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31,952 results on '"Knott A"'

1. Contactless Engergy Supply for Medical Drill with Integrated Temperature Sensor

Author

Knott Anna-Lena M. Sc., Sanders Mark M. Sc., Prinzen Tom, Klenzner Thomas, Schipper Jörg, Kristin Julia, and Schmitt Robert H.

Subjects
skull base surgery, medical drilling, temperature measurement, minimally invasive, contactless energy generation, Medicine
Abstract

An atraumatic approach to the inner ear is an important goal in the future development of cochlear implant surgery, as the current surgical procedure for electrode insertion is highly invasive. To reduce invasiveness, minimally invasive surgical options are being explored, such as the insertion of one or more drill channels. Due to the proximity to critical structures, a temperature sensor is incorporated into the medical drill for process parallel temperature monitoring to prevent thermal damage. The temperature data is sent to the analysis hardware via Bluetooth BLE. This requires a power supply for the electronics on the drill. There are a number of limitations to the design of such a power supply. For one, the drill is rotating. Therefore, the energy generation must be contactless. Secondly, the energy generation should not depend on the rotation of the drill, so that the temperature can be measured even without the drill rotating and the rotational speed of the drill is not limited. In this paper, we propose a solution for a contactless energy transmission system (ETS) based on inductive coupling. Considering the dimensional limitations of attaching the electronics to the drill shaft, a multilayer PCB coil with multiple contacts for different number of turns is developed. The electrical circuit is simulated and the best settings are identified. The power supply is validated according to the predefined requirements. In the future, the medical drill will be built with the newly developed ETS. Sterilization and wear tests are performed to confirm the suitability of the drill with integrated temperature sensor for the operating room.

Published
2024
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2. Suitability Analysis of Industrial Drills for Minimally Invasive Skull Base Surgery

Author

Knott Anna-Lena, Prinzen Tom, Klenzner Thomas, Schipper Jörg, Kristin Julia, and Schmitt Robert H.

Subjects
skull base surgery, medical drilling, temperature measurement, minimally invasive, Medicine
Abstract

Medical drilling processes, especially in the area of the otobase, pose a high risk to the patient due to the heat generated and introduced into the tissue. Two measures of reducing the heat are the parameters of the drilling process and the optimisation of the drill geometry. A spiral drill and a singlelip drill are evaluated for their suitability as medical drills in skull base surgery with the objective of integrating a temperature sensor in the tip of the drill. The two drills were chosen because of their promising heat generation properties in pretesting. In this paper the chip formation behaviour and drilling channel quality are analysed. The single-lip drill shows lack in precision of the drilling channel and unsuitable bone chip transport, as material powder is left in the drilling channel. In contrast, the spiral drill shows a good transport of the chips and the drilling channel follows the planned path. As a result, the spiral drill is chosen for the further development of a drill with integrated temperature sensor for minimally invasive drilling.

Published
2022
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3. A linguistic analysis of undesirable outcomes in the era of generative AI

Author

Gambetta, Daniele, Gezici, Gizem, Giannotti, Fosca, Pedreschi, Dino, Knott, Alistair, and Pappalardo, Luca

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Recent research has focused on the medium and long-term impacts of generative AI, posing scientific and societal challenges mainly due to the detection and reliability of machine-generated information, which is projected to form the major content on the Web soon. Prior studies show that LLMs exhibit a lower performance in generation tasks (model collapse) as they undergo a fine-tuning process across multiple generations on their own generated content (self-consuming loop). In this paper, we present a comprehensive simulation framework built upon the chat version of LLama2, focusing particularly on the linguistic aspects of the generated content, which has not been fully examined in existing studies. Our results show that the model produces less lexical rich content across generations, reducing diversity. The lexical richness has been measured using the linguistic measures of entropy and TTR as well as calculating the POSTags frequency. The generated content has also been examined with an $n$-gram analysis, which takes into account the word order, and semantic networks, which consider the relation between different words. These findings suggest that the model collapse occurs not only by decreasing the content diversity but also by distorting the underlying linguistic patterns of the generated text, which both highlight the critical importance of carefully choosing and curating the initial input text, which can alleviate the model collapse problem. Furthermore, we conduct a qualitative analysis of the fine-tuned models of the pipeline to compare their performances on generic NLP tasks to the original model. We find that autophagy transforms the initial model into a more creative, doubtful and confused one, which might provide inaccurate answers and include conspiracy theories in the model responses, spreading false and biased information on the Web.

Published
2024

4. Metrological Support of Medical Drillings at the Lateral Skull Base

Author

Knott Anna-Lena, Kristin Julia, Schipper Jörg, Klenzner Thomas, Prinzen Tom, and Schmitt Robert H.

Subjects
cochlear implant, minimally invasive, medical technology, temperature development, drilling process, risk model, Medicine
Abstract

For minimally invasive drilling processes, the temperature development in the drilling ground is of crucial importance for patient safety. To monitor the temperature during drilling, a drill prototype was developed by BREDEMANN ET AL. which can record the drill temperature in parallel to the process and in real time. The measurement principle of the thermistor (temperature sensor) integrated in the drill could be validated. [1] The prototype must be refined for use in the operating room, as the drill does not yet meet all the medical requirements that need to be fulfilled. In further development, the recorded temperature data in particular must be processed and communicated to the surgeon in order to provide added value for the surgical procedure.

Published
2021
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5. Social perception of faces in a vision-language model

Author

Hausladen, Carina I., Knott, Manuel, Camerer, Colin F., and Perona, Pietro

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

We explore social perception of human faces in CLIP, a widely used open-source vision-language model. To this end, we compare the similarity in CLIP embeddings between different textual prompts and a set of face images. Our textual prompts are constructed from well-validated social psychology terms denoting social perception. The face images are synthetic and are systematically and independently varied along six dimensions: the legally protected attributes of age, gender, and race, as well as facial expression, lighting, and pose. Independently and systematically manipulating face attributes allows us to study the effect of each on social perception and avoids confounds that can occur in wild-collected data due to uncontrolled systematic correlations between attributes. Thus, our findings are experimental rather than observational. Our main findings are three. First, while CLIP is trained on the widest variety of images and texts, it is able to make fine-grained human-like social judgments on face images. Second, age, gender, and race do systematically impact CLIP's social perception of faces, suggesting an undesirable bias in CLIP vis-a-vis legally protected attributes. Most strikingly, we find a strong pattern of bias concerning the faces of Black women, where CLIP produces extreme values of social perception across different ages and facial expressions. Third, facial expression impacts social perception more than age and lighting as much as age. The last finding predicts that studies that do not control for unprotected visual attributes may reach the wrong conclusions on bias. Our novel method of investigation, which is founded on the social psychology literature and on the experiments involving the manipulation of individual attributes, yields sharper and more reliable observations than previous observational methods and may be applied to study biases in any vision-language model.

