Refresh (RAP) and High-resolution Rapid Refresh (HRRR). Being available to the international WRF community, it was implemented in operational weather models in Austria, New Zealand, and Switzerland. Based on the feedback from the US weather service offices and the international WRF community and also based on our own validation, RUC LSM has matured over the years. Also, a sea-ice module was added to RUC LSM for surface predictions over the Arctic sea-ice. Other modifications include refinements to the snow model and a more accurate specification of albedo, roughness length, and other surface properties. At present, RUC LSM is being tested in the regional application of the Unified Forecast System (UFS). The next generation UFS-based regional Rapid Refresh FV3 Standalone (RRFS) model will replace operational RAP and HRRR at NCEP. Over time, RUC LSM participated in several international model intercomparison projects to verify its skill using observed atmospheric forcing. The ESM-SnowMIP was the last of these experiments focused on the verification of snow models for open and forested regions. The simulations were performed for ten sites located in different climatic zones of the world forced with observed atmospheric conditions. While most of the 26 participating models have more sophisticated snow parameterizations than in RUC, RUC LSM got a high ranking in simulations of both snow water equivalent and surface temperature. However, ESM-SnowMIP experiment also revealed some issues in the RUC snow model, which will be addressed in this paper. One of them is the treatment of grid cells partially covered with snow. RUC snow module computes energy and moisture budgets of snow-covered and snow-free areas separately by aggregating the solutions at the end of each time step. Such treatment elevates the importance of computing in the model snow cover fraction. Improvements to the original simplistic threshold-based approach have been implemented and tested both offline and in the coupled weather model. The detailed description of changes to the snow cover fraction and other modifications to RUC soil and snow parameterizations will be described in this paper.

Keywords: land-surface models, weather prediction, hydrology, boundary-layer processes

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Authors: Girish Chavadappanavar

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The impact of climate on agriculture could result in problems with food security and may threaten the livelihood activities upon which much of the population depends. In the present study, the development of a statistical yield forecast model has been carried out for sugarcane production over Belagavi district, Karnataka using weather variables of crop growing season and past observed yield data for the period of 1971 to 2010. The study shows that this type of statistical yield forecast model could efficiently forecast yield 5 weeks and even 10 weeks in advance of the harvest for sugarcane within an acceptable limit of error. The performance of the model in predicting yields at the district level for sugarcane crops is found quite satisfactory for both validation (2007 and 2008) as well as forecasting (2009 and 2010).In addition to the above study, the climate variability of the area has also been studied, and hence, the data series was tested for Mann Kendall Rank Statistical Test. The maximum and minimum temperatures were found to be significant with opposite trends (decreasing trend in maximum and increasing in minimum temperature), while the other three are found in significant with different trends (rainfall and evening time relative humidity with increasing trend and morning time relative humidity with decreasing trend).

Keywords: climate impact, regression analysis, yield and forecast model, sugar models

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Authors: Anna Cameron, Chunxia Zhao, Haofei Wang, Yun Liu, Guang Ze Yang

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Since the first publication in 1775, cancer research has built a comprehensive understanding of how cellular components of the tumor niche promote disease development. However, only within the last decade has research begun to establish the impact of non-cellular components of the niche, particularly the extracellular matrix (ECM). The ECM, a three-dimensional scaffold that sustains the tumor microenvironment, plays a crucial role in disease progression. Cancer cells actively deregulate and remodel the ECM to establish a tumor-promoting environment. Recent work has highlighted the need to further our understanding of the complexity of this cancer-ECM relationship. In vitro models use hydrogels to mimic the ECM, as hydrogel matrices offer biological compatibility and stability needed for long term cell culture. However, natural hydrogels are being used in these models verbatim, without tuning their biophysical characteristics to achieve pathophysiological relevance, thus limiting their broad use within cancer research. The biophysical attributes of these gels dictate cancer cell proliferation, invasion, metastasis, and therapeutic response. Evaluating the three most widely used natural hydrogels, Matrigel, collagen, and agarose gel, the permeability, stiffness, and pore-size of each gel were measured and compared to the in vivo environment. The pore size of all three gels fell between 0.5-6 µm, which coincides with the 0.1-5 µm in vivo pore size found in the literature. However, the stiffness for hydrogels able to support cell culture ranged between 0.05 and 0.3 kPa, which falls outside the range of 0.3-20,000 kPa reported in the literature for an in vivo ECM. Permeability was ~100x greater than in vivo measurements, due in large part to the lack of cellular components which impede permeation. Though, these measurements prove important when assessing therapeutic particle delivery, as the ECM permeability decreased with increasing particle size, with 100 nm particles exhibiting a fifth of the permeability of 10 nm particles. This work explores ways of adjusting the biophysical characteristics of hydrogels by changing protein concentration and the trade-off, which occurs due to the interdependence of these factors. The global aim of this work is to produce a more pathophysiologically relevant model for each tumor type.

