The concept of parity-time (PT) symmetry in the field of optics has been intensively explored. This study shows the absence of exceptional points in a three-dimensional system composed of a square waveguide array with diagonally-balanced gain/loss distribution. More specifically, we show that an array of four coupled waveguides supports eight fundamental propagation supermodes, four of which are singlet, and the other two pairs are double degenerated. It is found that the singlet states follow the routine PT phase transition; however, the double-degenerated modes never coalesce as the gain/loss-to-coupling strength level varies, showing no actual PT symmetry-derived behavior. This is evident in the phase rigidity which does not approach zero. The absence of exceptional points is ascribed to the coupling of non-symmetric supermodes formed in the diagonal waveguide pairs. Our results suggest comprehensive interplay between the mode pattern symmetry, the lattice symmetry, and the PT-symmetry, which should be carefully considered in PT-phenomena design in waveguide arrays.
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http://dx.doi.org/10.1038/srep22711 | DOI Listing |
Front Pharmacol
January 2025
Department of Psychiatry, Medical University of Warsaw, Warsaw, Poland.
Background: Due to its exceptional effectiveness, clozapine (CLO), whose metabolite is norclozapine (NCLO), is a drug of choice in the management of treatment-resistant schizophrenia. The purpose of this study was to assess the factors modifying the CLO/NCLO ratio (CNR).
Methods: A total of 446 blood samples (233 of which were drawn from females and 213 from males, aged from 18 to 77 years) were analyzed in this study.
Front Oncol
January 2025
Department of Surgical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, Oradea, Romania.
This study presents a rare case of three synchronous colon tumors with metastasis to the left inguinal lymph node, challenging the conventional understanding of the metastatic pathways and highlighting the exceptional nature of such occurrences. This highlights the importance of considering alternative atypical metastatic routes for the management of colon cancer. A literature search was performed to identify similar cases.
View Article and Find Full Text PDFFront Public Health
January 2025
Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Background: Machine learning is pivotal for predicting Peripherally Inserted Central Catheter-related venous thrombosis (PICC-RVT) risk, facilitating early diagnosis and proactive treatment. Existing models often assess PICC-RVT risk as static and discrete outcomes, which may limit their practical application.
Objectives: This study aims to evaluate the effectiveness of seven diverse machine learning algorithms, including three deep learning and four traditional machine learning models, that incorporate time-series data to assess PICC-RVT risk.
Front Med (Lausanne)
January 2025
Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Background: Gastroparesis following complete mesocolic excision (CME) can precipitate a cascade of severe complications, which may significantly hinder postoperative recovery and diminish the patient's quality of life. In the present study, four advanced machine learning algorithms-Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and -nearest neighbor (KNN)-were employed to develop predictive models. The clinical data of critically ill patients transferred to the intensive care unit (ICU) post-CME were meticulously analyzed to identify key risk factors associated with the development of gastroparesis.
View Article and Find Full Text PDFFront Cardiovasc Med
January 2025
Department of Cardiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
Introduction: The risk of mortality associated with cardiac arrhythmias is considerable, and their diagnosis presents significant challenges, often resulting in misdiagnosis. This situation highlights the necessity for an automated, efficient, and real-time detection method aimed at enhancing diagnostic accuracy and improving patient outcomes.
Methods: The present study is centered on the development of a portable deep learning model for the detection of arrhythmias via electrocardiogram (ECG) signals, referred to as CardioAttentionNet (CANet).
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