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A novel process for decolorizing dyes with sulfate radicals (SO 4•−) using an Fe(II)/sulfite system is reported in this manuscript. The objective of this study was to investigate the conditions under which Fe(II) activates Na 2 SO 3 to produce SO 4•− and decolorize organic dyes. Orange II, Rhodamine B, Indigo Carmine, and Reactive Brilliant Blue X-BR could be efficiently decolorized using this novel system, which was compared with the Fe(II)/persulfate and Fenton (Fe(II)/H 2 O 2 ) systems. The Fe(II)/sulfite system surpassed the other two in the decolorization of these dyes, and detailed mechanisms of the Fe(II)/sulfite system were researched. Primary radical identification through quenching experiments using tert-butyl alcohol and ethanol confirmed the existence of SO 4 •− , HO • , and SO 5 •− . A kinetic model was established for the halide ion effect, and k I − ,SO 4 •− (3.2 × 10 11 mol −1 L s −1 ) and R SO 4 •− f (10 −4 −10 −3 mol L −1 s −1 ) were indirectly derived.In conclusion, the Fe(II)/sulfite system is a good candidate for use in detoxifying water contaminants.

We prove the existence of flips for Q-factorial NQC generalized lc pairs, and the cone and contraction theorems for NQC generalized lc pairs. This answers a question of C. Birkar. As an immediate application, we show that we can run the minimal model program for Q-factorial NQC generalized lc pairs. In particular, we complete the minimal model program for Q-factorial NQC generalized lc pairs in dimension ≤ 3 and pseudo-effective Q-factorial NQC generalized lc pairs in dimension 4.

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Approaches to reliably predict the developmental potential of embryos and select suitable embryos for blastocyst culture are needed. The development of time-lapse monitoring (TLM) and artificial intelligence (AI) may help solve this problem. Here, we report deep learning models that can accurately predict blastocyst formation and usable blastocysts using TLM videos of the embryo’s first three days. The DenseNet201 network, focal loss, long short-term memory (LSTM) network and gradient boosting classifier were mainly employed, and video preparation algorithms, spatial stream and temporal stream models were developed into ensemble prediction models called STEM and STEM+. STEM exhibited 78.2% accuracy and 0.82 AUC in predicting blastocyst formation, and STEM+ achieved 71.9% accuracy and 0.79 AUC in predicting usable blastocysts. We believe the models are beneficial for blastocyst formation prediction and embryo selection in clinical practice, and our modeling methods will provide valuable information for analyzing medical videos with continuous appearance variation.