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The Sohar Basin, located offshore the East Coast of the Arabian Peninsula, is a long-ignored area adjacent to the super basins of the Arabian Plate. Three dry wells were drilled in the northern Sohar Basin between 1969 and 1980 on structural anomalies. In 2001, a 3D survey was acquired over the UAE portion of the basin in the belief that the Arabian Platform Carbonates extended from the Rub al Khali Basin, beneath the Semail Ophiolite to the east. This study depicts how this legacy 3D dataset has been re-evaluated to define new prospectivity. A tectonic history defined by a sequence stratigraphic and structural interpretation of the 3D data as well as a basin modeling exercise has revealed a previously unexamined petroleum system. These include Miocene to Eocene-aged turbidite channel and fan complexes representing potential reservoir units. In the deeper section, high reflectivity layers within ‘mini-basins’, have seismic characteristics consistent with organic rich source rock layers. Basin modelling indicates that these layers would be mature for hydrocarbon generation. Shallow bright spots provide evidence for hydrocarbon migration along deep-seated faults.

Compared to conventional fabrication, additive manufacturing enables production of far more complex geometries with less tooling and increased automation. However, despite the common perception of AM’s “free” geometric complexity, this freedom comes with a literal cost: more complex geometries may be challenging to design, potentially manifesting as increased engineering labor cost. Being able to accurately predict design cost is essential to reliably forecasting large-scale design for additive manufacturing projects, especially for those using expensive processes like laser powder bed fusion of metals. However, no studies have quantitatively explored designers’ ability to complete this forecasting. In this study, we address this gap by analyzing the uncertainty of expert design cost estimation. First, we establish a methodology to translate computer-aided design data into descriptive vectors capturing design for additive manufacturing activity parameters. We then present a series of case study designs, with varied functionality and geometric complexity, to experts and measure their estimations of design labor for each case. Summary statistics of the cost estimates and a linear mixed effects model predicting labor responses from participant and design attributes was used to estimate the significance of factors on the responses. A task-based, CAD model complexity calculation is then used to infer an estimate of the magnitude and variability of normalized labor cost to understand more generalizable attributes of the observed labor estimates. These two analyses are discussed in the context of advantages and disadvantages of relying on human cost estimation for additive manufacturing forecasts as well as future work that can prioritize and mitigate such challenges.

Modern digital manufacturing processes, such as additive manufacturing, are cyber-physical in nature and utilize complex, process-specific simulations for both design and manufacturing. Although computational simulations can be used to optimize these complex processes, they can take hours or days — an unreasonable cost for engineering teams leveraging iterative design processes. Hence, more rapid computational methods are necessary in areas where computation time presents a limiting factor. When existing data from historical examples is plentiful and reliable, supervised machine learning can be used to create surrogate models that can be evaluated orders of magnitude more rapidly than comparable finite element approaches. However, for applications that necessitate computationally-intensive simulations, even generating the training data necessary to train a supervised machine learning model can pose a significant barrier. Unsupervised methods, such as physics-informed neural networks, offer a shortcut in cases where training data is scarce or prohibitive. These novel neural networks are trained without the use of potentially expensive labels. Instead, physical principles are encoded directly into the loss function. This method substantially reduces the time required to develop a training dataset, while still achieving the evaluation speed that is typical of supervised machine learning surrogate models. We propose a new method for stochastically training and testing a convolutional physics-informed neural network using the transient 3D heat equation- to model temperature throughout a solid object over time. We demonstrate this approach by applying it to a transient thermal analysis model of the powder bed fusion manufacturing process.

Online data repositories provide designers and engineers with a convenient source of data. Over time, the wealth and type of readily-available data within online repositories has greatly expanded. This data increase permits new uses for machine learning approaches which rely on large, high-dimensional datasets. However, a comparison of the efficacies of attribute-based data, which lends itself well to traditional machine learning algorithms, and form-based data, which lends itself to deep machine learning algorithms, has not fully been established. This paper presents one such comparison for an exemplar dataset. As the efficacy of different machine learning approaches may be dependent on the specific application, this method is intended to lay the groundwork for future studies that produce more extensive comparisons. Specifically, this work makes use of a manufactured gear dataset for sale price prediction. Two traditional machine learning algorithms, M5Rules and SMOreg, are selected due to their applicability to the gear attribute-based data. These algorithms are compared to a neural network model that is trained on a voxelized version of the gears’ 3D models, defined in this work as form-based data. Results show that both data types provide comparable predictive accuracy.

