This paper aims to investigate failures induced by vibrations on machines, focusing on agricultural ones. The research on literature has brought to light a considerable amount of data on the driven vehicles and not much on the operating machines, including the ones that we looked for. For this reason, it was decided to direct a survey with the people who work with agricultural machinery every day: operators, sub-contractors, and producers. They were asked about the most frequent breakage, particularly in relation to the rotary harrow, the topic of this work. The questionnaire results showed the types of failures the harrow is most vulnerable to, indicating the times of failure and reparation and the need to set up a potentially useful preventive maintenance supporting system on these machines. Part of the work was then focused on the proposition of a method to investigate bearing failures in the rotary harrow, considering that these have been analyzed in the technical literature and in the survey as the most at-risk components. The proposed method in this work serves as a beginning for the development of a future on board sent-shore-based maintenance system for continuous monitoring of the bearing.
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http://dx.doi.org/10.3390/s21041547 | DOI Listing |
J Med Internet Res
January 2025
Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, China.
Background: Acute kidney injury (AKI) is a common complication in hospitalized older patients, associated with increased morbidity, mortality, and health care costs. Major adverse kidney events within 30 days (MAKE30), a composite of death, new renal replacement therapy, or persistent renal dysfunction, has been recommended as a patient-centered endpoint for clinical trials involving AKI.
Objective: This study aimed to develop and validate a machine learning-based model to predict MAKE30 in hospitalized older patients with AKI.
Neurogastroenterol Motil
January 2025
School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Background: Irritable Bowel Syndrome (IBS) is a prevalent condition characterized by dysregulated brain-gut interactions. Despite its widespread impact, the brain mechanism of IBS remains incompletely understood, and there is a lack of objective diagnostic criteria and biomarkers. This study aims to investigate brain network alterations in IBS patients using the functional connectivity strength (FCS) method and to develop a support vector machine (SVM) classifier for distinguishing IBS patients from healthy controls (HCs).
View Article and Find Full Text PDFChaos
January 2025
AIMdyn, Inc., Santa Barbara, California 93101, USA.
Koopman operator theory has found significant success in learning models of complex, real-world dynamical systems, enabling prediction and control. The greater interpretability and lower computational costs of these models, compared to traditional machine learning methodologies, make Koopman learning an especially appealing approach. Despite this, little work has been performed on endowing Koopman learning with the ability to leverage its own failures.
View Article and Find Full Text PDFJpn J Radiol
January 2025
Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece.
Objective: Calcific tendinopathy, predominantly affecting rotator cuff tendons, leads to significant pain and tendon degeneration. Although US-guided percutaneous irrigation (US-PICT) is an effective treatment for this condition, prediction of patient' s response and long-term outcomes remains a challenge. This study introduces a novel radiomics-based model to forecast patient outcomes, addressing a gap in the current predictive methodologies.
View Article and Find Full Text PDFInt Urol Nephrol
January 2025
Faculty of Medical Sciences, Pharmacology and Toxicology Department, University of Kragujevac, Kragujevac, Serbia.
Purposes: Intermediate-risk prostate cancer (IR PCa) is the most common risk group for localized prostate cancer. This study aimed to develop a machine learning (ML) model that utilizes biopsy predictors to estimate the probability of IR PCa and assess its performance compared to the traditional clinical model.
Methods: Between January 2017 and December 2022, patients with prostate-specific antigen (PSA) values of ≤ 20 ng/mL underwent transrectal ultrasonography-guided prostate biopsies.
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