B(C\u3csub\u3e6\u3c/sub\u3eF\u3csub\u3e5\u3c/sub\u3e)\u3csub\u3e3\u3c/sub\u3e-promoted tandem silylation and intramolecular hydrosilylation: diastereoselective synthesis of oxasilinanes and oxasilepanes

B(C6F5)3 promotes regio- and stereoselective cyclizations of unsaturated alkoxysilanes to generate oxasilinanes and oxasilepanes. The same products are available directly from alkenols via tandem silylation and hydrosilylation

Research on Optimization for Passenger Streamline of Hubs

AbstractThis paper proposes an optimization model for passenger streamline to promote the organization of hub management. Passengers are divided into two different categories, namely familiar type and unfamiliar type. Then the different route choice behaviors of these two types are analyzed. The graph theory is employed to abstract the hub network. The system cost is taken as the optimization objective, and then an optimization design model for passenger streamline is built. To find a solution, we adopt a traversal search algorithm to enumerate all the possible schemes, and then choose the scheme with the minimum system cost. Finally, a simple case is taken to verify the validity of the proposed model

How Do Consumers Identify Useful Review Information in a Social Media Environment?

The popularity of social network services (SNS) provides consumers with new channels to obtain information, and the convenience and diversity of information help consumers reduce the uncertainty and risk in online shopping. However, with the development of the internet, increasing information is available to consumers, and the information overload of online reviews (ORs) creates higher requirements for consumer information screening. Therefore, how to quickly and accurately identify useful information becomes important. There is much literature discussing the usefulness of online word of mouth and ORs, but these studies generally explore information selection and judgment in the traditional business to consumer environment and do not specifically explore social shopping in SNS. In addition, the existing results often only consider the quality factor of ORs and ignore the influence of consumers’ personal choice preferences and the characteristics of OR publishers on consumer behavior. From this, based on the theory of planned behavior and the online trust model, this paper determines the relevant factors and frameworks that affect the usefulness of ORs in the SNS environment. We start from the perspectives of reviewer-related and information-related aspects. Partial least squares structural equation modeling (PLS-SEM) was used to analyze 237 samples for eight factors: credibility, social distance, evaluation (positive and negative), information quality (accuracy, timeliness, and integrity), and presentation. This study enriches the theory of OR usefulness in the SNS environment and provides a reference for the online marketing of enterprises

Comparison of fibre migration in different yarn bodies

The qualities of yarn have been studied by analyzing the fibre migration in the yarn body. The effects of spinning method and raw fibre property on fibre migration have been studied using the tracer fibre technique. Compact-spun (60s Ne), ring-spun (60s and 10s Ne) and rotor-spun (10s Ne) (cotton) yarns have been prepared. However, for the compact-spun yarns, both pure cotton and polyester/cotton blended yarns have been prepared. Two mixing steps have been used, namely (i) the dyed cotton fibre mass is homogeneously mixed with the undyed cotton fibre mass by hand, and (ii) the mixed fibre agglomerates are then subjected to carding to obtain a more uniform mixture. Finally, five sets of yarns are obtained through the consequent spinning process. Fibre measuring system is used to watch the movement of tracer fibres and to get the migration parameters as well as their envelope lines. The results show that the fibre migration of the ring spinning yarn is the most obvious, followed by the compact spinning yarn. The rotor spinning yarn has so many wrapped structures that the fibre migration is not obvious. Polyester/cotton blended yarn, which has better yarn levelness, higher breaking strength and less hairiness, shows higher degree of fibre migration than the yarn made of pure cotton

Research on the Construction Mechanism of Consumers’ Trust Intentions and Behaviors in the Context of Live Streaming Shopping