The widespread growth of additive manufacturing, a field with a complex informatic “digital thread”, has helped fuel the creation of design repositories, where multiple users can upload distribute, and download a variety of candidate designs for a variety of situations. Additionally, advancements in additive manufacturing process development, design frameworks, and simulation are increasing what is possible to fabricate with AM, further growing the richness of such repositories. Machine learning offers new opportunities to combine these design repository components’ rich geometric data with their associated process and performance data to train predictive models capable of automatically assessing build metrics related to AM part manufacturability. Although design repositories that can be used to train these machine learning constructs are expanding, our understanding of what makes a particular design repository useful as a machine learning training dataset is minimal. In this study we use a metamodel to predict the extent to which individual design repositories can train accurate convolutional neural networks. To facilitate the creation and refinement of this metamodel, we constructed a large artificial design repository, and subsequently split it into sub-repositories. We then analyzed metadata regarding the size, complexity, and diversity of the sub-repositories for use as independent variables predicting accuracy and the required training computational effort for training convolutional neural networks. The networks each predict one of three additive manufacturing build metrics: (1) part mass, (2) support material mass, and (3) build time. Our results suggest that metamodels predicting the convolutional neural network coefficient of determination, as opposed to computational effort, were most accurate. Moreover, the size of a design repository, the average complexity of its constituent designs, and the average and spread of design spatial diversity were the best predictors of convolutional neural network accuracy.

Adolescent obesity is a major health issue facing today's youth. This may be the first generation to have a lower life expectancy than their parents. The Youth Empowerment Implementation Project's (YEIP) goal was to increase fruit and vegetable intake, lower junk food consumption, and increase physical activity among low-income African-American youth living in the Midwest. Thirty middle school aged youth participated in an evidenced-based program (i.e., Botvin's Life Skills Training) and were engaged in health education and physical activities. The results from baseline to follow-up demonstrated a reduction in junk food intake for participants and an increase in fruit and vegetable intake but not for physical activity. The health behaviors of participants improved for three out of four indicators following the intervention. Limitations, future research, and implications for future programs are also discussed.

Three-dimensional printing systems have expanded the access to low cost, rapid methods for attaining physical prototypes or products. However, a cyber attack, system error, or operator error on a 3D-printing system may result in catastrophic situations, ranging from complete product failure, to small types of defects which weaken the structural integrity of the product. Such defects can be introduced early-on via solid models or through G-codes for printer movements at a later stage. Previous works have studied the use of image classifiers to predict defects in real-time and offline. However, a major restriction in the functionality of these methods is the availability of a dataset capturing diverse attacks on printed entities or the printing process. This paper introduces an image processing technique that analyzes the amplitude and phase variations of the print head platform arising through induced system manipulations. The method uses an image sequence of the printing process to perform an offline spatio-temporal video decomposition to amplify changes attributable to a change in system parameters. The authors hypothesize that a change in the amplitude envelope and instantaneous phase response as a result of a change in the end-effector translational instructions to be correlated with an AM system compromise. Two case studies are presented, one verifies the hypothesis with statistical evidence in support of the method while the other studies the effectiveness of a conventional tensile test to identify system compromise. The method has the potential to enhance the robustness of cyber-physical systems such as 3D printers.

Machine learning can be used to automate common or time-consuming engineering tasks for which sufficient data already exist. For instance, design repositories can be used to train deep learning algorithms to assess component manufacturability; however, methods to determine the suitability of a design repository for use with machine learning do not exist. We provide an initial investigation toward identifying such a method using “artificial” design repositories to experimentally test the extent to which altering properties of the dataset impacts the assessment precision and generalizability of neural networks trained on the data. For this experiment, we use a 3D convolutional neural network to estimate quantitative manufacturing metrics directly from voxel-based component geometries. Additive manufacturing (AM) is used as a case study because of the recent growth of AM-focused design repositories such as GrabCAD and Thingiverse that are readily accessible online. In this study, we focus only on material extrusion, the dominant consumer AM process, and investigate three AM build metrics: (1) part mass, (2) support material mass, and (3) build time. Additionally, we compare the convolutional neural network accuracy to that of a baseline multiple linear regression model. Our results suggest that training on design repositories with less standardized orientation and position resulted in more accurate trained neural networks and that orientation-dependent metrics were harder to estimate than orientation-independent metrics. Furthermore, the convolutional neural network was more accurate than the baseline linear regression model for all build metrics.

