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  • AMERICAN HISTORY – LEGAL LESSONS LEARNED FOR EMERGENCY RESPONDERS

    Type:
    Document
    Description/Abstract:
    Modules to educate Officers in Officer I, II, III and Officer IV classes.
    Creator/Author:
    Bennett, Lawrence
    Submitter:
    Lawrence Bennett
    Date Uploaded:
    08/18/2020
    Date Modified:
    12/11/2025
    Date Created:
    2020-08
    License:
    Attribution-NonCommercial 4.0 International

  • FIRE & EMS LAW – RECENT CURRENT EVENTS

    Type:
    Document
    Description/Abstract:
    CHAPTERS 1- 18: Supplement to Prof. Larry Bennett’s textbook, FIRE SERVICE LAW (SECOND EDITION), Jan. 2017:  http://www.waveland.com/browse.php?t=708 SEE ALSO RECENT PUBLISHED COURT DECISIONS [NOV. 2018-PRESENT] FOR 18 CHAPTERS: From Prof. Bennett’s Fire & EMS Law monthly newsletters [send him e-mail if wish to receive]  https://scholar.uc.edu/concern/documents/n870zs553?locale=en
    Creator/Author:
    Bennett, Lawrence
    Submitter:
    Lawrence Bennett
    Date Uploaded:
    07/27/2021
    Date Modified:
    12/09/2025
    Date Created:
    2021-07
    License:
    Attribution 4.0 International

  • FIRE & EMS LAW - RECENT CASE SUMMARIES

    Type:
    Document
    Description/Abstract:
    Monthly newsletter for Fire and EMS.
    Creator/Author:
    Bennett, Lawrence
    Submitter:
    Lawrence Bennett
    Date Uploaded:
    11/02/2020
    Date Modified:
    12/04/2025
    Date Created:
    2020-11
    License:
    Attribution 4.0 International

  • Q2000 Deep Learning Model Package

    Type:
    Dataset
    Description/Abstract:
    Q2000 Deep Learning Model Package This Technical Resource Bundle provides the Q200 Deep Learning model for open access download and use. The Q2000 DL model is built to detect Maya structures in Lidar data visualized at one meter per pixel. Currently, this repository contains the ESRI ArcGIS compatible DL model in the format .dlpk. We expect to convert the model in its current form into Pytorch (.pt) and TensorFlow (.h5) formats to incude them also here for user access. The Q2000.dlpk file was created in 2025 at University of Cincinnati by Benjamin Britton, using ArcGIS Pro v.3.3 with data from the NASA Ames G-LiHT. It is intended as an experiment to evaluate the practicability of creating a broadscale deep learning model that can be used effectively to identify Maya structures in Lidar data across the length and breadth of the Yucatan Peninsula. The Q2000 model is the subject of an article, a draft of which is also included in this dataset, called "Evaluating Broadscale Deep Learning for Maya Settlement Detection in G-LiHT Lidar" which examines the process and rationale of the model development in detail. The article has been accepted for publication and this site is included in that article with a link to this permanent (DOI) publication site. To use the model with ArcGIS Pro, use a Lidar dataset converted to a 1m/pixel DEM file and visualized as a 3-channel RGB Hillshade or other customized visualization as source input. -Using the ArcGIS Pro Spatial Analyst Extension, the geoprocessing tool called "Detect Object Using Deep Learning" may be invoked. -For Input Raster, add your Lidar visualization (a Hillshade visualization might be easiest for most users). -For Output Detected Objects, specify a new Layer name, and this will be the layer on which the detections will be recorded and displayed. -For Model Definition, use Q2000.dlpk. -Unless you want to run "arguments", you can leave the Arguments boxes as their Default. -I suggest checking the box (On) for Non Max suppression because it will reduce the amount of overlapping detections if target objects are located very close to each other, and I suggest a Non-Maximum Suppression (NMS) ratio of 0.5. This will tend to suppress detections that overlap by more than 50 percent. -I suggest you use Pixel Space unchecked (Off), since it is for an unrelated sort of object detection. -Before you click run, open the "Environments" tab (at the top of the window, next to the "Parameters" tab). Leave all the settings at their defaults there - except scroll down to the bottom of Parameters tab to the section called "Processor Type", pull down the Processor type pull-down, and choose GPU (it is set to CPU by default). -Then click Run and it will generate a new layer showing its detections as bounding boxes around target objects. You can see details for each detection by opening the Attribute Table on the new layer. You can see a screen capture of such a configuration in the image called Q200DemoScreenCap.jpg, included in this site's dataset, showing a detection on G-LiHT transect Yucatan_South_GLAS_395 near Pixoyal, with a detection of a Maya staircase highlighted on the display, and its corresponding information highlighted in the Attribute Table for it.
    Creator/Author:
    Britton, Benjamin
    Submitter:
    Benjamin Britton
    Date Uploaded:
    08/23/2025
    Date Modified:
    11/13/2025
    Date Created:
    March 15, 2025
    License:
    Public Domain Mark 1.0

