Internet of Things (IoT) is being widely developed in various fields, and its penetration rate in daily life is continuously increasing. The nature of the social function of objects and giving them ...an identity has made it possible to successfully integrate this technology into many traditional systems and improve their performance by automation. Libraries are one of the most obvious examples of smartening by IoT architecture. So far, various architectures have been presented for smartening libraries through IoT technology. However, a low-cost and ideal architecture that can cover all the requirements in a wide range of smart library applications has not been provided. This article attempts to fill some of the existing research gaps in this field by presenting a new architecture for smartening libraries. In the proposed method, Software Defined Networking (SDN) is used to reduce implementation costs and improve the management process of network components. In this architecture, the communication platform of network active objects is formed based on a cluster-based topology. Also, passive Radio Frequency IDentification (RFID) tags are utilized to manage books and library property. Two stages of evaluation have been conducted for the suggested method's performance: actual deployment and computer simulations. Based on the findings, it can be concluded that this study has succeeded in creating an effective and affordable design for smart libraries, which is a major advancement over conventional libraries.
In this paper, based on the data of the Riiid education platform, the LSTM deep learning model is used to provide accurate prediction and guidance for the education management of colleges and ...universities. The Gini coefficient is also introduced to simplify the calculation process, focusing on predicting the development of students’ careers. To achieve this goal, the online education platform provided a dataset that was carefully pre-processed and cleaned of data, and feature engineering was performed to obtain more informative features. Comparing the AUC value of the offline area of the ROC curve, the AUC value of the LSTM deep learning model can reach 0.758, and the training time of a single model is about 41.8 seconds. Therefore, a deep learning model based on the LSTM algorithm can be used for innovation research.
A highly enantio‐ and regioselective hydrosulfonylation of 1,3‐dienes with sulfonyl hydrazides has been realized by using a palladium catalyst containing a monodentate chiral spiro phosphoramidite ...ligand. The reaction provided an efficient approach to synthetically useful chiral allylic sulfones. Mechanistic studies suggest that the reaction proceeds through the formation of an allyl hydrazine intermediate and subsequent rearrangement to the chiral allylic sulfone product. The transformation of the allyl hydrazine intermediate to the product is the enantioselectivity‐determining step.
A highly enantio‐ and regioselective hydrosulfonylation of 1,3‐dienes with sulfonyl hydrazides by using a palladium catalyst containing a monodentate chiral spiro phosphoramidite ligand is reported. The reaction provides a new strategy for the synthesis of chiral allylic sulfones in high enantioselectivity.
Amorphous solid dispersions (ASDs) are popular for enhancing the solubility and bioavailability of poorly water-soluble drugs. Various approaches have been employed to produce ASDs and novel ...techniques are emerging. This review provides an updated overview of manufacturing techniques for preparing ASDs. As physical stability is a critical quality attribute for ASD, the impact of formulation, equipment, and process variables, together with the downstream processing on physical stability of ASDs have been discussed. Selection strategies are proposed to identify suitable manufacturing methods, which may aid in the development of ASDs with satisfactory physical stability.
The review provides an updated overview of amorphous solid dispersion (ASD) manufacturing techniques. The impact of manufacturing variables of each method and downstream processing on the critical physical stability of ASDs are discussed. Display omitted
We have developed a nickel‐catalyzed desymmetric reductive cyclization/coupling of 1,6‐dienes. The reaction provides an efficient method for constructing a chiral tertiary alcohol and a quaternary ...stereocenter by a single operation. The method has excellent diastereoselectivity and high enantioselectivity, a broad substrate scope, as well as good tolerance of functional groups. Preliminary mechanism studies show that alkyl nickel(I) species are involved in the reaction.
A nickel‐catalyzed desymmetric reductive cyclization/coupling reaction of 1,6‐dienes and alkyl bromides is reported. A chiral tertiary alcohol and a quaternary stereocenter are constructed by a single operation with excellent diastereoselectivity and high enantioselectivity.