Published
2024

6. A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification

Author

Marks, Markus, Knott, Manuel, Kondapaneni, Neehar, Cole, Elijah, Defraeye, Thijs, Perez-Cruz, Fernando, and Perona, Pietro

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn about the data structure or context by solving a pretext task. With SSL, models can learn from abundant and cheap unlabeled data, significantly reducing the cost of training models where labels are expensive or inaccessible. In Computer Vision, SSL is widely used as pre-training followed by a downstream task, such as supervised transfer, few-shot learning on smaller labeled data sets, and/or unsupervised clustering. Unfortunately, it is infeasible to evaluate SSL methods on all possible downstream tasks and objectively measure the quality of the learned representation. Instead, SSL methods are evaluated using in-domain evaluation protocols, such as fine-tuning, linear probing, and k-nearest neighbors (kNN). However, it is not well understood how well these evaluation protocols estimate the representation quality of a pre-trained model for different downstream tasks under different conditions, such as dataset, metric, and model architecture. We study how classification-based evaluation protocols for SSL correlate and how well they predict downstream performance on different dataset types. Our study includes eleven common image datasets and 26 models that were pre-trained with different SSL methods or have different model backbones. We find that in-domain linear/kNN probing protocols are, on average, the best general predictors for out-of-domain performance. We further investigate the importance of batch normalization and evaluate how robust correlations are for different kinds of dataset domain shifts. We challenge assumptions about the relationship between discriminative and generative self-supervised methods, finding that most of their performance differences can be explained by changes to model backbones.

Published
2024

7. AKT and EZH2 inhibitors kill TNBCs by hijacking mechanisms of involution

Author

Schade, Amy E., Perurena, Naiara, Yang, Yoona, Rodriguez, Carrie L., Krishnan, Anjana, Gardner, Alycia, Loi, Patrick, Xu, Yilin, Nguyen, Van T. M., Mastellone, G. M., Pilla, Natalie F., Watanabe, Marina, Ota, Keiichi, Davis, Rachel A., Mattioli, Kaia, Xiang, Dongxi, Zoeller, Jason J., Lin, Jia-Ren, Morganti, Stefania, Garrido-Castro, Ana C., Tolaney, Sara M., Li, Zhe, Barbie, David A., Sorger, Peter K., Helin, Kristian, Santagata, Sandro, Knott, Simon R. V., and Cichowski, Karen

Published
2024
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10. Impacts of Extreme Heat on Labor Force Dynamics

Author

Ireland, Andrew, Johnston, David, and Knott, Rachel

Subjects
Economics - General Economics
Abstract

We use daily longitudinal data and a within-worker identification approach to examine the impacts of heat on labor force dynamics in Australia. High temperatures during 2001-2019 significantly reduced work attendance and hours worked, which were not compensated for in subsequent days and weeks. The largest reductions occurred in cooler regions and recent years, and were not solely concentrated amongst outdoor-based workers. Financial and Insurance Services was the most strongly affected industry, with temperatures above 38{\deg}C (100{\deg}F) increasing absenteeism by 15 percent. Adverse heat effects during the work commute and during outdoor work hours are shown to be key mechanisms.

Published
2024

11. Objective Photoanalysis of Feminizing Frontal Cranioplasty Outcomes.

Author

David, Abel, House, Adrian, Targ, Sonia, DeBusk, W, Park, Andrea, Knott, P, and Seth, Rahul

Subjects
cranioplasty, facial feminization, gender affirmation surgery
Abstract

STUDY DESIGN: Retrospective analysis at a tertiary care center. OBJECTIVE: This study describes a method of analyzing postoperative results using lateral view clinical photographs to create normalized projection ratios of the glabella and radix. METHODS: We reviewed preoperative and postoperative photographs of 15 patients. All photographs were in the lateral view Frankfort horizontal plane. We calculated the distances between the (a) tragus and cornea, (b) cornea and radix, (c) cornea and glabella, and the (θ) nasofrontal angle. RESULTS: Fifteen sets of patient photographs were analyzed and found that there was a favorable 14% reduction at the radix and an even greater reduction (78.9%) at the glabella. The nasofrontal angle was improved to a more feminine range from 131.84° preoperatively to 145.86° postoperatively. CONCLUSIONS: Normalized projection ratios of the glabella and radix, along with the nasofrontal angle, can be used to objectively measure outcomes of frontal feminizing cranioplasty.

Published
2024

12. Ergonomic Assessment of Septorhinoplasty Maneuvers During Simulated Pregnancy

Author

Bhethanabotla, Rohith M, Ledgister, Kaye, Soriano, Ian S, O'Sullivan, Patricia, Bigelow, Elaine, Knott, Philip Daniel, and Park, Andrea M

Subjects
Reproductive Medicine, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Musculoskeletal, ergonomics, pregnancy, rhinoplasty, simulation
Abstract

ObjectiveWomen represent an increasing proportion of the otolaryngology workforce. Work-related musculoskeletal disorders (WRMSD) are a little-studied yet important impediment to career completion. Scant attention has been directed to study the impact of pregnancy on surgeon posture and ergonomics. We piloted the use of a pregnancy simulation suit (Empathy Belly) to assess the risk of ergonomic compromise when performing open septorhinoplasty.Study designSurgical simulation.SettingSingle session, training simulation lab at academic medical center.MethodsMedical students and surgical residents performed the initial steps of a rhinoplasty procedure without and with a pregnancy simulation suit and were filmed with an artificial intelligence-based video analysis app from Kinetica Labs that calculates joint angles and categorizes the ergonomic risk factors. Still images from videos were taken and analyzed using validated posture-based analysis rubrics. Participants were asked to complete a qualitative questionnaire after the session.ResultsTwelve medical students and surgical residents participated in the study. Posture-based analysis indicated increased ergonomics risk factors among trainees when performing a rhinoplasty while wearing the pregnancy suit. Video analysis indicated trends of worsening back angle and shoulder postures. Trainees reported experiencing pain in the neck, suprapubic area, and lower back. They acknowledged the importance of ergonomics in otolaryngology and desired further education about workplace injury risk mitigation.ConclusionPregnancy impacts the ergonomics of performing septorhinoplasty and further investigation is required into interventions to reduce risk of WRMSDs.

