Search results for: structural equation modeling

Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

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Authors: Krishna Pal Singh, Shailendra Kumar Gupta, Olaf Wolkenhauer

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Background: Our study aimed to identify common genes and potential targets across the four diseases, which include rheumatoid arthritis, melanoma metastasis, ulcerative colitis, and Crohn’s disease. We used a network and systems biology approach to identify the hub gene, which can act as a potential target for all four disease conditions. The regulatory network was extracted from the PPI using the MCODE module present in Cytoscape. Our objective was to investigate the significance of hub genes in these diseases using gene ontology and KEGG pathway enrichment analysis. Methods: Our methodology involved collecting disease gene-related information from DisGeNET databases and performing protein-protein interaction (PPI) network and core genes screening. We then conducted gene ontology and KEGG pathway enrichment analysis. Results: We found that IL6 plays a critical role in all disease conditions and in different pathways that can be associated with the development of all four diseases. Conclusions: The theoretical importance of our research is that we employed various systems and structural biology techniques to identify a crucial protein that could serve as a promising target for treating multiple diseases. Our data collection and analysis procedures involved rigorous scrutiny, ensuring high-quality results. Our conclusion is that IL6 plays a significant role in all four diseases, and it can act as a potential target for treating them. Our findings may have important implications for the development of novel therapeutic interventions for these diseases.

Keywords: melanoma metastasis, rheumatoid arthritis, inflammatory bowel diseases, integrated bioinformatics analysis

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Authors: Zhiyong Zheng, Xu Li, Liang Huang, Zhengliang Sun, Jianhua Xu

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Yaw angle plays a significant role in many vehicle safety applications, such as collision avoidance and lane-keeping system. Although the estimation of the yaw angle has been extensively studied in existing literature, it is still the main challenge to simultaneously achieve a reliable and cost-effective solution in complex urban environments. This paper proposes a multi-stage learning framework to estimate the yaw angle with a monocular camera, which can deal with the challenge in a more reliable manner. In the first stage, an efficient road detection network is designed to extract the road region, providing a highly reliable reference for the estimation. In the second stage, a variational auto-encoder (VAE) is proposed to learn the distribution patterns of road regions, which is particularly suitable for modeling the changing patterns of yaw angle under different driving maneuvers, and it can inherently enhance the generalization ability. In the last stage, a gated recurrent unit (GRU) network is used to capture the temporal correlations of the learned patterns, which is capable to further improve the estimation accuracy due to the fact that the changes of deflection angle are relatively easier to recognize among continuous frames. Afterward, the yaw angle can be obtained by combining the estimated deflection angle and the road direction stored in a roadway map. Through effective multi-stage learning, the proposed framework presents high reliability while it maintains better accuracy. Road-test experiments with different driving maneuvers were performed in complex urban environments, and the results validate the effectiveness of the proposed framework.

Keywords: gated recurrent unit, multi-stage learning, reliable estimation, variational auto-encoder, yaw angle

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Authors: Andile Mkhohlakali, Tien-Chien Jen, James Tshilongo, Happy Mabowa

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Among different parameters, oxygen is one of the most important analytes of interest, dissolved oxygen (DO) concentration is very crucial and significant for various areas of physical, chemical, and environmental monitoring. Herein we report oxygen-sensitive luminophores -based lanthanum(III) trifluoromethanesulfonate), [La]³⁺ was encapsulated into SiO₂-based xerogel matrix. The nanosensor is composed of organically modified silica nanoparticles, doped with the luminescent oxygen–sensitive lanthanum(III) trifluoromethanesulfonate complex. The precursor materials used for sensing film were triethyl ethoxy silane (TEOS) and (3-Mercaptopropyltriethoxysilane) (MPTMS- TEOS) used for SiO2-baed matrices. Brunauer–Emmett–Teller (BET), and BJH indicate that the SiO₂ transformed from microporous to mesoporous upon the addition of La³⁺ luminophore with increased surface area (SBET). The typical amorphous SiO₂ based xerogels were revealed with X-Ray diffraction (XRD) and Selected Area Electron Diffraction (SAED) analysis. Scanning electron microscope- (SEM) and transmission electron microscope (TEM) showed the porous morphology and reduced particle for SiO₂ and La-SiO₂ xerogels respectively. The existence of elements, siloxane networks, and thermal stability of xerogel was confirmed by energy dispersive spectroscopy (EDS), Fourier-transform infrared spectroscopy (FTIR), and Thermographic analysis (TGA). UV-Vis spectroscopy and photoluminescence (PL) have been used to characterize the optical properties of xerogels. La-SiO₂ demonstrates promising characteristic features of an active sensing film for dissolved oxygen in the water. Keywords: Sol-gel, ORMOSILs, encapsulation, Luminophores quenching, O₂-sensing

Keywords: sol-gel, ORMOSILs, luminophores quenching, O₂-sensing

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Authors: Rudy Bahouth, Farouk Benmeddour, Emmanuel Moulin, Jamal Assaad

