How Do Designers Deal With Uncertainty

Uncertainty touches most aspects of life and cannot be avoided, anybody is frequently presented with situations wherein a decision must be made when he/she is uncertain of exactly how to proceed. Narrow down into Information Systems (IS) field, uncertainty could be regarded as a basic but difficult problem that every HCI designer need to deal with within their design process. The purpose of this thesis is to find out how do human-computer interaction (HCI) practitioners deal with the uncertainty in their daily work. Based on this purpose, we assume that design approaches could be the methods for the designers to deal with uncertainty. There is however very few existing research on how to deal with uncertainty. In this study, we firstly categorized the uncertainty into a logical taxonomy, also ranked four design approaches by the extent of user involvement. We interviewed five HCI practitioners in different organizations that are or were working as designers. We found that most uncertainties are resulted from their customers, which can also be the most difficult to handle by them. In order to solve uncertainty, the designers need to make a good communication with others in specific situation, and some of them also proposed other practical solutions, such as “Role Play” and “Instinct Follower”. Additionally, the designers all proposed that the relationship between uncertainty and design approaches can be weak or inexistent. Interestingly, modest user involvement can be a helper for designers to solve or avoid uncertainty in the design process

Utilizing Protein Structure to Identify Non-Random Somatic Mutations

Motivation: Human cancer is caused by the accumulation of somatic mutations in tumor suppressors and oncogenes within the genome. In the case of oncogenes, recent theory suggests that there are only a few key "driver" mutations responsible for tumorigenesis. As there have been significant pharmacological successes in developing drugs that treat cancers that carry these driver mutations, several methods that rely on mutational clustering have been developed to identify them. However, these methods consider proteins as a single strand without taking their spatial structures into account. We propose a new methodology that incorporates protein tertiary structure in order to increase our power when identifying mutation clustering. Results: We have developed a novel algorithm, iPAC: identification of Protein Amino acid Clustering, for the identification of non-random somatic mutations in proteins that takes into account the three dimensional protein structure. By using the tertiary information, we are able to detect both novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of clustering based on existing methods. For example, by combining the data in the Protein Data Bank (PDB) and the Catalogue of Somatic Mutations in Cancer, our algorithm identifies new mutational clusters in well known cancer proteins such as KRAS and PI3KCa. Further, by utilizing the tertiary structure, our algorithm also identifies clusters in EGFR, EIF2AK2, and other proteins that are not identified by current methodology

APPLICATION OF STATISTICAL PROCESS CONTROL THEORY IN COAL AND GAS OUTBURST PREVENTION

With Chinese coal exploitation extending to depth rapidly, a large number of coal and gas outburst accidents happened and resulted in thousands of casualties in the last decade. Coal and gas outburst prevention project has become the prerequisite of underground coal mining, but its process control ability is especially poor. By integrating statistical process control theory into the process of coal and gas outburst prevention, three urgent problems were solved. First at all, data structure of the process inspection parameters was designed asvectors, which only consisted of principle elements and formed data series as time went by. Secondly, based on sample data of the experimental area, statistical characteristic of inspection parameters was gained and their X-Rs control charts were drawn. Finally, performance of process running statuses that might be in control or beyond control were analyzed in detail. When the process was in control, curves should slightly fluctuate around their center lines and between upper control limits and lower control limits. Otherwise, the process was beyond control, in which X control charts were used to identify anomalies of data value fluctuation and Rs control charts were used to identify anomalies of data fluctuation amplitudes. By the experimental application in Hexi colliery of China, the interdisciplinary research was proved to be helpful to improve process control ability and then prevent coal and gas outburst accidents

A Spatial Simulation Approach to Account for Protein Structure When Identifying Non-Random Somatic Mutations

Background: Current research suggests that a small set of "driver" mutations are responsible for tumorigenesis while a larger body of "passenger" mutations occurs in the tumor but does not progress the disease. Due to recent pharmacological successes in treating cancers caused by driver mutations, a variety of of methodologies that attempt to identify such mutations have been developed. Based on the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of cluster identification algorithms has become critical. Results: We have developed a novel methodology, SpacePAC (Spatial Protein Amino acid Clustering), that identifies mutational clustering by considering the protein tertiary structure directly in 3D space. By combining the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC) and the spatial information in the Protein Data Bank (PDB), SpacePAC is able to identify novel mutation clusters in many proteins such as FGFR3 and CHRM2. In addition, SpacePAC is better able to localize the most significant mutational hotspots as demonstrated in the cases of BRAF and ALK. The R package is available on Bioconductor at: http://www.bioconductor.org/packages/release/bioc/html/SpacePAC.html Conclusion: SpacePAC adds a valuable tool to the identification of mutational clusters while considering protein tertiary structureComment: 16 pages, 8 Figures, 4 Table

Ensuring the authenticity of the conservation and reuse of modern industrial heritage architecture: a case study of the large machine factory, China

The Large Machine Factory (LMF) was built in the complex historical context of the late Qing Dynasty (1840–1912). Its space and construction faithfully record the architectural and cultural fusion between Chinese and western traditions and mark the beginning of modern architectural techniques in China. Through historical data and empirical studies, the historical background and architectural characteristics of the LMF were analyzed, and interventions aimed at ensuring authenticity were established. The cultural significance and results of construction were considered two crucial elements in terms of outstanding characteristics. Comprehensive inspection and assessment strategies were discussed, with minimal intervention and interpretation principles. Preventive reinforcement of the foundation, complementary reinforcement of the main structures, restoration of the historic façade and environment, and adaptive spatial interventions were found to be effective ways to ensure authenticity. The principles of minimal intervention and interpretability, which include prevention, recognizability, invisibility, subsidiarity, and intertextuality, were proposed through a comparison with the literature and practical experience. This study provides an appropriate technical reference for ensuring authenticity in the conservation and reuse of modern historic buildings with complex contexts. We propose a new understanding of intervention principles and suggest a guiding intervention path that avoids the complexities arising from the generalized interpretations of authenticity.Postprint (published version

Leveraging protein quaternary structure to identify oncogenic driver mutations.

