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Severe airborne particulate pollution frequently occurs over the North China Plain (NCP) region in recent years. To better understand the characteristics of carbonaceous components in particulate matter (PM) over the NCP region.

PM samples were collected at a typical area affected by industrial emissions in Handan, in January 2016. The concentrations of organic carbon (OC) and elemental carbon (EC) in PM of different size ranges (i.e. PM_2.5, PM₁₀ and TSP) were measured. The concentrations of secondary organic carbon (SOC) were estimated by the EC tracer method.

The results show that the concentration of OC ranged from 14.9 μg m⁻³ to 108.4 μg m⁻³, and that of EC ranged from 4.0 μg m⁻³ to 19.4μg m⁻³, when PM_2.5 changed from 58.0μg m⁻³ to 251.1μg m⁻³ during haze days, and the carbonaceous aerosols most distributed in PM_2.5 rather than large fraction. The concentrations of OC and EC PM_2.5 correlated better (r = 0.7) than in PM_2.5−10 and PM_>10, implying that primary emissions were dominant sources of OC and EC in PM_2.5. The mean ratios of OC/EC in PM_2.5, PM_2.5–10 and PM_>10 were 4.4 ± 2.1, 3.6 ± 0.9 and 1.9 ± 0.7, respectively. Based on estimation, SOC accounted for 16.3%, 22.0% and 9.1% in PM_2.5, PM_2.5–10 and PM_>10 respectively.

The ratio of SOC/OC (48.2%) in PM_2.5 was higher in Handan than those (28%–32%) in other megacities, e.g. Beijing, Tianjin and Shijiazhuang in the NCP, suggesting that the formation of SOC contributed significantly to OC. The mean mass absorption efficiencies of EC (MACEC) in PM₁₀ and TSP were 3.4 m² g⁻¹ (1.9–6.6 m² g⁻¹) and 2.9 m² g⁻¹ (1.6–5.6 m² g⁻¹), respectively, both of which had similar variation patterns to those of OC/EC and SOC/OC.

Smoke and dust emissions from industrial furnaces can do great harm to the environment and human health. This paper aims to analyze the morphology, diameter and elements of the submicron particles from the furnace flues and the nearby ambient air by using two typical industrial furnaces, the sintering furnace and the electric furnace.

Two typical industrial furnaces, the sintering furnace and the electric furnace, were chosen in this study, to analyze the morphology, diameter and elements of the submicron particles from the furnace flues and the near-by ambient air.

The results show that the particles from the two furnaces are mainly in the small sizes of 0.3-0.6 μm. Particles from sintering plant flue are mainly spherical and rich in K and Cl, whereas those from the electric plant flue are mainly particles rich in metal elements, such as Zn and Fe, and have different morphology.

The particles in the atmosphere nearby the two furnaces contain aged particles from the flue, lots of spherical particles, rectangle particles and various aggregations. The elements of those particles are complex.

This study aims to focus on the submicron particles (with diameter of 0.2-1.0 μm) of the ambient air from a coal-fired power plant. A systematic examination of their morphology, particle size and chemical element will be analyzed, so as to provide more scientific information and theoretical basis for the formation and control method of inhalable particles, as well as data support for environmental impact and ecological effects assessments.

In this paper, the morphology, size distribution and elemental characteristics of submicron particles from ambient air of a coal-fired power plant are studied by single particle analysis.

The results show that atmospheric particles in coal-fired power plant are mainly spherical particles, and most of them are soot aggregates adhered or coated with other particles with few rectangle particles. The particles collected in the afternoon and evening are mainly of spherical particles, and small-sized particles collected in the morning are mainly spherical ones, while the overall concentration is larger than that of the spherical particles in the size range above 0.5 μm. The results indicated that the larger-sized spherical particles have a lower concentration.

Coal-fired power plants are still the main supply of electricity in China, but the inhalable particles, especially sub-micron particles (0.1-1.0 μm) cannot be effectively captured by the dust removal device from the coal-fired power plant. Thus, a large amount of inhalable particles is emitted into the atmosphere, becoming the major air pollutants in China.

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

Research on the effects of feedback frequency has reported mixed findings. To tackle this problem, the current study focuses on specific feedback signs (i.e. negative feedback). By integrating the face management theory and attribution theory, this study examined the mediating effect of trust in supervisors and the moderating effect of employee-attributed performance promotion motives for negative feedback.

A field study with 176 participants and two supplemental experiments with 143 and 100 participants, respectively, were conducted to test the theoretical model.

Results revealed that the frequency of supervisory negative feedback negatively influenced employees’ trust in supervisors, which in turn influenced employees’ perceptions of feedback utility and learning performance. These indirect effects can be alleviated when employees have high degrees of performance promotion attribution for supervisor motives.

This research extends feedback research by integrating feedback frequency with a specific sign of feedback and revealing a moderated mediation effect of the negative feedback frequency.