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Guided ultrasonic waves are used in Non-Destructive Testing (NDT) and Structural Health Monitoring (SHM) for inspection and damage detection. Recently, wireless data transmission using ultrasonic waves in solid metallic channels has gained popularity in some industrial applications such as nuclear, aerospace and smart vehicles. The idea is to find a good substitute for electromagnetic waves since they are highly attenuated near metallic components due to Faraday shielding. The proposed solution is to use ultrasonic guided waves such as Lamb waves as an information carrier due to their capability of propagation for long distances. In addition to this, valuable information about the health of the structure could be extracted simultaneously. In this work, the reliable frequency bandwidth for communication is extracted experimentally from dispersion curves at first. Then, an experimental platform for wireless communication using Lamb waves is described and built. After this, coherent demodulation algorithm used in telecommunications is tested for Amplitude Shift Keying, On-Off Keying and Binary Phase Shift Keying modulation techniques. Signal processing parameters such as threshold choice, number of cycles per bit and Bit Rate are optimized. Experimental results are compared based on the average Bit Error Rate. Results have shown high sensitivity to threshold selection for Amplitude Shift Keying and On-Off Keying techniques resulting a Bit Rate decrease. Binary Phase Shift Keying technique shows the highest stability and data rate between all tested modulation techniques.

Keywords: lamb waves communication, wireless communication, coherent demodulation, bit error rate

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Authors: Mustafa Metin Donma

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Metabolic syndrome (MetS) components are noteworthy among children with obesity and morbid obesity because they point out the cases with MetS, which have the great tendency to severe health problems such as cardiovascular diseases both in childhood and adulthood. In clinical practice, considerable efforts are being observed to bring into the open the striking differences between morbid obese cases and those with MetS findings. The most privileged aspect is concerning cardiometabolic features. The aim of this study was to derive an index which behaves different in children with and without MetS from the cardiac point of view. For the purpose, aspartate transaminase (AST), a cardiac enzyme still being used independently to predict cardiac-related problems, was used. One hundred and twenty four children were recruited from the outpatient clinic of Department of Pediatrics in Tekirdag Namik Kemal University, Faculty of Medicine. Forty-three children with normal body mass index, forty-one and forty morbid obese (MO) children with MetS and without the characteristic features of MetS, respectively, were included in the study. Weight, height, waist circumference (WC), hip C (HC), head C (HdC), neck C (NC), systolic and diastolic blood pressure values were measured and recorded. Body mass index and anthropometric ratios were calculated. Fasting blood glucose (FBG), insulin (INS), triglycerides (TRG), high density lipoprotein cholesterol (HDL-C) analyses were performed. The values for AST, alanin transaminase (ALT) and AST/ALT were obtained. Advanced Donma cardiac index (ADCI) values were calculated. The formula for the index was [(TRG/HDL-C) * (INS/FBG)] * [(WC+HC)/Height] * [(HdC+NC)/Height]. Statistical evaluations including correlation analysis were done by a statistical package program. The statistical significance degree was accepted as p<0.05. The index, ADCI, was developed from both anthropometric and biochemical parameters. All anthropometric measurements except weight were included in the equation. Besides all biochemical parameters concerning MetS components were also added. This index was tested in each of three groups. Its performance was compared with the performance of cardiometabolic index (CMI). It was also checked whether it was compatible with AST activity. The performance of ADCI was better than that of CMI. Instead of double increase, the increase of three times was observed in children with MetS compared to MO children. The index was correlated with AST in MO group and with AST/ALT in MetS group. In conclusion, this index was superior in discovering cardiac problems in MO and in diagnosing MetS in MetS groups. It was also arbiter to point out cardiovascular and MetS aspects among the groups.

Keywords: aspartate transaminase, cardiac, children, index, obesity

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: Alma Khasenovna Zhakina, Arnt Oxana Vasilievna, Vasilets Evgeny Petrovich

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The aim of this project is to develop methods for obtaining new molecularly imprinted polymers from coal waste to study their structure, structural and morphological features and properties. Recently, the development of molecularly imprinted polymers has become one of the hot topics for researchers. Modern research indicates the broad prospects of rapidly developing molecular imprinting technologies for creating a new generation of sorption materials. The attractiveness of this area of research lies in the fact that the use of imprinted polymers is not limited to scientific research; they are already being introduced in the chemical, pharmaceutical and biotechnological industries, primarily at the stages of purification of the final product. For the use of molecularly imprinted polymers in the development of sorption material, their ability to selectively remove pollutants, including trace concentrations, is of fundamental importance, and the exceptional stability of polymeric materials under harsh conditions makes it possible to simplify the process of water purification as a whole. The scientific and technical effect is associated with the development of technologies for the production of new molecularly imprinted polymers, the establishment of optimal conditions for their production and the creation of effective imprinted sorbents on their basis for wastewater treatment from heavy metals. The social effect is due to the fact that the use of coal waste as a feedstock for the production of imprinted sorbents will make it possible in the future to create new industries with additional jobs and obtain competitive multi-purpose products. The economic and multiplier effect is associated with the low cost of the final product due to the involvement of local coal waste in the production, reduction of transport, customs and other costs.

