Machine Learning Optimization of p-Type Transparent Conducting Films

p-Type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, photodetectors, displays, and flexible sensors. Cu-Zn-S thin films prepared by chemical bath deposition (CBD) can have both high transparency in the visible range (>80%) as well as excellent hole conductivity (>1000 S cm-1). However, the interplay between the deposition parameters in the CBD process (metal and sulfur precursor concentrations, temperature, pH, complexing agents, etc.) creates a multidimensional parameter space such that optimization for a specific application is challenging and time-consuming. Here we show that strategic design of experiment combined with machine learning (ML) allows for the efficient optimization of p-TCM performance. The approach is guided by a figure of merit (FOM) calculated from the film conductivity and optical transmission in the desired spectral range. A specific example is shown using two steps of optimization using a selected subset of four experimental CBD factors. The ML model is based on support vector regression employing a radial basis function as the kernel function. 10-fold cross-validation was performed to mitigate overfitting. After the first round of optimization, predicted areas in the parameter space with maximal FOMs were selected for a second round of optimization. Films with optimal FOMs were incorporated into heterojunction solar cells and transparent photodiodes. The optimization approach shown here will be generally applicable to any materials synthesis process with multiple parameters

Radar for Assisted Living in the Context of Internet of Things for Health and Beyond

This paper discusses the place of radar for assisted living in the context of IoT for Health and beyond. First, the context of assisted living and the urgency to address the problem is described. The second part gives a literature review of existing sensing modalities for assisted living and explains why radar is an upcoming preferred modality to address this issue. The third section presents developments in machine learning that helps improve performances in classification especially with deep learning with a reflection on lessons learned from it. The fourth section introduces recent published work from our research group in the area that shows promise with multimodal sensor fusion for classification and long short-term memory applied to early stages in the radar signal processing chain. Finally, we conclude with open challenges still to be addressed in the area and open to future research directions in animal welfare

Hierarchical Clusters: Emergence and Success of the Automotive Districts of Barcelona and São Paulo

This article analyzes the causes for the long-term success of the Barcelona (Spain) and São Paulo (Brazil) automobile industry clusters. Comparative evidence suggests that both clusters emerged in the early twentieth century through the formation of Marshallian external economies. Nevertheless, neither Barcelona nor São Paulo reached mass automobile production before 1950. The consolidation of the clusters required the adoption of strategic industrial policy during the golden age of capitalism. This policy succeeded in encouraging a few hub firms to undertake mass production by using domestic parts. The strategic policy also favored these leading corporations transferring their technical, organizational, and distribution capabilities, which in turn amplified the advantages of the clusters. Local institutions did not make a significant contribution

IoT-Enabled flood severity prediction via ensemble machine learning models

© 2013 IEEE. River flooding is a natural phenomenon that can have a devastating effect on human life and economic losses. There have been various approaches in studying river flooding; however, insufficient understanding and limited knowledge about flooding conditions hinder the development of prevention and control measures for this natural phenomenon. This paper entails a new approach for the prediction of water level in association with flood severity using the ensemble model. Our approach leverages the latest developments in the Internet of Things (IoT) and machine learning for the automated analysis of flood data that might be useful to prevent natural disasters. Research outcomes indicate that ensemble learning provides a more reliable tool to predict flood severity levels. The experimental results indicate that the ensemble learning using the Long-Short Term memory model and random forest outperformed individual models with a sensitivity, specificity and accuracy of 71.4%, 85.9%, 81.13%, respectively

Evaluating the Feasibility of Mass Timber as a Mainstream Building Material in the US Construction Market: Industry Perception, Cost Competitiveness, and Environmental Performance Analysis

Mass timber has been considered as a promising building material because of its structural rigidity, environmental sustainability, and renewability nature. In Europe and Australia, mass timber materials have been used for many different types of construction such as residential, commercial, education, and industrial. However, the construction practitioners in the U.S. are still reluctant to consider mass timber as a mainstream building material. A limited number of case study projects make it difficult for industry personnel to evaluate the actual construction feasibility of mass timber. As a result, a significant knowledge gap has been created that is hindering the progress of mass timber material in the U.S. construction industry. To help solve the problem, this dissertation utilizes a range of research methodologies and data analysis techniques to evaluate the feasibility of mass timber building materials in the US construction industry. The dissertation focuses on four major objectives that will help the industry practitioners to adopt mass timber as a mainstream building material. The first objective of the study is to determine the existing perception of the industry practitioners regarding mass timber materials. Using industry-wide questionnaire surveys, this study determines the current awareness level among the practitioners regarding mass timber. It also identifies some of the major advantages and challenges associated with mass timber construction. Finally, the study provides several recommendations to overcome the challenges. The second objective of the study is to investigate the cost compatibility of mass timber materials compared to the other traditional building materials. A case study is used to evaluate the construction cost competitiveness of a mass timber building project with a modeled concrete building. The third objective of the study is to assess the air pollution potential of mass timber material. A mass timber building construction site, a steel building construction site, and a regular location are used to collect four different sizes of particulate matter (PM). The fourth and last objective of the dissertation is to develop a multi-criteria decision-making framework to evaluate the feasibility of mass timber material in the US market. A scientific decision-making tool named choosing-by-advantages (CBA) is used to develop the framework. The dissertation produces a total of nine peer-reviewed manuscripts summarizing the key research contributions. Findings from this dissertation will benefit both construction practitioners as well as the researchers with new knowledge on mass timber building materials. In addition to that, it will increase the acceptance of this material in the U.S. construction industry

IoT-enabled Flood Severity Prediction via Ensemble Machine Learning Models

River flooding is a natural phenomenon that can have a devastating effect on human life and economic losses. There have been various approaches in studying river flooding; however, insufficient understanding and limited knowledge about flooding conditions hinder the development of prevention and control measures for this natural phenomenon. This paper entails a new approach for the prediction of water level in association with flood severity using the ensemble model. Our approach leverages the latest developments in the Internet of Things (IoT) and machine learning for the automated analysis of flood data that might be useful to prevent natural disasters. Research outcomes indicate that ensemble learning provides a more reliable tool to predict flood severity levels. The experimental results indicate that the ensemble learning using the Long-Short Term memory model and random forest outperformed individual models with a sensitivity, specificity and accuracy of 71.4%, 85.9%, 81.13%, respectively