273 research outputs found
Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.
The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design
Welfare of broilers on farm
This Scientific Opinion considers the welfare of domestic fowl (Gallus gallus) related to the production of meat (broilers) and includes the keeping of day-old chicks, broiler breeders, and broiler chickens. Currently used husbandry systems in the EU are described. Overall, 19 highly relevant welfare consequences (WCs) were identified based on severity, duration and frequency of occurrence: 'bone lesions', 'cold stress', 'gastro-enteric disorders', 'group stress', 'handling stress', 'heat stress', 'isolation stress', 'inability to perform comfort behaviour', 'inability to perform exploratory or foraging behaviour', 'inability to avoid unwanted sexual behaviour', 'locomotory disorders', 'prolonged hunger', 'prolonged thirst', 'predation stress', 'restriction of movement', 'resting problems', 'sensory under- and overstimulation', 'soft tissue and integument damage' and 'umbilical disorders'. These WCs and their animal-based measures (ABMs) that can identify them are described in detail. A variety of hazards related to the different husbandry systems were identified as well as ABMs for assessing the different WCs. Measures to prevent or correct the hazards and/or mitigate each of the WCs are listed. Recommendations are provided on quantitative or qualitative criteria to answer specific questions on the welfare of broilers and related to genetic selection, temperature, feed and water restriction, use of cages, light, air quality and mutilations in breeders such as beak trimming, de-toeing and comb dubbing. In addition, minimal requirements (e.g. stocking density, group size, nests, provision of litter, perches and platforms, drinkers and feeders, of covered veranda and outdoor range) for an enclosure for keeping broiler chickens (fast-growing, slower-growing and broiler breeders) are recommended. Finally, 'total mortality', 'wounds', 'carcass condemnation' and 'footpad dermatitis' are proposed as indicators for monitoring at slaughter the welfare of broilers on-farm
Chapter 34 - Biocompatibility of nanocellulose: Emerging biomedical applications
Nanocellulose already proved to be a highly relevant material for biomedical
applications, ensued by its outstanding mechanical properties and, more importantly, its biocompatibility. Nevertheless, despite their previous intensive
research, a notable number of emerging applications are still being developed.
Interestingly, this drive is not solely based on the nanocellulose features, but also
heavily dependent on sustainability. The three core nanocelluloses encompass
cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial nanocellulose (BNC). All these different types of nanocellulose display highly interesting biomedical properties per se, after modification and when used in
composite formulations. Novel applications that use nanocellulose includewell-known areas, namely, wound dressings, implants, indwelling medical
devices, scaffolds, and novel printed scaffolds. Their cytotoxicity and biocompatibility using recent methodologies are thoroughly analyzed to reinforce their
near future applicability. By analyzing the pristine core nanocellulose, none
display cytotoxicity. However, CNF has the highest potential to fail long-term
biocompatibility since it tends to trigger inflammation. On the other hand, neverdried BNC displays a remarkable biocompatibility. Despite this, all nanocelluloses clearly represent a flag bearer of future superior biomaterials, being
elite materials in the urgent replacement of our petrochemical dependence
A review of commercialisation mechanisms for carbon dioxide removal
The deployment of carbon dioxide removal (CDR) needs to be scaled up to achieve net zero emission pledges. In this paper we survey the policy mechanisms currently in place globally to incentivise CDR, together with an estimate of what different mechanisms are paying per tonne of CDR, and how those costs are currently distributed. Incentive structures are grouped into three structures, market-based, public procurement, and fiscal mechanisms. We find the majority of mechanisms currently in operation are underresourced and pay too little to enable a portfolio of CDR that could support achievement of net zero. The majority of mechanisms are concentrated in market-based and fiscal structures, specifically carbon markets and subsidies. While not primarily motivated by CDR, mechanisms tend to support established afforestation and soil carbon sequestration methods. Mechanisms for geological CDR remain largely underdeveloped relative to the requirements of modelled net zero scenarios. Commercialisation pathways for CDR require suitable policies and markets throughout the projects development cycle. Discussion and investment in CDR has tended to focus on technology development. Our findings suggest that an equal or greater emphasis on policy innovation may be required if future requirements for CDR are to be met. This study can further support research and policy on the identification of incentive gaps and realistic potential for CDR globally
The Adirondack Chronology
The Adirondack Chronology is intended to be a useful resource for researchers and others interested in the Adirondacks and Adirondack history.https://digitalworks.union.edu/arlpublications/1000/thumbnail.jp
Computer Assisted Image Labeling for Object Detection Using Deep Learning
Deep learning-based object detectors have shown outstanding performance with state-of-the-art results on public benchmarks. However, they typically consist of millions of parameters and require a large number of training samples to tune these parameters appropriately. These samples are labeled by human annotators, which is a tedious, time-consuming, and expensive process. Moreover, object detectors have high computational costs both for the training and inference phase. This dissertation considers these two aspects of training and deploying deep learning object detectors.