Keywords: imprinted polymers, coal waste, polymerization, template, customized sorbents

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Authors: Ahmad Alsolami, Darren Mundy, Manuel Hernandez-Perez

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Social media networks, such as Twitter, offer the perfect opportunity to either positively or negatively affect political attitudes on large audiences. The existence of influential users who have developed a reputation for their knowledge and experience of specific topics is a major factor contributing to this impact. Therefore, knowledge of the mechanisms to identify influential users on social media is vital for understanding their effect on their audience. The concept of the influential user is related to the concept of opinion leaders' to indicate that ideas first flow from mass media to opinion leaders and then to the rest of the population. Hence, the objective of this research was to provide reliable and accurate structural mechanisms to identify influential users, which could be applied to different platforms, places, and subjects. Twitter was selected as the platform of interest, and Saudi Arabia as the context for the investigation. These were selected because Saudi Arabia has a large number of Twitter users, some of whom are considerably active in setting agendas and disseminating ideas. The study considered the scientific methods that have been used to identify public opinion leaders before, utilizing metrics software on Twitter. The key findings propose multiple novel metrics to compare Twitter influencers, including the number of followers, social authority and the use of political hashtags, and four secondary filtering measures. Thus, using ratio and percentage calculations to classify the most influential users, Twitter accounts were filtered, analyzed and included. The structured approach is used as a mechanism to explore the top ten influencers on Twitter from the political domain in Saudi Arabia.

Keywords: Twitter, influencers, structured mechanism, Saudi Arabia

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Authors: Shweta Verma

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Background: Epilepsy is a chronic non-communicable central nervous system (CNS) disorder which affects a large population of all ages. Different classes of drugs are used for the treatment of this neurological disorder, but due to augmented drug resistance and side effects, these drugs become incompetent. Therefore, we design the synthesis of ten new derivatives of Phenytoin. The moiety of Phenytoin was hybridized with different phenols by using three step approach. The synthesized molecules were then investigated for different physicochemical parameters, such as Log P values using diverse software programs and to predict the potential to cross the blood-brain barrier. Objective: The Phenytoin derivatives were designed, synthesized, and characterized to meet the structural necessities indispensable for antiepileptic activity. Method: Firstly, the chloroacetylation of the 5,5-diphenyl hydantoin was carried out, and then various substituted phenols were added to it. The synthesized compounds were characterized and evaluated for antianxiety activity by elevated plus maze method and antiepileptic activity by using subcutaneous pentylenetetrazole (scPTZ) and maximal electroshock (MES) models and neurotoxicity. Result: The number of derivatives of 5,5-diphenyl hydantoin was developed and optimized. The number of parameters was optimized which reveal that the compound containing chloro group such as C3 and C6 showed imperative potential when compared with the standard drug Diazepam. Other compounds containing nitro and methyl group were also found to possess activity. Conclusion: It was summarized that the new compounds of 5,5-diphenyl hydantoin derivatives were synthesized. The results of the data show that the compound containing chloro group is more potent for CNS activity. The new compounds have the probability of being optimized further to engender new scaffolds to treat various CNS disorders.

Keywords: phenytoin, parameters, CNS activity, blood-brain barrier, Log P, CNS active

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Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

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Authors: Wilman Septina, Rajiv R. Prabhakar, Thomas Moehl, David Tilley

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Cupric oxide (CuO) is a promising absorber material for the fabrication of scalable, low cost solar energy conversion devices, due to the high abundance and low toxicity of copper. It is a p-type semiconductor with a band gap of around 1.5 eV, absorbing a significant portion of the solar spectrum. One of the main challenges in using CuO as solar absorber in an aqueous system is its tendency towards photocorrosion, generating Cu2O and metallic Cu. Although there have been several reports of CuO as a photocathode for hydrogen production, it is unclear how much of the observed current actually corresponds to H2 evolution, as the inevitability of photocorrosion is usually not addressed. In this research, we investigated the effect of the deposition of overlayers onto CuO thin films for the purpose of enhancing its photostability as well as performance for water splitting applications. CuO thin film was fabricated by galvanic electrodeposition of metallic copper onto gold-coated FTO substrates, followed by annealing in air at 600 °C. Photoelectrochemical measurement of the bare CuO film using 1 M phosphate buffer (pH 6.9) under simulated AM 1.5 sunlight showed a current density of ca. 1.5 mA cm-2 (at 0.4 VRHE), which photocorroded to Cu metal upon prolonged illumination. This photocorrosion could be suppressed by deposition of 50 nm-thick TiO2, deposited by atomic layer deposition. In addition, we found that insertion of an n-type CdS layer, deposited by chemical bath deposition, between the CuO and TiO2 layers was able to enhance significantly the photocurrent compared to without the CdS layer. A photocurrent of over 2 mA cm-2 (at 0 VRHE) was observed using the photocathode stack FTO/Au/CuO/CdS/TiO2/Pt. Structural, electrochemical, and photostability characterizations of the photocathode as well as results on various overlayers will be presented.

