A Quixote in imagination might here find...an ideal baronage : Landscapes of Power, Enslavement, Resistance, and Freedom at Sherwood Forest Plantation

In the winter of 1862, two armed forces descended upon Fredericksburg; one blue, one gray. After suffering heavy losses during the Battle of Fredericksburg, the Union Army retreated to the northern banks of the Rappahannock River, making camp in Stafford County. From December 1862 until June 1863, the Union Army overran local plantations and small farm holdings throughout the area, including at Sherwood Forest, the home of the Fitzhugh family. Sherwood Forest was used as field hospital, a signal station, a balloon launch reconnaissance station, and a general encampment during the winter and spring of 1862/1863. Throughout the roughly six-month occupation of Sherwood Forest, many Union soldiers wrote of their time on the property, describing the house, outbuildings, and landscape of the plantation. A lawsuit regarding the Union Army occupation of the property filed by the antebellum owner, Henry Fitzhugh, in the Southern Claims Commission against the federal government also provides unprecedented documentation of life on the plantation before, during, and after the Civil War. These letters and official correspondences, in combination with archaeological evidence, extant landscape features, and oral history are examined to discuss how the landscape was used to convey power and control by the property owners during the antebellum period, with a brief consideration of the postbellum and Jim Crow eras. These same resources also provide evidence of active resistance to and undermining of these structures of power by those who were held in bondage on the property

An Evaluation of Tobacco Pipe Stem Dating Formulas

There are currently three formula dating techniques available to archaeologists studying 17th- and 18th-century colonial sites with imported white, ball-clay, tobacco-pipe stems. The formulas are based on Harrington’s 1954 histogram of time periods: Binford’s linear formula, Hanson’s ten linear formulas, and the Heighton and Deagan curvilinear formula. Data on pipe stem-bore diameters were collected from 28 sites in Maryland, Virginia, North Carolina, and South Carolina to test the accuracy and utility of the three formula dating methods. The results of this project indicate that current conventional use of Binford’s formula, to the exclusion of the other methods, may be problematic, and that the Heighton and Deagan formula is the most accurate of the three options

How do we teach authority in a culture where everyone’s an expert?

As one of the cornerstones of the CRAAP test to evaluate the validity and usefulness of sources, we rely on the idea of “authority” to inform our evaluation of the source, to decide if it is trustworthy. In the long history of authority, we’ve variously relied on royalty/aristocracy, the Church, professors/the University, the printed word, and the “cultural elite.” In today’s world, all knowledge is available to all people (who are literate and have access to technology) at the click of a mouse or the tap of a finger. The concept of authority has been destabilized and democratized. Credentials don’t matter. Authorities conflict with each other. All truth is just biased opinion. The distrust of historical sources of authority, along with the popularization of the idea of “fake news” and “fake news media”, coupled with the increasingly small number of people who read books, and the theory of confirmation bias, where we each inhabit our own bubble of information, have combined to create an almostan almost toxic (and certainly skewed) view of authority. Who needs authorities when I can find the “truth” on my own? I can crowd-source my answer. So how do we, as teachers of information literacy and critical thinking in the library and the composition classroom, combat this? We must change the conversation and must show students that authority still matters.. WWe will’d like to offer some practical applications and exercises you could can apply to your own classroom and to open up the conversation in ways that may be helpful and encourage students to think critically about who creates information

Crossing the threshold with the \u3ci\u3eFramework for Information Literacy\u3c/i\u3e

According to the projected timeline, the ACRL’s Information Literacy Competency Standards for Higher Education Task Force will have completed the Framework for Information Literacy for Higher Education by the time this conference convenes in October. The Framework expands and re-defines information literacy in ways that reflect how university students consume and produce information. Consequently, academic librarians will be challenged to revisit and revise their own understanding and teaching of information literacy. In this workshop, the presenters will lead up to 25 participants through a series of explanations and activities designed to explore an unfamiliar component of the Framework—threshold concepts. We will alternate explanations and activities in order to match participants experience with these key elements of the Framework. Introductions and preparation for discussion (10 min) Threshold concepts defined and introduced. (15-18 min) Think, pair, share activity to identify the problem areas participants currently experience. How are these problem areas addressed in the threshold concepts? (20 min) Re-framing assignments: Before the workshop begins, participants will succinctly describe a favorite or often used lesson plan on an index card. After discussing threshold concepts, small groups will “re-frame” assignments in terms of what they have learned about the threshold concepts. Each group reports one assignment. (25 min

Leakage Detection Framework using Domain-Informed Neural Networks and Support Vector Machines to Augment Self-Healing in Water Distribution Networks