Published
2024

19. CasPEDIA Database: a functional classification system for class 2 CRISPR-Cas enzymes

Author

Adler, Benjamin A, Trinidad, Marena I, Bellieny-Rabelo, Daniel, Zhang, Elaine, Karp, Hannah M, Skopintsev, Petr, Thornton, Brittney W, Weissman, Rachel F, Yoon, Peter H, Chen, LinXing, Hessler, Tomas, Eggers, Amy R, Colognori, David, Boger, Ron, Doherty, Erin E, Tsuchida, Connor A, Tran, Ryan V, Hofman, Laura, Shi, Honglue, Wasko, Kevin M, Zhou, Zehan, Xia, Chenglong, Al-Shimary, Muntathar J, Patel, Jaymin R, Thomas, Vienna CJX, Pattali, Rithu, Kan, Matthew J, Vardapetyan, Anna, Yang, Alana, Lahiri, Arushi, Maxwell, Micaela F, Murdock, Andrew G, Ramit, Glenn C, Henderson, Hope R, Calvert, Roland W, Bamert, Rebecca S, Knott, Gavin J, Lapinaite, Audrone, Pausch, Patrick, Cofsky, Joshua C, Sontheimer, Erik J, Wiedenheft, Blake, Fineran, Peter C, Brouns, Stan JJ, Sashital, Dipali G, Thomas, Brian C, Brown, Christopher T, Goltsman, Daniela SA, Barrangou, Rodolphe, Siksnys, Virginius, Banfield, Jillian F, Savage, David F, and Doudna, Jennifer A

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Biotechnology, Genetics, Generic health relevance, CRISPR-Cas Systems, Phylogeny, CRISPR-Associated Proteins, Databases, Genetic, Endodeoxyribonucleases, Encyclopedias as Topic, Environmental Sciences, Information and Computing Sciences, Developmental Biology, Biological sciences, Chemical sciences, Environmental sciences
Abstract

CRISPR-Cas enzymes enable RNA-guided bacterial immunity and are widely used for biotechnological applications including genome editing. In particular, the Class 2 CRISPR-associated enzymes (Cas9, Cas12 and Cas13 families), have been deployed for numerous research, clinical and agricultural applications. However, the immense genetic and biochemical diversity of these proteins in the public domain poses a barrier for researchers seeking to leverage their activities. We present CasPEDIA (http://caspedia.org), the Cas Protein Effector Database of Information and Assessment, a curated encyclopedia that integrates enzymatic classification for hundreds of different Cas enzymes across 27 phylogenetic groups spanning the Cas9, Cas12 and Cas13 families, as well as evolutionarily related IscB and TnpB proteins. All enzymes in CasPEDIA were annotated with a standard workflow based on their primary nuclease activity, target requirements and guide-RNA design constraints. Our functional classification scheme, CasID, is described alongside current phylogenetic classification, allowing users to search related orthologs by enzymatic function and sequence similarity. CasPEDIA is a comprehensive data portal that summarizes and contextualizes enzymatic properties of widely used Cas enzymes, equipping users with valuable resources to foster biotechnological development. CasPEDIA complements phylogenetic Cas nomenclature and enables researchers to leverage the multi-faceted nucleic-acid targeting rules of diverse Class 2 Cas enzymes.

Published
2024

20. Evaluation of a youth-focused suicide prevention HOPE aftercare service: protocol for a non-randomized hybrid effectiveness-implementation type I design

Author

Kartal, Dzenana, Jaeger, Tess, Lamblin, Michelle, Richards, Hannah, Witt, Katrina, Occhipinti, Jo-An, Mihalopoulos, Cathrine, Chatterton, Mary Lou, Chanen, Andrew, McGorry, Patrick, Skinner, Adam, Zbukvic, Isabel, Thompson, Andrew, Knott, Jonathan, Flego, Anna, Hamilton, Craig, Webb, Marianne, Mullen, Edward, Swingler, Natasha, Kenny, Bridget, and Robinson, Jo

Published
2024
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21. Metformin associates with higher myocardial perfusion reserve and survival in type 2 diabetes mellitus patients

Author

Sharrack, Noor, Knott, Kristopher D., Gulsin, Gaurav S., Kotecha, Tushar, Brown, Louise A. E., Yeo, Jian L., Porcari, Aldostefano, Adam, Robert D., Thirunavukarasu, Sharmaine, Chowdhary, Amrit, Levelt, Eylem, Moon, James C., McCann, Gerry P., Fontana, Marianna, Kellman, Peter, Munyombwe, Theresa, Gale, Chris P., Buckley, David L., Greenwood, John P., Swoboda, Peter P., and Plein, Sven

Published
2024
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26. Effectiveness of pharmacological treatments for severe agitation in real-world emergency settings: protocol of individual-participant-data network meta-analysis

Author

Siafis, Spyridon, Wu, Hui, Nomura, Nobuyuki, Schneider-Thoma, Johannes, Bighelli, Irene, Lorenz, Carolin, Dib, Joseph E., Tharyan, Prathap, Calver, Leonie A., Isbister, Geoffrey K., Chan, Esther W. Y., Knott, Jonathan C., Yap, Celene Y. L., Mantovani, Célia, Martel, Marc L., Barbic, David, Honer, William G., Hansen, Wulf-Peter, Huf, Gisele, Alexander, Jacob, Raveendran, Nirmal S., Coutinho, Evandro S. F., Priller, Josef, Adams, Clive E., Salanti, Georgia, and Leucht, Stefan

Published
2024
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30. Text-image Alignment for Diffusion-based Perception

Author

Kondapaneni, Neehar, Marks, Markus, Knott, Manuel, Guimaraes, Rogerio, and Perona, Pietro

