454 Background: Limited data exist on outcomes in mRCC patients with major venous involvement (MVI). Assessment of impact of a given variable on prognosis in cancer patients is challenging given competing causes of death (PMID: 25417239). This analysis aims to estimate cancer prognosis in mRCC patients with MVI, considering death from competing causes. Methods: Data from the Surveillance Epidemiology and End Results (SEER) database (2010-2020) were analyzed for survival and mortality in mRCC patients with MVI. 5-year (y) overall survival (OS) and 5-y probability of cancer death were calculated using the actuarial method in SEER*Stat 8.4.4, reported as percentages with 95% confidence intervals (CI). Results were based on the site of MVI (renal vein, inferior vena cava [IVC]) and stratified by age (<65, >65 years), gender (male, female), and race/ethnicity (Non-Hispanic [NH] White, NH Black, NH Asian/Pacific Islander [API], Hispanic). Results: A total of 89047 mRCC patients were included in our analysis of whom 10785 (12%) had major venous involvement (MVI). Overall, patients who experienced MVI had worse prognosis as compared to those who did not have MVI (5-y OS: 53.2% vs 84.4% and 5-y cancer death: 38.6% vs 8.2% respectively). Among cohort with MVI, prognosis was poor with IVC involvement as compared to involvement of renal vein (5-y OS: 36% vs 57%; 5-y cancer death: 56% vs 35% respectively). Patients who had the worst prognosis with MVI included older adults (>65 y) (5-y OS: 47.8%; 5-y cancer death: 40.8%) and NH Black (5-y OS: 41.5%; 5-y cancer death: 49.8%) (Table). Conclusions: Prognosis in mRCC with MVI, especially with IVC involvement, was poor, particularly for older adults (>65 y) and NH Black patients. Further research is needed for better prognostication and risk stratification to improve outcomes for high-risk patients. Subgroup No MVI MVI N 5-year OS (%), 95% CI 5-year Cancer death (%), 95% CI N 5-year OS (%), 95% CI 5-year Cancer death (%), 95% CI Overall population 78,262 84.4 (84.1-84.7) 8.2 (8.0-8.4) 10,785 53.2 (52.0-54.3) 38.6 (37.5-39.7) Age <65 y 49,217 89.0 (88.6-89.2) 6.5 (6.3-6.8) 5,897 57.5 (56.0-59.0) 36.8 (35.4-38.3) >65 y 29,045 77.0 (76.1-77.2) 10.9 (10.5-11.4) 4,888 47.8 (46.0-49.5) 40.8 (39.2-42.4) Gender Male 49,221 83.0 (82.7-83.5) 8.9 (8.6-9.2) 7,503 53.2 (51.8-54.6) 38.3 (37.0-39.6) Female 29,041 86.5 (86.0-86.9) 7.0 (6.7-7.4) 3,282 53.0 (51.0-55.0) 39.4 (37.4-41.3) Race/ethnicity NH White 49,520 84.0 (83.7-84.4) 8.3 (8.0-8.5) 7,182 53.5 (52.1-54.8) 38.0 (36.7-39.3) NH Black 8,878 82.0 (81.0-82.9) 8.1 (7.5-8.8) 685 41.5 (37.1-45.9) 49.8 (45.4-54.1) NH API 4,747 86.0 (85.1-87.4) 8.5 (7.6-9.4) 713 52.9 (48.2-57.3) 39.9 (35.5-44.1) Hispanic (All Races) 13,826 86.5 (85.8-87.2) 7.9 (7.4-8.4) 2,014 56.2 (53.4-58.8) 36.7 (34.1-39.2) N: number of cases; OS: overall survival; CI: confidence interval; NH: Non-Hispanic; API: Asian/Pacific Islander; MVI: major venous involvement.
A buck converter, sometimes known as a step-down converter, is a kind of DC-to-DC converter that belongs to the DC chopper family. Buck converters are a sort of switching power electrical device that is typically used to provide an alternating DC output voltage that is less than the constant DC input voltage source. The primary focus in the field of electronic appliances is on the root cause of harmonic distortion in a Buck converter system, where interference from harmonic signals frequently appears at both the input and output terminals of the load side, affecting the efficiency and performance of the buck converter. This research paper focuses on the load current ripple content and the harmonic problem encountered by a five-level buck converter. The research consists of ten buck converters with varying levels and two different loads, R load and RLC load, and all of these converters were used to compare one to the other as one of the objectives of this research. After repeated calculations, trial and error, and conclusion of the study hypothesis, the research got intriguing findings where the buck converter ripple and harmonic content were minimized by raising the buck converter level.
In the construction of robot applications, controller is very important in producing good performance the robot system. This paper presents the modeling and simulation of multi fingered robot hand (MFRH) with the conventional and modern controllers where the output responses have been analyzed. A swarm algorithm known as Particle Swarm Optimization (PSO) has been used to find optimum solutions in a large search space. Simulation of modern controller (PSO-PID) has produced better results than the conventional controller in terms of system parameters such as rise time, settling time steady state error and maximum overshoot in the DC Servomotor speed control MFRH.
