Hai Jiang, YiYing Lu, Liwen Ding, Wenzhong Lu, Guifen Fan and Yusheng Shi
Aluminum nitride (AlN) ceramics are suitable substrate and package materials for high-power integrated circuits.
Abstract
Purpose
Aluminum nitride (AlN) ceramics are suitable substrate and package materials for high-power integrated circuits.
Design/methodology/approach
Dense AlN ceramics with Y2O3 and LaF3 as sintering additives are prepared. The effects of these additives on the density, phase composition, microstructure and thermal conductivity of AlN ceramics are investigated.
Findings
Results show that 2 Wt.% Y2O3-doped additive is insufficient for the samples to achieve the full densification sintered at 1,700°C. When LaF3 is added with Y2O3, the samples are perfectly densified at the same sintering condition. The relative density and thermal conductivity of the samples are 97.8-99.07 per cent and 169.104-200.010 W·m-1·K-1, respectively. The density of the samples and their microstructure, especially the content and distribution of secondary phases, is necessary to control the thermal conductivity of AlN ceramics.
Originality/value
Y2O3 and LaF3 additives can effectively promote densification and enhance the thermal conductivity of AlN ceramics in a low sintering temperature, and the AlN ceramics added with Y2O3-LaF3 might have potential applications in package materials for high-power integrated circuits.
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Liwen Feng, Xiangyan Ding, Yinghui Zhang, Ning Hu and Xiaoyang Bi
The study delves into the influence of wear cycles on these parameters. The purpose of this paper is to identify characteristic patterns of σRS and εPEEQ that discern varying wear…
Abstract
Purpose
The study delves into the influence of wear cycles on these parameters. The purpose of this paper is to identify characteristic patterns of σRS and εPEEQ that discern varying wear situations, thereby contributing to the enrichment of wear theory. Furthermore, the findings serve as a foundational basis for nondestructive and in situ wear detection methodologies, such as nonlinear ultrasonic detection, known for its sensitivity to σRS and εPEEQ.
Design/methodology/approach
This paper elucidates the wear mechanism through the lens of residual stress (σRS) and plastic deformation within distinct fretting regimes, using a two-dimensional cylindrical/flat contact model. It specifically explores the impact of the displacement amplitude and cycles on the distribution of residual stress and equivalent plastic strain (εPEEQ) in both gross slip regime and partial slip regimes.
Findings
Therefore, when surface observation of wear is challenging, detecting the σRS trend at the center/edge, region width and εPEEQ distribution, as well as the maximum σRS distribution along the depth, proves effective in distinguishing wear situations (partial or gross slip regimes). However, discerning wear situations based on εPEEQ along the depth direction remains challenging. Moreover, in the gross slip regime, using σRS distribution or εPEEQ along the width direction rather than the depth direction can effectively provide feedback on cycles and wear range.
Originality/value
This work introduces a novel perspective for investigating wear theory through the distribution of residual stress (σRS) and equivalent plastic strain (εPEEQ). It presents a feasible detection theory for wear situations using nondestructive and in situ methods, such as nonlinear ultrasonic detection, which is sensitive to σRS and εPEEQ.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0005/
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This paper aims to help scholars to know the frontiers in the strategic management field. On studying, it was noted that business strategic management originated from America in…
Abstract
Purpose
This paper aims to help scholars to know the frontiers in the strategic management field. On studying, it was noted that business strategic management originated from America in the 1960s and has experienced more than half a century. However, strategic management development lacks systematical summary in the twenty-first century. The scientometric method was appliedto find out the frontiers and progress of the research of strategic management in the twenty-first century, based on the literature from 2001 to 2012 in the Strategic Management Journal.
Design/methodology/approach
In the paper, the authors mainly used the scientometric method and applied keywords, co-occurrence method combined with multistatistical methods and mutation words analysis, author co-citation, literature co-citation and keywords co-occurrence (national).
