Can dissonance engineering improve risk analysis of human–machine systems?

The paper discusses dissonance engineering and its application to risk analysis of human–machine systems. Dissonance engineering relates to sciences and technologies relevant to dissonances, defined as conflicts between knowledge. The richness of the concept of dissonance is illustrated by a taxonomy that covers a variety of cognitive and organisational dissonances based on different conflict modes and baselines of their analysis. Knowledge control is discussed and related to strategies for accepting or rejecting dissonances. This acceptability process can be justified by a risk analysis of dissonances which takes into account their positive and negative impacts and several assessment criteria. A risk analysis method is presented and discussed along with practical examples of application. The paper then provides key points to motivate the development of risk analysis methods dedicated to dissonances in order to identify the balance between the positive and negative impacts and to improve the design and use of future human–machine system by reinforcing knowledge

Machine Learning na previsão de Cancro Colorretal em função de alterações metabólicas

No mundo atual, a quantidade de informação disponível nos mais variados setores é cada vez maior. É o caso da área da saúde, onde a recolha e tratamento de dados biomédicos procuram melhorar a tomada de decisão no tratamento a aplicar a um doente, recorrendo a ferramentas baseadas em Machine Learning. Machine Learning é uma área da Inteligência Artificial em que através da aplicação de algoritmos a um conjunto de dados é possível prever resultados ou até descobrir relações entre estes que seriam impercetíveis à primeira vista. Com este projeto pretende-se realizar um estudo em que o objetivo é investigar diversos algoritmos e técnicas de Machine Learning, de modo a identificar se o perfil de acilcarnitinas pode constituir um novo marcador bioquímico para a predição e prognóstico do Cancro Colorretal. No decurso do trabalho, foram testados diferentes algoritmos e técnicas de pré-processamento de dados. Foram realizadas três experiências distintas com o objetivo de validar as previsões dos modelos construídos para diferentes cenários, nomeadamente: prever se o paciente tem Cancro Colorretal, prever qual a doença que o paciente tem (Cancro Colorretal e outras doenças metabólicas) e prever se este tem ou não alguma doença. Numa primeira análise, os modelos desenvolvidos apresentam bons resultados na triagem de Cancro Colorretal. Os melhores resultados foram obtidos pelos algoritmos Random Forest e Gradient Boosting, em conjunto com técnicas de balanceamento dos dados e Feature Selection, nomeadamente Random Oversampling, Synthetic Oversampling e Recursive Feature SelectionIn today´s world, the amount of information available in various sectors is increasing. That is the case in the healthcare area, where the collection and treatment of biochemical data seek to improve the decision-making in the treatment to be applied to a patient, using Machine Learning-based tools. Machine learning is an area of Artificial Intelligence in which applying algorithms to a dataset makes it possible to predict results or even discover relationships that would be unnoticeable at first glance. This project’s main objective is to study several algorithms and techniques of Machine Learning to identify if the acylcarnitine profile may constitute a new biochemical marker for the prediction and prognosis of rectal cancer. In the course of the work, different algorithms and data preprocessing techniques were tested. Three different experiments were carried out to validate the predictions of the models built for different scenarios, namely: predicting whether the patient has Colorectal Cancer, predicting which disease the patient has (Colorectal Cancer and other metabolic diseases) and predicting whether he has any disease. As a first analysis, the developed models showed good results in Colorectal Cancer screening. The best results were obtained by the Random Forest and Gradient Boosting algorithms, together with data balancing and feature selection techniques, namely Random Oversampling, Synthetic Oversampling and Recursive Feature Selectio

Welfare and labor market participation : a comparison of Saskatchewan and Alberta

This paper attempts to explain welfare and labor market participation differentials between Saskatchewan and Alberta, with greater emphasis placed on welfare participation. Generous benefit levels encourage welfare participation but discourage labor market participation. We are interested in explaining if generous welfare policy has contributed to an increase in welfare participation and discouraged labor market participation. We employ a probit model to analyze the decision to participate in the welfare or the labor market among lone parents and singles (unattached individuals) in the two provinces. The results are then decomposed into the explained and unexplained parts, and these results are used to illustrate which variables contribute to welfare differentials. We find that benefit levels have a significant positive effect on welfare participation and a significant negative effect on labor market participation. We also find that welfare participation differentials exist between Saskatchewan and Alberta; other factors in addition to benefit levels play a role in explaining that gap. We conclude that welfare differentials between Saskatchewan may be a reflection of program administration differences

