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The purpose of this paper is to investigate whether and how banks’ financial constraints affect their cash tax avoidance. The authors hypothesize that banks engage in more tax planning to generate additional cash to mitigate their financial constraints.

The authors use a sample of US banks to conduct the panel regression analysis. The authors measure the bank tax avoidance using the cash effective tax rate and measure the bank financial constraints using the Z-score and annual payout ratio. The authors further use the implementation of the Dodd–Frank Act as a quasi-natural experiment to conduct the difference-in-difference analysis.

The authors document that financially constrained banks exhibit lower cash effective tax rates. The authors further show that banks facing greater financial constraints are less likely to pursue tax-saving activities following the Dodd–Frank Act. Moreover, the authors find that non-performing loans increase the influence of financial constraints on tax avoidance, while a financial crisis amplifies the impact of financial constraints on bank cash tax savings.

By extending previous research on financial constraints and tax planning, this paper is the first study to recognize financial constraints, along with the Dodd–Frank Act, as determinants of banks’ tax avoidance. This study informs policymakers about the regulation of tax avoidance in the banking industry and sheds light on possible future research on banks’ tax-planning strategies.

This paper aims to improve the tribological properties of lithium complex greases using nanoparticles to investigate the tribological behavior of single additives (nano-TiO₂ or nano-CeO₂) and composite additives (nano-TiO₂–CeO₂) in lithium complex greases and to analyze the mechanism of their influence using a variety of characterization tools.

The morphology and microstructure of the nanoparticles were characterized by scanning electron microscopy and an X-ray diffractometer. The tribological properties of different nanoparticles, as well as compounded nanoparticles as greases, were evaluated. Average friction coefficients and wear diameters were analyzed. Scanning electron microscopy and three-dimensional topography were used to analyze the surface topography of worn steel balls. The elements present on the worn steel balls’ surface were analyzed using energy-dispersive spectroscopy and X-ray photoelectron spectroscopy.

The results showed that the coefficient of friction (COF) of grease with all three nanoparticles added was low. The grease-containing composite nanoparticles exhibited a lower COF and superior anti-wear properties. The sample displayed its optimal tribological performance when the ratio of TiO₂ to CeO₂ was 6:4, resulting in a 30.5% reduction in the COF and a 29.2% decrease in wear spot diameter compared to the original grease. Additionally, the roughness of the worn spot surface and the maximum depth of the wear mark were significantly reduced.

The main innovation of this study is the first mixing of nano-TiO₂ and nano-CeO₂ with different sizes and properties as compound lithium grease additives to significantly enhance the anti-wear and friction reduction properties of this grease. The results of friction experiments with a single additive are used as a basis to explore the synergistic lubrication mechanism of the compounded nanoparticles. This innovative approach provides a new reference and direction for future research and development of grease additives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2023-0291/

In recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.

Microstructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.

The comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.

This study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.

This paper aims to better control the mechanical properties and functional properties of NiTi alloy.

NiTi alloy samples with equal atomic ratio were formed by selective laser melting (SLM). X-ray diffraction (XRD), differential scanning calorimetry (DSC), scanning electron microscopy and tensile testing methods were used to study the effects of different laser power and scanning speed on the densification behavior, phase transformation characteristics and mechanical properties of NiTi alloy.

Compared with the laser power, the variation of the keyhole effect caused by the change of scanning speed is more intense, which has a greater effect on the densification behavior of SLM NiTi alloy. The effect of the laser power on the phase transition temperature is small. The increase of scanning speed weakens the burning degree of Ni element, so phase transition temperature decreases. The results of DSC test and tensile test show that the scanning velocity can significantly change the phase transition temperature, martensite twins reorientation and stress–strain behavior of SLM NiTi alloy.

This study provides a potential method to regulate the mechanical properties and functional properties of NiTi shape memory alloy in the future and NiTi alloys formed by SLM with good elongation were obtained because the Supercellular crystal structure formed during the nonequilibrium solidification of SLM and the superfine precipitates dispersed in the alloy prevented the dislocation formation.

