Quantum-based approach for laboratory risk assessment and decision-making

With the rising focus on academic safety, there has been an effort to improve the academic safety climate and develop lab-specific risk assessment tools. Despite the progress made in recent years, there is still a deficit of reliable data statistics on safety events and risk assessment in academia, making it difficult to perform full-scale analysis. Moreover, the rapid turnover of personnel and the highly dynamic research tasks in a laboratory environment impede the implementation of classical and well-established risk management strategies, which are usually time-consuming and resource-intensive. Laboratory safety management is always challenging due to lack of data and effective methods. The end of safety management is usually the selection of risk mitigation measures. In the laboratory risk management process, decisions are often made through discussion and compromise. In this regard, it would be beneficial to provide a tool that can support in resource allocation and prioritization of actions. The goal of this project is to develop a decision support tool to assist in the selection of optimal and appropriate preventive or corrective measures in the lab safety management process. The use of decision tools such as Multi-Criteria Decision Making (MCDM) can effectively abstract complex laboratory management problems into a decision framework. This study focuses on the ranking process of competitive corrective measures. Combining the results of interviews and questionnaires, five evaluation criteria were proposed for the normative process of multi-criteria decision making. They are: Residual Risk, Risk reduction, Personnel reliability, Technical reliability, and Cost. classical methods TOPSIS and WSM have been evaluated during the study, and emerging quantum decision making methods have been explored. All three methods are used to simulate the laboratory safety decision making process, and they all present good consistency. Different weighting methods have also been applied to the model and two case studies have been conducted. The first case study is designed to construct the model and verify its feasibility, and the second model is used to investigate the process of group decision making. By applying different MCDM methods, the obtained results are discussed and compared to verify the stability and accuracy of the model. The approach proposed in this research is a promising tool. It offers the possibility to be compatible with different MCDM methods and risk assessment methods, and the proposed decision model could be used as a decision support system to help lab managers make better decisions, or it can provide a neutral point of view as an aid. Future work may include fine-tuning the model. In the following stage, decision criteria should be precisely defined, and corresponding quantitative formulas should be proposed to serve the flexible and dynamic laboratory environment.

MoDN-Flip: Optimizing Modular Clinical Decision Support Networks with Decision Flippability Score

Kuan Tung

BACKGROUND. Clinical Decision Support Systems (CDSS) are point-of-care questionnaires that guide consultations to evidence-based predictions with the aim of standardizing care and optimizing resource allocation. The data collected in these tools contain potentially useful insights to further train its predictive algorithms, however, the questionnaire structure biases data with systematic missingness, making it unusable in traditional models. Further, privacy and interoperability issues make it challenging to synergize with data collected in related CDSS tools. The Modular Decision Support Network (MoDN) was previously proposed by our group to address these issues. It can predict diagnoses at each step in a consultation using sequentially encoded questions. However, it lacks the ability to provide information on unencoded questions, such as predicting their importance, which could potentially optimize questionnaire structure. AIM. In this work, we aim to optimize MoDN by deriving a flippability score (FS) which computes the importance of a question in the form of how likely its answer would flip (change) the predicted diagnoses. METHODS & FINDINGS. The FS is computed with the help of MoDN’s feature decoders that predict unknown feature values in the data. The optimal questionnaire order is then derived by feeding MoDN the question with the highest FS. This approach—-MoDN-flip—- is validated on two independent real world CDSS-derived data sets of pediatric outpatients. We show that FS-optimized question order can achieve the baseline predictive performance 14.71% faster (or with 3.53 fewer questions) compared to MoDN with the original question order. FS-optimized questionnaires are also able to improve the cumulative F1 score by 5.24%. Furthermore, we test the utility of FS-optimization when porting MoDN to an imperfectly interoperable data set (i.e. with slightly different questionnaire structures and partially-overlapping feature sets). The results show that the score is valid in a new dataset and can provide better predictive performance at an earlier stages, albeit a slight cost to final performance. CONCLUSION. With the aim of providing data-driven questionnaire optimization for CDSS tools, we propose the flippability score, which indicates the importance of each potential question in a consultation. The experimental results show that FS can express predictive importance of questions and has the potential to significantly optimize questionnaire structure. It is now necessary to evaluate this ordering against human consultation logic.