Keywords: CuO, hydrogen, photoelectrochemical, photostability, water splitting

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Authors: Mirco Tarozzi, Giacomo Pignagnoli, Andrea Benedetti

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Structural systems are often subjected to degradation processes due to different kind of phenomena like unexpected loadings, ageing of the materials and fatigue cycles. This is true especially for bridges, in which their safety evaluation is crucial for the purpose of a design of planning maintenance. This paper discusses the experimental evaluation of the stiffness reduction from frequency changes due to uniform damage scenario. For this purpose, a 1:4 scaled bridge has been built in the laboratory of the University of Bologna. It is made of concrete and its cross section is composed by a slab linked to four beams. This concrete deck is 6 m long and 3 m wide, and its natural frequencies have been identified dynamically by exciting it with an impact hammer, a dropping weight, or by walking on it randomly. After that, a set of loading cycles has been applied to this bridge in order to produce a uniformly distributed crack pattern. During the loading phase, either cracking moment and yielding moment has been reached. In order to define the relationship between frequency variation and loss in stiffness, the identification of the natural frequencies of the bridge has been performed, before and after the occurrence of the damage, corresponding to each load step. The behavior of breathing cracks and its effect on the natural frequencies has been taken into account in the analytical calculations. By using a sort of exponential function given from the study of lot of experimental tests in the literature, it has been possible to predict the stiffness reduction through the frequency variation measurements. During the load test also crack opening and middle span vertical displacement has been monitored.

Keywords: concrete bridge, damage detection, dynamic test, frequency shifts, operational modal analysis

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Authors: Silvano Salvador, Maryam Zarrinderakht, Alan Martin

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Open pits, which are the result of mining, are filled by water over time until the water reaches the elevation of the local water table and generates mine pit lakes. There are several specific regulations about the water quality of pit lakes, and mining operations should keep the quality of groundwater above pre-defined standards. Therefore, an accurate, acceptable numerical model predicting pit lakes’ water balance and water quality is needed in advance of mine excavation. We carry on analyzing and developing the model introduced by Crusius, Dunbar, et al. (2002) for pit lakes. This model, called “PitMod”, simulates the physical and geochemical evolution of pit lakes over time scales ranging from a few months up to a century or more. Here, a lake is approximated as one-dimensional, horizontally averaged vertical layers. PitMod calculates the time-dependent vertical distribution of physical and geochemical pit lake properties, like temperature, salinity, conductivity, pH, trace metals, and dissolved oxygen, within each model layer. This model considers the effect of pit morphology, climate data, multiple surface and subsurface (groundwater) inflows/outflows, precipitation/evaporation, surface ice formation/melting, vertical mixing due to surface wind stress, convection, background turbulence and equilibrium geochemistry using PHREEQC and linking that to the geochemical reactions. PitMod, which is used and validated in over 50 mines projects since 2002, incorporates physical processes like those found in other lake models such as DYRESM (Imerito 2007). However, unlike DYRESM PitMod also includes geochemical processes, pit wall runoff, and other effects. In addition, PitMod is actively under development and can be customized as required for a particular site.

Keywords: pit lakes, mining, modeling, hydrology

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Authors: Mohammadreza Vafaei, Amirali Moradi, Sophia C. Alih

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The method selected for the design of structures not only can affect their seismic vulnerability but also can affect their construction cost. For the design of steel structures, two distinct methods have been introduced by existing codes, namely allowable stress design (ASD) and load resistant factor design (LRFD). This study investigates the effect of using the aforementioned design methods on the seismic vulnerability and construction cost of steel structures. Specifically, a 20-story building equipped with special moment resisting frame and an eccentrically braced system was selected for this study. The building was designed for three different intensities of peak ground acceleration including 0.2 g, 0.25 g, and 0.3 g using the ASD and LRFD methods. The required sizes of beams, columns, and braces were obtained using response spectrum analysis. Then, the designed frames were subjected to nine natural earthquake records which were scaled to the designed response spectrum. For each frame, the base shear, story shears, and inter-story drifts were calculated and then were compared. Results indicated that the LRFD method led to a more economical design for the frames. In addition, the LRFD method resulted in lower base shears and larger inter-story drifts when compared with the ASD method. It was concluded that the application of the LRFD method not only reduced the weights of structural elements but also provided a higher safety margin against seismic actions when compared with the ASD method.

Keywords: allowable stress design, load resistant factor design, nonlinear time history analysis, seismic vulnerability, steel structures

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Authors: Goksel Ezgi Guzey, Bihrat Onoz

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The scarcity of streamflow gauging stations and the increasing effects of global warming cause designing water management systems to be very difficult. This study is a significant contribution to assessing regional regression models for estimating streamflow. In this study, simulated meteorological data was related to the observed streamflow data from 1971 to 2020 for 33 stream gauging stations of the Euphrates-Tigris Basin. Ordinary least squares regression was used to predict flow for 2020-2100 with the simulated meteorological data. CORDEX- EURO and CORDEX-MENA domains were used with 0.11 and 0.22 grids, respectively, to estimate climate conditions under certain climate scenarios. Twelve meteorological variables simulated by two regional climate models, RCA4 and RegCM4, were used as independent variables in the ordinary least squares regression, where the observed streamflow was the dependent variable. The variability of streamflow was then calculated with 5-6 meteorological variables and watershed characteristics such as area and height prior to the application. Of the regression analysis of 31 stream gauging stations' data, the stations were subjected to a clustering analysis, which grouped the stations in two clusters in terms of their hydrometeorological properties. Two streamflow equations were found for the two clusters of stream gauging stations for every domain and every regional climate model, which increased the efficiency of streamflow estimation by a range of 10-15% for all the models. This study underlines the importance of homogeneity of a region in estimating streamflow not only in terms of the geographical location but also in terms of the meteorological characteristics of that region.

