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Evaluating long-term operational data of a Very Large Crude Carrier: Assessing the diesel engines waste heat potential for integrating ORC systems
No full textGiven the spotlight on waste heat recovery (WHR) technologies for decarbonizing the shipping industry,
critically assessing waste heat from propulsion and auxiliary engines using actual data is crucial for accurately
predicting fuel and carbon savings. This paper addresses this by applying advanced data processing, considering
voyage characteristics, and developing methods for mapping waste heat from main engine (ME) and diesel
gensets (DGs) operations using long-term operational data from a Very Large Crude Carrier (VLCC) case
study. Two methods predict unrecorded air mass flows by implementing heat balance: one using measured
temperature values and the other estimating exhaust gas mass flow rates based on fuel oil consumption. Within
the main objectives of this study is to examine the impacts of employing the exhaust gases of both ME and
DGs to evaporate recuperative ORC working fluid on power output and vessel annual carbon savings, with
the formed diesel engines operational profiles serving as inputs. Simulations show a power output of 530 kW
using ME exhaust gases, increasing to 653 kW and 741 kW with DGs integration for R245fa and R1233zd(E)
as the selected ORC working fluids, respectively. The proposed solutions can reduce annual CO2 emissions by
4 % to 7 %
Deep brain stimulation and suicide attempts in treatment-resistant patients: a case report and neuroethical analysis
No full textDigital endpoints in clinical trials: emerging themes from a multi-stakeholder Knowledge Exchange event
No full textCombining the analytical hierarchy process, fuzzy expert systems, and the exponential risk priority number for the holistic evaluation of innovation projects in manufacturing
No full textWhy Do We Need a Territorial Typology of Gender-based Political Violence? An Analysis of the Online and Offline Harms Suffered by Brazilian Female Politicians Defending Territory
No full textAssessment of entropy accumulation in human subjects when exposed to low energy availability
No full textBackground & Aims
Adequate energy availability is essential for the body to maintain its physiological functions and achieve optimal health, especially among athletes. Unfortunately, low energy availability (LEA) is common among athletes, and it has been associated with impairments in health and performance. In contrast, an energy-restricted diet has been linked to longevity, but it is unclear how LEA affects athletes’ lifespans. The goal of the present study was to assess the impact of LEA thermodynamically on the lifespan of athletes.
Methods
Data from seven healthy young endurance-trained athletes (24 ± 4 years) who underwent short-term low energy availability (125 (kJ/day) per kg FFM) once with low protein content (LEA-LP; 0.8 g/kg) and with high protein content (LEA-HP; 125 (kJ/day) per kg FFM, 1.7 g/kg), as well as a control diet (CON; 230 (kJ/day) per kg FFM, 1.7 g/kg), were used in the calculations. The athletes followed each diet for five days and expended 67.5 (kJ/day) per kg FFM. entropy generation-based thermodynamic calculations were performed based on the metabolic activity of the athletes, which was determined from oxygen consumption and carbon dioxide production rates.
Results
Low energy availability was successfully induced during LEA-LP (62 ± 8 (kJ/day) per kg FFM; 95%Cl: 53-70) and LEA-HP (64 ± 8 (kJ/day) per kg FFM; 95%Cl: 56-71) diets. Despite of achieving energy deficit of -6658 ± 2110 kJ/day (95%Cl: -8609-(-) 4707) (LEA-LP), -5781 ± 623 (95%Cl: -26591-(-)4707) (LEA-HP) and excessive energy of 772 ± 1915 (95%Cl: -845-2388) (CON) statistical analyses revealed no significant differences in lifespan estimations among diets (CON: 72 ± 8 years (95%Cl: 65-79), LEA-LP: 74 ± 7 years (95%Cl: 68-80), and LEA-HP: 73 ± 11 (95%Cl: 62-83)).
Conclusions
This study suggests valuable insights into the intricate relationship between energy availability, entropy generation, and lifespan among athletes. Whereas entropy generation levels and the lifespan of athletes remained stable depending on diets, the distinguished differences in energy deficiency and energy availability underline the need for a profounder investigation of underlying physiological mechanisms to improve the health and performance of athletes
Metadata-enhanced contrastive learning from retinal optical coherence tomography images.
No full textDeep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-efficient downstream image analysis. However, the direct application of conventional contrastive methods to medical datasets introduces two domain-specific issues. Firstly, several image transformations which have been shown to be crucial for effective contrastive learning do not translate from the natural image to the medical image domain. Secondly, the assumption made by conventional methods, that any two images are dissimilar, is systematically misleading in medical datasets depicting the same anatomy and disease. This is exacerbated in longitudinal image datasets that repeatedly image the same patient cohort to monitor their disease progression over time. In this paper we tackle these issues by extending conventional contrastive frameworks with a novel metadata-enhanced strategy. Our approach employs widely available patient metadata to approximate the true set of inter-image contrastive relationships. To this end we employ records for patient identity, eye position (i.e. left or right) and time series information. In experiments using two large longitudinal datasets containing 170,427 retinal optical coherence tomography (OCT) images of 7912 patients with age-related macular degeneration (AMD), we evaluate the utility of using metadata to incorporate the temporal dynamics of disease progression into pretraining. Our metadata-enhanced approach outperforms both standard contrastive methods and a retinal image foundation model in five out of six image-level downstream tasks related to AMD. We find benefits in both a low-data and high-data regime across tasks ranging from AMD stage and type classification to prediction of visual acuity. Due to its modularity, our method can be quickly and cost-effectively tested to establish the potential benefits of including available metadata in contrastive pretraining
Virtual reality-empowered deep-learning analysis of brain cells.
No full textAutomated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease
Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA.
No full textThe Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically
