Histoire et patrimoine Burgess, Joanne
Histoire et patrimoine,
2019, 20190402, 2019, 2019-04-02
eBook
L’histoire et les études patrimoniales ont pendant longtemps évolué en parallèle. Mais, depuis quelques années, les collaborations et les échanges se sont accrus. C’est ce dont témoigne cet ouvrage ...dans lequel la relation entre histoire et patrimoine est explorée sous divers angles. On y met en lumière les apports originaux de travaux récents et on y esquisse des pistes de recherche. En outre, l’appropriation et la diffusion des résultats et les enjeux de la mise en valeur reçoivent une attention particulière. Ce livre est aussi l’occasion de souligner la contribution majeure du Laboratoire d’histoire et de patrimoine de Montréal, une équipe de recherche en partenariat, créée à l’Université du Québec à Montréal en 2006. Plusieurs articles illustrent l’apport de ses membres et partenaires à ce rapprochement entre histoire et patrimoine.
Waterfront blues Pathy, Alexander C
Waterfront blues,
c2004, 20040202, 2004, 2014, 2004-01-01, 20040101
eBook
Waterfront Bluesis the story of the dramatic events surrounding the labour battles at the Port of Montreal in the 1960s and 70s. During that time, the prospect - and reality - of technological change ...poisoned labour relations, provoking a series of bitter strikes as well as repeated exercises in government intervention. It was not until 1978 that management and labour were able to negotiate a collective agreement without a work stoppage or government intervention.
In this new study, Alexander Pathy probes deeply into the causes of this labour unrest and charts the efforts made by the parties concerned - management, labour, and government - to resolve the crisis. It draws upon the author's own experiences as a management representative and key figure at the Port of Montreal, as well as extensive research into the records generated by all the parties involved.
Exploring complicated issues of labour relations clearly and concisely,Waterfront Bluesalso boasts a fascinating cast of characters, including the colourful labour minister Bryce Mackasey; the shrewd shipping industry lawyer and future prime minister Brian Mulroney; the decisive and no-nonsense management spokesperson Arnie Masters; the fiery union leader Jean-Marc St-Onge; and the blunt, brutally effective mediator/arbitrator Judge Alan B. Gold.
During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, neurological symptoms increasingly moved into the focus of interest. In this prospective cohort study, we assessed ...neurological and cognitive symptoms in hospitalized coronavirus disease-19 (COVID-19) patients and aimed to determine their neuronal correlates. Patients with reverse transcription-PCR-confirmed COVID-19 infection who required inpatient treatment primarily because of non-neurological complications were screened between 20 April 2020 and 12 May 2020. Patients (age > 18 years) were included in our cohort when presenting with at least one new neurological symptom (defined as impaired gustation and/or olfaction, performance < 26 points on a Montreal Cognitive Assessment and/or pathological findings on clinical neurological examination). Patients with ≥2 new symptoms were eligible for further diagnostics using comprehensive neuropsychological tests, cerebral MRI and 18fluorodeoxyglucose (FDG) PET as soon as infectivity was no longer present. Exclusion criteria were: premorbid diagnosis of cognitive impairment, neurodegenerative diseases or intensive care unit treatment. Of 41 COVID-19 inpatients screened, 29 patients (65.2 ± 14.4 years; 38% female) in the subacute stage of disease were included in the register. Most frequently, gustation and olfaction were disturbed in 29/29 and 25/29 patients, respectively. Montreal Cognitive Assessment performance was impaired in 18/26 patients (mean score 21.8/30) with emphasis on frontoparietal cognitive functions. This was confirmed by detailed neuropsychological testing in 15 patients. 18FDG PET revealed pathological results in 10/15 patients with predominant frontoparietal hypometabolism. This pattern was confirmed by comparison with a control sample using voxel-wise principal components analysis, which showed a high correlation (R2 = 0.62) with the Montreal Cognitive Assessment performance. Post-mortem examination of one patient revealed white matter microglia activation but no signs of neuroinflammation. Neocortical dysfunction accompanied by cognitive decline was detected in a relevant fraction of patients with subacute COVID-19 initially requiring inpatient treatment. This is of major rehabilitative and socioeconomic relevance.