  • Walter E. Langsam's Presentation of Desjardins

    User Collection
    Type:
    Collection
    Description/Abstract:
    This is a digitized collection of slides that were presented by Walter E. Langsam on the Cincinnati architecture firm Desjardins and Hayward
    Creator/Author:
    Meyer, Elizabeth A. and Langsam, Walter E.
    Submitter:
    Elizabeth A. Meyer
    License:
    All rights reserved
    0Collections
    119Works

  • EMS LAW - Legal, Political & Regulatory Environment of EMS

    Type:
    Document
    Description/Abstract:
    Case summaries involving EMS cases.
    Creator/Author:
    Bennett, Lawrence
    Submitter:
    Lawrence Bennett
    Date Uploaded:
    07/10/2020
    Date Modified:
    10/29/2025
    Date Created:
    2020-07
    License:
    Attribution-NonCommercial 4.0 International

  • Thomas Scherz

    Type:
    Generic Work
    Description/Abstract:
    sts
    Creator/Author:
    Langsam, Walter
    Submitter:
    Walter Langsam
    Date Uploaded:
    10/25/2025
    Date Modified:
    10/25/2025
    License:
    Attribution-ShareAlike 4.0 International

  • Test after netapp

    Type:
    Image
    Description/Abstract:
    test
    Creator/Author:
    Scherz, Thomas
    Submitter:
    Thomas Scherz
    Date Uploaded:
    09/29/2025
    Date Modified:
    09/29/2025
    License:
    Attribution 4.0 International

  • How can a mobile living wall improve well-being and emotional health in palliative care settings?

    Type:
    Document
    Description/Abstract:
    This capstone explores how a calming mobile living wall can improve the well-being and emotional health of individuals with ALS in palliative care, as well as those who care for them. This project examines a mobile green wall as an adaptable solution that introduces the benefits of green design into various spaces within a care facility. The goals of this project are (1) to better understand how mobile green walls enhance users’ senses, thus reducing anxiety and influencing mood and stress and (2) to gain insight into a mobile green wall’s overall impact in palliative care environments.
    Creator/Author:
    Appel, Katie
    Submitter:
    Katie Appel
    Date Uploaded:
    04/09/2025
    Date Modified:
    09/23/2025
    Date Created:
    2025-04-09
    License:
    Attribution 4.0 International

  • Varieties of International Cyber Strategies (VoICS): Text Analysis of National Cybersecurity Documents

    Type:
    Dataset
    Description/Abstract:
    Varieties of International Cyber Strategies (VoICS): Text Analysis of National Cybersecurity Documents is a project that compares and contrasts the three main approaches to conceptualize national cybersecurity strategies (NSS): deterrence, norm-based approach (NBA) and cyber persistence engagement (CPE). Scholars and policymakers have initially conceptualized NSS in terms of deterrence or NBA. More recent academic research has demonstrated that these frameworks are inadequate for cyber space. As a result, Cyber Persistence Engagement (CPE) emerged as a third option. The first version (1.0) of the VoICS database on National Cybersecurity Strategies focuses on nations in Europe and North America and includes a total of 77 NCS of the states in the North Atlantic Area—NATO allies, EU members and Switzerland—released from 2003 until the end of 2023. The current 1.2 version includes 83 strategies from 36 allies and partners. It consists of 27 variables, including country and strategy identifiers, EU and NATO membership, their respective accession dates, and total length of the documents. VoICS include eighteen variables representing different measures of relative and absolute weights of the three NSS types—deterrence, NBA and CPE. The text analysis is based on official NSS documents provided by the NATO Cooperative Cyber Defence Centre of Excellence library (2024) and ENISA’s interactive map for National Cyber Security Strategies (2023). Both sources rely on voluntary submission from the member states. Unfortunately, some official documents were not available or accessible or were not listed at all. Authors have used various sources and contacts with a variety of cyber attachés in Brussels to determine if any additional strategies were released and to obtain the missing documents. The 18 text analysis variables compare and contrast the extent to which different NCS are associated with a specific strategy. They represent different frequency scores based either on words, phrases, or words and phrases combined. These calculations are associated with either deterrence, NBA, or CPE in each strategy. The authors have generated respective vocabularies for the three strategic ideas through which each of these approaches are operationalized. We have conducted a text analysis using WordStat text analysis software by Provialis ( https://provalisresearch.com/products/content-analysis-software/). A detailed codebook for NSS Dataset 1.2 along with a NSS Dictionary 1.2 have been included in this collection/ repository. The process of generating vocabulary associated with the three cybersecurity approaches involved several steps. First, upon reviewing the literature, the authors generated independently a list of words and phrases associated with each type of cybersecurity strategy. Second, the authors compared their lists to determine the degree of overlap in vocabulary. Those words and phrases that included in at least two different lists were reviewed and, if there was consensus, were incorporated in the dictionary. Finally, words and phrases which were identified in only one of lists were once again reviewed and, in case there was a consensus among the authors, these were also included in the dictionary. Third, the three vocabularies were updated on several instances when it was unanimously agreed that these words or phrases should be included in the analysis.
    Creator/Author:
    Millard, Matthew; Kovac, Igor, and Ivanov, Ivan Dinev
    Submitter:
    Ivan Ivanov
    Date Uploaded:
    05/12/2025
    Date Modified:
    08/29/2025
    Date Created:
    2025-04-18
    License:
    All rights reserved