Data Fusion and Systems Engineering Approaches for Quality and Performance Improvement of Health Care Systems: From Diagnosis to Care to System-level Decision-making

abstract: Technology advancements in diagnostic imaging, smart sensing, and health information systems have resulted in a data-rich environment in health care, which offers a great opportunity for Precision Medicine. The objective of my research is to develop data fusion and system informatics approaches for quality and performance improvement of health care. In my dissertation, I focus on three emerging problems in health care and develop novel statistical models and machine learning algorithms to tackle these problems from diagnosis to care to system-level decision-making. The first topic is diagnosis/subtyping of migraine to customize effective treatment to different subtypes of patients. Existing clinical definitions of subtypes use somewhat arbitrary boundaries primarily based on patient self-reported symptoms, which are subjective and error-prone. My research develops a novel Multimodality Factor Mixture Model that discovers subtypes of migraine from multimodality imaging MRI data, which provides complementary accurate measurements of the disease. Patients in the different subtypes show significantly different clinical characteristics of the disease. Treatment tailored and optimized for patients of the same subtype paves the road toward Precision Medicine. The second topic focuses on coordinated patient care. Care coordination between nurses and with other health care team members is important for providing high-quality and efficient care to patients. The recently developed Nurse Care Coordination Instrument (NCCI) is the first of its kind that enables large-scale quantitative data to be collected. My research develops a novel Multi-response Multi-level Model (M3) that enables transfer learning in NCCI data fusion. M3 identifies key factors that contribute to improving care coordination, and facilitates the design and optimization of nurses’ training, workload assignment, and practice environment, which leads to improved patient outcomes. The last topic is about system-level decision-making for Alzheimer’s disease early detection at the early stage of Mild Cognitive Impairment (MCI), by predicting each MCI patient’s risk of converting to AD using imaging and proteomic biomarkers. My research proposes a systems engineering approach that integrates the multi-perspectives, including prediction accuracy, biomarker cost/availability, patient heterogeneity and diagnostic efficiency, and allows for system-wide optimized decision regarding the biomarker testing process for prediction of MCI conversion.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Crop production and global food security in relation to climate variation: an empirical analysis

The challenge of meeting increasing global food demand is amplified by climate change. Crop yield is vulnerable to extreme conditions, including heatwaves, droughts and downpours, leading to widespread concern about negative effects of climate change on food security. This thesis describes a novel empirical analysis of total production, yields and harvested area data for three major crops (wheat, maize and soybean), using a unique, global, gridded agricultural time-series data set. Trend analysis is applied to changes in production, yield and harvested area of these three crops. Machine learning is used to quantify their responses to climate. A new methodology is introduced to identify “shocks”. Results show a more complex dynamics of agricultural production than is suggested by current liter- ature. Large changes in regional production, driven by harvested area rather than yield, have been driven by policy shifts. A large “killing degree-day” sum depresses yields for some regions and crops, but enhances them in others. Heat deficits can be as deleterious as heatwaves. Shocks can be negative or positive. Production variability has increased, but major negative shocks have been few, and have not become more frequent. Production shocks have been caused as often by changes in harvested area as in yield. These findings do not support a universal negative effect of climate change on crop production. More- over, stable global food supplies will not be assured by maximizing yields. It is equally important that farmers in different countries and environments grow a variety of crops. Climate-related risk is currently concentrated in the most productive baskets, exposing the global food supply to avoidably high risk. Increasing frequencies of climate extremes in the main producing areas only make such shocks more likely. Various measures that are not directly related to climate would help to make global food supplies more resilient.Open Acces

Resilient health care: a systematic review of conceptualisations, study methods and factors that develop resilience

Background Traditional approaches to safety management in health care have focused primarily on counting errors and understanding how things go wrong. Resilient Health Care (RHC) provides an alternative complementary perspective of learning from incidents and understanding how, most of the time, work is safe. The aim of this review was to identify how RHC is conceptualised, described and interpreted in the published literature, to describe the methods used to study RHC, and to identify factors that develop RHC. Methods Electronic searches of PubMed, Scopus and Cochrane databases were performed to identify relevant peer-reviewed studies, and a hand search undertaken for studies published in books that explained how RHC as a concept has been interpreted, what methods have been used to study it, and what factors have been important to its development. Studies were evaluated independently by two researchers. Data was synthesised using a thematic approach. Results Thirty-six studies were included; they shared similar descriptions of RHC which was the ability to adjust its functioning prior to, during, or following events and thereby sustain required operations under both expected and unexpected conditions. Qualitative methods were mainly used to study RHC. Two types of data sources have been used: direct (e.g. focus groups and surveys) and indirect (e.g. observations and simulations) data sources. Most of the tools for studying RHC were developed based on predefined resilient constructs and have been categorised into three categories: performance variability and Work As Done, cornerstone capabilities for resilience, and integration with other safety management paradigms. Tools for studying RHC currently exist but have yet to be fully implemented. Effective team relationships, trade-offs and health care ‘resilience’ training of health care professionals were factors used to develop RHC. Conclusions Although there was consistency in the conceptualisation of RHC, methods used to study and the factors used to develop it, several questions remain to be answered before a gold standard strategy for studying RHC can confidently be identified. These include operationalising RHC assessment methods in multi-level and diverse settings and developing, testing and evaluating interventions to address the wider safety implications of RHC amidst organisational and institutional change

