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An efficient route for the synthesis of valuable benzoxazoles has been developed through self-oxidative cyclization with N–O bond cleavage.

Chalcogen bonding catalysis with divalent chalcogenides required using heteroatoms as electron donors to give reactivity, while the activation of hydrocarbons such as alkenes by this concept remains an unresolved challenge. Herein, we discovered a powerful selenide catalyst that showed unprecedented capability in the activation of alkenes. The Se⋅⋅⋅π interactions were capable of catalyzing a broad range of transformations, including intermolecular cyclization and coupling reactions. Significantly, the Se⋅⋅⋅π bonding activation mode can be exploited to achieve intermolecular enyne cyclizations and controlled cross-coupling of triple alkenes. The activation of alkenes by divalent selenides opens up a new avenue for supramolecular catalysis.

Risk stratification of young patients with hypertension remains challenging. Generally, machine learning (ML) is considered a promising alternative to traditional methods for clinical predictions because it is capable of processing large amounts of complex data. We, therefore, explored the feasibility of an ML approach for predicting outcomes in young patients with hypertension and compared its performance with that of approaches now commonly used in clinical practice. Baseline clinical data and a composite end point—comprising all-cause death, acute myocardial infarction, coronary artery revascularization, new-onset heart failure, new-onset atrial fibrillation/atrial flutter, sustained ventricular tachycardia/ventricular fibrillation, peripheral artery revascularization, new-onset stroke, end-stage renal disease—were evaluated in 508 young patients with hypertension (30.83±6.17 years) who had been treated at a tertiary hospital. Construction of the ML model, which consisted of recursive feature elimination, extreme gradient boosting, and 10-fold cross-validation, was performed at the 33-month follow-up evaluation, and the model’s performance was compared with that of the Cox regression and recalibrated Framingham Risk Score models. An 11-variable combination was considered most valuable for predicting outcomes using the ML approach. The C statistic for identifying patients with composite end points was 0.757 (95% CI, 0.660–0.854) for the ML model, whereas for Cox regression model and the recalibrated Framingham Risk Score model it was 0.723 (95% CI, 0.636–0.810) and 0.529 (95% CI, 0.403–0.655). The ML approach was comparable with Cox regression for determining the clinical prognosis of young patients with hypertension and was better than that of the recalibrated Framingham Risk Score model.

A novel and efficient iodine-catalyzed one-pot reaction of aromatic amines, acetophenones, and elemental sulfur for the synthesis of 2-aryl benzothiazoles is described. The process involves sequential C−S and C-N bond formation followed by C(CO)−C bond cleavage from readily accessible starting materials. A wide range of functional groups is tolerated under oxidant and metal-free condition and moderate to good product yields are obtained.

A nonmetal-catalyzed oxidative cyclization to achieve 2,5-disubstituted oxazoles from inexpensive and readily available substituted chalcone, (diacetoxyiodo)benzene (PIDA) and ammonium acetate (NH4OAc) at room temperature is described. The reaction forms a variety of 2,5-diaryloxazoles in good to excellent yields with broad substrate scope under mild conditions without the requirement of ligands and additional bases.

A simple and practical elemental sulfur-catalyzed one-pot method for the synthesis of benzoxazole derivatives has been developed. The protocol uses inexpensive elemental sulfur as the catalyst and highly effective to promote this transformation without the aid of any metal salts. The corresponding benzoxazoles were achieved in good to high yields. Compared with previous methods, becouse of elemental sulfur promoted decarboxylative cyclization was unique and allow highly flexible and wide range of applications displays great potential for replacing existing methodologies.

The purpose of this paper is to establish a decision-making system for assembly clearance parameters and machine quality level by analyzing the data of assembly clearance parameters of diesel engine. Accordingly, we present an extension of the rough set theory based on mixed-integer linear programming (MILP) for rough set-based classification (MILP-FRST). Traditional rough set theory has two shortcomings. First, it is sensitive to noise data, resulting in a low accuracy of decision systems based on rough sets. Second, in the classification problem based on rough sets, the attributes cannot be automatically determined. MILP-FRST has the advantages of MILP in resisting noisy data and has the ability to select attributes flexibly and automatically. In order to prove the validity and advantages of the proposed model, we used the machine quality data and assembly clearance data of 29 diesel engines of a certain type to validate the proposed model. Experiments show that the proposed decision-making method based on MILP-FRST model can accurately determine the quality level of the whole machine according to the assembly clearance parameters.

The k-means algorithm is a widely used partition clustering algorithm. The traditional k-means algorithm has two problems: it is easy to fall into the local optimal solution; it is very sensitive to the initial solution. In this paper, a k-means algorithm model based on mixed integer linear programming is established. The experiment shows that the effect of the new algorithm is better than the traditional k-means algorithm, and the above two problems are solved well.