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However, the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically, it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps, leading to better perceptual performance. Our approach improves upon the current state-of-the-art (SOTA) in diffusion-based semantic segmentation on ADE20K and the current overall SOTA for depth estimation on NYUv2. Furthermore, our method generalizes to the cross-domain setting. We use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our cross-domain object detection model, trained on Pascal VOC, achieves SOTA results on Watercolor2K. Our cross-domain segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: https://www.vision.caltech.edu/tadp/. Code: https://github.com/damaggu/TADP., Comment: Project page: https://www.vision.caltech.edu/tadp/, Code page: github.com/damaggu/TADP

Published
2023

35. Human-AI Coevolution

Author

Pedreschi, Dino, Pappalardo, Luca, Ferragina, Emanuele, Baeza-Yates, Ricardo, Barabasi, Albert-Laszlo, Dignum, Frank, Dignum, Virginia, Eliassi-Rad, Tina, Giannotti, Fosca, Kertesz, Janos, Knott, Alistair, Ioannidis, Yannis, Lukowicz, Paul, Passarella, Andrea, Pentland, Alex Sandy, Shawe-Taylor, John, and Vespignani, Alessandro

Subjects
Computer Science - Artificial Intelligence
Abstract

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices on online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often ``unintended'' social outcomes. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., technical, epistemological, legal and socio-political.

