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Oxidations represent important reactions that are ubiquitous in academia and industry. Hydrogen peroxide (H2O2) is a common source of active oxygen for oxidations. H2O2 is usually diluted in water as it is too unstable to be used in pure form. However, the presence of water can complicate reactions because biphasic mixtures with organic solvents form. Furthermore, secondary reactions with water may lead to side products. Therefore, alternative forms of H2O2, such as peroxide adducts, are an active area of research. Di(hydroperoxy)alkane adducts of phosphine oxides are one attractive solution because they are soluble in organic solvents, crystallizable, shelf-stable and active towards a variety of oxidation reactions. The only drawback is that the phosphine oxide carrier has to be removed after the reaction. In this contribution, the bifunctional ligand (EtO)3Si(CH2)2PPh2 is immobilized on a silica (SiO2) support which is subsequently end-capped with EtOSi(CH3)3. The new surface-bound di(hydroperoxy)propane adduct is then generated with the immobilized phosphine oxide as carrier. The adduct and a deuterated analog are characterized with solid-state and solution NMR spectroscopy. It has been demonstrated that substrates in organic solvents easily access the surface-bound peroxide and are oxidized quantitatively. The phosphine oxide carrier remains bound to the surface and can be removed easily by settling of the silica. Using the oxidative esterification of nonyl aldehyde it is proven that the immobilized peroxide adduct does not leach from the silica support and is active and reusable over multiple cycles. 

Rigorous, mathematical reasoning, i.e., proof, is the foundation of any undergraduate computer science education. However, students find mathematical proof exceedingly challenging, but also at the same time do not see its relevance to programming. We address these concerns with Snowflake, an educational proof assistant designed to help undergraduates overcome these difficulties when authoring mathematical proof. Snowflake does this by operating in a context where mathematical proof is introduced alongside programming in either a CS1 or CS2 context. The lens that we use to unite the two concepts is program correctness, a topic that immediately makes relevant the concept of formal reasoning as students are perpetually faced with the issue of whether their code is correct. Snowflake is a proof assistant designed for the needs of undergraduates in courses that closely time programming and proof. It is a web-based application that helps students author proofs not only in the context of program correctness in-the-small, but also other topics found in discrete mathematics courses. We report on the design of Snowflake, the kinds of reasoning it enables, and our plans to deploy Snowflake in the classroom. 

Counting multi-vehicle motions via traffic cameras in urban areas is crucial for smart cities. Even though several frameworks have been proposed in this task, there is no prior work focusing on the highly common, dense and size-variant vehicles such as motorcycles. In this paper, we propose a novel framework for vehicle motion counting with adaptive label-independent tracking and counting modules that processes 12 frames per second. Our framework adapts hyperparameters for multi-vehicle tracking and properly works in complex traffic conditions, especially invariant to camera perspectives. We achieved the competitive results in terms of root-mean-square error and runtime performance.