Daily water demand forecasts are an important component of cost‐effective and sustainable management and optimization of urban water supply systems. In this study, a method based on coupling discrete ...wavelet transforms (WA) and artificial neural networks (ANNs) for urban water demand forecasting applications is proposed and tested. Multiple linear regression (MLR), multiple nonlinear regression (MNLR), autoregressive integrated moving average (ARIMA), ANN and WA‐ANN models for urban water demand forecasting at lead times of one day for the summer months (May to August) were developed, and their relative performance was compared using the coefficient of determination, root mean square error, relative root mean square error, and efficiency index. The key variables used to develop and validate the models were daily total precipitation, daily maximum temperature, and daily water demand data from 2001 to 2009 in the city of Montreal, Canada. The WA‐ANN models were found to provide more accurate urban water demand forecasts than the MLR, MNLR, ARIMA, and ANN models. The results of this study indicate that coupled wavelet‐neural network models are a potentially promising new method of urban water demand forecasting that merit further study.
Key Points
MLR, MNLR, ARIMA, ANN, and wavelet‐ANN models were developed
The water demand forecast models were compared using data from Montreal
The wavelet‐ANN model provided the most accurate forecasts
A new hybrid wavelet‐bootstrap‐neural network (WBNN) model is proposed in this study for short term (1, 3, and 5 day; 1 and 2 week; and 1 and 2 month) urban water demand forecasting. The new method ...was tested using data from the city of Montreal in Canada. The performance of the WBNN method was compared with the autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average model with exogenous input variables (ARIMAX), traditional NNs, wavelet analysis‐based NNs (WNN), bootstrap‐based NNs (BNN), and a simple naïve persistence index model. The WBNN model was developed as an ensemble of several NNs built using bootstrap resamples of wavelet subtime series instead of raw data sets. The results demonstrated that the hybrid WBNN and WNN models produced significantly more accurate forecasting results than the traditional NN, BNN, ARIMA, and ARIMAX models. It was also found that the WBNN model reduces the uncertainty associated with the forecasts, and the performance of WBNN forecasted confidence bands was found to be more accurate and reliable than BNN forecasted confidence bands. It was found in this study that maximum temperature and total precipitation improved the accuracy of water demand forecasts using wavelet analysis. The performance of WBNN models was also compared for different numbers of bootstrap resamples (i.e., 25, 50, 100, 200, and 500) and it was found that WBNN models produced optimum results with different numbers of bootstrap resamples for different lead time forecasts with considerable variability.
Key Points
Comparison of different methods for urban water demand forecasting
Wavelet‐bootstrap‐neural network method is found accurate and reliable.
Significance of input variables on forecasting performance.
Using location theory as a starting point, this paper aims to understand how coworking spaces (CSs) locate within the city and how they reacted to the stress of COVID-19. Through a case study of the ...city of Montreal (Canada), we show that most CSs locate in areas of high transit accessibility and in central districts, but there is a trend – possibly accelerated by COVID – towards more suburban locations. These location strategies follow logics similar to those of Knowledge intensive services (KIS), including the tendency of some to agglomerate and of others to disperse. For some CSs, there is also heightened sensitivity to interactions with, and contributions to, the local community. Hence, faced with COVID, CSs in transit-accessible places combine an inward strategy, centralizing their activities around members, with a networking strategy, pooling some services and developing partnerships with local or other nearby CSs. Furthermore, CSs in peri-central neighbourhoods are the most vulnerable and have adopted retraction strategies. In contrast, CSs located in low accessibility districts outside the agglomeration adopt an expansion strategy, opening new branches near suburban residential areas to attract nearby workers. As hybrid work evolves, these results can help urban planners better understand the location rationales of CSs, how they adapt, and to what extent they bring added value to local urban development.
In this research, proximal soil sensor data fusion was defined as a multifaceted process which integrates geospatially correlated data, or information, from multiple proximal soil sensors to ...accurately characterize the spatial complexity of soils. This has capability of providing improved understanding of soil heterogeneity for potential applications associated with crop production and natural resource management. To assess the potential of data fusion for the purpose of improving thematic soil mapping over the single sensor approach, data from multiple proximal soil sensors were combined to develop and validate predictive relationships with laboratory-measured soil physical and chemical properties. The work was conducted in an agricultural field with both mineral and organic soils. The integrated data included: topography records obtained using a real-time kinetic (RTK) global navigation satellite system (GNSS) receiver, apparent soil electrical conductivity (ECa) obtained using an electromagnetic induction sensor, and content of several naturally occurring radioisotopes detected using a mobile gamma-ray spectrometer. In addition, the soil profile data were collected using a commercial ruggedized multi-sensor platform carrying a visible and near-infrared (vis-NIR) optical sensor and a galvanic contact soil ECa sensor. The measurements were carried out at predefined field locations covering the entire study area identified from sensor measured a priori information on field elevation, ECa and gamma-ray count. The information was used to predict: soil organic matter (SOM), pH, lime buffer capacity (LBC), as well as concentration of phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and aluminum (Al). Partial least squares regressions (PLSRs) were used to predict soil properties from individual sensors and different sensor combinations (sensor data fusion). By integrating the data from all of the proximal soil sensors, SOM, pH, LBC, Ca, Mg, and Al were predicted simultaneously with R2 > 0.5 (RPD > 1.50). Improved predictions were observed for most soil properties based on sensor data fusion than those based on individual sensors. After choosing the optimal sensor combination for each soil property, the predictive capability was compared using different data mining algorithms, including support vector machines (SVM), random forest (RF), multivariate adaptive regression splines (MARS), and regression trees (CART). Improved predictions for SOM, Ca, Mg, and Al were observed using SVM over PLSR. The predictive capability was followed by RF and MARS, with CART. Predictions of pH and LBC were only feasible using MARS and PLSR, respectively. In this field, it was not possible to predict extractable P and K using all tested sensor combinations or algorithms. With large variability in SOM, the field presents a special situation and thus, the result could be specific to the study site. Further research includes an extended number of experimental sites covering different geographic areas around Eastern Canada.