As a new-type media form integrating text, image, video, and audio, live streaming itself is an excellent way of informationcarrying and interaction. And the development of information technology makes the use of live streaming shopping easier and more convenient. At the same time, with the popularity of live streaming marketing, there are also some consumption traps, which not only harm the rights and interests of consumers but also affect its own development. Thus, how to build trust and improve the credit evaluation mechanism has become a common concern of academic and industrial circles. Anchored in the theory of planned behavior (TPB) and other research results, this paper expounds on the definition and connotation of trust intention in live streaming shopping in detail. From the perspective of consumers, the trust model of live streaming shopping is constructed based on the comprehensive consideration of social presence, consumers\u27 personal attitude, and structural assurance. It adopts partial least squares (PLS) structural equation modeling (SEM) to evaluate the research model and hypothesis. On the basis of 259 samples, the result shows that consumers\u27 trust behavior in live streaming shopping is mainly affected by live streamers\u27 personalities, comment information, social presence, platform characteristics, usefulness, and structural assurance. The research result of this paper will play a positive role in building a more credible environment, improving the trust relationship with consumers, and promoting potential transactions. Meanwhile, it also lays a foundation for understanding consumers\u27 trust behavior and related theories in the context of China

DropMessage: Unifying Random Dropping for Graph Neural Networks

Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also faces some challenges, such as over-fitting, over-smoothing, and non-robustness. Previous works indicate that these problems can be alleviated by random dropping methods, which integrate noises into models by randomly masking parts of the input. However, some open-ended problems of random dropping on GNNs remain to solve. First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models. Second, random noises introduced to GNNs cause the incomplete coverage of parameters and unstable training process. In this paper, we propose a novel random dropping method called DropMessage, which performs dropping operations directly on the message matrix and can be applied to any message-passing GNNs. Furthermore, we elaborate the superiority of DropMessage: it stabilizes the training process by reducing sample variance; it keeps information diversity from the perspective of information theory, which makes it a theoretical upper bound of other methods. Also, we unify existing random dropping methods into our framework and analyze their effects on GNNs. To evaluate our proposed method, we conduct experiments that aims for multiple tasks on five public datasets and two industrial datasets with various backbone models. The experimental results show that DropMessage has both advantages of effectiveness and generalization

Effects of godet wheel position on compact siro-spun core yarn characteristics

Cotton-spandex compact siro-spun core yarns (29.2tex/44.4dtex and 14.6tex/44.4dtex) have been prepared on two kinds of compact spinning, viz complete condensing spinning system (CCSS) and lattice apron compact spinning system (LACSS) respectively. Three godet wheel positions on two kinds of compact system have been selected and corresponding yarn covering effect is studied respectively. Especially, the surface morphology and cross-sections of the core yarns are observed. Then, the covering effects are compared and affecting factors are analyzed. Moreover, other yarn properties including yarn hairiness, strength and evenness are also tested and compared. The results indicate that the covering effect of staple fibres is the most even when the godet wheel position is set on left side for both CCSS and LACSS

Inspecting Model Fairness in Ultrasound Segmentation Tasks

With the rapid expansion of machine learning and deep learning (DL), researchers are increasingly employing learning-based algorithms to alleviate diagnostic challenges across diverse medical tasks and applications. While advancements in diagnostic precision are notable, some researchers have identified a concerning trend: their models exhibit biased performance across subgroups characterized by different sensitive attributes. This bias not only infringes upon the rights of patients but also has the potential to lead to life-altering consequences. In this paper, we inspect a series of DL segmentation models using two ultrasound datasets, aiming to assess the presence of model unfairness in these specific tasks. Our findings reveal that even state-of-the-art DL algorithms demonstrate unfair behavior in ultrasound segmentation tasks. These results serve as a crucial warning, underscoring the necessity for careful model evaluation before their deployment in real-world scenarios. Such assessments are imperative to ensure ethical considerations and mitigate the risk of adverse impacts on patient outcomes.Comment: Submitted to ISBI 202

Waste-to-wealth: biowaste valorization into valuable bio(nano)materials

The waste-to-wealth concept aims to promote a future sustainable lifestyle where waste valorization is seen not only for its intrinsic benefits to the environment but also to develop new technologies, livelihoods and jobs. Based on the concept of waste valorization and circular economy, this review aims to provide an overview of present trends and future potential in the conversion of residues from different food sectors into valuable bio(nano)material