Francis Gildart Ruffin: A Jeffersonian Agrarian in the Old South and New Virginia, 1816 - 1892

For over forty years, Francis Frank Gildart Ruffin derived his livelihood from slave labor and agricultural pursuits. Like Jefferson, Ruffin believed that the nation should be based on the industry of small farmers. Once the Civil War began, he served the Confederacy on the staff of the Commissary Department. Following the war, Ruffin actively engaged in politics in the Old Dominion, though he never sought office. He became associated with the Readjuster party in Virginia. Its goal was to readjust or reduce the state\u27s debt, which was to be particularly beneficial to farmers. Ruffin was an original Readjuster, but later broke with the party believing that it had forsaken an agricultural emphasis in favor of big business and the black vote. As Virginia became industrial, Ruffin felt that the state would ultimately be ruined. He wrote political pamphlets and actively campaigned to return Virginia to her pre-war disposition. This did not occur, but Ruffin never stopped trying to return the Commonwealth to the traditions of his youth

Redistricting and the Origins of the Good Faith Presumption

Evidentiary and substantive presumptions are a well-established tool that courts employ when addressing a variety of legal problems. These presumptions streamline litigation, avoid debate over minor disputes, and ensure that the most important issues can be addressed. But these same presumptions just as frequently close the courthouse doors on meritorious claims and preemptively shut down litigation where a disputed issue exists. One area this occurs is redistricting. In the last fifty years, the Supreme Court of the United States has expanded its substantive presumptions in redistricting matters to permit facially discriminatory redistricting plans. By invoking the presumption of good faith, the Supreme Court insulates state legislators from their constitutional obligations on dubious grounds. Invoking a substantive presumption—especially in redistricting—lacks any constitutional, doctrinal, or historical basis. To draw this conclusion, this Article reviews the doctrine, history, and tradition of good faith in the Supreme Court of the United States. It explains how the Court’s understanding of good faith in redistricting matters reflects a significant departure from its good faith jurisprudence—and seems to have been created by accident. It then harmonizes the Court’s earlier good faith doctrines and explains how a substantive presumption for state-based redistricting contravenes the structure, context, and purpose of the Reconstruction Amendments and Constitution itself. To remedy this incongruous application, this Article examines the constitutional source of a substantive good faith presumption and where (and by whom) it may be properly invoked. By understanding good faith through these parameters, this Article explores the true constitutional interests that a presumption of good faith vindicates

A Jacksonian Theory of Estoppel in IP Litigation Against the United States

As an intellectual property infringer, the federal government occupies a unique position as both the entity that approved the infringed patent or trademark and an entity capable of arguing for its invalidity. By arguing for invalidity, the federal government assumes that it should be exempt from the traditional rules of procedural estoppel. Indeed, the government believes that even though it granted intellectual property rights (after careful research and deliberation and following the express review of an officer appointed with the advice and consent of the Senate), it should have a second bite at the apple to invalidate a patent or trademark when it risks liability. Ultimately, permitting the government to argue for inconsistent positions risks making intellectual property litigation—and the government itself—unpredictable and untrustworthy. To remedy this imbalance, this Article advocates for a rethinking of estoppel through Justice Jackson’s Youngstown Sheet concurrence when an examining attorney acts pursuant to unambiguous authority granted by Congress. In doing so, this position equalizes the playing field during litigation that heavily favors the federal government

Federalizing Professionalism: How the Seventh Circuit Rewrote Indiana Ethics Law

While sitting in diversity and adjudicating a federal question, federal courts are occasionally tasked with handling ethical questions about attorney conduct. Despite the frequency that such issues emerge, there are neither uniform federal ethics rules for attorneys nor a consensus amongst the circuits on how to apply (or even use) state ethics rules. Some federal courts have adopted the ethics rules of the state in which they sit. Others have no rules at all. In the Seventh Circuit, federal courts have adopted and interpreted the ethics rules of the states where they sit, independent from state courts and commissions that regulate the profession.In Watkins v Trans Union, the Seventh Circuit departed from principles of federalism, comity, and parity to chart its own path for professional ethics. In doing so, it displaced a role traditionally left to the states without any meaningful explanation as to why. As such, this Article argues that Watkins was an unforced error, unjustified by precedent or necessity. This error will continue to compound as attorneys are forced to balance different or contradictory ethical obligations—even without leaving their own district. There is a simple solution. Instead of this unneeded complexity, state court ethical principles should set the floor on permissible ethical conduct in federal court

Análisis de competencia universidades en Colombia.

