The complexities of managing historic buildings with BIM

Purpose The adoption of building information modelling (BIM) in managing built heritage is an exciting prospect, but one that presents complexities additional to those of modern buildings. If challenges can be identified and overcome, the adoption of historic BIM (HBIM) could offer efficiencies in how heritage buildings are managed. Design/methodology/approach Using Durham Cathedral as a case study, we present the workflows applied to create an asset information model to improve the way this unique UNESCO World Heritage Site is managed, and in doing so, set out the challenges and complexities in achieving an HBIM solution. Findings This study identifies the need for a better understanding of the distinct needs and context for managing historic assets, and the need for heritage information requirements (HIR) that reflect this. Originality/value This study presents first-hand findings based on a unique application of BIM at Durham Cathedral, a UNESCO World Heritage Site. The study provides a better understanding of the challenges and drivers of HBIM adoption across the heritage sector and underlines the need for information requirements that are unique to historical buildings/assets to deliver a coherent and relevant HBIM approach

Alien Registration- Kelly, Leo J. (Calais, Washington County)

https://digitalmaine.com/alien_docs/2591/thumbnail.jp

Evaluation of Teat Coverage Persistency and Teat Health for Four New Prototype Dry Period Persistent Barrier Teat Dips

Mastitis research has shown that 40–50% of intramammary infections (IMI) are contracted during the dry or non-lactating period with the greatest percentages of these occurring during the first and last two weeks of the dry period. The ability to develop and apply external persistent barrier teat dip products (like a liquid bandage) that can persist for these 1 week periods could decrease IMI, thus improving animal health and performance, and product quality and safety. The objective of this study was to evaluate 4 new prototype faster drying persistent barrier dry cow teat dips, with particular interest and comparisons of dip persistency in providing teat end protection, and overall teat end and skin health. Dipping with the new fast drying dip (A, B, C, D) resulted in similar excellent teat end and skin health. Overall persistency was lower (only 5-35% teats protected for 3days), especially compared to commercial T-Hexx Dry. The stranding and stringing of these dips upon drying created problems with the dip sticking to legs and/ or being pulled off easier. Dip C did not show this and had higher persistency @ day 2, but all dips showed inferior persistency @ day 3 (similar to other fast dry dip trials) compared to commercially available T-Hexx Dry teat dip product. Overall, the new experimental dips were inferior to the commercial T-Hexx Dry product!Also, every future trial should have commercial T-Hexx Dry incorporated into it so direct comparisons within cow and trial can be made

You Broke My Heart To Pass The Time Away.

https://digitalcommons.library.umaine.edu/mmb-vp/2786/thumbnail.jp

Menu Recommendation system using Machine Learning

Developing a recommendation menu system for restaurants based on the restaurant data and/or city food purchase data to help and change the way restaurants build their menu. Using Data Analysis and Machine Learning to build a project that aims to solve the problem of restaurants and chefs when it comes to preparing menus, the latter with ingredients and dishes that encourage their customers to order more, come back and recommend the restaurant. Helping chefs to create dishes for their restaurants with more accuracy and higher probability to be ordered by their customers. The project will cover tools to build the predictions, the project plan, collect datasets, manipulate data and evaluate the aspects of the situation. The main business goal of our project is to predict what ingredients customers would like to eat and, from that, give restaurants ingredient suggestions to create their next menu. By providing an efficient ingredients decision maker, it will simplify the way menus are elaborated and improve overall customers satisfaction. Another motivating factor in choosing this project was its potential to help society tackle the huge problem of food waste and the inequalities this entails

For those who don\u27t know (how) to ask: Building a dataset of technology questions for digital newcomers

While the rise of large language models (LLMs) has created rich new opportunities to learn about digital technology, many on the margins of this technology struggle to gain and maintain competency due to lexical or conceptual barriers that prevent them from asking appropriate questions. Although there have been many efforts to understand factuality of LLM-created content and ability of LLMs to answer questions, it is not well understood how unclear or nonstandard language queries affect the model outputs. We propose the creation of a dataset that captures questions of digital newcomers and outsiders, utilizing data we have compiled from a decade\u27s worth of one-on-one tutoring. In this paper we lay out our planned efforts and some potential uses of this dataset