Keywords: hydrology, streamflow estimation, climate change, hydrologic modeling, HBV, hydropower

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Authors: Sohayla Khaled El Fakahany

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Cultural and structural limitations and conservative social norms have direct effects on the availability of sources of sexual and reproductive health and rights (SRHR) in the Arab Region. Nevertheless, SRHR advocates, healthcare providers, and organizations have created online spaces like websites, blogs, and social media platforms to increase people’s access and ability to share information, experiences, and services. While these efforts help increase the accessibility to information and services, they also create and reflect inequalities based on limited internet access. Furthermore, these emergent ways of sharing and raising awareness online cannot be seen as a substitute for the urgent need for public healthcare systems and services to address SRHR issues in Arab states. This research aims to analyze the impact of the increasing importance of the role of social media platforms and technologies in the dissemination of SRHR-related information online to the youth as well as the associated inequalities of access. It also seeks to assess the effects and inequalities of the dependence on online platforms, which should be complementary to public and private SRHR services. The theoretical framework adopts Asef Bayat’s concept of social non-movements to analyze how collective mobilization around SRHR issues is exercised in repressive and conservative settings in the Arab region. Using digital ethnography of four prominent digital platforms and a qualitative survey of people aged 18-30 years, the research draws attention to the urgent need for better access to knowledge and services around gender, bodily autonomy, and sexual and reproductive health in the Arab region.

Keywords: sexual and reproductive health and rights, social non-movements, digital platforms, Arab region

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Authors: Muhammad Muzamil Khan, Asadullah Madni, Nina Filipczek, Jiayi Pan, Nayab Tahir, Hassan Shah, Vladimir Torchilin

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Lipid-polymer hybrid nanoparticles (LPHNP) are delivery systems for controlled drug delivery at tumor sites. The superior biocompatible properties of lipid and structural advantages of polymer can be obtained via this system for controlled drug delivery. In the present study, cisplatin-loaded lipid-chitosan hybrid nanoparticles were formulated by the single step ionic gelation method based on ionic interaction of positively charged chitosan and negatively charged lipid. Formulations with various chitosan to lipid ratio were investigated to obtain the optimal particle size, encapsulation efficiency, and controlled release pattern. Transmission electron microscope and dynamic light scattering analysis demonstrated a size range of 181-245 nm and a zeta potential range of 20-30 mV. Compatibility among the components and the stability of formulation were demonstrated with FTIR analysis and thermal studies, respectively. The therapeutic efficacy and cellular interaction of cisplatin-loaded LPHNP were investigated using in vitro cell-based assays in A2780/ADR ovarian carcinoma cell line. Additionally, the cisplatin loaded LPHNP exhibited a low toxicity profile in rats. The in-vivo pharmacokinetics study also proved a controlled delivery of cisplatin with enhanced mean residual time and half-life. Our studies suggested that the cisplatin-loaded LPHNP being a promising platform for controlled delivery of cisplatin in cancer therapy.

Keywords: cisplatin, lipid-polymer hybrid nanoparticle, chitosan, in vitro cell line study

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Authors: Hossein Askarinejad, Manicka Dhanasekar

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In railway tracks, two adjacent rails are either welded or connected using bolted jointbars. In recent years the number of bolted rail joints is reduced by introduction of longer rail sections and by welding the rails at location of some joints. However, significant number of bolted rail joints remains in railways around the world as they are required to allow for rail thermal expansion or to provide electrical insulation in some sections of track. Regardless of the quality and integrity of the jointbar and bolt connections, the bending stiffness of jointbars is much lower than the rail generating large deflections under the train wheels. In addition, the gap or surface discontinuity on the rail running surface leads to generation of high wheel-rail impact force at the joint gap. These fundamental weaknesses have caused high rate of failure in track components at location of rail joints resulting in significant economic and safety issues in railways. The mechanical behavior of railway track at location of joints has not been fully understood due to various structural and material complexities. Although there have been some improvements in the methods for analysis of track at jointed rails in recent years, there are still uncertainties concerning the accuracy and reliability of the current methods. In this paper the current methods in analysis of track with a rail joint are critically evaluated and the new advances and recent research outcomes in this area are discussed. This research is part of a large granted project on rail joints which was defined by Cooperative Research Centre (CRC) for Rail Innovation with supports from Australian Rail Track Corporation (ARTC) and Queensland Rail (QR).

Keywords: jointed rails, railway mechanics, track dynamics, wheel-rail interaction

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Authors: Mauricio Cruz, Andrés Díaz García, Martha Isabel Gómez, Juan Carlos Serrato Bermúdez

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The disadvantages of using natural substrates in SSF processes have been well recognized and mainly are associated to gradual decomposition of the substrate, formation of agglomerates and decrease of porosity bed generating limitations in the mass and heat transfer. Additionally, in several cases, materials with a high agricultural value such as sour milk, beets, rice, beans and corn have been used. Thus, the use of economic inert supports (natural or synthetic) in combination with a nutrient suspension for the production of biocontrol microorganisms is a good alternative in SSF processes, but requires further studies in the fields of modeling and optimization. Therefore, the aim of this work is to compare the performance of two inert supports, a synthetic (polyurethane foam) and a natural one (rice husk), identifying the factors that have the major effects on the productivity of T. asperellum Th204 and the maximum specific growth rate in a PROPHYTA L05® fixed bed bioreactor. For this, the six factors C:N ratio, temperature, inoculation rate, bed height, air moisture content and airflow were evaluated using a fractional design. The factors C:N and air flow were identified as significant on the productivity (expressed as conidia/dry substrate•h). The polyurethane foam showed higher maximum specific growth rate (0.1631 h-1) and productivities of 3.89 x107 conidia/dry substrate•h compared to rice husk (2.83x106) and natural substrate based on rice (8.87x106) used as control. Finally, a quadratic model was generated and validated, obtaining productivities higher than 3.0x107 conidia/dry substrate•h with air flow at 0.9 m3/h and C:N ratio at 18.1.

