Estimating Consumers’ Willingness to Pay for the Individual Quality Attributes with DEA

In a highly competitive environment a product’s commercial success depends increasingly more upon the ability to satisfy consumers’ preferences that are highly diversified. Since a consumer product typically comprises a host of technological attributes, its market value incorporates all of the individual values of technological attributes. If the willingness-to-pay (WTP) for individual technological characteristics of a product is known, one can conjecture the overall WTP or the imputed market price for the product. The market price listed by the producer has to be equal to or lower than this WTP for the commercial survival of the product. In this paper we propose a methodology for estimating the value of individual product characteristics and thus the overall WTP of the product with DEA. Our methodology is based on a model derived from the consumer demand theory on the one hand, and the recent theoretical developments on the flexible DEA frontiers on the other hand. The paper also presents a real case study for the mobile phone market, which is characterized by its high speed of innovation. The suggested model and its empirical applications has implications for the extension of DEA methodology to the estimation of market value of a complex multi-attribute product and/or of a value of quality attribute that is not explicitly marketable in isolation. We also expect that the framework will shed some light on the successful way of product differentiation when the cost information for individual characteristics is available.DEA; efficient consumption; willingness to pay; multi-attribute product pricing

Chemical Property-Guided Neural Networks for Naphtha Composition Prediction

The naphtha cracking process heavily relies on the composition of naphtha, which is a complex blend of different hydrocarbons. Predicting the naphtha composition accurately is crucial for efficiently controlling the cracking process and achieving maximum performance. Traditional methods, such as gas chromatography and true boiling curve, are not feasible due to the need for pilot-plant-scale experiments or cost constraints. In this paper, we propose a neural network framework that utilizes chemical property information to improve the performance of naphtha composition prediction. Our proposed framework comprises two parts: a Watson K factor estimation network and a naphtha composition prediction network. Both networks share a feature extraction network based on Convolutional Neural Network (CNN) architecture, while the output layers use Multi-Layer Perceptron (MLP) based networks to generate two different outputs - Watson K factor and naphtha composition. The naphtha composition is expressed in percentages, and its sum should be 100%. To enhance the naphtha composition prediction, we utilize a distillation simulator to obtain the distillation curve from the naphtha composition, which is dependent on its chemical properties. By designing a loss function between the estimated and simulated Watson K factors, we improve the performance of both Watson K estimation and naphtha composition prediction. The experimental results show that our proposed framework can predict the naphtha composition accurately while reflecting real naphtha chemical properties.Comment: Accepted at IEEE International Conference on Industrial Informatics 2023(INDIN 2023

Estimating Consumers’ Willingness to Pay for the Individual Quality Attributes with DEA

In a highly competitive environment a product’s commercial success depends increasingly more upon the ability to satisfy consumers’ preferences that are highly diversified. Since a consumer product typically comprises a host of technological attributes, its market value incorporates all of the individual values of technological attributes. If the willingness-to-pay (WTP) for individual technological characteristics of a product is known, one can conjecture the overall WTP or the imputed market price for the product. The market price listed by the producer has to be equal to or lower than this WTP for the commercial survival of the product. In this paper we propose a methodology for estimating the value of individual product characteristics and thus the overall WTP of the product with DEA. Our methodology is based on a model derived from the consumer demand theory on the one hand, and the recent theoretical developments on the flexible DEA frontiers on the other hand. The paper also presents a real case study for the mobile phone market, which is characterized by its high speed of innovation. The suggested model and its empirical applications has implications for the extension of DEA methodology to the estimation of market value of a complex multi-attribute product and/or of a value of quality attribute that is not explicitly marketable in isolation. We also expect that the framework will shed some light on the successful way of product differentiation when the cost information for individual characteristics is available

Predicting Site Energy Usage Intensity Using Machine Learning Models

Climate change is a shift in nature yet a devastating phenomenon, mainly caused by human activities, sometimes with the intent to generate usable energy required in humankind’s daily life. Addressing this alarming issue requires an urge for energy consumption evaluation. Predicting energy consumption is essential for determining what factors affect a site’s energy usage and in turn, making actionable suggestions to reduce wasteful energy consumption. Recently, a rising number of researchers have applied machine learning in various fields, such as wind turbine performance prediction, energy consumption prediction, thermal behavior analysis, and more. In this research study, using data publicly made available by the Women in Data Science (WiDS) Datathon 2022 (contains data on building characteristics and information collected by sensors), after appropriate data preparation, we experimented four main machine learning methods (random forest (RF), gradient boost decision tree (GBDT), support vector regressor (SVR), and decision tree for regression (DT)). The most performant model was selected using evaluation metrics: root mean square error (RMSE) and mean absolute error (MAE). The reported results proved the robustness of the proposed concept in capturing the insight and hidden patterns in the dataset, and effectively predicting the energy usage of buildings

Biological fixed film

The review includes literatures published in the year of 2014 regarding the uses of biofilm and bioreactor to treat wastewater. Topics considered are: biofilm formation and factors that impact biofilm formation; extracellular polymeric substance from biofilms; biofilm consortia and quorum sensing; biofilm reactors and biofilm in bioelectrochemical systems.</p

Biological fixed film

The review includes literatures published in the year of 2014 regarding the uses of biofilm and bioreactor to treat wastewater. Topics considered are: biofilm formation and factors that impact biofilm formation; extracellular polymeric substance from biofilms; biofilm consortia and quorum sensing; biofilm reactors and biofilm in bioelectrochemical systems.</p

Arabidopsis Carboxyl-Terminal Domain Phosphatase-Like Isoforms Share Common Catalytic and Interaction Domains But Have Distinct in Planta Functions

An Arabidopsis (Arabidopsis thaliana) multigene family (predicted to be more than 20 members) encodes plant C-terminal domain (CTD) phosphatases that dephosphorylate Ser residues in tandem heptad repeat sequences of the RNA polymerase II C terminus. CTD phosphatase-like (CPL) isoforms 1 and 3 are regulators of osmotic stress and abscisic acid (ABA) signaling. Evidence presented herein indicates that CPL3 and CPL4 are homologs of a prototype CTD phosphatase, FCP1 (TFIIF-interacting CTD-phosphatase). CPL3 and CPL4 contain catalytic FCP1 homology and breast cancer 1 C terminus (BRCT) domains. Recombinant CPL3 and CPL4 interact with AtRAP74, an Arabidopsis ortholog of a FCP1-interacting TFIIF subunit. A CPL3 or CPL4 C-terminal fragment that contains the BRCT domain mediates molecular interaction with AtRAP74. Consistent with their predicted roles in transcriptional regulation, green fluorescent protein fusion proteins of CPL3, CPL4, and RAP74 all localize to the nucleus. cpl3 mutations that eliminate the BRCT or FCP1 homology domain cause ABA hyperactivation of the stress-inducible RD29a promoter, whereas RNAi suppression of CPL4 results in dwarfism and reduced seedling growth. These results indicate CPL3 and CPL4 are a paralogous pair of general transcription regulators with similar biochemical properties, but are required for the distinct developmental and environmental responses. CPL4 is necessary for normal plant growth and thus most orthologous to fungal and metazoan FCP1, whereas CPL3 is an isoform that specifically facilitates ABA signaling