La pasantía con la Universidad de la Rioja consiste en escoger, según el ranking de universidades en Colombia las mejores universidades con modalidad “Online” y las dos mejores universidades en todo el ranking; adicional a eso, todas las universidades internacionales que tienen presencia en el país también con modalidad “Online”. Todas esas universidades tienen que tener el programa de administración de empresas y/o ingeniería informática y luego consignar todo en un archivo con los puntos principales de cada universidad como lo son el costo de la carrera, la cantidad de estudiantes que tiene la universidad, los puntos fuertes de dicha universidad y la cantidad de programas que tiene en general.The internship with the University of La Rioja consists in choosing, according to the ranking of universities in Colombia, the best universities with "Online" modality and the two best universities in the entire ranking; additional to that, all the international universities that have a presence in the country also with "Online" modality. All these universities have to have the program of business administration and/or computer engineering and then record everything in a file with the main points of each university such as the cost of the career, the number of students that the university has, the strengths of said university and the amount of programs it has in general

Event-Related Potential N100 Vs. N170 Wave Results Comparison On Driving Alertness

Driver’s attention, especially during long rides, is very crucial to avoid road accidents, which may lead to injuries and fatalities on the roadway. However, with modern technologies, the decline in driver’s attention can be investigated through the electrical activity of the brain. Event-Related Potential (ERP) is the electrophysiological brain response measurement that related to the sensory, cognitive and motor events. By using simple averaging techniques, ERPs can give reliable result to measure the attentiveness of the driver during driving. In this paper, N100 and N170 wave results were presented, which obtained from temporal and occipital lobes respectively and been compared to measure the driver’s attention. In term of attentiveness difference percentage, it is 0.09% and 38% differences were observed from N100 and N170 wave results respectively. From the results, it clearly can be seen that using N170 wave from occipital lobe is more significant to measure the driver’s alertness compared to N100 wave that recorded from the temporal lobe

N170 Wave Amplitude Analysis on Driving Performance on Highway Road

Attention in the physiological definition is taking possession of the mind in one of several simultaneously events of thoughts which implies withdrawing some things to deal effectively with other. In driving particularly, attention is essential to keep track of the driver’s vigilance to avoid the road accident. In this paper, analysis of the driver’s attention is done through their driving performance from both of Electroencephalographic (EEG) amplitude and accident score. During the driving experiment, two stimulations will be given to the subjects which are silent environment (in certain dB) and listening to the live streaming radio. The results show that when listening to the radio, the driving performance is improved and the score of the accident is reduced as well. This figure gave a concrete justification that driver’s attention able to maintain or even increase when the stimulation is triggered

Development of vocabulary learning application by using machine learning technique

Nowadays an educational mobile application has been widely accepted and opened new windows of opportunity to explore. With its flexibility and practicality, the mobile application can promote learning through playing with an interactive environment especially to the children. This paper describes the development of mobile learning to help children above 4 years old in learning English and Arabic language in a playful and fun way. The application is developed with a combination of Android Studio and the machine learning technique, TensorFlow object detection API in order to predict the output result. Developed application namely “LearnWithIman” has successfully been implemented and the results show the prediction of application is accurate based on the captured image with the list item. The inclusion of the user database for lesson tracking and new lesson will be added for improvement in the future

Signal processing for abnormalities estimation analysis

Pneumonia, asthma, sudden infant death syndrome (SIDS), and the most recent epidemic, COVID-19, are the most common lung diseases associated with respiratory difficulties. However, existing health monitoring systems use large and in-contact devices, which causes an uncomfortable experience. The difficulty in acquiring breathing signals for non-stationary individuals limits the use of ultra-wideband radar for breathing monitoring. This is due to ineffective signal clutter removal and body movement removal algorithms for collecting accurate breathing signals. This paper proposes a breathing signal analysis for non-contact physiological monitoring to improve quality of life. The radar-based sensors are used for collecting the breathing signal for each subject. The processed signal has been analyzed using continuous wavelet transform (CWT) and wavelet coherence with the Monte Carlo method. The finding shows that there is a significant difference between the three types of breathing patterns; normal, high, and slow. The findings may provide a comprehensive framework for processing and interpreting breathing signals, resulting in breakthroughs in respiratory healthcare, illness management, and overall well-being

The Comparative Study of Deep Learning Neural Network Approaches for Breast Cancer Diagnosis

Breast cancer is one of the life threatening cancer that leads to the most death due to cancer among the women. Early diagnosis might help to reduce mortality. Thus, this research aims to study on different approaches of the deep learning neural network model for breast cancer early detection for better prognosis. The performance of deep learning approaches such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) are evaluated using the dataset from the University of Wisconsin. The findings show ANN achieved high accuracy of 99.9 % compared to others in detecting breast cancer. ANN is able to deliver better results with the provided dataset. However, more improvement needed for better performance to ensure that the approach used is reliable enough for breast cancer early diagnosis