Published
2023

36. Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies

Author

Mason, James Paul, Werth, Alexandra, West, Colin G., Youngblood, Allison A., Woodraska, Donald L., Peck, Courtney, Lacjak, Kevin, Frick, Florian G., Gabir, Moutamen, Alsinan, Reema A., Jacobsen, Thomas, Alrubaie, Mohammad, Chizmar, Kayla M., Lau, Benjamin P., Dominguez, Lizbeth Montoya, Price, David, Butler, Dylan R., Biron, Connor J., Feoktistov, Nikita, Dewey, Kai, Loomis, N. E., Bodzianowski, Michal, Kuybus, Connor, Dietrick, Henry, Wolfe, Aubrey M., Guerrero, Matt, Vinson, Jessica, Starbuck, Peter, Litton, Shelby D, Beck, M. G., Fisch, Jean-Paul, West, Ayana, Muniz, Alexis A., Chavez, Luis, Upthegrove, Zachary T., Runyon, Brenton M., Salazar, J., Kritzberg, Jake E., Murrel, Tyler, Ho, Ella, LaFemina, Quintin Y., Elbashir, Sara I., Chang, Ethan C., Hudson, Zachary A., Nussbaum, Rosemary O., Kennedy, Kellen, Kim, Kevin, Arango, Camila Villamil, Albakr, Mohammed A., Rotter, Michael, Garscadden, A. J., Salcido-Alcontar JR, Antonio, Pearl, Harrison M., Stepaniak, Tyler, Marquez, Josie A., Marsh, Lauren, Andringa, Jesse C, Osogwin, Austin, Shields, Amanda M., Brookins, Sarah, Hach, Grace K., Clausi, Alexis R., Millican, Emily B., Jaimes, Alan A, Graham, Alaina S., Burritt, John J., Perez, J. S., Ramirez, Nathaniel, Suri, Rohan, Myer, Michael S., Kresek, Zoe M., Goldsberry, C. A., Payne, Genevieve K., Jourabchi, Tara, Hu, J., Lucca, Jeffrey, Feng, Zitian, Gilpatrick, Connor B., Khan, Ibraheem A., Warble, Keenan, Sweeney, Joshua D., Dorricott, Philip, Meyer, Ethan, Kothamdi, Yash S., Sohail, Arman S., Grell, Kristyn, Floyd, Aidan, Bard, Titus, Mathieson, Randi M., Reed, Joseph, Cisneros, Alexis, Payne, Matthew P., Jarriel, J. R., Mora, Jacqueline Rodriguez, Sundell, M. E., Patel, Kajal, Alesmail, Mohammad, Alnasrallah, Yousef A, Abdullah, Jumana T., Molina-Saenz, Luis, Tayman, K. E., Brown, Gabriel T., Kerr-Layton, Liana, Berriman-Rozen, Zachary D., Hiatt, Quinn, Kalra, Etash, Ong, Jason, Vadayar, Shreenija, Shannahan, Callie D., Benke, Evan, zhang, Jinhua, Geisman, Jane, Martyr, Cara, Ameijenda, Federico, Akruwala, Ushmi H., Nehring, Molly, Kissner, Natalie, Rule, Ian C., Learned, Tyler, Smith, Alexandra N., Mazzotta, Liam, Rounsefell, Tyndall, Eyeson, Elizabeth A., Shelby, Arlee K., Moll, Tyler S, Menke, Riley, Shahba, Hannan, House Jr., Tony A., Clark, David B., Burns, Annemarie C., de La Beaujardiere, Tristan, Trautwein, Emily D., Plantz, Will, Reeves, Justin, Faber, Ian, Buxton, B. W., Highhouse, Nigel, Landrey, Kalin, Hansen, Connor M, Chen, Kevin, Hales, Ryder Buchanan, Borgerding, Luke R., Guo, Mutian, Crow, Christian J., Whittall, Lloyd C., Simmons, Conor, Folarin, Adeduni, Parkinson, Evan J., Rahn, Anna L., Blevins, Olivia, Morelock, Annalise M., Kelly, Nicholas, Parker, Nathan L., Smith, Kelly, Plzak, Audrey E., Saeb, David, Hares, Cameron T., Parker, Sasha R., McCoy, Andrew, Pham, Alexander V., Lauzon, Megan, Kennedy, Cayla J., Reyna, Andrea B., Acosta, Daniela M. Meza, Cool, Destiny J., Steinbarth, Sheen L., Mendoza-Anselmi, Patricia, Plutt, Kaitlyn E., Kipp, Isabel M, Rakhmonova, M., Brown, Cameron L., Van Anne, Gabreece, Moss, Alexander P., Golden, Olivia, Kirkpatrick, Hunter B., Colleran, Jake R., Sullivan, Brandon J, Tran, Kevin, Carpender, Michael Andrew, Mundy, Aria T., Koenig, Greta, Oudakker, Jessica, Engelhardt, Rasce, Ales, Nolan, Wexler, Ethan Benjamin, Beato, Quinn I, Chen, Lily, Cochran, Brooke, Hill, Paula, Hamilton, Sean R., Hashiro, Kyle, Khan, Usman, Martinez, Alexa M., Brockman, Jennifer L., Mallory, Macguire, Reed, Charlie, Terrile, Richard, Singh, Savi, Watson, James Adam, Creany, Joshua B., Price, Nicholas K., Miften, Aya M., Tran, Bryn, Kamenetskiy, Margaret, Martinez, Jose R., Opp, Elena N., Huang, Jianyang, Fails, Avery M., Belei, Brennan J., Slocum, Ryan, Astalos, Justin, East, Andrew, Nguyen, Lena P., Pherigo, Callie C, East, Andrew N., Li, David Y., Nelson, Maya LI, Taylor, Nicole, Odbayar, Anand, Rives, Anna Linnea, Mathur, Kabir P., Billingsley, Jacob, Polikoff, Hyden, Driscoll, Michael, Wilson, Orion K., Lahmers, Kyle, Toon, Nathaniel J., Lippincott, Sam, Musgrave, Andrew J., Gregory, Alannah H., Pitsuean-Meier, Sedique, Jesse, Trevor, Smith, Corey, Miles, Ethan J., Kainz, Sabrina J. H. T., Ji, Soo Yeun, Nguyen, Lena, Aryan, Maryam, Dinser, Alexis M., Shortman, Jadon, Bastias, Catalina S, Umbricht, Thomas D, Cage, Breonna, Randolph, Parker, Pollard, Matthew, Simone, Dylan M., Aramians, Andrew, Brecl, Ariana E., Robert, Amanda M., Zenner, Thomas, Saldi, Maxwell, Morales, Gavin, Mendez, Citlali, Syed, Konner, Vogel, Connor Maklain, Cone, Rebecca A., Berhanu, Naomi, Carpenter, Emily, Leoni, Cecilia, Bryan, Samuel, Ramachandra, Nidhi, Shaw, Timothy, Lee, E. C., Monyek, Eli, Wegner, Aidan B., Sharma, Shajesh, Lister, Barrett, White, Jamison R., Willard, John S., Sulaiman, S. A, Blandon, Guillermo, Narayan, Anoothi, Ruger, Ryan, Kelley, Morgan A., Moreno, Angel J., Balcer, Leo M, Ward-Chene, N. R. D., Shelby, Emma, Reagan, Brian D., Marsh, Toni, Sarkar, Sucheta, Kelley, Michael P., Fell, Kevin, Balaji, Sahana, Hildebrand, Annalise K., Shoha, Dominick, Nandu, Kshmya, Tucker, Julia, Cancio, Alejandro R., Wang, Jiawei, Rapaport, Sarah Grace, Maravi, Aimee S., Mayer, Victoria A., Miller, Andrew, Bence, Caden, Koke, Emily, Fauntleroy, John T, Doermer, Timothy, Al-Ghazwi, Adel, Morgan, Remy, Alahmed, Mohammed S., Mathavan, Adam Izz Khan Mohd Reduan, Silvester, H. K., Weiner, Amanda M., Liu, Nianzi, Iovan, Taro, Jensen, Alexander V., AlHarbi, Yazeed A., Jiang, Yufan, Zhang, Jiaqi, Jones, Olivia M., Huang, Chenqi, Reh, Eileen N., Alhamli, Dania, Pettine, Joshua, Zhou, Chongrui, Kriegman, Dylan, Yang, Jianing, Ash, Kevin, Savage, Carl, Kaiser, Emily, Augenstein, Dakota N., Padilla, Jacqueline, Stark, Ethan K., Hansen, Joshua A., Kokes, Thomas, Huynh, Leslie, Sanchez-Sanchez, Gustavo, Jeseritz, Luke A., Carillion, Emma L., Vepa, Aditya V., Khanal, Sapriya, Behr, Braden, Martin, Logan