•A sensor fusion framework to integrate proximal soil sensor data is introduced.•Information of gamma ray, electrical conductivity, vis-NIR spectra and elevation were fused.•Sensor data fusion produced improved prediction over individual sensors.•SOM, pH, LBC, Ca, Mg, and Al were predicted simultaneously by sensor data fusion.•Data mining algorithm improved the prediction accuracy of soil properties.
Linguistic Rivalries weaves together anthropological accounts of diaspora, nation, and empire to explore and analyze the multifaceted processes of globalization characterizing the migration and ...social integration experiences of Tamil-speaking immigrants and refugees from India and Sri Lanka to Montréal, Québec in the late twentieth and early twenty-first centuries. In Montréal, a city with more trilingual speakers than in any other North American city, Tamil migrants draw on their multilingual repertoires to navigate longstanding linguistic rivalries by arguing that Indians speak “Spoken Tamil” and Sri Lankans speak “Written Tamil” as their respective heritage languages. Drawing on ethnographic, archival, and linguistic methods to compare and contrast the communicative practices and language ideologies of Tamil heritage language learning in Hindu temples, Catholic churches, public schools, and community centers, this book demonstrates how processes of sociolinguistic differentiation there are mediated by ethnonational, religious, class, racial, and caste hierarchies. Indian Tamils showcase their use of the “cosmopolitan” sounds and scripts of colloquial varieties of Tamil to enhance their geographic and social mobilities, whereas Sri Lankan Tamils, dispossessed of their homes by civil war and restricted in travel, instead emphasize the “primordialist” sounds and scripts of a pure “literary” Tamil to rebuild a homeland and launch a “global” critique of racism and environmental destruction. This book uses the ethnographic and archival study of Tamil mobility and immobility to expose the mutual constitution of elite and non-elite global modernities, defined here as language ideological projects in which migrants objectify dimensions of time and space through scalar metaphors.
In water resources applications (e.g., streamflow, rainfall‐runoff, urban water demand UWD, etc.), ensemble member selection and ensemble member weighting are two difficult yet important tasks in the ...development of ensemble forecasting systems. We propose and test a stochastic data‐driven ensemble forecasting framework that uses archived deterministic forecasts as input and results in probabilistic water resources forecasts. In addition to input data and (ensemble) model output uncertainty, the proposed approach integrates both ensemble member selection and weighting uncertainties, using input variable selection and data‐driven methods, respectively. Therefore, it does not require one to perform ensemble member selection and weighting separately. We applied the proposed forecasting framework to a previous real‐world case study in Montreal, Canada, to forecast daily UWD at multiple lead times. Using wavelet‐based forecasts as input data, we develop the Ensemble Wavelet‐Stochastic Data‐Driven Forecasting Framework, the first multiwavelet ensemble stochastic forecasting framework that produces probabilistic forecasts. For the considered case study, several variants of Ensemble Wavelet‐Stochastic Data‐Driven Forecasting Framework, produced using different input variable selection methods (partial correlation input selection and Edgeworth Approximations‐based conditional mutual information) and data‐driven models (multiple linear regression, extreme learning machines, and second‐order Volterra series models), are shown to outperform wavelet‐ and nonwavelet‐based benchmarks, especially during a heat wave (first time studied in the UWD forecasting literature).
Key Points
A stochastic data‐driven ensemble framework is introduced for probabilistic water resources forecasting
Ensemble member selection and weighting uncertainties are explicitly considered alongside input data and model output uncertainties
Wavelet‐based model outputs are used as input to the framework for an urban water demand forecasting study outperforming benchmark methods
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