Keywords: cancer, extracellular matrix, hydrogel, microfluidic

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Authors: Cecília M. Antão, Paulo Jorge Nogueira

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Breast cancer (BC) is the first cause of cancer mortality among Portuguese women. This retrospective observational study aimed at identifying comorbidities associated with BC female patients admitted to Portuguese public hospitals (2010-2018), investigating the effect of comorbidities on BC mortality rate, and building a predictive model using logistic regression. Results showed that the BC mortality in Portugal decreased in this period and reached 4.37% in 2018. Adjusted odds ratio indicated that secondary malignant neoplasms of liver, of bone and bone marrow, congestive heart failure, and diabetes were associated with an increased chance of dying from breast cancer. Although the Lisbon district (the most populated area) accounted for the largest percentage of BC patients, the logistic regression model showed that, besides patient’s age, being resident in Bragança, Castelo Branco, or Porto districts was directly associated with an increase of the mortality rate.

Keywords: breast cancer, comorbidities, logistic regression, adjusted odds ratio

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Authors: Sunidhi Sood, Drashti Narendrakumar Shah, Aakarsh Sharma, Nirali Harsh Panchal, Maria Karizhenskaia

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Cancer is a global health concern, causing a significant number of deaths, with chemotherapy being a standard treatment method. However, chemotherapy often induces side effects that profoundly impact the physical and emotional well-being of patients, lowering their overall quality of life (QoL). This research aims to investigate the potential of music and art therapy as holistic adjunctive therapy for cancer patients undergoing chemotherapy, offering non-pharmacological support. This is achieved through a comprehensive review of existing literature with a focus on the following themes, including stress and anxiety alleviation, emotional expression and coping skill development, transformative changes, and pain management with mood upliftment. A systematic search was conducted using Medline, Google Scholar, and St. Lawrence College Library, considering original, peer-reviewed research papers published from 2014 to 2023. The review solely incorporated studies focusing on the impact of music and art therapy on the health and overall well-being of cancer patients undergoing chemotherapy in North America. The findings from 16 studies involving pediatric oncology patients, females affected by breast cancer, and general oncology patients show that music and art therapies significantly reduce anxiety (standardized mean difference: -1.10) and improve perceived stress (median change: -4.0) and overall quality of life in cancer patients undergoing chemotherapy. Furthermore, music therapy has demonstrated the potential to decrease anxiety, depression, and pain during infusion treatments (average changes in resilience scale: 3.4 and 4.83 for instrumental and vocal music therapy, respectively). This data calls for consideration of the integration of music and art therapy into supportive care programs for cancer patients undergoing chemotherapy. Moreover, it provides guidance to healthcare professionals and policymakers, facilitating the development of patient-centered strategies for cancer care in Canada. Further research is needed in collaboration with qualified therapists to examine its applicability and explore and evaluate patients' perceptions and expectations in order to optimize the therapeutic benefits and overall patient experience. In conclusion, integrating music and art therapy in cancer care promises to substantially enhance the well-being and psychosocial state of patients undergoing chemotherapy. However, due to the small population size considered in existing studies, further research is needed to bridge the knowledge gap and ensure a comprehensive, patient-centered approach, ultimately enhancing the quality of life (QoL) for individuals facing the challenges of cancer treatment.

Keywords: anxiety, cancer, chemotherapy, depression, music and art therapy, pain management, quality of life

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Authors: Yushuai Wang, Feng Xu, Junbo Tan, Xueqian Wang, Bin Liang

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In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (RMPC) and set theoretic fault detection and isolation (FDI) is extended to linear parameter varying (LPV) systems. First, a group of set-valued observers are designed for passive fault detection (FD) and the observer gains are obtained through minimizing the size of invariant set of state estimation-error dynamics. Second, an input set for fault isolation (FI) is designed offline through set theory for actively isolating faults after FD. Third, an RMPC controller based on state estimation for LPV systems is designed to control the system in the presence of disturbance and measurement noise and tolerate faults. Besides, an FTC algorithm is proposed to maintain the plant operate in the corresponding mode when the fault occurs. Finally, a numerical example is used to show the effectiveness of the proposed results.