S., McMullan, Jesse J., Zhao, Tianwei, Williams, Abigail K., Alqabani, Emeen, Prinster, Gale H., Horne, Linda, Ruggles-Delgado, Kendall, Otto, Grant, Gomez, Angel R., Nguyen, Leonardo, Brumley, Preston J., Venegas, Nancy Ortiz, Varela, Ilian, Brownlow, Jordi, Cruz, Avril, Leiker, Linzhi, Batra, Jasleen, Hutabarat, Abigail P., Nunes-Valdes, Dario, Jameson, Connor, Naqi, Abdulaziz, Adams, Dante Q., Biediger, Blaine B., Borelli, William T, Cisne, Nicholas A., Collins, Nathaniel A., Curnow, Tyler L., Gopalakrishnan, Sean, Griffin, Nicholas F., Herrera, Emanuel, McGarvey, Meaghan V., Mellett, Sarah, Overchuk, Igor, Shaver, Nathan, Stratmeyer, Cooper N., Vess, Marcus T., Juels, Parker, Alyami, Saleh A., Gale, Skylar, Wallace, Steven P., Hunter, Samuel C, Lonergan, Mia C., Stewart, Trey, Maksimuk, Tiffany E., Lam, Antonia, Tressler, Judah, Napoletano, Elena R., Miller, Joshua B., Roy, Marc G., Chanders, Jasey, Fischer, Emmalee, Croteau, A. J., Kuiper, Nicolas A., Hoffman, Alex, DeBarros, Elyse, Curry, Riley T., Brzostowicz, A., Courtney, Jonas, Zhao, Tiannie, Szabo, Emi, Ghaith, Bandar Abu, Slyne, Colin, Beck, Lily, Quinonez, Oliver, Collins, Sarah, Madonna, Claire A., Morency, Cora, Palizzi, Mallory, Herwig, Tim, Beauprez, Jacob N., Ghiassi, Dorsa, Doran, Caroline R., Yang, Zhanchao, Padgette, Hannah M., Dicken, Cyrus A., Austin, Bryce W., Phalen, Ethan J., Xiao, Catherine, Palos, Adler, Gerhardstein, Phillip, Altenbern, Ava L., Orbidan, Dan, Dorr, Jackson A., Rivas, Guillermo A., Ewing, Calvin A, Giebner, B. C., McEntee, Kelleen, Kite, Emily R., Crocker, K. A., Haley, Mark S., Lezak, Adrienne R., McQuaid, Ella, Jeong, Jacob, Albaum, Jonathan, Hrudka, E. M., Mulcahy, Owen T., Tanguma, Nolan C., Oishi-Holder, Sean, White, Zachary, Coe, Ryan W., Boyer, Christine, Chapman, Mitchell G., Fortino, Elise, Salgado, Jose A., Hellweg, Tim, Martinez, Hazelia K., Mitchell, Alexander J., Schubert, Stephanie H., Schumacher, Grace K, Tesdahl, Corey D, Uphoff, C. H., Vassilyev, Alexandr, Witkoff, Briahn, Wolle, Jackson R., Dice, Kenzie A., Behrer, Timothy A., Bowen, Troy, Campbell, Andrew J, Clarkson, Peter C, Duong, Tien Q., Hawat, Elijah, Lopez, Christian, Olson, Nathaniel P., Osborn, Matthew, Peou, Munisettha E., Vaver, Nicholas J., Husted, Troy, Kallemeyn, Nicolas Ian, Spangler, Ava A, Mccurry, Kyle, Schultze, Courtney, Troisi, Thomas, Thomas, Daniel, Ort, Althea E., Singh, Maya A., Soon, Caitlin, Patton, Catherine, Billman, Jayce A., Jarvis, Sam, Hitt, Travis, Masri, Mirna, Albalushi, Yusef J., Schofer, Matthew J, Linnane, Katherine B., Knott, Philip Whiting, Valencia, Whitney, Arias-Robles, Brian A., Ryder, Diana, Simone, Anna, Abrams, Jonathan M., Belknap, Annelene L., Rouse, Charlotte, Reynolds, Alexander, Petric, Romeo S. L., Gomez, Angel A., Meiselman-Ashen, Jonah B., Carey, Luke, Dias, John S., Fischer-White, Jules, Forbes, Aidan E., Galarraga, Gabriela, Kennedy, Forrest, Lawlor, Rian, Murphy, Maxwell J., Norris, Cooper, Quarderer, Josh, Waller, Caroline, Weber, Robert J., Gunderson, Nicole, Boyne, Tom, Gregory, Joshua A., Propper, Henry Austin, von Peccoz, Charles B. Beck, Branch, Donovan, Clarke, Evelyn, Cutler, Libby, Dabberdt, Frederick M., Das, Swagatam, Figueirinhas, John Alfred D., Fougere, Benjamin L., Roy, Zoe A., Zhao, Noah Y., Cox, Corben L., Barnhart, Logan D. W., Craig, Wilmsen B., Moll, Hayden, Pohle, Kyle, Mueller, Alexander, Smith, Elena K., Spicer, Benjamin C., Aycock, Matthew C., Bat-Ulzii, Batchimeg, Murphy, Madalyn C., Altokhais, Abdullah, Thornally, Noah R., Kleinhaus, Olivia R., Sarfaraz, Darian, Barnes, Grant M., Beard, Sara, Banda, David J, Davis, Emma A. B., Huebsch, Tyler J., Wagoner, Michaela, Griego, Justus, Hale, Jack J. Mc, Porter, Trevor J., Abrashoff, Riley, Phan, Denise M., Smith, Samantha M., Srivastava, Ashish, Schlenker, Jared A. W., Madsen, Kasey O., Hirschmann, Anna E., Rankin, Frederick C, Akbar, Zainab A., Blouin, Ethan, Coleman-Plante, Aislinn, Hintsa, Evan, Lookhoff, Emily, Amer, Hamzi, Deng, Tianyue, Dvorak, Peter, Minimo, Josh, Plummer, William C., Ton, Kelly, Solt, Lincoln, AlAbbas, Batool H., AlAwadhi, Areej A., Cooper, Nicholas M., Corbitt, Jessica S, Dunlap, Christian, Johnson, Owen, Malone, Ryan A., Tellez, Yesica, Wallace, Logan, Ta, Michael-Tan D., Wheeler, Nicola H., Ramirez, Ariana C., Huang, Shancheng, Mehidic, Amar, Christiansen, Katherine E, Desai, Om, Domke, Emerson N., Howell, Noah H., Allsbrook, Martin, Alnaji, Teeb, England, Colin, Siles, Nathan, Burton, Nicholas David, Cruse, Zoe, Gilmartin, Dalton, Kim, Brian T., Hattendorf, Elsie, Buhamad, Maryam, Gayou, Lily, Seglem, Kasper, Alkhezzi, Tameem, Hicks, Imari R., Fife, Ryann, Pelster, Lily M., Fix, Alexander, Sur, Sohan N., Truong, Joshua K., Kubiak, Bartlomiej, Bondar, Matthew, Shi, Kyle Z., Johnston, Julia, Acevedo, Andres B., Lee, Junwon, Solorio, William J., Johnston, Braedon Y., McCormick, Tyler, Olguin, Nicholas, Pastor, Paige J., Wilson, Evan M., Trunko, Benjamin L., Sjoroos, Chris, Adams, Kalvyn N, Bell, Aislyn, Brumage-Heller, Grant, Canales, Braden P., Chiles, Bradyn, Driscoll, Kailer H., Hill, Hallie, Isert, Samuel A., Ketterer, Marilyn, Kim, Matthew M., Mewhirter, William J., Phillips, Lance, Phommatha, Krista, Quinn, Megan S., Reddy, Brooklyn J., Rippel, Matthew, Russell, Bowman, Williams, Sajan, Pixley, Andrew M., Gapin, Keala C., Peterson, B., Ruprecht, Collin, Hardie, Isabelle, Li, Isaac, Erickson, Abbey, Gersabeck, Clint, Gopalani, Mariam, Allanqawi, Nasser, Burton, Taylor, Cahn, Jackson R., Conti, Reese, White, Oliver S., Rojec, Stewart, Hogen, Blake A., Swartz, Jason R., Dick, R., Battist, Lexi, Dunn, Gabrielle M., Gasser, Rachel, Logan, Timothy W., Sinkovic, Madeline, Schaller, Marcus T., Heintz, Danielle A., Enrich, Andrew, Sanchez, Ethan S., Perez, Freddy, Flores, Fernando, Kapla, Shaun D., Shockley, Michael C., Phillips, Justin, Rumley, Madigan, Daboub, Johnston, Karsh, Brennan J., Linders, Bridget, Chen, Sam, Do, Helen C., Avula, Abhinav, French, James