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Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

The rich financial knowledge of small and micro business owners helps to enhance the formal credit demand of small and micro enterprises and change the credit channel preference of small and micro enterprises. The purpose of this paper is to explore the relationship between financial knowledge and the credit practices of 290 small and micro enterprises in China’s Jiangsu and Shandong provinces based on their formal credit needs and preferred channels of credit.

To measure the degree of the credit constraints of small and micro enterprises, this study applied questionnaire surveys to obtain information on the credit demand and supply of 363 small and micro enterprises in the Jiangsu and Shandong provinces. Firstly, a probit model is used to study the influence of financial knowledge on the formal credit demand and credit acquisition possibility of small and micro enterprises, and tool variables and a biprobit model are used to deal with the possible errors of endogenesis and sample selection. Secondly, a tobit model is used to study the influence of financial knowledge on the credit access of small and micro enterprises in different channels, and tool variables and a Heckman two-stage model are used to deal with endogenesis and possible errors in sample selection. Finally, this study carried out a series of robustness tests to make the conclusions more reliable.

This study is based on the perspective of the knowledge-based view to explore the impact of financial knowledge on the credit behaviour of small and micro enterprises. This study found that financial knowledge can increase a small and micro enterprise’s formal credit needs and drive the small and micro enterprise to actively apply for loans. Furthermore, financial knowledge has a significant and positive influence on the acquisition of formal credit and approved lines of formal credit and a significant and negative influence on the acquisition of informal credit and approved lines of informal credit.

The results indicated that increased financial knowledge can increase the likelihood of a small and micro enterprise to prefer formal credit and reduce the likelihood of it to prefer informal credit channels.

Financial knowledge is the ability to master basic economic knowledge and financial concepts as well as the ability to use knowledge to manage and allocate financial resources. The rich financial knowledge of small and micro business owners helps to enhance the formal credit demand of small and micro enterprises and change their credit channel preference. This paper offers a new perspective on the problems of credit constraint, low participation in formal credit markets and high participation in private credit markets among China’s small and micro enterprises and valuably supplements the research literature.

Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improved optimization algorithm, named, adaptive resistance and stamina strategy-based dragonfly algorithm (ARSSDA).

To speed up the convergence, ARSSDA applies an adaptive resistance and stamina strategy (ARSS) to conventional dragonfly algorithm so that the search step can be adjusted appropriately in each iteration. In ARSS, it includes the air resistance and physical stamina of dragonfly during a flight. These parameters can be updated in real time as the flight status of the dragonflies.

The performance of ARSSDA is verified by 30 benchmark functions of Congress on Evolutionary Computation 2014’s special session and 3 well-known constrained engineering problems. Results reveal that ARSSDA is a competitive algorithm for solving the optimization problems. Further, ARSSDA is used to search the optimal parameters for a bucket wheel reclaimer (BWR). The aim of the numerical experiment is to achieve the global optimal structure of the BWR by minimizing the energy consumption. Results indicate that ARSSDA generates an optimal structure of BWR and decreases the energy consumption by 22.428% compared with the initial design.

A novel search strategy is proposed to enhance the global exploratory capability and convergence speed. This paper provides an effective optimization algorithm for solving constrained optimization problems.

The mesoscale structure (MS) has a significant impact on the mechanical performance of parts made by additive manufacturing (AM). This paper aims to explore the design and fabrication of force-flow guided reinforcement mesoscale structure (FFRMS) compared with the homogeneous mesoscale structure (HMS), which is inconsistent with the stress field for a given load condition. Some cases were presented to demonstrate the mechanical properties of FFRMS in terms of MS combined with quasi-isotropy and anisotropy.

The paper consists of four main sections: the first developed the concept of FFRMS design based on HMS, the second explored volume fraction control for the proportion of force-flow lines in terms of mechanical property requirement, and the third presented a sequence stacking theory and practical manufacturing process framework and the final sections provided some application case studies.

The main contributions of this study were the definition and development of the FFRMS concept, the application framework and the original case studies. As an example, a typical lug designed with the proposed FFRMS method was fabricated by three different AM processes. The test results showed that both the strength and stiffness of the specimens are improved greatly by using the FFRMS design method.

The superposition of HMS as the basement and force-flow as an indication of the stiffener, leading to a heterogeneous structure, which exhibits more efficient and diversified means compared with the traditional way of increasing the HMS density merely.

The purpose of this study is to propose extended potentials and investigate the applicability of extended Finnis–Sinclair (FS) potential to Cr with the unit cell structure of body-centered cubic (BCC Cr).

The parameters of each potential are determined by fitting the elastic constants, cohesive energy and mono-vacancy formation energy. Furthermore, the ability of the extended FS potential to describe the crystal defect properties is tested. Finally, the applicability of reproducing the thermal properties of Cr is discussed.

The internal relationship between physical properties and potential function is revealed. The mathematical relationship between physical properties and potential function is derived in detail. The extended FS potential performs well in reproducing physical properties of BCC Cr, such as elastic constants, cohesive energy, surface energy and the properties of vacancy et al. Moreover, good agreement is obtained with the experimental data for predicting the melting point, specific heat and coefficient of thermal expansion.