Keywords: fault detection, linear parameter varying, model predictive control, set theory

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Authors: Anod H. Alhazmi, Hanaa A. Yamani

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The lack of digital transformation professionals could prevent Saudi Arabia’s universities from providing digital services. The task of understanding what digital skills are needed within an organization, measuring the existing skills, and developing or attracting talents is a complex task. This paper provides a comprehensive analysis of the digital transformation skills needed in the organizations who seek digital transformation and identifies the skills and competencies framework DigSC built on Skills Framework for the Informational Age (SFIA) framework that is adopted by the Ministry of Communications and Information Technology (MCIT) in Saudi Arabia. The framework adopted identifies the main digital transformation skills clusters, categories and levels of responsibilities for each job description to fill the gap between this requirement and the digital skills supplied by the Umm Alqura University (UQU).

Keywords: competencies, digital transformation, framework, skills, Umm Alqura university

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: Peter Smolianov, Jed Smith, Lisa Chen, Steven Dion, Christopher Schoen, Jaclyn Norberg

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This study compared general conditioning and sport specific fitness tests used in China, European Union (EU), Russia, and the United States. A constant content comparison method was used to identify the differences. Data from the study indicated that there were shared test components, including aerobic fitness, muscular strength, endurance, and flexibility. However, the testing components and items, as well as the cut-off values of the analyzed tests varied by country for the same gender and age. Culturally unique methods of testing and preparing for the tests were identified. Additionally, evidence revealed support for lifelong fitness and long-term sport participation through comprehensive fitness and wellness guidelines for different age groups and through new internet-based technologies.

Keywords: fitness testing, global trends, new developments, worldwide comparison

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Authors: Muhammad Abu-Khaizaran, Ahmad Faza’, Tariq Othman, Yahia Yousef

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This paper presents a study to provide sufficient and reliable information about constructing a Photovoltaic energy profile of the Birzeit University campus (BZU) based on the weather forecast. The developed Photovoltaic energy profile helps to predict the energy yield of the Photovoltaic systems based on the weather forecast and hence helps planning energy production and consumption. Two models will be developed in this paper; a Clear Sky Irradiance model and a Cloud-Cover Radiation model to predict the irradiance for a clear sky day and a cloudy day, respectively. The adopted procedure for developing such models takes into consideration two levels of abstraction. First, irradiance and weather data were acquired by a sensory (measurement) system installed on the rooftop of the Information Technology College building at Birzeit University campus. Second, power readings of a fully operational 51kW commercial Photovoltaic system installed in the University at the rooftop of the adjacent College of Pharmacy-Nursing and Health Professions building are used to validate the output of a simulation model and to help refine its structure. Based on a comparison between a mathematical model, which calculates Clear Sky Irradiance for the University location and two sets of accumulated measured data, it is found that the simulation system offers an accurate resemblance to the installed PV power station on clear sky days. However, these comparisons show a divergence between the expected energy yield and actual energy yield in extreme weather conditions, including clouding and soiling effects. Therefore, a more accurate prediction model for irradiance that takes into consideration weather factors, such as relative humidity and cloudiness, which affect irradiance, was developed; Cloud-Cover Radiation Model (CRM). The equivalent mathematical formulas implement corrections to provide more accurate inputs to the simulation system. The results of the CRM show a very good match with the actual measured irradiance during a cloudy day. The developed Photovoltaic profile helps in predicting the output energy yield of the Photovoltaic system installed at the University campus based on the predicted weather conditions. The simulation and practical results for both models are in a very good match.

Keywords: clear-sky irradiance model, cloud-cover radiation model, photovoltaic, weather forecast

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Authors: Taki Eddine Seghier, Mohd Hamdan Ahmad, Yaik-Wah Lim, Samuel Opeyemi Williams

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The assessment of building sustainability to achieve a specific green benchmark and the preparation of the required documents in order to receive a green building certification, both are considered as major challenging tasks for green building design team. However, this labor and time-consuming process can take advantage of the available Building Information Modeling (BIM) features such as material take-off and scheduling. Furthermore, the workflow can be automated in order to track potentially achievable credit points and provide rating feedback for several design options by using integrated Visual Programing (VP) to handle the stored parameters within the BIM model. Hence, this study proposes a BIM-based tool that uses Green Building Index (GBI) rating system requirements as a unique input case to evaluate the building sustainability in the design stage of the building project life cycle. The tool covers two key models for data extraction, firstly, a model for data extraction, calculation and the classification of achievable credit points in a green template, secondly, a model for the generation of the required documents for green building certification. The tool was validated on a BIM model of residential building and it serves as proof of concept that building sustainability assessment of GBI certification can be automatically evaluated and documented through BIM.