M., Bertuccio, Chrisanna, Hand, Tyler, Lee, Adrianna J., Neeland, Brenna K, Salazar, Violeta, Andrew, Carter, Barmore, Abby, Beatty, Thomas, Alonzi, Nicholas, Brown, Ryan, Chandler, Olivia M., Collier, Curran, Current, Hayden, Delasantos, Megan E., Bonilla, Alberto Espinosa de los Monteros, Fowler, Alexandra A., Geneser, Julianne R., Gentry, Eleanor, Gustavsson, E. R., Hansson, Jonathan, Hao, Tony Yunfei, Herrington, Robert N., Kelly, James, Kelly, Teagan, Kennedy, Abigail, Marquez, Mathew J., Meillon, Stella, Palmgren, Madeleine L., Pesce, Anneliese, Ranjan, Anurag, Robertson, Samuel M., Smith, Percy, Smith, Trevor J, Soby, Daniel A., Stratton, Grant L., Thielmann, Quinn N., Toups, Malena C., Veta, Jenna S., Young, Trenton J., Maly, Blake, Manzanares, Xander R., Beijer, Joshua, George, Jacob D., Mills, Dylan P., Ziebold, Josh J, Chambers, Paige, Montoya, Michael, Cheang, Nathan M., Anderson, Hunter J., Duncan, Sheridan J., Ehrlich, Lauren, Hudson, Nathan C., Kiechlin, Jack L., Koch, Will, Lee, Justin, Menassa, Dominic, Oakes, S. H., Petersen, Audrey J., Bunsow, J. R. Ramirez, Bay, Joshua, Ramirez, Sacha, Fenwick, Logan D., Boyle, Aidan P., Hibbard, Lea Pearl, Haubrich, Calder, Sherry, Daniel P., Jenkins, Josh, Furney, Sebastian, Velamala, Anjali A., Krueger, Davis J., Thompson, William N., Chhetri, Jenisha, Lee, Alexis Ying-Shan, Ray, Mia G. V., Recchia, John C., Lengerich, Dylan, Taulman, Kyle, Romero, Andres C., Steward, Ellie N., Russell, Sloan, Hardwick, Dillon F., Wootten, Katelynn, Nguyen, Valerie A., Quispe, Devon, Ragsdale, Cameron, Young, Isabel, Atchley-Rivers, N. S., Stribling, Jordin L., Gentile, Julia G, Boeyink, Taylor A., Kwiatkowski, Daniel, Dupeyron, Tomi Oshima, Crews, Anastasia, Shuttleworth, Mitchell, Dresdner, Danielle C., Flackett, Lydia, Haratsaris, Nicholas, Linger, Morgan I, Misener, Jay H., Patti, Samuel, Pine, Tawanchai P., Marikar, Nasreen, Matessi, Giorgio, Routledge, Allie C., Alkaabi, Suhail, Bartman, Jessica L., Bisacca, Gabrielle E., Busch, Celeste, Edwards, Bree, Staudenmier, Caitlyn, Starling, Travis, McVey, Caden, Montano, Maximus, Contizano, Charles J., Taylor, Eleanor, McIntyre, James K., Victory, Andrew, McCammon, Glen S., Kimlicko, Aspen, Sheldrake, Tucker, Shelchuk, Grace, Von Reich, Ferin J., Hicks, Andrew J., O'neill, Ian, Rossman, Beth, Taylor, Liam C., MacDonald, William, Becker, Simone E., Han, Soonhee, O'Sullivan, Cian, Wilcove, Isaac, Brennan, David J., Hanley, Luke C., Hull, Owen, Wilson, Timothy R., Kalmus, Madison H., Berv, Owen A., Harris, Logan Swous, Doan, Chris H, Londres, Nathan, Parulekar, Anish, Adam, Megan M., Angwin, Abigail, Cabbage, Carter C., Colleran, Zachary, Pietras, Alex, Seux, Octave, Oros, Ryan, Wilkinson, Blake C., Nguyen, Khoa D, Trank-Greene, Maedee, Barone, Kevin M., Snyder, G. L., Biehle, Samuel J, Billig, Brennen, Almquist, Justin Thomas, Dixon, Alyssa M., Erickson, Benjamin, Evans, Nathan, Genne, SL, Kelly, Christopher M, Marcus, Serafima M., Ogle, Caleb, Patel, Akhil, Vendetti, Evan, Courtney, Olivia, Deel, Sean, Del Foco, Leonardo, Gjini, Michael, Haines, Jessica, Hoff, Isabelle J., Jones, M. R., Killian, Dominic, Kuehl, Kirsten, Kuester, Chrisanne, Lantz, Maxwell B., Lee, Christian J, Mauer, Graham, McKemey, Finbar K., Millican, Sarah J., Rosasco, Ryan, Stewart, T. C., VanEtten, Eleanor, Derwin, Zachary, Serio, Lauren, Sickler, Molly G., Blake, Cassidy A., Patel, Neil S., Fox, Margaret, Gray, Michael J, Ziegler, Lucas J., Kumar, Aman Priyadarshi, Polly, Madelyn, Mesgina, Sarah, McMorris, Zane, Griffin, Kyle J., Haile, L. N., Bassel, Claire, Dixon, Thomas J., Beattie, Ryan, Houck, Timothy J, Rodgers, Maeve, Trofino, Tyson R., Lukianow, Dax, Smart, Korben, Hall, Jacqueline L., Bone, Lauren, Baldwin, James O., Doane, Connor, Almohsen, Yousef A., Stamos, Emily, Acha, Iker, Kim, Jake, Samour II, Antonio E., Chavali, S., Kanokthippayakun, Jeerakit, Gotlib, Nicholas, Murphy, Ryan C., Archibald, Jack. W., Brimhall, Alexander J, Boyer, Aidan, Chapman, Logan T., Chadda, Shivank, Sibrell, Lisa, Vallery, Mia M., Conroy, Thomas C., Pan, Luke J., Balajonda, Brian, Fuhrman, Bethany E. S., Alkubaisi, Mohamed, Engelstad, Jacob, Dodrill, Joshua, Fuchs, Calvin R., Bullard-Connor, Gigi, Alhuseini, Isehaq, Zygmunt, James C., Sipowicz, Leo, Hayrynen, Griffin A., McGill, Riley M., Keating, Caden J., Hart, Omer, Cyr, Aidan St., Steinsberger, Christopher H., Thoman, Gerig, Wood, Travis M., Ingram, Julia A., Dominguez, J., Georgiades, Nathaniel James, Johnson, Matthew, Johnson, Sawyer, Pedersen, Alexander J., Ralapanawe, Anoush K, Thomas, Jeffrey J., Sato, Ginn A., Reynolds, Hope, Nasser, Liebe, Mizzi, Alexander Z., Damgaard, Olivia, Baflah, Abdulrahman A., Liu, Steven Y., Salindeho, Adam D., Norden, Kelso, Gearhart, Emily E., Krajnak, Zack, Szeremeta, Philip, Amos, Meggan, Shin, Kyungeun, Muckenthaler, Brandon A., Medialdea, Melissa, Beach, Simone, Wilson, Connor B., Adams, Elena R, Aldhamen, Ahmed, Harris, Coyle M., Hesse, Troy M., Golding, Nathan T., Larter, Zachary, Hernandez, Angel, Morales, Genaro, Traxler, Robert B., Alosaimi, Meshal, Fitton, Aidan F., Aaron, James Holland, Lee, Nathaniel F., Liao, Ryan Z., Chen, Judy, French, Katherine V., Loring, Justin, Colter, Aurora, McConvey, Rowan, Colozzi, Michael, Vann, John D., Scheck, Benjamin T., Weigand, Anthony A, Alhabeeb, Abdulelah, Idoine, Yolande, Woodard, Aiden L., Medellin, Mateo M., Ratajczyk, Nicholas O, Tobin, Darien P., Collins, Jack C., Horning, Thomas M., Pellatz, Nick, Pitten, John, Lordi, Noah, Patterson, Alyx, Hoang, Thi D, Zimmermann, Ingrid H, Wang, Hongda, Steckhahn, Daniel, Aradhya, Arvind J., Oliver, Kristin A., Cai, Yijian, Wang, Chaoran, Yegovtsev, Nikolay, Wu, Mengyu, Ganesan, Koushik, Osborne, Andrew, Wickenden, Evan, Meyer, Josephine C., Chaparro, David, Visal, Aseem, Liu, Haixin, Menon, Thanmay S., Jin, Yan, Wilson, John, Erikson, James W., Luo, Zheng, Shitara, Nanako, Nelson, Emma E, Geerdts, T. R., Ortiz, Jorge L Ramirez, and Lewandowski, H. J.