The reduction of water leakage is essential for ensuring sustainable and resilient water supply systems. Despite recent investments in sensing technologies, pipe leakage remains a significant challenge for the water sector, particularly in developed nations like the UK, which suffer from aging water infrastructure. Conventional models and analytical methods for detecting pipe leakage often face reliability issues and are generally limited to detecting leaks during nighttime hours. Moreover, leakages are frequently detected by the customers rather than the water companies. To achieve substantial reductions in leakage and enhance public confidence in water supply and management, adopting an intelligent detection method is crucial. Such a method should effectively leverage existing sensor data for reliable leakage identification across the network. This not only helps in minimizing water loss and the associated energy costs of water treatment but also aids in steering the water sector towards a more sustainable and resilient future. As a step towards ‘self-healing’ water infrastructure systems, this study presents a novel framework for rapidly identifying potential leakages at the district meter area (DMA) level. The framework involves training a domain-informed variational autoencoder (VAE) for real-time dimensionality reduction of water flow time series data and developing a two-dimensional surrogate latent variable (LV) mapping which sufficiently and efficiently captures the distinct characteristics of leakage and regular (non-leakage) flow. The domain-informed training employs a novel loss function that ensures a distinct but regulated LV space for the two classes of flow groupings (i.e., leakage and non-leakage). Subsquently, a binary SVM classifier is used to provide a hyperplane for separating the two classes of LVs corresponding to the flow groupings. Hence, the proposed framework can be efficiently utilised to classify the incoming flow as leakage or non-leakage based on the encoded surrogates LVs of the flow time series using the trained VAE encoder. The framework is trained and tested on a dataset of over 2000 DMAs in North Yorkshire, UK, containing water flow time series recorded at 15-minute intervals over one year. The framework performs exceptionally well for both regular and leakage water flow groupings with a classification accuracy of over 98 % on the unobserved test datase

Machine-Learning-Based Health Monitoring and Leakage Management of Water Distribution Systems

The reduction of pipe leakage is one of the top priorities for water companies, with many investing in greater sensor coverage to improve the forecasting of flow and detection of leaks. The majority of research on this topic is focused on leakage detection through the analysis of sensor data from district metered areas (DMAs), with the aim of identifying bursts after occurrence. This study is a step towards development of ‘self-healing’ water infrastructure systems. In particular, the concepts of machine-learning (ML) and deep-learning (DL) are applied to the forecasting of water flow in DMAs at various temporal scales, thereby aiding in the health monitoring of water distribution systems.  This study uses a dataset for ~2500 DMAs in Yorkshire, containing flow time-series recorded at every 15-minute interval over the period of a year. Firstly, the method of isolation forests is used to identify anomalies in the dataset which are verified as corresponding to entries in water mains repair log, indicating the occurrence of bursts. Going beyond leakage detection, this research proposes a hybrid framework of DL models - such as recurrent neural networks (RNNs) and transformer neural networks) - and state-space ML algorithms - such as Kalman filter and autoregressive integrated moving average (ARIMA). The ML algorithms are trained to forecast the stationary component of the expected flow patterns in real-time, which is then combined (through Bayesian updating) with the non-stationary component obtained from DL models. As well as providing expected day-to-day flow demands, this framework aims to issue sufficient early warning for any upcoming anomalous flow or possible leakages. For a given forecast period, the framework can be used to compute the probability of flow exceeding a pre-defined threshold, thus allowing decisions to be made regarding any necessary interventions. This can inform targeted repair strategies which best utilise resources to minimise leakage and disruptions by addressing both detected and predicted burst events

Machine-Learning-Based Health Monitoring and Leakage Management of Water Distribution Systems

The reduction of pipe leakage is one of the top priorities for water companies, with many investing in greater sensor coverage to improve the forecasting of flow and detection of leaks. The majority of research on this topic is focused on leakage detection through the analysis of sensor data from district metered areas (DMAs), with the aim of identifying bursts after occurrence. This study is a step towards development of ‘self-healing’ water infrastructure systems. In particular, the concepts of machine-learning (ML) and deep-learning (DL) are applied to the forecasting of water flow in DMAs at various temporal scales, thereby aiding in the health monitoring of water distribution systems.  This study uses a dataset for ~2500 DMAs in Yorkshire, containing flow time-series recorded at every 15-minute interval over the period of a year. Firstly, the method of isolation forests is used to identify anomalies in the dataset which are verified as corresponding to entries in water mains repair log, indicating the occurrence of bursts. Going beyond leakage detection, this research proposes a hybrid framework of DL models - such as recurrent neural networks (RNNs) and transformer neural networks) - and state-space ML algorithms - such as Kalman filter and autoregressive integrated moving average (ARIMA). The ML algorithms are trained to forecast the stationary component of the expected flow patterns in real-time, which is then combined (through Bayesian updating) with the non-stationary component obtained from DL models. As well as providing expected day-to-day flow demands, this framework aims to issue sufficient early warning for any upcoming anomalous flow or possible leakages. For a given forecast period, the framework can be used to compute the probability of flow exceeding a pre-defined threshold, thus allowing decisions to be made regarding any necessary interventions. This can inform targeted repair strategies which best utilise resources to minimise leakage and disruptions by addressing both detected and predicted burst events