Keywords: green building rating system, GBRS, building information modeling, BIM, visual programming, VP, sustainability assessment

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Authors: Vahid Nourani, Atefeh Ashrafi

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Prediction of treated wastewater quality is a matter of growing importance in water treatment procedure. In this way artificial neural network (ANN), as a robust data-driven approach, has been widely used for forecasting the effluent quality of wastewater treatment. However, developing ANN model based on appropriate input variables is a major concern due to the numerous parameters which are collected from treatment process and the number of them are increasing in the light of electronic sensors development. Various studies have been conducted, using different clustering methods, in order to classify most related and effective input variables. This issue has been overlooked in the selecting dominant input variables among wastewater treatment parameters which could effectively lead to more accurate prediction of water quality. In the presented study two ANN models were developed with the aim of forecasting effluent quality of Tabriz city’s wastewater treatment plant. Biochemical oxygen demand (BOD) was utilized to determine water quality as a target parameter. Model A used Principal Component Analysis (PCA) for input selection as a linear variance-based clustering method. Model B used those variables identified by the mutual information (MI) measure. Therefore, the optimal ANN structure when the result of model B compared with model A showed up to 15% percent increment in Determination Coefficient (DC). Thus, this study highlights the advantage of PCA method in selecting dominant input variables for ANN modeling of wastewater plant efficiency performance.

Keywords: Artificial Neural Networks, biochemical oxygen demand, principal component analysis, mutual information, Tabriz wastewater treatment plant, wastewater treatment plant

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Authors: Anteneh Dagnachew Sewenet, Paola Pisano

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The concept of circular economy, circularity, and sustainability are interconnected and promote a more sustainable future. However, previous studies have mainly focused on each concept individually, neglecting the relationships and gaps in the existing literature. This study aims to integrate and link these concepts to expand the theoretical and practical methods of scholars and professionals in pursuit of sustainability. The aim of this systematic literature review is to comprehensively analyze and summarize the interconnections between circular economy, circularity, and sustainability. Additionally, it seeks to develop a conceptual framework that can guide practitioners and serve as a basis for future research. The review employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. A total of 78 articles were analyzed, utilizing the Scopus and Web of Science databases. The analysis involved summarizing and systematizing the conceptualizations of circularity and its relationship with the circular economy and long-term sustainability. The review provided a comprehensive overview of the interconnections between circular economy, circularity, and sustainability. Key themes, theoretical frameworks, empirical findings, and conceptual gaps in the literature were identified. Through a rigorous analysis of scholarly articles, the study highlighted the importance of integrating these concepts for a more sustainable future. This study contributes to the existing literature by integrating and linking the concepts of circular economy, circularity, and sustainability. It expands the theoretical understanding of how these concepts relate to each other and provides a conceptual framework that can guide future research in this field. The findings emphasize the need for a holistic approach in achieving sustainability goals. The data collection for this review involved identifying relevant articles from the Scopus and Web of Science databases. The selection of articles was made based on predefined inclusion and exclusion criteria. The PRISMA protocol guided the systematic analysis of the selected articles, including summarizing and systematizing their content. This study addressed the question of how circularity is conceptualized and related to both the circular economy and long-term sustainability. It aimed to identify the interconnections between these concepts and bridge the gap in the existing literature. The review provided a comprehensive analysis of the interconnections between the circular economy, circularity, and sustainability. It presented a conceptual framework that can guide practitioners in implementing circular economy strategies and serve as a basis for future research. By integrating these concepts, scholars, and professionals can enhance the theoretical and practical methods in pursuit of a more sustainable future. The findings emphasize the importance of taking a holistic approach to achieve sustainability goals and highlight conceptual gaps that can be addressed in future studies.

