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Atomic orbital (AO) normalization is a foundational assumption in electronic structure theory, yet in practice, the norm of contracted basis functions can deviate from unity due to internal reduction and transformation mechanisms applied by quantum chemistry packages. This work presents a systematic framework for analyzing the physical and numerical consequences of primitive basis function elimination and AO-level norm inconsistency. The implemented methodology quantifies norm loss, separates constructive and destructive contributions, and enables precise renormalization by retaining both positive and negative terms within AO representations. Using two representative systems-a Raman-active carotenoid (lycopene) and a phosphorus dimer with through-space (P-P) coupling-sensitivity to AO normalization was evaluated. While vibrational frequencies remained stable across normalization schemes, Raman intensities and -coupling constants showed non-negligible shifts: up to 6 Hz for phosphorus and over 50 units in Raman activity. The results demonstrate that AO normalization is not merely a numerical refinement, but a physically impactful step with implications for precision spectroscopy and quantum computing applications.
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http://dx.doi.org/10.1039/d5cp01681a | DOI Listing |
Entropy (Basel)
August 2025
Department of Physics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), P.O. Box 90950, Riyadh 11623, Saudi Arabia.
In this work, we investigate the quantum coherence and purity in hydrogen atoms under dissipative dynamics, with a focus on the hyperfine structure states arising from the electron-proton spin interaction. Using the Lindblad master equation, we model the time evolution of the density matrix of the system, incorporating both the unitary dynamics driven by the hyperfine Hamiltonian and the dissipative effects due to environmental interactions. Quantum coherence is quantified using the L1 norm and relative entropy measures, while purity is assessed via von Neumann entropy, for initial states, including a maximally entangled Bell state and a separable state.
View Article and Find Full Text PDFVet Radiol Ultrasound
September 2025
School of Agricultural, Environmental and Veterinary Sciences, Charles Sturt University, Wagga Wagga, New South Wales, Australia.
Habitat loss, road trauma, predation, disease, and natural disasters impact the health and survival of the family Macropodidae, including kangaroos. Cardiac disease has been reported, including hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), nutritional myodegeneration, valvular pathology, cardiovascular parasites, toxoplasmosis, and toxicities. Human research has evaluated macropod pericardium and aortic valves as possible bioprostheses.
View Article and Find Full Text PDFJ Pharm Bioallied Sci
July 2025
Department of Ophthalmology, Sree Balaji Medical College and Hospital, Chennai, Tamil Nadu, India.
We describe an unusual instance of a juvenile with proptosis and progressive one sided vision loss. Radiological imaging showed an intraorbital lesion indicative of optic nerve sheath meningioma (ONSM). An endoscopic endonasal surgical method was used for tumor excision, given the medial orbital position of the lesion and growing vision loss.
View Article and Find Full Text PDFIEEE Trans Comput Biol Bioinform
January 2025
Discovering miRNAs associated with diseases can contribute to understanding the pathogenesis and treatment strategies of diseases. In the commonly used graph regularized non-negative matrix factorization methods for miRNA-disease association prediction, there exist issues such as interference from low-dimensional matrix noise and loss of network topology information from partial original data. To solve these issues, we propose a method called L$_{21}$ S-NPFM, which combines L$_{21}$ similarity constrain graph matrix factorization and network projection fusion for miRNA-disease association prediction.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
August 2025
Post-training quantization (PTQ) for transformer-based large foundation models (LFMs) significantly accelerates model inference and relieves memory constraints, without incurring model training. However, existing methods face three main issues: 1) The scaling factors, which are commonly used in scale reparameterization based weight-activation quantization for mitigating the quantization errors, are mostly hand-crafted defined which may lead to suboptimal results; 2) The formulation of current quantization error defined by L2-norm ignores the directional shifts after quantization; 3) Most methods are devised tailored for single scenario, i.e.
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