BACKGROUND: Identifying key "driver" mutations which are responsible for tumorigenesis is critical in the development of new oncology drugs. Due to multiple pharmacological successes in treating cancers that are caused by such driver mutations, a large body of methods have been developed to differentiate these mutations from the benign "passenger" mutations which occur in the tumor but do not further progress the disease. Under the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of algorithms that identify these clusters has become a critical area of research. RESULTS: We have developed a novel methodology, QuartPAC (Quaternary Protein Amino acid Clustering), that identifies non-random mutational clustering while utilizing the protein quaternary structure in 3D space. By integrating the spatial information in the Protein Data Bank (PDB) and the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), QuartPAC is able to identify clusters which are otherwise missed in a variety of proteins. The R package is available on Bioconductor at: http://bioconductor.jp/packages/3.1/bioc/html/QuartPAC.html . CONCLUSION: QuartPAC provides a unique tool to identify mutational clustering while accounting for the complete folded protein quaternary structure.This work was supported in part by NSF Grant DMS 1106738 (GR, HZ); NIH Grants GM59507 and CA154295 (HZ), and GM102869 (YM); and Wellcome Trust Grant 101908/Z/13/Z (YM)

Response of the metastable pitting corrosion of laser powder bed fusion produced Ti–6Al–4v to H+ concentration changes

There is limited research on metastable pitting corrosion in an acidic environment, and acid is a major challenge for material corrosion. Therefore, this work investigated the metastable pitting corrosion of laser powder bed fusion (LPBF)-produced Ti–6Al–4V, in Hank’s solution, at different pH values (pH = 3, 5, and 7). This work investigated the effect of acid on the characteristics of passive films, as well as the change in metastable pitting behavior. Based on the results of electrochemical impedance spectroscopy (EIS) and X-ray photoelectron spectroscopy (XPS), the passive film will be inhibited and dissolved under the influence of H+. The higher the concentration of H+, the thinner the passive film. Potentiodynamic polarization tests reveal that LPBFed Ti–6Al–4V in Hank’s solution, at pH 3, has more obvious metastable pitting corrosion. This is because the higher the H+ concentration, the more Cl- is adsorbed on the surface of the passive film, which is prone to generate soluble chlorides by competitive adsorption with oxygen atoms and thus develop into metastable pitting corrosion

A Graph Theoretic Approach to Utilizing Protein Structure to Identify Non-Random Somatic Mutations

Background: It is well known that the development of cancer is caused by the accumulation of somatic mutations within the genome. For oncogenes specifically, current research suggests that there is a small set of "driver" mutations that are primarily responsible for tumorigenesis. Further, due to some recent pharmacological successes in treating these driver mutations and their resulting tumors, a variety of methods have been developed to identify potential driver mutations using methods such as machine learning and mutational clustering. We propose a novel methodology that increases our power to identify mutational clusters by taking into account protein tertiary structure via a graph theoretical approach. Results: We have designed and implemented GraphPAC (Graph Protein Amino Acid Clustering) to identify mutational clustering while considering protein spatial structure. Using GraphPAC, we are able to detect novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of prior clustering based on current methods. Specifically, by utilizing the spatial information available in the Protein Data Bank (PDB) along with the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), GraphPAC identifies new mutational clusters in well known oncogenes such as EGFR and KRAS. Further, by utilizing graph theory to account for the tertiary structure, GraphPAC identifies clusters in DPP4, NRP1 and other proteins not identified by existing methods. The R package is available at: http://bioconductor.org/packages/release/bioc/html/GraphPAC.html Conclusion: GraphPAC provides an alternative to iPAC and an extension to current methodology when identifying potential activating driver mutations by utilizing a graph theoretic approach when considering protein tertiary structure.Comment: 25 pages, 8 figures, 3 Table

Effects of Government R&D Grants on IT Entrepreneurial Firm Performance: A New Perspective on Exploration vs. Exploitation

Governments keep subsidizing R&D of IT entrepreneurial firms greatly. However, the effect of these grants remains unclear. Acknowledging this gap, this study provides a nuanced perspective to understand the influence of government R&D grants on IT entrepreneurial firm performance. Based on the literature on organizational learning, we categorize government R&D grants into two types: explorative vs. exploitative. Moreover, drawing on resource complementarity theory, we articulate how the two types of government R&D grants interact with firms’ private R&D resources. In particular, we hypothesize that in the innovation stage, government explorative R&D grants complement a firm’s internal exploration in influencing innovation performance, but substitute a firm’s external exploration. We further posit that in the commercialization stage, government exploitative R&D grants complement a firm’s innovation performance and internal exploitation in impacting financial performance, but substitute a firm’s external exploitation. We advance a theory of public-private R&D interaction for IT entrepreneurial firms