Segmenting Consumers Purchase Intention towards Edible Bird’s Nest Products Using the Decision Tree Techniques

Domestic consumption of nutritional products and food supplements are on the rise. This is due to the fact that consumers have become more affluent and aware of their health. Edible birds nest (EBN) is used as a health supplement for medicinal benefits to improve health quality. However, issues such as contamination and counterfeit EBN have caused fluctuation of the products price over time and consumers are slowly shunning away from consuming EBN products. Marketing effort is a strategy tool often used to convince buying intention among consumers and therefore relieve the publics anxiety. Presently, the extent of marketing mix that can convince consumers intention to purchase EBN products remains unknown. Thus, this study aimed to analyze the influence of marketing mix towards consumers intention to purchase EBN products. Principle component analysis and decision tree models were used to analyze the data. The performance of three decision tree models was compared based on accuracy and sensitivity rate. Result showed that all three models possessed similar accuracy rate (CART = 84.35%, C5.0 = 84.73%, QUEST = 83.08%), while C5.0 had the highest sensitivity (CART = 84.7%, C5.0 = 87.46%, QUEST = 85.59%). The important variables derived from C5.0 model are health conscious, gender, promotion, race, price, employment, and income. The outcomes from the present study through the performance prediction have provided informative profile of the consumers which will be useful to target potential consumers and to narrow down the market segment for the marketers benefit

Determining construction method patterns to automate and optimise scheduling – a graph-based approach

Construction projects have been experiencing project delays for decades. As an executive guide to construction activities, construction schedules can mitigate delay risks and are essential to project success. Yet, creating a quality construction schedule is often the outcome of experienced schedulers, and what makes it harder is the fact that historic information including decision reasoning was not documented and disseminated for future use. This study proposes a graph-based method to find the time- and risk-efficient construction method patterns from historic projects to help schedulers improve productivity and accuracy. The method leverages schedule data (including activity names, Work Breakdown Structure, and start and finish date) that were obtained from a Tier-1 contractor for this study. The method was validated for excavation activities. The results indicate that the most time-efficient excavation activities can be done in 0.6% of total project time. The proposed method can help industry professionals standardise scheduling guidelines and automate the generation of construction schedules for critical subtasks

Integrated Organizational Machine Learning for Aviation Flight Data

Increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations: 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) developing an embedded machine learning framework. Data cleanup and preparation have been a well-known challenge since database systems were first invented. While integration and automation of data collection efforts within many organizations is quite mature, there are special challenges for flight-based organizations (i.e., the automatic and efficient transmission of aircraft flight data to centralized analytical data processing systems). Furthermore, this creates additional constraints for the operationalization of embedded machine learning methods for classical tasks such as classification and prediction; and magnifying design challenges for the more novel ‘prescriptive-based’ architectures. Our research is focused on the application of a design pattern for a) the integration and automation of data collection and b) an organizationally embedded ensemble machine learning method

Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators

In the last decades, the accumulation of municipal solid waste in urban areas has become a latent concern in our society due to its implications for the exposed population and the possible health and environmental issues it may cause. In this sense, this research study contributes to the timely identification of these sectors according to the anthropogenic characteristics of their residents as dictated by 10 social indicators (i.e., age, education, income, among others) sorted into three assessment categories (sociodemographic, sociocultural, and socioeconomic). Then, the data collected was processed and analyzed using two machine learning algorithms (random forest (RF) and logistic regression (LR)). The primary information that fed the machine learning model was collected through field visits and local/national reports. For this research, the Puente Piedra and Chaclacayo districts, both located in the province of Lima, Peru, were selected as case studies. Results suggest that the most relevant social indicators that help identifying these sectors are monthly income, consumption patterns, age, and household population density. The experiments showed that the RF algorithm has the best performance, since it efficiently identified 63% of the possible solid waste accumulation zones. In addition, both models were capable of determining different classes (AUC – RF = 0.65, AUC – LR = 0.71). Finally, the proposed approach is applicable and reproducible in different sectors of the national Peruvian territory.Campus Lima Centr

Characterization of extracellular vesicles derived from mesenchymal stromal cells by surface-enhanced Raman spectroscopy.

Extracellular vesicles (EVs) are secreted by all cells into bodily fluids and play an important role in intercellular communication through the transfer of proteins and RNA. There is evidence that EVs specifically released from mesenchymal stromal cells (MSCs) are potent cell-free regenerative agents. However, for MSC EVs to be used in therapeutic practices, there must be a standardized and reproducible method for their characterization. The detection and characterization of EVs are a challenge due to their nanoscale size as well as their molecular heterogeneity. To address this challenge, we have fabricated gold nanohole arrays of varying sizes and shapes by electron beam lithography. These platforms have the dual purpose of trapping single EVs and enhancing their vibrational signature in surface-enhanced Raman spectroscopy (SERS). In this paper, we report SERS spectra for MSC EVs derived from pancreatic tissue (Panc-MSC) and bone marrow (BM-MSC). Using principal component analysis (PCA), we determined that the main compositional differences between these two groups are found at 1236, 761, and 1528 c

Characterization of extracellular vesicles derived from mesenchymal stromal cells by surface-enhanced Raman spectroscopy.

Extracellular vesicles (EVs) are secreted by all cells into bodily fluids and play an important role in intercellular communication through the transfer of proteins and RNA. There is evidence that EVs specifically released from mesenchymal stromal cells (MSCs) are potent cell-free regenerative agents. However, for MSC EVs to be used in therapeutic practices, there must be a standardized and reproducible method for their characterization. The detection and characterization of EVs are a challenge due to their nanoscale size as well as their molecular heterogeneity. To address this challenge, we have fabricated gold nanohole arrays of varying sizes and shapes by electron beam lithography. These platforms have the dual purpose of trapping single EVs and enhancing their vibrational signature in surface-enhanced Raman spectroscopy (SERS). In this paper, we report SERS spectra for MSC EVs derived from pancreatic tissue (Panc-MSC) and bone marrow (BM-MSC). Using principal component analysis (PCA), we determined that the main compositional differences between these two groups are found at 1236, 761, and 1528 c

Characterization of extracellular vesicles derived from mesenchymal stromal cells by surface-enhanced Raman spectroscopy.

Extracellular vesicles (EVs) are secreted by all cells into bodily fluids and play an important role in intercellular communication through the transfer of proteins and RNA. There is evidence that EVs specifically released from mesenchymal stromal cells (MSCs) are potent cell-free regenerative agents. However, for MSC EVs to be used in therapeutic practices, there must be a standardized and reproducible method for their characterization. The detection and characterization of EVs are a challenge due to their nanoscale size as well as their molecular heterogeneity. To address this challenge, we have fabricated gold nanohole arrays of varying sizes and shapes by electron beam lithography. These platforms have the dual purpose of trapping single EVs and enhancing their vibrational signature in surface-enhanced Raman spectroscopy (SERS). In this paper, we report SERS spectra for MSC EVs derived from pancreatic tissue (Panc-MSC) and bone marrow (BM-MSC). Using principal component analysis (PCA), we determined that the main compositional differences between these two groups are found at 1236, 761, and 1528 c