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Carbon minibeam radiation therapy results in tumor growth delay in an osteosarcoma murine model
FDA approves TROP2-targeted antibody–drug conjugate for breast cancer
A high throughput, high content screen for non-toxic small molecules that reduce levels of the nuclear lamina protein, Lamin B1
Directing autophagy to degrade cell surface receptors
Unveiling soil thermal behavior under ultra-high voltage power cable operations
Abstract The optimal operation of high-voltage underground power cables is crucial for powering our communities, and it hinges on the intricate dynamics of insulation temperature around the conductor, primarily influenced by joule heating. This temperature responsiveness is further molded by seasonal and diurnal fluctuations in power demand, as well as the moisture content in the surrounding soil. Past research concentrated on theoretical analyses and experiments under dry conditions, but our study expands this scope. Through extensive laboratory tests exploring static and cyclic thermal loads in both dry and saturated sand environments, we uncovered valuable insights. Cyclic thermal loads in dry sand demonstrated a significant thermal charging effect, especially with shorter relaxation times. In static thermal loading, utilizing saturated sand enhanced heat dissipation due to higher thermal conductivity. However, it also revealed a noteworthy observation: a robust convection cell formed after three days of continuous heating, presenting challenges for cables under crop fields despite facilitating efficient cooling. Highlighting the importance of high-voltage power cable infrastructure, our study delves into the critical intersection between infrastructure and the underground soil. Understanding these interactions becomes imperative for the sustainable development of clean energy initiatives. As the world transitions to cleaner energy practices, optimizing the performance of underground power cable systems becomes pivotal in realizing their full potential and aligning with broader clean energy goals. This research contributes essential knowledge to enhance the safety, efficiency, and sustainability of high-voltage underground power cable systems in support of a cleaner and more sustainable energy future.
Impact of inoculating various lactic acid bacteria on vitamin A levels in total mixed ration silage
Designer proteins take the bite out of snake venom
Risk assessment of land subsidence in Shanghai municipality based on AHP and EWM
Peptide blocks phase separation and oncogenic transcription
The correlation between serum sex hormone levels and clear cell renal cell carcinoma in male patients
A multicenter, retrospective cohort study on the diagnosis, treatment and natural history of eosinophilic gastrointestinal disorders in the Netherlands
New target to tackle coronaviruses
The relationship between genetic prediction of 486 blood metabolites and the risk of COPD: mendelian randomization study
Development of a new software for pore measurements in foraminifera and the constraints of pore proxy under high oxygen conditions
PKC modulator promotes remyelination
Oncofertility Barriers in Nurses Caring for Women with Breast Cancer
A cross dataset meta-model for hepatitis C detection using multi-dimensional pre-clustering
Abstract Hepatitis C is a liver infection triggered by the hepatitis C virus (HCV). The infection results in swelling and irritation of the liver, which is called inflammation. Prolonged untreated exposure to the virus can lead to chronic hepatitis C. This can result in serious health complications such as liver damage, hepatocellular carcinoma (HCC), and potentially death. Therefore, rapid diagnosis and prompt treatment of HCV is crucial. This study utilizes machine learning (ML) to precisely identify hepatitis C in patients by analyzing parameters obtained from a standard biochemistry test. A hybrid dataset was acquired by merging two commonly used datasets from individual sources. A portion of the dataset was used as a hold-out set to simulate real-world data. A multi-dimensional pre-clustering approach was used in this study in the form of k-means for binning and k-modes for categorical clustering. The pre-clustering approach was used to extract a new feature. This extracted feature column was added to the original dataset and was used to train a stacked meta-model. The model was compared against baseline models. The predictions were further elaborated using explainable artificial intelligence. The models used were XGBoost, K-nearest neighbor, support vector classifier, and random forest (RF). The baseline score obtained was 94.25% using RF, while the meta-model gave a score of 94.82%.