Investigating Emotions in Creative Design

A wealth of research has suggested that emotions play a significant role in the creative problem solving process, but less work has focused on investigating the role of emotions in the design process. This is surprising given that creative problem solving lies at the heart of the design processes. In an exploratory study we interviewed 9 expert designers about their emotions during the design process. The content analysis allowed us to identify the various types of emotions relevant in the design process and to extend Wallas’ model of creative problem solving with emotional components for each of its stages. In addition, we identified two important roles of emotions in design and several ways in which expert designers regulate their emotions. We discussed the theoretical and practical applications of our work

Application of Dynamic Centrifugal Compressor Model for Mechanical Vapor Recompression System Simulation

In order to reducing energy costs and CO2 foot-print, mechanical vapor recompression system (MVR) is used for thermal separation processes such as evaporation and distillation are energy intensive instead of multiple-effect evaporation system. For medium and high capacities, centrifugal compressor (fan) is the most commonly used type for gas compression with a limited operational range and control of the compressors is crucial for safe and efficient operation. The model based on first principles is developed for dynamic performance, which is determined from the compressor geometry and not from the experimentally determined characteristic performance curves. Impeller losses are studied: incidence, skin friction, choking, jet-wake mixing, blade loading, hub to shroud, tip clearance, shock and distortion losses. The vaneless diffuser outlet is calculated using a one-dimensional numerical solution to the underlying differential equations. Dynamic model of a centrifugal compressor capable of system simulation computational environment is presented. A model has been created for simulation of a separation and gas compression system. Based on the theory for centrifugal compressors and control theory a control strategy has been applied to the model based on the available equipment. The model has been used to investigate how the gas compression system responds to changes in the compressor inlet flows and conditions. The model has been used to investigate the performance of the gas compression system at off-design conditions. The surge line for the compressor can also be determined from the simulation results. Furthermore, the model presented here provides a valuable tool for evaluating the system performance as a function of various operating parameters

Exploiting Data and Human Knowledge for Predicting Wildlife Poaching

Poaching continues to be a significant threat to the conservation of wildlife and the associated ecosystem. Estimating and predicting where the poachers have committed or would commit crimes is essential to more effective allocation of patrolling resources. The real-world data in this domain is often sparse, noisy and incomplete, consisting of a small number of positive data (poaching signs), a large number of negative data with label uncertainty, and an even larger number of unlabeled data. Fortunately, domain experts such as rangers can provide complementary information about poaching activity patterns. However, this kind of human knowledge has rarely been used in previous approaches. In this paper, we contribute new solutions to the predictive analysis of poaching patterns by exploiting both very limited data and human knowledge. We propose an approach to elicit quantitative information from domain experts through a questionnaire built upon a clustering-based division of the conservation area. In addition, we propose algorithms that exploit qualitative and quantitative information provided by the domain experts to augment the dataset and improve learning. In collaboration with World Wild Fund for Nature, we show that incorporating human knowledge leads to better predictions in a conservation area in Northeastern China where the charismatic species is Siberian Tiger. The results show the importance of exploiting human knowledge when learning from limited data.Comment: COMPASS 201

Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation

Hyper-partisan misinformation has become a major public concern. In order to examine what type of misinformation label can mitigate hyper-partisan misinformation sharing on social media, we conducted a 4 (label type: algorithm, community, third-party fact-checker, and no label) X 2 (post ideology: liberal vs. conservative) between-subjects online experiment (N = 1,677) in the context of COVID-19 health information. The results suggest that for liberal users, all labels reduced the perceived accuracy and believability of fake posts regardless of the posts' ideology. In contrast, for conservative users, the efficacy of the labels depended on whether the posts were ideologically consistent: algorithmic labels were more effective in reducing the perceived accuracy and believability of fake conservative posts compared to community labels, whereas all labels were effective in reducing their belief in liberal posts. Our results shed light on the differing effects of various misinformation labels dependent on people's political ideology

Investigation of air injection to enhanced oil recovery from medium oil reservoir of Upper Indus basin of Pakistan