•Fast and accurate prediction of non-isothermal airflow distribution are achieved.•Suitable data preprocessing methods are proposed.•Data preprocessing and training dataset have impact on neural ...network generality.•The error submergence occurs when neural network output has no preprocessing.•Separate prediction of multiple variables without data preprocessing is possible.
The indoor environment is important to the daily lives of humans. Fast and accurate prediction of indoor environments is desirable with regard to practical applications, such as coupled simulation, inverse design, and system control. Neural network (NN) is a popular machine learning model used to build mappings between target variables with nonlinear relations. To confirm the feasibility of an NN for fast and accurate prediction of indoor environments (including both velocity and temperature distributions), two-dimensional non-isothermal cases are set and an NN model is constructed in this study, where the inputs are boundary conditions (i.e. inlet velocity, temperature and window surface temperature) and outputs are velocity and temperature distributions. Various data preprocessing methods are utilized, and their results are compared to reveal the impact of data preprocessing on NN performance. The results show that, for most cases, different preprocessing methods can lead to similar NN performances with a prediction time of approximately 350 μs for each case and a prediction error of less than 10% for the maximum value and 5% for the mean value. Without data preprocessing, error submergence is likely to occur, and the gradient descent algorithm may fail to reduce errors of variables with smaller orders of magnitude during the training process. Separate prediction of multiple variables without data preprocessing can achieve accurate predictions as simultaneous prediction with data preprocessing; however, the computation cost for training multiple NNs for separate predictions should be considered.
Chiral benzylic amines are privileged motifs in pharmacologically active molecules. Intramolecular enantioselective radical C(sp3)−H functionalization by hydrogen‐atom transfer has emerged as a ...straightforward, powerful tool for the synthesis of chiral amines, but methods for intermolecular enantioselective C(sp3)−H amination remain elusive. Herein, we report a cationic copper catalytic system for intermolecular enantioselective benzylic C(sp3)−H amination with peroxide as an oxidant. This mild, straightforward method can be used to transform an array of feedstock alkylarenes and amides into chiral amines with high enantioselectivities, and it has good functional group tolerance and broad substrate scope. More importantly, it can be used to synthesize bioactive molecules, including chiral drugs. Preliminary mechanistic studies indicate that the amination reaction involves benzylic radicals generated by hydrogen‐atom transfer.
A highly enantioselective intermolecular benzylic C(sp3)−H bond amination by using a chiral cationic copper catalyst and oxidant di‐tert‐butyl peroxide is reported. This mild, straightforward method can be used to transform an array of feedstock alkylarenes and amides into chiral amines with high enantioselectivities, and it has good functional group tolerance and a broad substrate scope.
In this study, a comprehensive model for suitable carrying capacity of resources and environment was proposed based on ecological footprint method. Using the spatiotemporal distribution data of land ...use in Chongqing Section of Three Gorges Reservoir Area from 2001 to 2016, the response changes of carrying capacity of resources and environment under the evolution of land use structure were investigated. The analytical results showed that the suitable carrying capacity of resources and environment in Chongqing decreased first and then increased. In the early stage of the Three Gorges Project, some districts and counties exhibited the phenomenon of suitable carrying capacity deficit, especially in the northeast of Chongqing. In the main urban area of Chongqing, the suitable carrying capacity was also mainly restricted by the ecological resources conditions, the deficit was getting worse with the increase of population density. In the later stage, by restoring ecology and improving the living and economic conditions, the phenomenon of deficit was gradually alleviated. These findings will provide some references for the protection of ecological environment and the development of social economy in Chongqing Section of the Three Gorges Reservoir Area.
Display omitted
•A concept of suitable carrying capacity of resources and environment is proposed.•Response changes of carrying capacity for land use structure evolution are analyzed.•Carrying capacity surplus and deficit of environment in Chongqing are investigated.
The authors examine the enantioselective hydrogenation of enamines and imines in metal-catalyzed transitions. The enantioselective hydrogenation of several catalysts is also studied.
PDF