The purpose of this study is to address the mixed predictions about the relationship between general skills training and turnover intention of early career graduates by examining the mediating mechanisms of perceived organizational support (POS) and job satisfaction (JS) through which this relationship might be enacted. This study adopts organizational support theory as the guiding theory and examines the concept of POS as critical for predicting and explaining relationships in the conceptual framework.

A quantitative survey method was used on a sample of 147 Chinese early career graduate trainees. Analysis was conducted using partial least square-based structural equation modelling (PLS-SEM).

The main finding is that participation in general skills training (PGST) does not directly impact turnover intention, rather POS is a mechanism through which this negative relationship operates. This study also found significant evidence for serial mediation by POS on PGST and its relationship with turnover intention. Importantly, JS only has an effect on turnover intention when in the presence of serial mediation by POS.

Cross-sectional study of a small survey sample. Nonetheless, the findings have major implications for research theories on the relationship of general skills training with employee turnover.

PGST does not directly impact turnover intention, rather POS is a mechanism through which this negative relationship operates.

This research emphasizes the important role of POS in the relationship between early career graduate trainees’ PGST and their turnover intentions.

The purpose of this paper is to investigate the dynamic responses of a spur gear pair with unloaded static transmission error (STE) excitation numerically and experimentally and the influences of the system factors including mesh stiffness, error excitation and torque on the dynamic transmission error (DTE).

A simple lumped parameters dynamic model of a gear pair considering time-varying mesh stiffness, backlash and unloaded STE excitation is developed. The STE is calculated from the measured tooth profile deviation under the unloaded condition. A four-square gear test rig is designed to measure and analyze the DTE and vibration responses of the gear pair. The dynamic responses of the gear transmission are studied numerically and experimentally.

The predicted numerical DTE matches well with the experimental results. When the real unloaded STE excitation without any approximation is used, the dynamic response is dominated by the mesh frequency and its high order harmonic components, which may not be result caused by the assembling error. The sub-harmonic and super-harmonic resonant behaviors are excited because of the high order harmonic components of STE. It will not certainly prevent the separations of mesh teeth when the gear pair is under the condition of high speed and heavy load.

This study helps to improve the modeling method of the dynamic analysis of spur gear transmission and provide some reference for the understanding of the influence of mesh stiffness, STE excitation and system torque on the vibration behaviors.

This paper aims to study the impact of individual relationships on tacit knowledge sharing in the company setting of compulsory bond, expressive bond, instrumental bond and self-monitoring by empirical explorations.

The paper raises seven hypotheses that focus on the impact of employees’ relationship with tacit knowledge sharing in knowledge-intensive industries and positions based on relationship theory. Before distributing the formal questionnaires, a pre-research was done in a college by collecting comments and suggestions so as to correct and modify the questionnaires. A four-page questionnaire based on the Likert scale with 45 questions was used for data collection, and 210 valid questionnaires were collected from a research institute, a software company and an educational institute. Finally, SPSS17.0 was used to analyze these data, including reliability analysis, validity analysis, correlation analysis and regression analysis, etc.

The findings include: there is a positive correlation between employees’ compulsory bond and the efficiency of tacit knowledge sharing; there is a positive correlation between employees’ expressive bond and the efficiency of tacit knowledge sharing; there is a negative correlation between employees’ instrumental bond and the efficiency of tacit knowledge sharing; the more apt employees are at self-monitoring, the more effectively they will share tacit knowledge; the interaction between compulsory bonds and self-monitoring has a positive and stimulating impact on tacit knowledge sharing; the interaction between expressive bonds and self-monitoring has a positive and stimulating impact on tacit knowledge sharing; and the interaction between instrumental bonds and self-monitoring has a certain impact on tacit knowledge sharing.

However, the efficiency of tacit knowledge sharing cannot be measured easily and how to share the tacit knowledge based on employees’ relationships should be further concerned by knowledge industries.