Previously, air injection is exclusively used in light oil reservoirs; however, laboratory research has shown that air injection can also be very efficient for medium and heavy oil recovery. Due to the low cost of air injection and its indefinite availability, it has an economic advantage over other Enhanced Oil Recovery methods. This study is carried out in an experiment conducted on air injection into medium oil reservoirs. To better understand the air injection procedure for enhancing oil recovery from the X field\u27s medium oil (26.12 °API) of Pakistan reservoir, 14 runs were performed. The effects of air flux, porous media, temperature, and pressure on oxidation reaction rates were explored and measured. The consumption of oxygen at a rate of 90% was determined. At a moderate pressure of 7300 kPa, a significant oil recovery of around 81% of the original oil in place was observed. Increased air flux and low permeability can have a more significant effect on medium oil recovery. The technique produced flue gases that were exceptionally low in carbon oxides, with a typical gas composition of 12% CO2, 6% CO, and unreacted oxygen. This research will contribute to a better knowledge of the air injection method and allow for the optimum performance for a specified reservoir. In the Enhanced oil recovery, a less costly process using this method will be inspiring due to recovering oil in this region

Composition Spectrum of Oral Microbiota Diversity in Patients with Pancreatic Cancer: A Systematic Review

Objective To systematically evaluate the diversity of oral flora in patients with pancreatic cancer. Methods A cross-sectional study was conducted, focusing on the oral flora diversity profiles of patients with pancreatic cancer. The studies were retrieved from PubMed, Web of science, EMbase, The Cochrane Library, CBM, CNKI, Wanfang, and VIP databases, and the search period was from the establishment of the database to July 15, 2023. According to the inclusion and exclusion criteria, two researchers screened intensive review literature, extracted data and information, and carried out Meta-analysis using qualitative systematic review and Review Manager 5.4. Results Seven cross-sectional studies were reviewed, including 187 patients with pancreatic cancer and 440 healthy controls. The results of meta-analysis showed that the oral microbiota diversity Simpson index of patients with pancreatic cancer was reduced compared with that of healthy controls. Qualitative analysis showed that the relative abundance of Firmicute, Prevotella, Roseburia, and Streptococcus in patients with pancreatic cancer was higher than that in healthy people. The relative abundance of Proteobacteria, Neisseria, Haemophilus, porphyromonas, and Haemophilus parainfluenza in patients with pancreatic cancer was lower than that in healthy people. Conclusion Patients with pancreatic cancer have distinct oral flora, which has high relative abundance of Firmicutes, Prevotella etc. and low relative abundance of Proteobacteria, Neisseria, etc

Data Augmentation for Abstractive Query-Focused Multi-Document Summarization

The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets. We present two QMDS training datasets, which we construct using two data augmentation methods: (1) transferring the commonly used single-document CNN/Daily Mail summarization dataset to create the QMDSCNN dataset, and (2) mining search-query logs to create the QMDSIR dataset. These two datasets have complementary properties, i.e., QMDSCNN has real summaries but queries are simulated, while QMDSIR has real queries but simulated summaries. To cover both these real summary and query aspects, we build abstractive end-to-end neural network models on the combined datasets that yield new state-of-the-art transfer results on DUC datasets. We also introduce new hierarchical encoders that enable a more efficient encoding of the query together with multiple documents. Empirical results demonstrate that our data augmentation and encoding methods outperform baseline models on automatic metrics, as well as on human evaluations along multiple attributes.Comment: AAAI 2021 (13 pages

Towards Interpretable Natural Language Understanding with Explanations as Latent Variables

Recently generating natural language explanations has shown very promising results in not only offering interpretable explanations but also providing additional information and supervision for prediction. However, existing approaches usually require a large set of human annotated explanations for training while collecting a large set of explanations is not only time consuming but also expensive. In this paper, we develop a general framework for interpretable natural language understanding that requires only a small set of human annotated explanations for training. Our framework treats natural language explanations as latent variables that model the underlying reasoning process of a neural model. We develop a variational EM framework for optimization where an explanation generation module and an explanation-augmented prediction module are alternatively optimized and mutually enhance each other. Moreover, we further propose an explanation-based self-training method under this framework for semi-supervised learning. It alternates between assigning pseudo-labels to unlabeled data and generating new explanations to iteratively improve each other. Experiments on two natural language understanding tasks demonstrate that our framework can not only make effective predictions in both supervised and semi-supervised settings, but also generate good natural language explanation