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This paper aims to clarify the relationship between digital transformation and labor structure from the perspectives of microenterprise business strategies and factor allocation efficiency. It attempts to address the gap in existing research by explaining the impact of digital transformation on multidimensional workforce structures and the positive effects of this structural adjustment on labor allocation efficiency. In addition, the study further explores the economic ramifications of digital transformation, clarifying the correlation between changes in labor force structure and enterprise human resource allocation, thus enhancing the employment mobility effects of digital innovation at the enterprise level.

In contrast to prior research, our approach uses text analytics to assess the internal labor structure, incorporating labor skill, position and age into the analytical framework. This approach yields a more comprehensive data set, shedding light on variations in multidimensional employment structures.

The paper asserts that digital transformation significantly influences labor structure changes, evidenced by increased proportions of high-skilled, non-routine and younger laborers, as well as decreased shares of low-skilled, routine and older-age workers. Furthermore, it captures internal labor structure impacts, influenced by enterprise size, ownership, industry density and regional digitization levels. Mechanism analysis indicates moderation of digital transformation effects on labor structure by innovative tasks, labor productivity and management shareholding.

The paper reveals the specific impact of corporate digital transformation on workforce structure, enriching the employment mobility effects of digital innovation at the enterprise level and providing theoretical support for the formulation and implementation of relevant policies.

First, this paper delves into the impact of digital transformation on the internal labor structure from a microlevel perspective, elucidating its mechanisms. Second, in contrast to prior research, it uses text analytics to assess the internal labor structure, incorporating labor skill, position and age into the analytical framework. This approach yields a more comprehensive data set, shedding light on variations in multidimensional employment structures. Lastly, the study investigates the economic ramifications of shifts in employment structures. The findings of this study furnish novel empirical evidence for the debate regarding whether digital transformation can indeed enhance labor allocation efficiency.

It is a complex and dynamic process to provide high-quality rural infrastructure. However, there lacks a holistic performance evaluation method for rural infrastructure provision that reflects changing rural social needs and takes a village as a whole. This study aims to develop a holistic and dynamic performance evaluation model for rural infrastructure in Mainland China.

This study established an evaluation index system by combining the lifecycle theory and the economy, efficiency, effectiveness and equity (4E) theory. This study developed an evaluation model by using the analytic network process (ANP) and matter-element analysis theory (MEAT). The model was validated by two representative villages in Mainland China.

The developed model can reflect dynamic social needs and effectively evaluate the overall infrastructure provision performance of a village. The weight of indicators reflects the changes in Mainland China’s contemporary rural social needs, with particular emphasis on the impact and output performance. The evaluation result shows that the overall performance of the representative villages was excellent but had a tendency toward good. Although the output performance was excellent, different input, process and impact performances resulted in different downgrade trends.

This study provides a theoretical basis for disaggregating the complex issue of the performance of rural infrastructure provision. The results can be used by relevant authorities to make a holistic and dynamic evaluation of the performance of rural infrastructure provision and timely revise planning and management policies.

Data-driven models are increasingly being used to predict the fatigue life of many engineering components exposed to multiaxial loading. However, owing to their high data requirements, they are cost-prohibitive and underperforming for application scenarios with limited data. Therefore, it is essential to develop an advanced model with good applicability to small-sample problems for multiaxial fatigue life assessment.

Drawing inspiration from the modeling strategy of empirical multiaxial fatigue models, a modular neural network-based model is proposed with assembly of three sub-networks in series: the first two sub-networks undergo pretraining using uniaxial fatigue data and are then connected to a third sub-network trained on a few multiaxial fatigue data. Moreover, general material properties and necessary loading parameters are used as inputs in place of explicit damage parameters, ensuring the universality of the proposed model.

Based on extensive experimental evaluations, it is demonstrated that the proposed model outperforms empirical models and conventional data-driven models in terms of prediction accuracy and data demand. It also holds good transferability across various multiaxial loading cases.

The proposed model explores a new avenue to incorporate uniaxial fatigue data into the data-driven modeling of multiaxial fatigue life, which can reduce the data requirement under the promise of maintaining good prediction accuracy.

