UNDERSTANDING THE DIFFERENCES BETWEEN INDUSTRY OBJECTIVES AND INSTITUTIONAL LEARNING

To combat understaffing in the construction industry, it is necessary to employ the best candidates possible. By identifying the most desirable skills in a construction employee from an industry perspective, institutional learning can better prepare graduates for the construction workforce. Currently there exists an information gap in the objectives of the construction industry and institutional learning. This gap produces graduates, professors, and employers with expectations that do not align. Reducing this gap will aid in the success of hiring recently graduated construction students who can meet the ever-changing demands of the industry. Construction students who are fresh out of college have a general idea of what to expect from prospective organizations looking to hire. Just as well, organizations have a general idea of what to expect from prospective employees and professors have their own idea of what should be taught in the classroom to make for successful graduates. By aligning the objectives of all the parties in question, this gap can be closed. This study examined what is desired in graduates from construction industry professionals. By developing an understanding of industry objectives, institutional learning can be more targeted in its own objectives, facilitating gradates that are more desirable to hire

Comparing the Evolution of Risk Culture in Radiation Oncology, Aviation, and Nuclear Power

Objectives: All organizations seek to minimize the risks that their operations pose to public safety. This task is especially significant if they deal with complex or hazardous technologies. Five decades of research in quantitative risk analysis have generated a set of risk management frameworks and practices that extend across a range of such domains. Here, we investigate the risk culture in three commercial enterprises that require exceedingly high standards of execution: radiation oncology, aviation, and nuclear power. Methods: One of the characteristics of high reliability organizations is their willingness to learn from other such organizations. We investigate the extent to which this is true by compiling a database of the major publications on risk within each of the three fields. We conduct a bibliographic coupling analysis on the combined database to identify connections among publications. This analysis reveals the strength of engagement across disciplinary boundaries and the extent of cross-adoption of best practices. Results: Our results show that radiation oncology is more insulated than the other two fields in its adoption and propagation of state-of-the-art risk management tools and frameworks that have transformed aviation and nuclear power into high reliability enterprises with actuarially low risk. Conclusions: Aviation and nuclear power have established risk cultures that cross-pollinate. In both nature and extent, we found a distinct difference in radiation oncology's engagement with the risk community, and it lags behind the other two fields in implementing best practices that might mitigate or eliminate risks to patient safety

Robust self-scheduling of a price-maker energy storage facility in the New York electricity market

Recent progress in energy storage raises the possibility of creating large-scale storage facilities at lower costs. This may bring economic opportunities for storage operators, especially via energy arbitrage. However, storage operation in the market could have a noticeable impact on electricity prices. This work aims at evaluating jointly the potential operating profit for a price-maker storage facility and its impact on the electricity prices in the New York state market. Based on historical data, lower and upper bounds on the supply curve of the market are constructed. These bounds are used as an input for the robust self-scheduling problem of a price-maker storage facility. Our computational experiments show that the robust strategies thus obtained allow to reduce significantly the loss exposure while maintaining reasonably high expected profits

AI Without Math: Making AI and ML Comprehensible

If we want nontechnical stakeholders to respond to artificial intelligence developments in an informed way, we must help them acquire a more-than-superficial understanding of artificial intelligence (AI) and machine learning (ML). Explanations involving formal mathematical notation will not reach most people who need to make informed decisions about AI. We believe it is possible to teach many AI and ML concepts without slipping into mathematical notation

Computational Models for Renewable Energy Target Achievement & Policy Analysis

To date, over 84% of countries worldwide have renewable energy targets (RET), requiring that a certain amount of electricity be produced from renewable sources by a target date. Despite the worldwide prevalence of these policies, little research has been conducted on ex-ante RET policy analysis. In an effort to move toward evidence-based policymaking, this thesis develops computational models to assess the tradeoffs associated with alternatives for both RET achievement and RET policy formulation, including the option of creating renewable energy credit (REC) markets to facilitate meeting an RET goal. A mixed integer linear program (MILP), a probabilistic cost prediction model and a mixed complementarity problem (MCP) serve as the theoretical bases for the RET alternative and policy formulation analyses. From these models it was found, inter alia, that RET goals set too low run the risk of creating technological lock-in and could inhibit achievement of higher goals; probabilistic cost predictions give decision-makers essential risk information, when cost estimation is an integral part of alternatives assessment; and though REC markets may facilitate RET achievement, including REC markets in an RET policy formulation may not result in the lowest possible greenhouse gas emissions (GHG).</p

ATTnet: An explainable gated recurrent unit neural network for high frequency electricity price forecasting

The primary contribution of this study is the proposal of an explainable deep-learning neural network (ATTnet) that employs an attention mechanism to achieve accurate electricity spot price forecasting and an explainable model pipeline. The concise, single-stream network consists of a 5-head attention mechanism and gated recurrent units, which have been developed to model the temporal dependencies of the volatile market data. In addition to introducing a novel neural network architecture for volatile time series data, this study makes a substantial contribution by investigating prediction factors in two ways: temporally via the attention scores from the input sequences and globally via feature Shapely values. In real-time electricity price prediction, historical prices, temperature, hour, and zonal load are found to be the most important variables. The deep learning model was tested on real-time price profiles from eight generators within the New York Independent System Operator (NYISO) network. The proposed model achieves performance gains of 21% in MAE and 22% in MAPE over the state-of-the-art benchmark methods

Explaining successful and failed investments in U.S. carbon capture and storage using empirical and expert assessments

Most studies of deep decarbonization find that a diverse portfolio of low-carbon energy technologies will be required, including carbon capture and storage (CCS) that mitigates emissions from fossil fuel power plants and industrial sources. While many projects essential to commercializing the technology have been proposed, most (>80%) end in failure. Here we analyze the full universe of CCS projects attempted in the U.S. that have sufficient documentation ( N =39)—the largest sample ever studied systematically. We quantify 12 project attributes that the literature has identified as possible determinants of project outcome. In addition to costs and technological readiness, which prior research has emphasized, we develop metrics for attributes that are widely thought to be important yet have eluded systematic measurement, such as the credibility of project revenues and policy incentives, and the role of regulatory complexity and public opposition. We build three models—two statistical and one derived through the elicitation of expert judgment—to evaluate the relative influence of these 12 attributes in explaining project outcome. Across models, we find the credibility of revenues and incentives to be among the most important attributes, along with capital cost and technological readiness. We therefore develop and elicit experts’ judgment of 14 types of policy incentives that could alter these attributes and improve the prospects for investment in CCS. Knowing which attributes have been most responsible for past successes and failures allows developers to avoid past mistakes and identify clusters of near-term CCS projects that are more likely to succeed

AI Without Math: Making AI and ML Comprehensible

If we want nontechnical stakeholders to respond to artificial intelligence developments in an informed way, we must help them acquire a more-than-superficial understanding of artificial intelligence (AI) and machine learning (ML). Explanations involving formal mathematical notation will not reach most people who need to make informed decisions about AI. We believe it is possible to teach many AI and ML concepts without slipping into mathematical notation