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This paper aims to introduce the history and major achievement of the Chinese private enterprise survey (CPES), which is one of the most enduring large-scale nationwide sample surveys in China, providing important micro firm-level data for understanding and studying the development of Chinese enterprises and entrepreneurs over the past 26 years.

The main body of this paper is based on a bibliometric analysis of all literature using CPES until 2017.

This paper discusses problems that users may encounter during data mining. By doing so, it can assist other researchers to get a better understanding of what has been done (e.g. journals, topics, scholars and institutions) and do their research in a more targeted way.

As members of the survey project team, the authors also take a prospect of the future data design and use, as well as offer some suggestions about how to use the CPES data to improve high-quality development and business environment evaluation in China.

This paper is the first to provide an overall picture of academic papers in China and abroad that have used the CPES data.

While the impact of digitalization on businesses has been extensively studied, the influence of digitalization on marketing outcomes in private enterprises has not received sufficient attention. The current study aims to examine how and when digitalization affects international marketing decisions in the context of private enterprises.

This study employs data from a survey of Chinese private enterprises conducted in 2020, which constitutes the world's largest dataset of its kind. Nearly 19,000 samples were included in the study. Additionally, we also incorporate supplementary data on digitalization in the Chinese region. Employing various methods, this study empirically and robustly examines the proposed research framework within the context of Chinese private enterprises.

Based on the resource-based view and agency theory, this paper found that digitalization can positively impact private enterprises’ direct and indirect international marketing decisions. Furthermore, we introduce the inclusion of innovation capacity and board governance as moderators in the model and find that board governance attenuates the influence of digitalization on international marketing decisions, while innovation capacity enhances the impact of digitalization on direct international marketing but diminishes its effect on indirect international marketing.

This study advances the understanding of the impact of digitalization on international marketing in private enterprises, thereby addressing the gap in the limited focus on digitalization in private enterprises. It also demonstrates how private enterprises effectively utilize digitalization to gain marketing advantages in the international market.

This paper aims to investigate the influences of process parameters on part quality, electrical energy consumption. Moreover, the relationship between part quality and energy consumption of UTR9000 photosensitive resin fabricated by stereolithography apparatus (SLA) was also assessed.

Main effect plots and contour maps were used to analyze the interactions and effects of various parameters on energy consumption and part quality, respectively. Then, a growth rate was used defined as the percentage of the value of energy consumption (or the part quality) of the sample compared to the minimum value of the energy consumption (or the same part quality), to jointly analyze relationships between part quality and energy consumption on a specific process parameter.

The part qualities can be improved with increased energy consumption via adjusting layer thickness, without further increasing energy consumption through adjusting laser power, over-cure and scanning distance. Energy consumption can be highly saved while slightly decreasing the tensile strength by increasing layer thickness from 0.09 mm to 0.12 mm. Energy consumption and surface roughness can be decreased when setting laser power near 290 mW. Setting an appropriate over-cure of about 0.23 mm will improve tensile strength and dimensional accuracy with a little bit more energy consumption. The tensile strength increases nearby 5% at a scanning distance of 0.07 mm compared to that at a scanning distance of 0.1 mm while the energy consumption only increases by 1%.

In this research, energy consumption and multiple part quality for SLA are jointly analyzed first to accelerate the development of sustainable additive manufacturing. This can be used to assist designers to achieve energy-effective fabrication in the process design stage.

Collaborative robots (cobots) are widely used in various manipulation tasks within complex industrial environments. However, the manipulation capabilities of cobot manipulation planning are reduced by task, environment and joint physical constraints, especially in terms of force performance. Existing motion planning methods need to be more effective in addressing these issues. To overcome these challenges, the authors propose a novel method named force manipulability-oriented manipulation planning (FMMP) for cobots.

This method integrates force manipulability into a bidirectional sampling algorithm, thus planning a series of paths with high force manipulability while satisfying constraints. In this paper, the authors use the geometric properties of the force manipulability ellipsoid (FME) to determine appropriate manipulation configurations. First, the authors match the principal axes of FME with the task constraints at the robot’s end effector to determine manipulation poses, ensuring enhanced force generation in the desired direction. Next, the authors use the volume of FME as the cost function for the sampling algorithm, increasing force manipulability and avoiding kinematic singularities.

Through experimental comparisons with existing algorithms, the authors validate the effectiveness and superiority of the proposed method. The results demonstrate that the FMMP significantly improves the force performance of cobots under task, environmental and joint physical constraints.

To improve the force performance of manipulation planning, the FMMP introduces the FME into sampling-based path planning and comprehensively considers task, environment and joint physical constraints. The proposed method performs satisfactorily in experiments, including assembly and in situ measurement.

