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Key core technology is the most important weapon of the country, and breaking through the “strangled” problem is one of the real problems that China’s emerging industries and enterprises must solve. Accurately identifying the “strangled” problem will help China accelerate the realization of high-level scientific and technological self-reliance and win the battle against key core technologies.

Combined with the characteristics of key core technologies, the key core technology evaluation system was constructed from four dimensions: technology innovation, technology radiation, technology economy and technology safety. We adopt the entropy TOPSIS method to evaluate the patents, and the patents with the top 5% scores are identified as key core technology patents. Then, this study identifies key core technology “strangled” problems in three dimensions: technology value advantage, competitive advantage and quantitative advantage.

Taking the patent data of the global new generation information technology industry from 2011 to 2023 as a sample, 178 moderately “strangled” technologies and 49 severely “strangled” technologies are selected. The study results are consistent with the current situation of the new generation information technology industry’s development, and verify the feasibility and reliability of the key core technology “strangled” problem identification model.

This study uses patent data to identify key core technologies and “Strangled” in the new generation information technology industry. It can provide a reference for relevant national departments and agencies, as well as universities and enterprises.

This paper aims to explore how thermal activation enhances the oxidation complexation of the titanium alloy, aiming to enhance surface quality and processing efficiency.

The titanium alloys were chemically mechanically polished under various temperatures. The removal rate and surface roughness were characterized using a three-dimensional topography tester. The surface composition, content and valence state were characterized by X-ray photoelectron spectroscopy. The abrasion performance of the surface reaction layers was conducted using a friction wear testing machine.

The thermal activation temperature can enhance the chemical-mechanical polishing effect of titanium alloy. The thermal activation temperature can enhance the oxidation complexation synergistic effect of K₂S₂O₈ and KF on titanium alloy, thereby improving the polishing effect. With the increase in temperature, the wear resistance of titanium alloy decreases after oxidation corrosion, making it more susceptible to removal through friction. By promoting the oxidation and corrosion of K₂S₂O₈ and KF on the titanium alloy, higher temperatures can facilitate the formation of easily removable film layers on the surface, thereby enhancing the polishing effect.

This research contributes to enriching the theoretical framework of precision machining of titanium alloy and enhancing surface quality and machining efficiency.

With this statement, the authors hereby certify that the manuscript is the result of their own effort and ability. They have indicated all quotes, citations and references. Furthermore, the authors have not submitted any essay, paper or thesis with similar content elsewhere. No conflict of interest exists in the submission of this manuscript.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0167/

This study aims to propose and numerically analyze a weakly-coupled polarization-maintaining few-mode fiber sensor based on stimulated Brillouin scattering (SBS) for simultaneous multiple-parameter measurement.

First, a weakly-coupled double-edged-eyelid-core polarization-maintaining few-mode fiber (DEEC-PM-FMF) supporting 12 eigenmodes across the whole C + L band is designed to improve the performance of simultaneous multiple-parameter sensing by optimizing the structural parameters of the fiber. Then, the intramodal Brillouin characteristics of the designed fiber, such as Brillouin gain spectrum and Brillouin frequency shift (BFS), are analyzed. Finally, based on intramodal SBS, the performance of the temperature and strain sensing in the designed fiber are studied and discussed.

Numerical investigation shows the minimum errors for synchronized temperature and strain measurement can reach 0.25 and 5.78 µe, respectively.

This work is significant for studying intramodal SBS in the FMFs. Furthermore, the proposed weakly-coupled DEEC-PM-FMF sensor also provides a potential guide for further improving the performance of simultaneous multiple-parameter sensing.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.

This study aims to investigate the efficacy of Ag@GO/Na₂SiO₃ nanocomposite in eliminating As from aqueous solutions. Employing response surface methodology, the research systematically examines the adsorption process.

Various experimental parameters including sample pH, contact time, As concentration and adsorbent dosage are optimized to enhance the As removal process.

Under optimized conditions, the initial As concentration, contact time, pH and adsorbent dosage are determined to be 32 ppm, 50 mins, 6.5 and 0.4 grams, respectively. While the projected removal of As stands at 97.6% under these conditions, practical application achieves a 93% removal rate. Pareto analysis identifies the order of significance among factors as follows: adsorbent dosage > contact time > pH > As concentration.

This study highlights the potential Ag@GO/Na₂SiO₃ as a promising adsorbent for efficiently removing industrial As from aqueous solutions, and it is likely to have a good sufficiency in the filtration of water and wastewater treatment plans to remove some chemical pollution, including paints and heavy metals.

The simplicity of the nanocomposite preparation method without the need for advanced equipment and the cheapness of the raw materials and its potential ability to remove As are the prominent advantages of this research.

This study aims to describe the effect of ironing process parameters on mixing efficiency and gradient generation in Y-micromixers and microfluidic gradient generators (MGGs), respectively.

Material extrusion (MEX) enables the production of miniaturized devices with the advantage of lower manufacturing costs and higher design freedom. However, surface finishing is the most important drawback when it comes to microfluidic applications where flow splitting is not required. First, the effect of ironing line spacing (LS) and speed (IS) on mixing efficiency in Y-micromixers was experimentally investigated. Then, the best ironing settings were chosen to further study the spatial stability of the normalized concentration gradient in MGGs.

Lower ironing LS and IS enhance the microchannel surface smoothness. The best combination of ironing parameters (lowest values of LS and IS) leads to an increase in mixing length of 191% at Q = 10 µL/min and 198% at Q = 20 µL/min, with respect to a similar Y-micromixer geometry where ironing was not performed. These findings were applied in the production of a MGG, showing that the normalized concentration gradient in the crosswise flow direction does not depend on the streamwise position when ironing is performed.