Flow Forecasting for Leakage Burst Prediction in Water Distribution Systems using Long Short-Term Memory Neural Networks and Kalman Filtering

Reducing pipe leakage is one of the top priorities for water companies, with many investing in higher quality sensor coverage to improve flow forecasting and detection of leaks. Most research on this topic is focused on leakage detection through the analysis of sensor data from district metered areas (DMAs), aiming to identify bursts after their occurrence. This study is a step towards the development of ‘self-healing’ water infrastructure systems. In particular, machine learning and deep learning-based algorithms are applied to forecasting the anomalous water flow experienced during bursts (new leakage) in DMAs at various temporal scales, thereby aiding in the health monitoring of water distribution systems. This study uses a dataset for over 2,000 DMAs in Yorkshire, containing flow time series recorded at 15-minute intervals over one year. Firstly, the method of isolation forests is used to identify anomalies in the dataset, which are verified as corresponding to entries in the water mains repair log, indicating the occurrence of bursts. Going beyond leakage detection, this research proposes a hybrid deep learning framework named FLUIDS (Forecasting Leakage and Usual flow Intelligently in Distribution Systems). A recurrent neural network (RNN) is used for mean flow forecasting, which is then combined with forecasted residuals obtained through real-time Kalman filter. While providing expected day-to-day flow demands, this framework also aims to issue sufficient early warning for any upcoming anomalous flow or possible leakages. For a given forecast period, the FLUIDS framework can be used to compute the probability of flow exceeding a pre-defined threshold, thus allowing decisions to be made regarding any necessary interventions. This can inform targeted repair strategies that best utilize resources to minimize leakage and disruptions by addressing detected and predicted burst events. The proposed FLUIDS framework is statistically assessed and compared against state-of-practice minimum night flow (MNF) methodology. Finally, it is concluded that the framework performs well on the unobserved test dataset for both regular and leakage water flows

Flow forecasting for leakage burst prediction in water distribution systems using long short-term memory neural networks and Kalman filtering

Reducing pipe leakage is one of the top priorities for water companies, with many investing in higher quality sensor coverage to improve flow forecasting and detection of leaks. Most research on this topic is focused on leakage detection through the analysis of sensor data from district metered areas (DMAs), aiming to identify bursts after their occurrence. This study is a step towards the development of ‘self-healing’ water infrastructure systems. In particular, machine learning and deep learning-based algorithms are applied to forecasting the anomalous water flow experienced during bursts (new leakage) in DMAs at various temporal scales, thereby aiding in the health monitoring of water distribution systems. This study uses a dataset of over 2,000 DMAs in North Yorkshire, UK, containing flow time series recorded at 15-minute intervals for a period of one year. Firstly, the method of isolation forests is used to identify anomalies in the dataset, which are cross referenced with entries in the water mains repair log, indicating the occurrence of bursts. Going beyond leakage detection, this research proposes a hybrid deep learning framework named FLUIDS (Forecasting Leakage and Usual flow Intelligently in water Distribution Systems). A recurrent neural network (RNN) is used for mean flow forecasting, which is then combined with forecasted residuals obtained through real-time Kalman filtering. While providing expected day-to-day flow demands, this framework also aims to issue sufficient early warning for any upcoming anomalous flow or possible leakages. For a given forecast period, the FLUIDS framework can be used to compute the probability of flow exceeding a pre-defined threshold, thus allowing decision-making for any necessary interventions. This can inform targeted repair strategies that best utilize resources to minimize leakages and disruptions. The FLUIDS framework is statistically assessed and compared against the state-of-practice minimum night flow (MNF) methodology. Based on the statistical analyses, it is concluded that the proposed framework performs well on the unobserved test dataset for both regular and leakage water flows.</p

Put This in Your Pipe and Smoke it : An Evaluation of Tobacco Pipe Stem Dating Methods

There are currently three formula dating techniques available to archaeologists studying 17th and 18th century sites using imported English clay tobacco pipe stems based on Harrington's histogram of time periods; Binford's linear formula, Hanson's formulas and the Heighton and Deagan formula. Pipe stem bore diameter data were collected from 26 sites in Maryland, Virginia, North Carolina and South Carolina in order to test the accuracy and utility of the three formula dating methods. Of the formulas, the Heighton and Deagan proved to be the most accurate, producing formula mean dates closest to the dates assigned to the sites using other dating techniques. It was also determined that all three formula dating methods work better in Maryland and Virginia than in North and South Carolina. Other aspects of pipe stem dating were explored in this paper including regional consumption patterns and the influences Dutch pipes have on formula dating. These questions were addressed specifically on sites from the Chesapeake. This analysis supports recent assertions that the Chesapeake should be split into two sub-regions, the Upper and Lower Chesapeake.  M.A