2022

Du savetier au financier: la famille Borel, de Neuchâtel à San Francisco

The Borel Family From Neuchâtel to San Francisco : From shoemaker to banker Did you know that the Cable Car was owned by Swiss citizens? BACKTRACKING : PRIVATE FINANCING OF PUBLIC METROPOLITAN INFRASTRUCTURES Executive Summary: Did you know that one of San Francisco most famous fixtures, the Cable car, was owned by Swiss citizens? Through this detailed study of the Borel family, and its business and investment plans we will observe an example of private financing of public metropolitan infrastructures in the 19th Century and see if it can teach us lessons for tomorrow’s needs? TOPICS Looking at today’s infrastructures, one does not always remember how much work contracting firms had to accomplish in order to produce our contemporary standard of urban comfort . What is to be said of yesterday’s planners or engineers and the credit they deserve? We often hear stories of local events brought to epic proportions, but who are the responsible players who really proved the feasibility of regional infrastructures? Who actually did support, sponsor or finance such achievements? The present study is a trial to propose a new view over such apparently hidden business phenomenons. This research shows us an inspiring case-study of local entrepreneurs who gather key success-factors. The author concentrates on the economic aspect of infrastructure financing to which modern metropolitan society owes its specific urban growth. Contemporary challenges such as job creation, globalisation, technology transfer and local economic development are all interconnected to metropolitan development. XIXTH CENTURY METROPOLITAN INFRASTRUCTURES FINANCING Le Temps, 30th July 1998, D. Masmejan : «Alfred Borel, Auguste’s elder son, enters the finance world. He meets an extraordinary businessman fate. His understanding of the economic impact of the Californian Gold Rush allows him to seize the opportunity of the westward American expansionism. He sails to San Francisco in 1855 where he is the founder of the «Alfred Borel & Company», followed in 1856 by a Mortgage fund (the “Caisse Hypothécaire” bank). The same year sees the foundation of Credit Suisse in Zurich. Alfred Borel investment bank participates in very important infrastructure projects in California and elsewhere in the second half of the XIXth century. At first, its investment into the worldwide famous San Francisco Cable Car is marginal and then becomes overwhelming. Other investments include the “Los Angeles Railway Company” or the “United Railroad Company”, the San Francisco Waterworks, other banks and real estate.» DETAILED RESEARCH ALLOWING TO UNDERSTAND FINANCIAL DECISION MAKING The author reviews financial decision-making through personal correspondence and private accounting in some of those completely documented yearly archives. The «sixth Switzerland» well-connected to the Presbyterian «Huguenot International» cosmopolitan network leads us to detailed research and discoveries. The accounting of 1866 to 1884 goes through the 3rd and 4th San Francisco City real estate crises that the mortgage bankers Borel brothers had to endure. Alfred Borel faced the 2nd Californian real estate crisis (which resulted from the first loss of public interest in gold mines in the years 1854-1858). Then he went back to Switzerland after the death of his father and uncles in order to reconstitute the Swiss end of the pipe connecting his bank’s investors to the Borel family network. He left duty of the day-to-day San Francisco business to his younger brother Antoine after having set up asset management rules. Together both brothers explored new ways of diversifying the investment of the funds they are managing and place them into infrastructures networks. Taking shares «upstream» into equipment industries (for instance the wire works delivering the precise traction rope of cable cars) or other real estate assets, allowed them to guarantee superior interests payments to European investors or creditors in comparison with other financial markets. This anticyclical economic strategy set up their financial success that occurred during the 5th local economic recession, which coincides with the beginning of the big worldwide Victorian economic crisis (1880-1895). UNIQUE SPECIFICITY OF THIS RESEARCH This research process is rarely accomplished in engineering and architecture schools, construction and infrastructures institutes or research centers. The integrity of construction professionals precludes their questioning « where does the whole process originate from?». Builders ultimately considered themselves as «honest craftsmen» that have to dedicate their energy and resources to the proper technical execution of the clients wishes and orders. Customers duty is then to write the project’s specifications and thus to initiate its process. The author of this case-study demonstrates the feasibility of comparisons between similar structural change periods in the XIXth century and the years 1990-2000. Hence, a new theme of interdisciplinary studies for engineers could be implemented by promoting this systemic approach between urban infrastructure financiers, suppliers and users. Last but not least, the insider observations and outsider revelations which are featured in this work are seldom considered in an engineering historical approach. It is not often that an author with a contemporary outsider vision has access to family archives that entirely document bicentennial confidential banker correspondence.

EPFL1999

Automated analysis of product related data generated from PLM ontology

Ana Milicic

In recent years, ontology for the Product Lifecycle Management domain has raised a lot of interest in research communities, both academic and industrial. It has emerged as a convenient method for supporting the concept of closed lifecycle information loop, which is one of the most important issues of PLM. By modeling relevant aspects collected from all lifecycle stages of a product, within one ontology, a common knowledge structure is created accessible to all actors. Assuming that appropriate mechanisms for updating ontology (or rather, instances that populate it) are provided, ontology becomes a base layer for a knowledge management platform. Useful experience and information from all productsâ life-cycle stages, can influence designerâs decisions and business strategies. The industrial research community has recognized this added value of ontological implementation, and there is an increasing number of developed ontologies for this purpose. Application of ontology contributes to time efficiency by reducing the time required to retrieve information. Furthermore, it allows for the enhancement of design decisions which are supported through additional information at the appropriate moment. Finally, ontology gives an overall perspective on a product's lifecycle, allowing from-the-top optimization. Different domains modeled in ontology, and software platforms that use them as a base layer, become interoperable and convenient to merge. The purpose of ontology as it is today is not to store data, for the most part because there are more efficient data base systems to handle large data amounts. Still, the domain modeled within ontology is composed of structured and un-structured data sets, and ontology itself can give us a top view on relations and dependences between these data sets. In this perspective, it holds a strong similarity to a relational data base, if relations in the data base where defined so that they depicted the real world in the most precise possible manner. In large companies today, handling a growing amount of data generated every day is becoming an increasingly relevant problem. Managing and storing them, although challenging, is still feasible, but holding data without understanding it carries little added value. In an effort to exploit useful information contained in unstructured data sources, a number of decision support systems and enterprise resource planning systems have been developed. They can be very diverse in functionality and efficiency but the one thing that they all have in common is that the user has to be the one making the initiative and defining the queries. This means that the user has to know which information he is looking for, or hoping to extract. As a consequence, the number of relevant correlations and dependencies between different factors of real life captured in the data are left unnoticed, simply because they were not assumed. In the PLM domain, this is particularly present since it involves a number of actors and most of them are interacting only with a small subset of domain concepts. Data mining as a discipline, gives a number of tools for resolving this issue. All of the algorithms are designed to detect correlations, underlying patterns or functions that generated the data. The problem of data mining techniques is that they are still performed mostly manually. Although deterministic steps of data mining procedures can be supported by existing software tools, the others remain an obstac

EPFL2015