Mapping and analyzing energy use and efficiency in a modified hydroponic shipping container

In urban centers today, vertical farming is becoming a popular alternative to conventional agriculture in an effort to increase local food production and improve urban food security by growing crops using hydroponic methods in controlled environment spaces. More specifically, one vertical method involves growing crops inside refurbished shipping containers, or a “farm-in-a-box” concept, which offers a flexible, mobile, and scalable means of year-round food production in a variety of climates. Despite benefits of producing food locally, some of the concerns associated with these vertical farming systems include high energy consumption from climate control and electric lighting systems as well as expensive capital investments. Therefore, this study investigated the viability and effectiveness of shipping container farms as alternative food systems through analysis of system energy requirements and resulting crop yields. A Modified Hydroponic Shipping Container (MHSC) system was designed, and a Nutrient Film Technique (NFT) hydroponics system was tested by growing lettuce plants and monitoring energy use throughout the growth period. Additionally, theoretical energy use was quantified for one year of production at full scale by modeling energy consumption of major system components. Crop production and energy consumption were assessed using a crop production efficiency metric created to evaluate the ratio of system outputs to inputs. A baseline crop production efficiency value was determined, and scenarios for improving system efficiency from the baseline value were then analyzed. As a result, alternative energy scenarios reduced yearly consumption up to 53 percent from baseline consumption. Improvements to the MHSC design through suggested energy use reduction strategies will allow for the creation of a viable and sustainable alternative food system that is capable of providing local, accessible foods year-round for a variety of urban communities

The Use of Turning and Repositioning Versus Pressure Redistributing Support Surfaces in the Prevention of Pressure Ulcers

Currently, 1.3-3 million adults in the United States are affected by pressure ulcers, costing $37,800 to$70,000 per ulcer (Smith, 2013). This costs the United States 11 billion dollars annually (Smith, 2013). This review’s PICO question is “In hospitalized critically ill patients, how does turning and repositioning every two to four hours compared to the use of pressure redistributing support surfaces prevent the occurrence of pressure ulcers?” For this review, the articles found were rated as excellent (n=7), good (n=2), and fair (n=1). Appendix A shows the critical appraisal of all pertinent articles used. Findings suggest that there is minimal statistically significant evidence that the use of one intervention is more effective than another (pressure redistributing support surfaces versus turning and repositioning every two to four hours) (Bergstrom, 2013; Chou, 2013; Huang, 2013; Manzano, 2013; Manzano, 2014; Rich, 2011b; Smith, 2013). Furthermore, findings indicate that when both interventions are used together, pressure ulcer prevention is increased (Chou, 2013; Rich, 2011a; Smith, 2013)

ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance

Accurately segmenting brain lesions in MRI scans is critical for providing patients with prognoses and neurological monitoring. However, the performance of CNN-based segmentation methods is constrained by the limited training set size. Advanced data augmentation is an effective strategy to improve the model's robustness. However, they often introduce intensity disparities between foreground and background areas and boundary artifacts, which weakens the effectiveness of such strategies. In this paper, we propose a foreground harmonization framework (ARHNet) to tackle intensity disparities and make synthetic images look more realistic. In particular, we propose an Adaptive Region Harmonization (ARH) module to dynamically align foreground feature maps to the background with an attention mechanism. We demonstrate the efficacy of our method in improving the segmentation performance using real and synthetic images. Experimental results on the ATLAS 2.0 dataset show that ARHNet outperforms other methods for image harmonization tasks, and boosts the down-stream segmentation performance. Our code is publicly available at https://github.com/King-HAW/ARHNet.Comment: 9 pages, 4 figures, 3 table

Pregnancy predictors in the fresh cycle using dual trigger protocol

Dual trigger protocol using a combination of GnRH agonist and hCG for final oocyte maturation has been shown to minimize ovarian hyperstimulation syndrome (OHSS) risk without compromising fresh embryo transfer outcomes. Therefore, we sought to determine if any cycle characteristics were associated with predictive of pregnancy outcomes in fresh cycles that utilized this protocol for in-vitro fertilization

Negative differential electrical resistance of a rotational organic nanomotor

A robust, nanoelectromechanical switch is proposed based upon an asymmetric pendant moiety anchored to an organic backbone between two C60 fullerenes, which in turn are connected to gold electrodes. Ab initio density functional calculations are used to demonstrate that an electric field induces rotation of the pendant group, leading to a nonlinear current–voltage relation. The nonlinearity is strong enough to lead to negative differential resistance at modest source–drain voltages

MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation

Accurate stroke lesion segmentation plays a pivotal role in stroke rehabilitation research, to provide lesion shape and size information which can be used for quantification of the extent of the stroke and to assess treatment efficacy. Recently, automatic segmentation algorithms using deep learning techniques have been developed and achieved promising results. In this report, we present our stroke lesion segmentation model based on nnU-Net framework, and apply it to the Anatomical Tracings of Lesions After Stroke (ATLAS v2.0) dataset. Furthermore, we describe an effective post-processing strategy that can improve some segmentation metrics. Our method took the first place in the 2022 MICCAI ATLAS Challenge with an average Dice score of 0.6667, Lesion-wise F1 score of 0.5643, Simple Lesion Count score of 4.5367, and Volume Difference score of 8804.9102. Our code and trained model weights are publicly available at https://github.com/King-HAW/ATLAS-R2-Docker-Submission.Comment: Challenge Report, 1st place in 2022 MICCAI ATLAS Challeng

TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning

Processing of medical images such as MRI or CT presents unique challenges compared to RGB images typically used in computer vision. These include a lack of labels for large datasets, high computational costs, and metadata to describe the physical properties of voxels. Data augmentation is used to artificially increase the size of the training datasets. Training with image patches decreases the need for computational power. Spatial metadata needs to be carefully taken into account in order to ensure a correct alignment of volumes. We present TorchIO, an open-source Python library to enable efficient loading, preprocessing, augmentation and patch-based sampling of medical images for deep learning. TorchIO follows the style of PyTorch and integrates standard medical image processing libraries to efficiently process images during training of neural networks. TorchIO transforms can be composed, reproduced, traced and extended. We provide multiple generic preprocessing and augmentation operations as well as simulation of MRI-specific artifacts. Source code, comprehensive tutorials and extensive documentation for TorchIO can be found at https://github.com/fepegar/torchio. The package can be installed from the Python Package Index running 'pip install torchio'. It includes a command-line interface which allows users to apply transforms to image files without using Python. Additionally, we provide a graphical interface within a TorchIO extension in 3D Slicer to visualize the effects of transforms. TorchIO was developed to help researchers standardize medical image processing pipelines and allow them to focus on the deep learning experiments. It encourages open science, as it supports reproducibility and is version controlled so that the software can be cited precisely. Due to its modularity, the library is compatible with other frameworks for deep learning with medical images.Comment: Submitted to Computer Methods and Programs in Biomedicine. 27 pages, 7 figures. Documentation for TorchIO can be found at http://torchio.rtfd.io