Keywords: bioprocess, scale up, fractional design, C:N ratio, air flow

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Authors: Joonas Pääkkönen

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In sports, individuals and teams are typically interested in final rankings. Final results, such as times or distances, dictate these rankings, also known as places. Places can be further associated with ordered random variables, commonly referred to as order statistics. In this work, we introduce a simple, yet accurate order statistical ordinal regression function that predicts relay race places with changeover-times. We call this function the Fenton-Wilkinson Order Statistics model. This model is built on the following educated assumption: individual leg-times follow log-normal distributions. Moreover, our key idea is to utilize Fenton-Wilkinson approximations of changeover-times alongside an estimator for the total number of teams as in the notorious German tank problem. This original place regression function is sigmoidal and thus correctly predicts the existence of a small number of elite teams that significantly outperform the rest of the teams. Our model also describes how place increases linearly with changeover-time at the inflection point of the log-normal distribution function. With real-world data from Jukola 2019, a massive orienteering relay race, the model is shown to be highly accurate even when the size of the training set is only 5% of the whole data set. Numerical results also show that our model exhibits smaller place prediction root-mean-square-errors than linear regression, mord regression and Gaussian process regression.

Keywords: Fenton-Wilkinson approximation, German tank problem, log-normal distribution, order statistics, ordinal regression, orienteering, sports analytics, sports modeling

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Authors: Yogendra K. Karna, Lauren T. Bennett

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Fire-tolerant eucalypt forests are one of the major forest ecosystems of south-eastern Australia and thought to be highly resistant to frequent high severity wildfires. However, the impact of different severity wildfires on the canopy structure of fire-tolerant forest type is under-studied, and there are significant knowledge gaps in relation to the assessment of tree and stand level canopy structural dynamics and recovery after fire. Assessment of canopy structure is a complex task involving accurate measurements of the horizontal and vertical arrangement of the canopy in space and time. This study examined the utility of multitemporal, small-footprint lidar data to describe the changes in the horizontal and vertical canopy structure of fire-tolerant eucalypt forests seven years after wildfire of different severities from the tree to stand level. Extensive ground measurements were carried out in four severity classes to describe and validate canopy cover and height metrics as they change after wildfire. Several metrics such as crown height and width, crown base height and clumpiness of crown were assessed at tree and stand level using several individual tree top detection and measurement algorithm. Persistent effects of high severity fire 8 years after both on tree crowns and stand canopy were observed. High severity fire increased the crown depth but decreased the crown projective cover leading to more open canopy.

Keywords: canopy gaps, canopy structure, crown architecture, crown projective cover, multi-temporal lidar, wildfire severity

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Authors: MyungGu Yeo, JongHan Ha, Gi-Hoon Yang, JaeYoon Lee, SeungHyun Ahn, Hyeongjin Lee, HoJun Jeon, YongBok Kim, Minseong Kim, GeunHyung Kim

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Tissue engineering is a rapidly growing interdisciplinary research area that may provide options for treating damaged tissues and organs. As a promising technique for regenerating various tissues, this technology requires biomedical scaffolds, which serve as an artificial extracellular matrix (ECM) to support neotissue growth. Electrospun micro/nanofibers have been used widely in tissue engineering because of their high surface-area-to-volume ratio and structural similarity to extracellular matrix. However, low mechanical sustainability, low 3D shape-ability, and low cell infiltration have been major limitations to their use. In this work, we propose new hybrid scaffolds interlayered with cell-laden electrospun micro/nano fibers and poly(caprolactone) microstructures. Also, we applied various concentrations of alginate and electric field strengths to determine optimal conditions for the cell-electrospinning process. The combination of cell-laden bioink (2 ⅹ 10^5 osteoblast-like MG63 cells/mL, 2 wt% alginate, 2 wt% poly(ethylene oxide), and 0.7 wt% lecithin) and a 0.16 kV/mm electric field showed the highest cell viability and fiber formation in this process. Using these conditions and PCL microstructures, we achieved mechanically stable hybrid scaffolds. In addition, the cells embedded in the fibrous structure were viable and proliferated. We suggest that the cell-embedded hybrid scaffolds fabricated using the cell-electrospinning process may be useful for various soft- and hard-tissue regeneration applications.