The purpose of this paper is to provide theoretical guidance and an experimental basis for a smart anti-corrosion coating of halloysite nanocontainers loaded with benzotriazole (BTA) inhibitors on copper in a marine corrosion environment.

In the present study, the smart anti-corrosion coatings of halloysite nanocontainers loaded inhibitors on copper were synthesized by adding BTA inside the halloysite nanocontainers. Then, the halloysite carrier’s surface topography and composition in the halloysite were observed using scanning electron microscopy. After the successful synthesis of the coating, the inhibitor’s physical and chemical properties, as well as the mass change in halloysite, were evaluated in terms of temperature fluctuation and time using thermal gravity analysis (TGA). Finally, electrochemical impedance spectroscopy was used to check the pH selectivity for the self-releasing of BTA out of the nanocontainers.

The results indicate that the efficiency of the nanotubes was enhanced by calcination at high temperatures. The thermal gravity analysis by TGA shows that halloysite nanoparticles store inhibitors BTA and there are approximately 37.39 Wt.% BTA loaded in each nanocontainer. The release of the preloaded BTA from the halloysite nanocontainers is pH 7 in a 3.5% NaCl solution.

The development of a new environmentally safe coating for corrosion protection of metallic surfaces has attracted great interest in material science over the past few years. At present, halloysite nanotubes (HNTs) have become a research hotspot internationally and are widely used in nanocomposites, catalysis, nanofiltration, drug sustained-release and other fields. However, the application of HNT is limited by its modification methods. As the carrier of metal nanocorrosion inhibitor in the Marine corrosive environment, the modification research of HNT still needs to be further studied and improved so as to expand the practical application of HNT in the Marine corrosive environment. In this paper, the modification of HNTs was investigated and observed. Four different modification schemes were used to observe and compare the structural properties of the nanotubes under different conditions so as to provide a theoretical basis for the further loading of HNTs as corrosion inhibitors.

This paper aims to develop an architecture for 3D printers in an Industrial Internet of Things (IIoT) controlled automated manufacturing environment. An algorithm is proposed to estimate the electrical energy consumption of 3D printing jobs, which is used, 3D Printing, Sustainable Manufacturing, Industry 4.0, Electrical Energy Estimation, IIoT to schedule printing jobs on optimal electrical tariff rates.

An IIoT-enabled architecture with connected pools of 3D printers and an Electrical Energy Estimation System (EEES) are used to estimate the electrical energy requirement of 3D printing jobs. EEES applied the combination of Maximum Likelihood Estimation and a dynamic programming–based algorithm for estimating the electrical energy consumption of 3D printing jobs.

The proposed algorithm decently estimates the electrical energy required for 3D printing and able to obtain optimal accuracy measures. Experiment results show that the electrical energy usage pattern can be reconstructed with the EEES. It is observed that EEES architecture reduces the peak power demand by scheduling the manufacturing process on low electrical tariff rates.

Proposed algorithm is validated with limited number of experiments.

IIoT with 3D printers in large numbers is the future technology for the automated manufacturing process where controlling, monitoring and analyzing such mass numbers becomes a challenging task. This paper fulfills the need of an architecture for industries to effectively use 3D printers as the main manufacturing tool with the help of IoT. The electrical estimation algorithm helps to schedule manufacturing processes with right electrical tariff.

Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set.

Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set.

The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM.

With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting.

To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests.

過去的研究顯示 Google 搜尋趨勢資料有助於改善旅遊需求預測的準確度，本研究就此進一步探討 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定的使用對於組合預測準確度所造成的影響。

本研究以 Google 搜尋趨勢指標做為多變量灰色預測模式的解釋變數，並以單變量與多變量灰色模式產生各別預測值。在分別產生由所有的單變量模式 (CUGM)所有的模式 (CAGM), 以及經過涵蓋性檢定所留存下來之模式 (CSET) 所組成之集合後，就各別的組合集以常用的組合方法產生預測值。

以台灣的四個熱搜旅遊城市的旅遊人數進行三個組合集的預測準確度分析。涵蓋性檢定顯示多變量灰色模式在組合預測中扮演重要的角色，而結果亦呈現線性組合方法在 CSET優於在 CUGM 與 CAGM 的準確度，突顯搜尋趨勢指標與涵蓋性檢定對於線性組合方法的有用性。

藉由 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定，旅遊部門應可透過線性組合方法的預測規劃旅遊目的地的經營策略。新冠疫情下於各季的事前預測亦可結合情境預測具體呈現。

為提升組合預測在旅遊需求的預測準確度，本研究結合涵蓋性檢定以分析 Google 搜尋趨勢指標與組合預測準確度之間的關聯性。

旅遊需求,涵蓋性檢定,Google 搜尋趨勢,灰色預測,組合預測

研究型论文

Dado que el uso de los datos de Google Trends es útil para mejorar la precisión de las predicciones, este estudio examina si el uso del índice de búsqueda web de Google Trends combinado con la agregación de relevancia puede mejorar la precisión del predictor.