The purpose of this paper is to study and report the effects of silver (Ag) content, glass phase particle size and Ag/antimony-doped tin oxide (ATO) particle size on the properties of ATO thick film resistor pastes, especially on the microstructure, square resistance, temperature coefficient of resistance (TCR), resistance temperature curve and other properties of the pastes.

Thick film resistor pastes with different Ag content, glass phase particle size and ATO particle size were printed on stainless steel substrates by screen printing technology, and a series of Ag/ATO thick film resistors (TFRs) were obtained after high-temperature sintering. The electrical properties of TFRs were evaluated. The microstructure development, square resistance, TCR and other properties of the developed TFRs were evaluated with the change in Ag content and the particle size.

The results show that with the increase of Ag content, the square resistance of the pastes decreases and the TCR increases. The change rate of resistance after resintering is less than 4%, and the pastes show excellent antiaging properties. Meanwhile, with the increase of the particle size of the glass phase, the square resistance decreases first and then increases, and the TCR increases first and then decreases, which has little effect on the conductive behavior. The increase in ATO particle size leads to an increase in the square resistance of TFRs and a decrease in the TCR.

This paper provides a useful evaluation of the square resistance, TCR and other properties of Ag/ATO thick film resistor pastes, which are related to the Ag content, glass phase particle size and ATO particle size of the developed TFRs. The thick film resistor pastes with zero TCR can be obtained using Ag/ATO as the functional phase without Pd or Pt.

Objective is to investigate the effects of COVID-19 on the performance and productivity of infodemic research. A comprehensive bibliometric analysis is conducted using data extracted from Thomson Reuters' Web of Science, and the analysis is facilitated by the bibliometrix and biblioshiny tools.

Data was extracted from the Web of Science (WoS) database provided by Thomson Reuters. Therefore, literature published outside of the WoS database was not included. Results were extracted about the Document Type, Research Area, Language, Publication year, and country or countries for all authors because this study was interested in scholarly international collaboration. The researcher also used the Thomson Reuters Web of Science’s InCites Essential Science Indicators database, which allowed the researcher to measure the scientific output performance of countries over a period of time. In addition to InCites data, citation data and international collaboration for all countries were also downloaded.

Inclusion and exclusion criteriax: this study focused on literature published by authors identified by each author’s affiliation in each publication. Thus, the WoS topic field was searched by “infodemic” or “information epidemic” or “info ebidemic”. The time span selected for this study started from 2018 to 2022, allowing the researcher to survey the nature of the literature during the last 6 years before COVID-19 and 4 years after COVID-19 to identify the effects of COVID-19 on research in the world regarding both performance and productivity. The study included various types of materials, such as articles, early access, and review articles.

A comprehensive bibliometric analysis is conducted using data extracted from Thomson Reuters' Web of Science, and the analysis is facilitated by the bibliometrix and biblioshiny tools. The findings reveal that prior to the COVID-19 outbreak, researchers contributed a total of 3,960 documents, with the United States leading with 2,933 publications, followed by China with 2,561. However, the production of infodemic research doubled following the onset of the pandemic, resulting in a total of 6,979 documents. Both before and after COVID-19.

The purpose of this study is to systematically investigate the novel phenomenon of rail corrugation on small radius curves with rail joints in mountainous city metros, characterized by the coexistence of short and long wavelengths (30–40 mm and 150–200 mm) on the low rail.

The finite element model of the wheel-rail system in the section with rail joint is constructed based on field surveys. The friction-coupled vibration characteristics of the wheel-rail system are studied from the perspective of friction self-excited vibration of the wheel-rail system and feedback vibration of the rail irregularity.

The rail corrugation with short wavelength is primarily induced by the friction self-excited vibration of wheel-rail system. In contrast, the rail corrugation with long wavelength is predominantly caused by the feedback vibration of rail joint irregularity. Additionally, the feedback vibration of corrugated irregularity accelerates the progression of corrugation depth without triggering the emergence of rail corrugation with new wavelength.

The research advances the understanding of the vibration inducement behind rail corrugation in mountainous city metros.

Recent progress in additive manufacturing methods alleviated manufacturing constraints on devices. Topology optimization (TO) methods can leverage these reduced limitations and this paper aims to study the use of these algorithms in induction heating for injection molding.