Keywords: circularity, circular economy, sustainability, innovation

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Authors: H. Krarchaa, A. Ferroudj

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This work presents the results of thermodynamic properties of intermetallic rare earth-gold compounds at different stoichiometric structures. It mentions the existence of the AuRE AuRE2, Au2RE, Au51RE14, Au6RE, Au3RE and Au4RE phases in the majority of Au-RE phase diagrams. It's observed that equiatomic composition is a common compound for all gold rare earth alloys and it has the highest melting temperature. Enthalpies of the formation of studied compounds are calculated based on a new reformulation of Miedema’s model.

Keywords: rare earth element, enthalpy of formation, thermodynamic properties, macroscopic model

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Authors: Zina Ghiloufi, Tahar Khir

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A transient three-dimensional computational fluid dynamics (CFD) model is developed to determine the velocity and temperature distribution in different positions cold room during pre-cooling of dates. The turbulence model used is the k-ω Shear Stress Transport (SST) with the standard wall function, the air. The numerical results obtained show that cooling rate is not uniform inside the room; the product at the medium of room has a slower cooling rate. This cooling heterogeneity has a large effect on the energy consumption during cold storage.

Keywords: CFD, cold room, cooling rate, dDates, numerical simulation, k-ω (SST)

Procedia PDF Downloads 237

Authors: Man-Yu Wong, Siyu Zhou, Zhiqiang Cao

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In quality of life, health service utilization is an important determinant of medical resource expenditures on Colorectal cancer (CRC) care, a better understanding of the increased utilization of health services is essential for optimizing the allocation of healthcare resources to services and thus for enhancing the service quality, especially for high expenditure on CRC care like Hong Kong region. In assessing the association between the health-related quality of life (HRQOL) and health service utilization in patients with colorectal neoplasm, count data models can be used, which account for over dispersion or extra zero counts. In our data, the HRQOL evaluation is a self-reported measure obtained from a questionnaire completed by the patients, misreports and variations in the data are inevitable. Besides, there are more zero counts from the observed number of clinical consultations (observed frequency of zero counts = 206) than those from a Poisson distribution with mean equal to 1.33 (expected frequency of zero counts = 156). This suggests that excess of zero counts may exist. Therefore, we study tests for detecting zero-inflation in models with measurement error in covariates. Method: Under classical measurement error model, the approximate likelihood function for zero-inflation Poisson regression model can be obtained, then Approximate Maximum Likelihood Estimation(AMLE) can be derived accordingly, which is consistent and asymptotically normally distributed. By calculating score function and Fisher information based on AMLE, a score test is proposed to detect zero-inflation effect in ZIP model with measurement error. The proposed test follows asymptotically standard normal distribution under H0, and it is consistent with the test proposed for zero-inflation effect when there is no measurement error. Results: Simulation results show that empirical power of our proposed test is the highest among existing tests for zero-inflation in ZIP model with measurement error. In real data analysis, with or without considering measurement error in covariates, existing tests, and our proposed test all imply H0 should be rejected with P-value less than 0.001, i.e., zero-inflation effect is very significant, ZIP model is superior to Poisson model for analyzing this data. However, if measurement error in covariates is not considered, only one covariate is significant; if measurement error in covariates is considered, only another covariate is significant. Moreover, the direction of coefficient estimations for these two covariates is different in ZIP regression model with or without considering measurement error. Conclusion: In our study, compared to Poisson model, ZIP model should be chosen when assessing the association between condition-specific HRQOL and health service utilization in patients with colorectal neoplasm. and models taking measurement error into account will result in statistically more reliable and precise information.

Keywords: count data, measurement error, score test, zero inflation

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Authors: Peter Kress, Alexander Pflaum, Ulrich Loewen

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The number of Internet-of-Things (IoT) platforms is steadily increasing across various industries, especially for smart factories, smart homes and smart mobility. Also in the manufacturing industry, the number of Industrial-IoT platforms is growing. Both IT players, start-ups and increasingly also established industry players and small-and-medium-enterprises introduce offerings for the connection of industrial equipment on platforms, enabled by advanced information and communication technology. Beside the offered functionalities, the established ecosystem of partners around a platform is one of the key differentiators to generate a competitive advantage. The key question is how platform operators design the business model around their platform to attract a high number of customers and partners to co-create value for the entire ecosystem. The present research tries to answer this question by determining the key characteristics of business models of successful platforms in the manufacturing industry. To achieve that, the authors selected an explorative qualitative research approach and created an inductive comparative case study. The authors generated valuable descriptive insights of the business model elements (e.g., value proposition, pricing model or partnering model) of various established platforms. Furthermore, patterns across the various cases were identified to derive propositions for the successful design of business models of platforms in the manufacturing industry.