3D printing systems have expanded the access to low cost, rapid methods for attaining physical prototypes or products. However, a cyber attack, system error, or operator error on a 3D printing system may result in catastrophic situations, ranging from complete product failure, to small types of defects which weaken the structural integrity of the product, making it unreliable for its intended use. Such defects can be introduced early-on via solid models or through G-codes for printer movements at a later stage. Previous works have studied the use of image classifiers to predict defects in real-time as a print is in progress and also by studying the printed entity once the print is complete. However, a major restriction in the functionality of these methods is the availability of a dataset capturing diverse attacks on printed entities or the printing process. This paper introduces a visual inspection technique that analyzes the amplitude and phase variations of the print head platform arising through induced system manipulations. The method uses an image sequence of a 3D printing process captured via an off the shelf camera to perform an offline multi-scale, multi-orientation decomposition to amplify imperceptible system movements attributable to a change in system parameters. The authors hypothesize that a change in the amplitude envelope and instantaneous phase response as a result of a change in the end effector translational instructions, to be correlated with an AM system compromise. A case study is presented that tests the hypothesis and provides statistical validity in support of the method. The method has the potential to enhance the robustness of cyber-physical systems such as 3D printers that rely on secure, high quality hardware and software to perform optimally.

Oil and gas interests and the government recently offered a First Nation in the Skeena River estuary (BC, Canada) $1 billion to consent to construction of a controversial terminal to load fossil fuels onto tankers ([ 1 ][1]). The proposal highlights a troubling blind spot in Canada's environmental

Challenging the conventional portrait of Huey P. Long as a Southern demagogue, this essay argues for a new understanding of the Kingfish that better accounts for how he simultaneously forged a fiercely loyal following of "common folk" and outraged, even terrorized, a "better" class of citizens. Focusing on Long's rise to national prominence between 1933 and 1935, we locate the rhetorical and symbolic power of Long's Share Our Wealth crusade not in the economic "deal" he offered poor people, but in the rusticity of his political persona and the religiosity of his radio addresses.

By measures of impact alone, Thomas Paine might be considered the charismatic leader of the American Revolution and his Common Sense its most important manifesto. Psychological and sociological theories of charisma, however, are hard‐pressed to account for the impact of Paine and his pamphlet. Written anonymously, Common Sense did not derive its power from the reputation of its author, nor did the text itself project the image of a charismatic leader as conventionally understood. Instead, Common Sense exemplified a “new rhetoric” and a new style of charismatic leadership‐a “republican charisma"‐more in tune with the emerging egalitarian ethos of revolutionary America.

Clostridium perfringens is a common cause of food-borne illness. The illness is characterized by profuse diarrhea and acute abdominal pain. Since the illness is usually self-limiting, many cases are undiagnosed and/or not reported. Investigations are often pursued after an outbreak involving large numbers of people in institutions, at restaurants, or at catered meals. Serotyping has been used in the past to assist epidemiologic investigations of C. perfringens outbreaks. However, serotyping reagents are not widely available, and many isolates are often untypeable with existing reagents. We developed a pulsed-field gel electrophoresis (PFGE) method for molecular subtyping of C. perfringens isolates to aid in epidemiologic investigations of food-borne outbreaks. Six restriction endonucleases ( Sma I, Apa I, Fsp I, Mlu I, Ksp I, and Xba I) were evaluated with a select panel of C. perfringens strains. Sma I was chosen for further studies because it produced 11 to 13 well-distributed bands of 40 to ∼1,100 kb which provided good discrimination between isolates. Seventeen distinct patterns were obtained with 62 isolates from seven outbreak investigations or control strains. In general, multiple isolates from a single individual had indistinguishable PFGE patterns. Epidemiologically unrelated isolates (outbreak or control strains) had unique patterns; isolates from different individuals within an outbreak had similar, if not identical, patterns. PFGE identifies clonal relationships of isolates which will assist epidemiologic investigations of food-borne-disease outbreaks caused by C. perfringens.

We introduce a new approach to three important problems in ray tracing: antialiasing, distributed light sources, and fuzzy reflections of lights and other surfaces. For antialiasing, our approach combines the quality of supersampling with the advantages of adaptive supersampling. In adaptive supersampling, the decision to partition a ray is taken in image-space, which means that small or thin objects may be missed entirely. This is particularly problematic in animation, where the intensity of such objects may appear to vary. Our approach is based on considering pyramidal rays (pyrays) formed by the viewpoint and the pixel. We test the proximity of a pyray to the boundary of an object, and if it is close (or marginal), the pyray splits into 4 sub-pyrays; this continues recursively with each marginal sub-pyray until the estimated change in pixel intensity is suciently small. The same idea also solves the problem of soft shadows from distributed light sources, which can be calculated to any required precision. Our approach also enables a method of defocusing reflected pyrays, thereby producing realistic fuzzy reflections of light sources and other objects. An interesting byproduct of our method is a substantial speedup over regular supersampling even when all pixels are supersampled. Our algorithm was implemented on polygonal and circular objects, and produced images comparable in quality to stochastic sampling, but with greatly reduced run times.