In this paper, TO is used to reduce the volume of ferrite in an injection molding tool while aiming at maximizing the performance of the device. Characteristics of the proposed solution such as efficiency and power density are compared to the ones of the original device.

The study shows that it is possible to reduce significantly the amount of ferrite used without impacting the efficiency. The thermal performances of the proposed solution present also slight improvements compared to the original solution.

Optimization algorithms are important for understanding how to design efficient electrical devices. In this paper, the application of TO for injection molding applications presents a new perspective in designing such components.

Loss of control and air crashes are frequently caused by aircraft faults. Therefore, a practical control strategy can prevent aircrafts from losing control without control laws reconstruction. The purpose of this paper is to propose a sideslip trim fault-tolerant control strategy for wing damage and aileron stuck.

The six degree of freedom model of the damaged aircraft is constructed by using the non-center-of-mass approach on the basis of aerodynamic database, which is calculated in XFlow. This paper adopts the sideslip command for trim, combining with the adaptive nonlinear dynamic inversion control to achieve fault-tolerant control.

This strategy can effectively improve the control margin of the remaining control surface and guarantee maneuverability of the aircraft after serious faults.

The original and wing-damaged aircraft models are reconstructed in CATIA, and the aerodynamic data is calculated in XFlow. Sideslip angle is adopted to compensate additional roll moment caused by wing damage or aileron stuck. Adaptive nonlinear dynamic inversion control, combined with sideslip trim, is applied to achieve fault-tolerant control.

The purpose of this study is to evaluate the tea stem natural dye was extracted from tea stem waste and applied to dyeing silk fiber, after which the properties of dyed samples were tested and analyzed.

The dyeing process was optimized using the response surface methodology (RSM) approach. Dyeing temperature, pH and time were chosen as variables and the color difference value as a response. The properties of dyed samples were tested and analyzed.

The optimized dyeing process was as follows: dyeing temperature 70°C, pH 3.5 and time 110 min. The K/S and color difference value of silk fiber dyed with the optimal process dye enzymatic oxidation with laccase was 1.4 and 27.8, respectively. The silk fiber dyed has excellent color fastness, antioxidant and antibacterial property, which greatly increases the added value of the dyed products. Furthermore, the optimized dyeing process did not significantly affect the strength properties and handle of the silk fiber.

Researchers have not used statistical analysis to optimize the process of dyeing process of silk fiber by tea stem natural dye enzymatic oxidation with laccase using response surface methodology. Additionally, this dyeing process was a low-temperature dyeing process, which not only saves energy consumption and reduces silk fiber damage but also obtains superbly dyeing results and biological functional properties, achieve the effects of waste utilization and clean dyeing.

Surface roughness and delamination during the milling of carbon fiber reinforced polymer (CFRP) composite parts in aviation can lead to component rejection. This article aims to optimize cutting conditions to reduce these failures while ensuring compliance with aviation standards. By improving machinability, the goal is to minimize part rejection rates and scrap, optimizing costs and increasing safety.

Full factorial experimental design and response surface methodology (RSM) were used to establish relationships between the cutting parameters and the cutting force, delamination and surface roughness. To validate the model and identify significant parameters, analysis of variance (ANOVA) was performed. The cutting parameters were optimized to reduce cutting force and improve surface quality using ANOVA and RSM.

The lowest response values can be achieved with a cutting speed of 285.35 m/min and a feed of 358.57 mm/min using the Aluminum Chromium Nitride (AlCrN)-coated tool. Accordingly, the optimum cutting force was obtained as 190.97 N, delamination depth as 1.562 mm and surface roughness as 1.431 µm. It has been seen that the obtained surface roughness and delamination values are consistent with aviation literature studies, sectoral data and standards.

This study uniquely examines cutting force, surface roughness and delamination using Titanium Aluminum Nitride (TiAlN)- and AlCrN-coated tools instead of traditional Poly Cyristaline Diamond (PCD) tools. It employs a two-stage experimental framework, starting with a full factorial design followed by RSM. The initial data have been used as inputs for optimization in the second stage to achieve more accurate results.