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We report the serendipitous discovery of a map drawn by an army surgeon during the First World War. The map, entitled 'Loos 36[degrees] NW3,'' was drawn by 24-year-old Captain Alexander Edward MacDonald in fall 1917 and was found in his old surgery textbook. MacDonald's map depicts the positions of Canadian frontlines and medical units after the Battle of Hill 70. During the battle, Dr. MacDonald tended to the wounded in an aid post that he constructed in a ruined coal mine near the Front. MacDonald would go on to serve with distinction in the Battle of Passchendaele and Canada's Hundred Days, and he received the Military Cross for gallantry. He maintained a passionate interest in cartography throughout his life and eventually became an authority among map collectors. Artifacts such as MacDonald's map remind us of the realities of war and the sacrifices of our surgeon predecessors., Virtually every town, city and church in Canada and the rest of the British Empire created memorials to their participation in the First World War. Veterans, however, were often hesitant [...]

Alfalfa is critical to global food security, and its data is abundant in the U.S. nationally, but often scarce locally, limiting the potential performance of machine learning (ML) models in predicting alfalfa biomass yields. Training ML models on local-only data results in very low estimation accuracy when the datasets are very small. Therefore, we explore synthesizing non-local data to estimate biomass yields labeled as high, medium, or low. One option to remedy scarce local data is to train models using non-local data; however, this only works about as well as using local data. Therefore, we propose a novel pipeline that trains models using data synthesized from non-local data to estimate local crop yields. Our pipeline, synthesized non-local training (SNLT pronounced like sunlight), achieves a gain of 42.9% accuracy over the best results from regular non-local and local training on our very small target dataset. This pipeline produced the highest accuracy of 85.7% with a decision tree classifier. From these results, we conclude that SNLT can be a useful tool in helping to estimate crop yields with ML. Furthermore, we propose a software application called Predict Your CropS (PYCS pronounced like Pisces) designed to help farmers and researchers estimate and predict crop yields based on pretrained models. [ABSTRACT FROM AUTHOR]

After the First World War, Canadian veterans created a culture that celebrated the camaraderie, sense of purpose, and light-hearted moments of their experience as soldiers. Much like the trench culture of the war years, it poked fun at misfortune, satirized the enemy, and presumed that a stiff drink could make any situation better. Veteran culture provided ex-soldiers in the 1920s and 1930s with the mutual support they needed to get through difficult times, but it was a milieu in which the excessive consumption of alcohol was accepted and even encouraged. This had little impact on the settled, well-adjusted veteran but for the ex-soldier who was struggling in the postwar world, it could be a recipe for disaster. [ABSTRACT FROM AUTHOR]

A nonaffine discrete-time system represented by the nonlinear autoregressive moving average with eXogenous input (NARMAX) representation with unknown nonlinear system dynamics is considered. An equivalent affinelike representation in terms of the tracking error dynamics is first obtained from the original nonaffine nonlinear discrete-time system so that reinforcement-learning-based near-optimal neural network (NN) controller can be developed. The control scheme consists of two linearly parameterized NNs. One NN is designated as the critic NN, which approximates a predefined long-term cost function, and an action NN is employed to derive a near-optimal control signal for the system to track a desired trajectory while minimizing the cost function simultaneously. The NN weights are tuned online. By using the standard Lyapunov approach, the stability of the closed-loop system is shown. The net result is a supervised actor-critic NN controller scheme which can be applied to a general nonaffine nonlinear discrete-time system without needing the affinelike representation. Simulation results demonstrate satisfactory performance of the controller. Index Terms--Adaptive critic, adaptive dynamic programming, Lyapunov stability, neural network control, reinforcement learning control.

Past research has shown substantial reductions in the oxides of nitrogen (N[O.sub.x]) concentrations by using 10%-25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The stability analysis of the closed-loop system is given and the boundedness of the control input is demonstrated by relaxing separation principle, persistency of excitation condition, certainty equivalence principle, and linear in the unknown parameter assumptions. Online training is used for the adaptive NN and no offline training phase is needed. This online learning feature and model-free approach is used to demonstrate the applicability of the controller on a different engine with minimal effort. Simulation results demonstrate that the cyclic dispersion is reduced significantly using the proposed controller when implemented on an engine model that has been validated experimentally. For a single cylinder research engine fitted with a modern four-valve head (Ricardo engine), experimental results at 15% EGR indicate that cyclic dispersion was reduced 33% by the controller, an improvement of fuel efficiency by 2%, and a 90% drop in N[O.sub.x] from stoichiometric operation without EGR was observed. Moreover, unburned hydrocarbons (uHC) drop by 6% due to NN control as compared to the uncontrolled scenario due to the drop in cyclic dispersion. Similar performance was observed with the controller on a different engine. Index Terms--Adaptive control, neural networks (NNs), non-linear systems, observers, output feedback.

During the Second World War, hundreds of organizations were created in Canada to provide amenities to prisoners of war. So the relief effort could proceed efficiently, the government attempted to coordinate the activities of the three main organizations, the Canadian Red Cross Society, the War Prisoners' Aid of the YMCA and the Canadian vow Relatives Association, but this endeavour was frustrated by the desire of each to control the aid programme. The result was ill-will, dissension and unnecessary work for leading philanthropists and the bureaucrats who dealt with them. Unaffected by this confusion was the most famous part of Canada's effort in aid of vows, the provision of Red Cross food parcels. This campaign was a complete success and remains the symbol of Canada's concern for Allied prisoners of war.