Subjects
Astrophysics - Solar and Stellar Astrophysics
Abstract

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating., Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 71

Published
2023
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37. Second Primary Cancer Risks After Breast Cancer in BRCA1 and BRCA2 Pathogenic Variant Carriers

Author

Allen, Isaac, Hassan, Hend, Walburga, Yvonne, Huntley, Catherine, Loong, Lucy, Rahman, Tameera, Allen, Sophie, Garrett, Alice, Torr, Bethany, Bacon, Andrew, Knott, Craig, Jose, Sophie, Vernon, Sally, Lüchtenborg, Margreet, Pethick, Joanna, Santaniello, Francesco, Goel, Shilpi, Wang, Ying-Wen, Lavelle, Katrina, McRonald, Fiona, Eccles, Diana, Morris, Eva, Hardy, Steven, Turnbull, Clare, Tischkowitz, Marc, Pharoah, Paul, and Antoniou, Antonis C.

Published
2024
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38. Heat and Worker Health

Author

Ireland, Andrew, Johnston, David, and Knott, Rachel

Subjects
Economics - General Economics
Abstract

Extreme heat negatively impacts cognition, learning, and task performance. With increasing global temperatures, workers may therefore be at increased risk of work-related injuries and illness. This study estimates the effects of temperature on worker health using records spanning 1985-2020 from an Australian mandatory insurance scheme. High temperatures are found to cause significantly more claims, particularly among manual workers in outdoor-based industries. These adverse effects have not diminished across time, with the largest effect observed for the 2015-2020 period, indicating increasing vulnerability to heat. Within occupations, the workers most adversely affected by heat are female, older-aged and higher-earning. Finally, results from firm-level panel analyses show that the percentage increase in claims on hot days is largest at "safer" firms.

Published
2023
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39. Human Dignity in Natural Resource Social Sciences Career Pathways

Author

Andrews, Evan J., primary, Knott, Christine, additional, Nadeau, Solange, additional, Parlee, Courtenay, additional, Rastogi, Archi, additional, Kelly, Rachel, additional, López Gómez, María Andrée, additional, Galappaththi, Madu, additional, and Esteves Dias, Ana Carolina, additional

Published
2024
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40. Traceable dynamic measurement of mechanical quantities: objectives and first results of this european project

Author

Bartoli C., Beug M. F., Bruns T., Elster C., Esward T., Klaus L., Knott A., Kobusch M., Saxholm S., and Schlegel C.

Subjects
dynamic force, dynamic torque, dynamic pressure, traceability, measurement uncertainty, Technology
Abstract

Nine european national metrology institutes (NMIs) are collaborating in a new project funded by the european metrology research programme (EMRP) to establish traceable dynamic measurement of the mechanical quantities force, pressure, and torque. The aim of this joint research project (JRP) is to develop appropriate calibration methods, mathematical models, and uncertainty evaluation. The duration of the project is 3 years for a global amount of €3.6 million. It began in September 2011.

Published
2012
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47. Competency-Based Development and Implementation of a Learning Factory Environment for Human-Robot-Interaction

Author

Knott, Marius, Milloch, Elias, Christ, Lukas, Dückershoff, Tim, Kuhlenkötter, Bernd, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Thiede, Sebastian, editor, and Lutters, Eric, editor

Published
2024
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48. Evaluating CAD Usage to Generate Synthetic Images for Machine Learning-Based Object Detection

Author

Schmelter, T., Nowak, T., Knott, M., Kuhlenkötter, B., Behrens, Bernd-Arno, Series Editor, Grzesik, Wit, Series Editor, Ihlenfeldt, Steffen, Series Editor, Kara, Sami, Series Editor, Ong, Soh-Khim, Series Editor, Tomiyama, Tetsuo, Series Editor, Williams, David, Series Editor, Bauernhansl, Thomas, editor, Verl, Alexander, editor, Liewald, Mathias, editor, and Möhring, Hans-Christian, editor

Published
2024
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