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Raul Valverde
John Molson School of Business, Concordia University, 1450 Guy, Montréal, QC H3H 0A1, Canada

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Journal article
Published: 31 July 2021 in Machine Learning and Knowledge Extraction
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The performance of a photovoltaic (PV) system is negatively affected when operating under shading conditions. Maximum power point tracking (MPPT) systems are used to overcome this hurdle. Designing an efficient MPPT-based controller requires knowledge about power conversion in PV systems. However, it is difficult for nontechnical solar energy consumers to define different parameters of the controller and deal with distinct sources of data related to the planning. Semantic Web technologies enable us to improve knowledge representation, sharing, and reusing of relevant information generated by various sources. In this work, we propose a knowledge-based model representing key concepts associated with an MPPT-based controller. The model is featured with Semantic Web Rule Language (SWRL), allowing the system planner to extract information about power reductions caused by snow and several airborne particles. The proposed ontology, named MPPT-On, is validated through a case study designed by the System Advisor Model (SAM). It acts as a decision support system and facilitate the process of planning PV projects for non-technical practitioners. Moreover, the presented rule-based system can be reused and shared among the solar energy community to adjust the power estimations reported by PV planning tools especially for snowy months and polluted environments.

ACS Style

Farhad Khosrojerdi; Stéphane Gagnon; Raul Valverde. Proposing an Ontology Model for Planning Photovoltaic Systems. Machine Learning and Knowledge Extraction 2021, 3, 582 -600.

AMA Style

Farhad Khosrojerdi, Stéphane Gagnon, Raul Valverde. Proposing an Ontology Model for Planning Photovoltaic Systems. Machine Learning and Knowledge Extraction. 2021; 3 (3):582-600.

Chicago/Turabian Style

Farhad Khosrojerdi; Stéphane Gagnon; Raul Valverde. 2021. "Proposing an Ontology Model for Planning Photovoltaic Systems." Machine Learning and Knowledge Extraction 3, no. 3: 582-600.

Journal article
Published: 21 December 2020 in Sensors
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Cloud computing has emerged as the primary choice for developers in developing applications that require high-performance computing. Virtualization technology has helped in the distribution of resources to multiple users. Increased use of cloud infrastructure has led to the challenge of developing a load balancing mechanism to provide optimized use of resources and better performance. Round robin and least connections load balancing algorithms have been developed to allocate user requests across a cluster of servers in the cloud in a time-bound manner. In this paper, we have applied the round robin and least connections approach of load balancing to HAProxy, virtual machine clusters and web servers. The experimental results are visualized and summarized using Apache Jmeter and a further comparative study of round robin and least connections is also depicted. Experimental setup and results show that the round robin algorithm performs better as compared to the least connections algorithm in all measuring parameters of load balancer in this paper.

ACS Style

Bhavya Alankar; Gaurav Sharma; Harleen Kaur; Raul Valverde; Victor Chang. Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud. Sensors 2020, 20, 7342 .

AMA Style

Bhavya Alankar, Gaurav Sharma, Harleen Kaur, Raul Valverde, Victor Chang. Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud. Sensors. 2020; 20 (24):7342.

Chicago/Turabian Style

Bhavya Alankar; Gaurav Sharma; Harleen Kaur; Raul Valverde; Victor Chang. 2020. "Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud." Sensors 20, no. 24: 7342.

Journal article
Published: 24 June 2020 in Sustainability
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As one of the most significant components of financial technology (FinTech), blockchain technology arouses the interests of numerous investors in China, and the number of companies engaged in this field rises rapidly. The emotion of investors has an effect on stock returns, which is a hot topic in behavioral finance. Blockchain is an essential part of FinTech, and with the fast development of this technology, investors’ sentiment varies as well. The online information that directly reflects investors’ mood could be utilized for mining and quantifying to construct a sentiment index. For a better understanding of how well some factors adequately explain the return of stocks related to blockchain companies in the Chinese stock market, the Fama-French three-factor model (FFTFM) will be introduced in this paper. Furthermore, sentiment could be a new independent variable to enhance the explanatory power of the FFTFM. A comparison between those two models reveals that the sentiment factor could raise the explanatory power. The results also indicate that the Chinses blockchain industry does not own the size effect and book-to-market effect.