Keywords: bioink, cell-laden scaffold, micro/nanofibers, poly(caprolactone)

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Authors: Amani Abdallah, Isam Shahrour

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The quality of drinking water is a major concern of public health. The control of this quality is generally performed in the laboratory, which requires a long time. This type of control is not adapted for accidental pollution from sudden events, which can have serious consequences on population health. Therefore, it is of major interest to develop real-time innovative solutions for the detection of accidental contamination in drinking water systems This paper presents researches conducted within the SunRise Demonstrator for ‘Smart and Sustainable Cities’ with a particular focus on the supervision of the water quality. This work aims at (i) implementing a smart water system in a large water network (Campus of the University Lille1) including innovative equipment for real-time detection of abnormal events, such as those related to the contamination of drinking water and (ii) develop a numerical modeling of the contamination diffusion in the water distribution system. The first step included verification of the water quality sensors and their effectiveness on a network prototype of 50m length. This part included the evaluation of the efficiency of these sensors in the detection both bacterial and chemical contamination events in drinking water distribution systems. An on-line optical sensor integral with a laboratory-scale distribution system (LDS) was shown to respond rapidly to changes in refractive index induced by injected loads of chemical (cadmium, mercury) and biological contaminations (Escherichia coli). All injected substances were detected by the sensor; the magnitude of the response depends on the type of contaminant introduced and it is proportional to the injected substance concentration.

Keywords: distribution system, drinking water, refraction index, sensor, real-time

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Authors: Gamal Seliem, Hassan Seliem

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Aswan area is one of the important areas in Egypt and because it encompasses the vital engineering structure of the High dam, so it has been selected for the present study. The study of the crustal deformation and gravity associated with earthquake activity in the High Dam area of great importance for the safety of the High Dam and its economic resources. This paper deals with using micro-gravity, precise leveling and GPS data for geophysical and geodetically studies. For carrying out the detailed gravity survey in the area, were established for studying the subsurface structures. To study the recent vertical movements, a profile of 10 km length joins the High Dam and Aswan old dam were established along the road connecting the two dams. This profile consists of 35 GPS/leveling stations extending along the two sides of the road and on the High Dam body. Precise leveling was carried out with GPS and repeated micro-gravity survey in the same time. GPS network consisting of nine stations was established for studying the recent crustal movements. Many campaigns from December 2001 to December 2014 were performed for collecting the gravity, leveling and GPS data. The main aim of this work is to study the structural features and the behavior of the area, as depicted from repeated micro-gravity, precise leveling and GPS measurements. The present work focuses on the analysis of the gravity, leveling and GPS data. The gravity results of the present study investigate and analyze the subsurface geologic structures and reveal to there be minor structures; features and anomalies are taking W-E and N-S directions. The geodetic results indicated lower rates of the vertical and horizontal displacements and strain values. This may be related to the stability of the area.

Keywords: repeated micro-gravity changes, precise leveling, GPS data, Aswan High Dam

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Authors: Arjun G. Koppad

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The study was conducted in Joida taluk of Uttara Kannada district, Karnataka, India, to assess the land use land cover (LULC) and forest aboveground biomass using L band SAR data. The study area covered has dense, moderately dense, and sparse forests. The sampled area was 0.01 percent of the forest area with 30 sampling plots which were selected randomly. The point center quadrate (PCQ) method was used to select the tree and collected the tree growth parameters viz., tree height, diameter at breast height (DBH), and diameter at the tree base. The tree crown density was measured with a densitometer. Each sample plot biomass was estimated using the standard formula. In this study, the LULC classification was done using Freeman-Durden, Yamaghuchi and Pauli polarimetric decompositions. It was observed that the Freeman-Durden decomposition showed better LULC classification with an accuracy of 88 percent. An attempt was made to estimate the aboveground biomass using SAR backscatter. The ALOS-2 PALSAR-2 L-band data (HH, HV, VV &VH) fully polarimetric quad-pol SAR data was used. SAR backscatter-based regression model was implemented to retrieve forest aboveground biomass of the study area. Cross-polarization (HV) has shown a good correlation with forest above-ground biomass. The Multi Linear Regression analysis was done to estimate aboveground biomass of the natural forest areas of the Joida taluk. The different polarizations (HH &HV, VV &HH, HV & VH, VV&VH) combination of HH and HV polarization shows a good correlation with field and predicted biomass. The RMSE and value for HH & HV and HH & VV were 78 t/ha and 0.861, 81 t/ha and 0.853, respectively. Hence the model can be recommended for estimating AGB for the dense, moderately dense, and sparse forest.

Keywords: forest, biomass, LULC, back scatter, SAR, regression

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Authors: Lina Vyas

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Gender gap, unfortunately, is still prevalent in the workplace around the world. In most countries, women are less likely than men to participate in the workplace. They earn considerably less than men for doing the same work and are generally expected to prioritize family obligations over work responsibilities. Women often face more conflicts while balancing the increasingly normalized roles of both worker and mother. True gender equality in the workplace is still a long way off. In Hong Kong, no less is this true. Despite the fact that female students are outnumbered by males at universities, only 55% of women are active participants in the labour market, and for those in the workforce, the gender pay gap is 22%. This structural inequality also exacerbates the issues of confronting biases at work for choosing to be employed as a mother, as well as reinforces the societal expectation of women to be the primary caregiver at home. These pressures are likely to add up for women and contribute to increased levels of work-life conflict, which may be a further barrier for the inclusion of women into the workplace. Family-friendly policies have long been thought to be an alleviator of work-life conflict through helping employees balance the demands in both work and family. Particularly, for women, this could be a facilitator of their integration into the workplace. However, little research has looked at how family-friendly policies may also have a gender differential in effect, as opposed to traditional notions of having universal efficacy. This study investigates both how and how much the gender dimension impacts work-family conflict. In addition to disentangling the reasons for gender gaps existing in work-life conflict for women, this study highlights what can be done at an organizational level to alleviate these conflicts. Most importantly, the policies recommendations derived from this study serve as an avenue for more active participation for women in the workplace and can be considered as a pathway for promoting greater gender egalitarianism and fairness in a traditionally gender-segregated society.