Findings
The findings show that the strategic management research focuses on the following theories and academic thoughts: knowledge-based view, network organization research and dynamic capability are the mainstream; besides, strategy risk, the stakeholders analysis of strategy management, corporate reputation and strategic concept also attract the attention of researchers; Barney, Teece and Porter have made significant contributions to strategy management research since the twenty-first century.
Originality/value
The findings in the paper will help scholars in the field of strategic management to know the main frontiers of the theory, as well as the main contributors.
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Jing Yang, Jie Zhong, Fang Xie, Xiaoyang He, Liwen Du, Yaqian Yan, Meiyu Li, Wuqian Ma, Wenxin Wang and Ning Wang
The purpose of this work is to controllably synthesize a carbon aerogel with programmable functionally graded performance via a simple and effective strategy.
Abstract
Purpose
The purpose of this work is to controllably synthesize a carbon aerogel with programmable functionally graded performance via a simple and effective strategy.
Design/methodology/approach
This work uses polyvinyl alcohol (PVA) via the controllable sol-gel, lyophilization, and carbonization approach to achieve a programmable carbon aerogel. This design has the advantages of low raw material and preparation cost, simple and controllable synthetic process and low carbonization temperature.
Findings
The thermal stability and microstructure of PVA aerogel can be controlled by the crosslinking agent content within a certain range. The crosslinking agent content and the carbonization temperature are the key factors for functionally graded programming of carbon aerogels, including microstructure, oxygen-containing functional groups and adsorption performance. The adsorption ratio and adsorption rate of uranium can be controlled by adjusting initial concentration and pH value of the uranium solution. The 2.5%25 carbon aerogel with carbonization temperature of 350 °C has excellent adsorption performance when the initial concentration of uranium solution is 32 ppm at pH 7.5.
Research limitations/implications
As a new type of lightweight nano-porous amorphous carbon material, this carbon aerogel has many excellent properties.
Originality/value
This work presents a simple, low cost and controllable strategy for functionally graded programming of novel carbon aerogel. This carbon aerogel has great potential for application in various fields such as uranium recovery, wastewater treatment, sound absorption and shock absorption.
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Pinsheng Duan and Jianliang Zhou
The construction industry is an industry with a high incidence of safety accidents, and the interactions of unsafe behaviors of construction workers are the main cause of…
Abstract
Purpose
The construction industry is an industry with a high incidence of safety accidents, and the interactions of unsafe behaviors of construction workers are the main cause of accidents. The neglect of the interactions may lead to serious underestimation of safety risks. This research aims to analyze the cascading vulnerability of unsafe behaviors of construction workers from the perspective of network modeling.
Design/methodology/approach
An unsafe behavior network of construction workers and a cascading vulnerability analysis model were established based on 296 actual accident cases. The cascading vulnerability of each unsafe behavior was analyzed based on the degree attack strategy.
Findings
Complex network with 85 unsafe behavior nodes is established based on the collected accidents in total. The results showed that storing in improper location, does not wear a safety helmet, working with illness and working after drinking are unsafe behaviors with high cascading vulnerability. Coupling analysis revealed that differentiated management strategies of unsafe behaviors should be applied. Besides, more focus should be put on high cascading vulnerability behaviors.
Originality/value
This research proposed a method to construct the cascading failure model of unsafe behavior for individual construction workers. The key parameters of the cascading failure model of unsafe behaviors of construction workers were determined, which could provide a reference for the research of cascading failure of unsafe behaviors. Additionally, a dynamic vulnerability research framework based on complex network theory was proposed to analyze the cascading vulnerability of unsafe behaviors. The research synthesized the results of dynamic and static analysis and found the key control nodes to systematically control unsafe construction behaviors.
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Bingzi Jin and Xiaojie Xu
Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly…
Abstract
Purpose
Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.
Design/methodology/approach
In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.
Findings
Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.
Originality/value
Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.