El modelo predictivo gris genera predicciones bajo un único modelo, mientras que el modelomultivariado utiliza el indicador Google Trends como variable explicativa. Se generaron tresensamblajes generales, incluido el Modelo armónico único (CUGM), los ensamblajes de todos loscomponentes (CAGM) y la prueba de presencia de componentes con predicción (CSET). Laspredicciones individuales encada grupo luego se combinan utilizando métodos de correlación deuso común.

Utilizando el número de turistas en las cuatro ciudades más investigadas de Taiwán, los tresgrupos combinados se clasificaron según su precisión. Las pruebas incluidas muestran que losmodelos multivariados en escala de grises son importantes para la predicción. Además, losresultados de las pruebas muestran que el índice de Google Trends y las pruebas que incluyenmétodos de suma lineal son útiles porque los métodos combinados con CSET funcionan majorque los métodos combinados con CSET. CAGM y VCUG.

La industria de viajes puede usar el índice de búsqueda web de Google Trends para desarrollarestrategias comerciales para atracciones basadas en un conjunto lineal de componentes.

Con el objetivo de mejorar la precisión de los pronósticos agregados, este estudio investiga larelación entre el índice de tendencias de Google y las expectativas generales de viaje junto con laevidencia global.

Demanda de viajes, Experiencia global, Tendencias de Google, Predicción gris

Trabajo de investigación

Molecular dynamics is an emerging simulation technique in the field of machining in recent years. Many researchers have tried to simulate different processing methods of various materials with the theory of molecular dynamics (MD), and some preliminary conclusions have been obtained. However, the application of MD simulation is more limited compared with traditional finite element model (FEM) simulation technique due to the complex modeling approach and long computation time. Therefore, more studies on the MD simulations are required to provide a reliable theoretical basis for the nanoscale interpretation of grinding process. This study investigates the crystal structures, dislocations, force, temperature and subsurface damage (SSD) in the grinding of iron-nickel alloy using MD analysis.

In this study the simulation model is established on the basis of the workpiece and single cubic boron nitride (CBN) grit with embedded atom method and Morse potentials describing the forces and energies between different atoms. The effects of grinding parameters on the material microstructure are studied based on the simulation results.

When CBN grit goes through one of the grains, the arrangement of atoms within the grain will be disordered, but other grains will not be easily deformed due to the protection of the grain boundaries. Higher grinding speed and larger cutting depth can cause greater impact of grit on the atoms, and more body-centered cubic (BCC) structures will be destroyed. The dislocations will appear in grain boundaries due to the rearrangement of atoms in grinding. The increase of grinding speed results in the more transformation from BCC to amorphous structures.

This study is aimed to study the grinding of Fe-Ni alloy (maraging steel) with single grit through MD simulation method, and to reveal the microstructure evolution within the affected range of SSD layer in the workpiece. The simulation model of polycrystalline structure of Fe-Ni maraging steel and grinding process of single CBN grit is constructed based on the Voronoi algorithm. The atomic accumulation, transformation of crystal structures, evolution of dislocations as well as the generation of SSD are discussed according to the simulation results.

The purpose of this paper is to improve the performance and quality of Ti-6Al-4V fabricated by laser powder bed fusion.

Single-track experiments were conducted during the fabrication process to obtain the single tracks with excellent wettability to narrow the process parameter window. The effects of process parameters on the build surface, cross-section, relative density, defects, surface roughness, microstructure and mechanical properties of the parts were analyzed through multilayer fabrication experiments and surface optimization experiments.

The point distance has the greatest influence on the build surface of the fabricated parts, and the unmelted defects can be eliminated when the point distance is 35 µm. The relative density of the fabricated parts decreased with the increase of the point distance, and the hatch spacing has different characteristics with respect to the relative density of the fabricated parts under different laser powers. It was observed that the most of experimental groups with higher relative densities than 99%, and the highest density could reach 99.99%. The surface roughness can be reduced to less than 10 µm through remelting optimization.

The research results can provide theoretical support for scientific researchers and data support for engineers.

This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.

The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.

This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.