In recent years, the need for integrated engineering courses has increased.Due to its multidisciplinary nature, Industrial Automation and Robotics degree course is an ideal example of curriculum integration.This paper discusses several issues such as course offerings, topical content, student profile, student performance and other pertinent matters related to the recent development of an Industrial Automation and Robotics undergraduate degree programme at the
This research paper presents the study on design of arm exoskeleton for stroke rehabilitation purpose. The mechanical design of the exoskeleton focuses on few aspects of the arm exoskeleton which are length and the design of the exoskeleton and motor specification. Besides, the experiment of obtaining surface electromyography (sEMG) signal for repetition training for physiotherapy patient purpose is carried out to observe the difference in amplitude and muscle signal of different subjects (four males and four females) due to the amount of training and the angle of the training. The signals are filtered and the average of the root mean square of the data is compared.
The conventional robotic assistive device was based on pre-programmed functions by the robot expert. This makes it difficult for stroke patients use it effectively due to difficulty of torque setting that is suitable for the user movement. Electromyography (EMG) signal measures the electrical signal of muscle contraction.The EMG-based robotics assistive technology would enable the stroke patients to control the robot movement according to the user's own strength of natural movement.This paper discusses the mapping of surface electromyography signals (sEMG) to torque for robotic rehabilitation. Particle swarm optimization (PSO) has been applied as a control algorithm for a number of selected mathematical models. sEMG signals were determined as input data to the mathematical model where parameters of the mathematical model were optimized using PSO. Hence, the good correlated estimated torque as output was obtained.
Federated learning is an emerging approach that enables large-scale decentralized learning without the need to share data among different data owners.This approach is particularly valuable in addressing data privacy concerns in medical image analysis.However, existing methods often impose a strict requirement for label consistency across clients, which significantly limits its applicability.Various clinical sites may only provide annotations for specific organs of interest, and there may be limited or no overlap in the labeled data among different sites.The human brain receives nutrients and oxygen through blood vessels in the brain.The pathology of small vessels, i.e. mesoscopic scale, is a vulnerable component of the cerebral blood supply and can result in major complications such as Cerebral Small Vessel Diseases (CSVD).In this paper, we propose a hybrid architecture for medical image segmentation to produce efficient representations from global and local features and adaptively aggregate them, aiming to fully exploit their strengths to obtain better segmentation performance in federated learning.Furthermore, we propose a multiscale feature extraction module embedded at the bottom of the proposed model, which can efficiently extract hidden multi-scale contextual information and aggregate multi-scale features.Experiments on segmentation over threedimensional rotational angiography of internal Carotid Artery with aneurysm (SHINY-ICARUS) challenge dataset show the effectiveness of the proposed multiscale framework.
Since the COVID-19 pandemic, numerous jobs have become necessary, including the storing and sharing of printed material across computers. One simple way to save data from printed papers to a computer system is to scan them first and then save them as images. However, it would be quite challenging to extract or query text or other information from these photo files to reuse this information. As a result, a method for automatically retrieving and storing information, particularly text, from picture files is required. Optical character recognition (OCR) is an ongoing research topic that aims to create a computer system capable of extracting and processing text from images. To accomplish successful automation, certain significant problems must be identified and addressed. The font properties of characters in paper documents, as well as image quality, are only a few of the latest problems. Characters may not be recognized correctly by the computer system because of many complexities. So, in this study, authors look into OCR in four different contexts and apply them to get our results. However, every OCR is further followed by these two steps. First, a comprehensive explanation of the challenges that may develop during the OCR phases is provided. The key phases of an OCR system are then executed, including pre-processing, segmentation, normalization, feature extraction, classification, and post-processing. It can be used with deep learning software to provide OCR data which is very useful for robotic and AI applications.
The human brain is a complex and heterogeneous organ composed of distinct compartments such as cerebral cortex, the cerebellum, the brainstem, and the subcortical regions.To analyze the chemical composition of tissues in brain, in vivo magnetic resonance spectroscopy allows non-invasive measurements of neurochemicals in either single voxel or multiple voxels.The reconstruction spectra using 1/3rd of original data than current Edited-MRS scans will not only result in four times faster edited-MRS scans but also extensively reduction in radiations.In this work, we present a deep depth-wise channel attention module (DCAM) based fine-tuned network for magnetic resonance spectroscopy image reconstruction.Besides, we have used channel-wise convolutions and average pooling without dimensionality reduction.We have trained the initial network from scratch on track-1 simulated dataset, however due to the limited dataset, we finetune the network on track-2 and track-3.Experiments are conducted on Edited-MRS-Rec-Challenge dataset1 that showed significantly better performance.
In recent years, research into energy saving has been increasing in view of dealing with environmental problems and effectively using energy resources. In a plant, power consumption monitoring of individual inductive devices like motors would have significant impact on energy savings in the long run. However, the current practice of measurement of power consumption of the whole plant rather than individual devices results in penalties for energy losses due to variation of demand charges in a plant. Therefore, electrical power consumption monitoring on a real-time basis is essential to keep it from exceeding the critical demand level. Power meters are practical energy saving devices that can help monitor electricity consumption in a plant. This paper discusses the development and implementation of a micro-controller based portable digital power meter that has the capability to measure three phase power supply for a single device in order to optimize power usage in a plant. It could also be used as an educational tool for undergraduate studies.
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