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Salamo‐type ligand H2L was designed and synthesized and reacted with Cu (II) and Zn (II) acetate, respectively, to obtain two binuclear Cu (II) and Zn (II) complexes: [Cu2(L)2]·2CH3OH (1) and [Zn2(L)2] (2) having the same coordination number but distinct geometries. Complex 1 is a Cu (II) complex containing two crystalline ethanol molecules, where two Cu (II) atoms are surrounded by two utterly deprotonated (L)2− parts, forming slightly twisted tetragonal pyramidal configurations of pentacoordinate. In complex 2, two utterly deprotonated (L)2− parts surround two Zn (II) atoms, forming pentadentate twisted triangular bipyramidal configurations. The complexes were elucidated via IR analyses and ultraviolet spectroscopy. Using DFT theoretical calculation, MEPs, IRI, and Hirshfeld surface analysis to investigate further the molecular interactions and reaction sites. Ultimately, the fluorescence characteristics of H2L and its complexes 1 and 2 were discussed. [ABSTRACT FROM AUTHOR]

In Australia there is no centrally administered nationwide assessment system for students who complete their final year (Year 12) of high school. Each state and territory uses its own Year 12 assessment system. Apart from Queensland and the Australian Capital Territory, all states and territories use externally set competitive examinations. The Queensland and Australian Capital Territory systems focus on continuous assessment that commences when students begin Year 11. Hence, policymakers at the state, territory and federal levels in Australia are debating over which of the Year 12 systems is superior, that is, an externally set competitive examination or a school/college-based continuous assessment system? If one of the assessment systems is superior to the other, then it is expected that the superiority should be reflected by the students' subsequent academic performances at their tertiary-level studies. In this article we specifically addressed this issue and compared the academic performances of these two groups of students at an Australian university in their first year of a business degree. We found no difference in their academic performances in the subjects studied. The empirical evidence provided in the article does not, therefore, support the claim that one system is superior to the other. Essentially, both systems have their inherent merits and demerits. The merits of one system are offset by the merits of the other system and vice versa.

Background: Continuous contrast-enhanced ultrasound (CEUS) video is a challenging direction for radiomics research. We aimed to evaluate machine learning (ML) approaches with radiomics combined with the XGBoost model and a convolutional neural network (CNN) for discriminating between benign and malignant lesions in CEUS videos with a duration of more than 1 min. Methods: We gathered breast CEUS videos of 109 benign and 81 malignant tumors from two centers. Radiomics combined with the XGBoost model and a CNN was used to classify the breast lesions on the CEUS videos. The lesions were manually segmented by one radiologist. Radiomics combined with the XGBoost model was conducted with a variety of data sampling methods. The CNN used pretrained 3D residual network (ResNet) models with 18, 34, 50, and 101 layers. The machine interpretations were compared with prospective interpretations by two radiologists. Breast biopsies or pathological examinations were used as the reference standard. Areas under the receiver operating curves (AUCs) were used to compare the diagnostic performance of the models. Results: The CNN model achieved the best AUC of 0.84 on the test cohort with the 3D-ResNet-50 model. The radiomics model obtained AUCs between 0.65 and 0.75. Radiologists 1 and 2 had AUCs of 0.75 and 0.70, respectively. Conclusions: The 3D-ResNet-50 model was superior to the radiomics combined with the XGBoost model in classifying enhanced lesions as benign or malignant on CEUS videos. The CNN model was superior to the radiologists, and the radiomics model performance was close to the performance of the radiologists. [ABSTRACT FROM AUTHOR]