First, we study data labeling for the training phase and the robustness of object detectors towards label noise. We classify possible label noise scenarios in 2D object detection and study the sensitivity of one-stage object detectors to label noise in the training phase. We then propose methods for efficient bounding box annotation by utilizing human-machine collaboration. Extensive experiments have been done to study an efficient and effective bounding box annotation scheme for deep learning object detectors. Additionally, we created an easy-to-use, medium-sized, multiclass, fully labeled object detection dataset from indoor premises and released it publicly for registration-free use.
Second, we study the practical problem of object detection network deployment with an efficient implementation of the object detection network for applications such as facial analysis, human detection and tracking, and the path prediction of mobile objects on resource-limited devices. We implemented object detection in an image processing pipeline integrating with other tasks for multiple applications and studied the optimal design process. We present the details of the system-level design to incorporate a multitasking network efficiently with the proper system architecture design
3-я Міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні аспекти (ICSF 2022) 24-27 травня 2022 року, м. Кривий Ріг, Україна
Матеріали 3-ої Міжнародної конференції зі сталого майбутнього: екологічні, технологічні, соціальні та економічні аспекти (ICSF 2022) 24-27 травня 2022 року, м. Кривий Ріг, Україна.Proceedings of the 3rd International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2022) 24-27 May 2022, Kryvyi Rih, Ukraine
2022-2023 Graduate School Catalog
Graduate students from more than 67 counties are providing outstanding leadership during the pandemic, as they conduct vital research to inform public health, contribute to the greater good, and stimulate the economy. Their scholarship spans 140 programs - from biomedical engineering to business administration, from history to horticulture, and from marine sciences to music performance
Haptics: Science, Technology, Applications
This open access book constitutes the proceedings of the 13th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2022, held in Hamburg, Germany, in May 2022. The 36 regular papers included in this book were carefully reviewed and selected from 129 submissions. They were organized in topical sections as follows: haptic science; haptic technology; and haptic applications
Real-Time Detection of Vine Trunk for Robot Localization Using Deep Learning Models Developed for Edge TPU Devices
The concept of the Internet of Things (IoT) in agriculture is associated with the use of high-tech devices such as robots and sensors that are interconnected to assess or monitor conditions on a particular plot of land and then deploy the various factors of production such as seeds, fertilizer, water, etc., accordingly. Vine trunk detection can help create an accurate map of the vineyard that the agricultural robot can rely on to safely navigate and perform a variety of agricultural tasks such as harvesting, pruning, etc. In this work, the state-of-the-art single-shot multibox detector (SSD) with MobileDet Edge TPU and MobileNet Edge TPU models as the backbone was used to detect the tree trunks in the vineyard. Compared to the SSD with MobileNet-V1, MobileNet-V2, and MobileDet as backbone, the SSD with MobileNet Edge TPU was more accurate in inference on the Raspberrypi, with almost the same inference time on the TPU. The SSD with MobileDet Edge TPU achieved the second-best accurate model. Additionally, this work examines the effects of some features, including the size of the input model, the quantity of training data, and the diversity of the training dataset. Increasing the size of the input model and the training dataset increased the performance of the model.info:eu-repo/semantics/publishedVersio
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