Winthrop Farm Excavation Artifacts - Accession 1462

The collection consists of various artifacts obtained from the Winthrop University Farm Excavation conducted during the Fieldwork in Archaeology class directed by Dr. Christina Brooks and Darwin Ramsey, Spring of 2008. The artifacts consist of glass jars and bottles, pots, bowls, building materials, sherds, and other materials found at a dig conducted near the Aragon Cotton Mill in Rock Hill, SC near what is now known as Winthrop Farm. The Aragon Cotton Mill employed 275 operators and the associated mill housing had 450-500 residents. The collection also contains various printed photographs, student papers, display cards, and photo displays from the class exhibit.https://digitalcommons.winthrop.edu/manuscriptcollection_findingaids/2416/thumbnail.jp

Imagining Our Future: Preserving Pennsylvania's Collections

PennsylvaniaSurveyMonkeyFinal version of the preservation plan for the Commonwealth of Pennsylvania, completed in January 2010.Institute of Museum and Library ServicesPennsylvania Historical and Museum Commission, Pennsylvania Federation of Museums and Historical Organization

Evaluation of denoising strategies to address motion-correlated artifacts in resting-state functional magnetic resonance imaging data from the human connectome roject

Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR

Boundary Objects and their Use in Agile Systems Engineering

Agile methods are increasingly introduced in automotive companies in the attempt to become more efficient and flexible in the system development. The adoption of agile practices influences communication between stakeholders, but also makes companies rethink the management of artifacts and documentation like requirements, safety compliance documents, and architecture models. Practitioners aim to reduce irrelevant documentation, but face a lack of guidance to determine what artifacts are needed and how they should be managed. This paper presents artifacts, challenges, guidelines, and practices for the continuous management of systems engineering artifacts in automotive based on a theoretical and empirical understanding of the topic. In collaboration with 53 practitioners from six automotive companies, we conducted a design-science study involving interviews, a questionnaire, focus groups, and practical data analysis of a systems engineering tool. The guidelines suggest the distinction between artifacts that are shared among different actors in a company (boundary objects) and those that are used within a team (locally relevant artifacts). We propose an analysis approach to identify boundary objects and three practices to manage systems engineering artifacts in industry

Artifact-Aware Analogue/Mixed-Signal Front-Ends for Neural Recording Applications

This paper presents a brief review of techniques to overcome the problems associated with artifacts in analog frontends for neural recording applications. These techniques are employed for handling Common-Mode (CM) Differential-Mode (DM) artifacts and include techniques such as Average Template Subtraction, Channel Blanking or Blind Adaptive Stimulation Artifact Rejection (ASAR), among others. Additionally, a new technique for DM artifacts compression is proposed. It allows to compress these artifacts to the requirements of the analog frontend and, afterwards, it allows to reconstruct the whole artifact or largely suppress it.Ministerio de Economía y Empresa TEC2016-80923-