Keywords: industrial-internet-of-things, business models, platforms, ecosystems, case study

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Authors: S. Werrlich, E. Eichstetter, K. Nitsche, G. Notni

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Augmented Reality (AR) is a strong growing research topic in different training domains such as medicine, sports, military, education and industrial use cases like assembly and maintenance tasks. AR claims to improve the efficiency and skill-transfer of training tasks. This paper gives a comprehensive overview of evaluations using AR for assembly and maintenance training tasks published between 1992 and 2017. We search in a structured way in four different online databases and get 862 results. We select 17 relevant articles focusing on evaluating AR-based training applications for assembly and maintenance tasks. This paper also indicates design guidelines which are necessary for creating a successful application for an AR-based training. We also present five scientific limitations in the field of AR-based training for assembly tasks. Finally, we show our approach to solve current research problems using Design Science Research (DSR).

Keywords: assembly, augmented reality, survey, training

Procedia PDF Downloads 282

Authors: Jacob Livingston Slosser, Naja Holten Moller, Thomas Troels Hildebrandt, Henrik Palmer Olsen

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In this paper, we propose a model for human-AI interaction in public administration that involves legal decision-making. Inspired by Alan Turing’s test for machine intelligence, we propose a way of institutionalizing a continuous working relationship between man and machine that aims at ensuring both good legal quality and higher efficiency in decision-making processes in public administration. We also suggest that our model enhances the legitimacy of using AI in public legal decision-making. We suggest that case loads in public administration could be divided between a manual and an automated decision track. The automated decision track will be an algorithmic recommender system trained on former cases. To avoid unwanted feedback loops and biases, part of the case load will be dealt with by both a human case worker and the automated recommender system. In those cases an experienced human case worker will have the role of an evaluator, choosing between the two decisions. This model will ensure that the algorithmic recommender system is not compromising the quality of the legal decision making in the institution. It also enhances the legitimacy of using algorithmic decision support because it provides justification for its use by being seen as superior to human decisions when the algorithmic recommendations are preferred by experienced case workers. The paper outlines in some detail the process through which such a model could be implemented. It also addresses the important issue that legal decision making is subject to legislative and judicial changes and that legal interpretation is context sensitive. Both of these issues requires continuous supervision and adjustments to algorithmic recommender systems when used for legal decision making purposes.

Keywords: administrative law, algorithmic decision-making, decision support, public law

Procedia PDF Downloads 218

Authors: Gyoomi Kim, Jiyoung Bae

Abstract:

The purpose of this study was to propose a framework that verifies the structural relationships among students’ use of digital English learning strategy (DELS), affective domains, and their individual variables. The study developed a hypothetical model based on previous studies on language learning strategy use as well as digital language learning. The participants were 720 Korean high school students and 430 university students. The instrument was a self-response questionnaire that contained 70 question items based on Oxford’s SILL (Strategy Inventory for Language Learning) as well as the previous studies on language learning strategies in digital learning environment in order to measure DELS and affective domains. The collected data were analyzed through structural equation modeling (SEM). This study used quantitative data analysis procedures: Explanatory factor analysis (EFA) and confirmatory factor analysis (CFA). Firstly, the EFA was conducted in order to verify the hypothetical model; the factor analysis was conducted preferentially to identify the underlying relationships between measured variables of DELS and the affective domain in the EFA process. The hypothetical model was established with six indicators of learning strategies (memory, cognitive, compensation, metacognitive, affective, and social strategies) under the latent variable of the use of DELS. In addition, the model included four indicators (self-confidence, interests, self-regulation, and attitude toward digital learning) under the latent variable of learners’ affective domain. Secondly, the CFA was used to determine the suitability of data and research models, so all data from the present study was used to assess model fits. Lastly, the model also included individual learner factors as covariates and five constructs selected were learners’ gender, the level of English proficiency, the duration of English learning, the period of using digital devices, and previous experience of digital English learning. The results verified from SEM analysis proposed a theoretical model that showed the structural relationships between Korean students’ use of DELS and their affective domains. Therefore, the results of this study help ESL/EFL teachers understand how learners use and develop appropriate learning strategies in digital learning contexts. The pedagogical implication and suggestions for the further study will be also presented.

Keywords: Digital English Learning Strategy, DELS, individual variables, learners' affective domains, Structural Equation Modeling, SEM

Procedia PDF Downloads 126