During the Stamp Act crisis, colonial protest leaders relied upon a tradition of legitimate, extralegal coercion to justify mob actions against stamp distributors. As the conflict between Britain and its colonies escalated, however, whig leaders broadened their definition of “the enemy” to include a variety of fellow colonists, making it more difficult to justify coercion and violence on traditional grounds. Following the outbreak of war, radical whigs justified the suppression of all remaining dissent with appeals, not to tradition, but to a revolutionary theory of popular sovereignty. This change marked a key rhetorical “moment” in the coming of the Revolution, but its legacy also threatened the political stability of the new American Republic.

The 1990s has been heralded as the decade of the environment for the advanced capitalist countries. Are the practitioners of the new Canadian political economy prepared? The exploration of environmental issues requires an analytical framework that can incorporate geographic space, production and trade, technology, and the organization of social power. Despite possessing such a framework, there has been remarkably little written within the Canadian political economy tradition that directly addresses the degradation of our environment.

The implementation of safeguards for computer security is based on the ability to verify the identity of authorized computer systems users accurately. The most common form of identity verification in use today is the password, but passwords have many poor traits as an access control mechanism. To overcome the many disadvantages of simple password protection, we are proposing the use of the physiological characteristics of keyboard input as a method for verifying user identity. After an overview of the problem and summary of previous efforts, a research study is described which was conducted to determine the possibility of using keystroke characteristics as a means of dynamic identity verification. Unlike static identity verification systems in use today, a verifier based on dynamic keystroke characteristics allows continuous identity verification in real-time throughout the work session. Study results indicate significant promise in the temporal personnel identification problem.

Currently, the most popular method of visualizing music is music notation. Through music notation, an experienced musician can gain an impression of how a particular piece of music sounds simply by looking at the notes on paper. However, most listeners are unfamiliar or uncomfortable with the complex nature of music notation. The goal of this project is to present an alternate method for visualizing music that makes use of color and 3D space. This paper describes one method of visualizing music in 3D space. The implementation of this method shows that music visualization is an effective technique, although it is certainly not the only possible method for accomplishing the task. Throughout the course of this project, several variations and alternative approaches were discussed. The final version of this project reflects the decisions that were made in order to present the best possible representation of music data.

Noting that the United States government had refused to allow the U.S. branch plants of Canadian companies to sit on its free trade talks advisory committees for "national security" reasons, New Democratic Party (NDP) trade critic Steven Langdon questioned in the fall of 1986 why the Canadian government had appointed at least fifteen representatives of U.S. subsidiaries to sit on Canada's trade advisory committees.

This paper reports on an experiment that was conducted to assess the viability of using keystroke digraph latencies (time between two successive keystrokes) as an identity verifier. Basic data are presented and discussed that characterize the class of keystroke digraph latencies that are found to have good potential as static identity verifiers as well as dynamic identity verifiers. Keystroke digraph latencies would be used in conjunction with other security measures to provide a total security package.

Les observations photometriques confirment les caracteres attribues a V1223 Sgr par Warner et Cropper, et permettent de deriver une limite superieure |P/P

Une tentative récente d'expliquer les tarifs de la « politique nationale » de 1879 soutient qu'ils furent conçus par l'Etat canadien à la demande de la fraction hégétnonique de la bourgeoisie désireuse de noyer la bourgeoisie nationale naissante sous un déluge de succursales américaines entraînées par les tarifs douaniers. Nous remettons en cause cette thèse du conflit entre les deux bourgeoisies et nous supposons au contraire qu'il y avait un consensus général au sein de l'élite politique et économique autour d'une stratégie industrielle qui, à bien des égards, se rapproche du développement industriel par le remplacement des importations et qui a connu peu de succès dans le Tiers-Monde. Nous nous servons de cette interprétation pour mettre en lumière la préoccupation des industriels canadiens et des dirigeants de succursales de l'époque pour le marché national et pour sa dépendence à l'égard de la technologie étrangère. Nous concluons en examinant un certain nombre de facteurs propres à une économie de produits de base qui ont montré à de grands secteurs de la bourgeoisie canadienne les avantages d'une stratégie industrielle comme l'industrialisation par le remplacement des importations.