Keywords: family-friendly policies, Hong Kong, work-family conflict, workplace

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Authors: Saba Daneshmand, Assal Habibi

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Studying musicians has become a prominent approach in macrostructural neuroscience research aimed at exploring the influence of environmental factors on brain development due to the significant impact of musical training on the brain. Although longitudinal studies can establish a direct causal relationship between musical training and brain development, only a limited number of studies have been conducted for a long enough duration. We recruited children for the experimental music group to participate in an after-school music program which was compared to the control group that had no such after-school program or enrichment activities. We ultimately calculated cortical thickness, a distinct measure of development. When a task such as playing an instrument occurs frequently, the associated neural processes become quicker and more refined over time, causing only the necessary pathways to remain; this, therefore, results in cortical thinning. The Brain and Music Lab has identified the anterior and posterior superior temporal gyrus, Heschl's gyrus, and the inferior regions to be involved with musicianship. The past study only found that the posterior superior temporal gyrus experienced a larger thinning in the music group compared to the control; however, we expect our ongoing study to produce similar but more intense results, including thinning in the other regions associated with musicianship. We believe the limited results of the previous study are due to its short duration which is why this ongoing and more lengthy longitudinal study is a significant and indispensable contribution in helping us discover the important developmental aspects of musical training.

Keywords: cortical thickness, music, neuroimaging, child development

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Authors: Nail Nazarov, Alexandra Vyshtakalyuk, Vyacheslav Semenov, Irina Galyametdinova, Vladimir Zobov, Vladimir Reznik

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Among hepatic pyrimidine used as a means of stimulating protein synthesis and recovery of liver cells in her damaged toxic and infectious etiology. When an experimental toxic hepatitis hepatoprotective activity detected some pyrimidine derivatives. There are literature data on oksimetiluratcila hepatoprotective effect. For analogs of pyrimidine nucleobases - drugs Methyluracilum pentoxy and hepatoprotective effect of weakly expressed. According to the American scientists broad spectrum of biological activity, including hepatoprotective properties, have a 2,4-dioxo-5-arilidenimino uracils. Influenced Xymedon medicinal preparation (1- (beta-hydroxyethyl) -4,6-dimethyl-1,2-dihydro-2-oksopirimidin) developed as a means of stimulating the regeneration of tissue revealed increased activity of microsomal oxidases human liver. In studies on the model of toxic liver damage in rats have shown hepatoprotective effect xymedon and stimulating its impact on the recovery of the liver tissue. Hepatoprotective properties of the new compound in the series of pyrimidine derivatives L-ascorbate 1-(2-hydroxyethyl)-4,6-dimethyl-1,2-dihydropirimidine-2-one synthesized on the basis Xymedon preparation were firstly investigated on rats under the carbon tetrachloride action. It was shown the differences of biochemical parameters from the reference value and severity of structural-morphological liver violations decreased in comparison with control group under the influence of the compound injected before exposure carbon tetrachloride. Hepatoprotective properties of the investigated compound were more pronounced in comparison with Xymedon.

Keywords: hepatoprotectors, pyrimidine derivatives, toxic liver damage, xymedon

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Authors: M. C. Schloesser

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The purpose of this paper is to explore the use and impact of business analytics in European football. Despite good evidence from other major sports leagues, research on this topic in Europe is currently very scarce. This research relies on expert interviews on the use and objective of business analytics. Along with revenue data over 16 seasons spanning from 2004/05 to 2019/20 from Manchester City FC, we conducted a time series analysis to detect a structural breakpoint on the different revenue streams, i.e., sponsorship and ticketing, after analytical tools have been implemented. We not only find that business analytics have indeed been applied at Manchester City FC and revenue increase is the main objective of their utilization but also that business analytics is indeed a good means to increase revenues if applied sufficiently. We can thereby support findings from other sports leagues. Consequently, professional sports organizations are advised to apply business analytics if they aim to increase revenues. This research has shown that analytical practices do, in fact, support revenue growth and help to work more efficiently. As the knowledge of analytical practices is very confidential and not publicly available, we had to select one club as a case study which can be considered a research limitation. Other practitioners should explore other clubs or leagues. Further, there are other factors that can lead to increased revenues that need to be considered. Additionally, sports organizations need resources to be able to apply and utilize business analytics. Consequently, findings might only apply to the top teams of the European football leagues. Nonetheless, this paper combines insights and results on usage, objectives, and impact of business analytics in European professional football and thereby fills a current research gap.

Keywords: business analytics, expert interviews, revenue management, time series analysis

Procedia PDF Downloads 79