The aim of this study is to understand thermal effects and surface topography of roller bearings with misaligned load under combination of multifactors by an experimental method.

A series of orthogonal experiments would need to be planned and performed. A ranking of impact degree of factors on edge effect and eccentric load effect can be learned with multivariate analysis of variance by Statistical Product and Service Solutions software. Influence rules of each individual factor can also be obtained through more experiments. A roller surface phase diagram both before and after test can be observed with metallographic microscope. An axial profile data of roller can be measured by PGI 3D Profiler, then a roller generatrix contour can be achieved through filtering measured signal with empirical mode decomposition method.

Slip fraction has most impact on edge effect, whereas tilting angle plays a key role in eccentric load effect. For the case of low temperature, skidding damage does not occur. Inversely, because of the high pressure in partial elastohydrodynamic lubrication caused by roller tilt, running-in occurs and micro asperity flattening is observed on a rough surface. And, the larger the tilting angle, the more obvious the micro-flattening and the greater the reduction of roller surface roughness after the test.

A lot of theoretical studies on thermal effect of roller bearings surface morphology have been published. However, there are little on relevant experimental study, especially on thermal effect with an integration of sliding, tilting and unbalance loading.

In high-speed processing, the influence on the machining accuracy of a machine tool is greatly caused by the thermal deformation of the motorized spindle; a further study on the thermal characteristics of the spindle is given in this paper. This study aims to reduce the thermal error and improve the performance of the machine tool by discussing the relationships between the temperature distributions and rotating accuracy caused by the thermal deformations of the spindle.

The paper opted for a method combining the theoretical analysis and the experimental study to study the thermal stability of the high-speed motorized spindle. First of all, a finite element model of the spindle was built with ANSYS, whereby temperature distributions and the thermal deformations were successively obtained at different speeds. And then, both the temperature field and the rotating accuracy of the motorized spindle were measured simultaneously by the thermal stability experiment. Finally, the experimental and theoretical results were compared and validated.

The thermal stability of the motorized spindle was studied in this paper, and some findings from the study were as follows: the spindle’s rotating accuracy maintained good in X direction but bad in Y and Z directions in terms of the deformations; the higher front-end temperature of the spindle which can significantly affect the rotating accuracy is needed to be controlled mainly; the recovery speed of the spindle deformation lagged behind the temperature’s fallback speed; the vibration graph about radial rotating sensitivity synthesized by X1 and X2 presented a trifoliate shape.

Based on a built test-bed which can synchronously measure the motorized spindle’s temperature distribution and rotating accuracy with five-point method, the coupling effects of the thermal deformation and temperature are embodied, and not only the vibration graph but also the thermal tilt angles can be gained. Therefore, considering the influence of the thermal deformation on the heat generated by the bearings, the paper fulfilled a study by which it was obtained that the front-end temperature of the spindle, which was higher and could significantly affect the rotating accuracy, needed to be controlled mainly.

This paper aims to explore the impact of digital inclusive finance (DIF) on regional economic growth and innovation-driven development.

Based on the panel data of 31 provinces (autonomous regions and municipalities directly under the central government) in China from 2011 to 2018, this paper explores the impact of DIF on economic growth and innovative development.

(1) DIF has a direct positive effect on economic growth and innovative development; (2) there is significant regional heterogeneity in the impact of DIF on economic growth and innovative development. (3) DIF can indirectly affect economic growth and innovative development by increasing residents’ personal disposable income, increasing fiscal expenditure and improving educational level.

Exploring the relationship between them and digital inclusive financial development can provide a reference for national productivity construction and development.

Economic growth and innovation-driven development have been one of the main concerns of China’s policymakers. Exploring the relationship between them, digital inclusive financial development can provide a reference for national productivity construction and development.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

This paper aims to investigate the problem of adaptive bipartite tracking control in nonlinear networked multi-agent systems (MASs) under the influence of periodic disturbances. It considers both cooperative and competitive relationships among agents within the MASs.

In response to the inherent limitations of practical systems regarding transmission resources, this paper introduces a novel approach. It addresses both control signal transmission and triggering conditions, presenting a two-bit-triggered control method aimed at conserving system transmission resources. Additionally, a command filter is incorporated to address the problem of complexity explosion. Furthermore, to model the uncertain nonlinear dynamics affected by time-varying periodic disturbances, this paper combines Fourier series expansion and radial basis function neural networks. Finally, the effectiveness of the proposed methodology is demonstrated through simulation results.

Based on neural networks and command filter control method, an adaptive two-bit-triggered bipartite control strategy for nonlinear networked MASs is proposed.

The proposed control strategy effectively addresses the challenges of limited transmission resources, nonlinear dynamics and periodic disturbances in networked MASs. It guarantees the boundedness of all signals within the closed-loop system while also ensuring effective bipartite tracking performance.