ACS Style

Ziyang Ji; Victor Chang; Hao Lan; Ching-Hsien Robert Hsu; Raul Valverde. Empirical Research on the Fama-French Three-Factor Model and a Sentiment-Related Four-Factor Model in the Chinese Blockchain Industry. Sustainability 2020, 12, 5170 .

AMA Style

Ziyang Ji, Victor Chang, Hao Lan, Ching-Hsien Robert Hsu, Raul Valverde. Empirical Research on the Fama-French Three-Factor Model and a Sentiment-Related Four-Factor Model in the Chinese Blockchain Industry. Sustainability. 2020; 12 (12):5170.

Chicago/Turabian Style

Ziyang Ji; Victor Chang; Hao Lan; Ching-Hsien Robert Hsu; Raul Valverde. 2020. "Empirical Research on the Fama-French Three-Factor Model and a Sentiment-Related Four-Factor Model in the Chinese Blockchain Industry." Sustainability 12, no. 12: 5170.

Journal article
Published: 30 April 2020 in Applied Sciences
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Financialization has contributed to economic growth but has caused scandals, misselling, rogue trading, tax evasion, and market speculation. To a certain extent, it has also created problems in social and economic instability. It is an important aspect of Enterprise Security, Privacy, and Risk (ESPR), particularly in risk research and analysis. In order to minimize the damaging impacts caused by the lack of regulatory compliance, governance, ethical responsibilities, and trust, we propose a Business Integrity Modeling and Analysis (BIMA) framework to unify business integrity with performance using big data predictive analytics and business intelligence. Comprehensive services include modeling risk and asset prices, and consequently, aligning them with business strategies, making our services, according to market trend analysis, both transparent and fair. The BIMA framework uses Monte Carlo simulation, the Black–Scholes–Merton model, and the Heston model for performing financial, operational, and liquidity risk analysis and present outputs in the form of analytics and visualization. Our results and analysis demonstrate supplier bankruptcy modeling, risk pricing, high-frequency pricing simulations, London Interbank Offered Rate (LIBOR) rate simulation, and speculation detection results to provide a variety of critical risk analysis. Our approaches to tackle problems caused by financial services and the operational risk clearly demonstrate that the BIMA framework, as the outputs of our data analytics research, can effectively combine integrity and risk analysis together with overall business performance and can contribute to operational risk research.

ACS Style

Victor Chang; Raul Valverde; Muthu Ramachandran; Chung-Sheng Li. Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis. Applied Sciences 2020, 10, 3145 .

AMA Style

Victor Chang, Raul Valverde, Muthu Ramachandran, Chung-Sheng Li. Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis. Applied Sciences. 2020; 10 (9):3145.

Chicago/Turabian Style

Victor Chang; Raul Valverde; Muthu Ramachandran; Chung-Sheng Li. 2020. "Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis." Applied Sciences 10, no. 9: 3145.

Chapter
Published: 29 August 2018 in Understanding Complex Systems
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System dynamics is an approach to modeling complex systems using feedback loops to explain relationships between variables and to reflect their nonlinear interdependencies through time, along with their underlying driving forces (Sterman J (2000) Business dynamics: systems thinking and modeling for a complex world (p. c2000). Irwin/McGraw-Hill, Boston). Systems are graphically represented by a set of active nodes, with qualitative and quantitative attributes, along with passive nodes, modeled as flows and stocks acting as buffers between active nodes. In the context of this chapter, we use the system dynamics approach to model the operations of a procurement function within the supply chain. In this chapter, we propose a system dynamics interpretation of procurement drivers and link to the operational and strategic levels of decision-making.

ACS Style

Sherif Barrad; Raul Valverde; Stéphane Gagnon. The Application of System Dynamics for a Sustainable Procurement Operation. Understanding Complex Systems 2018, 179 -196.

AMA Style

Sherif Barrad, Raul Valverde, Stéphane Gagnon. The Application of System Dynamics for a Sustainable Procurement Operation. Understanding Complex Systems. 2018; ():179-196.

Chicago/Turabian Style

Sherif Barrad; Raul Valverde; Stéphane Gagnon. 2018. "The Application of System Dynamics for a Sustainable Procurement Operation." Understanding Complex Systems , no. : 179-196.