Resilience engineering for sociotechnical safety management

Modern societies call for a reconsideration of risk and safety, in light of the increasing complexity of human-made systems. Technological artefacts, and the respective role of humans, as well as the organizational contexts in which they operate, dramatically changed in the last decades with an even more severe transformation expected in the future. Rooted in human factors, ergonomics, cognitive engineering, systems thinking and complexity theory, the discipline of resilience engineering proposes innovative approaches for safety challenges imposed by the dynamic, uncertain, and intertwined nature of modern sociotechnical systems. Resilience engineering aims to provide support means for ensuring that systems can sustain required operations under both expected and unexpected conditions. This chapter aims to provide a summary of the scientific field of resilience engineering, as well as a description of two methods common in the field, the resilience analysis grid and the functional resonance analysis method. Following two examples, the chapter proposes a multidisciplinary research agenda for the field

Tapaustutkimus joukkoliikenteen tasataksasta Helsingin seudulla

Thesis studies flat tariff as potential pricing model for Helsinki Region Transport. Flat tariff is compared to current zone model through financial analysis, user experiences, benchmarking other cities and evaluating effects on least well-off passengers. Thesis utilizes qualitative methods of expert, individual and group interviews and quantitative methods of price elasticity and trip/income analyses. Behavioural effects are recognized through theoretical framework. Results show that flat tariff is realizable but would demand increased subsidies. Experience of fairness relates with losses; if flat tariff is implemented with the current lowest price level, feeling of injustice should not occur. If price increases would be needed, negative emotions of “losers” are stronger than the joy of “winners”. Flat tariff with AB-zone price level would improve transport justice for all users. Flat tariff is not the optimal model to maximize both revenue and usage, unless the behavioural value for simplicity is expected to be high. However, defining the exact value of simplicity would demand further empirical preference studies. Behavioural eco-nomics is relevant framework for tariff planning, and planners need quantitative methods to combine psychological analysis and economical effects of pricing. In conclusion, thesis recommends remaining to zone model, but to lower prices of C- and D-zones in relation to AB-region.Diplomityö tutkii tasatariffia hinnoitteluvaihtoehtona Helsingin seudun liikenteessä. Tariffia verrataan vyöhykkeisiin rahoituksen, käyttäjäkokemusten ja muiden kaupunkien kokemusten kautta sekä arvioidaan vaikutuksia pienituloisille matkustajille. Tutkimus perustuu asiantuntija-, henkilö- ja ryhmähaastatteluiden laadulliseen analyysiin sekä hinta-joustojen ja matkojen kvantitatiiviseen analyysiin. Käyttäytymistaloustieteellisiä vaikutuksia analysoidaan teorian avulla. Tulosten perusteella tasatariffi on toteutettavissa, mutta edellyttää lisäsubventioita. Kokemus oikeudenmukaisuudesta liittyy hinnankorotuksiin; jos tasataksa toteutetaan ilman hinnankorotuksia, epäoikeudenmukaisuuden kokemus ei ole ongelma. Jos osalle käyttäjistä aiheutuu hinnankorotuksia, ”häviäjien” negatiiviset tunteet ovat voimakkaampia kuin “voittajien” tyytyväisyys. Tasatariffi nykyisellä AB-hintatasolla parantaisi liikkumisen oikeudenmukaisuutta kaikille käyttäjille. Tasataksa ei ole optimaalinen malli tulojen ja käytön maksimoimiseksi, ellei yksinkertaisuuden arvo asiakkaalle ole korkea. Yksinkertaisuuden arvon määrittäminen vaatisi kuitenkin empiirisiä preferenssitutkimuksia. Työ osoittaa käyttäytymistaloustieteen keskeisen roolin hinnoittelussa, ja suunnittelijoiden täytyy hallita kvantitatiiviset menetelmät hinnoittelun psykologisten ja taloudellisten vaikutusten analysoimiseksi. Johtopäätöksenä suositellaan pysyttäytymistä vyöhykemallissa ja CD-vyöhykkeiden hintojen laskua