To the best of the authors’ knowledge, for the first time, the possibility of optimizing ironing parameters to enhance the surface roughness in MEX microfluidic devices has been investigated. Ironing of the channel bottom surface allows to reduce ridges-induced flow convection, thus delaying mixing in Y-micromixers and achieving stable concentration gradient in MGGs.

This study proposes a novel algorithm based on the finite volume method for simulating groundwater flows and presents the practical application of this method in geotechnical engineering.

The matrix-free implicit iteration method based on the finite volume method and preconditioning conjugate gradient algorithm was used to discretize and solve the groundwater seepage governing equation. Implicit residual smoothing and GPU parallel techniques were utilized to speed up the computation with the solver.

The new method was assessed and evaluated using benchmark and typical infiltration cases. Both the analytical solutions and solutions of the commercial software GEO-Studio were used to verify the accuracy of the proposed algorithm. The speedup performance of the GPU parallel algorithm was also well reflected.

The results demonstrate that the new algorithm is simple and practical, with fast convergence and high accuracy and can satisfy engineering application requirements.

This study aims to comprehensively analyze the current developments and applications of paper-based electrochemical platforms for blood glucose detection, focusing on their potential to revolutionize point-of-care testing through cost-effective and accessible diagnostic solutions.

The review systematically examines fundamental principles of paper-based platforms, including substrate properties, fluid transport mechanisms and electrochemical detection methods. It critically evaluates recent technological advances in materials science, fabrication techniques and signal amplification strategies while analyzing various case studies demonstrating successful implementations.

Recent innovations in paper-based glucose sensors have achieved remarkable performance metrics, with detection limits reaching sub-millimolar ranges and response times within seconds. The integration of nanomaterials, particularly graphene-based composites and carbon nanotubes, has significantly enhanced sensor sensitivity and stability. Advanced enzyme immobilization techniques using layer-by-layer assembly have demonstrated sustained activity for up to 10 weeks, while novel signal amplification strategies incorporating bimetallic nanoparticles have pushed detection limits into the sub-picogram range.

This review uniquely synthesizes the latest developments in paper-based electrochemical glucose sensing, providing critical insights into the synergistic integration of advanced materials, fabrication methods and detection strategies. It offers valuable perspectives on overcoming current technical challenges and highlights emerging opportunities in smart device integration and artificial intelligence applications, serving as a comprehensive resource for researchers and practitioners in the field of point-of-care diagnostics.

This study aims to explore the entrepreneur–venture nexus in transnational entrepreneurial ventures conducting operations in culturally similar countries. Theoretically, smaller cultural differences imply fewer challenges inherent to the liability of outsidership, thus facilitating cultural arbitrage activities. However, these minor differences can also hinder learning and increase competition with local firms, threatening the survival of ventures in the host country. To clarify this perspective, the research looks at how entrepreneurs’ resources affect venture survival, focusing on transnational entrepreneurs operating in countries that have relatively small cultural differences from their own.

This research is outlined by the capital approach in entrepreneurial opportunity development through a qualitative configurational design.

The results indicate that there are four strategies for achieving venture survivability for transnational entrepreneurship operations in culturally similar countries. This is reflected in both the core configurations associated with long-term venture survivability and in the model of venture survivability for cultural arbitrage opportunity development.

This study’s findings, which include the configurations and the model, speak to the burgeoning conversation of cultural arbitrage by illuminating venture survival strategies for international entrepreneurs operating in countries similar to their home countries. They are relevant for the international entrepreneurship discourse and the larger conversation in entrepreneurship, focusing on the entrepreneur–venture nexus.

The purpose of the study was to investigate the effects of artificial intelligence (A.I.) awareness, advertisement models and source-message incongruence on consumer evaluations of A.I.-generated advertisements. It explores how these factors interact in shaping consumer perceptions and advertising effectiveness.

A 2 (source-message (in)congruence: incongruent vs. congruent) x 3 (A.I. awareness: unawareness, pre-advertisement, post-advertisement) x 3 (advertisement model: traditional human, virtual human, digital twin) between-subjects design was employed in this study. Using stratified random sampling, a total of 231 undergraduate students were recruited from course groups and randomly assigned to one of nine experimental treatments, each involving the viewing of a specific A.I.-generated advertisement followed by a survey. Data were analyzed using two-way ANCOVA and regression analyses, controlling for participants' involvement in sports and brand.

The results indicated that A.I. awareness timing, advertisement model types and source-message incongruence significantly affected consumer evaluations of advertisements. A.I. awareness generally had a positive impact on evaluations, with the most favorable outcomes when awareness of the A.I.-generated nature occurred after viewing the advertisement. Virtual human models were rated the lowest, while digital twin and traditional human models received similarly positive evaluations. Source-message incongruence negatively influenced evaluations. An interaction effect was observed between A.I. awareness timing and advertisement model types under high source-message incongruence, where virtual human models showed the highest effectiveness when A.I. awareness occurred after viewing.

Given that sports are characterized by the transcendence of human limitations and the emphasis on physical and emotional challenges – elements that A.I. cannot replicate – it is essential to examine how sports consumers perceive A.I., which, despite offering efficiency and personalization advantages, contrasts with the fundamentally human nature of athletic performance. This research contributes to the literature on A.I.-generated advertising by uniquely investigating the interaction between A.I. awareness timing and advertisement model types within the context of source-message incongruence. It offers critical insights for practitioners and researchers on strategically timing A.I.-generated ad disclosures and selecting appropriate advertisement models to optimize their effectiveness. By addressing these underexplored variables, the study enhances understanding of consumer perceptions and provides a foundation for more effective A.I. integration in advertising practices.