28 research outputs found
Machine Learning Based Survival Analysis for Breast Cancer Using Clinical and Genetic Data
No full textThis project studies patient data from the TCGA PanCanAtlas to find clinical and genetical factors affecting survival. Using machine learning, it explores links between gene changes, tumor features, and outcomes. The goal is to improve predictions and support personalized treatment in breast cancer care.Biomedical and Health InformaticsBichindaritz, Isabell
Unsteady MHD free convection flow of casson fluid over an inclined vertical plate embedded in a porous media
No full textToken passing techniques to support real time communications on WTPN used for industrial control applications
No full textWireless Token Passing Networks (WTPN) have gained significant attention in the recent times due to the ability of these networks to overcome the challenge of channel access sharing faced by the wireless networks supporting real-time communications. A WTPN is specified as the network implementing token passing techniques in order to share the wireless channel access medium. In the token passing techniques, only the wireless node holding the token acquires the chance to access the channel, which thereby helps in offering guaranteed channel access time to the nodes connected in the WTPN. The efficiency of token passing techniques in offering guaranteed channel access when compared with master-slave and polling mechanisms resulted in the development of several token passing techniques. This paper intends in surveying and simulating the state-of-art token passing techniques that are applicable for supporting real-time communications over WTPN used for industrial control applications. The theoretical and practical comparison of the token passing techniques presented in this paper can help the practitioners in choosing the suitable token passing technique
Machine Learning-Based Human Movements Mimicking System for Animation and Virtual Reality
No full textA trend, which can be noticed over the last several years, is the increasing interest to the machine learning methods in the animation and virtual reality (VR) industries to process the human motion data. The present abstract studies the possible contribution of human like machine learning simulations on human motions, which aim to improve animation and motion synthesis personalization. In this research, we propose a novel framework that utilizes camera-captured human motion data. This technique, which takes advantage of comes of motion data to simulate these animated characters movements in real time, will allow animators to create more creature-like animated figures with subtle actions as seen in real life. Additionally, in virtual reality, it links users’ movements with their avatars so the whole interaction is more engaging and realistic
Robotic-assisted versus conventional hip arthroplasty: a comparative analysis of perioperative blood management and early outcomes
No full textPurpose: This study aimed to evaluate the impact of Robotic-Assisted Total Hip Arthroplasty (RATHA) versus Conventional Total Hip Arthroplasty (CTHA) on perioperative blood loss, blood transfusion requirements, and early clinical outcomes. Methods: A prospective cohort study was conducted at a high-volume tertiary care center from January 2021 to January 2023. A total of 200 patients undergoing primary THA were equally divided into RATHA (using the MAKO SmartRobotics system) and CTHA cohorts. Propensity score matching adjusted for demographics and baseline characteristics, resulting in 172 matched patients. Primary outcomes included changes in perioperative hemoglobin, estimated blood loss (EBL), and transfusion rates. Secondary outcomes assessed were operative time, length of stay, and transfusion-related adverse events. Results: The RATHA group demonstrated significantly lower post-operative hemoglobin drops (2.49 ± 0.6 g/dL vs. 3.38 ± 1.0 g/dL; p < 0.001), reduced EBL on post-operative day 3 (1125.52 ± 361.2 mL vs. 1611.12 ± 501.4 mL; p < 0.0001), and lower transfusion rates (7.96% vs. 20.4%; p = 0.0175) compared to the CTHA group. Operative time was significantly shorter in the RATHA group (68.01 ± 8.7 minutes vs. 77.1 ± 10.5 minutes; p < 0.0001). All robotic cohort patients were discharged within 3 days, whereas 14% of CTHA patients required extended hospitalization. Conclusion: This study demonstrates that robotic-assisted total hip arthroplasty (RATHA) significantly reduces perioperative blood loss, hemoglobin drop, and blood transfusion rates compared to conventional total hip arthroplasty (CTHA). The observed decrease in operative time and hospital stay in the RATHA group further suggests that robotic assistance may enhance procedural efficiency and support faster patient recovery
Robotic-assisted versus conventional hip arthroplasty: a comparative analysis of perioperative blood management and early outcomes
No full textPurpose: This study aimed to evaluate the impact of Robotic-Assisted Total Hip Arthroplasty (RATHA) versus Conventional Total Hip Arthroplasty (CTHA) on perioperative blood loss, blood transfusion requirements, and early clinical outcomes. Methods: A prospective cohort study was conducted at a high-volume tertiary care center from January 2021 to January 2023. A total of 200 patients undergoing primary THA were equally divided into RATHA (using the MAKO SmartRobotics system) and CTHA cohorts. Propensity score matching adjusted for demographics and baseline characteristics, resulting in 172 matched patients. Primary outcomes included changes in perioperative hemoglobin, estimated blood loss (EBL), and transfusion rates. Secondary outcomes assessed were operative time, length of stay, and transfusion-related adverse events. Results: The RATHA group demonstrated significantly lower post-operative hemoglobin drops (2.49 ± 0.6 g/dL vs. 3.38 ± 1.0 g/dL; p < 0.001), reduced EBL on post-operative day 3 (1125.52 ± 361.2 mL vs. 1611.12 ± 501.4 mL; p < 0.0001), and lower transfusion rates (7.96% vs. 20.4%; p = 0.0175) compared to the CTHA group. Operative time was significantly shorter in the RATHA group (68.01 ± 8.7 minutes vs. 77.1 ± 10.5 minutes; p < 0.0001). All robotic cohort patients were discharged within 3 days, whereas 14% of CTHA patients required extended hospitalization. Conclusion: This study demonstrates that robotic-assisted total hip arthroplasty (RATHA) significantly reduces perioperative blood loss, hemoglobin drop, and blood transfusion rates compared to conventional total hip arthroplasty (CTHA). The observed decrease in operative time and hospital stay in the RATHA group further suggests that robotic assistance may enhance procedural efficiency and support faster patient recovery
Attack Graphs for EPCglobal RFID
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