In this study, new extended potentials are proposed. The extended FS potential is able to reproduce the physical and thermal properties of BCC Cr. Therefore, the new extended potential can be used to describe the crystal defect properties of BCC Cr.

The purpose of this paper is to fabricate a high-performance filtration electrospun nanofiber membrane with antibacterial function. The Ag nanoparticles (AgNPs) gotten by reducing AgNO₃ act as antimicrobial agent. Then the AgNPs/Polyacrylonitrile (AgNPs/PAN) composite nanofiber membrane was prepared by electrospinning.

The electrospun Ag/PAN composite membrane was prepared by one step, in which the Ag particles were acting as antibacterial agent and PAN nanofiber as the upholder of the composite mat. AgNPs were obtained by reducing AgNO3 in N,N-Dimethylformamide (DMF) solution at high temperature. Meanwhile, the PAN particles were added to DMF solution and dissolved. Then the Ag/PAN nanofiber was obtained by electrospinning.

The thinner nanofiber can be produced with PAN concentration of 12 per cent and AgNPs concentration of 10 per cent. Finally, the filtration resistance of the composite membrane with antibacterial property is as high as 99.1 per cent, and the filtration efficiency is only 83 Pa. Therefore, the AgNPs/PAN composite membrane is the ideal choice for air filtration with antibacterial property.

The AgNPs/PAN composite nanofiber membrane has high filtration performance for particulate matter (PM)25 and outstanding antibacterial property to Escherichia coli and Staphylococcus aureus, which can be used with masks, air-conditioning filters (including car air-conditioning filters), window screening and other similar objects.

The purpose of this paper is to invent a new functional coated fabric based on nanomaterials to shield UV and IR. Multifunctional surface coatings with ultraviolet (UV)/near infrared radiations protection and waterproof were widely applied in outdoor fabrics. Herein, ultrafine TiO2 and nano-antimony doped tin dioxide (ATO) were prepared and embedded into water-based polyurethane (PU) coatings and then coated on the nylon fabric.

ATO was prepared using the sol–gel method and the two powders were dispersed by ball milling. The results of zeta potential and particle size distribution showed that the ultrafine TiO2 and nano-ATO could be stably dispersed in water at pH 8 with the presence of sodium polycarboxylate. The optimal process was screened out by orthogonal design and scanning electron microscopy (SEM), UV protection, thermal insulation and water-pressure resistance were tested. SEM images indicated the nanoparticles could be uniformly dispersed in the coatings.

The effect of UV prevention can get to UPF > 50, UVA < 5 per cent, which meet up with the AATCC 183-2014. Coatings can effectively lower the temperature of fabric surface by 8∼9ºC through the self-made closed test system and by 3ºC through the open test system.

These PU coatings are environment-friendly and adhesive to impart waterproof, UV-proof and thermal insulation properties to nylon fabrics by coating finishing.

This article investigates the mechanism of the direct and indirect effects of epidemics on agricultural production and projects the impact of COVID-19 on agricultural output in China.

This article first adopts a dynamic panel model and spatial Durbin model to estimate the direct and indirect effects, followed by a growth accounting method to identify the channels by which epidemics affect agriculture; finally, it projects the overall impact of COVID-19 on agriculture.

The incidence rate of epidemics in a province has a negative impact on that province's own agricultural productivity, but the increase in the input factors (land, fertilizer and machinery) can make up for the loss and thus lead to insignificant direct effects. However, this “input-offset-productivity” mechanism fails to radiate to the surrounding provinces and therefore leads to significant indirect/spillover effects. It is projected that COVID-19 will lower China's agricultural growth rate by 0.4%–2.0% in 2020 under different scenarios.

It is crucial to establish a timely disclosure and sharing system of epidemic information across provinces, improve the support and resilience of agricultural production in the short run and accelerate the process of agricultural modernization in the long run.

Considering the infectivity of epidemics, this article evaluates the mechanism of the direct and indirect effects by introducing a spatial dynamic model into the growth accounting framework. Moreover, besides the impact on input portfolio and productivity, this article also investigates whether epidemics reshape agricultural production processes due to panic effects and control measures.

This paper aims to examine the joint influence of incidental affect and mood-changing prices on consumers’ hotel booking intention in an online purchase context.

Based on the integrative framework of affect evaluation and affect regulation, a 3 × 2 full factorial between-subject online experiment in an online booking scenario is developed to investigate how consumers’ booking intentions change by mood inductions (happy, neutral and sad) and price levels (below versus above reference price).

Results showed that when the observed price was a mood-threatening cue, participants who were induced to feel either happy or sad by a commercial had a higher booking intention than those who were induced to feel neutral. However, there were no significant differences in participants’ booking intentions across pre-purchase affective states when the observed price was a mood-lifting cue.

The current study contributes to a better understanding and prediction of consumers’ action tendencies resulting from the interactions between specific incidental affects and mood-changing opportunities in an online hotel reservation environment.

Online booking companies and online travel agencies in general may wish to incorporate mood-changing components into their booking web pages to enhance potential bookers’ purchase intentions at any given price.

This research is one of the first empirical studies to instantiate the integrative affective mechanism in an online purchase setting. As e-commerce and online marketplaces are taking the place of traditional brick-and-mortar retailing, it is critical for hospitality industry marketers to fully understand how consumers’ pre-purchase emotions influence their purchase decisions.