This paper illustrates multiple, in-depth approaches to research on knowledge sharing. It shows why it is important to pay attention to employees’ relationships during the process of tacit knowledge sharing. The author argued some key factors such as compulsory bond, emotional bond and self-monitoring that may have a certain impact on the tacit knowledge sharing. The paper also further discussed the influence on the sharing of tacit knowledge as for the interaction between different relationship types and self-monitoring.

The knowledge is critical to enhance enterprises’ performance, and it will become more useful when the new knowledge is shared with others. However, tacit knowledge cannot be measured easily, and how to share the tacit knowledge based on employees’ relationships should be further concerned by knowledge industries. A series of findings are proposed in this paper.

Integrating the knowledge of different individuals, of which 90 per cent is tacit knowledge, in an organization that engages in producing products and providing service is instrumental to the sustainability and productivity of that organization. This study addressed the factors and dynamics of tacit knowledge sharing that can be used in knowledge management to effectively capture, store and disseminate tacit knowledge across an organization.

To provide a selective bibliography in the emerging area of library content personalization for the benefit of library and information professionals.

A range of recently published works (in the period 1993–2004), which aim to provide pragmatic application of content personalization rather than theoretical works, are discussed and sorted into “classified” sections to help library professionals understand more about the various options for formulating content as per the specific needs of their clientele.

This paper provides information about each category of tool and technique of personalization, indicating what is achieved and how particular developments can help other libraries or professionals. It recognises that personalization of library resources is a viable way of helping users deal with the information explosion, conserving their time for more productive intellectual tasks. It identifies how computer and information technology has enabled document mapping to be more efficient, especially because of the ease with which a document can be indexed and represented with multiple terms, and confirms that this same functionality can be used to represent a user's interests, facilitating the easy linking of relevant sources to prospective users. Personalization of library resources is an effective way for maximizing user benefit.

This is not an exhaustive list of developments in personalization. Rather it identifies a mix of products and solutions that are of immediate use to librarians.

A very useful source of pragmatic applications of personalization so far, that can guide a practicing professional interested in creating similar solutions for more productive information support in his/her library.

This paper fulfils an identified need for a “review of technology” for LIS practitioners and offers practical help to any professional exploring solutions similar to those outlined in this paper.

The purpose of this study is to investigate starch mucor (SM) in potassium iodide (KI) as corrosion inhibitor of aluminium in hydrochloric acid (HCl) medium.

The SM in KI was characterized by gravimetric, scanning electron microscopy, electrochemical impedance spectroscopy measurements, potentiodynamic polarization and gas chromatography-mass spectrometer techniques. The inhibition efficiency was optimized using response surface methodology.

The result revealed that the inhibitor inhibited corrosion at a low concentration with the rate of inhibition increasing as the concentration of the inhibitor increased. The inhibition efficiency increases as the temperature was increased with slight incorporation of the inhibitor (SM in KI). This indicates that the corrosion control is both inhibitor (SM in KI) and temperature dependent.

The research results can provide the basis for using SM in KI as corrosion inhibitor of aluminium in HCL medium. Mixed-type inhibitor nature of SM was proved by cathodic and anodic nature of the polarization curves.

In this work, the authors aim to provide a set of convenient methods for generating training data, and then develop a deep learning method based on point clouds to estimate the pose of target for robot grasping.

This work presents a deep learning method PointSimGrasp on point clouds for robot grasping. In PointSimGrasp, a point cloud emulator is introduced to generate training data and a pose estimation algorithm, which, based on deep learning, is designed. After trained with the emulation data set, the pose estimation algorithm could estimate the pose of target.

In experiment part, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor and a base platform with adjustable inclination. A data set that contains three subsets is set up on the experimental platform. After trained with the emulation data set, the PointSimGrasp is tested on the experimental data set, and an average translation error of about 2–3 mm and an average rotation error of about 2–5 degrees are obtained.

The contributions are as follows: first, a deep learning method on point clouds is proposed to estimate 6D pose of target; second, a convenient training method for pose estimation algorithm is presented and a point cloud emulator is introduced to generate training data; finally, an experimental platform is built, and the PointSimGrasp is tested on the platform.